IE 401 - Motorcycle Riding Safety Assessment for Commuter's Accident Prevention in the Philippines

MOTORCYCLE RIDING SAFETY ASSESSMENT FOR COMMUTER’S ACCIDENT

PREVENTION IN THE PHILIPPINES: A MULTIPLE REGRESSION APPROACH

CHAPTER 1. THE PROBLEM AND ITS BACKGROUND

This chapter focuses on presenting the problem of motorcycle related accidents on different factors with the use of problem identifying tool like Pareto, Ishikawa and Why-Why analysis. This also include the introduction, background of the study, statement of the problem, objectives, significance and the scope and limitation of the study.

1.1. Introduction

Motorcycle, usually called bike, motorbike or cycle, is a two-wheeled or three-wheeled motor vehicle. The design of motorcycle vastly varies to correspond to a range of various purposes such as lengthy travel, commuting, sport, and off-road riding. On the year 1894, the first series production of the motorcycle was released by Hilderbrand and Wolfmuller, and their production of the motorcycle is the first to be called motorcycle. The motorcycle was first used in supplying effective communications during World War I. Today's motorcycle industry is mainly dominated by the Indian and Japanese motorcycle companies.

Motorcycles are generally used globally, with 314 million powered two and threewheelers registered in 154 countries in 2010 (Ivers, Sakashita, et al., 2016). In the Philippines, the annual sales report of motorcycle for the year 2018 is 21%, an upsurge compared to the year 2017 where the sales were only 15.7%. In terms of sales the Scooter segment leads at

577,722 units sold, Backbone model segments comes in 2 nd at 494,842 units sold, the Moped segment comes in 3 rd at with a 1% growth in sales with 494,842 units sold, and lastly the Street or Standard segment at 69,627 units sold (Motorcycle Development Program Participants

Association, 2019). The growing sales of motorcycles have benefitted mainly from the traffic problem in the metro. Sigua, 2010 stated that “there are several reasons for the upsurge of motorcycles in the Philippines. Motorcycle becomes a necessity for the commuters because of the increase in fuel prices and the fare in public transportation. The motorcycle offers fast travel than a car because of the congested roads and can easily weave between vehicl es”. In contrast with cars, motorcycles are the most likely sustainable means of transportation it also provides an economical form of personal transport that employs fewer resources. (Jittrapirom,

Knoflacher, et al., 2016).

IE 401 – ERGONOMICS / HUMAN FACTORS ENG’G 1



1.2. Project Rationale

Since motorcycles are vulnerable compare to other vehicles, they have the highest percentage of accidents. According to Metropolitan Manila Development Authority (MMDA) road crashes involving motorcycle riders increased by 21 percent in 2018. This shows that the safety use of motorcycles is not that recognized in Metro Manila.

1.3. The Client

A Motorcycle rider is someone who rides a two-wheeled motor vehicle with a strong frame known as Motorcycle. They are required to complete a Compulsory Basic Traning course

(CBT) and to have a valid driving license before riding any type of motorcycle on the road. As a rider, being responsible for the roadworthiness of the motor vehicle is a must. By simply carrying out a consistent inspection on the moto rcycle they’ll be able to maintain it working efficiently and safely.

Some motorcycle riders selecting their motor vehicle not as how it looks but how it fits.

Considering, the seat height and the engine size. Protective gears such as helmet, face shield, goggles, jackets, pants, riding suits, gloves, and footwear are worn as it lessens impacts and injuries to the riders. They have the same rights and obligations as other road users as well.

However, there are also risks when riding on the road including traffic conditions, road surfaces, and weather that has a huge impact on the safety of the rider, the passenger and the motorcycle itself. Riding with a passenger can be challenging as motor vehicles react to the movement of the riders. For such responsiveness, both passenger and rider need to be wellprepared.

Considering passengers as a second rider, they can assist in ensuring the safety and if procedural operations are followed precisely. Both the rider and the passenger are required to practice. As the passenger must be mindful of basic riding skills as well.

1.4. Statement of the Problem

According to the statistics of (MMDA, 2019) about the road crash accidents of motorcycles. The researchers found out that there is a total of 86,838 cases of motorcycle related accidents under the Damages to Property, Fatal, and Non-Fatal Injury. The average accident per day per cases in year 2016 is 59 cases, in year 2017 it has 60.44 cases and in year 2018 it has 73.01 cases. The percentage of motorcycle accidents increases from 3.08% to 20.80% (See Appendix A). Thus, this study will focus on the driving safety of motorcycle



PREVENTION IN THE PHILIPPINES: A MULTIPLE REGRESSION APPROACH riders. The result of increasing number of motorcycle accidents is that the drivers will loss its productivity, the quality of their life and medical cost due to the injuries they incurred. Road accident fatality will cost an average of P200,000.00. (DOH, 2017).

Using the questionnaires that the researchers provided, the researchers identified the main problems that contributed to the safety of the driver. Out of the 40 respondents, 31 respondents said that they experienced a Reckless Driver of Another Vehicle, Overtaking at Unsafe Distance, Reckless Driver of the Pillion Passenger, Weight of the Customer,

Comfortability and Slippery Road.

1.5. Objectives of the Study

1.5.1 General Objectives

•

To identify factors that have significant effect to the safety of motorcycle riders in driving and to lessen the motorcycle-related accidents by 50-70%.

1.5.2 Specific Objectives

•

To find related literatures concerning the factors related to motorcycle safety.

•

To determine the factors contributing to the motorcycle safety driving.

•

To gather data among motorcycle riders in Quezon City.

•

To assess and interpret the results from the gathered data using charts, figures and tables.

•

To use different statistical tools in analyzing the relationship of motorcycle safety with the determined variables.

•

To recommend feasible solution based on the factors that have significant relationship regarding motorcycle safety driving.

1.6 Significance of the Study

The findings of this study will contribute to the improvement of motorcycles in the Metro

Manila. Furthermore, this study will benefit the drivers of motorcycles by providing them necessary information about the safety of using a motorcycle. Also, this study will give knowledge to the Department of Transportation (DOTr) about the risk and safeness of motorcycles as this will contribute on the research development of traffic in Metro Manila.




CHAPTER 2. THE REVIEW OF RELATED LITERATURE AND STUDIES

This chapter contains different ideas and studies that came from different resources like journals, books, and articles that combines information that will help the recipient/reader to understand more about the study and to be able to familiarize them with information that are relevant to the topic.

2. Review of Related Literature

Mental workload might be a problem while driving a motorcycle vehicle; the driver should establish good perceiving, identifying and anticipating road elements and other road user's behavior while having maintained an appropriate steering control and speed (Faure et al., 2015). As defined by Ohtsuka et al. (2015) in their study, the mental workload is a cognitive load that a motorcycle rider takes while a rider processes various information to operate the motorcycle safely. In the study of Faure et al. (2015) found out that the mental workload level of drivers increased with the complexity of the driving environment and this report the German part of a Swedish-German cooperative study in which the PDT was investigated focusing on its specific sensitivity compared with alternative workload measures. Subjective workload ratings reflected overall route demands and also did not indicate differing effects of the two displays. The physiological measures were less sensitive to workload and indicated emotional strain as well. An assessment of the PDT as a method for the measurement of safety-related workload is given. (G, john, et al, 2009). The other technique that the eye movement parameters of the motorcycle rider are measured under two-run objectives, the one leads to a high MWL and the other one leads to a low MWL.

On another side, were to continue the presence of factors affecting the safeness of using a motorcycle may contribute the safety attitudes from every motorcycle rider. Ching Fu

Chen, 2009, has proved that the results show attitudes toward traffic safety are directly associated with risky driving behaviors while having direct effects on attitudes toward traffic safety, personality traits are also found to influence risky driving behaviors indirectly mediated by traffic safety attitudes. Practical implications for the traffic safety of young motorcyclists are also discussed. From the 257 respondents in Taiwanese university. Despite the risk of being involved in a traffic accident is the same for motorcyclists as other road users, the risk for a motorcyclist of being severely injured is much higher in a Previous studies on motorcycle traffic accidents can be classified into three types pre-crash phase, (Aare and von Holst, 2003;

Zambon and Hasselberg, 2006). By analyzing the problem with safety attitudes this article uses a combination of factor analysis and tree-based regression to determine driver groups with



PREVENTION IN THE PHILIPPINES: A MULTIPLE REGRESSION APPROACH homogeneous self-reported behavior and determine whether regional differences in driving behaviors exist. (Golias and Karlaftis, 2001).

The Motorcycle riders are the most vulnerable types of road users where the focus on this study is to deepen the understanding of the correlation of different subtypes of visual attention and driving violation behaviors and their effect on hazard perception between accident-free and accident-involved motorcycle riders (A, Cheng et al., 2011) and also cited that the results of the regression analysis showed that aggressive driving violation CMRDV score significantly predicted hazard perception and accident involvement of motorcycle riders.

Given that all participants were mature and experienced motorcycle riders, the most plausible explanation for the differences between them is their driving style (influenced by an undesirable driving attitude), rather than skill deficits per se. The present study points to the importance of conceptualizing the influence of different driving behaviors to enrich our understanding of the role of human factors in road accidents and consequently develop effective countermeasures to prevent traffic accidents involving motorcycles. (Ferguson, 2003; Groeger, 2000; Groeger and Chapman, 1996. Reliable measures of hazard perception have been achieved and these hazard perception tests also appear to have some degree of validity. Hazard perception can be improved through relatively quick and cheap training techniques and this training has been demonstrated to transfer to real driving (M.S, Horswill, F.P, McKenna, 2004).

Kontogiannis, 2006, cited that the Drivers high in confidence reported fewer mistakes and violations which, in association with a low perception of risk, was a particularly worrying aspect of driving. A contextual model of accident involvement was tested with LISREL in which violations yielded a direct effect whilst aggression yielded an indirect effect mediated by violations. Alertness and confidence were both related to safety orientation but failed to predict accident rates and speeding convictions. This paper refers to a driver who is having a high regression composed of stress, coping strategies and aberrant driving of a Greek sample of company employees (N= 714). The results supported the main factor structures of the Driver

Behavior Inventory (Matthews, G., Tsuda, A., Xin, G., Ozeki, and Y.).

A dimension of habitual dislike of driving was associated with reduced control skills, greater caution, and disturbance of moods. A measure of aggressive driving predicted more frequent and more error-prone overtaking, which are effects attributed to the use of confronted coping strategies in interaction with other vehicles. An alertness measure predicted speed of



PREVENTION IN THE PHILIPPINES: A MULTIPLE REGRESSION APPROACH reaction to pedestrian hazards. This research has practical applications for system design, automated monitoring of driver performance, selection and assessment of drivers, and training.

Matthews, et al, (1998) addresses the issue of having the motorcycle driver's stress. A new scale, the Driving Behavior Inventory (DBI) was developed to study dimensions of driver stress. The DBI was administered to two independent samples of drivers who commuted daily to work and/or for whom driving was part of their job. In both studies' driver stress was defined by five factors which accounted for over 40% of the variance. These were identified as driving aggression, dislike of driving, tension and frustration connected with successful or unsuccessful overtaking, irritation when overtaken and heightened alertness and concentration. Multiple regression analyses pointed toward variables extraneous to driving as predictors of driver stress, among which life stresses appear to play a predominant role.

Gulian, et al, 1989; Hills, 1980, Siva, 1996, stated that Driving a motor vehicle at night requires artificial light to improve the driver's vision, which is the main sensory channel used by the information acquisition and information processing while driving and according to

Mayeur, et al, 2009, cited that the results show that the passengers' performances (detection distance) were higher than the drivers' performances (p = .0014). Furthermore, the higher the

VL, the higher the detection distances (p < .0001). These results lead up to modify the reference scenario to take into account human factor components for road lighting design.

On the other side that according to P. Raynham, (2004) the current principles of road lighting design are well established and have been adopted in several lighting design standards. This paper questions the basis of these principles and looks at some of the anomalies and problems of current road lighting design practice. The development of road lighting for pedestrians is examined, and the benefits of having a relevant visual task are explored. The paper concludes by suggesting that the fundamentals of road lighting need further examination. Much of visibility research related to road lighting is based on static tests with small objects. The improvement in visual performance is found to be considered up to a lighting level of 3.4 cd/m2.

W. J. M. Van Bommel and J.Telekenburg, (1986) the human error must convey to avoid the hazards from the darkened part of the road. Chen, Chen, 2010, have proved that the results reveal that the psychological flow variables have greater predictive power in explaining speeding behavior than the TPB variables, providing useful insights into the unique nature of this group of motorcyclists, who are more prone to engage in speeding. Group differences concerning both sensation-seeking and rider experiences in speeding behavior are highlighted, and the implications of the findings are discussed. The vehicle dynamics are represented by



PREVENTION IN THE PHILIPPINES: A MULTIPLE REGRESSION APPROACH four degrees of freedom model which includes the vehicle's longitudinal slip and sideslip angle.

The driver behavior model considers the ability in deceleration maneuvers according to the mobilized friction. The infrastructure characteristics introduce precise handling of the road geometry and the maximal available friction. (H.Slimi, et al, 2010).

Amrutkar and Rajhans (2011) stated that motorcycle riders tend to change posture in the duration of their ride due to discomfort that they were experienced while riding in a motorcycle. One of the most prominent disorders that motorcycle riders experience is back pain as they cannot maintain proper ergonomic posture and this may lead to musculoskeletal disorders. Karmegam, Sapuan, Ismail, et al. (2011) stated in their study that motorcycle riders' experiences discomfort in their body while riding motorcycle. It had a lot of techniques and studies about the proper posture that might help to scrutiny the specific part of the severe part of the body. To study the patterns and characteristics of lower limb injuries in motorcyclists, the morbidity and mortality among motorcyclists involved in road traffic accidents (RTA) in

Singapore is high.

The motivation behind this examination was to have a superior comprehension of the hazard variables of strong distress among motorcyclists because of bike riding. Overall, five risk factors were identified that might affect muscle activity and cause discomfort in various parts of the body among motorcyclists. Risk factors such as biological, environmental, vehicle, physiological and life activities factors were related to each other and might contribute to the discomfort among motorcyclists during the riding process. (A, alias, et al, 2016).

According to the records that 252 cases and 820 controls were studied, including 185 professional drivers. Strong associations were found with poor mental health and belief in work as a causal factor for LBP, and with occupatio nal sitting for ≥3 hours while not driving.

Associations were also seen with taller stature, consulting propensity, BMI, smoking history, fear-avoidance beliefs, frequent twisting, low decision latitude and low support at work.

However, associations with the six metrics of WBV were weak and not statistically significant, and no exposure-response relationships were found. (Palmer KT, et al.Case control study of low back pain presenting for MRI with special relation to whole-body vibration. (2008).

Traffic Management Bureau, (2012) reported that the Road safety is a basic issue over the world, particularly since it opened the door to the world during the 1980s and experienced resulting major financial changes and expanded motorization. It depicts some of the major changes that have occurred in mortality rates since 1951.death rates since 1951.




In any case, this information ought to be utilized cautiously Traffic Management Bureau,

2012. stated that Limited awareness of legal alcohol limits might contribute to drunk driving offenses. The high level of alcohol consumption by many offenders suggests that hazardous drinking levels may be a contributor. Recidivist drivers also had higher AUDIT scores, which suggest there may be benefits in using the AUDIT to identify potential drink drivers and recidivists, subject to further research. The Local media and posters are used by the Public

Security Bureau which is responsible for education about safe driving but participants thought that the education campaigns are limited in scope.

The knowledge and attitudes of drivers arrested for drunk driving differ from those of a sample drawn from the general community. In terms of existing empirical outcomes. The analysis uses logistic regression to investigate the differences between the responses given by the two samples. The findings illustrate several significant differences in knowledge and attitudes between offenders and the general community. (S, Baum, 1999).

Elliot, et al, 2006, proved that principal components analysis revealed a 5-factor solution (traffic errors, control errors, speed violations, the performance of stunts and use of safety equipment). Generalized linear modeling showed that, while controlling for the effects of age, experience, and annual mileage, traffic errors were the main predictors of crash risk.

For crashes in which respondents accepted some degree of blame, control errors and speed violations were also significant predictors of crash risk. Implications of the findings are discussed in deciding which countermeasures may be most effective at reducing motorcycle casualty rates. (Elliott et al., 2003; Huang and Preston, 2004)

In the study of Penumaka et al., where they investigated in-depth PTW (motorcycle)car accidents in which human error is the only factor that contributes to accidents. The study state that the most common causes of PTW (motorcycle)-car accident happens due to human error that classified as perception and execution failures. According to the study of Christie, R.

(2001) that driver training could not be considered an effective crash countermeasure. Other approaches such as increased supervision and graduated licensing for novice drivers and traffic law enforcement for all drivers are likely to make greater and more lasting contributions to road safety.

Low individual crash risk and decay of learning work against the potential effectiveness of driver training programs that concentrate on car control skills or deal with rare events such as emergencies. In this study aims to assess the time and day how much this cause in driving the motorcycle which may limit the results interpretations, these discrepancies suggest that



PREVENTION IN THE PHILIPPINES: A MULTIPLE REGRESSION APPROACH during complex tasks, compensation mechanisms may be set up between different resources to maintain a good level of performance (Bougard, et al, 2007).

According to MacLean, A. W., Davies, D. R., & Thiele, K. (2003), states that association between sleepiness and driving and sleepy may be a factor in about 20% of motor vehicle accidents where studies carried out. The majority of prevention efforts to date have focused on short-term solutions that only mask underlying sleepiness and it is suggested that more emphasis be directed toward primary prevention efforts such as educating drivers about the importance of getting sufficient sleep and avoiding circadian performance troughs. Finally, the important role that health professionals can play in the identification, treatment, and education of sleepy drivers is highlighted. Besides, W.Verwey, D, Zaidel, 1999, had an experiment with

26 participants drove at night for 135 min on a simulated two-lane rural road with light traffic and filled out a battery of questionnaires to prove that feeling drowsy while driving may because of having accidents, especially at night. The best predicting measures for poor driving were the frequency of eye closures exceeding 1 s and the number of times that time-to-line crossings were below 0.5 s. The participants' judgments on susceptibility to drowsiness were a poor predictor. The dissociation of physiological and subjective measures was observed and explained by a two-level information processing model.

2.2 Summary of Related Literature

Table 2.2.1 Summary of Related Literature

YEAR TITLE AUTHOR

HIGHLIGHTS OF THE

STUDY

2009

The conundrum of the motorcycle in the mix of sustainable urban transport

Okonda, Aliata, Aila, et.al.,

Motorcycles are potentially the more sustainable means of transport. Motorcycles need less space, consume fewer resources, and pollute less than cars with typically low

2015

Impact of motorcycle tax on the emergency of other related business

Okonda, Michael

W; Aliata, Victor

L; Aila, Fredrick

O; Ombok, occupancy

The increase of taxi business that would be a competitive framework among the industry of transportation




2018 activities in Siaya

District

Benjamin; Nyongesa,

Destaings

The Danger Zone:

Injuries and Conditions

Associated with

Immediately Fatal

Motorcycle Crashes in the State of Michigan

Rachel N. Saunders,

MD, Matthew B. Dull,

MD, Amanda B.

Witte, MD, James M.

Regan, MD, Alan T.

Davis, PhD, Tracy J.

