Accounting for systematic heterogeneity across car commuters in response to multiple TDM policies: case study of Tehran

Modeling commuters’ choice behavior in response to transportation demand management (TDM) helps in predicting the consequences of TDM policies. Although research looking at choice behavior has evolved to investigate preference heterogeneity in response to factors influencing mode choice, as far as we know, no study has considered taste variation across commuters in response to multiple TDM policies. This paper investigates the presence of systematic preference heterogeneity across commuters, in response to the TDM policies that can be explained by their socio-economic or commuting-related characteristics. Analysis is based on results of a stated preference survey developed using a Design of Experiments approach. Five policies were assessed in order to study the impact they had on how commuters chose their mode of transportation. These include increasing parking cost, increasing fuel cost, implementing cordon pricing, reducing transit time and improving access to transit facilities. For the sake of assessing both systematic and random preference heterogeneity across car commuters, a form of the Mixed Multinomial Logit (MMNL) model that identifies sources of heterogeneity and consequently makes the choice models less restrictive in considering both systematic and random preference variation across individuals was developed. The sample includes 366 individuals who regularly commute to their workplace in the city center of Tehran, Iran. The likelihood function value of this model shows a significant improvement compared to the base MNL model, using the same variables. The MMNL model shows that taste variation across the studied commuters results in differences in influences estimated for three policies: increasing parking cost, reducing transit time and improving access to transit. The analysis examines several distributions for random parameters to test the impacts of restricting distributions to allow for only normality. The results confirm the potential to improve model fit with alternative distributions.