Assessing the consistency between observed and modelled route choices through GPS data

In traffic engineering, different assumptions on user behaviour are adopted in order to model the traffic flow propagation on the transport network. This paper deals with the classical hypothesis that drivers use the shortest possible path for their trip, pointing out the error related to using such approximation in practice, in particular in the context of dynamic origin-destination (OD) matrix estimation. If this problem is already well known in the literature, only few works are available, which provide quantitative and empirical analysis of the discrepancy between observed and modelled route sets and choices. This is mainly related to the complexity of collecting suitable data: to analyse route choice in a systematic way, it is necessary to have observations for a large period of time, since observing trajectories for the single user on a specific day could not be enough. Information is required for several days in order to analyse the repetitiveness and understand which elements influence this choice. In this work the use of the real shortest path for a congested network is evaluated, showing the differences between what we model and what users do. Results show that there is a systematic difference between the best possible choice and the actual choice, and that users clearly consider route travel time reliability in their choice process.

[1]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[2]  Erhan Erkut,et al.  On finding dissimilar paths , 2000, Eur. J. Oper. Res..

[3]  David Bernstein,et al.  FINDING ALTERNATIVES TO THE BEST PATH , 1997 .

[4]  Robin Sibson,et al.  SLINK: An Optimally Efficient Algorithm for the Single-Link Cluster Method , 1973, Comput. J..

[5]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[6]  Zhong-Ren Peng,et al.  Using Global Positioning System Data to Understand Variations in Path Choice , 2000 .

[7]  Tom Thomas,et al.  Drivers' Perception of Route Alternatives as Indicator for the Indifference Band , 2013 .

[8]  Shlomo Bekhor,et al.  Applying Branch-and-Bound Technique to Route Choice Set Generation , 2006 .

[9]  Hartwig H. Hochmair,et al.  Network Structure and Travel Time Perception , 2013, PloS one.

[10]  Mohamed Abdel-Aty,et al.  USING STATED PREFERENCE DATA FOR STUDYING THE EFFECT OF ADVANCED TRAFFIC INFORMATION ON DRIVERS' ROUTE CHOICE , 1997 .

[11]  J. Wielinski,et al.  Annual Meeting of the Transportation Research Board , 2010 .

[12]  Shlomo Bekhor,et al.  Evaluation of choice set generation algorithms for route choice models , 2006, Ann. Oper. Res..

[13]  E. Martins,et al.  An algorithm for the ranking of shortest paths , 1993 .

[14]  Shanjiang Zhu,et al.  Do People Use the Shortest Path? An Empirical Test of Wardrop’s First Principle , 2015, PloS one.

[15]  Toshiyuki Yamamoto,et al.  Development of map matching algorithm for low frequency probe data , 2012 .

[16]  F. Rohlf,et al.  NTSYS-pc Numerical Taxonomy and Multivariate Analysis System, version 2.1: Owner manual , 1992 .

[17]  Heidrun Belzner,et al.  A New Measure of Travel Time Reliability for In-Vehicle Navigation Systems , 2008, J. Intell. Transp. Syst..

[18]  N. J. V. Zijpp,et al.  Path enumeration by finding the constrained K-shortest paths , 2005 .

[19]  Nils J. Nilsson,et al.  A Formal Basis for the Heuristic Determination of Minimum Cost Paths , 1968, IEEE Trans. Syst. Sci. Cybern..

[20]  G. Bifulco,et al.  Evaluating the effects of information reliability on travellers’ route choice , 2014 .