Modeling Travel Behavior Under Urban Dynamic Road Traffic Information

Advanced traveler information systems (ATIS) can provide real-time traffic information for travelers'choosing reasonable travel mode, route and departure time, which is helpful to alleviate urban traffic congestion. The accuracy of information can influence travelers' trustiness in travel information, and therefore their travel behavior. It is noted that most studies about trustiness of travelers focus on the information attributes of cost and convenience. Meanwhile, few researchers discuss the effect of the change of travel time on travelers' behavior. Also the comparison between absolutely rational and boundedly rational decision-making theory for choice behavior analysis is still lacking. In order to deal with the aforementioned problems, this study analyzes the travel behavior under urban dynamic road traffic information, including route choice and departure time choice behavior. The stated preference survey for travel data collection is conducted with drivers in Dalian city. The most significant factors influencing the trustiness in traffic information are identified and the mechanism of travel behavior under travel information is analyzed using three decision strategies. The main findings in this paper are summarized as follows:Firstly, an Ordered Probit model is developed to evaluate the trustiness of travelers for providing urban road traffic information with six different attributes on the basis of extensive collection of literature review within travel behavior analysis under ATIS. The stated preference experiment using orthogonal method is designed and the survey is conducted with drivers in Dalian city. The attributes of traffic information accuracy are taken into account, which include the prediction error of travel time and the occurrence frequency of large prediction error. The model estimation results indicate that the occurrence frequency of large error is the most sensitive parameter affecting travelers'trustiness in traffic information. The travelers'education level and years of driving experience can influence their choices significantly. Also the effect of other socioeconomic attributes is discussed in detail. Then the relevant suggestions for the construction and operation of ATIS in China are proposed.Secondly, a Logit model and a Mixed Logit model are developed for analyzing travel behavior with time parameter fixed and cost parameter having a normal distribution, lognormal distribution or SB distribution, which contain route choice and departure time choice considering the change of travel time in traffic information. The stated preference survey is designed and conducted to obtain data from travelers with driving experience in order to identify the most important attributes within urban road traffic information which can affect travelers'choice behavior. The model estimation results indicate that the parameters of travel time and the change of travel time are very similar, while the values are very different for the maximum travel time and minimum travel time. The travelers'age, years of driving experience and income significantly impact their travel behavior.Finally, the mechanism of travel behavior under traffic information is analyzed using expected utility theory, prospect theory and regret theory, considering the change of travel time in traffic information. The different outcomes from three theories are discussed in detail. In order to examine the applicability of regret function in travelers'decision-making process, the stated preference survey is designed and conducted to investigate the commuting departure time choice behavior. A utility-based model and a regret-based model are developed with the collected survey data, incorporating the attribute of travel time uncertainty. The model estimation results show that the sensitivity of maximum and minimum travel time is very similar in regret-based model, which is obviously different from the estimated coefficients in utility-based model. The difference indicates that bad performance in terms of explanatory variables can not be compensated by the strong performance of other variables in the framework of regret theory. So the travelers that apply regret minimization criteria may arrive at different choices for departure time than the travelers that maximize utility. The hit ratio of regret model is lower than utility model, which implies the regret approach is not superior to expected utility theory. As an alternative to utility maximization method, prospect theory or regret theory for travel behavior analysis should be further improved.