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Comparison of the regulated air pollutant emission characteristics
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of real-world driving cycle and ECE cycle for motorcycles
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Hung-Lung Chianga．Pei-Hsiu Huanga, Yen-Ming Lai a,Ting-Yi Leea
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a
Department of Health Risk Management, China Medical University, Taichung,
Taiwan
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Abstract
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Motorcycles are an important means of transportation, and their numbers have
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increased significantly in recent years. However, motorcycles can emit
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significant amounts of air pollutants; therefore, the emission characteristics and
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driving patterns of motorcycles are necessary baseline information for the
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implementation of control measures for motorcycles in urban areas. The
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selected motorcycles were equipped with global positioning systems (GPS) to
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obtain speed-time data for determination of the characteristics of real-world
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driving parameters, and an on-board exhaust gas analyser with data logger
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was employed to determine the instantaneous concentration of regulated air
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pollutants from motorcycle exhaust. Results indicated that the time proportions
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of acceleration, cruising, and deceleration are different from those of the
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Economic Commission for Europe (ECE) driving cycle, and the time
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percentages of acceleration and deceleration of the ECE cycle are much less
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than those in Taichung city. In general, the emission factors of the Taichung
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motorcycle driving cycle (TMDC) were higher HC and lower NOx emission than
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those of the ECE cycle. The average fuel consumption of tested motorcycles
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on three roads during workdays was 5% higher than that on weekends. The
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fuel consumption in the real-world motorcycle driving cycle was also about 7%
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higher than that of the ECE cycle, which again indicates that the ECE cycle is
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unsuitable for measuring fuel consumption in the Taichung metropolitan area.
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Therefore, understanding the local driving cycle is necessary for developing
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accurate emission data for air pollution control measures for urban areas.
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Keywords: Global positioning system (GPS); Taichung motorcycle driving
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cycle (TMDC); fuel consumption; workdays; weekend
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1. Introduction
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Motorcycles are an important means of transportation in Asia, which has the
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world’s highest concentration of motorcycles. There are currently 350 million
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motorcycles in the world, and that number is expected to rise to 500 million by
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2015. The number of motorcycles is forecast to grow 7.2% annually to 134.5
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million units in 2016. The predominant types in Asia, especially in China, India
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and Indonesia, are inexpensive, small motorcycles (The Freedonia Group,
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2013). Motorcycles play an increasing role in private mobility, particularly in
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metropolitan areas, where traffic congestion and parking difficulties influence
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the choice of transport mode. Unfortunately, motorcycles not only present
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traffic problems but also emit significant air pollutants, contributing a large
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fraction of air pollution in urban areas.
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A decade ago, motorcycles accounted for about 54% of the total vehicle
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population in Jakarta (Asian Development Bank, 2002a).
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contributed to more than 50% of air pollutants in Hanoi, Vietnam, with an
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incredible annual motorcycle increase in southern Asia (Asian Development
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Bank, 2002b; Tung et al., 2011). In 2011, it was reported that motorcycles were
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contributing 94% CO, 68% NMVOC, 61% SO2 and 99% CH4 of traffic emission
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in Ho Chi Minh City, Vietnam (Ho and Clappier, 2011). In Taiwan, the source
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profiles from mobile emissions indicated that 20% of CO, 1.5% of NOx, 30% of
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NMHC and 18% of PM, or 1800, 14, 82 and 3.8 thousand metric tons,
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respectively, were emitted from motorcycles (TEPA, 2013).
Motorcycles
The same
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problems of motorcycle traffic and resultant pollution are plaguing many Asian
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cities. Therefore, it is clear that priority must be given to developing and
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implementing air pollution control strategies for these two-wheelers if Asian
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cities are to achieve clean and healthy ambient air, especially in urban areas.
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Taiwan EPA has employed a variety of control programs to improve air quality,
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such as stringent emission standards and fees to control stationary sources
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and reduce some pollutants. Therefore, mobile source control is important to
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further improve the air quality. Government strategies include enhancing and
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extending public transport infrastructures, accelerating the adoption and
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deployment of improved technology, creating effective inspection and
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maintenance programs, and implementing optimized programs for traffic
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management
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associated with engine design, fuel efficiency improvement, and catalysis
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development have been implemented by manufacturers.