Koehler, PhD,

Charles J. Gibson,

MD, Gaby A.

Iskander, MD, Carlos

H. Rodriguez, MD,

Stephen D. Cohle,

MD, Alistair J.

Chapman, MD

Immediately fatal motorcycle crashes have not been well characterized, and we aimed to catalogue injuries sustained in fatal motorcycle crashes and to assess the impact of crash conditions on injury patterns.

2015

Impact Of Motorcycle

Taxi on the Emergence of Other Related

Business Activities in

Siaya District

Michael Washika

Okonda, Fredrick

Onyango Aila, Victor

Lusala Aliata, Ombok

Benjamin

. The capacity to ship men and materials to a point where the work power is best is a well-established factor in the fighting

2011

2005

Motorcyclist

Acceptability on Road

Safety Policy:

Motorcycle Exclusive

Lane in Makassar

Determination of

Comfortable Safe Width in an Exclusive

Motorcycle Lane

Arifin Asri,

Muhammad Isran

Ramli, Lawalenna

Samang

Teik Hua Law

IE 401 – ERGONOMICS / HUMAN FACTORS ENG’G

Discusses the road safetiness among motorcycle users and making an exclusive lane for motorcycles.

Intended width size on motorcycle lane.

10



2016

A Study of Motorcycle

Lane Design in Some

Asian Countries

Trid.trb.org,Elsevier appropriate layout for the motorcycle lane

2006

2016

2015

2011

Simulation of crash tests for high containment levels of road safety barriers

The effects of driving environment complexity and dual tasking on drivers’ mental workload and eye blink behavior

M. Borovinsˇek *, M.

Vesenjak, M. Ulbin, Z.

Ren

Verane faure,regis lobojis,Nicolas benguigui

Estimation of Mental

Workload during

Motorcycle Operation

Ryoichi ohtsuka, jian wang,takanori chihara,kimihiro yamanaka,keisuke morishima,hiroshi daimoto

An experiment on how much helpful the barriers in the road

Disadvantage of doing unnecessary motion together while driving

The assuming values on how to measure the mental work load during driving a motorcyle

Ergonomic Posture for

Motorcycle Riding

Neela Ravindra

Rajhans

Ergonomically posture to prove a proper body position while driving

2008

Case-Control Study of

Low-Back Pain

Presenting for Mri, With

Special Relation to

Whole-Body Vibration

KT, kalmer,ez harris, mj griffin,j benetti reading, m

Sampson,d coggon

2006

Patterns of driver stress and coping strategies in a Greek sample and their relationship to

Tom kotonggianis

The study of having a lower back pain due to prolonged driving

A study about the stress and coping of the problem how to have a solution.



PREVENTION IN THE PHILIPPINES: A MULTIPLE REGRESSION APPROACH aberrant behaviors and traffic accidents

2010

1986

1998

1989

2010

Individual differences in driver stress vulnerability in a

Japanese sample

Stress, coping and problem solving

Driver Stress and

Performance on a

Driving Simulator

Dimensions of driver stress

Geral Matthews,akira tsuda,gu xin, yukako uzeki

Individual stresses among the motorcycle riders.

Tom cox

Knowing the measurement on how to eliminate the stressors

Gerald Matthews, lisa dorm, Thomas w. hoyes,roy davies, ian glendon ray G. taylor.

E Gulian, g.

Matthews,a.i glendon, d.r davies,l. m debney

A study about the probability of involving in accident while carrying a bunch of stress.

Physical and mental involvement of stress

Speeding for fun?

Exploring the speeding behavior of riders of heavy motorcycles using the theory of planned behavior and psychological flow theory

Ching fu chen,cheng wen chen

A study on how to realize that speeding might be the cause in trouble.

2010

Motorcycle Speed

Profile in Cornering

Situation

H. Slimi, H. Arioui, L.

Nouveliere and S.

Mammar

2011

A comparison of the hazard perception ability of accident-involved and

Andy S.K. Chenga, ∗ ,

Terry C.K. Nga, Hoe

C. Lee a formula citing on how the motorcycle will get slow passing the corner on the road

A safety attitudes of knowing the hazard perception ability of accident-free while driving




2004

2015

2013

2009

2004

1986 accident-free motorcycle riders

Drivers’ hazard perception ability:

Situation awareness on the road

Drunk driving offenders’ knowledge and 13 behavior in relation to alcohol-involved driving in Yinchuan and a comparison with

Guangzhou, China

Reducing Alcohol-

Related Driving on

China ’s Roads: Traffic

Police Officers’

Perceptions and

Practice

The effect of the driving activity on target detection as a function of the visibility level:

Implications for road lighting

An examination of the fundamentals of road lighting for pedestrians and drivers

Visibility research for road lighting based on a dynamic situation

M.S. Horswill and

F.P. McKenna

Keqin jia, mark king, judyj. Fleeter,mary

Sheehan,wenjun ma jing lei,jianzhen zhang.

Keqin Jia,

Judy J. Fleiter,

Mark J. King

Mary Sheehan

Michael Dunne

Wenjun Ma

P raynham

W.J.M Van

Bommel,J.

Tekelenburg

Awareness to avoid the possible incidents/accidents on the road.

Talks with a fine/penalty when the driver caught drinking/drunk while driving a motorcycle

A knowledge on the possible chance to avoid of getting in accident

Anais mayuer, roland

Bremond,J.M

Christian bastien

A factor that proving of having a visible sign might help the drivers to use the public road

The fundamentals of having a road lights for the pedestrian and drivers

Discusses about having the visible solution for the public




2006

Errors and violations in relation to motorcyclists’ crash risk

Maerk A. Elliot,

Christpoher j.

Bougham, Baryy F.

Sexton

The factors of being involved to accidents

2001

The Effectiveness of

Driver

Training/Education as a

Road Safety Measure

Royal Automobile

Club of Victoria

(RACV) Ltd

The advantage of attending a seminar before in actual driving and being a smart driver to know how to manage in avoiding the possible accidents occur

2009

2001

Personality, safety attitudes and risky driving behaviors —

Evidence from young

Taiwanese motorcyclists

An international comparative study of self-reported driver behavior

Ching fu chen

John Golias, Matthew

G. Karlaftis

Character, security frames of mind and unsafe driving practices —Evidence from youthful Taiwanese motorcyclists

A global relative investigation of self-revealed driver conduct

2007

1999

An assessment of the relevance of laboratory and motorcycling tests for investigating time of day and sleep deprivation influences on motorcycling performance

Predicting drowsiness accidents from personal attributes, eye blinks

Clemen Bougard, sebastien

Moussay,Damien

Davenne

Willem verwey, David

M. Zaidel

An appraisal of the importance of research facility and motorcycling tests for examining time of day and lack of sleep impacts on motorcycling execution

Focuses on avoiding the probability of having a



PREVENTION IN THE PHILIPPINES: A MULTIPLE REGRESSION APPROACH and ongoing driving behavior

2.3 Conceptual Framework sleepiness feeling while driving

Individual Factors

•Age

•BMI

•Gender

Psychological

Factors

•Mental Workload

•Safety Attitude

•Alertness

•Stress

Motorcycle Safety in Driving

Environmental

Factors

•Road Lighting

Physical Factors

•Posture

•Alcohol

Consumption

•Human Error

•Sleep Deprivation

•Speeding

Figure 2.3.1 Conceptual Framework

Figure 2.3.1 Shows the Conceptual Framework by the researchers as it has indicated it has four Factors: Psychological, Individual, Environmental and Physical Factors that contributes to the Motorcycle Safety in Driving.

2.4 Definition of terms




•

Motorcycle or Motorbike - is a single-track, two-wheeled engine vehicle that is used worldwide for transportation

•

Workload – the amount of work to be done by someone or something

•

Stress - the body's reaction to any change that requires an adjustment or response

•

Sleep Deprivation – Condition of not having enough sleep

•

Posture - Posture is the position in which we hold our bodies while standing, sitting, or lying down.

•

Human Error something has been done that was "not intended by the actor; not desired by a set of rules or an external observer; or that led the task or system outside its acceptable limits

•

Road safety - is referred to as the forms and measures used to avoid road users from being killed or seriously injured.

•

All-Terrain Vehicle or ATV - designed with four wheels and cannot be operated on public roads. Conversely, the work capacity of the motorcycle can affect the possibility of unsafe riding behavior like over speeding.

•

Transport - is the method by which individuals and items are moved starting with one spot then onto the next. It, in this manner, makes connections conceivable

•

Alertness - the quality of being alert.




CHAPTER 3: METHODOLOGIES

This chapter represents methodologies on how the researcher conducted the study.

Also, this chapter contains sampling frame, sample size determination, a definition of variables with the factors, survey design, administration of data gathering and data collection, statistical too and analysis.

Literature Review

(Motorcycle Accidents, Motorcycle Safety, Factors related to Motorcycle Accidents)

Tool Validation

(Driver Behaviour Inventory, Motorcycle Rider

Behaviour Questionnaire, Photometric Measurement,

Rapid Entire Body Assessment, Safety Attitude Scale,

Speeding Behavior Scale, Alcohol Use Disorders

IdentificationTest, Digit Vigilance Test, NASA Task

Load Index, Sign Cancellation)

Actual Data Gathering

(Driver Behaviour Inventory, Motorcycle Rider Behaviour

Questionnaire, Photometric Measurement, Rapid Entire

Body Assessment, Safety Attitude Scale, Speeding

Behavior Scale, Alcohol Use Disorders IdentificationTest,

Digit Vigilance Test, NASA Task Load Index, Sign

Cancellation)

Analysis of Data and Results

(Normality Test, Stepwise Regression, Linear Regression

Using Minitab 19)

Conclusion and Recommendations

Figure 3.1 Methodologies

Figure 3.1 shows how the researcher conducted a research study to come up with the conclusion and recommendations. The study starts with finding a literature review about motorcycle accidents and the factors related to this. By determining all the factors that



PREVENTION IN THE PHILIPPINES: A MULTIPLE REGRESSION APPROACH contribute to the safety of motorcycle riders, tools were identified and validated. Thus, each tool produces a measurement of each factor. Then, followed by data gathering and data analysis that will give the researcher a basis for the conclusions and recommendations for the research.

3.1 Sampling Frame

This study is concerned with all the motorcycle riders operating in Quezon City. The researchers selected samples among the motorcycle riders to represent them as a whole.

Stratified random sampling among motorcycle riders are considered and also the anonymity of the respondents to take part in the research.

3.2 Sample Size Determination

In determining the sample size, the researchers used the following parameters as a basis for the study. a. Primary Variables of Measurement – This study uses different types of scale modified by the researchers to measure the categorical variables (e.g. stress, speeding, attitude, human error). b. Alpha Level – In most educational or academic research an alpha level of 0.05 is the most prevalent use as an alpha value when conducting a research study. (Bartlett, J., et.al. (2001). c. Acceptable Margin of Error – Krejcie and Morgan (1970) explained that, the rule for acceptable margin of error in conducting a research study is set to be as 3% margin of error to be acceptable in continuous data and 5% margin of error in categorical data. d. Variance Estimation – According to Barlett, J., et al. (2001), to estimate the variances, identify first the range of scale and divide it by the number of standard deviations that is included in the range. In this study, the researchers use 0.5 as estimation to maximize the sample size. e. Sample Size Computation – This study will use Cochran’s (1977) formula about sample size using categorical data. The computation is shown below: 𝑛

0

= 𝑡 2 ×𝑝.𝑞 𝑑 2

(1)



PREVENTION IN THE PHILIPPINES: A MULTIPLE REGRESSION APPROACH 𝑛

0

=

(1.96) 2 × (.50)(.50)

(.05) 2

= 𝟑𝟖𝟒 𝒔𝒂𝒎𝒑𝒍𝒆𝒔 where: t = selected value of the alpha level (two tailed) = 1.96 p, q = estimate of variance = 0.50 d = acceptable margin of error for mean that is estimated = .05

3.3 Definition of Variables

There are two (2) ways of determining Continuous variables (Quantitative Data) and

Categorical (Qualitative Data). A continuous variable is a variable where the findings obtain an actual value. A categorical variable is a qualitative data in which the data obtained are assigned to a set of categories.

A. Independent Variables

A.1 Psychological Factors a. Stress (X1) – a condition of mental state that causes mental tension b. Mental Workload (X2) – the capacity of an individual to process the information c. Attitude (X3) – the way of thinking or feeling in motorcycle driving d. Alertness (X4) – awareness in the environment while driving

A.2 Individual Factors a. Age (X5) – the length of time that the motorcycle driver existed until present b. Gender (X6) – characterized as male or female c. Body Mass Index (X7) – based on the height and mass of an individual to measure the size of body fat

A.3 Environmental Factors a. Road Lighting (X8) – the luminance of the road

A.4 Physical Factors a. Posture (X9) – the position of an individual when they are sitting or standing b. Alcohol Consumption (X10) – defined as 0 to 40 if there is a presence of alcohol in the individual c. Human Error (X11)

– refers to unintentional action or decision



PREVENTION IN THE PHILIPPINES: A MULTIPLE REGRESSION APPROACH d. Sleep Deprivation (X12) - a condition where the individual suffers from lack of sleep e. Speeding(X13) - characterized as motorcycle riders exceeding the speed limit

B. Dependent Variable a. Safety (Y1) – the condition of being away from cause of danger and hazards

𝑿

𝟏

𝑿

𝟐

𝑿

𝟖

𝑿

𝟗

𝑿

𝟏𝟎

𝑿

𝟏𝟏

𝑿

𝟏𝟐

𝑿

𝟏𝟑

𝑿

𝟑

𝑿

𝟒

𝑿

𝟓

𝑿

𝟔

𝑿

𝟕

Table 3.3.1 Properties of Principal Variables

Variable Classification Data Type

Stress Independent Categorical 0

Level

Low High

4

Independent Continuous 0 100 Mental

Workload

Attitude

Alertness

Age

Gender

Body Mass

Index

Road Lighting

Posture

Alcohol

Consumption

Human Error

Sleep

Deprivation

Speeding

Independent

Independent

Independent

Independent

Independent

Independent

Independent

Independent

Independent

Independent

Independent

Categorical 1

Continuous 0

Continuous 18

Categorical 1

Continuous 18

Continuous 0

Continuous 1

Continuous 0

Categorical 1

Continuous 0

Categorical 1

14

15

40

5

420

65

2

30

5

222

5

Categorical 0 4 𝒀

𝟏

3

.4 Survey Design

Safety

3.4.1 Driver Behavior Inventory

Dependent




Driver Behavior Inventory is a scale used to measure the stress of the driver in riding a motorcycle. The questionnaire is composed of 19 DBI items which measures the aggression, alertness, confidence and dislike of driving. Participants rate the questionnaire by a five-point

Likert scale, with 0 = never, 1=seldom, 2 =sometimes, 3 = frequently, and 4 = almost continually by how often they had the feelings and disposition described in the questionnaire.

3.4.2 Speeding Behavior Scale

The Speeding Behavior Scale is a modified questionnaire from different kinds of questionnaire related to speeding like speeding behavioral attention, actual speeding behavior, sensation seeking and etc. It is a 10-item questionnaire, participants rate it by a five-point likert scale, with 1 = extremely disagree, 2 = disagree, 3 = not sure, 4 = agree, and 5 = extremely agree.

3.4.3 Safety Attitude Scale

Safety Attitude Scale is a questionnaire that deals with the measurement of safety attitude composed of three dimensions namely: traffic flow vs. rule obedience, speeding, and fun-riding. It is a 15-item questionnaire that the participants rate it from 1 as strongly disagree and 5 as strongly agree.

3.4.4 Motorcycle Rider Behavior Questionnaire (MRBQ)

The Motorcycle Rider Behavior Questionnaire (MRBQ) is a questionnaire that measures the behavior among motorcyclists. It is composed of a 43 item MRBQ to measure the behaviors of motorcyclists. The MRBQ uses five-factor structure in measuring driver behavior, namely: traffic errors, speed violations, stunts, safety equipment, and control errors.

The questionnaire is administered by involving the participants in rating each fivefactor on a (6) six-point rating scale with 1 = never, 2 = hardly ever, 3 = occasionally, 4 = quite often, 5 = frequently and 6 = nearly all the time.

3.4.5 Photometric Measurement

Photometric measurement is the science of measuring the light in regards on how human eye sees the light source. It is concerned with the luminous power and luminance intensity. To assess the optical characteristic of a light source a photometric measurement is commonly use like light meter in determining the light intensity of the designated area.




3.4.6 Rapid Entire Body Assessment (REBA)

Rapid Entire Body Assessment is a tool used to evaluate the whole posture of the body and the risk associated with task done by individual. To evaluate and collect data of the required body posture, forceful extortions, type of movement or actions, repetition and coupling, a single worksheet is used. The scoring is from 1 to 15 and above, 1 = no action required, 2-3 = low risk, 4-7 = medium risk, 8-10 = high risk and 11 and above = very high risk that represent the level of Musculoskeletal Disorders risk.

3.4.7 The Alcohol Use Disorders Identification Test (AUDIT)

The Alcohol Use Disorders Identification Test (AUDIT) is a tool to identify individual who are at risk in developing alcohol problems. It is developed by the World Health

Organization (WHO) in year 1982. The test is composed of multiple questions about the frequency and quantity of alcohol consumption, drinking behavior, alcohol-related problems or reaction. The score is based on a point system, a total score of 8 or more indicates that an individual has alcohol problem.

3.4.8 Digit Vigilance Test

The Digit Vigilance Test is used to measure the visual vigilance and sustained attention during rapid visual tracking or selection of target stimuli. In measuring this, the respondents are asked to cross out the numbers (6 or 9) as quickly as possible that appears randomly within

59 rows of single digit on two pages. The score is provided by adding the times in first and second pages which is the total time score in seconds. High total time score shows that an individual has a problem regarding attention.

3.4.9 NASA Task Load Index (NASA-TLX)

NASA Task Load Index (NASA-TLX) is a tool in measuring the mental workload. It is developed by the Human Performance Group in 3 years at NASA. It consists of six dimensions to determine the performance rate of an individual. The six dimensions are mental demand, physical demand, temporal demand, effort, performance and frustration. Participants rate their score on an interval (0) low and (100) high.

3.4.10 Sign Cancellation




Sign Cancellation is a test used to measure the level of sleepiness of an individual. It is evaluated by a Subject Vigilance Level. The test has 3 sign shapes to be marked. The instruction is to mark all the signs that correspond to the 3 sign shapes.

In this study, the researchers used a survey questionnaire and distributed it to 384 motorcycle drivers in Quezon City to gather data and have a relevant result in conducting statistical analysis. The survey questionnaire was limited in the following guidelines:

1. The selection of participants was done by random sampling method for private motorcycles in Quezon City.

2. The participants should be 18 to 65 years old and can be any of gender.

3. The participant should be a licensed driver.

3.6 Data Collection

The instrument used in this study is a questionnaire. To collect all the necessary data to be analyzed, the researchers used different following sources of data in the study:

1. The demographics (e.g. age, gender, weight, height and etc.) among motorcycle drivers in Quezon City.

2. In measuring the stress of the motorcycle driver, Driver Behavior Inventory (DBI) was used by a 19-item questionnaire. The result of the questionnaire will measure from 0 to 4.

