Does Travel Information Influence Commuter and Noncommuter Behavior?: Results from the San Francisco Bay Area TravInfo Project

Abstract

Improved information received from public and private advanced traveler information systems can help travelers make more informed decisions, shorten times spent in traffic congestion, and reduce anxiety and stress. The behavioral responses of automobile and transit commuters as well as those of noncommuters to travel information received from radio, television, and telephone are analyzed. The influence of information has seldom been studied in terms of these different users. The data were collected through a computer-aided telephone interview conducted in the San Francisco Bay Area ( n = 947). The study analyzes the impacts of socioeconomic, context, and information variables on individuals’ decisions to adjust travel before beginning their trips; given an adjustment, the frequency of trip changes; and the type of most recent trip changes in terms of route, departure time, or mode shifts. The statistical analysis involves estimation of probit models with and without sample selectivity and a multinomial logit model. The results of the study indicate that a sample selectivity model is more appropriate for understanding respondents’ frequency of trip changes only when members of a subset of the sample population change their trips. Travel time uncertainty and travel information received from the electronic media increase the pretrip adjustment propensity. Furthermore, information from the most widely available and accessed medium, the radio, is highly likely to result in behavioral adjustments. Noncommuters have a high receptivity to canceling their trips in response to travel information. This has important implications for congestion relief in transportation networks.

Modeling the Impact of Traffic Information Acquisition From Mobile Devices During the Primary Tour of the Day

“…These findings are consistent with findings from other surveys presented in the literature (Al-Deek, Chandra, & Flick, 2009;Han, Timmermans, Dellaert, & van Raaij, 2008;Khattak, Yim, & Stalker, 1999;Mahmassani, Huynh, Srinivasan, & Kraan, 2003;Mahmassani & Liu, 1999;Roorda & Andre, 2007;Tsirimpa & Polydoropoulou, 2011).…”

Traveler Response to New Dynamic Information Sources: Analyzing Corridor and Areawide Behavioral Surveys

“…Table 4 presents illustrative comparisons of sub-types of automobile commuters in the second Broad Area survey. Note that other statistically rigorous analyses that we have conducted cannot be presented here due to space limitations (e.g., 12,13,18). The purpose here is to integrate the survey results, interpret them using a conceptual model and draw suggestive inferences regarding the impact of information on various types of travel decisions, and the contribution of different information sources to those decisions.…”

“…Finally, the specific content demanded by travelers needs more attention. Few studies combine knowledge from revealed choice data with stated preference data to find the true information needs of end users (12,13).…”

Can information promote transportation-friendly location decisions? A simulation experiment

“…Hickman and Wilson (1995) question the benefits of real-time transit information on the basis of finding few changes in route choices, travel time, and trip reliability when such information is provided to transit users. Furthermore, transit users tend to make scant use of static information resources such as maps and schedules when they are available (Balcombe and Vance, 1996;Khattak and de Palma, 1997;Khattak et al, 1999). Non-regular users of transit appear to be even more impervious to efforts at transit information dissemination (Abdel-Aty, 2001; Chorus et al, 2006c).…”