G. VISWANATH & Dr. KARTHIK K. SRINIVASAN

TRANSPORTATION ENGINEERING DIVISION

CIVIL ENGINEERING DEPARTMENT

IIT-MADRAS

1

Introduction

Objectives

Inferences from Literature review

Data Description

Methodology

Results

Conclusions

2

Vehicle ownership/presence - one of causal factors for trip

frequency

Generally uni-directional nature of relationship between vehicle

presence and trip frequency is assumed

The effect of trip frequency on vehicle presence has not received

sufficient attention

This is known as reverse causation effect or endogeneity

This could result in inaccurate predictions and errors in estimates

about travel

3

To investigate key factors influencing vehicle presence

To examine the main determinants of trip frequency and their

magnitudes of influence

To test for presence of endogeneity between vehicle presence and

trip frequency and if present, quantify its magnitude

4

Socio economic factors, Built environment and transportation

system characteristics -the main causal factors

Vehicle ownership was car ownership in most of the literature review

which would be different for Indian conditions

Endogeneity between vehicle presence and trip frequency was not

studied in any of these studies

5

This study is based on data collected as part of Chennai

Household Travel Survey (CHTS)

Analysis used data from 1000 households of which a subset

of 600 households were used after cleaning outliers and

missing data.

Representative of Chennai population in terms of key

variables such as household size, vehicle ownership, income,

trips made etc.

81% of all households in the sample own a vehicle either two

wheeler or car

6

Average number of two wheelers and cars per household

were 1.06 and 0.28 in study sample

Average trip frequency was 4.15 with a standard deviation of

2.47

Two Wheelers Presence/Holding: Number of two wheelers

which are owned/available at the time of survey

Cars Presence/Holding: Number of cars which are

owned/available at the time of survey

Trip Frequency: Total number of trips made by all members

above age 5 years) in household on survey day

7

Model set A : Dimensions considered

◦ Two wheeler presence, car presence and number of trips

Model set B : Dimensions considered

◦ Two wheeler presence and car presence

Model set C : Dimensions considered

◦ Combined vehicle ownership and number of trips

For each model set, 3 estimation techniques used:

Ordinary Least Square without endogenous variables (OLS1)

Ordinary Least Square With Endogenous regressors (OLS 2)

Three Stage Least Square Estimation (3SLS).

8

In model set A, trip making had a positive and significant influence (coefficient = 0.13) on two-wheeler presence and significant negative influence on car presence(0.02 ).

Car presence was a positive determinant (0.49) of two-wheeler presence and two wheeler presence was a positive (0.10) determinant of car presence.

While two-wheeler presence positively (0.64) affected number of trips, car presence negatively affected it (1.36 ).

Thus as the number of trips increased, trip makers preferred using a two wheeler than a car which was logical.

For Model set B, the relationship of car presence on two-wheeler presence was positive (0.55) and was significant.

9

Whereas the relationship of two-wheeler presence on car presence

was very small positive influence (0.065) but not significant

statistically.

In Model set C, when classified vehicle presence is not considered,

the effect of vehicles on trips turned out to be negative (0.17) and

significant.

In the other direction, the number of trips exerts a very small

positive influence (0.0065) on number of vehicles and was not

significant.

Thus, model set A provides a more complete and logical

representation of the interactions.

10

Two specification test were used to test the endogeneity – Hausman

test and Sargan test

Hausman test is used to check for bias towards particular variable

OLS1 and OLS2 were found to give biased coefficients for

endogeneous variables and other explanatory variables

This indicates that there exists endogeneity between vehicle

presence and trip frequency

Hence these must be simultaneously estimated

11

Sargan test is used to check for relevance of instrumental variables

used in the 3 SLS (i.e. if instrumental variables and residuals are

correlated)

The residuals of the number of two wheelers, number of cars and

number of trips made were regressed against all the Instrumental

variables

The product of degrees of freedom and coefficient of determination

(R2) was checked with Chi squared value at 99 % significance level

The results indicated that there is no correlation between error

terms and Instrumental variables

12

Household characteristics

o number of children in age group 0 to 5 years (-ve)

Connectivity characteristics o the number of cell phones (+ve)

Accessibility characteristics o proximity to train stations (+ve)

13

Social Activity characteristics o presence of two wheelers in neighborhood (+ve)

o presence of cars in neighborhood (-ve)

User characteristics

o importance given by users to comfort levels of owning vehicles(+ve)

o importance given by users to cost of owning a vehicle (+ve) Income characteristics

o medium income of households(-ve)

14

Household characteristics o number of children in age group 0 to 5 years(-ve) o number of drivers(+ve)

o number of educated people(+ve)