3. To measure the speeding behavior of the driver, Speeding Behavior Scale was used which is a modified questionnaire. To assess this, the questionnaire is measure by 1 to 5.

4. For the human error, Motorcycle Riders Behavior Questionnaire was utilized by a 43-item questionnaire. The assessment of this tool will be measured by rating it from 1 to 6, respectively.

5. To measure the road lighting, photometric measurement was used by using light meter in measuring light intensity in different parts of Quezon City.

6. Using Rapid Entire Body Assessment (REBA), the posture of the motorcycle drivers will be assessed by scoring it from 1 to 15.

7. To measure the Alcohol Consumption of the subject, AUDIT was used to determine the individual who has an alcohol problem by scoring it from 0 to 40.




8. Alertness measurement will be used a Digital Vigilance Test. It will be used a time measurement. The high total time score will indicate an individual have a problem in attention

9. In measuring the mental workload, the researchers used NASA-TLX. It is composed of six dimensions, namely: mental demand, physical demand, temporal demand, effort, performance and frustration. To assess this 0 to 100 is the scoring.

10. Using the safety attitude scale, the researchers are able to measure the attitudes of the driver in a 3 dimension which are: speeding, traffic flow vs. rule obedience and fun-riding. To measure this, the scoring is 1 to 5.

11. By using the sign cancellation, the researchers were able to measure the level of sleepiness of an individual. To assess this, number of errors were considered.

The high the total number of errors will indicate an individual have a problem with sleeping.

3.7 Statistical Tool

3.7.1 Normality Test

The Normality Test is a statistical process used to identify if the sample or any group of data is usable or reliable by understanding the data around the mean. This test will help the researchers in determining the outlier in the data.

3.7.2 Correlation Analysis

Correlation Analysis is a method of statistical evaluation used to study the relationship between two variables categorical or continuous data. In this study the researchers used this analysis to know if there is a strong relationship between the dependent variables and independent variables.

3.7.3 Stepwise Regression

The Stepwise Regression is a statistical tool used to create a reliable data and to arranged based on the importance of variables. Stepwise Regression is a step by step process of regression model that involves automatic selection of variables by adding and removing variables based on the factors that has a big impact to the dependent variable. In this study, the stepwise regression method used is the adjusted R-squared, in which the value of the Rsquared identifies if the data is fit for the study.




3.7.4 Multiple Regression

Multiple Linear Regression is a statistical technique used to explain the relationship between multiple independent variable with one dependent variable. Independent variables are variable used to predict the value of dependent variable. The dependent variable is the variable needed to be predicted. Also, this method will determine the overall fit of the model with the relative contribution of the predictors to the total variance.

3.8 Data Analysis

The researcher used Minitab 17 software as a statistical tool in analyzing the collected data to develop a model and analysis. Also, to know the reliability and significance of the independent variable to the dependent variable. First, to know the variation of the data,

Normality Test was utilized and then the researchers removed the outliers. After that, identify the most significant predictors of the psychological factors, individual factors, environmental factors, and physical factors by using the Stepwise Regression. Then, Multiple Regression will be used by generating the predicted response on the dependent variable. The obtain result of the data analysis will allow the researchers to come up with the conclusion and recommendation.




CHAPTER 4. RESULTS AND DISCUSSION

The Chapter covers the presentation of the gathered data coming for the survey questionnaires and answered by the motorcycle riders of Quezon City. Charts and tables are utilized for a simpler way of understanding the results of the data gathering are guided by the methodology presented in the previous chapter, by using multiple regression the researchers identify the factor that mostly cause the motorcycle accidents.

4.1 Data Presentation

4.1.1 Continuous Data

The variables from which information was acquired from the review directed were named either clear cut or nonstop. Table 4.1.1.1 condenses the insights got from the constant information. Recipes were utilized for processing the scores of the mental and work practice factors, which empowered the scientists to determine the mean and standard deviation for each factor.

Variable

Posture

Alertness

Sleepiness

Table 4.1.1.1 Summary Table of Continuous Data

Unit Mean Std. Deviation n/a

Sec n/a

11.4036

392.41911

153.648438

1.62265

18.129103

48.2313165

Alcohol Consumption n/a

Road Lighting Lux

Workload

Age n/a

Years

BMI n/a

28.6979

10.37760417

67.203125

29

23.69651798

6.39455

1.170935441

19.122947

6.8

4.080978687




4.1.2 Categorical Data

The researchers used Pie Graphs to present the categorical data used in this study such as

Gender, Stress, Safety Attitude, Speeding and Human Error.

4.1.2.1 Gender

GENDER

90

294

Boys Girls

Figure 4.1.2.1 The Gender of the respondents

Figure 4.1.2.1 Shows 76.56% of the respondents are men while 23.44% are women




4.1.2.2 Human Error

Human Error

4

107

136

137

1-1.99

2-2.99

3-3.99

4-5.00

Figure 4.1.2.2 Measurement of Human Error of the Respondents

Figure 4.1.2.2 Shows the measurement of Human Error of the respondents with the use of the MRBQ, the researchers measure the Human Error of the drivers.

4.1.2.3 Safety Attitude

Safety Attitude

1.2

4

78

196

1 to 2 2 to 3 3 to 4 4 to 5




Figure 4.1.2.3 Measurement of Safety Attitude of the Respondents

Figure 4.1.2.3 Shows the data gathered from the safety attitude scale to measure the safety attitude of the respondents.

4.2 Data Analysis

The researchers used the Minitab 19 computer program in conducting data analysis.

The results that follow are generated through the use of different statistical analyses available in the computer program.

4.2.1 Correlation

4.2.1.1 Pearson’s Correlation Analysis (Continuous)

Pearson’s Correlation: Safety, Mental Workload, Alertness, Age, BMI, Road

Lighting, Posture, Alcohol, Sleep (Continuous)

The correlation coefficient can range in value from −1 to +1. The larger the absolute value of the coefficient, the stronger the relationship between the variables. For the Pearson correlation, an absolute value of 1 indicates a perfect linear relationship. A correlation close to 0 indicates no linear relationship between the variables.

The results of Pearson's correlation indicate that the correlation between mental workload and safety is about 0.005, which indicates that there is a moderate positive relationship between the variables. The Pearson correlation between alertness and safety is about -0.074, which indicates that there is a negative relationship between the variable, and between alertness and mental workload is about 0.079, the result indicates that there is a positive relationship between the variables. The relationship between age, safety, and mental workload are about 0.008 and 0.067, which indicates that there is a positive relationship between the variables, while the relationship between age and alertness is about -0.016 and indicates that there is a negative relationship between the variables. The correlation between

BMI, safety, mental workload, and alertness are all negative with a value of -0.049, -0.073, and

-0.046, respectively. The relationship between BMI and age is a positive relationship with a value of 0.093. The relationship between road lighting and safety is about -0.095, which indicates that there is a negative relationship, it also indicates that when metal workload and safety increase, road lighting decreases. The relationship between road lighting, mental workload and alertness, and BMI are all positive, with a value of 0.006, 0.016, and 0.069, respectively. The relationship between road lighting and age is a negative relationship with a



PREVENTION IN THE PHILIPPINES: A MULTIPLE REGRESSION APPROACH value of -0.031. The correlation between posture, safety, mental workload, and BMI are all positive relationship with a value of 0.013, 0.073, and 0.058, respectively. The correlation between posture, alertness, age, and road lighting are all negative relationship with a value of

-0.062, -0.066, and -0.024, respectively. The correlation between alcohol, safety, age, BMI, and road lighting is about 0.056, 0.017, and 0.079, which indicates a positive relationship. The correlation between alcohol, mental workload, alertness, and posture is about -0.011, -0.028, and -0.074, which indicates a negative relationship. The correlation between sleep, safety, mental workload, alertness, and BMI are about 0.050, 0.015, 0.055, and 0.038, which indicates positive relationship, while the correlation between sleep, age, road lighting, and alcohol are about -0.142, -0.008, -0.018, and -0.018 which indicates a negative relationship.




Safety

Table 4.2.1.1.1 Pearson’s Correlation

Mental

Alertness Age BMI

Workload

Road

Lighting

Posture Alcohol

Mental

Workload

0.005

Alertness -0.074 0.079

Age 0.008 0.067

BMI -0.049 -0.073

Road

Lighting

-0.095 0.006

Posture 0.013 0.073

Alcohol 0.056 -0.011

-0.016

-0.046 0.093

0.016 -0.031 0.069

-0.062 -0.066 0.058 -0.024

-0.028 0.017 0.079 0.079 -0.074

Sleep 0.050 0.015 0.055 -0.142 0.038 -0.008 -0.018 -0.118

Table 4.2.1.1.1 shows the significant correlations between continuous variables.

The analysis shows that safety was positively correlated to mental workload

(𝑋

2

)

, age

(𝑋

5

)

, posture

(𝑋

9

)

, alcohol consumption

(𝑋

10

)

, and sleep deprivation

(𝑋

12

)

. Safety was negatively correlated to alertness

(𝑋

4

)

, BMI

(𝑋

7

)

, and road lighting

(𝑋

8

)

.




Spearman Correlation: Safety, Stress, Safety Attitude, Gender, Human

Error, Speeding (Categorical)

The results of the Spearman Correlation between stress and safety is 0.066, which indicates that there is a positive relationship between the variables. The Spearman Correlation between safety attitude and safety is 0.188 and between safety attitude and stress is 0.069.

The relationship between these variables is positive, which indicates that as safety and stress increase, safety attitude increase. The Spearman Correlation between gender and safety is

0.034 and between gender and stress is 0.086 and between gender and safety attitude is

0.148, which indicates that as safety attitude, safety and stress increase, gender increase. The

Spearman Correlation between human error and safety is 0.137 and between human error and stress is 0.100 and between human error and safety attitude is 0.079 and between human error and gender is 0.061, which indicates that as gender, stress, and safety attitude increase, the human error also increase. The Spearman Correlation between speeding and safety is 0.037 and between speeding and stress is 0.041 and between speeding and safety attitude is 0.081 and between speeding and gender is 0.061 and between speeding and human error is 0.049, which indicates that as human error, safety, stress, safety attitude, and gender increase, speeding also increases.




Stress

Safety Attitude

Table 4.2.1.1.2 Spearman Rho Correlation

Safety Stress

0.066

0.188 0.069

Safety

Attitude

Gender Human

Error

Gender

Human Error

0.034

0.137

0.086

0.100

0.148

0.079 0.061

Speeding 0.037 0.041 0.081 0.061 0.049

Table 4.2.1.1.2 shows the significant correlations between categorical variables. The analysis shows that safety was positively correlated to stress (𝑋

1

) , safety attitude (𝑋

3

) , gender (𝑋

6

) , human error (𝑋

11

) , and speeding (𝑋

13

) .




4.2.2 Multiple Regression (Step-by-Step Process)

Multiple regression analysis examines the linear relationships between one continuous response and two or more predictors. It is used to determine or predict the response or value of the dependent variable to the value of the independent variables. Also, it is used to determine the overall fit of the model to the data and how each of the predictors contributes to the total variance observed.

Figure 4.2.2.1 Step-by-Step Process of Multiple Regression in Minitab Step 1

The figure shows how to use Multiple Regression. First click Stat, second click regression, third click regression, and lastly click regression model.





The figure shows the responses and the different predictors for continuous variable and categorical variables. The figure shows what the researchers set to responses, continuous predictors, and categorical predictors to proceed with multiple regression analysis. It also shows that there are eight continuous predictors and five categorical predictors.




`


The researchers choose the Stepwise method to analyze the regression model. By using the

Stepwise method, the researchers selectively remove unusual observations from the sample that the Stepwise method generated.




Figure 4.2.2.4

Step-by-Step Process of Multiple Regression in Minitab Step 4

The researchers choose Expanded tables rather than a simple table. The researchers aim to see more data from the sample and to be more precise on interpreting the data.

Table 4.2.2.1 Multiple Regression Analysis Results

Dependent Variable Predicting Factors Coefficient p-value

(Safety) 𝑌

1

Mental Workload ( 𝑋

2

)

Road Lighting ( 𝑋

9

)

Safety Attitude ( 𝑋

3

)

-0.1018

-2.166

-9.44

2.01

0.015

0.002

0.000

Human Error ( 𝑋

12

)

6.30

11.55 0.000

11.68

17.12

The results of the multiple factor regression analysis yield the p-values and coefficients for each predicting factor, which is summarized in Table 4.3.3.1. Based on the results, each of the following predicting factors has a p-value of less than 0.05.




4.2.3 Residual Plots

4.2.3.1 Normal Probability Plot

Figure 4.2.3.1.1 Normal Probability Plot

Based on the normality probability plot shown above, the researchers can say that the data is normally distributed. In the residual plot, the independent variable is represented in the horizontal axis, while residuals are on the vertical axis. Based on the data above, the points are randomly dispersed around the horizontal axis, so, therefore, using linear regression is appropriate for the data.

4.2.3.2

Figure 4.2.3.2.1 Versus Fits Plot




Using Versus Fits Plot will help the researchers verify the assumption if it is normally distributed to have a constant variance. If the points are randomly spread out around the horizontal axis, a linear regression model is appropriate for the data. As the figure shows, all the dots were spread out which means the data are appropriate.

4.2.3.3 Histogram

Figure 4.2.3.3.1 Histogram

The Histogram Plot is related to the Normal Probability Plot, and it shows that the data is suitable for the linear regression because of the dots that are arbitrarily scattered around the horizontal axis.

4.2.3.4 Versus Order Plot

Figure 4.2.3.4.1 Versus Order Plot




The use of Versus Order Plot is to identify if the residual is independent of one another if it has no pattern. The data shows that the residuals show no trend which means the residuals are independent.




CHAPTER 5. CONCLUSIONS AND RECOMMENDATIONS

This chapter contains all the findings of the researcher after conducting the study. The summary of results through gathered data, the conclusions based on the result of the method or technique used in analyzing the data, and the recommendations made for the subjects of this research study.

5.1 Summary

In this study, the researchers were able to gain and broaden their knowledge about the principles of ergonomics by applying this in the research study with the chosen topic. To know the relationship of motorcycle accidents with safety among motorcycle drivers, the researchers applied the concepts of cognitive ergonomics. The researchers identified the problem based on the data of (MMDA, 2019). Based on the data, motorcycle crashes are increasing every year and found out that human error is the most contributing factor. Past studies gave researchers an idea concerning the factors contributing to motorcycle accidents that affect motorcycle safety. Factors were categorized as psychological, individual, environmental, and physical factors.

Collected data are needed to construct a predictive model related to the causes of motorcycle accidents. Researchers used a survey questionnaire for the study. Different tools like DBI, MRBQ, Speeding Behavior Scale, etc are used to identify which of among factors have a significant effect on motorcycle safety. Different Statistical tool was used to determine the factors affecting the safety of motorcycle drivers. The Normality test shows that the p-value is 0.024 which means that the data are normally distributed. In the correlation analysis, we used Pearson’s correlation for the continuous data and Spearman correlation for categorical data. In the stepwise regression, all unusual observations are removed. In the results of multiple regression analysis, four predicting factors are obtained namely: mental workload, road lighting, safety attitude, and human error.




5.2 Conclusions

Many contributing factors affect the safety of motorcycle riders that results in motorcycle accidents. In this study, the researchers were able to identify the contributing factors that affect motorcycle safety among motorcycle riders with the help of related studies and literature.

These factors were classified as dependent and independent variables. After conducting the study, with the use of Minitab software, the study shows that 4 out of 10 factors are a significant cause of motorcycle accidents. Road lighting, Stress, Mental Workload, and Human Error have a p-value lower to a significant level of 0.05. Therefore, these factors are the key to improving the safety of motorcyclists and to reduce motorcycle accidents.

5.3 Recommendations

The researchers proposed an ergonomically designed motorcycle and suggested some solutions to address the problem regarding the safety of motorcycle drivers that can reduce motorcycle accidents among motorcyclists. This will help them to understand the importance of safeness while riding a motorcycle. The researchers proposed design and recommendations are as follows:




5.3.1 Proposed Design of Motorcycle

After conducting the research study, the researchers were able to come up with an ergonomically designed motorcycle that aims to reduce motorcycle accidents among motorcyclists in Metro Manila. With the proposed design of the researchers, the contributing factors that affect the safety of motorcycle riders can be addressed now.

Figure 5.3.1 Perspective View of the Proposed Design of Motorcycle

The researchers used the scooter type of a motorcycle as the majority of motorcyclists preferred a scooter type of a motorcycle. The researchers focused on re-designing the headlight, soft cushion seat with leg rest, and the side mirrors of the motorcycle. The researchers modified the dimension of the scooter type of motorcycle with the dimensions of

70cm by 50cm by 60cm, length, width, and height, respectively. The researchers come up with the following dimensions, the length of the proposed motorcycle design is 75cm, the width is 55cm, and the height of the proposed motorcycle design is 69cm. The researcher comes up with the design because of the following aspects: The engine is 150cc that is why it has more length and width, the researchers also designed a bigger compartment that can stack one bag pack and one full-faced helmet other than scooter type motorcycle than can stack only one full-face helmet and one half-faced helmet.




Figure 5.3.2 Top View of the Proposed Design of Motorcycle

The figure shows the proposed dimension of the soft cushion seat with a length of 45cm and a width of 35cm. The soft cushion seat also has a leg/thigh rest, the researchers come up with this design to lessen the driver's stress while driving and to help the driver to relax more.

The present design of the motorcycle seat has a dimension of 43cm by 32cm, respectively.

The present seat design is uncomfortable to the drivers because of its width and then it has no leg/thigh rest.

Based on the contributing factors of motorcycle safety in driving we suggest the following:

1. Human Error and Mental Workload

In the proposed design of a motorcycle, the researchers allocated the side mirror at the end bar of the steering wheel to see more clearly the back compare to the common design of the side mirror. Also, it can help the motorcycle riders to hold the steering wheel easily in case there would be an accident. Also, the researchers apply aerodynamics in designing the body of the motorcycle to enhance the stability of the motorcycle while running in the middle of other large vehicles like trucks. Furthermore, the researchers designed a soft cushion seat with leg rest for the comfortability of the riders while driving. Thus, this can reduce the human error and lessen the mental workload of the riders in driving the motorcycle.




2. Road Lighting

In the proposed design of a motorcycle, the researchers used 2-3 light bulbs with 3-5 light reflectors to widen the range of the light by 180 degrees. The headlight is more focus on the lower level to avoid other vehicles being dazzled by the bright light of the usual motorcycle. Therefore, this can be a solution to the road lighting problem.

3. Safety Attitude – The researchers suggested to attend seminars and training about safety in driving a motorcycle like Safety Riding Seminar and Safety Driving Seminar of Honda Company. This seminar will help the riders to understand their limitations in driving, the characteristics of their vehicle and also the condition of the environment which affects the safety in driving.




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9. Verane faure,regis lobojis,Nicolas benguigui, (2016), The effects of driving environment complexity and dual tasking on drivers’ mental workload and eye blink behavior, Department of Psychology, Queen's University, Ontario, Canada.

10. Ryoichi ohtsuka, jian wang,takanori chihara,kimihiro yamanaka,keisuke morishima,hiroshi daimoto, (2015), Estimation of Mental Workload during Motorcycle

Operation.

