Transit travel strategy as solution of a Markov decision problem: Theory and applications

The search of optimal travel strategy on unreliable transit network is analyzed as solution of a Markov decision problem and two uses of this approach, recently presented by the authors, are recalled and compared. The first application is relative to normative strategy search, such as for path recommendation in innovative transit trip planners. The second use concerns subjective optimal strategy search in a dynamic strategy-based path choice modelling, especially suitable for real-time run-oriented simulation-based mesoscopic assignment models. The paper concludes with an overview of the benefits of the approach and outlines scopes for further research.

[1]  Martin L. Puterman,et al.  Markov Decision Processes: Discrete Stochastic Dynamic Programming , 1994 .

[2]  Umberto Crisalli,et al.  A mesoscopic transit assignment model including real-time predictive information on crowding , 2016, J. Intell. Transp. Syst..

[3]  Stefano Pallottino,et al.  Equilibrium traffic assignment for large scale transit networks , 1988 .

[4]  Mohamed Wahba,et al.  MILATRAS: MIcrosimulation Learning-based Approach to TRansit ASsignment , 2009 .

[5]  Agostino Nuzzolo,et al.  A normative optimal strategy in intelligent transit networks , 2017 .

[6]  Antonino Vitetta,et al.  Real-time Reverse Dynamic Assignment for Multiservice Transit Systems , 2016 .

[7]  R. Bellman.  A Markovian Decision Process , 1957 .

[8]  Alan C. Elliott,et al.  Applied Time Series Analysis with R , 2016 .

[9]  Michael Florian,et al.  Optimal strategies: A new assignment model for transit networks , 1989 .

[10]  Avishai Ceder,et al.  New Applications of ITS to Real-time Transit Operations , 2017 .

[11]  Mark Hickman,et al.  Transit origin-destination estimation , 2017 .

[12]  J. Neumann,et al.  Theory of games and economic behavior , 1945, 100 Years of Math Milestones.

[13]  Oded Cats,et al.  Dynamic Modeling of Transit Operations and Passenger Decisions , 2011 .

[14]  Fumitaka Kurauchi,et al.  Generation and calibration of transit hyperpaths , 2013 .

[15]  George Athanasopoulos,et al.  Forecasting: principles and practice , 2013 .

[16]  Agostino Nuzzolo,et al.  Advanced public transport and intelligent transport systems: new modelling challenges , 2016 .