Connectivity characteristics

o number of cell phones(+ve) o land line telephone connection (+ve)

15

Social Activity characteristics

o presence of two wheelers in neighborhood (-ve)

o presence of cars in neighborhood (+ve)

Transportation System characteristics

o road surface condition (-ve)

16

Household characteristics

o household size (+ve)

o number of children in age group 0 to 5 years (+ve)

o number of drivers (+ve) Accessibility characteristics

o proximity to train station (+ve) o proximity to movie hall (+ve)

17

Social Activity characteristics o presence of two wheelers in neighborhood (-ve) o presence of cars in neighborhood (+ve)

User characteristics o importance given by users to comfort levels of owning vehicles

(-ve) o importance given by users to cost of owning a vehicle (-ve) o user’s perception of safety levels of a particular road (+ve)

18

Contributing factors for vehicle presence and trip frequency were

investigated in this study using 3SLS models

Two wheeler holding, Car holding and Trip Frequency were found to

be mutually endogenous.

Neglecting endogeneity leads to insignificance of some key

variables and spurious significance of other variables in trip

frequency model.

Classified vehicle holding should be modeled instead of total

number of vehicles present in a household

19

A number of new variables were found to influence the vehicle

presence and number of trips

Two wheeler holding is influenced by: number of cellular phones,

presence of two wheelers in neighborhood, and user’s valuation of

cost and comfort while owning a vehicle.

Car holding is affected by: telephone connectivity, car presence in

the neighborhood, road condition

Trip frequency is influenced by access to recreational centers near

home, user's valuation of comfort and cost .

20

Study the impact of endogenous vehicle ownership and trip

frequency on congestion levels and pollution levels

Evaluate alignment of transit in new areas and/or evaluation of

transit stops on trip frequency and vehicle ownership decisions.

21

1. Bhat, C.R., and Guo, Y.J. (2005). A comprehensive analysis of built environment characteristics on household residential choice and auto ownership levels, Transportation Research Part B 41 (2007), 506–526.

2. Bhat, C.R., and Pulugurta, V. (1998). A comparison of two alternative behavioral choice mechanisms for household auto presence decisions, Transportation Research Part B 32(1), 61-75.

3. Eluru, N., Bhat, C. R., and Sen, S. (2008). The impact of demographics, built environment attributes, vehicle characteristics, and gasoline prices on household vehicle holdings and use, Transportation Research Part B 43 (2), 1-18.

4. Dargay, J., and Gately, D. (1998). Income's effect on car and vehicle ownership, worldwide: 1960-2015, Transportation Research Part A 33(1),101-138.

22

5. Dugundji, E. R., Walker, J.L., Ehlers, E., and Banerjee, I. (2011).Correcting for endogeneity in behavioral choice models with social influence variables, Transportation Research Part A 45(1), 362-374.

6. Greene, W.H. (2007). Econometric Analysis: (Sixth Edition). Prentice Hall Inc.

7. Kitamura, R. (1987). A panel analysis of household car ownership and mobility, Proceedings of JSCE, Infrastructure Planning and Management 383(IV – 7), 13 – 27.

8. Kumar, M., and Krishna Rao, K.V. (2006). A Stated Preference study for a car ownership model in the context of developing countries, Transportation Planning and Technology 29(5), 409-425.

9. Matas, A., Raymond, J.L., and Roig, J.L. (2009). Car ownership and access to jobs in Spain, Transportation Research Part A 43 (6), 607-617.

23

10. Gim, T-H. T., (2011). A meta-analysis of the relationship between density and travel behavior, Transportation 39, 491–519.

11. Pendyala, R. M., Kostyniuk, L.P., and K. G. Goulias. (1995). A repeated cross-sectional evaluation of car presence, Transportation 22 (1), 165-184.

12. Potoglou, D., and Kanaroglou, P.S. (2008). Modelling car ownership in urban areas: A case study of Hamilton, Canada. Journal of Transport Geography 16, 42-54.

13. Sargan, John D. (1958). The estimation of economic relationships using instrumental variables, Econometrica 26, 393-415. 14. Zellner, A., and Theil, H. (1962). Three-stage least squares: simultaneous estimation of simultaneous equations, Econometrica 30 (1), 54–78.

24

25

26

27

• In Stage 1, regress exogenous variables against each endogeneous

variable and use regression values as instruments to estimate each

equation using OLS.

•In Stage 2, residuals from cross-equation disturbance term correlations

are obtained from the above estimates of stage 1.

• In Stage 3, ordinary least squares (OLS) is used to obtain coefficient

estimates

•Because of the additional information considered (correlation of

disturbances), 3SLS produces more efficient coefficient estimates than

single-equation estimation methods.

28

29

THANK YOU