11. Neela Ravindra Rajhans, (2011) Ergonomic Posture for Motorcycle Riding.

Production Engineering Department, College of Engineering, Shivaji Nagar, Pune-

411005, INDIA.

12. KT, kalmer,ez harris, mj griffin,j benetti, reading, m Sampson,d coggon,(2008), CASE-

CONTROL STUDY OF LOW BACK PAIN PRESENTING FOR MRI, WITH SPECIAL

RELATION TO WHOLE-BODY VIBRATION.

13. Tom kotonggianis, (2006), Patterns of driver stress and coping strategies in a Greek sample and their relationship to aberrant behaviors and traffic accidents. Department of Production Engineering and Management, Technical University of Crete, University campus, Chania 73100, Crete, Greece.

14. Geral Matthews,akira tsuda,gu xin, yukako uzeki, (2010), Individual differences in driver stress vulnerability in a Japanese sample.

15. Tom cox, (1986), Stress, coping and problem solving.

16. Gerald Matthews, lisa dorm, Thomas w. hoyes,roy davies, ian glendon ray G. taylor,

(1998), Driver Stress and Performance on a Driving Simulator.




17. E Gulian, g. Matthews,a.i glendon, d.r davies,l. m debney, (1989), Dimensions of driver stress

18. Ching fu chen,cheng wen chen, (2010), Speeding for fun? Exploring the speeding behavior of riders of heavy motorcycles using the theory of planned behavior and psychological flow theory, Department of Transportation & Communication

Management Science, National Cheng Kung University, 1 University Rd., Tainan 701,

Taiwan.

19. H. Slimi, H. Arioui, L. Nouveliere and S. Mammar, (2010), Motorcycle Speed Profile in

Cornering Situation.

20. Andy S.K. Chenga, Terry C.K. Nga, Hoe C. Lee, (2011), A comparison of the hazard perception ability of accidentinvolved and accident-free motorcycle riders.

21. M.S. Horswil l and F.P. McKenna, (2004), Drivers’ hazard perception ability: Situation awareness on the road, Curtin University Driving Rehabilitation Clinic, Curtin Health

Innovation Research Institute, Curtin University, Australia.

22. Keqin jia, mark king, judyj. Fleeter,mary Sheehan,wenjun ma jing lei,jianzhen zhang,(

2015), Drunk driving offenders’ knowledge and behavior in relation to alcoholinvolved driving in Yinchuan and a comparison with Guangzhou, China, Centre for Accident

Research and Road Safety-Queensland (CARRS-Q), Queensland University of

Technology (QUT), Australia. , School of Medicine, University of Queensland,

Australia, Yinchuan City Centre for Disease Control and Prevention of Ningxia Hui

Autonomous Region, China.

23. Keqin Jia, Judy J. Fleiter, Mark J. King Mary Sheehan Michael Dunne Wenjun Ma,

(2013, REDUCING ALCOHOLRELATED DRIVING ON CHINA’S ROADS: TRAFFIC

POLICE OFFICERS’ PERCEPTIONS AND PRACTICE, Email: k.jia@qut.edu.au




24. Anais mayuer, roland Bremond,J.M Christian bastien,( 2009), The effect of the driving activity on target detection as a function of the visibility level: Implications for road lighting, Université paris Est, LEPSIS, INRETS-LCPC, 58 bd Lefebvre, 75015 Paris,

France.

25. P Raynham, (2004), An examination of the fundamentals of road lighting for pedestrians and drivers, Found at http://lrt.sagepub.com/content/36/4/307 .

26. W.J.M Van Bommel,J. Tekelenburg, (1986), Visibility research for road lighting based on a dynamic situation, Université Paul Verlaine-Metz, BP 30309, île du Saulcy,

57006 Metz, France.

27. Maerk A. Elliot, Christpoher j. Bougham, Baryy F. Sexton, (2006), Errors and violations in relation to motorcyclists’ crash risk, Department of Psychology, University of Strathclyde, United Kingdom, Transport Research Laboratory (TRL), United

Kingdom.

28. Royal Automobile Club of Victoria (RACV) Ltd, (2001), The Effectiveness of Driver

Training/Education as a Road Safety Measure.

29. Ching fu chen, (2009), Personality, safety attitudes and risky driving behaviors —

Evidence from young Taiwanese motorcyclists.

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APPENDIX A

PROBLEM

IDENTIFICATION

TOOL




1. PARETO CHART

APPENDIX A

PROBLEM IDENTIFICATION TOOLS

Problem

Reckless Driver of another

Vehicle

Table A.1 Pareto Table of Safety Concerns of Drivers and Passengers

Frequency Percentage Cumulative

Percentage

8 20.00 20.00

Overtaking at unsafe distance 7

Reckless Driver of the Passenger 5

Weight of the Customer 4

17.50

12.5

10.00

37.5

50

60

Comfortability

Slippery Road

Lack of Traffic Signages

Size of Motorcycle

Wobbling and Wiggling of

Steering

Darkness of the Road

4

3

2

2

1

1

10.00

7.5

5.0

5.0

2.5

2.5

70

77.5

82.5

87.5

90

92.5

Drunk Customer

Not Wearing Proper Gear

Over Speeding

1

1

1

2.5

2.5

2.5

95

97.5

100

Table A.1 Shows the different kinds of problem where 6 problem falls into the vital few which are the following: 1. Reckless Driver of another Vehicle, 2. Overtaking at unsafe distance, 3. Reckless Driver of the Passenger, 4. Weight of the Customer, 5. Comfortability, and. 6. Slippery Road. The Pareto rule, also known as the 80/20 rule. States that, for many events, roughly 80% of the effects come from 20% of the causes . Below is the Figure A.1

Pareto Chart of the Safety Concerns of the Drivers and Passengers.




Figure A.1 Pareto Chart of Problem Concerns of the Drivers and Passengers

Road Crash

Accidents per

Vehicle type

Motorcycle

Car

PUJ

Van

Truck

Tricycle

Bike/Pedicab

Bus

Taxi

Unknown

Train

Table A.2 for Sub-Pareto Chart

Frequency

19

12429

7778

2295

1882

1753

1626

997

896

885

552

31112

Percentage

39.95

25.00

7.38

6.05

5.63

5.23

3.20

2.88

2.84

1.77

0.06

Cum percentage

39.95

64.95

72.33

78.37

84.01

89.24

92.44

95.32

98.16

99.94

100.00

This table shows the road crash accidents per vehicle type where motorcycle constitute the highest frequency with 12,429 fatalities. Other accidents per vehicle type that follows are car with 7,7778 fatalities, PUJ with 2295 fatalities, Van with 1,882 fatalities, Truck with 1,753 fatalities, Tricycle with 1,626 fatalities, Bike/Pedicab with 997 fatalities, Bus with 896 fatalities,

Taxi with 552 fatalities, Unknown with 552 fatalities, and Train with 19 fatalities.




Figure A.2 Sub Pareto for Road Crash Accidents per Vehicle Type

This figure of the sub pareto chart for road crash accidents per vehicle type shows that motorcycle accidents fall under the vital few with cumulative percentage of 39.95% which is the main contributor of the road crash accidents in Metro Manila.

Percentage of Motorcycle accident increase (2016-2018)

MMDA report of 2016 and 2017:

2016 = 21,403 accidents

2017 = 22, 063 accidents

Formula:

|𝑂𝑙𝑑 𝑣𝑎𝑙𝑢𝑒−𝑁𝑒𝑤 𝑉𝑎𝑙𝑢𝑒|

× 100 =

𝑂𝑙𝑑 𝑉𝑎𝑙𝑢𝑒

|21,403−22,063|

× 100 = 𝟑. 𝟎𝟖%

21,403

MMDA report of 2017 and 2018:

2017 = 22, 063 accidents

2018 = 26, 652 accidents

Formula:




|𝑂𝑙𝑑 𝑣𝑎𝑙𝑢𝑒−𝑁𝑒𝑤 𝑉𝑎𝑙𝑢𝑒|

× 100 =

𝑂𝑙𝑑 𝑉𝑎𝑙𝑢𝑒

|22,063−26,652|

× 100 = 𝟐𝟎. 𝟖𝟎%

22,063

2. ISHIKAWA DIAGRAM

Figure A.3 Ishikawa Diagram

Figure A.2 shows the possible reason why motorcycle accidents happen it includes four (4)

Categories that causes motorcycle accidents:

Man

Overtaking at unsafe Distance, Rushing, Heavy Flow of traffic, too much volume of vehicles, Weight of the Passenger, Stability, Hard Steering are the factors of motorcycle accidents.

Machine

The weight of the customer, the stability of the motorcycle, and the weight capacity of it are the factors of a motorcycle accidents.

Environment

Lack of traffic signages and improper location of signages are some of the factors that might cause motorcycle accidents.

Materials

Not wearing proper gear due to unfitted ridding gear and comfortability of the gear itself may cause motorcycle accident.







FACTORS

MAN

Overtaking at unsafe Distance

CONTROLLABLE UNCONTROLLABLE

✔

Rushing ✔

Heavy Traffic ✔

Too much volume of vehicles ✔

Weight of the Passenger ✔

Stability ✔

Hard Steering ✔

MACHINE

Weight Capacity ✔

Stability ✔

Heavy Load ✔

ENVIRONMENT

Lack of Traffic Signages ✔

Visibility is not clear ✔

Improper Location ✔

MATERIAL

Not Wearing Proper Gear ✔

Unfitted Riding Gear ✔

Not Comfortable ✔

TOTAL 10 6

Table A.3 Controllable and Uncontrollable

Table A.3 shows the root causes of the problem, Motorcycle Accidents. Which is shown in the

Ishikawa diagram as well. These factors were rated by the researchers based on their observation in whether the factors are controllable or uncontrollable. Based from the results.




3. Why-Why Analysis

PROBLEM •Motorcycle Accident

WHY

•Going too fast/Over

Speeding

WHY

•trying to over run the rush hour

WHY

•Trying to

EscapeTraffic

WHY

•Getting late in appointment

WHY

•Woke Up

Late

Figure A.4 Why-Why Analysis

The figure shown above is the causes and effect underlying the specific problem.

Having a problem with motorcycle crash which is answered by the factor of going too fast/over speeding, answers with the trying to overrun the rush hour, answers with a trying to escape the traffic, answered with getting late in appointment and the root cause of woke up late.




APPENDIX B

MINITAB RESULTS




APPENDIX B

MINITAB RESULTS

Regression Analysis: Safety versus Mental Workload, Alertness, Age, BMI, Road

Lighting, Posture, Alcohol, Sleep, Stress, Safety Attitude, Gender, Human Error,

Speeding

Method

Categorical predictor coding

(1,

0)

Rows unused 16

Stepwise Selection of Terms

α to enter = 0.15, α to remove = 0.15

Regression Equation

Safety Human

Attitude Error

1

1

2 Safety = 68.7

- 1.897 Road Lighting

3 Safety = 78.8


1

1

2

2

4 Safety = 80.3


5 Safety = 82.2


2 Safety = 84.82


3 Safety = 94.88



2



2

3

3

3

3

4

4

4

4

5

4 Safety = 96.42


5 Safety = 98.29


2 Safety = 80.53


3 Safety = 90.58


4 Safety = 92.13


5 Safety = 94.00

2


Safety = 87.71


3 Safety = 97.76


4 Safety = 99.31


5 Safety = 101.18


2 Safety = 91.79





5 3 Safety = 101.85


5

5

4 Safety = 103.39


5 Safety = 105.26


Coefficients

Term

Constant

Road

Lighting

Coef

SE

Coef 95% CI T-Value P-Value VIF

68.7 20.6 (28.2, 109.2) 3.34 0.001

0.810 (-3.491, -2.34 0.020 1.02

1.897 0.303)

Safety

Attitude

2

3

16.1 18.3 (-19.9, 52.2) 0.88 0.380 26.25

11.8 18.0 (-23.6, 47.3) 0.66 0.513 86.49

4

5

Human Error

19.0 18.0 (-16.5, 54.5) 1.05 0.293 89.28

23.1 18.1 (-12.6, 58.7) 1.27 0.204 52.20

3

4

10.06

11.60

3.78 (2.63, 17.49)

3.83 (4.08, 19.13)

2.66

3.03

0.008 4.00

0.003 3.81

5 13.47 4.17 (5.26, 21.68) 3.23 0.001 2.70

Model Summary

S R-sq

Rsq(adj) PRESS

17.8929 9.83% 7.82% *

Analysis of Variance

Rsq(pred) AICc BIC

* 3178.76 3217.23

Source

Regression

DF

Seq

SS Contribution Adj SS Adj MS F-Value P-Value

8 12534 9.83% 12534 1566.7 4.89 0.000




Road

Lighting

Safety

Attitude

1

4

1867

7026

1.46%

5.51%

1755 1754.7

6655 1663.7

5.48

5.20

0.020

0.000

Human

Error

3 3641 2.86% 3641 1213.6

Error

Total

359 114936

367 127470

90.17% 114936 320.2

100.00%

Fits and Diagnostics for Unusual Observations

3.79 0.011

Std

Resid

-2.19

Del

Resid HI

-2.20 0.01163

Cook’s

D

0.01

Obs Safety Fit

SE

Fit 95% CI Resid

7 33.00 71.99 1.93

10 56.00 56.00 17.89

(68.20,

75.79)

(20.81,

91.19)

92 109.83 74.30 2.22 (69.92,

78.67)

102 31.36 68.58 2.04 (64.57,

72.58)

112 43.09 79.86 3.15 (73.67,

86.06)

-

38.99

-0.00

35.54

-

37.21

-

36.77

129 38.96 79.17 1.92 (75.40,

82.94)

156 46.63 85.91 3.00 (80.00,

91.81)

161 28.63 75.55 3.69 (68.30,

82.81)

164 31.87 73.13 2.13 (68.95,

77.31)

209 40.86 79.17 2.87 (73.53,

84.81)

-

40.21

-

39.27

-

46.92

-

41.26

-

38.31

*

2.00

-2.09

-2.09

-2.26

-2.23

-2.68

-2.32

-2.17

* 1.00000

2.01 0.01546

-2.10 0.01297

-2.10 0.03099

-2.27 0.01148

-2.24 0.02815

-2.70 0.04248

-2.34 0.01413

-2.18 0.02565

*

0.01

0.01

0.02

0.01

0.02

0.04

0.01

0.01




222 106.62 70.85 1.84 (67.23,

74.47)

231 107.92 72.37 2.81 (66.84,

77.90)

246 42.86 81.85 2.10 (77.72,

85.99)

270 121.49 79.84 2.75 (74.43,

85.25)

287 45.40 82.61 2.26 (78.17,

87.06)

301 43.67 86.29 3.01 (80.36,

92.22)

310 112.15 70.12 2.28 (65.63,

74.61)

337 24.06 65.85 4.62 (56.76,

74.94)

384 112.04 73.89 2.31 (69.35,

78.43)

Obs DFITS

7 R

0.238992

10 * X

92 0.251837 R

102 -

0.241104

R

112 -

0.375126

R

129 -

0.245052

R

156 -

0.381056

R

161 -

0.569393

R

35.77

35.55

-

39.00

41.65

-

37.22

-

42.62

42.03

-

41.78

38.15


2.01

2.01

-2.19

2.36

-2.10

-2.42

2.37

-2.42

2.15

2.02 0.01060

2.02 0.02470

-2.21 0.01384

2.37 0.02362

-2.11 0.01595

-2.43 0.02838

2.38 0.01626

-2.43 0.06675

2.16 0.01662

0.00

0.01

0.01

0.01

0.01

0.02

0.01

0.05

0.01

64



164 -

0.279741

R

209 -

0.353782

R

222 0.208872 R

231 0.321520 R

246 -

0.261396

R

270 0.368761 R

287 R

301

0.268199

R

0.415798

310 0.306437 R

337 -

0.650877

R

384 0.280977 R

R Large residual

X Unusual X




WORKSHEET 1



Speeding

Method


(1,

0)

Rows unused 16




Safety Human

Attitude Error

2 2 Safety = 94.97 - 0.0699 Mental Workload


2

2

3 Safety = 103.98 - 0.0699 Mental Workload




2

3








3

3

3

4

4

4

4

5












5


Safety = 109.06 - 0.0699 Mental Workload




5

5

5







Coefficients




Term

Constant

Coef

SE

Coef 95% CI

94.97 9.07 (77.13,

T-Value P-Value VIF

10.47 0.000

112.81)

-

0.0699

0.0455 (-0.1595,

0.0196)

-1.54 0.126 1.04 Mental

Workload

Road Lighting -2.008 0.735 (-3.454, -

0.561)

Safety Attitude

-2.73 0.007 1.01

3 -8.51 3.50 (-15.40, -

1.63)

0.88 3.47 (-5.94, 7.70)

-2.43 0.015 3.91

4 0.25 0.800 4.01

5

Human Error

4.76 3.82 (-2.76, 12.28) 1.25 0.214 2.77

3

4

9.01 3.41 (2.30, 15.72)

10.48 3.46 (3.67, 17.30)

5

Model Summary

13.22 3.79 (5.76, 20.67)

2.64 0.009 3.93

3.03 0.003 3.76

3.49 0.001 2.64

S R-sq

Rsq(adj) PRESS


15.8698 14.88% 12.88% 90306.2 10.23% 2931.51 2969.41


Source

Regression

Safety

Attitude

DF

Seq

SS Contribution Adj SS Adj MS F-Value P-Value

8 14971 14.88% 14970.8 1871.3 7.43 0.000

0.18% 593.8 593.8 2.36 0.126 Mental

Workload

1 181

Road Lighting 1 2141

3 9418

2.13% 1877.3 1877.3 7.45 0.007

9.36% 8983.1 2994.4 11.89 0.000

Human Error 3 3231

Error 340 85629

3.21% 3230.6 1076.9 4.28 0.006

85.12% 85628.9 251.8




Total 348 100600 100.00%


Obs Safety Fit

SE


19 31.00 64.18 3.89 (56.52,

71.83)

129 103.69 68.53 1.76 (65.08,

71.99)

167 101.09 67.98 1.87 (64.30,

71.66)

168 49.87 83.14 2.09 (79.03,

87.24)

185 47.96 82.95 2.51 (78.01,

87.89)

202 49.16 80.97 2.69 (75.67,

86.26)

273 45.55 80.63 2.08 (76.54,

84.72)

312 49.76 86.46 2.82 (80.90,

92.01)

335 92.63 73.74 4.44 (65.01,

82.46)

338 108.17 74.18 2.22 (69.81,

78.54)

363 46.40 83.16 3.94 (75.42,

90.91)

Obs DFITS

19 -

0.548444

R

129 0.249728 R

167 0.250715 R

-

33.18

35.15

33.11

-

33.27

-

34.98

-

31.80

-

35.08

-

36.70

18.89

34.00

-

36.77

Std

Resid

-2.16

2.23

2.10

-2.11

-2.23

-2.03

-2.23

-2.35

1.24

2.16

-2.39

Del

Resid HI

-2.17 0.0601421

Cook’s

D

0.03

2.24 0.0122553

2.11 0.0138965

-2.13 0.0172961

-2.25 0.0250443

-2.04 0.0287653

-2.24 0.0171497

-2.37 0.0316669

1.24 0.0781516

2.18 0.0195741

-2.41 0.0616205

0.01

0.01

0.01

0.01

0.01

0.01

0.02

0.01

0.01

0.04




168 -

0.281973

R

185 -

0.359923

R

202 -

0.351585

R

273 -

0.296299

R

312 -

0.427836

R

335 0.361335 X

338 0.307378 R

363 -

0.617177

R

R Large residual

X Unusual X




WORKSHEET 1



Speeding

Method


(1,

0)

