Predictive Rerouting of Connected and Automated Vehicles Using Traffic and Charging Demand Forecasts

2026-10-07

AI Summary

This study addresses the collaborative optimization of routing and charging for connected and autonomous vehicles (CAVs) in mixed traffic environments by proposing a multi-objective rerouting framework integrated with deep spatiotemporal prediction. Methodologically, a diffusion convolutional recurrent neural network (DCRNN) is employed to forecast traffic flow and charging demand. Coupled with the SUMO simulation platform, the framework dynamically generates alternative routes under multidimensional constraints encompassing congestion, emissions, driving range, and charging availability. Experimental results demonstrate that across CAV penetration rates ranging from 5% to 45%, the average travel time index is reduced by approximately 1.8%. The proposed approach achieves minimal congestion, maximum average speeds, and lowest emissions, while yielding significantly higher rerouting acceptance rates compared to baseline methods.

Abstract

In this paper, we consider the problem of predictive rerouting of connected and automated vehicles (CAVs) in mixed traffic with electric vehicle charging demand. We provide a framework that combines a diffusion convolutional recurrent neural network (DCRNN) with a routing policy that accounts for congestion, CO2 emissions, route length, and charging demand. The DCRNN uses historical network observations to forecast traffic conditions and charging demand. These forecasts are then used to evaluate feasible alternative routes for eligible CAVs. A route change is accepted when the alternative preserves connectivity to the original destination and improves the prescribed route cost. We evaluate the proposed framework in SUMO under controlled traffic disruptions at five CAV penetration levels, ranging from 5% to 45%. We compare its performance with K-shortest-path routing, predictive-density routing, V2X proactive routing, and a reference scenario without rerouting. In the considered scenarios, the proposed framework reduces the average travel-time index by approximately 1.8% relative to the reference scenario and achieves the lowest average travel-time index, highest average speed, and lowest aggregate CO2 emissions among the active routing methods. Across all five penetration levels, it accepts 41 route changes, compared with 146 for K-shortest-path routing and 153 for V2X proactive routing. The reference scenario retains lower aggregate emissions and distance traveled, illustrating the tradeoff between congestion reduction and the additional travel associated with rerouting.

Problem

• Predictive Rerouting
• Connected and Automated Vehicles
• Mixed Traffic
• Charging Demand
• Traffic Forecasting

Innovation

• Predictive Rerouting
• Connected and Automated Vehicles
• Diffusion Convolutional Recurrent Neural Network
• Charging Demand Forecasting
• Mixed Traffic