Simulation-Based Dynamic Traffic Assignment with Continuously Distributed Value of Time for Heterogeneous Users

The value of time (VOT) attribute is usually utilized to represent the trade-off between time and monetary expenses in transportation problems. A good representation of VOT is essential for evaluation of any road pricing scheme. Conventionally, in dynamic traffic assignment models, VOT is considered as either constant or finite discrete among travelers because of memory and computational limitations, which in turn could introduce bias in the results. This research explicitly models the individual bi-criteria dynamic user equilibrium (IBDUE) problem and presents a distinct simulation-based solution algorithm that enables individual-based traffic assignment within reasonable run time with a successful implementation of variable and continuously distributed VOT in a simulation-based dynamic traffic assignment package. Numerical analysis reveals that the constant and discrete VOT models tend to overestimate toll road usage compared with the continuous VOT model when the toll charge is low, and underestimate it when the toll charge is high, which reflects previous studies. In the meantime, an experiment on a real-world congestion pricing scheme demonstrates the capability of the proposed algorithm on evaluating flow-dependent pricing schemes.

Uncertainties of Sub-Scaled Supply and Demand in Agent-Based Mobility Simulations with Queuing Traffic Model

Agent-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.