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motorcycle emissions are still contributing a large portion of air pollution in
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metropolitan areas. These emissions can be used as baseline data to
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determine effective control measures, and real-world driving patterns can
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reflect the emission of on-road motorcycles in urban areas.
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Some studies have indicated that currently used driving cycles are unable to
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accurately assess vehicle emissions because they are not representative of
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real-world driving (Tsai et al., 2005; Wang et al., 2008; Tong et al., 2011). City
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size, local road infrastructure, and driving behavior are the most important
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factors affecting differences in vehicle driving patterns (Wang et al., 2008), and
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pollutant emission factors are extremely dependent on the selected vehicle
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class, the engine size, and the applied driving model (Kumar et al., 2011;
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Zamboni et al., 2011). In addition, regulated limits, vehicle deterioration, and
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after- treatment technology also influence emission factors (Zhang et al., 2008;
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Prati et al 2011).
and
enforcement.
Some
stringent
emission
standards
However,
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The test motorcycle was equipped with a GPS and an on-board exhaust gas
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analyser with a data logger to obtain the speed-time data and the
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concentration of regulated air pollutants, respectively. The objective of this
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study was to obtain a better understanding of actual motorcycle driving
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patterns in the Taichung metropolitan area in central Taiwan.
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methodology in the present study, including the road conditions as well as
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analysis of temporal-spatial difference and the driving conditions, was selected
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to provide data that is broadly representative of driving cycles in each
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respective jurisdiction; the results from one study in Kaohsiung (Tsai et al.,
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2005) are certainly not applicable to Taichung city. Specifically, three roads
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were selected (with and without dividers for cars and motorcycles) to
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investigate differences in driving patterns between morning and evening rush
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hour and non-rush hour conditions for workdays and weekends. Also, an
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important element is that characteristics of the driving cycle change with time
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and should be updated to reflect the current status. In addition, the motorcycle
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was equipped with an on-board exhaust gas analyser to yield instant exhaust
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concentration data as it travelled on the highway. We used a chase motorcycle
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to follow the speed and track of the selected motorcycle on the road.
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Comparison of the Taichung motorcycle driving cycle (TMDC) with those of
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ECE, FTP-75 and Japanese models are the major tasks. The TMDC cycle and
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the current use of the ECE cycle were also investigated for motorcycle exhaust
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characteristics (CO, HC, NOx and CO2) and fuel consumption.
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that the results of the present study should represent driving conditions in
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Taichung city.
The
It is believed
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2. Experimental
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2.1 The selected routes and motorcycles
Taichung is the third largest city in Taiwan, with a metropolitan population
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of 2.69 million in 2013 and an area of about 2,214 km2.
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population density is 1200 people/km2, but the density could be over 20,000
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people/km2 in downtown. In Taichung, there are over 1.7 million motorcycles,
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which was about 65% of the total vehicles in 2012 (Ministry of Transportation
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and
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transportation means for daily activity. The mobile source accounts for
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approximately 98% of CO, 32% of HCs and 89% of NOx in downtown Taichung
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(TEPA, 2013). Consequently, it is important to develop a strategy for reducing
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pollutant emissions from mobile sources.
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Communication,
The average
2012). The motorcycle is the most important
Test motorcycles were selected on the basis of mileage and engine type
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for in-use motorcycles in Taiwan.
Eight in-use 4-stroke motorcycles were
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tested in this study. All of them were equipped with a two-way catalyst, mileage
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ranged from 2.1 to 39.9103 Km, and their ages were from brand new to 12
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years.
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Three main routes were selected to investigate motorcycle driving cycles.
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Route A (Zhong-Gang road, green road) and route C (Da-Ya road, red road)
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are the main connection from Taichung downtown to the rural area, and route
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B (Wen-Xin road, blue road) is the circle connection for downtown Taichung
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city (Figure 1).
