Probabilistic Time-Dependent Travel Time Computation Using Monte Carlo Simulation

This paper presents an experimental evaluation of probabilistic time-dependent travel time computation. Monte Carlo simulation is used for the computation of travel times and their probabilities. The simulation is utilizing traffic data regarding incidents on roads to compute the probability distribution of travel time on a selected path. Traffic data has the information about an optimal speed, a traffic incident speed and a probability of a traffic incident to occur. The exact algorithm is used for the comparison of the simulation.

[1]  Shichao Sun,et al.  How to find the optimal paths in stochastic time-dependent transportation networks? , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

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

[3]  Jan Martinovic,et al.  viaRODOS: Monitoring and Visualisation of Current Traffic Situation on Highways , 2014, CISIM.

[4]  Haibin Ling,et al.  An Efficient Earth Mover's Distance Algorithm for Robust Histogram Comparison , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Adaptive least-expected time paths in stochastic, time-varying transportation and data networks , 2001 .

[6]  Ivo Vondrák,et al.  Time-Dependent Route Planning for the Highways in the Czech Republic , 2015, CISIM.

[7]  R. Kalaba,et al.  Arriving on Time , 2005 .

[8]  Ariel Orda,et al.  Shortest-path and minimum-delay algorithms in networks with time-dependent edge-length , 1990, JACM.

[9]  Qingquan Li,et al.  Finding Reliable Shortest Paths in Road Networks Under Uncertainty , 2013 .

[10]  Alexandre M. Bayen,et al.  This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 1 Learning the Dynamics of Arterial Traffic From Probe , 2022 .

[11]  Dirk P. Kroese,et al.  Simulation and the Monte Carlo Method (Wiley Series in Probability and Statistics) , 1981 .

[12]  Christian S. Jensen,et al.  Stochastic skyline route planning under time-varying uncertainty , 2014, 2014 IEEE 30th International Conference on Data Engineering.

[13]  Jian Pei,et al.  Probabilistic path queries in road networks: traffic uncertainty aware path selection , 2010, EDBT '10.

[14]  Dirk P. Kroese,et al.  Simulation and the Monte Carlo Method: Solutions Manual to Accompany , 2007 .

[15]  Andrew V. Goldberg,et al.  Alternative routes in road networks , 2010, JEAL.

[16]  Qingquan Li,et al.  Reliable Shortest Path Problems in Stochastic Time-Dependent Networks , 2014, J. Intell. Transp. Syst..

[17]  Erik van Zwet,et al.  A simple and effective method for predicting travel times on freeways , 2004, IEEE Transactions on Intelligent Transportation Systems.

[18]  Roland Bader,et al.  Defining and Computing Alternative Routes in Road Networks , 2010, ArXiv.