Modeling Traveler ’ s Day ‐ to ‐ day Departure Time Choice with Social Network Information : A Bayesian Belief Approach

Benefit from the prevalence of social network applications in recent years, people could share information with friends very conveniently. Meanwhile, the widely use of mobile devices, such as mobile phone and car GPS, helps people get their current locations promptly. Combining social networks and location-aware devices, travelers could share their travel experience and location information to friends, and get other’s information in return. This becomes a new type of information about traffic conditions, generated by travelers themselves and transmitted through social networks. Compared with traditional traffic information, this information has many unique properties: up-to-date, user specific and multi-source (travelers may receive information from several friends at once time). So a natural question arises: Can we exploit the shared traffic information in social networks to help tailor the travel plans for users? This problem is drawing increasing attention from researchers. Existing studies on this topic could be categorized into two folds according to the activities of their concerns: one is traveler’s own decision making, and the other is their social activities. When planning their own trips, travelers may be affected by the advices or feedbacks from friends on destinations (Han et al., 2011; Iryo et al., 2012; Kowald and Axhausen, 2012). Besides, people could get to know new friends and negotiate the time and location of their social activities by using social networks (Ronald et al., 2012; van den Berg et al., 2013). In these related works, most of them focus on non-work travel activities, such as sightseeing and shopping, which are flexible in trip-making. In this study, we consider a more frequent and time-constrained travel behavior, namely, commuting, and focus on people’s daily departure time choice, with the influence of social networks. Departure time choice is an important component of commuters’ daily travel plan and its outcome will be reflected in the traffic flow and has direct effect on congestion. Understanding and modeling commuters’ departure time choices is an important problem in transportation research (Jou et al., 2008). When making decisions, travelers not only learn from their own experience, but also from external traffic information (e.g. ATIS, online map, apps for mobile phones) to adjust their departure times dynamically. In this study, we target to understand 2 whether the information from social network would have significant influence on travelers’ day-to-day departure time choices, how they deal with this multi-source information in their decision, and its influence on the performance of the transportation network. We model the learning process of travelers by using a Bayesian belief updating approach. Bayesian learning is a method widely used in transportation to describe travelers’ decision and learning process from information under uncertainty, such as route choice (Chen and Mahmassani, 2004; Chorus et al., 2009; Parvaneh et al., 2012). In this study, we consider the case of a new commuter corridor, where travelers need to go through every day from home to work with the same work start time. The free flow travel time of the corridor is known, while the capacity is variable from day to day. Let D denote the set of all latent departure times, and T denote the set of perceived possible travel times. Both departure time and perceived travel time are modeled to be discrete time intervals for simplicity with | | D m  and | | T n  . At the beginning, we consider that travelers have prior knowledge about the corridor based on their previous travel experience. Specifically, for each departure time k d D  ( 1,2, , ) k m   , traveler i’s perception of travel time j t T  ( 1, 2, , ) j n   is denoted by Pr( ) j t , and the expected utility (EU) of k d is calculated as Equation (1), where ( , ) k j f d t is the travel cost associated with departure time k d and arrival time j t .