International Journal of Civil Engineering and Technology (IJCIET)
Volume 10, Issue 1, January 2019, pp.233–241, Article ID: IJCIET_10_01_023
Available online at http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=10&IType=1
ISSN Print: 0976-6308 and ISSN Online: 0976-6316
©IAEME Publication
Scopus Indexed
EVALUATION OF URBAN ACCESSIBILITY
THROUGH TRAVEL BEHAVIOR FOR MIXED
LAND USE ZONES
Jayesh Juremalani
Research Scholar, Civil Engineering Department,
Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat, India
Krupesh A. Chauhan
Associate Professor, Civil Engineering Department,
Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat, India. 395007
ABSTRACT
In general, urban accessibility shows the ease of reaching destinations and the
interaction between the land-use and transportation systems. Integrating transport
and land-use mix is one of the goals of planning policies around the world. In this
paper, an attempt is made to assess the urban accessibility through commuters’ travel
behavior for shopping trip only. The effects of trip characteristics like trip length, trip
time and trip cost and socio-economic characteristics like gender, age, income,
occupation and vehicle ownership on travel behavior and mode choice are studied for
shopping trips for different mixed land use zones (wards) of Vadodara city. Urban
accessibility Index is prepared for different neighborhoods. It is found that the change
in the land-use mix affects the commuters' travel behaviour and mode choice selection.
Keywords: Trip Distance, Trip Cost, Trip Time, Utility Function, Accessibility.
Cite this Article: Jayesh Juremalani and Krupesh A. Chauhan, Evaluation of Urban
Accessibility Through Travel Behavior For Mixed Land Use Zones, International
Journal of Civil Engineering and Technology (IJCIET), 10 (1), 2019, pp. 233–241.
http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=10&IType=1
1. INTRODUCTION
Accelerated industrialization throughout the world has led to higher growth rates, increased
income and high demand for mobility. The increasing number of vehicles in the city causes
congestion and environmental problems that lead to disrupted traffic conditions like delay,
accidents which cause huge economic loss every year. Urban accessibility is a term often used
in transport and land-use planning, and is generally understood to mean approximately 'ease
of reaching'. However, the detailed definitions may vary. The related terms 'accessibility' and
'mobility' are the subjects of considerable confusion, and it is useful here to describe their
http://www.iaeme.com/IJMET/index.asp
233
editor@iaeme.com
Jayesh Juremalani and Krupesh A. Chauhan
distinct meanings. Traditionally, measures used to evaluate the transportation system have
focused on the concept of mobility. Mobility measures assess the potential for movement.
They typically include elements that describe the level of service, road capacity, and design
speed. On the other hand, accessibility measures assess the potential for interaction. Elements
of an accessibility measure would describe the spatial distribution of destinations, the ease of
reaching those destinations, and the quality of the destinations. Mobility is one element of
accessibility. A typical accessibility measure has two components. One is related to the
destinations and is commonly called the attractions portion of the measure. Typical attractions
measures for shopping include a number of employees, amount of sales, or square feet of
space. The second component describes the ease of reaching those attractions. Since difficulty
increases over the distance this component is commonly called the impedance factor. Typical
impedance factors include the distance to the attraction, the amount of time it takes to reach
the attraction or the cost of travelling to the attraction. Utility-based accessibility measures,
first introduced by Ben-Akiva and Lerman: This model is defined based on the “log sum”
expression of a random utility model in which the probability of an individual making a
particular choice is related to the utility of all choices. In this paper, accessibility is found
using this model under heterogeneous traffic conditions and mix neighborhoods.
2. INTRODUCTION OF THE STUDY AREA
Vadodara also is known as Baroda is the third largest city in the western Indian state of
Gujarat after Ahmedabad and Surat. It is located on the banks of the Vishwamitri River. It is
selected to be converted into the smart city under smart cities mission. BRTS is proposed for
it. The railway line and NH 8 that connects Delhi and Mumbai pass through Vadodara. As per
census 2011, the population of Vadodara is 16,70,806: of which male and female are 8,69,647
and 8,01,159 respectively. Figure 1 shows the zoning of the Vadodara city.
