Modeling and Optimization of Spatio-Temporal Differentiated Speed Limit Strategies for Electric Bicycles

According to statistics, China has over 400 million electric bicycles, which account for 72% of urban road conflicts and 45% of traffic accidents, highlighting the limitations of a uniform speed limit of 25 km/h in balancing safety and efficiency. Based on the current situation, this study proposes a two-dimensional optimization framework of “region-time period,” integrating micro-behavior simulation and macro-strategy optimization. Using cellular automata to quantify the probability ofviolation behaviors, a multi-objective optimization model is constructed with regional-time slot speed limit values and resource allocation as decision variables. A genetic algorithm combined with Monte Carlo simulation is employed to solve the nonlinear mixed optimization problem. The results indicate that differentiated speed limit strategies significantly outperform uniform speed limit strategies. Safety risks were reduced by 8% while maintaining reasonable efficiency; optimal speed limits concentrated in the17–20 km/h range effectively avoided extreme risks such as low-speed-induced red light running and high-speed-induced motor vehicle lane encroachment; violations exhibit a nonlinear correlation with speed limits. This study establishes a closed-loop framework of “behavior analysis-strategy generation-effect verification” for the first time, providing operational, fine-grained spatio-temporal speed limit and resource allocation schemes for urban traffic management, and promoting the transition from a “one-size-fits-all” policy to scientific dynamic governance.