Long waiting delays for users and significant imbalances in vehicle distribution are bothering traditional station-based one-way electric car-sharing system operators.To address the problems above,a“demand forecast-s...Long waiting delays for users and significant imbalances in vehicle distribution are bothering traditional station-based one-way electric car-sharing system operators.To address the problems above,a“demand forecast-station status judgement-vehicle relocation”multistage dynamic relocation algorithm based on the automatic formation cruising technology was proposed in this study.In stage one,a novel trip demand forecast model based on the long short-term memory network was established to predict users'car-pickup and car-return order volumes at each station.In stage two,a dynamic threshold interval was determined by combining the forecast results with the actual vehicle distribution among stations to evaluate the status of each station.Then vehicle-surplus,vehicleinsufficient,vehicle-normal stations,and the number of surplus or insufficient vehicles for each station were counted.In stage three,setting driving mileage and carbon emission as the optimization objectives,an integer linear programming mathematical model was constructed and the optimal vehicle relocation scheme was obtained by the commercial solver Gurobi.Setting 43 stations and 187 vehicles in Jiading District,Shanghai,China,as a case study,results showed that rapid vehicle rebalancing among stations with minimum carbon emissions could be realized within 15 min and the users’car-pickup and car-return demands could be fully satisfied without any refusal.展开更多
基金supported by the Science and Technology Project of State Grid Corporation of China“Research on urban power grid dispatching technology for large-scale electric vehicles integration”(grant number 5108202119040A-0-0-00)。
文摘Long waiting delays for users and significant imbalances in vehicle distribution are bothering traditional station-based one-way electric car-sharing system operators.To address the problems above,a“demand forecast-station status judgement-vehicle relocation”multistage dynamic relocation algorithm based on the automatic formation cruising technology was proposed in this study.In stage one,a novel trip demand forecast model based on the long short-term memory network was established to predict users'car-pickup and car-return order volumes at each station.In stage two,a dynamic threshold interval was determined by combining the forecast results with the actual vehicle distribution among stations to evaluate the status of each station.Then vehicle-surplus,vehicleinsufficient,vehicle-normal stations,and the number of surplus or insufficient vehicles for each station were counted.In stage three,setting driving mileage and carbon emission as the optimization objectives,an integer linear programming mathematical model was constructed and the optimal vehicle relocation scheme was obtained by the commercial solver Gurobi.Setting 43 stations and 187 vehicles in Jiading District,Shanghai,China,as a case study,results showed that rapid vehicle rebalancing among stations with minimum carbon emissions could be realized within 15 min and the users’car-pickup and car-return demands could be fully satisfied without any refusal.
