Closing the Gap Between Static and Dynamic User Equilibrium in Traffic Assignment: A Multiple Cities Study

Static user equilibrium model and dynamic user equilibrium model are two commonly used traffic assignment models. Compared to dynamic model, static model costs much less time, needs less information but cannot assign the traffic in time dimension so it cannot obtain the time series of traffic flow. We can divide the time interval into segments and apply static model on each segment, so that the time series of traffic flow can be obtained. A challenge of this method is how to determine the scale of the time segment so that the result of static model can be close to dynamic model. This paper proposes a big data-driven analysis framework and uses China’s 40 cities’ road networks and traffic flow data as samples. We calculate the gap between the results of dynamic and static models using different scales of time segment. It is shown that the unevenness of the distribution of travel demand in time dimension and other indexes which measure the volume of the city, such as GDP, have a strong correlation with the optimal scale of time segment for static model.