Dynamic Electric Vehicle Route Planning via Traffic Flow Prediction and Charging Service Integration

Abstract

The rapid growth of vehicle ownership has led to increasingly congested road networks, which significantly reduces the energy efficiency of electric vehicles (EVs) and intensifies user range anxiety. To address these challenges, a dynamic EV route planning process is proposed by integrating traffic flow (TF) prediction, charging service modelling, and time-varying path optimization within a unified framework. First, future TF is predicted using a data-driven forecasting module based on the iTransformer model, which captures multivariate temporal dependencies across road links and provides accurate inputs for downstream decision-making. Based on the predicted traffic states, a time-dependent queuing process is formulated to estimate charging station waiting times by modelling the dynamic interaction between vehicle arrivals and service capacity. These components are then embedded into a time-varying shortest path optimization process that explicitly considers mid-journey charging constraints, with the objective of minimizing total travel time and economic cost. The proposed framework establishes a closed-loop decision-making process that couples traffic evolution, charging service dynamics, and routing behaviour. Extensive comparative experiments against classical Time-Dependent Shortest Path (TDSP) methods under different network scales, together with a real-world case study, demonstrate that the proposed approach achieves higher computational efficiency and improved routing performance under dynamic conditions. The results indicate that the proposed process-oriented method provides an effective and practical solution for EV routing in intelligent transportation systems characterized by time-varying traffic and service processes.