To identify electrical vehicle (EV) distribution paths with high robustness, insensitivity to uncertainty factors, and detailed road-by-road schemes, optimization of the distribution path problem of EV with multiple distribution centers and considering the charging facilities is necessary. With the minimum transport time as the goal, a robust optimization model of EV distribution path with adjustable robustness is established based on Bertsimas’ theory of robust discrete optimization. An enhanced three-segment genetic algorithm is also developed to solve the model, such that the optimal distribution scheme initially contains all road-by-road path data using the three-segment mixed coding and decoding method. During genetic manipulation, different interlacing and mutation operations are carried out on different chromosomes, while, during population evolution, the infeasible solution is naturally avoided. A part of the road network of Xifeng District in Qingyang City is taken as an example to test the model and the algorithm in this study, and the concrete transportation paths are utilized in the final distribution scheme. Therefore, more robust EV distribution paths with multiple distribution centers can be obtained using the robust optimization model.

Background: An intersection is an area with more energy consumption and emissions by motor vehicles, and the energy consumption and emissionsof vehicles at intersections should be reduced in road planning and traffic control to improve the urban traffic environment. Objectives: In order to analyze the influence of signal timing on CO₂ emission of traffic flow under the mixed traffic environment of fuel vehicles and electric vehicles. Methods: A set of CO₂ incremental emission models is established to estimate the CO₂ emissions of fuel vehicles and electric vehicles at signalized intersections. Then, a signal timing model with minimum CO₂ emissions is established, and the influence of signal timing with minimum CO₂ emissions on vehicle control delay and stop rates under different traffic conditions is analyzed. Conclusions: The case study shows that optimizing of the timing parameters of intersections from the perspective of vehicle CO₂ emissions is different from the perspective of control delay or stop rate; the model’s timing optimization will effectively balance the CO₂ emissions generated by vehicles during the acceleration, deceleration and idling stages, essentially achieving a comprehensive consideration of vehicle control delay and stop rates. When the road section speed and the mixed proportion of electric vehicles are low, the timing results tend to reduce the vehicle delay at intersections, but when the road section speed and the mixed proportion of electric vehicles are high, the timing results tend to reduce the vehicle stop rate.