Staggered working hours has the potential to alleviate excessive demands on urban transport networks during the morning and afternoon peak hours and influence the travel behavior of individuals by affecting their activity schedules and reducing their commuting times. This study proposes a multi-agent-based Q-learning algorithm for evaluating the influence of staggered work hours by simulating travelers’ time and location choices in their activity patterns. Interactions among multiple travelers were also considered. Various types of agents were identified based on real activity–travel data for a mid-sized city in China. Reward functions based on time and location information were constructed using Origin–Destination (OD) survey data to simulate individuals’ temporal and spatial choices simultaneously. Interactions among individuals were then described by introducing a road impedance function to formulate a dynamic environment in which one traveler’s decisions influence the decisions of other travelers. Lastly, by applying the Q-learning algorithm, individuals’ activity–travel patterns under staggered working hours were simulated. Based on the simulation results, the effects of staggered working hours were evaluated on both a macroscopic level, at which the space–time distribution of the traffic volume in the network was determined, and a microscopic level, at which the timing of individuals’ leisure activities and their daily household commuting costs were determined. Based on the simulation results and experimental tests, an optimal scheme for staggering working hours was developed.
EVALUATING STAGGERED WORKING HOURS USING A MULTI-AGENT-BASED Q-LEARNING MODEL
