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Aiming to alleviate traffic jams, many traffic management strategies/policies are adopted to nudge travelers to re-arrange their departure time or switch from driving to public transit or non-motorized mode. Analytical travel behavior model is needed to predict travelers’ departure time choice and mode switch under such strategies. In this paper, we developed an agent-based model for travellers’ choices of mode and departure time. Departing from the traditional utility maximization theory, this model focuses on the decision-making process based on imperfect information, bounded and distinctive rationalities. In the modeling framework, travelers accumulate experiences and update their spatial and temporal knowledge through a Bayesian learning process. Before making a trip, travelers decide whether to search for alternative departure time and/or travel mode according to their expected search gain and cost. When an additional search happens, travelers decide whether or not to switch to the new departure time and travel mode according to a series of decision conditions. Both the search and decision processes are represented by production (if–then) rules derived from a joint revealed/stated-preference survey data collected in Beijing. Then the agent-based model is applied to evaluate congestion charge policies with various demand scenarios in the 2nd ring road of Beijing. Results suggest that the model can display the peak spreading and mode switch process practically. [ABSTRACT FROM AUTHOR]

• Passengers' behavioural responses toward passenger flow control strategies. • NL model for departure time and mode shift decisions. • Mathematical programming with NL-based SUE constraints. • Trade-off between operational efficiency and service fairness. • Empirical case studies of Guangzhou and Beijing metros. This paper presents a methodology for developing demand-responsive passenger flow control strategies for oversaturated metro networks. A stated preference-off-revealed preference survey was conducted to capture passengers' behavioural responses to the passenger flow control strategies, and revealed significant behavioural change intentions in terms of departure times and mode choices. Currently, such behavioural responses are often ignored, leading to deviations in estimating the size of a target group and performance of a proposed strategy. To address this issue, in this study, a mathematical programming with equilibrium constraints (MPEC) approach was developed to optimise demand-responsive passenger flow control strategies. The proposed MPEC model highlights the importance of balancing the operational efficiency of a metro system with service fairness perceived by passengers. In particular, a nested logit-based stochastic user equilibrium problem was included to accommodate potential changes in demand patterns driven by candidate passenger flow control strategies. Two empirical cases based on the Guangzhou and Beijing metros were used to demonstrate the effectiveness of the proposed model and solution algorithm. The results show that the inclusion of service fairness considerations does not contradict the pursuit of efficiency. Instead, dual emphases on service fairness and passengers' behavioural responses help create a win–win situation for both metro operators and passengers. In the Guangzhou and Beijing metros, the highest section load rate decreased from 126.6% to 105.7% and from 122.98% to 106.87%, respectively, under the optimal passenger flow control strategies. The corresponding network load Gini coefficients improved from 0.278 to 0.259 and from 0.269 to 0.248, demonstrating the remarkable performance of this approach in regards to peak cutting and load balancing. [ABSTRACT FROM AUTHOR]