Rows unused 16




Safety Human

Attitude Error



2

2





2

3








3

3

3

4

4

4

4

5

5

5

5
























Coefficients



Term

Constant

Coef

95.42

SE


8.81 (78.10,

112.74)

10.84 0.000

-1.92 0.056 1.04 Mental

Workload

-

0.0834

0.0435 (-0.1690,

0.0021)

Road Lighting -1.858 0.700 (-3.236, -

0.481)

-2.65 0.008 1.01

Safety Attitude

3 -9.92 3.41 (-16.62, -

3.21)

-2.91 0.004 4.08

4

5

0.90 3.37 (-5.73, 7.52) 0.27 0.790 4.17

5.10 3.72 (-2.21, 12.41) 1.37 0.171 2.87

Human Error

3 8.20 3.32 (1.67, 14.72) 2.47 0.014 4.09

4 10.07 3.37 (3.44, 16.69)

5

Model Summary

14.56 3.69 (7.30, 21.81)

2.99 0.003 3.95

3.95 0.000 2.70

S R-sq

Rsq(adj) PRESS


14.8940 19.72% 17.77% 77012.4 15.29% 2796.60 2834.16


Source

Regression

DF Seq SS Contribution Adj SS Adj MS F-Value P-Value

8 17926.2 19.72% 17926.2 2240.8 10.10 0.000

0.39% 816.9 816.9 3.68 0.056 Mental

Workload

1 355.8

Road Lighting 1 2052.2 2.26% 1561.8 1561.8 7.04 0.008

12.97% 11359.1 3786.4 17.07 0.000 Safety

Attitude

3 11791.9

Human Error 3 3726.3 4.10% 3726.3 1242.1 5.60 0.001




Error

Total

329 72982.8

337 90909.1

80.28% 72982.8 221.8

100.00%


Std

Resid

-0.63

Del

Resid HI

-0.63 0.0821040

Cook’s

D

0.00

Obs Safety Fit

SE


11 62.00 71.04 4.27 (62.65, -9.04

79.44)

20 44.00 74.10 2.43 (69.31,

78.89)

-

30.10

207 50.29 80.75 1.69 (77.42,

84.07)

-

30.46

-

31.00

30.77

233 54.46 85.46 2.38 (80.79,

90.14)

267 88.67 57.90 3.41 (51.18,

64.61)

289 109.07 79.08 2.57 (74.03,

84.13)

342 73.05 71.22 4.35 (62.67,

79.76)

Obs DFITS

29.99

1.83

11 -

0.189318

X

20 -

0.340934

R

207 -

0.236272

R

233 -

0.342449

R

267 0.502314 R

289 0.359392 R

342 0.039149 X

-2.05

-2.06

-2.11

2.12

2.04

0.13

-2.06 0.0266995

-2.07 0.0128764

-2.12 0.0254345

2.13 0.0525096

2.05 0.0297106

0.13 0.0851083

0.01

0.01

0.01

0.03

0.01

0.00




R Large residual

X Unusual X

WORKSHEET 1



Speeding

Method


(1,

0)

Rows unused 16




Safety Human

Attitude

2

Error



2

2

2








3



3

3

3

4

4

4

4

5

5

5












4













5





Coefficients

Term

Constant

Coef

96.52

SE


8.84 (79.13,

113.92)

10.92 0.000

Mental

Workload

-

0.0922

0.0428 (-0.1764, -

0.0079)

Road Lighting -1.991 0.690 (-3.349, -

0.634)

Safety Attitude

3

4

-2.15

-2.89

0.032 1.04

0.004 1.02

-10.52 3.48 (-17.36, -3.68) -3.02 0.003 4.36

1.04 3.42 (-5.69, 7.78) 0.30 0.761 4.44

5

Human Error

4.88 3.78 (-2.55, 12.31)

3 9.73 3.45 (2.95, 16.52)

4

5

Model Summary

11.07 3.50 (4.19, 17.96)

15.62 3.80 (8.14, 23.10)

1.29

2.82

3.16

4.11

0.198 3.09

0.005 4.54

0.002 4.43

0.000 2.90

S R-sq

Rsq(adj) PRESS


14.5487 21.79% 19.84% 72026.6 17.34% 2723.40 2760.74


Source

Regression

DF Seq SS Contribution Adj SS Adj MS F-Value P-Value

8 18984.3 21.79% 18984.3 2373.0 11.21 0.000

0.53% 980.3 980.3 4.63 0.032 Mental

Workload

1 460.5

Road Lighting 1 2209.7

Safety 3 12612.1

Attitude

2.54% 1762.3 1762.3

14.47% 12211.0 4070.3

8.33

19.23

0.004

0.000




Human Error 3 3701.9

Error 322 68155.7

4.25% 3701.9 1234.0

78.21% 68155.7 211.7

Total 330 87140.0 100.00%


5.83 0.001

Std

Resid

2.04

Del

Resid HI

2.05 0.0295642

Cook’s

D

0.01

Obs Safety Fit

SE


194 104.08 74.89 2.50 (69.97,

79.81)

29.19

-

30.00

224 51.88 81.88 1.79 (78.37,

85.40)

Obs DFITS

194 0.357252 R

224 -

0.258691

R

-2.08 -2.09 0.0151061 0.01

R Large residual




WORKSHEET 1



Speeding

Method


(1,

0)

Rows unused 16




Safety Human

Attitude Error





2

2

3

3












3

3

4







4

4

4

5

5






2






5

5





Coefficients

Term Coef

SE





Constant

Mental

Workload

99.40 8.82 (82.04,

116.75)

-

0.0978

0.0425 (-0.1814, -

0.0142)

Road Lighting -2.234 0.689 (-3.588, -

0.879)

11.27

-2.30

-3.24

0.000

0.022 1.04

0.001 1.02

Safety Attitude

3 -10.56 3.44 (-17.33, -3.78) -3.07 0.002 4.34

4

5

Human Error

1.04

4.85

3.39 (-5.63, 7.72)

3.74 (-2.50, 12.21)

0.31

1.30

0.759 4.40

0.195 3.09

3

4

10.03

10.85

3.41 (3.32, 16.75)

3.47 (4.03, 17.67)

5

Model Summary

15.62 3.77 (8.22, 23.03)

2.94 0.004 4.51

3.13 0.002 4.40

4.15 0.000 2.90

S R-sq

Rsq(adj) PRESS


14.4006 22.64% 20.70% 70160.6 18.21% 2700.29 2737.56


Source

Regression

Mental

Workload

DF Seq SS Contribution

8 19416.6

1 522.5

Adj

SS Adj MS F-Value P-Value

22.64% 19417 2427.1

0.61% 1098 1098.0

11.70

5.29

0.000

0.022


Safety 3 12582.9

Attitude


3.12% 2183 2182.5

14.67% 12240 4080.1

10.52

19.67

0.001

0.000

Error

Total

320 66360.9

328 85777.5

4.23% 3631 1210.5 5.84 0.001

77.36% 66361 207.4

100.00%





Obs Safety Fit

SE


177 95.20 66.96 3.52 (60.04,

Std

Resid

Del

Resid HI

28.24 2.02 2.03 0.0595965

Cook’s

D

0.03

73.87)

28.26 2.02 2.03 0.0539240 0.03 310 108.75 80.49 3.34 (73.91,

87.07)

Obs DFITS

177 0.511611 R

310 0.484039 R

R Large residual




WORKSHEET 1



Speeding

Method


(1,

0)

Rows unused 16




Safety Human

Attitude Error





2

2

3

3












3

3

4







4

4

4

5

5






2






5

5





Coefficients

Term Coef

SE





Constant

Mental

Workload

97.60 8.76 (80.35,

114.84)

-

0.1041

0.0422 (-0.1870, -

0.0211)

Road Lighting -2.296 0.684 (-3.641, -

0.951)

11.14

-2.47

-3.36

0.000

0.014 1.04

0.001 1.02

Safety Attitude

3 -9.08 3.47 (-15.92, -2.25) -2.61 0.009 4.49

4

5

Human Error

2.31

6.43

3.43 (-4.44, 9.05)

3.76 (-0.98, 13.83)

0.67

1.71

0.502 4.56

0.089 3.19

3

4

11.59

12.19

3.46 (4.78, 18.41)

3.51 (5.28, 19.11)

5

Model Summary

17.14 3.80 (9.67, 24.61)

3.35 0.001 4.71

3.47 0.001 4.58

4.51 0.000 3.00

S R-sq

Rsq(adj) PRESS


14.2606 23.45% 21.53% 68303.3 19.15% 2677.56 2714.77


Source

Regression

Mental

Workload


8 19814.5

1 634.8

Adj


23.45% 19814 2476.8

0.75% 1239 1239.4

12.18

6.09

0.000

0.014


Safety 3 12335.0

Attitude


3.16% 2293 2293.4

14.60% 12007 4002.4

11.28

19.68

0.001

0.000

Error

Total

318 64670.1

326 84484.5

4.94% 4176 1391.9 6.84 0.000

76.55% 64670 203.4

100.00%





Obs Safety Fit

SE


89 102.21 73.86 1.94 (70.05,

Std

Resid

Del

Resid HI

28.35 2.01 2.02 0.0184584

Cook’s

D

0.01

77.67)

28.76 2.03 2.04 0.0134871 0.01 258 111.74 82.98 1.66 (79.72,

86.24)

Obs DFITS

89 0.276474 R

258 0.238568 R

R Large residual




WORKSHEET 1



Speeding

Method


(1,

0)

Rows unused 16




Safety Human

Attitude Error





2

2

3

3












3

3

4







4

4

4

5

5






2






5

5





Coefficients

Term Coef

SE





Constant

Mental

Workload

96.40 8.69 (79.31,

113.50)

-

0.1018

0.0418 (-0.1840, -

0.0197)

Road Lighting -2.166 0.678 (-3.501, -

0.832)

11.09

-2.44

-3.19

0.000

0.015 1.03

0.002 1.02

Safety Attitude

3 -9.44 3.44 (-16.21, -2.66) -2.74 0.006 4.47

4

5

Human Error

2.01

6.30

3.40 (-4.67, 8.70)

3.73 (-1.03, 13.63)

0.59

1.69

0.554 4.54

0.092 3.18

3

4

11.55

11.68

3.43 (4.81, 18.30)

3.48 (4.82, 18.53)

5

Model Summary

17.12 3.76 (9.72, 24.52)

3.37 0.001 4.69

3.35 0.001 4.54

4.55 0.000 3.00

S R-sq

Rsq(adj) PRESS


14.1198 23.80% 21.87% 66593.0 19.46% 2654.81 2691.95


Source

Regression

Mental

Workload


8 19677.8

1 590.6


Safety 3 12507.6

Attitude


Error

Total

316 63000.5

324 82678.2

Adj


23.80% 19678 2459.7

0.71% 1186 1185.9

2.92% 2033 2032.7

15.13% 12206 4068.8

5.04% 4165 1388.3

76.20% 63000 199.4

100.00%

12.34

5.95

10.20

20.41

6.96

0.000

0.015

0.002

0.000

0.000




WORKSHEET 1



Speeding

Method


(1,

0)

Rows unused 16




Safety Human

Attitude Error

2

2





2

2





3

3








3

3





4

4

4

4









5

5

5

5





4

5





Coefficients




Term

Constant

Coef

SE

Coef 95% CI

96.40 8.69 (79.31,

T-Value P-Value VIF

11.09 0.000

113.50)

-

0.1018

0.0418 (-0.1840, -

0.0197)

-2.44 0.015 1.03 Mental

Workload

Road Lighting -2.166 0.678 (-3.501, -

0.832)

Safety Attitude

3

-3.19 0.002 1.02

-9.44 3.44 (-16.21, -2.66) -2.74 0.006 4.47

4

5

2.01 3.40 (-4.67, 8.70)

6.30 3.73 (-1.03, 13.63)

Human Error

3 11.55 3.43 (4.81, 18.30)

4 11.68 3.48 (4.82, 18.53)

5

Model Summary

17.12 3.76 (9.72, 24.52)

0.59

1.69

3.37

3.35

4.55

0.554 4.54

0.092 3.18

0.001 4.69

0.001 4.54

0.000 3.00

S R-sq

Rsq(adj) PRESS


14.1198 23.80% 21.87% 66593.0 19.46% 2654.81 2691.95


Source

Regression


8 19677.8

Adj


23.80% 19678 2459.7 12.34 0.000

0.71% 1186 1185.9 5.95 0.015 Mental

Workload

1 590.6


Safety 3 12507.6

Attitude


Error

Total

316 63000.5

324 82678.2

2.92% 2033 2032.7

15.13% 12206 4068.8

5.04% 4165 1388.3

76.20% 63000 199.4

100.00%

10.20

20.41

6.96

0.002

0.000

0.000







APPENDIX C

QUESTIONNAIRE

SCORE

COMPUTATIONS

GUIDE




APPENDIX C

Questionnaire Score Computations

SAFETY ASSESSMENT (DULA DANGEROUS DRIVING INDEX)

#1 + #2 + #3 + … + #26 + #27 + #28

STRESS (DRIVER’S STRESS SCALE)

(#1 + #2 + #3 + … + #16 + #17 + #18) / 18

ATTITUDE ASSESSMENT (SAFETY ATTITUDE SCALE)

(#1 + #2 + #3 + … + #13 + #14 + #15) / 15

SPEEDING ASSESSMENT (SPEEDING BEHAVIOR SCALE)

#1 + #2 + #3 + … + #8 + #9 + #10

BEHAVIOR ASSESSMENT (MACHESTER RIDER BEHAVIOR QUESTIONNAIRE)

(#1 + #2 + #3 + … + #31 + #32 + #33) / 33

POSTURE ASSESSMENT (RAPID ENTIRE BODY ASSESSMENT)

TABLE C SCORE + ACTIVITY SCORE = REBA SCORE

ALCOHOL USE DISORDER IDENTIFICATION

NO. OF DRINKS * 10 = GRAMS OF PURE ALCOHOL




APPENDIX D

ACTUAL DATA

GATHERING







APPENDIX E

SAMPLE

QUESTIONNAIRE




KUWESTYONARYO

Magandang araw! Ikaw ay napili na lumahok sa pagaaral na ito. Anumang impormasyon na iyong maibibigay sa pagaaral na ito ay ituturing na kompidensyal at hindi ipapaubaya sa sinuman. Walang sinumang indibidwal ang kikilalanin na may koneksyon sa mga matutuklasan ng pananaliksik na ito.

Maraming salamat sa pagpapaunlak ng pagsagot sa pagsisisyasat na ito.

Lubos na nakikiusap na sagutan ang mga tanong ng wasto.

DULA DANGEROUS DRIVING INDEX (DDDI)

1.1 Ito ay naglalarawan sa ligtas na pagmamaneho ng isang motorista. Maaari lamang na bilugan ang numero ng iyong sagot. Basahin maigi ang bawat tanong. Tiyakin ang pagtugon sa bawat tanong.

1.Ako ay nagmamaneho kapag ako ay nagagalit o masama ang loob

2. Nawawala ang pasensya habang nagmamaneho

3. Itinuturing kong hindi naaangkop o kawalan ng pinag-aralan ang aksyon ng ibang drayber

4. Nag-flash ako ng headlight ng aking motorsiklo kapag naiinis sa ibang drayber

5. Gumagawa ng mga nakakabastos na kilos at nag mumura patungo sa kinaiinisang drayber

6. Iniinsulto ang kinaiisang drayber

7. Sinasadya kong harangan ang mga sasakyan na masyadong malapit sa aking likuran

8. Magmamaneho ng malapit sa likuran ng aking sinusundan na drayber

9. Ako ay nakikipag-karera sa ibang drayber sa stop light upang mauna

10. Iligal na lalagpasan ang mga mababagal na sasakyan

11. Sa aking palagay, karapat-dapat lamang na ako’y gumanti, kung ang ibang drayber ay agresibo sa akin

12. Kapag ako ay naipit sa tindi ng trapiko, ako ay naiirita

13. Kinakarera ko ang tren kapag mabagal ang takbo nito

14. Ako ay lumulusot sa iba’t-ibang sasakyan dahil sa bagal ng trapiko

15. Magmamaneho kahit ako ay medyo naka-inom na

16. Kapag ang ibang sasakyan ay pininahan ako, parang gusto kong gumanti sakanya

17. Nauubos ang aking pasensya kapag ako ay nahuhuli sa iskedyul habang nagmamaneho

18. Pinapakalma ako ng aking angkas

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0


1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

3

3

3

3

3

3

3

3

3

3

3

3

3

3

3

3

3

3

4

4

4

4

4

4

4

4

4

4

4

4

4

4

4

4

4

4

99



19. Ako ay naiirita kapag ang sasakyan sa aking harapan ay biglang bumagal ang takbo ng walang dahilan

20. Dadaan sa “double yellow lines” upang malagpasan ang mababagal na sasakyan

21. Sa aking palagay, Karapatan kong makarating sa aking pupuntahan sa lalong madaling panahon

22. Sa aking pananaw, ang mga drayber na passive ay dapat matutong magmaneho o di kaya ay huwag ng umalis ng bahay

23. Ako ay magmamaneho sa gitna ng daan upang makaiwas sa trapik

0

0

0

0

0

24. Kapag nilalagpasan ang ibang sasakyan sa 2-lane road, halos hindi

0 ako sumasablay

25. Ako ay magmamaneho kahit lasing 0

0 26. Sa aking palagay, mawawala ako sa sarili kung aking kokomprontahin ang nakasagutang drayber

27. Itinuturing ko ang aking sarili na isang “risk-taker”

0

1

1

1

1

1

1

1

1

1

2

2

2

2

2

2

2

2

2

3

3

3

3

3

3

3

3

3

4

4

4

4

4

4

4

4

4

28. Para sa akin mga batas trapiko ay dapat konsiderahin na isa na lamang suhestyon

0 1 2 3 4

DRIVER’S STRESS SCALE

Ito ay ang mga pangungusap na naglalarawan sa nararamdamang stress ng tao. Mangyaring basahin ng mabuti ang mga bawat isa at pagkatapos ay bilugan ang numero na naglalarawan sa stress mo sa kasalukuyan. Tiyakin ang pag tugon sa bawat tanong.

