Leave the expressway or not? Impact of dynamic information

Abstract

This study investigates drivers' diversion decision behavior under expressway variable message signs that provide travel time of both an expressway route and a local street route. Both a conventional cross-sectional logit model and a mixed logit model are developed to model drivers' response to travel time information. It is based on the data collected from a stated preference survey in Shanghai, China. The mixed logit model captures the heterogeneity in the value of ''travel time'' and ''number of traffic lights'' and accounts for correlations among repeated choices of the same respondent. Results show that travel time saving and driving experience serve as positive factors, while the number of traffic lights on the arterial road, expressway use frequency, being a middle-aged driver, and being a driver of an employer-provided car serve as negative factors in diversion. The mixed logit model obviously outperforms the cross-sectional model in dealing with repeated choices and capturing heterogeneity regarding the goodness-of-fit criterion. The significance of standard deviations of random coefficients for travel time and number of traffic lights evidences the existence of heterogeneity in the driver population. The findings of this study have implications for future efforts in driver behavior modeling and advanced traveler information system assessment.

A multi-user decision support system for online city bus tour planning

“…Similar studies and overviews have been proposed, among others, by [45][46][47][48]. Finally, several recent studies show an approach to travelers' decision behavior modeling more similar in spirit to a non-regrettable choice setting than to a risk/uncertainty one (see, for example, [49,50]). …”

Agent-based en-route diversion: Dynamic behavioral responses and network performance represented by Macroscopic Fundamental Diagrams

“…Stated preference and revealed preference data were typically applied to estimate these types of models. More recently, mixed logit, which can incorporate more heterogeneous taste variations, has been broadly employed in modeling diversion behavior (Gan and Ye, 2014;Ben-Elia and Shiftan, 2010). Gao et al (2010) developed non-linear utility functions based on cumulative prospect theory (CPT) to consider the flexibility towards risk when real-time traffic information is provided.…”