AbstractAgent-based models for dynamic traffic assignment simulate the behaviour of individual, or group of, agents, and then the simulation outcomes are observed on the scale of the system. As large-scale simulations require substantial computational power and have long run times, most often a sample of the full population and downscaled road capacities are used as simulation inputs, and then the simulation outcomes are scaled up. Using a massively parallelized mobility model on a large-scale test case of the whole of Switzerland, which includes 3.5 million private vehicles and 1.7 million users of public transit, we have systematically quantified, from 6 105 simulations of a weekday, the impacts of scaled input data on simulation outputs. We show, from simulations with population samples ranging from 1% to 100% of the full population and corresponding scaling of the traffic network, that the simulated traffic dynamics are driven primarily by the flow capacity, rather than the spatial properties, of the traffic network. Using a new measure of traffic similarity, that is based on the chi-squared test statistic, it is shown that the dynamics of the vehicular traffic and the occupancy of the public transit are adversely impacted when population samples less than 30% of the full population are used. Moreover, we present evidence that the adverse impact of population sampling is determined mostly by the patterns of the agents’ behaviour rather than by the traffic model.
Automatic calibration of agent-based public transit assignment path choice to count data