TANONG

1.Ako ay naiirita habang oras ng siksikan sa kalye 0

2. Ako ay naiinis kapag mabagal ang aking sinusundan

0

3. Kapag ako ay nabigong lampasan ang isang sasakyan, ako ay nakakaramdam ng pagka-bigo

0

0 4. Sa pangkalahatan, lagi kong naiisip na ako ay maabutan at malalampasan ng ibang sasakyan

5. Ako ay nagiging agresibo sa pagmamaneho kapag ako ay naiinis.

6. Hilig kong lampasan o lusutan ang mga sasakyan

7. Ako ay tutok sa pagmamaneho habang matindi ang trapik

8. Ako ay umiiwas sa mga posibleng panganib

9. Ang aksidente ay madalas na mangyari dahil sa maling disisyon

10. Mataas ang konsentrasyon at alerto sa mga daang hindi pamilyar

0

0

0

0

0

0


1

1

1

1

1

1

1

1

1

1

2

2

2

2

2

2

2

2

2

2

4

4

4

4

4

4

4

4

4

4

3

3

3

3

3

3

3

3

3

3

100



11. Sa pangkalahatan, hindi ako masaya sa aking pagmamaneho

12. Nakakaramdam ako ng pagkabigo kapag ako ay nagmamaneho

13. Ang pagmamaneho ay hindi nagbibigay ng saya sa akin

14. Sa pangkalahatan, ang pagmamaneho ay aksaya lamang ng oras

15. Ako ay nababahala kapag masama ang panahon

16. Nagtitiwala sa kakayahan na maiwasan ang aksidente

17. . Madali para sa akin na kontrolin ang galit kapag ako ay nagmamaneho

18. Ako ay laging handa sa mga hindi inaasahang aksyon ng ibang drayber

0

0

0

0

0

0

0

0

1

1

1

1

1

1

1

1

2

2

2

2

2

2

2

2

3

3

3

3

3

3

3

3

4

4

4

4

4

4

4

4

SAFETY ATTITUDE SCALE

3.1 Ito ay mga pangugusap na naglalarawan sa kaligtasan ng tao habang nagmamaneho. Mangyaring basahin ng mabuti ang mga bawat isa at pagkatapos ay bilugan ang numero na naglalarawan sa stress mo sa kasalukuyan. Tiyakin ang pag tugon sa bawat tanong.

TANONG

Masidhing

Hindi Sangayon

Hindi

Sangayon

Hindi

Sigurado

Sangayon

Masidhing

Sang-ayon

1.Maraming batas trapiko ang pwedeng hindi sundin upang mapabilis ang galaw ng trapiko

2. Kailangan minsan na hindi sundin ang mga regulasyon upang tuloy- tuloy ang pag galaw trapiko

3. Mas mahalagang panatilihin ang daloy ng trapiko kesa sundin ang mga batas trapiko

4. Kailangan minsan na wag sundin ang batas trapiko upang mauna

5. Kailangan minsan na makipagsapalaran sa trapik

6. Kailangan minsan na wag sundin ang batas trapiko upang makarating ng mas maaga sa pupuntahan

7. Ang nakikipagsapalaran at lumalabag paminsan minsan sa batas trapiko ay hindi basehan ng pagiging isang hindi ligtas na drayber

8. Kung ikaw ay mayroong magaling na kakayahan, ito ay naaayon lamang upang ikaw ay magmabilis

9. Sa aking panananaw, tama lamang na magmabilis kung ang kundisyon ng trapiko ay naaayon din

1

1

1

1

1

1

1

1

1


3

3

3

3

3

3

3

3

3

2

2

2

2

2

2

2

2

2

5

5

5

5

5

5

5

5

5

4

4

4

4

4

4

4

4

4

101



10. Ang pagmamaneho ng higit sa 10 o 20 kilometro habang lagpas sa naaayong bilis ay okay lamang dahil ito ay ginagawa ng nakararami.

1 2 3 4 5

11. Kung ikaw ay isang ligtas na drayber, katanggap-tanggap lamang na lumagpas ng

10kph sa nakatakdang bilis.

12. Kung ikaw ay isang ligtas na drayber, katanggap-tanggap lamang na lumagpas ng

20kph sa nakatakdang bilis.

13. Kailangan ng mga kabataan ang katuwaan sa trapiko

14. Ang pagmamabilis at kagalakan ay magkasabay habang nagmamaneho

15. Ang pagmamaneho ay hindi lamang isang uri ng transportasyon, ito rin ay isang

1.Ako ay nasisiyahan sa pagmamaneho ng motorsiklo ayon

1

1

1

1

1

1

2

2

2

2

2

2

3

3

3

3

3

3

4

4

4

4

4

5

5

5

5

5 uri ng pagmamabilis at kagalakan

SPEEDING BEHAVIOR SCALE

4.1 Ito ay mga pangugusap na naglalarawan sa ugaling pagmamabilis habang nagmamaneho ng motorsiklo. Mangyaring basahin ng mabuti ang mga bawat isa at pagkatapos ay bilugan ang numero na naglalarawan sa ugaling pagmamabilis habang nagmamaneho ng motorsiklo. Tiyakin ang pag tugon sa bawat tanong.

Masidhing

Hindi Sang-

Hindi Sangayon

Hindi

Sigurado

Sang-ayon Masidhing

Sang-ayon

4 5

1 2 3 4 5 2. Ako ay masaya sa pagmamaneho ng motorsiklo

3. Kaaya-aya ang pagsakay sa motorsiklo

4. Nakatutok ng husto sa pagmamabilis

1

1

2

2

3

3

4

4

5

5

5. Masaya kapag nagmamabilis

6. Alam ang mga nangyayari sa

Kapaligiran

7. Laging pinapaalalahanan ng mga taong malapit sa akin tungkol sa aking pagmamabilis

8. Hindi pabor ang mga taong malapit sa akin tungkol sa pagmamabilis

1

1

1

1

2

2

2

2

3

3

3

3

4

4

4

4

5

5

5

5

1 2 3 4 5




9. . Hindi nagmamabilis sa pagmamaneho ang mga taong malapit sa akin

10

Habang nagmamabilis, naniniwaala ako sa aking kakayahan sa pagmamaneho

1 2 3 4 5

MANCHESTER RIDER BEHEAVIOR QUESTIONNAIRE (MRBQ)

5.1 Ito ay mga tanong tungkol sa pag-uugali ng isang rider ng motorsiklo. Mangyaring basahin ng maigi ang bawat katanungan at bilugan ang numero na iyong tugon.

Masidhing

Hindi Sang-

Hindi

Sang-

Hindi

Sigurado

Sangayon

Masidhing

Sangayon ayon ayon

1.Nabigo na mapansin ang mga naglalakad sa tawiran kapag lumiliko sa gilid ng kalye mula sa pangunahing kalsada?

2. Hindi napansin ang isang tao na lumabas mula sa likuran ng isang naka park na sasakyan?

3. Hindi napansin ang naglalakad na naghintay makatawid sa isang tawiran habang kakapula lamang ng ilaw ng trapiko?

4. Habang nasa pangunahing kalsada, pumunta sa harap ng isang sasakyan na hindi nakita o hindi napansin ang bilis ng pagpapatakbo ng sasakyan?

1

1

1

1

2

2

2

2

3

3

3

3

4

4

4

4

5

5

5

5

5. Hindi napansin ang mga "bigyang daan" na sensyales nang maiwasan ang mga pagbangga sa mga sasakyang nasa tamang daanan?

6. Nabigo na mapansin ang isa pang sasakyan na maaaring pumunta sa iyong harapan at nahihirapang makahinto? 1

1

7. Habang nakapila upang makaliko pakaliwa sa pangunahing kalsada, iyong pinagtutuunan ng pansin ang trapiko na halos iyong matamaan ang sasakyan sa iyong harap?

1


2

2

2

3

3

3

4

4

4

5

5

5

103



8. Habang abala, iyong hindi napansin na ang sasakyan sa iyong harap ay bumagal at kailangan mong mag preno ng malakas upang maiwasan ang pagbangga?

9. Pagtangkang malampasan ang isang tumatakbong sasakyan ng hindi napapansin ang pagsenyas ng pagliko nito?

10. Habang tumatakbo sa kaparehong bilis ng iba pang sasakyan, ika'y nahihirapang makahinto sa tamang oras laban sa ilaw ng trapiko?

1

1

1

2

2

2

3

3

3

4

4

4

5

5

5

11. Magpatakbo ng napakalapit sa sasakyan sa iyong harapan na ika'y mahihirapang makahinto pagdating ng hindi inaasahang pangyayari?

12. Pagtakbo ng malawak habang papunta sa isang sulok?

13. Magpatakbo ng napakabilis papunta sa isang sulok kahit na baka ika'y mawalan ng kontrol?

14. Humigit sa limitasyon ng bilis ng pagpapatakbo ng motorsiklo ng isang bansa o kalsada sa kanayunan?

1

1

1

1

15. Hindi pagtuon ng pansin sa limitasyon ng bilis ng pagpapatakbo ng motorsiklo maging sa gabi man o sa umaga?

16. Humigit sa limitasyon ng bilis ng pagpapatakbo sa daanan ng motorsiklo?

1

1

17. Humigit sa limitasyon ng bilis ng pagpapatakbo sa daan na may mga tirahan?

1

2

2

2

2

2

2

2

3

3

3

3

3

3

3

4

4

4

4

4

4

4

5

5

5

5

5

5

5




18. Pagtakbo papalayo sa ilaw ng trapiko sa layon na malampasan ang sasakyan o motorsiklo na malapit sa iyo? 1 2 3 4 5

19. Hindi pagpigil sa sarili at sumugal lamang sa kalsada?

20. Pagpapatakbo sa pagitan ng dalawang linya habang mabilis na gumagalaw ang trapiko?

21. Pakikisali sa hindi opisyal na karera kasama ang iba pang mga nagpapatakbo ng motorsiklo at sasakyan?

22. Pagpapatakbo ng napakabilis papunta sa isang sulok na ika'y kinabahan sa iyong sarili?

23. Pagtangka o hindi pagtangkang pagpapataas ng harapang gulong ng motorsiklo at pagpapanati nitong nakataas sa hangin?

24. Habang tumatakbo natanggalan ng gulong sa harapan

25. Pagsadyang pagpapaikot ng gulong?

26. Hindi pagsadyang pagpapaikot ng gulong?

27. Pagsuot ng tamang sapatos sa pagpapatakbo ng motorsiklo?

28. Pagsuot ng pamproteksyong pantalon? (katad o hindi)

29. Pagsuot ng pamproteksyong tsaketa? (katad o hindi)

30. Pagsuot ng proteksyon sa katawan ( pad sa siko, pad sa balikat, pad sa tuhod, atbp.)

1

1

1

1

1

1

1

1

1

1

1

1

2

2

2

2

2

2

2

2

2

2

2

2

3

3

3

3

3

3

3

3

3

3

3

3

4

4

4

4

4

4

4

4

4

4

4

4

5

5

5

5

5

5

5

5

5

5

5

5




31. Pagpapatakbo kapag sa iyong palagay ika'y maaaring nasa ibabaw ng ligal na limitasyon para sa alkohol? 1 2 3 4 5

32. Pagsuot ng pambuong katawang katad na damit pamproteksyon? 1 2 3 4 5

33. Pagsuot ng maliwanag o makinang na damit? 1 2 3 4 5

RAPID ENTIRE BODY ASSESSMENT (REBA)

6.1 Ito ay naglalarawan sa postura ng tao habang nagmamaneho ng motorsiklo. Ang paksa ay kukuhanan ng larawan habang nakasakay sa kanyang motorsiklo. Mangyaring ipostura ang sarili sa akto ng pagmamaneho ng motorsiklo.

Table A Neck

Legs

1

1 2 3 4

2

1 2 3 4

3

1 2 3 4

Neck Score

Trunk Score

Trunk

Posture

Score

1

2

1

1

2

2

3

3

4

4

1

1

2

2

3

3

4

4

1

1

2

2

3

3

4

4

Leg Score

3

4

1 2 3 4 1 2 3 4 1 2 3 4

1 2 3 4 1 2 3 4 1 2 3 4

5 1 2 3 4 1 2 3 4 1 2 3 4

Posture Score A

Table

B

Lower Arm

Force/Load Score

1

Score A

2

Wrist

1 2 3 1 2 3


Upper

Arm

Score

4

5

6

1

2

3



4

6

7

1

1

3

5

7

8

2

2

4

5

8

8

2

3

5

5

7

8

1

2

4

6

8

9

2

3

5

Posture Score B

Score A

(score from table

A + load/force score)

1

2

5

6

3

4

7

8

9

10

11

12

Table C Score

Coupling Score

TABLE C

Score B, (table B value + coupling score)

1 2 3 4 5

7

8

9

4

6

1

2

3

4

10

11

12

7

8

9

4

6

1

1

2

3

10

11

12

1

2

3

4

4

6

7

8

9

10

11

12

2

3

3

4

5

7

8

9

10

11

11

12

6

8

9

10

10

3

4

4

5

11

12

12

6

7

8

9

10

10

3

4

5

6

11

12

12

Activity Score

Score B

Final REBA Score

7

8

9

9

10

11

4

5

6

7

11

12

12

7

8

9

3

4

5

9

9

10

10

10

11

6

6

7

8

12

12

12

8

8

9

10

10

11

5

6

7

8

12

12

12

Upper Arm Score

Lower Arm Score

Wrist Score

11

9

10

11

11

12

7

7

8

9

12

12

12

10

9

10

11

11

12

7

7

8

9

12

12

12

12

9

10

11

11

12

7

8

8

9

12

12

12




DIGIT VIGILANCE TEST

7.1 Maaaring tingnan maigi ang papel. Ang mga numero na nakikita ay magkaka-iba ang ayos sa bawat hilera. Ang mga numero ay iba’t -iba ang pagkaka-ayos. Maaari lamang na kanselahin ang numerong 6 at 9 sa pinakamabilis na paraan. Wag kanselahin ang ibang numero maliban sa 6 at 9 at siguraduhing hindi magmimintis sa kakanselahing numero. Maari rin lamang na tapusin ito sa loob ng 7 minuto. Gawin sa pinakamabilis na paraan.

2 3 5 1 8 7 6 5 4 9 1 3 4 7 5 6 9 7 5 1 3 5 4 4 7 6 8 9 7 9

1 4 3 4 7 5 8 6 9 7 4 2 3 9 4 5 7 8 1 2 2 1 6 7 3 1 2 4 9 9

3 4 7 6 8 1 9 2 4 5 7 5 6 7 8 1 2 9 1 2 5 4 3 8 6 7 9 4 9 6

4 5 4 6 7 8 8 3 3 9 1 2 5 4 7 8 6 5 1 2 3 9 1 4 8 7 1 5 3 7

8 6 7 1 3 4 5 9 7 4 2 1 5 3 6 9 7 8 1 3 4 1 7 8 9 7 6 4 2 3

9 4 6 7 8 1 3 4 7 8 5 1 6 7 4 2 9 7 5 3 6 4 1 7 2 5 9 7 5 6

5 7 4 1 3 6 4 1 2 9 7 3 8 9 6 1 4 2 3 4 1 5 6 8 7 9 7 6 8 1

7 5 3 1 6 8 7 4 5 2 9 5 4 6 3 1 2 9 7 8 7 5 1 3 5 6 9 7 8 6

6 1 2 4 5 7 8 3 1 4 9 4 5 6 7 2 1 3 4 5 9 8 5 2 3 4 7 5 8 9

1 5 6 7 1 2 3 4 2 8 7 9 6 1 3 5 7 9 6 7 8 2 4 3 1 3 3 2 4 7

5 4 2 4 6 7 1 3 5 6 9 7 8 4 2 3 4 6 7 8 4 1 3 9 7 8 4 9 5 1

2 3 4 6 7 8 5 4 3 7 8 9 6 1 3 5 7 8 4 2 5 6 7 9 7 8 2 1 3 4

2 5 3 1 6 8 7 4 5 2 9 5 4 6 3 1 2 9 7 8 7 5 1 3 5 6 9 7 8 6

3 4 3 4 7 5 8 6 9 7 4 2 3 9 4 5 7 8 1 2 2 1 6 7 3 1 2 4 9 9

5 7 4 1 3 6 4 1 2 9 7 3 8 9 6 1 4 2 3 4 1 5 6 8 7 9 7 6 8 1

6 5 3 1 6 8 7 4 5 2 9 5 4 6 3 1 2 9 7 8 7 5 1 3 5 6 9 7 8 6

7 7 4 1 3 6 4 1 2 9 7 3 8 9 6 1 4 2 3 4 1 5 6 8 7 9 7 6 8 1

8 5 3 1 6 8 7 4 5 2 9 5 4 6 3 1 2 9 7 8 7 5 1 3 5 6 9 7 8 6

7 4 3 4 7 5 8 6 9 7 4 2 3 9 4 5 7 8 1 2 2 1 6 7 3 1 2 4 9 9

5 5 3 1 6 8 7 4 5 2 9 5 4 6 3 1 2 9 7 8 7 5 1 3 5 6 9 7 8 6

4 7 4 1 3 6 4 1 2 9 7 3 8 9 6 1 4 2 3 4 1 5 6 8 7 9 7 6 8 1

1 5 3 1 6 8 7 4 5 2 9 5 4 6 3 1 2 9 7 8 7 5 1 3 5 6 9 7 8 6

3 7 4 1 3 6 4 1 2 9 7 3 8 9 6 1 4 2 3 4 1 5 6 8 7 9 7 6 8 1

1 3 4 6 7 8 5 4 3 7 8 9 6 1 3 5 7 8 4 2 5 6 7 9 7 8 2 1 3 4

2 5 3 1 6 8 7 4 5 2 9 5 4 6 3 1 2 9 7 8 7 5 1 3 5 6 9 7 8 6

5 4 3 4 7 5 8 6 9 7 4 2 3 9 4 5 7 8 1 2 2 1 6 7 3 1 2 4 9 9

9 7 4 1 3 6 4 1 2 9 7 3 8 9 6 1 4 2 3 4 1 5 6 8 7 9 7 6 8 1

9 5 3 1 6 8 7 4 5 2 9 5 4 6 3 1 2 9 7 8 7 5 1 3 5 6 9 7 8 6

9 3 4 6 7 8 5 4 3 7 8 9 6 1 3 5 7 8 4 2 5 6 7 9 7 8 2 1 3 4

7 5 3 1 6 8 7 4 5 2 9 5 4 6 3 1 2 9 7 8 7 5 1 3 5 6 9 7 8 6

8 7 4 1 3 6 4 1 2 9 7 3 8 9 6 1 4 2 3 4 1 5 6 8 7 9 7 6 8 1

5 5 3 1 6 8 7 4 5 2 9 5 4 6 3 1 2 9 7 8 7 5 1 3 5 6 9 7 8 6

2 7 4 1 3 6 4 1 2 9 7 3 8 9 6 1 4 2 3 4 1 5 6 8 7 9 7 6 8 1

5 4 3 4 7 5 8 6 9 7 4 2 3 9 4 5 7 8 1 2 2 1 6 7 3 1 2 4 9 9




SIGN CANCELLATION

8.1 Ito ay naglalarawan sa pagsukat ng kaantukan ng isang tao. Suriing mabuti ang imahe at kanselahin lamang ang 2 simbolo sa itaas. Maaaring sagutan lamang ito ng tama. Gawin sa mabilis na paraan.

Oras: ________________________

Bilang ng Maling Sagot: _________________




ALCOHOL USE DISORDER IDENTIFICATION TEST (AUDIT)

9.1 Ito ay naglalarawan tungkol sa pagkonsumo, pagiging dependent, at problema sa pag-inom ng alak ng isang tao. Maaari lamang na basahing maigi ang bawat tanong. Maaari rin lamang na markahan ng ekis (X) ang numero na iyong tugon.

Hindi Kailanman Buwanan o mas kaunti sa isang

2-4 na beses sa

2-3 beses sa isang

4 o mas higit pa sa buwan isang buwan lingo isang linggo

Iskor

1.Gaano ka kadalas umiinom ng nakalalasing na inumin?

