mode choice analysis for work trips using multinomial logit model for windsor, ontario, canada
TRANSCRIPT
Mode Choice modelUSING MULTINOMIAL LOGIT MODEL
TRANSPORTATION SYSTEMS ANALYSISSUMMER 2015
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)
Aakash Bagchi (104296114)
Source: www.bikehub.co.uk
ObjectiveFrom 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.
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
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
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
Aakash Bagchi (104296114)
Hypothesis formulation – Data exploration
0 1 2 3 4 50
20
40
60
80
100
120
Number of Vehicles & Mode Share
Walk/BikeTransitAuto
0 1 2 3 4 580
85
90
95
100
105
Number of Bicycles & Mode Share
Walk/BikeTransitAuto
1 2 3 4 5 675
80
85
90
95
100
105
Household size & Mode Share
Walk/BikeTransitAuto
Aakash Bagchi (104296114)
Auto Transit Walk/Bike0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
Employment-type & Mode Share
Full-TimeHome-makerPart-TimeRetiredSelf-EmployedStudentUnemployed
Auto Transit Walk/Bike0.00
10.0020.0030.0040.0050.0060.0070.0080.0090.00
100.00
House-type & Mode Share
ApartmentDuplexSingle-FamilyTownhouseOther
Auto Transit Walk/Bike0.00
20.00
40.00
60.00
80.00
100.00
120.00
Age-group & Mode Share
<=1516-2526-3536-4546-5556-65>65
Hypothesis formulation – Data exploration
Aakash Bagchi (104296114)
Hypothesis formulation – From past research and given data
Household incomeTrip distanceGender-FemaleHousehold sizeVehicles Ownership
Travel CostTravel timeAge 5
Age 6Age 7
Travel CostTravel timeHousehold incomeTrip distanceGender-FemaleVehicle Ownership
Auto Transit
Aakash Bagchi (104296114)
HypothesisMode
Variable Auto Transit Walk/Bike
Socio-Economi
c
HOUSEHOL +VEHICLES +BICYCLES +GENDER +APT +DUPLEXSING_FAM +THOUSE +OTHERDFULL_TIM +HMAKER +PTIME +RTRDSELFEMPSTUDENT + +UNEMPINC + - -
ModeVariable Auto Transit Walk/Bike
Level of Service
TRP_DISTANCE + - -TT_ATUO -TT_TRANS -TT_WB -Ttime - - -TC - - -
Demographic
AGE1 +AGE2AGE3AGE4AGE5 +AGE6 +AGE7 +
Aakash Bagchi (104296114)
Utility MatrixAlt A1 A2 Vehicles Bicycles Ttime TC Full_tim Student Thouse Sing_fam Age3+Age4+Age5 Vehicles/Househol
AT CA 0 NVEH 0 TT TC FTE 0 TH 0 WORKAGE 0TR 0 CT 0 0 TT TC 0 STDT 0 0 0 NVEHHHTWB 0 0 0 NBIKE TT TC 0 STDW 0 SINGFAM 0 NVEHHHW
Aakash Bagchi (104296114)
Goodness of Fit of modelρ2= 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
Aakash Bagchi (104296114)
Model ResultsProb
95% confidence intervalMODE Coefficient Error z |z|>Z*CA -2.955 0.784 -3.77 0.00 -4.49 -1.42NVEH 1.152 0.330 3.49 0.00 0.51 1.80TT -0.057 0.013 -4.47 0.00 -0.08 -0.03TC -0.349 0.349 -1.00 0.32 -1.03 0.33FTE 0.639 0.479 1.33 0.18 -0.30 1.58TH 1.719 1.156 1.49 0.14 -0.55 3.99WORKAGE 0.689 0.431 1.60 0.11 -0.15 1.53CT -2.656 0.967 -2.75 0.01 -4.55 -0.76NVEHHHT -1.486 1.124 -1.32 0.19 -3.69 0.72STDT 2.138 1.016 2.10 0.04 0.15 4.13NVEHHHW -1.080 0.714 -1.51 0.13 -2.48 0.32NBIKE 0.310 0.133 2.33 0.02 0.05 0.57STDW 1.381 0.847 1.63 0.10 -0.28 3.04SINGFAM -1.574 0.443 -3.55 0.00 -2.44 -0.70
Aakash Bagchi (104296114)
Comparison of results and hypothesis
ModeVariable Auto Transit Walk/Bike
Socio-Economic
HOUSEHOL + VEHICLES + 1.152VEHICLES/HOUSEHOL -1.486 -1.080BICYCLES + 0.310GENDER + APT + DUPLEXSING_FAM + -1.574THOUSE + 1.719OTHERDFULL_TIM + 0.639HMAKER + PTIME + RTRDSELFEMPSTUDENT + 2.138 + 1.381UNEMPINC + - -
Level of Service
TRP_DISTANCE + - - TT_ATUO - TT_TRANS - TT_WB - Ttime - -0.057 - -0.057 - -0.057
TC - -0.349 - -0.349 - -0.349
Demographic
AGE1 + AGE2AGE3AGE4AGE5 +AGE3+AGE4+AGE5 0.689AGE6 + AGE7 +
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
Aakash Bagchi (104296114)
Thank You!