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

Abstract

In this study the impact of real-time travel information acquired through mobile devices on traveler behavior is investigated. The study takes into consideration individuals' attitudes toward the use of smart mobile devices. The effect of traffic information acquisition in activity rescheduling is also considered. A case study was developed for the Athens Metropolitan Area, Greece, in 2009. A survey was conducted that included both revealed and stated preference data, as well as attitudes and perceptions of individual decision makers. This article focuses on the modeling and analysis of the stated preferences experiment conducted. An integrated error component latent variable model was estimated, predicting the probability of travelers' switching behavior as a function of travel information and traffic information seekers through mobile devices. The analysis of the results indicates that traffic information seekers through mobile devices are more likely to switch travel behavior, while traffic information appears to affect the rescheduling of individuals' activities that in many cases is also accompanied by mode or route switching.

Urban Day-to-Day Travel and Its Development in an Information Environment: A Review

“…Improve urban transportation conditions [76][77][78] Willing to accept information prompts [79-81] Provide travel decision-making assistance [35,[82][83][84][85] Willing to face risk with the help of information [86] Reduce emissions, protect environment [83] Information provided by mobile devices affects most [87] Negative Bad effects of improper information dissemination [88] Unwilling to accept information prompts [89][90][91][92][93][94] Real road network information may not lead to best traffic distribution [95] Information prompts do not lead to traffic condition improvement [96] A majority of travelers are indifferent to information prompts [97-99] Better to follow intuition than follow information prompts [100] Neutral Interaction in social platforms is also a part of travel information [101,102] Different attitudes towards information prompts [103][104][105][106] Information prompts may not always have a fixed effect [107,108] Effectiveness varies depending on the penetration rate of ATIS [109,110] Information dissemination has different strategies [111] Differentiated information dissemination considering different personalities of receivers [112] Correct and incorrect information may both have good effects [113] Different impact of information prompts inside/outside congestion area [114] 3.…”

Bus passenger path choices after consulting ubiquitous real-time information

“…Car drivers are offered with more flexibility and options to make route choices compared to PT users. Since the introduction of travel information, a considerable number of studies on its impact has been dedicated to car drivers' route choice (such as Khattak et al, 1993;Emmerink et al, 1996;Abdel-Aty et al, 1997;Lotan, 1997;Wardman et al, 1997;Chen and Mahmassani, 1999;Hato et al, 1999;Srinivasan and Mahmassani, 2000;Dia, 2002;Chatterjee and Mcdonald, 2004;Mahmassani and Srinivasan, 2004;Tsirimpa et al, 2005;Buscema et al, 2009;Wang et al, 2009;Kattan et al, 2010;Lu et al, 2011;Tian et al, 2011;Ramos et al, 2012;Ben-Elia et al, 2013;Tseng et al, 2013;Ma et al, 2014;Venkatraman et al, 2014;Tsirimpa, 2015;van Essen et al, 2016). Existing literature on the impact of PT passenger information on path choice is still limited.…”

“…Edinburgh bus network has a hub and spoke structure (Figure 1). Mahmassani, 1999;Jou, 2001;Peirce and Lappin, 2004;Tsirimpa et al, 2005;Chorus et al, 2007;Ferris et al, 2010;Politis et al, 2010;Ferris et al, 2011;Chorus et al, 2013;Kattan et al, 2013;Tseng et al, 2013;Veiga Simão, 2014;Tsirimpa, 2015;Zhang et al, 2015;Maréchal, 2016b;Ge et al, 2017).…”

Mobile Technology and Studies on Transport Behavior: A Literature Analysis, Integrated Research Model, and Future Research Agenda

“…Watkins et al investigated the impact of real-time data on transit riders through the convenience of an information system [12], providing evidence that realtime mobile information reduces the perceived and actual wait time. Tsirimpa [13] presented a case study for the Athens Metropolitan Area, Greece, in 2009 to explore realtime travel information from mobile devices. Both individuals' attitudes and the effect of information acquisition on activity rescheduling were considered.…”