Benefit of Road Reservation Travel Mode Under Traffic Network Equilibrium Theory

To meet the escalating demand for sophisticated urban traffic demand management measures, the introduction of a Travel Reservation Strategy (TRS) in managing urban road congestion is expected to play an important role in shaping the future of intelligent transportation systems. However, existing research on TRS frequently encounters challenges, including oversimplified assumptions about total reservation volumes on reserved roads and homogeneous travel choices across users, without adequately considering the multi-dimensional decision variables of individual travel and the complexities of integrating urban multimodal transportation. In this study, the road capacity distribution function was estimated using censored data models and the product limit method, and the sustained flow index was introduced to determine the optimal reservation volume for designated roads. Additionally, considering user heterogeneity, a comprehensive model for multi-user, multi-criteria, and multi-modal traffic mode split and traffic assignment is formulated within the urban multimodal transportation framework. The findings reveal that the optimal reservation volume falls within a range of approximately 79% to 89% of the actual road capacity. Following the implementation of TRS, notable improvements were observed, with average road network speed increasing by 7.6%, average saturation enhancing by 7.9%, and total travel cost decreasing by 1.6%, compared to pre-implementation levels. Notably, the proportion of private car users declined by 4.19%, while the share of public transportation users grew by 3.19% . Heterogeneous travelers with different time values demonstrated distinct responses to TRS, highlighting the need for tailored strategies. These findings provide valuable insights into the potential benefits and challenges of TRS implementation, providing policymakers with essential theoretical underpinnings and contributing to the development of more scientific and effective traffic demand management strategies. © 2024 Science Press. All rights reserved.