2. Gaano karaming standard na inumin ang naiinom mo sa tuwing ikaw ay nag-iinom ng alcohol?

3. Gaano kadalas kang nag-iinom ng anim o mas higit pang standard na inuming alak sa isang okasyon?

4. Gaano kadalas noong nakaraang taon na kapag ikaw ay naka-inom na ng alak ay hindi kana tumitigil?

5. Gaano kadalas noong nakaraang taon na hindi mo na nagagawa ang mga dapat mong gawin nang dahil sa alak?

6. Gaano kadalas noong nakaraang taon na kailanganin mo munang uminom sa umaga upang mawala ang epekto ng alak?

7. Gaano kadalas kang nagsisisi pagkatapos uminom ng alak?

0

0

0

0

0

0

1

1

1

1

1

1

2

2

2

2

2

2

3

3

3

3

3

3

4

4

4

4

4

4

0 1 2 3 4

8. Gaano mo natatandaaan ang isang pangyayari matapos mong uminom ng alak noong nakaraang taon?

9. Ikaw ba ay nasaktan o nakasakit ng ibang tao dahil sa pag-inom ng alak?

10. Napagsabihan ka na ba ng doctor, kaibigan, o kapamilya tungkol sa pagbabawas mo sa pag inom ng alak?

TOTAL ISKOR

0

0

0

1

1

1

2

2

2

3

3

3

4

4

4

PHOTOMETRIC MEASUREMENT

10.1 Ito ay talaan ng lebel ng luminance sa bilang na lugar na madalas daanan ng motorsiklo sa Lungsod ng Quezon.

Mga Daan: Lebel ng Luminance

EDSA (BONI SERRANO)

EDSA (MAIN AVENUE)


EDSA (P. TUAZON)



EDSA (AURORA BOULEVARD)

EDSA (E. RODRIGUEZ)

NASA TASK LOAD INDEX (NASA – TLX)

11.1 Ito ay naglalarawan sa dami ng trabaho na ginagawa ng isang tao upang suriin ang isang trabaho, sistema, o pagiging epektibo ng kanyang perpormans.

Kard ng paghaham bing ng iba’t -ibang uri ng Workload:

Pagsisikap

O

Perpormans

Temporal na Demand

O

Pagkabigo

Temporal na Demand

O

Pagsisikap

Perpormans

O

Pagkabigo

Pisikal na Demand

O

Perpormans

Pagkabigo

O

Pagsisikap

Pisikal na Demand

O

Pagkabigo

Pisikal na Demand

O

Temporal na Demand

Temporal na Demand

O

Mental na Demand

Perpormans

O

Mental na Demand

Perpormans

O

Temporal na Demand

Mental na Demand

O

Pisikal na Demand

Pagkabigo

O

Mental na Demand

Papel para sa Rating:

MENTAL NA DEMAND

Low

PISIKAL NA DEMAND

Low

TEMPORAL NA DEMAND

Low

Mental na Demand

O

Pagsisikap

Pagsisikap

O

Pisikal na Demand

High

High

High




PERPORMANS NA DEMAND

Low

EPORT NA DEMAND

Low

PRUSTRASYON NA DEMAND

Low

High

High

High




Dear Respondents,

Greetings! We, the 4th year students taking up Bachelor of Science in Industrial Engineering

(BSIE) from Technological Institute of the Philippines would like to ask about your personal opinion on the problems that you encounter when riding ANGKAS for our Ergonomics Project

Study.

May we please take a few minutes of your time in answering the questions below. Your answers will surely provide benefits to the users of motorcycle taxis. Thank you!

Gender:

Male

Female

Age: _______

Are you an ANGKAS:

Driver

Customer

If you're an ANGKAS Driver, what are the problems that you encounter when driving?

Wobbling/wiggling of the steering wheel due to improper loading

Weight of the customer

Slippery roads

Reckless drivers of another car

Darkness of the roads

Lack of traffic signs on main roads

Numbness of arm

Back pain

Drunk customers

If you're an ANGKAS customer, what are the problems that you encounter when riding

ANGKAS?




Reckless drivers

Driver disregards traffic signs

Overtaking at unsafe distance

Not wearing proper riding gear

Over speeding of the driver

Size of the motorcycle

Comfortability while riding




APPENDIX F

SUMMARY OF

DATA FROM

QUESTIONNAIRE




C1: SAFETY, C2: STRESS, C3: WORKLOAD, C4: SAFETY ATTITUDE, C5: ALERTNESS,

C6: AGE, C7: GENDER, C8: BODY MASS INDEX (BMI), C9: ROAD LIGHTING, C10:

