Collaborated eco-routing optimization for continuous traffic flow based on energy consumption difference of multiple vehicles

Liu, Yonggang; Chen, Qianyou; Li, Jie; Zhang, Hongjie; Chen, Zheng; Lei, Zhenzhen

doi:10.1016/j.energy.2023.127277

Cited by 7 publications

(2 citation statements)

References 33 publications

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“…References [4][5][6] studied the dynamic vehicle routing problem with time windows and capacity constraints. References [7][8][9] studied the multi-vehicle routing problem under dynamic road networks.…”

Section: Research On Path Optimizationmentioning

confidence: 99%

“…Formulas ( 3)-( 5) are travel path constraints to ensure that passengers at each demand point have and only have the same vehicle for service; Formula (6) is the vehicle capacity constraint, indicating that the maximum number of drivers and passengers per vehicle is 4; Formulas (7) and ( 8) are the constraints of the ride in sequence, and the vehicle must travel in the sequence of passing through the passenger demand point from its starting point and then to the destination. Formula (9) specifies the latest arrival time of the carpool.…”

Section: Objective Functionmentioning

confidence: 99%

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Multi-Objective Planning of Commuter Carpooling under Time-Varying Road Network

Li,

Zhang,

Liu

et al. 2024

Sustainability

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Aiming at the problem of urban traffic congestion in morning and evening rush hours, taking commuter carpool path planning as the research object, the spatial correlation of traffic flow at adjacent intersections is mined using convolutional neural networks (CNN), and the temporal features of traffic flow are mined using long short-term memory (LSTM) model. The extracted temporal and spatial features are fused to achieve short-term prediction. Considering the travel willingness of drivers and passengers, a multi-objective optimization model with minimum driver and passenger loss time and total travel time is established under the constraints of vehicle capacity, time windows and detour distances. An Improved Non-dominated Sorted Genetic Algorithm-II (INSGA-II) is proposed to solve it. The open-loop saving algorithm is used to generate an initial population with better quality, and the 2-opt local search strategy is adopted in the mutation operation to improve search efficiency. The influence of vehicle speed on the matching scheme is analyzed. The research results show that under the same demand conditions, the total travel distance of the carpool scheme is reduced by about 56.19% and total travel time is reduced by about 65.52% compared with the non-carpool scheme. Research on carpool matching under time-varying road networks will help with urban commuting efficiency and environmental quality, and play a positive role in alleviating traffic congestion and promoting carpool services.

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