Why do people change routes? Impact of information services

Abstract

Purpose -The aim of this study is to identify factors that influence drivers' route choice response to travel time information about both the expressway and local streets provided by variable message signs 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 this study. Research limitations/implications -This 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.

Leave the expressway or not? Impact of dynamic information

“…In previous studies, many researchers used stated preference (SP) data from questionnaire surveys to model drivers' response behavior, e.g., [5][6][7][8][9][10][11][12][13][14][15][16]. Some other studies used SP data from travel simulator experiments (e.g., [17][18][19][20][21][22][23][24][25]).…”

“…With the increasing popularity of simulation-based estimation, panel data models that address repeated observations are gaining more attention. Methodologies that have been applied to address repeated observations include mixed logit models (e.g., [19,23]), random effect models (e.g., [14,29]), multinomial probit (e.g., [13,18]), normal mixing distributions (e.g., [6]), generalized estimating equations (e.g., [5,15]), and mixed linear models (e.g., [25]). …”

Whether to enter expressway or not? The impact of new variable message sign information

“…The effectiveness of advanced traveler information systems (ATIS) depends highly on travelers' behavior in response to real‐time information. It is well recognized that it is very important to indentify the factors that influence travelers' decision behavior under ATIS . Research results in this challenging field can facilitate better investment, design, and operation of ATIS technologies.…”

“…In the literature, many studies on travel behavior under ATIS exist. In terms of collecting data about traveler response to ATIS, many researchers used SP data from questionnaire surveys (e.g., ). Some other studies used SP data from travel simulator experiments (e.g., ).…”

“…A recent review was given in . While developing discrete choice models of travel behavior under ATIS, approaches applied to address repeated observations in previous studies include mixed logit (e.g., ), multinomial probit (e.g., ), normal mixing distributions (e.g., ), generalized estimating equations (e.g., ), and mixed linear models (e.g., ). In contrast to the aforementioned studies, this study proposes a random effect panel data logit model to address correlations among repeated observations of the same respondent.…”