2. Aakash Bagchi (104296114)
Introduction
Mode Choice modelling
◦ Third stage in 4-stage transport modelling
Data : Household travel survey
◦ Variable groups: Socio-economic, Level of Service, Demographic
Location: Windsor, ON
◦ High level of vehicle ownership (automotive capital of Canada)
◦ Spread out geographically
◦ No transit services to suburbs-Lasalle, Amherstberg, Lakeshore etc
Modelling technique: Multinomial Logit model
Software tool: NLOGIT5 (Student version)
3. Aakash Bagchi (104296114)
Source: www.bikehub.co.uk
Objective
From the given data,
find the variables
which have a
significant impact on
the choice of mode for
work-trips and analyse
the effect of the
variables
(positive/negative) on
the choice of each
mode using a discrete
choice method.
4. Aakash Bagchi (104296114)
Literature Review
[Ding et al., 2014 (Exploring the influence of built environment on tour-based commuter mode choice: A cross-classified
multilevel modeling approach)]
◦ Distance of home zone from the work location is significant and has a positive effect on auto
mode
◦ Employment density at work location and population density at home location both
significant, but employment density at work location more so
◦ Travel time has a negative impact on auto mode
◦ Highly mixed land-use living areas encourage the use of transit for work while mixed land use
at work location not significant
[Yong Le Loo et al., 2015 (Transport mode choice in South East Asia: Investigating the relationship between transport
users’ perception and travel behaviour in Johor Bahru, Malaysia)]
◦ Variables having a positive effect on public transport use were location of residence,
students studying in Singapore, education-trade and technical skills institution and
education-post secondary institution
◦ Variables having a negative impact were, gender-female, age(45-54), employed in Johor
Bahru and employed in Singapore
5. Aakash Bagchi (104296114)
Literature Review
[Owen A., 2013 (Modeling the commute mode share of transit using continuous accessibility to jobs)]
◦ Transit mode share was found to decrease with increase in household income, increase in
population of white, non-hispanics and vehicle ownership.
◦ Household size and education had a negative association with transit ridership.
[de Palma and D Rochat, 2000 (Mode choices for trips to work in Geneva: an empirical analysis)]
◦ Variables having a positive impact on number of auto trips: Number of years of commuting,
cross-border travel, duration of daily congestion, weather, female, size of the household,
children going to school, young people with age less than 30years
◦ Variables having a negative impact on number of auto trips: Travel time, travel cost, flexible
work hours, frequency of congestion, senior people with age more than 50 years, employed
in top management, education level
6. Aakash Bagchi (104296114)
Literature Review
[M El-Sayed El-Bany et al., 2014 (Policy sensitive mode choice analysis of Port-Said City, Egypt)]
◦ High income has a positive effect on car/taxi use
◦ Out of vehicle travel time has larger impact (negative) than in-vehicle travel time on auto use
[J Zhou, 2012 (Sustainable commute in a car-dominant city: Factors affecting alternative mode choices among university
students)]
◦ Possessing a discounted transit pass has a positive effect on alternative mode use
◦ Commute distance is positively related to carpool. Distance not significant for walking, biking
or transit modes
◦ Gender, education level and age significant and positive co-relation to alternate modes
7. Aakash Bagchi (104296114)
Hypothesis formulation – Data
exploration
0
20
40
60
80
100
120
0 1 2 3 4 5
Number of Vehicles & Mode Share
Walk/Bike
Transit
Auto
80
85
90
95
100
105
0 1 2 3 4 5
Number of Bicycles & Mode Share
Walk/Bike
Transit
Auto
75
80
85
90
95
100
105
1 2 3 4 5 6
Household size & Mode Share
Walk/Bike
Transit
Auto
9. Aakash Bagchi (104296114)
Hypothesis formulation – From
past research and given data
Household income
Trip distance
Gender-Female
Household size
Vehicles Ownership
Travel Cost
Travel time
Age 5
Age 6
Age 7
Travel Cost
Travel time
Household income
Trip distance
Gender-Female
Vehicle Ownership
Auto Transit
12. Aakash Bagchi (104296114)
Goodness of Fit of model
ρ2= 0.34
AT TR WB Total
AT 717 10 28 754
TR 10 1 1 12
WB 27 2 17 46
Total 754 12 46 812
Crosstab: Comparison of
actual and model results
15. Aakash Bagchi (104296114)
Simulation
Travel times for transit decreased by 50%, and that of auto increased by 25%
Travel cost for transit decreased by 10% and that of auto increased by 10%
Choice
Base Scenario Scenario - Base
% Number % Number % Number
AT 92.86 754 91.0 740 -1.85 -14
TR 1.48 12 3.4 25 1.93 13
WB 5.67 46 5.9 48 0.19 2
Total 100 812 100 813 0.27 1
Windsor:
Transit system last comprehensively revised in 1977.
Average 3% of transit trips (Windsor Area Long range Transportation Study)
Population of around 2M.
Ding et al.
employment density at the home zone is not statistically significant in the model
The distance of home zone from the work location is significant at a 95% CI in the traditional model and significant at 90% in the cross-classified multilevel probit model
It was found that having a highly mixed land-use living areas encourage the use of transit for work tours. While mixed land use at the work location did not have a significant impact on the mode choice
Employment density at the work location is more important than population density at the home location
As the travel time increases, the probability of choosing car/auto mode decreases.
Yong Le Loo et al. (2015)
Variables having a positive effect on public transport use were location of residence, students studying in Singapore, education-trade and technical skills institution and education-post secondary institution
Variables having a negative impact were gender, age(45-54), employed in Johor Bahru and employed in Singapore
Owen (2013) found that transit mode share was found to decrease with increase in household income, increase in population of white, non-hispanics and vehicle ownership. Unexpectedly, household size and education had a negative association with transit ridership.
Geneva
+ve auto: No of years of use, cross-border(france), duration of daily congestion, weather, females, size of the household, children going to school, young people (<30years)
-ve auto: Travel time, travel cost, flexible work hours, frequency of congestion, seniors (>50 years), top management, high education
Port Said, Egypt
(1) Income is the most important attribute affecting the mode choice behavior model. The higher income earners are more likely to use car than taxi or bus. This is reflected by the high values and positive signs of income parameters.
(2) Contrary to most cases in developed countries, out-ofvehicle time which represents the accessibility shows higher impacts than the in-vehicle time as a result of poor access facilities in developing countries.
(3) A positive raise in speed and time budget with the reduction in monetary travel cost caused by applying new policy.
LA
It shows that being multimodal and having a discounted transit pass increase the utility of alternative modes such as public transit, biking, walking, carpool and telecommuting while holding a parking permit reduces the utility of these modes
Commute distance probably has a mixed impact on the utility or odds of these modes. This study identifies that commute distance is positively related to carpool and telecommuting. It cannot confirm that commute distance and biking, walking and usage of public transit are signifi- cantly correlated
t gender, status (undergraduate vs. gradate) and age are significantly correlated to biking, walking or usage of public transit
Students living alone are more likely to commute by driving alone than other students