POSTURE, C11: ALCOHOL CONSUMPTION, C12: HUMAN ERROR, C13: SLEEPINESS,

C14: SPEEDING

C1 C2 C3 C4 C5

65 2 70 2 420

66 2 75 4 350

70 2 75 4 380

72 2 80 3 420

63 2 20 3 420

44 2 35 3 420

33 2 35 3 420

52 3 55 3 420

56 2 60 4 310

56 2 30 1 345

61 3 50 3 323

62 2 45 3 368

62 2 65 2 420

50 2 55 3 420

65 3 80 3 349

53 2 90 3 413

65 2 85 3 397

77 2 60 3 321

61 2 50 3 373

40 3 90 3 366

31 2 20 3 349

46 2 50 3 352

44 2 30 3 389

65 3 80 4 378

77 3 90 4 420

86 3 60 4 420

86 3 85 4 365

71 2 80 4 350

69 3 40 3 369

66 2 30 3 413

76 4 50 4 401

92 4 60 4 400

92 4 40 4 363

C6 C7

38

40

32

47

1

1

1

1

C8

27 1 0.487

24.69041

0.8141

20.70313

0.257

C9 C10 C11 C12 C13 C14

40 1 19.59632 11.8 9 60 4 47 3

25 1 19.62323 10.6 12 60 3 38 3

35 1 34.22222 9.4 9 60 3 35 4

34 1 18.30577 8.8 11 90 4 31 3

36 1 17.95918 10.2 12 100 4 51 3

24 1 24.55775 11.2 9 40 2 38 3

26 1 24.56033 9.8 10 50 3 49 4

28 1 20.22703 10.4 13 70 3 59 4

42 1 28.32658 12.4 13 50 3 33 2

47 1 28.32658 12 9 60 3 32 4

29 1 24.25867 11 14 110 3 32 4

37 1 30.46875 10 12 50 2 32 4

20 1 24.55775 10.2 11 120 2 37 5

18 1 20.22703 12.6 9 35 3 43 4

44 1 16.65306 11.6 10 60 3 44 3

33 1 23.1405 8.6 12 40 3 46 3

56 1 22.59814 9.4 13 30 3 45 4

22 1 30.46875 8.4 13 80 3 45 3

43 1 22.27531 11.6 10 100 3 44 4

34 1 34.22222 10.4 9 55 3 37 4

20 1 23.53304 10.4 9 85 2 38 4

31 1 25.21736 10.4 14 35 3 40 4

24 1 23.53304 9.2 12 40 3 53 4

22 1 32.04588 10.4 13 70 3 44 4

10

10.8

11.8

9.8

8.4

11

24 1 0.5918 12.4 10 110 3 42 4

32 1 16.06979 13.2 9 90 3 83 3

11

11

9

9

120

90

130

85

30

4

3

3

3

4

41

73

78

97

68

3

4

3

3

3

29 1 30.22222 10.6 12 110 4 87 4

23 1 23.33547 10.6 13 80 4 75 4




90 4 40 5 352

93 3 75 4 347

91 4 60 4 415

96 3 70 4 353

94 3 70 4 358

85 3 55 4 359

92 3 60 4 418

90 3 60 4 388

98 3 40 5 366

104 3 50 5 398

103 4 40 5 399

104 5 90 4 413

68 2 90 3 418

77 3 70 4 392

97 3 80 4 378

91 3 70 4 333

94 3 25 3 325

36 1 30.45766 12 12 120 4 65 4

50 1 23.83673 10.6 12 80 4 71 4

21 1 18.73278 10.6 9 110 4 63 4

29 1 24.50895 10.4 9 100 4 74 4

39 1 26.83518 10.6 11 70 4 78 5

29 2 23.18339 9 10 120 4 84 4

28 1 26.83518 11.6 13 110 4 72 4

30 1 22.94812 8.8 14 90 4 74 4

22 2 20.54989 9.8 12 80 5 65 5

40 2 22.83737 11.4 10 100 4 61 5

31 2 16.00048 10.2 9 100 5 67 5

37 1 21.36752 10 9 100 5 66 5

50 1 23.05456 12.2 11 90 3 81 3

20 1 23.18339 9.6 11 50 4 83 5

38 1 22.03857 12 13 70 3 70 4

24 2 24.4646 9.4 14 60 4 69 4

38 2 18.83239 10.2 14 30 4 85 4

70 2 92 5 374.41 27 1 18.83239 10.2 11 90 4 187 3

45 3 95 3 418.27 38 1 32 9.2 11 70 2 206 4

81 3 73 4 375.65 36 1 0.7055 9.4 9 70 3 134 4

60 3 89 3 412.13 37 1 30.22222 9.2 9 110 4 188 4

92 3 75 4 391.99 25 1 23.33547 9.6 14 20 2 182 4

64 4 37 4 400.27 22 1 30.45766 11.4 11 110 5 143 3

109 3 46 4 4.46 36 1 23.83673 9.6 12 100 3 138 2

63 4 50 3 382.91 22 1 18.73278 8.4 12 40 4 221 3

82 4 54 3 384.4 20 1 24.50895 12 11 110 5 176 3

77 4 36 3 4.08 22 1 26.83518 9.8 13 60 4 114 5

62 2 71 3 406.05 28 1 23.18339 9.2 11 50 3 180 4

103 2 50 5 408.49 21 1 22.26563 9.8 13 20 3 134 3

97 2 77 5 387.37 21 1 21.36752 11.4 9 20 5 180 3

64 3 41 4 406 21 1 16.54131 10 9 90 5 189 3

106 2 85 4 400.99 27 1 16.78719 9 10 10 5 0

72 2 82 4 372.71 20 1 20.54989 11.6 11 90 4 2

5

4

71 2 76 3 376.92 20 1 22.94812 10 14 20 4 212 3

67 3 63 3 380.86 25 1 23.30109 12 12 70 4 167 4

65 3 72 3 398.96 24 1 24.16327 9.2 12 90 4 146 3

53 2 96 3 384.8 36 1 25.0995 11 10 30 5 154 3

71 2 46 3 399.86 21 1 32 9 9 70 3 155 4

64 2 77 3 395.74 23 1 25.0995 8.8 9 30 2 0 4

106 4 54 4 383.75 26 1 22.83737 10.4 13 50 4 214 5




65 3 60 3 389.92 37 1 25.77778 10.8 14 20 5 155 4

86 3 82 4 385.61 30 1 20.83082 9.6 13 80 3 180 4

96 3 71 5 406.38 28 1 26.39798 10.2 14 70 3 182 5

80 3 66 3 381.73 25 1 23.05456 10.2 14 60 3 187 4

112 4 36 5 383.87 22 1 22.03857 9.4 14 20 4 209 3

105 3 61 3 396.6 27 1 23.18339 9.4 13 110 5 177 5

64 4 38 4 412.34 23 2 24.4646 11.2 12 20 4 211 3

54 2 83 4 399.87 27 2 21.67211 12.4 14 90 3 137 4

84 3 75 4 417.81 18 2 18.83239 12.4 14 60 4 151 4

87 2 91 4 376.77 34 1 16.00048 8.8 9 40 5 220 4

90 3 57 4 387.32 32 2 20.17715 11.8 10 70 3 220 5

81 2 48 3 415.22 34 2 21.97735 10 10 70 4 116 5

74 2 47 3 418.77 25 1 18.92915 8.6 14 60 4 115 4

62 2 42 5 372.7 28 1 30.46875 10.4 11 120 3 6 3

79 3 75 5 411.86 30 1 24.53512 8.4 11 20 5 183 4

85 4 97 4 404.4 29 1 17.95918 8.8 13 60 3 216 3

90 4 92 4 385.15 31 1 27.33564 10.8 10 110 3 172 4

90 2 60 4 396.73 32 1 16.65306 10.4 10 60 3 142 3

110 4 64 3 417.91 28 1 0.257 9.4 10 30 4 127 3

88 2 67 3 375.49 32 1 18.72601 12 10 90 3 162 4

64 3 94 3 377.72 20 1 0.7055 10.2 13 70 3 154 5

102 3 55 3 393.14 18 1 28.99931 9.2 12 20 4 175 4

104 3 36 4 396.96 26 2 24.55775 9.2 12 70 5 133 4

97 4 36 5 376.72 33 2 27.94214 11 9 20 4 163 3

94 3 65 4 373.28 38 2 24.56033 7.6 14 10 4 117 3

63 4 63 4 375.18 26 2 24.67105 11 11 50 5 163 3

65 2 50 5 411.48 35 2 21.73 10 14 70 2 141 4

82 2 43 4 415.83 20 2 20.22703 10.6 13 100 4 179 5

31 4 90 3 401.83 23 2 23.1405 11.6 14 70 3 6 4

86 2 36 3 418.24 39 1 24.25867 10 12 110 5 145 3

66 2 75 4 404.26 26 2 24.67105 9.6 14 20 3 201 3

75 4 81 5 411.43 35 2 23.23346 12.6 9 10 3 178 5

74 2 96 4 416.76 24 2 22.59814 9.2 12 40 4 2 2

83 3 65 4 416.93 22 2 28.32658 9.6 12 110 3 221 4

77 3 97 3 391.97 38 1 26.95984 7.8 11 30 3 169 4

53 4 39 3 378.8 34 2 32.88919 10 10 90 5 2

82 3 43 3 387.53 23 2 24.55775 11.6 14 90 2 7

4

4

64 3 60 4 406.5 37 1 22.89282 11 10 60 4 144 3

43 2 61 5 391.89 21 2 24.88889 12.4 12 40 4 116 4

93 3 90 4 392.59 39 1 29.47584 10.8 13 40 4 8 3




51 2 91 4 401.25 24 1 22.27531 10.8 14 20 2 121 5

79 3 64 3 383.03 39 1 0.6232 10.6 13 90 4 177 4

45 4 55 3 407 28 1 18.30577 11.4 10 80 4 207 4

78 2 97 4 416.29 26 1 25.15315 11 14 20 3 200 3

108 4 54 4 401.51 25 1 32.04588 9.6 14 70 4 206 5

86 3 61 4 382.29 18 1 27.34375 10.4 14 100 4 116 5

68 3 80 3 405 25 1 0.5717 12.6 14 20 3 155 3

80 2 82 2 384.14 26 1 17.95918 8.8 12 50 5 3 3

84 4 68 3 409.29 23 1 26.29758 12 10 70 3 1 3

80 3 48 3 397.71 26 1 34.22222 9.8 9 10 3 210 3

95 3 58 4 399.29 36 1 23.53304 10.2 12 70 3 125 4

72 2 88 3 414.14 27 1 28.93407 11.8 14 50 3 207 5

100 3 63 5 391.99 38 2 25.21736 8.8 13 90 5 131 4

43 2 85 3 400.5 32 2 26.17 11.8 10 10 4 135 4

102 3 62 4 4.68 37 2 22.46003 10.2 13 70 4 146 3

39 3 48 4 406.6 26 2 31.21748 9.8 12 60 3 205 2

61 3 74 4 400.81 30 2 28.306 11.8 10 80 3 144 4

99 2 64 4 375.91 31 2 25.28257 11.6 12 120 3 203 3

84 2 93 4 406.05 21 2 18.72601 11 10 60 3 163 4

69 2 62 4 378.08 21 1 27.26801 11.4 10 20 5 147 3

100 2 58 5 417.56 28 1 22.59814 8.6 11 70 2 161 4

104 2 75 3 381.94 22 1 25.55933 10.8 11 90 3 174 4

77 4 73 4 404.78 29 1 23.33768 7.8 10 70 4 113 2

72 2 51 4 396.61 37 1 20.86112 9.6 11 50 3 204 3

107 4 87 5 395.28 33 1 18.93878 9.4 9 60 5 138 3

65 3 56 4 392.66 24 1 33.33 11.8 12 20 3 132 4

73 3 93 4 385.29 24 1 22.89307 10.6 11 60 3 187 4

82 2 55 4 390.72 29 1 17.35892 10.8 10 70 3 154 3

69 3 67 5 381.09 33 1 20.9042 10.4 10 30 4 213 3

80 3 77 3 381.51 36 1 22.0384 10 10 90 3 0 4

75 2 79 4 392.15 35 1 32.89333 11.6 12 20 4 186 4

111 4 66 4 414.16 32 1 20.32444 9.2 10 60 5 181 3

75 4 87 2 372.92 33 1 33.32052 12.4 11 70 3 0 4

74 2 35 4 384.46 25 1 26.83865 9.6 11 110 3 186 3

66 2 73 3 373 24 1 17.11002 10.2 11 10 4 150 5

54 3 79 3 391.32 23 1 23.25502 12.4 12 70 4 210 5

82 4 73 4 386.49 28 1 26.21882 10.4 11 20 3 189 5

84 2 97 4 392.4 20 1 0.6927 8.8 9 70 3 186 5

77 4 53 3 402.63 38 1 34.66667 12.2 9 20 3 154 2

66 2 97 3 379.11 38 1 28.16249 10 14 50 3 136 4




65 3 93 5 372.81 18 1 24.25867 11 13 60 3 164 3

62 2 90 4 395.23 35 1 18.93878 9.8 10 70 3 159 3

47 3 97 5 378.34 33 1 15.39669 10.2 12 90 5 132 4

70 2 85 4 398.59 36 1 26.5625 9.6 14 90 3 4 3

58 2 71 3 389.43 36 1 16.09645 10.2 9 40 3 133 3

72 3 91 3 401.23 37 1 24.60938 11.4 9 50 4 128 2

69 2 78 5 372.53 38 1 21.84701 12.6 10 110 4 111 3

29 3 59 2 417.33 24 1 24.74094 11 9 50 4 122 4

79 2 59 4 413.74 34 1 18.82716 10.2 11 50 5 185 3

74 4 93 4 409.47 36 2 18.21494 9.6 11 10 4 117 4

32 4 86 3 380.85 28 2 21.33821 9.2 11 70 3 175 3

50 3 66 3 400.29 39 2 28.47989 10.8 9 110 4 153 4

48 4 52 3 373.44 24 2 22.60026 12.6 11 50 4 123 3

68 3 55 3 373.86 26 2 20.89796 13.8 10 70 4 202 4

96 2 76 3 414.82 34 1 31.55556 10.6 12 10 4 122 4

104 2 96 5 403.16 32 1 0.8347 10.4 13 10 3 167 4

64 3 99 5 411.18 34 1 25.76571 11.4 13 20 2 121 3

101 2 97 4 398.94 24 1 21.77755 9 11 30 3 122 3

78 3 72 3 383.27 25 1 27.30997 10.4 10 50 5 168 5

93 2 99 2 372.22 27 1 22.30936 11.4 12 70 2 218 5

53 3 57 3 372.16 21 1 0.53 12.2 11 30 3 137 3

76 3 49 3 400.17 31 2 0.3625 10 9 80 3 220 5

101 4 60 3 380.15 31 1 18.25311 11.6 14 70 3 182 4

50 2 79 4 402.29 37 2 23.01118 8.8 13 60 4 122 4

88 4 38 5 386.23 33 2 27.91552 10.6 9 100 4 202 4

79 2 98 3 403.27 36 1 22.40879 11.4 14 60 3 127 3

77 2 94 5 387.24 39 2 25.39343 9 13 80 2 171 5

60 4 73 4 404.63 26 2 22.31328 9.8 13 90 5 132 4

104 2 68 4 383.39 30 1 26.40235 9.8 10 20 3 221 3

62 2 96 3 383.72 30 1 23.72529 9 11 30 4 177 5

106 4 53 4 395.61 39 1 27.53123 8.2 10 30 3 136 2

79 3 35 3 414.66 35 1 25.35154 10.8 10 60 3 131 4

62 3 39 4 406.25 23 1 22.27531 8.4 13 90 4 220 5

94 2 77 5 401.8 26 1 0.487 9 11 30 3 165 2

76 4 42 3 383.53 35 1 21.82995 12 12 40 4 137 4

98 3 65 5 416.02 24 1 23.56663 10.8 12 110 5 139 5

77 3 99 4 414.87 29 1 26.25958 11 14 10 5 221 4

92 3 75 3 397.4 36 1 25.96953 10.6 10 40 3 151 3

95 2 82 4 375.33 26 1 17.0935 11.4 13 80 2 212 4

52 2 58 3 375.25 39 1 15.26205 9.2 11 30 3 134 3




48 2 53 5 383.63 23 1 24.34961 11 9 60 3 135 3

60 2 45 3 387.81 27 1 30.0215 10.6 12 80 3 201 3

80 2 61 5 417.31 32 1 20.56 10.8 11 60 3 5 4

68 3 75 3 391.58 30 1 26.29172 11 14 40 3 177 4

76 4 81 3 390.82 34 1 31.23859 9.6 11 100 4 174 3

82 2 59 2 377.14 30 1 16.51473 8.6 9 12 3 170 3

80 3 41 4 401.07 34 2 18.92915 11.4 11 110 5 112 5

97 2 64 5 414 20 2 0.7777 8 13 40 4 111 4

74 2 99 4 402.48 35 2 0.4874 8.2 11 60 4 0 5

76 3 85 5 388.75 23 2 22.66271 9.2 14 50 3 162 4

94 2 83 4 373.15 37 2 25.71101 11.8 11 110 4 8

78 2 61 3 411.26 35 1 18.72417 10.6 11 40 3 9

3

4

73 3 52 3 395.52 22 1 0.4674 9 13 10 3 170 3

82 2 75 2 382.88 35 1 23.12467 9.4 10 110 4 3 3

96 4 42 3 381.59 35 1 21.3578 10.8 13 50 4 116 3

41 4 92 4 403.29 23 1 22.30936 11.6 12 60 5 149 5

86 3 93 4 390.63 30 1 18.50777 12.2 11 120 4 201 3

104 3 94 4 404.54 27 1 22.28259 12.6 12 50 4 205 4

49 3 63 4 380.47 29 1 23.45092 11.8 12 30 5 125 4

62 2 37 4 417.69 30 1 26.67276 11 13 70 4 114 4

63 2 38 4 392.31 34 1 21.70513 11 10 40 3 177 3

95 2 70 3 397.13 26 1 21.64412 10 10 60 3 205 4

66 3 75 3 418 39 2 32.89333 11.2 10 100 3 129 4

97 4 41 3 372.42 39 2 23.8054 8.6 14 30 3 2

84 2 94 4 405.8 21 2 20.80856 10.6 12 60 3 1

3

3

68 3 68 4 372.57 25 2 26.49151 10.2 14 70 4 146 4

99 2 85 4 399.68 32 2 20.69049 12 12 20 5 209 3

94 2 98 4 390.48 39 2 24.08822 11.2 13 80 4 2 5

107 2 83 3 410.77 36 2 26.57313 10.4 12 110 3 133 4

100 4 51 5 391.68 24 2 22.66271 10.2 14 10 4 4 3

50 3 71 4 405.38 23 2 24.16716 9.6 13 60 3 163 3

59 3 82 4 374.4 32 1 21.6041 9.2 14 30 4 170 3

88 4 83 4 415.53 21 1 23.95123 9.8 14 10 3 163 3

77 3 83 4 404.63 32 1 24.67105 10.6 10 80 3 202 3

76 2 71 3 410.8 34 1 22.94812 11.4 10 50 4 206 4

55 3 38 2 395.69 28 1 29.38468 11 11 10 3 164 4

83 2 90 5 418.55 36 1 22.79033 12 9 70 5 182 4

108 2 51 3 408.93 25 1 21.20311 11.4 11 10 5 184 3

78 2 40 3 386.36 40 1 16.78825 11.6 10 20 5 173 4

61 2 48 5 372.26 32 1 25.71101 10.2 10 60 2 146 4




64 4 87 4 403.59 37 1 20.54989 8.4 12 90 3 135 4

81 2 71 3 387.18 32 1 25.55885 13 14 80 5 127 5

66 3 37 3 408.02 24 1 24.72518 9.4 13 60 4 161 5

62 2 57 3 398.89 20 1 17.50562 12.4 12 10 4 128 4

77 2 53 5 393.71 35 1 18.5132 11 9 30 3 203 4

47 2 36 3 399.14 29 1 22.78331 10.6 10 60 4 214 4

65 3 90 4 394.83 40 1 18.00922 11.2 14 10 3 129 5

80 3 36 5 378.04 24 1 30.44384 11.8 12 30 3 165 5

105 3 70 2 403.13 34 1 25.1559 10.8 11 40 4 169 4

101 3 77 5 392.81 29 1 24.02381 9.4 11 90 5 206 3

94 2 65 4 406.89 38 1 29.24211 11.4 11 50 4 150 3

52 4 77 4 386.13 25 1 25.10388 9.2 12 50 3 208 5

43 2 48 4 384.77 28 1 25.76298 9.2 9 90 4 207 3

69 4 100 4 382.16 38 1 20.74755 9 13 80 2 9 5

81 3 78 2 378.35 37 2 32.89329 12 9 60 3 203 4

56 4 90 3 384.72 26 2 26.3656 8.2 13 70 4 9 5

52 4 58 3 373.23 34 1 29.6875 10.2 13 50 3 200 3

70 2 45 3 402.04 26 1 24.53896 12.4 11 30 3 181 5

54 3 64 4 411 23 1 18.937 10.8 11 40 5 183 5

70 3 63 4 385.25 30 1 29.99671 8.6 10 60 3 206 3

74 3 65 4 378.83 27 1 24.03171 9.4 10 80 4 147 4

91 4 83 3 410.35 33 1 24.55775 11.6 14 110 3 131 3

68 3 75 4 375.68 35 1 31.22945 11 10 120 3 166 4

56 3 95 5 396.73 31 1 24.28098 11.8 12 120 3 203 4

92 4 79 4 387.54 22 1 23.35564 10.6 14 10 4 4 5

96 3 38 4 384.08 37 2 26.63892 10.6 9 80 3 160 5

106 4 51 3 400.76 40 2 27.63037 10 11 110 2 172 3

93 2 74 4 409.68 23 2 22.64086 12.4 14 50 4 112 4

81 4 90 4 390.82 18 2 23.11 10.2 10 80 4 163 4

80 4 59 2 416.21 37 2 17.30612 10.6 13 30 4 166 3

63 3 66 3 412.74 27 2 24.24242 9 10 10 4 145 3

43 2 39 3 409.36 21 2 28.959 12.6 10 100 3 166 2

77 4 75 4 380.33 20 1 0.2653 11 9 100 5 206 4

56 3 62 4 409.59 33 1 24.00549 10.4 14 50 4 180 3

98 4 54 5 374.6 31 2 20.28651 8.6 12 100 5 122 3

62 3 68 3 398.47 29 2 21.67126 9.4 12 90 3 201 3

121 3 87 5 376.79 32 2 18.42404 11.6 13 10 3 122 3

63 4 86 5 374.83 33 2 22.02432 11 12 80 3 176 5

40 4 38 3 375.87 40 2 22.94812 7.8 12 60 3 204 3

93 2 80 5 399.5 29 2 25.03992 10.6 11 80 3 154 3




97 2 38 5 396.81 27 2 17.28395 9.8 12 50 5 201 3

87 2 48 5 4.67 20 2 20.33833 10.6 11 20 5 128 4

69 2 54 3 382.35 30 2 26.12495 9 11 50 5 133 4

89 2 95 2 384.35 23 2 24.4898 10.6 10 110 3 146 4

75 3 63 3 406.81 30 1 23.66524 11.4 13 110 3 182 4

108 2 61 4 384.22 26 1 28.62147 10.8 11 30 4 135 3

55 2 93 5 374.67 21 1 18.51852 10.8 10 60 2 110 5

68 2 72 2 372.28 35 1 25.95156 12.4 11 110 4 175 3

65 4 40 3 401.17 31 1 23.50781 11.2 12 60 3 185 5

74 3 71 3 380.45 38 1 21.2963 9.8 14 10 2 133 4

70 2 42 4 394.54 20 1 24.96495 11.8 13 40 4 218 4

112 3 68 4 385.48 28 1 24.55775 9.6 12 50 4 152 5

84 3 70 4 415.44 28 1 31.24499 9.8 14 70 4 155 4

45 4 56 4 375.6 39 2 30.80125 8.8 11 30 4 187 3

46 3 88 4 402.72 40 2 23.32342 9 13 30 3 210 3

89 2 68 3 411.43 24 2 22.76147 11.8 10 110 2 156 3

56 2 42 4 381.66 26 2 0.6052 8.4 11 30 3 132 4

58 2 50 3 376.43 35 2 24.07407 11.8 14 40 3 176 3

68 3 67 4 402.15 29 1 25.86451 11 13 100 3 161 3

96 4 95 5 395.6 36 1 20.32444 10.6 11 110 3 187 4

60 3 85 5 404.86 29 2 26.77593 8.6 14 70 2 124 5

56 4 78 4 417.82 29 1 27.6601 10.2 13 70 4 6 4

53 3 83 4 374.92 27 2 21.35991 10.6 10 20 3 168 5

70 4 86 3 372.58 36 2 23.22543 11.4 13 40 5 212 4

104 4 79 5 376.66 38 1 32.46753 10.6 11 60 4 213 4

78 4 57 4 381.51 33 1 20.76125 10.8 10 30 5 5

98 3 73 4 418.41 28 1 24 10.2 13 30 3 147

4

3

44 2 69 5 382.98 34 1 0.433 10 12 40 5 110 4

77 2 56 4 410.47 33 2 26.75321 8.6 11 50 4 166 5

87 3 83 3 407.47 32 2 21.0772 12.2 13 20 4 138 3

95 4 74 4 373.93 29 2 22.89282 11.6 12 110 3 114 4

93 2 49 5 387.29 20 2 28.35306 11.4 12 10 3 185 3

84 2 73 4 372.74 28 2 30.73061 10.8 14 120 4 110 3

61 2 99 3 416.62 26 1 26.63892 11 12 80 5 142 5

64 2 99 5 409.42 20 1 28.90625 10 12 60 4 178 4

91 2 70 3 403.65 20 1 22.91303 10.8 11 80 4 168 3

112 3 56 3 393.8 40 1 26.31464 11.6 11 40 4 125 5

63 3 86 3 410.73 35 1 15.39669 11.8 12 30 3 3

96 2 62 3 382.48 20 1 22.21368 10.6 9 110 4 7

109 2 51 3 390.2 38 1 20.83082 9 11 20 5 141 4

3

5




74 4 98 3 393.53 26 1 26.53376 10 13 10 4 110 3

58 4 62 3 378.02 23 1 18.62139 9.2 9 30 3 215 3

59 2 81 5 395.26 24 1 0.1001 10.8 10 10 4 176 4

82 2 59 5 404.39 32 1 20.79673 10.2 10 50 5 140 3

76 2 70 3 415.23 32 1 27.47624 9.8 10 60 5 163 4

57 3 88 3 380.1 23 1 25.22409 11.4 10 80 3 153 5

48 3 97 3 409.33 31 1 21.29529 11.6 11 40 4 143 3

90 2 38 5 397.85 28 1 29.39 10.2 11 40 3 133 3

54 2 89 5 379.13 37 1 26.98962 12.8 13 70 3 137 4

70 4 44 4 384.72 39 1 25.0995 10.2 14 100 3 174 3

80 2 80 4 380.64 34 1 28.50752 12.2 12 60 3 159 4

95 2 71 4 407.96 31 1 23.1405 9.8 10 80 4 185 3

55 3 81 3 393.3 40 1 24.45606 10.2 12 60 5 139 3

84 2 38 3 396.03 22 1 22.7244 10.6 12 80 3 181 4

90 2 41 2 397.86 27 1 21.75547 10.4 11 90 5 212 4

50 3 63 5 389.62 22 1 25.0995 11 10 120 5 184 5

77 3 40 3 403.87 29 1 27.47563 10.6 13 70 4 215 3

85 3 96 3 406.81 28 1 0.4874 9.2 11 10 5 143 3

65 4 99 4 379.99 37 1 24.60938 9.6 9 40 4 123 4

100 3 56 5 391.87 20 1 22.91303 10.4 10 120 3 169 5

78 2 98 3 396.71 24 1 22.30936 8 13 120 5 206 2

85 2 45 4 417.22 31 1 22.75831 9 9 70 5 141 3

71 2 55 2 393.82 31 1 24.07407 10.2 14 20 4 220 4

24 4 36 2 388.82 34 1 22.49135 10 10 10 2 126 2

88 4 76 3 393.91 37 1 30.29778 8.8 8 40 3 131 5

54 4 39 4 397.94 30 1 22.79033 11.6 13 60 5 205 2

85 2 58 3 377.46 31 1 28.82753 11.2 11 70 3 6 5

90 3 42 3 396.73 26 1 18.30577 10.8 9 60 3 217 2

91 3 90 4 394.08 28 1 16.72769 9.6 10 60 3 160 4

94 2 95 2 397.58 37 1 23.61275 9.8 12 60 3 208 3

109 2 85 2 377.04 29 1 17.63265 9.6 11 110 4 161 4

105 3 90 4 384.58 23 2 0.6072 11.4 9 30 5 139 4

72 3 56 5 394.13 33 1 28.51563 9.2 11 80 4 2 4

108 2 72 4 382.73 37 1 23.2438 10.4 13 100 3 147 5

78 4 67 5 383.63 29 1 20.56 10 13 80 4 151 4

96 3 48 2 385.23 20 2 23.18367 11.4 8 50 4 177 3

93 2 84 4 385.65 21 1 17.0935 9.2 9 100 4 187 4

57 3 55 3 397.3 37 1 22.36744 10 10 100 4 113 3

52 3 68 4 388.5 25 1 23.14815 11.6 11 20 3 214 3

93 2 51 2 375.3 29 1 27.0538 8.8 11 10 2 150 2




96 3 36 4 417 33 1 29.04866 9.6 10 10 3 159 2

48 2 90 3 406.35 28 1 17.67454 10.2 13 30 2 159 4

108 4 46 3 398.51 37 1 25.59374 9 9 80 3 8 2

76 2 74 5 389.69 33 1 24.11404 7.8 12 70 4 138 3

63 3 71 4 406.65 23 1 24.44728 8.6 11 110 3 214 3

71 3 48 3 369.45 37 1 27.12032 10.8 12 20 4 178 2

53 2 95 2 408.55 34 1 27.70083 9.8 14 70 3 171 2

77 3 61 3 381.79 36 1 23.05175 7.6 9 30 3 183 3

77 3 83 4 384.44 34 1 18.25311 10.2 10 50 3 217 3

72 2 96 2 4.59 28 1 28.06894 9.6 12 60 5 142 4

86 3 82 3 376.07 35 1 30.22222 10.8 10 90 3 217 2

79 3 37 4 378.69 37 1 25.63692 11.2 12 20 2 115 4

67 2 52 2 400.84 34 1 24.14152 9 13 50 4 137 3

84 3 42 3 367.92 22 1 21.60494 9.4 12 110 2 125 4

95 2 38 2 403.26 33 1 23.87511 9.6 11 10 4 188 2

86 2 81 4 412.24 30 1 24.34961 10 10 80 4 134 2

73 3 54 2 413.26 23 1 25.18079 10.6 10 10 2 127 3

84 3 55 5 378.25 28 1 0.2672 10.2 13 70 3 170 4

99 3 85 5 376.94 27 1 27.39092 11 9 70 3 147 4

54 3 95 4 388.8 39 1 26.91273 11

92 2 79 4 394.91 38 1 26.17 8.8

8

10

70

30

2

3

215

166

2

4

101 3 59 3 389.33 36 1 17.16551 8.6 14 90 5 8

59 2 73 4 389.48 37 1 21.87755 11.6 11 120 4 7

98 2 63 2 396.54 28 1 25.52964 7.8 11 30 3 177 4

86 3 82 4 398.51 26 1 30.07813 11 11 120 4 184 4

5

3

72 2 81 4 412.91 31 1 27.85201 10.8 11 10 3 124 2

70 3 61 3 375.42 25 1 0.3337 10.2 11 20 4 9 2

46 4 42 2 405.41 34 1 24.14152 11 12 40 5 112 4

93 4 92 4 367.33 20 1 27.47624 10.2 10 20 3 163 3

74 2 85 2 384.5 35 1 17.43285 10.2 10 90 4 0 3

112 4 69 3 379.98 33 1 30.84442 8.8 11 70 3 203 4


IE 401 - Motorcycle Riding Safety Assessment for Commuter's Accident Prevention in the Philippines

CHAPTER 3: METHODOLOGIES

Figure 3.1 Methodologies

Variable Classification Data Type

Level

Low High

3

GENDER

Human Error

Safety Attitude

Table 4.2.1.1.1 shows the significant correlations between continuous variables.

The analysis shows that safety was positively correlated to mental workload

, age

, posture

, alcohol consumption

, and sleep deprivation

. Safety was negatively correlated to alertness

, BMI

, and road lighting

.

Spearman Correlation: Safety, Stress, Safety Attitude, Gender, Human

Error, Speeding (Categorical)

APPENDIX A

PROBLEM

IDENTIFICATION

TOOL

|𝑂𝑙𝑑 𝑣𝑎𝑙𝑢𝑒−𝑁𝑒𝑤 𝑉𝑎𝑙𝑢𝑒|

𝑂𝑙𝑑 𝑉𝑎𝑙𝑢𝑒

|21,403−22,063|

21,403

|𝑂𝑙𝑑 𝑣𝑎𝑙𝑢𝑒−𝑁𝑒𝑤 𝑉𝑎𝑙𝑢𝑒|

𝑂𝑙𝑑 𝑉𝑎𝑙𝑢𝑒

|22,063−26,652|

22,063

APPENDIX B

MINITAB RESULTS

APPENDIX C

QUESTIONNAIRE

SCORE

COMPUTATIONS

GUIDE

APPENDIX D

ACTUAL DATA

GATHERING

APPENDIX E

SAMPLE

QUESTIONNAIRE

APPENDIX F

SUMMARY OF

DATA FROM

QUESTIONNAIRE

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