Figure 1. Zoning map of Vadodara city.
http://www.iaeme.com/IJCIET/index.asp
234
editor@iaeme.com
Evaluation of Urban Accessibility Through Travel Behavior For Mixed Land Use Zones
3. SOCIO-ECONOMIC BACKGROUND OF THE COMMUTERS
The Vadodara city has 19 election wards as per census, so 19 wards were considered as 19
zones of the study area. A questionnaire was prepared and using Krejcie & Morgan formula
sample size is decided. Simple random sampling technique is used for data collection. Total
1400 samples were collected. After removing outliers total 1358 respondents’ forms were
selected for analysis from all the 19 wards. The questionnaire was prepared to collect
information about all the trips but in this paper, only shopping trip analysis is shown. There
were total 983 male and 375 female respondents. The information on various social and
economic variables like age, income, gender, occupation, vehicle ownership was collected
and analyzed. Table 1 below shows the age distribution of respondents ward-wise. For
example, ward number 7 and 14 have younger respondent having age below 35 years as high
as 77.67 and 72.97 percentage respectively and ward number 8 and 15 have a lower
percentage of younger respondent whose age is below 35 years 39.32 and 40.62 percent
respectively.
Table 1 Statistical analysis of age of respondents.
Ward
No
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
16 to 25
years
25
34
11
12
4
4
49
10
6
2
2
30
12
16
6
10
12
13
12
270
26 to 35
years
24
30
36
36
26
22
31
25
18
16
15
29
21
38
20
34
19
36
17
493
36 to 45
years
13
13
17
19
23
14
16
15
11
14
15
22
22
12
15
23
22
15
12
313
http://www.iaeme.com/IJCIET/index.asp
235
46 to 55
years
8
14
9
10
10
10
4
32
5
4
4
14
10
7
8
14
11
9
9
192
55 years
above
4
10
12
6
1
0
3
7
3
4
2
5
5
1
15
3
3
4
2
90
Total
Respondents
74
101
85
83
64
50
103
89
43
40
38
100
70
74
64
84
67
77
52
1358
editor@iaeme.com
Jayesh Juremalani and Krupesh A. Chauhan
Table 2 shows the occupation of the respondents. The average percentage of the
government employee, private sector employee, service industry employee, businessmen and
housewife are 8.7%, 20.91%, 17.54%, 26.32% and 26.53% respectively. So it is clear from
the analysis that more number of shopping trips are made by business class people and
housewife.
Table 2 Statistical analysis of occupation of respondents.
WARD
GOV.
EMPLOYEE
PRIVET
SECTOR
SERVICE
INDUSTRY
BUSINESSMAN
HOUSEWIFE
TOTAL
1
8
27
20
14
5
74
2
16
28
27
20
10
101
3
10
30
12
16
17
85
4
7
23
18
21
14
83
5
6
5
5
15
33
64
6
5
6
9
2
28
50
7
6
24
24
41
8
103
8
11
18
16
20
24
89
9
1
11
5
20
6
43
10
1
1
3
11
24
40
11
0
1
4
13
20
38
12
16
32
25
23
4
100
13
17
19
13
12
9
70
14
2
17
12
34
9
74
15
9
15
10
14
16
64
16
10
14
14
17
29
84
17
0
4
0
29
34
67
18
4
19
26
22
6
77
19
1
12
11
10
18
52
The segregation of respondents based on income is shown in Table 3 below. The
maximum respondents fall under the income group of 20,000 to 30,000 and 30,000 to 40,000.
Total 53.21 % respondents have their income between Rs. 20,000 to Rs. 40,000. Only 6%
respondents have income between Rs. 50,000 to Rs. 1,00,000 and merely 2% are having
monthly income more than 1 lakh.
http://www.iaeme.com/IJCIET/index.asp
236
editor@iaeme.com
Evaluation of Urban Accessibility Through Travel Behavior For Mixed Land Use Zones
Table 3. Statistical analysis of income of respondents.
