Pricing is considered an effective management policy to reduce traffic congestion in transportationnetworks. This paper combines a macroscopic modeling of traffic congestion in urban networkswith an agent-based simulator to study congestion pricing schemes. The macroscopic model,which has been tested with real data in previous studies, represents an accurate and robustapproach to model the dynamics of congestion. The agent-based simulator can represent thecomplexity of travel behavior in terms of departure time choice and heterogeneous users. Whiletraditional traffic simulators (including car-following, lane-changing and route choice models)consider traffic demand as input, i.e. inelastic to level of congestion conditions. On the other hand,most of traditional congestion pricing models, utilize a network supply curve which is notconsistent with the physics of traffic and the dynamics of congestion, as they are sensitive todemand fluctuations and non-stationary conditions. Also, many of the existing pricing models areassuming deterministic and homogeneous population characteristics. In this paper, we firstdemonstrate by case studies in Zurich urban road network, that the output of a multi-agent basedsimulator, is consistent with the physics of traffic flow dynamics, as expressed by a macroscopicfundamental diagram (MFD). We then apply a dynamic cordon-based congestion pricing scheme,in which tolls are controlled by an MFD, and investigate the effectiveness of the proposed pricingscheme. Results show that by applying such a congestion pricing, (i) the savings of travel time atboth aggregated and disaggregated level outweighs the costs, (ii) the congestion inside cordon iseased while no congestion is shifted to outside cordon, (iii) during toll period, fewer work-relatedactivities shift starting time than leisure-related activities do; while the impact of toll is moresignificant in the evening than morning. Future work can apply the same methodology to othernetwork-based pricing schemes. Equity issues can be investigated more carefully, if provided withdata such as income of agents. Time-dependent or value-of-time-dependent pricing schemes thencan also be determined.
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