Mode Choice analysis for work trips using Multinomial Logit model for Windsor, Ontario, Canada
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Mode Choice analysis for work trips using Multinomial Logit model for Windsor, Ontario, Canada
- 1. Mode Choice model USING MULTINOMIAL LOGIT MODEL TRANSPORTATION SYSTEMS ANALYSIS SUMMER 2015
- 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
- 8. Aakash Bagchi (104296114) 0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 100.00 Auto Transit Walk/Bike Employment-type & Mode Share Full-Time Home-maker Part-Time Retired Self-Employed Student Unemployed 0.00 20.00 40.00 60.00 80.00 100.00 Auto Transit Walk/Bike House-type & Mode Share Apartment Duplex Single-Family Townhouse Other 0.00 20.00 40.00 60.00 80.00 100.00 120.00 Auto Transit Walk/Bike Age-group & Mode Share <=15 16-25 26-35 36-45 46-55 56-65 >65 Hypothesis formulation – Data exploration
- 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
- 10. Aakash Bagchi (104296114) Hypothesis Mode Variable Auto Transit Walk/Bike Socio-Economic HOUSEHOL + VEHICLES + BICYCLES + GENDER + APT + DUPLEX SING_FAM + THOUSE + OTHERD FULL_TIM + HMAKER + PTIME + RTRD SELFEMP STUDENT + + UNEMP INC + - - Mode Variable Auto Transit Walk/Bike LevelofService TRP_DISTANCE + - - TT_ATUO - TT_TRANS - TT_WB - Ttime - - - TC - - - Demographic AGE1 + AGE2 AGE3 AGE4 AGE5 + AGE6 + AGE7 +
- 11. Aakash Bagchi (104296114) Utility Matrix Alt 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 0 TR 0 CT 0 0 TT TC 0 STDT 0 0 0 NVEHHHT WB 0 0 0 NBIKE TT TC 0 STDW 0 SINGFAM 0 NVEHHHW
- 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
- 13. Aakash Bagchi (104296114) Model Results Prob 95% confidence intervalMODE Coefficient Error z |z|>Z* CA -2.955 0.784 -3.77 0.00 -4.49 -1.42 NVEH 1.152 0.330 3.49 0.00 0.51 1.80 TT -0.057 0.013 -4.47 0.00 -0.08 -0.03 TC -0.349 0.349 -1.00 0.32 -1.03 0.33 FTE 0.639 0.479 1.33 0.18 -0.30 1.58 TH 1.719 1.156 1.49 0.14 -0.55 3.99 WORKAGE 0.689 0.431 1.60 0.11 -0.15 1.53 CT -2.656 0.967 -2.75 0.01 -4.55 -0.76 NVEHHHT -1.486 1.124 -1.32 0.19 -3.69 0.72 STDT 2.138 1.016 2.10 0.04 0.15 4.13 NVEHHHW -1.080 0.714 -1.51 0.13 -2.48 0.32 NBIKE 0.310 0.133 2.33 0.02 0.05 0.57 STDW 1.381 0.847 1.63 0.10 -0.28 3.04 SINGFAM -1.574 0.443 -3.55 0.00 -2.44 -0.70
- 14. Aakash Bagchi (104296114) Comparison of results and hypothesis Mode Variable Auto Transit Walk/Bike Socio-Economic HOUSEHOL + VEHICLES + 1.152 VEHICLES/HOUS EHOL -1.486 -1.080 BICYCLES + 0.310 GENDER + APT + DUPLEX SING_FAM + -1.574 THOUSE + 1.719 OTHERD FULL_TIM + 0.639 HMAKER + PTIME + RTRD SELFEMP STUDENT + 2.138 + 1.381 UNEMP INC + - - LevelofService TRP_DISTANCE + - - TT_ATUO - TT_TRANS - TT_WB - Ttime - -0.057 - -0.057 - -0.057 TC - -0.349 - -0.349 - -0.349 Demographic AGE1 + AGE2 AGE3 AGE4 AGE5 + AGE3+AGE4+AGE 5 0.689 AGE6 + AGE7 +
- 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
- 16. Aakash Bagchi (104296114) Thank You!
Editor's Notes
- 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