An iterative learning approach for network contraction: Path finding problem in stochastic time‐varying networks

Abstract

Path finding problem has a broad application in different fields of engineering. Travel time uncertainty is a critical factor affecting this problem and the route choice of transportation users. The major downside of the existing algorithms for the reliable path finding problem is their inefficiency in computational time. This study aims to develop a network contraction approach to reduce the network size of each specific origin and destination (OD) pair in stochastic time‐dependent networks. The network contraction is based on the comparison of optimistic and pessimistic solutions resulting from minimum and maximum travel time realizations of a Monte‐Carlo simulation (MCS)‐based approach. In this respect, the researchers propose a learning approach to utilize the information of the realizations in the initial iterations of the MCS approach. Implementation of this approach is in place for several OD pairs of two real‐world large‐scale applications. First, it is calibrated for the Chicago downtown network; the performance and accuracy of the proposed approach are investigated by comparing the results against that of the approach without any network contraction. In addition, the Salt Lake City network illustrates the transferability of the approach to other networks. The results demonstrate significant computational improvements, with an acceptable accuracy level relative to the approach without network contraction.

Assessing the Network-Wide Impacts of Dedicated Lanes for Connected Autonomous Vehicles

“…The average computational time of each simulation run is about 35 min. Please refer to Fakhrmoosavi et al ( 45 , 46 ) and Xu et al ( 47 ) for more information about the computation time for similar simulations. The estimated NFDs based on the three simulation runs for the high demand level in the freeway network and the entire network are presented in Figure 3.…”

A Gaussian sampling heuristic estimation model for developing synthetic trip sets

“…However, the developed heuristic model is fully flexible to consider other metrics such as observed travel times instead of measured travel distances or other models that calculate trips in urban networks. For this, it is just necessary to replace the shortest-trip calculation with any other approach discussed in the literature (see, e.g., Fischer, 2020;Flötteröd & Bierlaire, 2013), time-dependent trips (Fakhrmoosavi et al, 2019), or focusing on the most reliable trips (Hadjidimitriou et al, 2015;Zockaie et al, 2016). On the other hand, the implementation of the developed heuristic model assumes independence between the regional OD pairs.…”

Pareto Optimal Path Generation Algorithm in Stochastic Transportation Networks

“…The route generating problem is the main problem in numerous applications of different fields, such as communications, electrical networks, and transportation networks [14]. The shortest path in deterministic networkers is the core of all routing algorithms.…”

“…Travel times for both links and paths are continuously estimated and recorded. The performance of each path considering Z 1 & Z 2 is calculated numerically as in steps (13,14). The expected travel time of a path is calculating by averaging all times resulted through different iterations, whereas the cumulative distribution is drawn to predict the time that would not be delayed over with a risk equals to the r.p.…”