Bandwidth-delay constrained path selection under inaccurate state information
IEEE/ACM Transactions on Networking (TON)
Transportation Science
Updating Paths in Time-Varying Networks Given Arc Weight Changes
Transportation Science
Stochastic shortest paths via Quasi-convex maximization
ESA'06 Proceedings of the 14th conference on Annual European Symposium - Volume 14
A new model for path planning with interval data
Computers and Operations Research
Shortest paths in stochastic networks with correlated link costs
Computers & Mathematics with Applications
IEEE Transactions on Intelligent Transportation Systems
Ant colony optimization for stochastic shortest path problems
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Dynamic routing under recurrent and non-recurrent congestion using real-time ITS information
Computers and Operations Research
High-Performance heuristics for optimization in stochastic traffic engineering problems
LSSC'09 Proceedings of the 7th international conference on Large-Scale Scientific Computing
Finding the K shortest hyperpaths using reoptimization
Operations Research Letters
On the minimum risk-sum path problem
ESCAPE'07 Proceedings of the First international conference on Combinatorics, Algorithms, Probabilistic and Experimental Methodologies
Dynamic shortest path problems: Hybrid routing policies considering network disruptions
Computers and Operations Research
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We consider stochastic, time-varying transportation networks, where the arc weights (arc travel times) are random variables with probability distribution functions that vary with time. Efficient procedures are widely available for determining least time paths in deterministic networks. In stochastic but time-invariant networks, least expected time paths can be determined by setting each random arc weight to its expected value and solving an equivalent deterministic problem. This paper addresses the problem of determining least expected time paths in stochastic, time-varying networks. Two procedures are presented. The first procedure determines the a priori least expected time paths from all origins to a single destination for each departure time in the peak period. The second procedure determines lower bounds on the expected times of these a priori least expected time paths. This procedure determines an exact solution for the problem where the driver is permitted to react to revealed travel times on traveled links en route, i.e., in a time-adaptive route choice framework. Modifications to each of these procedures for determining least expected cost (where cost is not necessarily travel time) paths and lower bounds on the expected costs of these paths are given. Extensive numerical tests are conducted to illustrate the algorithms' computational performance as well as the properties of the solution.