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The test street distance ranges from 4.3 to 5.4 km, which closely matches
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the common travel distance, 4-5 km/trip, in Taiwan. The width of the
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motorcycle-driving lane ranges from 3.5 to 5.0 m; route A has an exclusive
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motorcycle lane; the other two routes (B and C) do not have dedicated
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motorcycle lanes. The number of traffic signals in the three testing routes
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varies from 19 to 26, which could affect driving patterns.
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Testing durations were 07:00-09:00 (rush hour in the morning, RHM),
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17:00-19:00 (rush hour in the evening, RHE) and 14:00-15:00 (non-rush hour,
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NRH).
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workdays and weekends. In total, 202 test runs (including duplicate test runs)
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were conducted.
Each motorcycle was run for three periods for each route during
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2.2 Data collection system
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The test motorcycle was equipped with a global positioning system (GPS)
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in a personal digital assistant (PDA), and the accepted signals were logged in
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the data logger system. The GPS data follow the standard of the National
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Marine Electronics Association (NMEA), and the signal is transferred into time
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and velocity. The device records time series data by using its internal flash
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memory for every second. Each record contains speed, time, and position
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data.
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To ensure that the measurement of speed is accurate, the speed and position
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data detected from the GPS module are then compared with the previous
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method, where the motorcycle was equipped with a frequency-voltage
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transducer (magnetic induction device) and a data acquisition system to
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acquire time-speed data during the testing period (Tsai et al., 2005). The GPS
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was selected for verification of the garage-made magnetic sensor. It has an
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accuracy of  2.5 meter and  0.1 m/sec for position and velocity measurement
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respectively, and results indicated that the difference of the two methods was
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less than 3.5%, confirming that the GPS could be employed in development of
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the driving cycle.
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The system recorded data every 0.1 s, and it averaged the data every
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second (10 sets of data). Thus, data was time-weighed, and only those values
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within 5% of the mean value were selected. Essentially, this serves as a
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“filtering” function, resulting in a “smoothing” pattern.
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subsequent data filtering was performed.
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weighing on the rush-hour (morning and evening) and non-rush hour data. All
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data were selected randomly and treated equally for the TMDC development.
Consequently, no
Further, there is no additional
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The speed of the motorcycle at various times was then recorded by the
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data acquisition system for further analysis in the laboratory. The time scale
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resolution of the data acquisition system was 0.1 s. The lower limit of the
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measurement of driving speed was 0.3 km h-1 (those with speed < 0.3 km h-1
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treated as idle stage) The preliminary work assured the stability of time-speed
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data recording, the stability of the refitted equipment on the motorcycle, and the
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accuracy and precision of the motorcycle driving speed.
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2.3 Real world gas sampling and analysis
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Motorcycle exhaust gas was analyzed for CO, THC, NOx and CO2 by
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an on-board automated instrument (HORIBA MEXA-584L) associated with a
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computer to record the real-time variation of exhaust concentration.
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sampling tube of the monitor was connected directly to the tailpipe of the
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motorcycle. The packing of the stability system was conducted for the exhaust
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gas analyzer to reduce the vibration effect on the sensitivity of the analyzer on
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the chase motorcycle.
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in Figure 2.
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2.4 Selection of the motorcycle driving cycle
The
The sampling equipment schematic diagram is shown
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Condensing a large amount of speed data into a cycle of reasonable
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duration was the most difficult task in developing a driving cycle. Different
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methods have been used in synthesizing driving cycles; for example, only the
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average speed, RMS acceleration and percentage idle time were used (Kent et
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al., 1978). The study follows the work of Tong et al. (1999 and 2011) and Tsai
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et al. (2005) in selecting 11 parameters for development of the driving cycle.
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The present study employed the approach of fixed route vehicle chasing.
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There are eleven parameters in the assessment analysis including average
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speed of the entire driving cycle including the idle periods, v1 (km h-1); average
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running speed excluding the idle periods, v2 (km h-1); average acceleration of all
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acceleration phases, a; (m s-2); average deceleration of all deceleration
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phases, d; (m s-2); mean length of a driving period, c (s); time proportion of the
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driving cycle, i.e., idle (pi); acceleration (pa); cruising (pc); and deceleration (pd)
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(%); average number of acceleration-deceleration changes within one driving
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period, M; and root mean square acceleration, RMS (m s-2).
