Complete Street Design informed by Multi-Agent Simulation
Objectives: Complete Street design is currently guided largely by qualitative principles despite the increasing complexity of multimodal urban traffic. This paper develops a microscopic simulation framework for quantitatively evaluating alternative street designs by explicitly representing heterogeneous road users, including Autonomous Vehicles (AVs), Connected Vehicles (CVs), Human-Driven Vehicles (HDVs), pick-up and drop-off (PUDO) services, and micromobility users. The framework jointly evaluates mobility and safety within a unified testbed. Methods:The framework enriches microscopic simulation with behavioral models for heterogeneous road users. It explicitly captures lateral movements, parking maneuvers, pedestrian crossings and jaywalking, and shared-lane bicycle and scooter interactions. Safety is quantified using a conflict severity measure grounded in collision physics, while candidate street layouts are optimized using a genetic algorithm. Findings: Applied to a segment of Belmont Avenue in Chicago, Illinois, the framework shows that dedicated bicycle lanes reduce conflict severity at the expense of vehicular throughput. Increasing AV penetration mitigates congestion by reducing traffic shockwaves while improving interactions with pedestrians relative to CVs and HDVs. The optimization framework efficiently identifies Pareto-efficient street layouts that balance mobility and safety. Novelty: The proposed framework integrates heterogeneous behavioral modeling, physics-based conflict assessment, stochastic performance evaluation, and design optimization within a single quantitative framework for Complete Street design. Practical Applications: The framework provides transportation planners with a predictive virtual testbed for evaluating street designs and quantifying the tradeoff between mobility and safety before implementation.