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

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.