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different driving modes are defined as follows: idling mode, zero speed;
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acceleration mode, the positive incremental speed changes > 0.1 m s-2;
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cruising mode, the absolute incremental speed changes  0.1 m s-2;
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deceleration mode, the negative incremental speed changes > 0.1 m s -2; and
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driving period, the time of driving cycle(s).
In addition, the
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The selection of the representative driving cycle is as follows: From the
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data (time, speed, etc.) collected, the 11 parameters mentioned above were
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determined from 202 driving trips.
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periods, separated by idle stages, e.g., stops due to traffic jams or traffic lights.
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Out of 202 trips, there were approximately 1200 driving periods of which five
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were randomly selected.
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(cycle) for which the 11 parameters were calculated based on the time
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duration of each period.
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then calculated from the mean value of all test runs (n = 202). The absolute
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relative error < 5% was used as a criterion for determining the fitness of each
Each trip comprised multiple driving
The five periods were then combined into one trip
The absolute relative error of each parameter was
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parameter.
The procedure was repeated about 2000 times of randomly
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selected 5 other driving periods to combined the tested driving cycle, with the 5
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selected driving periods for the previous test cycle eliminated. The sum of
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absolute relative errors of all 11 parameters was calculated for each driving
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cycle, with the least sum of absolute relative errors as a representative driving
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cycle in the Taichung metropolitan area.
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development procedures for the Taichung motorcycle driving cycle.
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2.5 Dynamometer testing
Figure 3 summarizes the
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The test motorcycles were first examined for safety in a certified
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motorcycle manufacture laboratory, and the fuel was then replaced. The next
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day, the test motorcycles were loaded on the dynamometer (HORIBA,
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MEXA-8320).
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2.6 Data Analysis
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The emission factors of various pollutants were assessed with the exhaust
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concentrations, the volume of the exhaust, and the total running mileage in
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one test cycle.
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patterns were not available, the mean rotating speed of the engine, the volume
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of the cylinder, and the running time in that driving period were applied to
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derive the emission factors.
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coworkers (2005), and the equations are as follows:
Because the actual exhaust volumes in various driving
The method follows the study of Tsai and
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Vm  R  t  E v  10 6  F
(1)
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M i  C i  VSTP  10 3
(2)
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EFi  M i/L
(3)
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where Vm is the exhaust gas volume of a specific driving mode (m 3), R is the
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rotating speed of the engine (rpm), t is the total time in that driving mode
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(minute), Ev is the volume of the engine cylinder (cc), and F is the correction
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factor of the engine type, which is 0.5 for a 4-stroke engine. The parameter Mi
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is the exhaust amount of the pollutant i (g) in a specific driving mode, Ci is the
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concentration of the pollutant i (mg Nm-3), VSTP is the normalized value of V by
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temperature and pressure correction (1 atm and 0oC,Nm3), L is the running
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mileage during the test procedure (km), and EFi is the emission factor of that
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pollutant i (g km-1).
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3. Results and Discussion
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3.1 Development of Taichung motorcycle driving cycle
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3.1.1 Characteristics of all testing runs
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Table 1 shows the mean values and standard deviations of all test runs.
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The time proportions of acceleration, cruising, and deceleration are obviously
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different than those of the ECE cycle (25.07, 9.15 and 26.63% for the average
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testing cycle in Taichung versus 18.5, 32.3 and 18.5% in the ECE cycle,
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respectively); the time percentages of acceleration and deceleration of ECE
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cycle are much lower than those values in Taichung city. In contrast, the
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duration of cruising mode (9.15%) in the Taichung area driving cycle is lower
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than those of the ECE (32.3%), FTP 75 (20.4%), Japanese 10-15 mode
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(21.2%), and Hong Kong (9.4%) cycles (shown as Table 3). In addition, the
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average number of acceleration-deceleration changes within one driving period
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(M = 13.8) is much higher than those of the other models, e.g., ECE (1.0), US
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FTP 75 (6.0), Japanese 10-15 mode (2.4), and Hong Kong (6.01) (Tong et al.,
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1999). But the parameters of the TMDC driving cycle were similar to the work
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conducted in Kaohsiung city a few years ago (Tsai et al., 2005). Although part
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1 of the WMTC is similar to Taiwan’s city conditions, it still reveals a higher
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percentage in curing fraction (The curing is maintain a constant speed in this
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study) and a lower percentage in idle fraction. The results, therefore, support
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the notion that the ECE and WMTC are not suitable as a standard testing cycle
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for use in Taiwan.
