LAUSR

Previous studies established traffic demand equilibrium with an assumption that all the traffic information on road is easily accessed by the in-need traffic participants, which is not true in the real applications (due to data collections difficulty). The newly emerging smart portable devices (e.g., smart phones) generate massive on-site traffic data (speed, density, etc.), which stimulates us re-consider designing the traffic demand equilibrium. For the purpose of analyzing mobile internet service influence on traffic demand, we build up a Probit-based model with consideration of multiple traffic constraints (i.e., traveler type, actual travelling time, perceiving travelling time). The Monte Carlo method is introduced to simulating initial route selection probability distributions, and the Method of Successive Averages (MSA) is developed to help the Monte Carlo algorithm converge at optimal solution. We have implemented our model on typical traffic travelling scenario with very complex traffic network demands. The experimental results suggested that larger mobility service coverage can significantly reduce the overall traffic time in the free flow state, and the mobility service coverage rate ranging from 0.6 to 0.8 is supposed to provide minimum travelling time for the overall traffic network, while larger coverage rate at congested state may reduce the traffic network efficiency.