1
2
3
4
LESS
THAN
5000
1
1
1
1
5
6
7
8
9
10
0
0
5
1
5
1
16
2
1
2
9
5
10
16
12
3
11
12
13
14
15
16
0
0
3
4
1
1
1
3
5
6
7
2
2
15
11
13
12
13
3
18
14
36
22
22
10
29
17
15
17
26
16
19
11
0
5
15
3
13
5
0
0
4
3
3
4
0
0
1
38
100
70
74
64
84
17
18
19
1
4
3
33
1
5
3
78
6
7
11
207
10
23
8
365
22
25
17
369
24
8
7
199
3
4
3
78
0
1
0
29
67
77
52
1358
WARD
500010000
1000120000
2000130000
3000140000
4000150000
50001100000
ABOVE
100000
TOTAL
7
3
2
9
19
21
7
15
19
16
26
22
15
21
31
25
8
17
14
8
5
10
4
2
0
12
0
1
74
101
85
83
3
17
12
58
29
6
4
15
9
14
25
14
22
17
10
0
10
5
5
2
13
0
4
0
3
1
1
0
2
0
0
64
50
103
89
43
40
Urban accessibility is measured here in terms of the log value of utility functions. Here
utility value is nothing but the advantages one gets from the particular area. The equations for
the utility function are generated using SPSS software from the collected data of three
parameters travel distance, travel time and travel cost. The mean values are shown above in
Table number 4. It also shows the area in square Km and density which is nothing but
population per square KM.
http://www.iaeme.com/IJCIET/index.asp
237
editor@iaeme.com
Jayesh Juremalani and Krupesh A. Chauhan
Table 4 below shows the mean values of the three parameters trip distance, trip time and trip cost for
shopping trips.
Ward
Distance
Time
Cost
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
2.78
16.65
31.97
2.54
16.27
44.35
1.38
17.12
36.31
5.36
23.67
21.57
2.22
16.63
18.08
1.10
12.32
3.78
3.34
14.07
14.05
1.38
14.55
11.35
1.35
13.91
40.23
1.25
16.55
49.35
.92
15.68
41.21
4.50
18.82
27.96
3.79
19.99
21.69
.88
14.72
4.78
6.72
21.92
57.77
1.60
12.24
8.82
1.70
12.13
9.24
5.06
16.90
42.91
2.38
13.04
14.58
Area in SQ
KM
9.49
9.17
5.98
11.50
5.34
5.18
16.14
6.60
7.08
6.84
3.27
7.38
6.78
4.43
10.15
5.34
8.09
20.58
9.30
Population
above 18 as per
census 2011
64860
73258
68124
57669
68216
67050
69640
70092
66099
73779
69004
57493
63618
75990
62888
62101
69908
62556
67553
4. ANALYSIS OF URBAN ACCESSIBILITY
The mean values are then normalized. Analysis of ward number 1 is shown in table 5. For
example utility for ward 1 can be found, U ward 1 = 4.440-0.450* 0.41 -0.035*0.70- 0.005*
0.55= 4.280. Here normalized mean values of distance, time and cost are used to find out
utility value. Similarly, all the utility values are found for all the wards. After finding utility
values they are converted into log values and the accessibility index is prepared for the wards
for shopping trips.
http://www.iaeme.com/IJCIET/index.asp
238
editor@iaeme.com
Evaluation of Urban Accessibility Through Travel Behavior For Mixed Land Use Zones
Table 5. Calculation of log of utility functions for ward 1
ward
Coefficient
U Value
Log U
Normalized mean Values for ward 1
Distance
Time
Cost
0.41
0.70
0.55
4.440
-.450
1
4.28
.035
1.45
.005
Table 6 shows the final score of accessibility for all zones of Vadodara. The highest value
is secured by ward 6 and the lowest value is achieved by ward 19. So accessibility of Warsia
Sangam area is highest while Maneja has the lowest accessibility as far as shopping trips are
concerned.
Table 6 Final score of accessibility for all zones of Vadodara.
Ward/ Zone
6
17
16
14
4
8
1
11
5
7
9
13
15
10
18
2
12
3
19
Score
0.87
0.80
0.74
0.62
0.56
0.46
0.45
0.45
0.42
0.41
0.38
0.38
0.37
0.29
0.20
0.20
0.11
0.07
0.30
Name
WARSIA SANGAM AREA
TARSALI
PRATAPNAGAR
MANDVI CBD AREA
SARDAR ESTATE
GORWA
CHHANI AREA
AKOTA AREA
WAGHODIA AJWA AREA
FATEHGUNJ AREA
GOTRI TALAV
KOTHI RAOPURA
MANJALPUR
VASNA TANDALJA
MAKARPURA AREA
NIZAMPURA
ATLADRA AREA
HARNI AIRPORT
MANEJA AREA
Chart 1 shows the mode share and shopping trip distance relationship. It can be seen from
chart 1 that after 2 km use of non-motorized transport like walking and bicycle reduces
drastically. This is useful for designing cities.
http://www.iaeme.com/IJCIET/index.asp
239
editor@iaeme.com
Jayesh Juremalani and Krupesh A. Chauhan
Chart 1 The relationship between mode choice and working trip distance.