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3.1.2 Characteristics of different testing routes
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Figure 1 also shows the parameters from the three routes. Route A is the
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shortest distance of three routes with the lowest driving speed. Route C
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exhibited the highest value of driving speed and smallest value of deceleration
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and acceleration. From Figure 1 data, the number of motorcycles per lane
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width for route C is the lowest (about 137-371 # hr-1) with the highest number of
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traffic lights per length (5.6 per km).
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responsible for the highest driving speed in route C. In route C, there is no
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barrier to separate the motorcycles from other vehicles, and low traffic volume
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could enable motorcycles to maintain their travelling speed. Although many
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traffic lights were set on route C, they did not reduce the motorcycle speed on
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route C. With the ability to pass from one lane to another, motorcycles could
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increase their driving speed on Route C. In contrast, on route A, the exclusive
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motorcycle and vehicle lanes inhibit the speed of motorcycles, despite the fact
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that this route had the lowest number of traffic lights per kilometer. The
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sequence of average acceleration of all acceleration phases, a, was as follows:
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route B > A > C. The lowest pi value in Route C may be due to the highest
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speed, and the highest pa, pc and pd values may be due to lower traffic loading
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on the road.
All of these characteristics are
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Vehicle and motorcycle were
not separated (B and C routes)
Vehicle and motorcycle were
separated by barrier (A route)
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The figure is for your reference.
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3.1.3 Characteristics of different testing periods
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The testing periods were divided into three parts, which included RHM,
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NRH, and RHE; results are shown in Table 1. The 1 and 2 were 19.3-23.43
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and.33.4-37.7 km h-1, respectively. The a and d values ranged from 0.51 to
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0.58 and from 0.50 to 0.55 m s-2, respectively. The sequence of different
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driving modes was pi >pa > pd > pc. The mean length of a driving period, C, and
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the average number of acceleration-deceleration changes within one driving
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period, M, were about 150 s and 14, respectively. The lowest values of
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acceleration, deceleration and mean length of a driving period were measured
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in the RHM period. In the morning, drivers travel from home to work or school.
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The driving speed in the RHE period was the slowest among the three periods,
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which could be attributed to poor visibility and heavy traffic, with people coming
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home from work and classes. The data revealed typical traffic characteristics of
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middle Taiwan. As would be expected, the NRH period had the highest driving
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speed, v2, and acceleration among the three testing periods.
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3.1.4 Taichung motorcycle driving cycle (TMDC)
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Table 2 shows 8 typical synthesized driving cycles with 11 parameters in
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each cycle. Each parameter was compared with the mean value of all test
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runs (absolute relative error < 5%).
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cycle among these synthesized cycles, the sum of absolute relative error was
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calculated for each cycle. The data within the parentheses in Table 1 show
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the absolute relative error of 11 typical parameters between the mean values of
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all test runs (n = 202) and those of each synthesized driving cycle. As it turns
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out, the TMDC cycle 1 had the smallest sum of absolute relative error
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(13.35%). (Cycle 5 was not selected as the best driving cycle because the sum
To choose the representative driving
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of relative error of 11 parameters for cycle 5 was 29.67%, which was higher
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than that of cycle 1 (13.35%). Therefore, cycle 1 was selected as the typical
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motorcycle driving cycle for Taichung.)
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3.1.5 TMDC cycle and other driving cycle
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Table 3 compares the parameter characteristics of the selected TMDC
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driving cycle with others. The mean length (about 151 s) of the TMDC cycle
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was much higher than those of the other cycles (45-111s). The number of
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acceleration-decelerations within one driving period of the TMDC cycle (13.8)
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was also higher than that of the other cycles, except for Kaochiung. This
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demonstrated that there was a high frequency of speed variations in motorcycle
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driving in the Taichung metropolitan area. Furthermore, the TMDC cycle had
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a much lower percentage of cruising time (9.3%) than that of other driving
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cycles, and the low percentage of curing stage (The curing is maintain a
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constant speed of motorcycle in this study) was similar to the HK (Hong Kong)
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and KHM (Kaohsiung, Taiwan) cycles. This may indicate that the frequent
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speed variation was due to the fact that the driver had to change speed to adapt
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to the traffic conditions.
