Impact of smartphone-delivered real-time multi-modal information

En-trip mode switch decisions under the smartphone-delivered multi-modal information SMMI seem to have been rarely explored. This study investigated the impact on commute drivers' en-trip mode switch behaviour of SMMI. This was based on a stated preference SP survey in Shanghai which collected over 2000 observations of the choice between 'auto' and 'park-and-ride' P+R under SMMI. SMMI provides travel time for auto and P+R, delay for auto, cause of delay, P+R cost and comfort level of rail transit. A generalised estimating equations GEEs-based analysis was conducted to address the potential correlations between repeated observations from the same individual. Among the tested candidate GEEs models, the model with the 'unstructured' working correlation structure leads to the best fit for our SP data. Results showed that SMMI can significantly influence mode switch. Statistically significant explanatory variables in the model are gender, education level, usual commute mode, the number of sources of dynamic information of a driver access, frequency of driving, P+R use experience, seat availability in subway car, real time road condition, auto delay and P+R cost.

[1]  Glenn Lyons,et al.  The value of integrated multimodal traveller information and its potential contribution to modal change , 2003 .

[2]  Yang Bai,et al.  Why do people change routes? Impact of information services , 2013, Ind. Manag. Data Syst..

[3]  Choong C. Lee,et al.  Identifying drivers for continual usage of wireless broadband , 2011, Int. J. Mob. Commun..

[4]  W. Pan.  Akaike's Information Criterion in Generalized Estimating Equations , 2001, Biometrics.

[5]  Mohamed A. Abdel-Aty,et al.  Examination of Multiple Mode/Route-Choice Paradigms Under ATIS , 2006, IEEE Transactions on Intelligent Transportation Systems.

[6]  Anna Kramers.  Advanced multimodal traveller information system for reduced energy and GHG emissions , 2012 .

[7]  June Wei,et al.  A simulation tool for in-vehicle navigation systems evaluation , 2012, Int. J. Mob. Commun..

[8]  Brian Caulfield,et al.  Does green make a difference: The potential role of smartphone technology in transport behaviour , 2013 .

[9]  Hans Westerheim,et al.  National multimodal travel information - a strategy based on stakeholder involvement and intelligent transportation system architecture , 2007 .

[10]  Serge P. Hoogendoorn,et al.  Validation of a multimodal travel simulator with travel information provision , 2007 .

[11]  Chin-Lung Hsu,et al.  A study of the adoption behaviour for In-Car GPS navigation systems , 2010, Int. J. Mob. Commun..

[12]  Marius G. Minea,et al.  Efficiency of multimodal real-time travel and traffic information services employing mobile communications , 2011, 2011 10th International Conference on Telecommunication in Modern Satellite Cable and Broadcasting Services (TELSIKS).

[13]  Hongcheng Gan,et al.  To switch travel mode or not? Impact of Smartphone delivered high-quality multimodal information , 2015 .

[14]  S. Zeger,et al.  Longitudinal data analysis using generalized linear models , 1986 .

[15]  Kun Zhou,et al.  Traveler Information Tool with Integrated Real-Time Transit Information and Multimodal Trip Planning , 2011 .

[16]  D. Hensher,et al.  Stated Choice Methods: Analysis and Applications , 2000 .

[17]  Audun Vennesland,et al.  Flexible organisation of multimodal travel information services , 2009 .

[18]  Caspar G. Chorus,et al.  Responses to Transit Information among Car-drivers: Regret-based Models and Simulations , 2006 .

[19]  Gerit Götzenbrucker,et al.  Advanced traveller information systems for intelligent future mobility: the case of 'Anachb' in Vienna , 2012 .

[20]  Wen Long Yue,et al.  Integrating multimodal travel information at major transport interchanges : the prospective of intelligent public transport information system , 2009 .