Abstract
Purpose - This study identifies factors that influence drivers’ route choice response to travel time information about both the expressway and local streets provided by VMS on arterial roads.Design/methodology/approach - A stated preference questionnaire survey was conducted to collect behavioral data. The generalized estimating equations (GEEs) method with a logit link function was used to model driver response and account for correlations within repeated observations from the same respondent. Four GEEs-based estimations with different working correlation structures were conducted and compared with each other as well as the conventional maximum likelihood estimation.Findings - Driving experiences, expressway delays, causes of delay, and the number of traffic lights on local streets are factors influencing route choice decisions. A new finding is that there exist differences in response behavior among employer-provided car, taxi and private car drivers. On the modeling aspect, the exchangeable structure was the most appropriate in our study.Research limitations/implications - Our study indicates the effectiveness and appropriateness of the GEEs method and suggests further examination of GEEs’ performance.Practical implications - The route choice probability model established by this study will facilitate better investment, design and assessment of dynamic information services in transportation management.Originality/value - The dynamic information this study concerns has rarely been addressed in the literature. Little literature to date has applied the GEEs method in information response modeling. This study reaches solider conclusions about the GEEs method.
Purpose - This study identifies factors that influence drivers’ route choice response to travel time information about both the expressway and local streets provided by VMS on arterial roads.Design/methodology/approach - A stated preference questionnaire survey was conducted to collect behavioral data. The generalized estimating equations (GEEs) method with a logit link function was used to model driver response and account for correlations within repeated observations from the same respondent. Four GEEs-based estimations with different working correlation structures were conducted and compared with each other as well as the conventional maximum likelihood estimation.Findings - Driving experiences, expressway delays, causes of delay, and the number of traffic lights on local streets are factors influencing route choice decisions. A new finding is that there exist differences in response behavior among employer-provided car, taxi and private car drivers. On the modeling aspect, the exchangeable structure was the most appropriate in our study.Research limitations/implications - Our study indicates the effectiveness and appropriateness of the GEEs method and suggests further examination of GEEs’ performance.Practical implications - The route choice probability model established by this study will facilitate better investment, design and assessment of dynamic information services in transportation management.Originality/value - The dynamic information this study concerns has rarely been addressed in the literature. Little literature to date has applied the GEEs method in information response modeling. This study reaches solider conclusions about the GEEs method.
