Volume 23 Issue 6
Dec.  2023
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2023  >  23(6): 257-269
HUANG Sen, XU Xiang-dong. Reliable path planning model and algorithm in transportation networks with heterogeneous stochastic travel time in road links[J]. Journal of Traffic and Transportation Engineering, 2023, 23(6): 257-269. doi: 10.19818/j.cnki.1671-1637.2023.06.017
Citation: HUANG Sen, XU Xiang-dong. Reliable path planning model and algorithm in transportation networks with heterogeneous stochastic travel time in road links[J]. Journal of Traffic and Transportation Engineering, 2023, 23(6): 257-269. doi: 10.19818/j.cnki.1671-1637.2023.06.017
shu

Reliable path planning model and algorithm in transportation networks with heterogeneous stochastic travel time in road links

doi: 10.19818/j.cnki.1671-1637.2023.06.017

  • Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai 201804, China

Funds:

National Natural Science Foundation of China 72021002

Fundamental Research Funds for the Central Universities 22120230192

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    Abstract
  • In view of the concerns of travel time reliability and unreliability from travelers in path selection decisions under the heterogeneous distribution of stochastic travel time in road links in a transportation network, a new reliable path planning model was proposed with the mean-excess travel time (METT) as the optimization criterion. To characterize the heterogeneous distributions of stochastic travel times in different road links in the transportation network, the first four order moments of travel time were used as inputs to analytically estimate the METT, thus avoiding the assumption of homogenous distributions in existing studies. The exact and approximate two-stage algorithms based on the Dijkstra and K-shortest path algorithms were developed according to the theoretical properties of METT. The accuracy of the analytical estimation method of METT based on the first four order moments was verified by using the Monte Carlo simulation method as the benchmark. The accuracy and computational efficiency of the approximate two-stage algorithm were analyzed by finding the shortest mean-excess paths of all OD pairs in the test network. Research results indicate that the commonly used normal distribution assumption of stochastic travel time in road links in existing studies ignores the influences of skewness and kurtosis coefficient on the reliable path selection. The relative error between the approximated value from the analytical estimation method and the true value of the METT is not greater than 0.13%. When K is set as 10 in the K-shortest path algorithm, in the shortest mean-excess paths of OD pairs in the whole network solved by the approximate two-stage algorithm, the minimum mean-excess values of 0.11% OD pairs are different from those solved by the exact two-stage algorithm, with a maximum relative error of 3.35%. For the OD pairs composed of randomly selected five nodes and other nodes, the ratio range of average calculation time of the approximate two-stage algorithm to that of the exact two-stage algorithm is 0.87%-22.96%, indicating that the approximate two-stage algorithm can not only ensure the solution accuracy, but also can improve the calculation efficiency. The proposed model and algorithms can capture the realistic characteristics regarding the heterogeneous distributions of stochastic travel times in different road links, and the corresponding path planning results can better reflect travelers' concerns about the reliability of arriving on time and the risk of encountering delay.

     

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