Assessment of the Impact of Multi-Agent Model-Based Traffic Optimization Interventions on Urban Travel Behavior

Abstract

With the continuous increase in car ownership, alleviating traffic congestion and reducing carbon emissions have become key challenges in urban traffic management. This study constructs a multi-agent model to evaluate the impact of various traffic optimization interventions on citizens’ travel behavior and traffic carbon emission levels. Different from previous mathematical models, this model integrates computer technology and geographic information systems, abstracting travelers as agents with self-control capabilities who can make independent decisions based on their own circumstances, thus reflecting individual differences in travel behavior. Using the real geographical and social environment of the high-density travel area in Xiaodian District, Taiyuan City as a case study, this research explores the overall improvement in the urban transportation system through the implementation of multiple traffic optimization interventions, such as a parking reservation system, the promotion of the park-and-ride mode, and the optimization of public transportation services. Studies have demonstrated that, compared to reducing bus fares, travelers exhibit a greater sensitivity to waiting times. Reducing bus departure intervals can increase the proportion of park-and-ride trips to 25.79%, surpassing the 19.19% increase observed with fare adjustments. A moderate increase in the proportion of reserved parking spaces can elevate the public transport load to 49.85%. The synergistic effect of a combined strategy can further boost the public transport share to 50.62%, while increasing the park-and-ride trip proportion to 33.6%, thereby highlighting the comprehensive benefits of implementing multiple strategies in tandem. When the parking reservation system is effectively implemented, carbon dioxide emissions can be reduced from over 800 kg to below 200 kg, and the proportion of vehicle cruising can decrease from over 20% to under 15%. These results underscore the critical role of the parking reservation strategy in optimizing traffic flow and advancing environmental sustainability.

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