Negative Effect Prediction and Refueling Traffic Flow Equilibrium of Urban Energy Supply Network during Weekends and Holidays

• Published in: Applied sciences 2023-02, Vol.13 (4), p.2498
• Main Authors: Ge, Xianlong, Yin, Zuofa, Zou, Yuanqiu, Wang, Bo
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Automobiles, Electric; Carbon; Continuous improvement; Electric vehicles; Emissions; Emissions (Pollution); Energy consumption; Energy industry; Equilibrium; Genetic algorithms; Holidays & special occasions; Hybrid vehicles; negative effect prediction; Queuing; Queuing theory; Refueling; refueling station network; Replenishment; Supply and demand; Time series; Traffic assignment; Traffic congestion; Traffic flow; traffic flow equilibrium; Traffic management; Transportation networks; Travel demand; urban transportation
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app13042498

With the continuous improvement of people’s living standards, the travel demand for vehicles increases rapidly during weekends and holidays. This situation leads to a number of negative impacts on the transport network, such as traffic congestion, carbon emissions, and long queues at refueling stations. The impact of vehicles requiring energy replenishment on the negative effect is nonlinear. Therefore, the negative effect data of working days cannot be directly used to evaluate the cost, and the traffic equilibrium allocation problem also needs to be solved. An equilibrium allocation model of hybrid vehicles considering the energy replenishment demand in holidays is established, aiming at minimizing the total time cost and energy consumption cost. The Frank–Wolfe algorithm is used to solve the traffic allocation problem in advance, and the negative effect of the urban energy network during holidays is predicted by the Genetic Algorithm—Back Propagation (GA-BP) algorithm. Then, the energy-replenishing vehicles in the traffic network are induced to reduce the total cost. Finally, taking the actual road network in Jiulongpo District of Chongqing City, southwest of China, as an example, the negative effects of multiple stations are predicted. The results show that the prediction method proposed in this paper is effective during holiday periods. In addition, as the market share of electric vehicles increases, the negative cost can decrease gradually. The predicted results can provide a reference for traffic managers.