An adaptive routing system for location-aware mobile devices on the road network

As congestion problems become a greater concern and negatively impact society, solutions which alleviate them are needed to improve the performance of the transportation system. Routing systems which take into account the travel-time experienced by the driver have been largely unexplored in the domain of adaptive routing. In this article, we present a system which enables users of smartphones to obtain directions generated using an algorithm which provides an optimal routing policy for reliable on-time arrival; that is, directions which seek to maximize the probability of arriving to the destination within a given time budget, rather than to minimize the travel time based on posted speed limits. Our work leverages the geolocation capabilities of smartphones to provide optimal routing directions along the route dependent on the realized (experienced) travel time. The adaptive routing scheme we implement allows for significant power savings and improved driver safety compared to classical routing algorithms; special attention is paid to minimizing driver distraction by emphasizing aural and graphical components over textual elements during route guidance. Finally, we illustrate system performance and design choices on synthetic examples and real traffic data from the Mobile Millennium system in San Francisco.

[1]  Yueyue Fan,et al.  Optimal Routing for Maximizing the Travel Time Reliability , 2006 .

[2]  Mohan M. Trivedi,et al.  Examining the impact of driving style on the predictability and responsiveness of the driver: Real-world and simulator analysis , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[3]  Seiichi Sugiura,et al.  HUMAN FACTORS CONSIDERATIONS FOR VOICE ROUTE GUIDANCE , 1993 .

[4]  Y. Nie,et al.  Arriving-on-time problem : Discrete algorithm that ensures convergence , 2006 .

[5]  Sivan Toledo,et al.  VTrack: accurate, energy-aware road traffic delay estimation using mobile phones , 2009, SenSys '09.

[6]  Sebastien Blandin,et al.  A Tractable Class of Algorithms for Reliable Routing in Stochastic Networks , 2011 .

[7]  H. Frank,et al.  Shortest Paths in Probabilistic Graphs , 1969, Oper. Res..

[8]  Alexandre M. Bayen,et al.  Using Mobile Phones to Forecast Arterial Traffic through Statistical Learning , 2010 .

[9]  A. Bayen,et al.  A traffic model for velocity data assimilation , 2010 .

[10]  Matthew Brand,et al.  Stochastic Shortest Paths Via Quasi-convex Maximization , 2006, ESA.

[11]  Vladimir G. Danilov,et al.  Shock wave formation process for a multidimensional scalar conservation law , 2011 .

[12]  P. Abbeel,et al.  Path and travel time inference from GPS probe vehicle data , 2009 .

[13]  Y. Nie,et al.  Shortest path problem considering on-time arrival probability , 2009 .

[14]  Geir Evensen,et al.  The Ensemble Kalman Filter: theoretical formulation and practical implementation , 2003 .

[15]  David R. Karger,et al.  Optimal Route Planning under Uncertainty , 2006, ICAPS.

[16]  M J Lighthill,et al.  On kinematic waves II. A theory of traffic flow on long crowded roads , 1955, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.

[17]  P. I. Richards Shock Waves on the Highway , 1956 .

[18]  Alexandre M. Bayen,et al.  Scaling the mobile millennium system in the cloud , 2011, SoCC.

[19]  Richard Bellman,et al.  ON A ROUTING PROBLEM , 1958 .