5 CONCLUSIONS
From the analysis, it may be concluded that the ward numbers 6, 17, 16 and 14 are more
accessible for shopping trips than their other counterparts. Similarly, ward numbers 2, 12, 3
and 19 are less accessible for the shopping trips.
From chart 1, it can be concluded that the use of walking and bicycle decreases as the
distance between home and shop place increases. Especially after 2 and 3 KM use of nonmotorized mode decreases drastically. While designing the neighborhood for making smart
and sustainable cities this point should be kept in mind. Vadodara is a tier II city. Most of the
urban population lives in tire II cities so this study may be helpful to other similar settlements.
REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]
A.G.O. Yeh, Fiona F. Yang, Jiejing Wang “Economic Transition and Urban
Transformation of China: The Interplay of the State and the Market”, Urban Studies, Vol.
52, No. 15, pp. 2822-2848. (2015).
Ben-Akiva, M.E., and J.L. Bowman. "Activity-Based Disaggregate Travel Demand
Model System with Daily Activity Schedules." Transportation Research Part A 35.1: 1–
28. Print. (2000).
Bertolini, L. “Sustainable accessibility: a conceptual framework to integrate transport and
land use plan-making.” Two test-applications in the Netherlands and a reflection on the
way forward. Transport Policy, 12(3). (2005).
Bhat, C.R. and Gossen, R. “A mixed multinomial logit model analysis of weekend
recreational episode type choice.” Transportation Research Part B, 38(9), 767-787. (2004).
Cerda, A. “Accessibility: a performance measure for land-use and transportation planning
in the Montreal Metropolitan Region.” School of Urban Planning McGill University.
(2009).
Chandra Bhat, Susan Handy, Kara Kockelman, Hani Mahmassani, Anand Gopal, Issam
Srour, Lisa Weston.“Development of an urban accessibility index: formulations,
http://www.iaeme.com/IJCIET/index.asp
240
editor@iaeme.com
Evaluation of Urban Accessibility Through Travel Behavior For Mixed Land Use Zones
[7]
[8]
[9]
[10]
[11]
[12]
[13]
aggregation, and application.” Technical Report. The University of Texas at Austin.
(2002).
El-Geneidy, A. M. “Understanding Relationships Between Regional Accessibility, Travel
Behaviour and Home Values.” Paper presented at the Transportation Research Board,
Washington DC.2010
Koppleman, F.; Bhat, C. A.: Self Instructing Course in Mode Choice Modeling:
Multinomial and Nested Logit Models. U.S. Department of Transportation, Federal
Transit Administration. (2006).
Krizek, K. J. “Neighborhood services, trip purpose, and tour-based travel.”
Transportation, 30(4), 387-410. doi: 10.1023/a:1024768007730. (2003).
Levine, J., Grengs, J., & Shen, Q. “The demographics of transportation accessibility: an
intermetropolitan comparison.” Paper presented at the Transed 2010, Hong Kong, China.
(2010).
Song, Y., & Rodriguez, D. “The Measurement of the Level of Mixed Land Uses: A
Synthetic Approach.” Carolina Transportation Program White Paper Series. White Paper.
Carolina Transportation Program. Chapel Hill. (2005).
Subarao, SSV, and Krishna Rao KV. "Analysis of Household Activity and Travel
Characteristics in Mumbai Metropolitan Region (MMR)." Analysis of Household Activity
and Travel Behavior: A Case of Mumbai Metropolitan Region 3.1: 1–18. Print. (2013).
Thill, J.-C., & Kim, M. “Trip making, induced travel demand, and accessibility.” Journal
of Geographical Systems, 7(2), 229-248. doi: 10.1007/s10109-005-0158-3.(2005).
http://www.iaeme.com/IJCIET/index.asp
241
editor@iaeme.com