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Highlights • Formulate the day-to-day evolution of departure time choice under bounded rationality. • Show the evolution has stationary points equivalent to boundedly rational user equilibria. • Propose a dynamic pricing policy implemented in the day-to-day evolution process to reach system optimum. Abstract We extend the proportional swap system by Smith (1984a) and the network tatonnement process by Friesz et al. (1994) to formulate the day-to-day evolution of departure time choice under bounded rationality in the bottleneck model. It is shown that the stationary points of the doubly dynamical systems exist and are equivalent to the boundedly rational user equilibrium (BRUE) points. We propose a dynamic pricing policy implemented in the day-to-day evolution process. Despite that the stationary points correspond to a set of BRUE points and the day-to-day departure rate without pricing may not converge to the set of BRUE points, the pricing policy can drive the day-to-day departure rate to reach a new equilibrium state, which is system-optimal (SO). The convergence to the SO state is theoretically proved. Finally, by numerical examples, we demonstrate the properties of the doubly dynamical systems and the effectiveness of the pricing policy. [ABSTRACT FROM AUTHOR]

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• Propose a data-driven framework to reroute some users to improve system performance. • Define trips that contribute the most to system congestion based on trajectory features. • Unravel system-optimum path by applying machine learning to trajectory feature data. • Propose a robust simulation-based regression method to compute path marginal costs. • Validate the proposed methods through case studies on a medium size real-world network. This work investigates network-related trajectory features to unravel trips that contribute most to system under-performance. When such trips are identified, feature analysis also permits determining the best alternatives in terms of routes to bring the system to its optimum. First, we define a combination of network-related trajectory features that helps us unravel the critical trips which contribute the most to the network under-performance, based on the literature review and a factor selection process. Second, based on supervised learning methods, we propose a two-step data-driven methodological framework to reroute a part of the users and make the system close to its optimum. The learning models are trained with trajectory features to identify which users should be selected, and which alternative routes should be assigned, thanks to the data and features obtained from two reference dynamic traffic assignment (DTA) simulations, under User-Equilibrium (UE) and System-Optimum (SO). We only focus on trajectory features that are accessible in real time, such as network features and regular travel time information, so that the methods proposed can be implemented without requiring cumbersome network monitoring and prediction. Finally, we evaluate the efficiency of the methods proposed through microscopic DTA simulations. The results show that by targeting 20% of the users according to our selection model and moving them onto paths predicted as optimal alternative paths based on our rerouting model, the total travel time (TTT) of the system is reduced by 5.9% in comparison to a UE DTA simulation. This represents 62.5% of the potential TTT reduction from UE to SO, when all the users choose their path under the SO condition. [ABSTRACT FROM AUTHOR]