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patterns, including lane width, type and number of cars and motorcycles,
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topography, weather conditions, time (rush and non-rush hour), and driving
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behavior.
Again, many factors contribute to site-specific
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The European Union is currently developing a new test for WMTC
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(Worldwide Harmonised Motorcycle Emissions Certification/Test Procedure)
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cycle for 2006 regulations. Comparison of the TMDC with the current WMTC
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shown in Table 3 indicates significant differences with respect to speed, curing
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(The curing is maintain a constant speed of motorcycle in this study) time and
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idle time. Therefore, WMTC is not suitable to be used in the investigated area
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in this study.
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For better comparison, ECE, KHM and TMDC speed diagrams with
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respect to time in the representative driving cycle (TMDC 1) are shown in
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Figure 4. The ECE cycle is a regulated, smooth driving cycle. In contrast,
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the TMDC cycle displays a random driving condition that is similar to KHM.
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The speed of the TMDC cycle is also slightly higher than that of the ECE cycle
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and KHM.
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only 5 stages in the TMDC cycle for the same time duration. There were 10
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stages in the KHM cycle, and the total driving time (1126 s) was longer for
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KHM that that (755 s) for TMDC). The driving cycle is considered to be unique
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in different cities due to local traffic conditions and driver behavior. Therefore,
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the localized driving pattern developed in the present study may better reflect
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typical driving conditions. This should replace the ECE cycle currently used
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and the WMTC in the future for emission testing procedures.
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3.2 Emission characteristics and fuel consumption of TMDC
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3.2.1 Motorcycle emission factor and fuel consumption
As for the idle stage, there were 11 stages in the ECE cycle, yet
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The localized motorcycle driving cycle developed in the present study was
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further used to establish useful baseline information in a mobile source
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management program. Table 4 shows the exhaust emission factor of CO,
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HC, NOx, CO2 and fuel consumption of real-world conditions for Taichung city.
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For better comprehension, Table 5 depicts the motorcycle emission
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factors of the TMDC and ECE driving cycles, respectively. In general, the
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emission factors of the TMDC driving cycle were higher than those of the ECE
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cycle, due to frequent acceleration-deceleration as shown in Figure 4.
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Therefore, when the emission factors of the ECE cycle are used to measure the
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emission of motorcycles, the results are underestimated and not appropriate to
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serve as the basis for an effective air pollution control strategy and
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implementation plan. In the future, the real-world TMDC driving cycle should
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be implemented to reflect actual traffic conditions in the Taichung metropolitan
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area.
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Table 4 also shows the fuel consumption of motorcycles.
High fuel
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consumption was determined on the A and B routes, associated with high CO2
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and HC emission. High motorcycle volume loading per meter width on the
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route (Figure 1) could be the reason for the high fuel consumption in route B,
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and the motorcycle lane exclusion to limit the driving speed could also cause
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high fuel consumption. The lowest CO2 and HC emissions but high CO
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emission were determined for route C, which could be due to the driving
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characteristics (such as high speed, high M value (average number of
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acceleration-deceleration changes within one driving period)) that could reduce
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the engine combustion efficiency.
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The real-world driving tests showed 5% higher fuel consumption, 15%
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higher HC, 11% higher NOx, 5% lower CO and equal CO2 emission on
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workdays compared with those on the weekend. During the workdays, for the
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real world motorcycle, fuel consumption was 5% higher, CO2 emission was 7%
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higher, and CO emission was up to 15% higher during the RHM than the NRH.
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In the RHE, fuel consumption was 3% higher than during the NRH. HC
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emission was higher and NOx and CO emission were lower in the RHE than
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during the RHM. At night, poor visibility can inhibit driver behaviour and reduce
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engine combustion efficiency.
