Statistical Modeling of Traffic Flow in Commercial Clusters Based on a Street Network

Abstract

Traffic flow characterizes vitality in commercial clusters, and the accurate prediction of traffic flow based on the street network has significant implications for street planning and vitality regulation in commercial clusters. However, existing studies are limited by certain problems, such as difficulty in obtaining traffic flow data and carrying out technical methods. The purpose of this study is to use urban physical data to study traffic flow so as to quickly and effectively estimate the traffic flow in commercial clusters. This study takes the street networks of 100 commercial clusters in China as the research objects and classifies them into three forms according to the theory of “A city is not a tree”. Taking typical commercial clusters in these three forms as the research unit, space syntax was used to study five metrics of street network connectivity, and integration (Dn) was selected as a proxy variable for street network connectivity. The results show that the traffic flow in the three forms of commercial clusters can be predicted using the multiple regression models established based on the three metrics of integration, the traffic level, and the operation cycle. This study establishes the connection between the street network form and the traffic flow, which enables the possibility of obtaining the traffic flow of commercial clusters quickly and effectively. For areas with poorly structured urban data, the results can help urban planning administrators to predict the potential economic attributes using easily accessible street network data in commercial clusters.

Initial-state dependence of phase behaviors in a dense active system

“…[28] In this model, an active reorientation rule is incorporated for particles at collisions, to mimic the collision avoidance behavior commonly adopted in cell colonies, [29,30] animal herds, [31,32] swarming robots, [33] and traffic flows. [34] The coupling between self-propulsion and active reorientation produces richer dynamics compared with previous models merely involving translation or rotation. As a result, these particles may collectively aggregate to dynamic clusters or self-organize into aligned flocking.…”