Multistrategy Bus Guidance Method Incorporating Right-Turn Lane Sharing: A Hybrid Bayesian Network Approach

Enhancing bus movements along urban arterial roads to reduce waiting times and stops at intersections is critical for improving overall bus operational efficiency. Although extensive research has investigated transit signal priority (TSP) and bus guidance to achieve these objectives, mixed traffic conditions without dedicated bus lanes remain constrained due to inefficient traffic organization and underutilized intersection lanes. To address these limitations, this study proposes a multi-strategy bus guidance approach for connected environments that maximizes intersection lane utilization through right-turn lane sharing (RTS), particularly in China where dedicated and uncontrolled right-turn lanes are common. The approach is implemented via a dual-stage hybrid Bayesian network (HBN): Stage I forecasts bus travel times and queuing states using real-time bus positions and signal states data; Stage II determines the combination of speed guidance, dwell-time management, and RTS by accounting for passenger car speed distributions and allowable dwell times at stops. We evaluated the approach in a SUMO simulation environment of a real-world urban arterial in Kunshan, China, under four demand scenarios with varying volume-to-capacity (V/C) ratios of total traffic flow. Simulation results demonstrated that the proposed approach significantly reduced bus waiting times and stops at intersections. Under low-to-moderate demand situations, the combination of three strategies outperformed other strategies, and RTS-incorporated strategies achieved the greatest benefits under high-demand conditions.