The dynamic shortest path problem with time-dependent stochastic disruptions

The dynamic shortest path problem with time-dependent stochastic disruptions consists of finding a route with a minimum expected travel time from an origin to a destination using both historical and real-time information. The problem is formulated as a discrete time finite horizon Markov decision process and it is solved by a hybrid Approximate Dynamic Programming (ADP) algorithm with a clustering approach using a deterministic lookahead policy and value function approximation. The algorithm is tested on a number of network configurations which represent different network sizes and disruption levels. Computational results reveal that the proposed hybrid ADP algorithm provides high quality solutions with a reduced computational effort.

[1]  Warren B. Powell,et al.  An Adaptive Dynamic Programming Algorithm for the Heterogeneous Resource Allocation Problem , 2002, Transp. Sci..

[2]  D. Helbing,et al.  Theoretical vs. empirical classification and prediction of congested traffic states , 2009, 0903.0929.

[3]  Tom Van Woensel,et al.  Dynamic shortest path problems: Hybrid routing policies considering network disruptions , 2013, Comput. Oper. Res..

[4]  Alexander Skabardonis,et al.  Measuring Recurrent and Nonrecurrent Traffic Congestion , 2008 .

[5]  Ratna Babu Chinnam,et al.  Dynamic routing under recurrent and non-recurrent congestion using real-time ITS information , 2012, Comput. Oper. Res..

[6]  Samer Madanat,et al.  Incorporating network considerations into pavement management systems: A case for approximate dynamic programming , 2013 .

[7]  Loo Hay Lee,et al.  An approximate dynamic programming approach for the empty container allocation problem , 2007 .

[8]  Chelsea C. White,et al.  The dynamic shortest path problem with anticipation , 2007, Eur. J. Oper. Res..

[9]  Chelsea C. White,et al.  State space reduction for nonstationary stochastic shortest path problems with real-time traffic information , 2005, IEEE Transactions on Intelligent Transportation Systems.

[10]  J. Tsitsiklis,et al.  Stochastic shortest path problems with recourse , 1996 .

[11]  Warren B. Powell,et al.  Approximate Dynamic Programming - Solving the Curses of Dimensionality , 2007 .

[12]  Martijn R.K. Mes,et al.  Patient admission planning using Approximate Dynamic Programming , 2015, Flexible Services and Manufacturing Journal.

[13]  Warren B. Powell,et al.  An Approximate Dynamic Programming Algorithm for Large-Scale Fleet Management: A Case Application , 2009, Transp. Sci..

[14]  Atle Riise,et al.  Dynamic and Stochastic Aspects in Vehicle Routing-A Literature Survey , 2005 .

[15]  Warren B. Powell,et al.  Approximate dynamic programming for high dimensional resource allocation problems , 2005 .

[16]  T. V. Woensel,et al.  A selected review on the negative externalities of the freight transportation: Modeling and pricing , 2015 .

[17]  N. Geethanjali,et al.  A Survey on Shortest Path Routing Algorithms for Public Transport Travel , 2010 .

[18]  Liping Fu,et al.  An adaptive routing algorithm for in-vehicle route guidance system with real-time information , 2001 .

[19]  Hubert Rehborn,et al.  Common traffic congestion features studied in USA, UK, and Germany based on kerner's three-phase traffic theory , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[20]  Chelsea C. White,et al.  Optimal vehicle routing with real-time traffic information , 2005, IEEE Transactions on Intelligent Transportation Systems.

[21]  Chen Cai,et al.  Adaptive traffic signal control using approximate dynamic programming , 2009 .

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