DocumentCode :
28146
Title :
Adaptive Vehicle Navigation With En Route Stochastic Traffic Information
Author :
Lin Xiao ; Lo, Hong K.
Author_Institution :
Univ. of Minnesota, Minneapolis, MN, USA
Volume :
15
Issue :
5
fYear :
2014
fDate :
Oct. 2014
Firstpage :
1900
Lastpage :
1912
Abstract :
This paper develops an adaptive approach for vehicle navigation in a stochastic network with real-time en route traffic information. This stochastic and adaptive approach is formulated as a probabilistic dynamic programming problem and is solved through a backward recursive procedure. The formulation, as a modeling framework, is designed to be able to incorporate various sources of information and real-time traffic states to improve routing quality. In this paper, we prove that the approach outperforms deterministic instantaneous shortest paths in a statistical sense. We also analyze the algorithm´s computational efficiency. The results from numerical examples are included to illustrate the performance of the adaptive routing policy that was generated by the formulation.
Keywords :
dynamic programming; network theory (graphs); road traffic; road vehicles; stochastic processes; vehicle routing; adaptive routing policy; adaptive vehicle navigation; backward recursive procedure; deterministic instantaneous shortest paths; information source; probabilistic dynamic programming problem; routing quality; stochastic network; stochastic traffic information; Adaptive systems; Algorithm design and analysis; Navigation; Real-time systems; Routing; Vectors; Vehicles; Adaptive navigation; dynamic programming; real traffic information;
fLanguage :
English
Journal_Title :
Intelligent Transportation Systems, IEEE Transactions on
Publisher :
ieee
ISSN :
1524-9050
Type :
jour
DOI :
10.1109/TITS.2014.2303491
Filename :
6763062
Link To Document :
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