A fuel-efficient reliable path finding algorithm in stochastic networks under spatial correlation

• Published in: Fuel (Guildford) Vol. 349; p. 128733
• Main Authors: Teng, Wenxin, Zhang, Yi, Chen, Xuan-Yan, Duan, Xiaoqi, Wan, Qiao, Yu, Yue
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2023
• Subjects: Fuel consumption; Path-finding; Spatial correlation; Stochastic networks; Travel time; Fuel consumption; Travel time; Path-finding; Spatial correlation; Stochastic networks
• ISSN: 0016-2361; 1873-7153
• DOI: 10.1016/j.fuel.2023.128733

•A reliable path finding model is proposed to minimize the fuel consumption budget.•Specified on-time arrival probability index is ensured by reliable path finding model.•A heuristic label setting algorithm is developed to exactly solve the formulated problem.•Real traffic data is applied for the applicability and efficiency verification. Transport activities are regarded as a major source of fuel consumption and CO2 emission production. To reduce the negative impact of traffic-related CO2 emission, this paper proposes a reliable path-finding algorithm for improving fuel efficiency in stochastic networks under the uncertainty of travel time and fuel consumption with the consideration of spatial correlation. A reliable constrained path-finding model is developed and formulated to minimize the fuel consumption budget while guaranteeing the specified on-time arrival probability. A heuristic label setting algorithm is developed to precisely solve the formulated problem. The proposed algorithm overcomes the time-consuming drawbacks of traditional path enumeration algorithms. The applicability and efficiency of the proposed algorithm are verified on real-world traffic data acquired from the Beijing and Xi‘an networks in China. The experiments demonstrate that our proposed algorithm can significantly reduce fuel consumption compared to existing studies. The experiment in Beijing shows that using the proposed algorithm can reduce 0.9 kg of CO2 emissions on average per trip compared to existing studies.