Multi-Objective Transportation System Optimization Using Agent-Based Simulation-A Study of Cordon-and Mileage-Based Congestion Pricing

Congestion pricing policies are increasingly being considered to aid in congestion management and transportation funding in urban areas. This article presents a case study of the optimization of congestion pricing policy design using the Berkeley Integrated System for Transportation Optimization (BISTRO), an open-sourced transportation planning and decision support system (DSS) that uses an agent-based simulation (ABS) and optimization framework to evaluate transportation system interventions. The study exemplifies how the granularity offered by activity-based travel demand models and ABS can be leveraged to enhance the interpretability of multi-objective transportation policy optimization through rich analyses of the effects of policy design on both individual-and system-level outcomes. The location and size of a circular charging zone with two different pricing schemes (a cordon fee and a cordoned mileage fee) are encoded as inputs to an ABS with an activity-based travel model of 15,000 travelers. Through an analysis of the effects of various weighting schemes across congestion-, social-, and revenue-based objectives, we present a method for interpretation of the inherent trade-offs in transportation policy optimization and demonstrate the importance of cultivating transparency in policy DSS that use black-box optimization in order to produce explainable, defensible policy strategies. We find that cordoned mileage fees Pareto-dominate cordon tolls, enabling greater improvements across all objectives studied. However, the prioritization of congestion reduction poses a challenge for pricing optimization in that it may result in unnecessarily large mode shifts away from driving which significantly worsens travel cost burden. We explore the role of weighting schemes in shifting the priority of optimal pricing schemes to social equity while mitigating congestion and maintaining toll revenue.