Enhancing Travel Reservation Benefits Through Incentive and Penalty Mechanisms in Urban Congested Roads

To enhance the refinement of urban traffic demand management, this study explores the impact of introducing incentive and penalty mechanisms in the urban road travel reservation strategy (TRS) on both heterogeneous users and the road network. The existing research on TRS has primarily focused on static evaluations, which have limitations in terms of the one-sidedness of travel service methods and the homogeneity of users’ travel choices. Moreover, these studies overlook the multidimensional decision-making of travelers and the synergistic effects of urban multimodal transportation systems. To overcome these limitations, this paper introduces incentives and penalties for users and develops an agent-based multi-objective optimization model. The model optimizes travel incentive schemes to maximize social benefits, considering the interests of both system managers and travelers. Additionally, an agent-based dynamic traffic simulation model is constructed, incorporating individual travel decisions, real-time traffic conditions, and the balance of road supply and demand. The findings indicate that the introduction of incentive and penalty mechanisms increased the transportation system’s revenue by 17.26% and reduced travel costs by 2.67%. TRS implementation significantly improved traffic performance and reduced congestion across the road network. Specifically, the average speed, road saturation, and network traffic volume increased by 6.7%, 9.3%, and 3.7%, respectively. Moreover, the proportion of users participating in reservation travel increased by 48.5%, with travelers more willing to adjust their travel times. Heterogeneous travelers with different time valuations showed distinct responses to the TRS. In conclusion, TRS offers significant potential in promoting sustainable urban transportation, providing both theoretical insights and practical implications for urban planners and policymakers.