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On the weekend, low traffic volume loading in the morning could reduce
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pollution emission. HC was 21% lower and NOx was 8% lower, but CO
400
emission was 23% higher in the RHM than the NRH. In the evening, HC and
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NOx were also reduced, and CO emission increased 13% in the RHE over the
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NRH. A different traffic flow pattern was observed between workdays and
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weekends; that is, the low traffic flow could reduce motorcycle emissions in the
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NRH compared to the RHM and RHE. In addition, the average fuel
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consumption of tested motorcycles on the three routes on workdays was 5%
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higher than that on the weekend.
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3.2.2 Emission factor of TMDC and ECE
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Table 5 presents the emission factor of motorcycle exhaust and fuel
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consumption. Most driving cycles were conducted on the dynamometer test
411
under careful environment control including the ECE cycle for the study. The
412
ECE testing results are similar (less than 10% difference for CO, CO2, NOx
413
and fuel consumption and 24% difference for HC) to the work of Tsai et al.
414
(2005). In the on-board real-world test of TMDC, low CO and NOx emission
415
factors were determined compared to other studies. The CO2 emissions of
416
TMDC were in the range of a model stimulation in Hanoi (Kim Oanh et al.,
417
2012), lower than that of WMTC (Alvarez et al., 2009), but higher than a
418
dynamometer test in Hanoi (Tung et al., 2011). Hydrocarbon emissions were
419
significantly higher in the TMDC, which could be attributed to environmental
420
factors such as temperature, humidity, topography and flow pattern affecting
421
driving and reducing combustion efficiency to cause HC emission. Real-world
422
driving conditions (such as temperature, humidity, topography and flow pattern)
423
could reduce the combustion efficiency, causing high HC emission and low
424
NOx emission in the TMDC. Because the TMDC was conducted outdoors, it is
425
a real-world test. In contrast, other driving cycles such as ECE, WMTC were
426
carried out on a dynamometer in a well-controlled environment. The fuel
427
consumption of TMDC was higher than that of the dynamometer in Hanoi
428
(Tung et al., 2011). More low-engine-volume (70-150 cc) motorcycles were
429
employed to the dynamometer test, which could account for the low fuel
430
consumption in Hanoi. Fuel consumption in the real-world motorcycle driving
431
cycle was also about 7% lower than that of the ECE cycle, which again
16
432
indicates that the ECE cycle is unsuitable to measure the fuel consumption in
433
the Taichung metropolitan area. Consequently, a localized driving cycle that
434
can accurately describe the traffic conditions and represent the drivers’ habits
435
should be used.
436
4. Conclusions
437
The study used the chase-car with GPS technique to develop a motorcycle
438
driving cycle in the Taichung metropolitan area of middle Taiwan. Comparing
439
the TMDC cycle to other cycles, the speed and acceleration values were lower
440
than those of the FTP 75, but higher than others. In addition, the speed of the
441
TMDC cycle was similar to the 10-15 mode of Japan. On the weekend, the low
442
traffic volume loading in the morning could reduce the pollution emission.
443
Average fuel consumption of tested motorcycles on three routes on workdays
444
was 5% higher than on the weekend. The TMDC/ECE emission factor ratios of
445
CO, HC, NOx and CO2 for all types of motorcycles were 1.3, 5.4, 0.53 and 0.99,
446
respectively. Results indicated the exhaust CO and HC emission factors of
447
the TMDC driving cycle were significantly higher than those of the ECE. But the
448
NOx emission factor of TMDC was lower than that of ECC. Furthermore, the
449
fuel consumption during the TMDC driving cycle was nearly 7% lower than that
450
of the ECE cycle. Based on the results, the dynamometer test and different
451
driving cycle did not reflect the real-world motorcycle exhaust emission.
452
Therefore, the development of a localized motorcycle driving cycle and an
453
on-board exhaust analyzer is important for the estimation of motorcycle
454
pollution and other assessment of mobile source pollution control programs.
455
456
Acknowledgments:
457
The authors express their sincere thanks to the National Science Council,
458
Taiwan
and
Taiwan
Environmental
Protection
Agency
17
459
(NSC-96-2221-E-039-012) for their support.
460
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