Unlocking the potential of cooperative staggered shifts in urban networks

Staggered shifts strategies effectively alleviate traffic pressure and promote the rational allocation of traffic resources by dispersing peak-hour traffic demands. The development of advanced traveler information systems (ATIS) platforms has facilitated the rapid transmission and precise delivery of traffic information. Current studies have combined ATIS platforms with staggered shifts strategies to propose cooperative staggered shifts (CSS) strategies, which can enhance the sophistication of staggered shifts strategies and, consequently, improve their effectiveness. However, current studies on CSS inadequately consider the heterogeneity in the willingness of travelers with different travel behaviors to adjust their departure times. Additionally, existing studies have used traffic state optimization as the sole objective function, without considering system costs. To fill this gap, this study integrates multi-source spatiotemporal big data and survey data to analyze the willingness of travelers with different travel behaviors to adjust their departure times. Based on this analysis, a modeling framework for CSS that considers system costs is constructed. The framework is designed with the dual objectives of optimizing traffic conditions and minimizing system costs. Using the fast-solving algorithm proposed in this study for large-scale scenarios, the Pareto front of the CSS framework is analyzed. Taking Hangzhou city, China as an example, the results indicates that an 11.1% optimization effect on the traffic state can be achieved with only 2.4% of the maximum system cost; As the system cost increases, the marginal benefits of CSS diminish. The research findings can provide effective support for the modeling and policy formulation of CSS strategies.