Algorithms for most reliable routes on stochastic and time-dependent networks

• Published in: Transportation research. Part B: methodological Vol. 138; pp. 202 - 220
• Main Author: Arun Prakash, A.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.08.2020; Elsevier Science Ltd
• Subjects: Adaptive algorithms; Algorithms; Computer applications; Dynamic and random link travel times; Most reliable policy; Network reliability; On-time arrival probability; Reliability analysis; Stochastic dynamic traffic assignment; Stochastic time-dependent networks; Time dependence; Time-dependent link travel time distributions; Transportation networks; Travel time reliability; Stochastic dynamic traffic assignment; Travel time reliability; On-time arrival probability; Stochastic time-dependent networks; Most reliable policy; Time-dependent link travel time distributions; Dynamic and random link travel times
• ISSN: 0191-2615; 1879-2367
• DOI: 10.1016/j.trb.2020.05.013

highlights•We present algorithms to determine the most reliable strategy and path on stochastic and time-dependent networks.•The measure of reliability chosen is the on-time arrival probability at the destination.•We present a decreasing order-of-time algorithm for optimal time-adaptive strategy and a pruning algorithm for optimal path.•We derive the correctness of the proposed procedures and show their efficacy on large-scale transportation networks. This study presents algorithms to determine the most reliable routes on stochastic and time-dependent networks. The measure of reliability adopted is the probability of on-time arrival at the destination, given a threshold arrival-time. We propose two distinct algorithms to determine optimal time-adaptive strategy and optimal apriori path on stochastic and time-dependent networks. First, a decreasing order-of-time algorithm is proposed to determine the optimal strategy to the sink from all node and departure-time combinations. Second, a label-correcting, network pruning algorithm is proposed to determine the optimal path between the source and the sink for a given departure-time. The correctness of both the proposed algorithms is proved and their computational complexity expressions are derived. The efficacy of the proposed procedures is demonstrated on large-scale transportation networks. This work has the potential to facilitate wider application of stochastic and time-dependent networks in reliability-based modeling and analysis.