As the products of intelligent transportation systems, parking apps have become convenient platforms for implementing parking policies, which can be provided as parking app services. This paper proposes a traffic simulation model for evaluating the impacts of parking app services on the travellers’ choice behaviour and traffic dynamics. Travellers are assumed to use three types of parking app services: the provision of information on real-time parking lot occupancies, parking reservation, and the display of dynamic parking fees. The behaviour of travellers, such as travellers’ mode choices, departure time choices, and learning behaviour, are considered in this model. Numerical experiments show that providing information on real-time parking lot occupancies can be helpful in reducing the use ratio of commercial parking lots, but the effect will ultimately be smoothed during the evolution of traffic dynamics. Moreover, parking reservation is an effective way to reduce travel costs and encourage travellers to choose park-and-ride. Furthermore, dynamic parking fees usually lead to the oscillation of traffic dynamics and travellers’ choices, in addition to an increase in travel costs. This model is a useful tool for analysing the impacts of other parking management policies that can be implemented as parking app services and can be a reference for evaluating the impacts of other parking polices.
Based on the pay-per-use charge in mobile Apps, value-added advanced traveler information systems (VATIS) service can be provided in traffic networks. From the perspective of the traffic manager, we propose a two- stage policy for improving the efficiency of traffic networks by providing VATIS service. At the first stage, the traffic manager selects several ATIS service providers, then offers seed fund to each of them to encourage VATIS service provision. At the second stage, the traffic manager announces a given target of the total market penetration of VATIS service and encourages service providers to achieve it together. If they could complete the task, compensation will be paid. To evaluate the efficiency of this policy, four scenarios are formulated by four bi-level models, and the total travel time is set as the evaluation indicator. Numerical experiments show that, the suggested policy can reduce total travel time dramatically if the market penetration target has been appropriately selected.
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