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Modeling the effects of real time traffic information on travel behavior: A case study of Istanbul Technical University.

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Title: Modeling the effects of real time traffic information on travel behavior: A case study of Istanbul Technical University.
Authors: Gokasar, Ilgin1 ilgin.gokasar@boun.edu.tr, Bakioglu, Gozde1 bakioglugo@itu.edu.tr
Source: Transportation Research: Part F. Oct2018, Vol. 58, p881-892. 12p.
Subjects: Real-time control, Traffic engineering software, Mobile communication systems, Socioeconomics, Commuters, Global Positioning System
Abstract: Highlights • Driver behavior under the real-time information through traffic apps is studied. • MNL model for travel and commuter responses to traffic information is estimated. • The outcomes are analyzed separately for self-driving and public transit commutes. • The results could help developing vehicular communication systems such as V2I. Abstract This article adds to the literature on the investigation of choice behavior of travelers under the real-time traffic information acquired through some traffic applications such as GPS navigation devices for car, mobile traffic applications and radio traffic reports on traveler behavior on highways. Self-deviced survey of travelers was conducted for the civil engineering undergraduate – graduate students, academicians and supporting staff at Istanbul Technical University, Turkey in 2016. Multinomial logit mode choice model of the decision making for travel and commuter responses to traffic information were estimated separately in two different commute modes, including private cars and public transit. The attributes that influence travelers’ decision-making patterns were broadly categorized into three groups, which were socioeconomics, travel and technological characteristics. The analysis of the results indicated that travelers who obtained traffic information from some traffic applications were more likely to switch their route with respect to their different characteristics. Moreover, the travel pattern of the commuters regarding whether to change their choice of route or not varied with respect to their aforementioned characteristics as well as their selection of commute modes. The results of this research could also help to develop vehicular communication systems such as vehicle-to-infrastructure V2I communications. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Highlights • Driver behavior under the real-time information through traffic apps is studied. • MNL model for travel and commuter responses to traffic information is estimated. • The outcomes are analyzed separately for self-driving and public transit commutes. • The results could help developing vehicular communication systems such as V2I. Abstract This article adds to the literature on the investigation of choice behavior of travelers under the real-time traffic information acquired through some traffic applications such as GPS navigation devices for car, mobile traffic applications and radio traffic reports on traveler behavior on highways. Self-deviced survey of travelers was conducted for the civil engineering undergraduate – graduate students, academicians and supporting staff at Istanbul Technical University, Turkey in 2016. Multinomial logit mode choice model of the decision making for travel and commuter responses to traffic information were estimated separately in two different commute modes, including private cars and public transit. The attributes that influence travelers’ decision-making patterns were broadly categorized into three groups, which were socioeconomics, travel and technological characteristics. The analysis of the results indicated that travelers who obtained traffic information from some traffic applications were more likely to switch their route with respect to their different characteristics. Moreover, the travel pattern of the commuters regarding whether to change their choice of route or not varied with respect to their aforementioned characteristics as well as their selection of commute modes. The results of this research could also help to develop vehicular communication systems such as vehicle-to-infrastructure V2I communications. [ABSTRACT FROM AUTHOR]
ISSN:13698478
DOI:10.1016/j.trf.2018.07.013