Spatial and Temporal Analysis of Congestion in Urban Transportation Networks

Abstract

Research on congestion and propagation in large urban city networks has been mainly based on microsimulations of link-level traffic dynamics. However, both the unpredictability of travel behaviors and high complexity of accurate physical modeling remain challenging and simulation results may be far time consuming and not realistic. It has been recently shown that amacroscopic fundamental diagram (MFD) linking space-mean network flow, density and speed exists in the urban transportation networks under some conditions. An MFD is further well defined if the network is homogeneous with links of similar properties. This collective behavior concept can also be utilized to introduce simple control strategies to improve mobility in homogeneous city centers without the need for details in individual links. However many real urban transportation networks are heterogeneous with different levels of congestion. In order to study the existence of MFD and the feasibility of simple control strategies to improve network performance in heterogeneously congested networks, this thesis mainly focuses on three issues: (1) static partitioning of traffic networks, (2) congestion propagation and dynamic partitioning, and (3) the effect of network structure on congestion propagation. Firstly, we study the static partitioning of transportation networks based on the spatial features of congestion during a specific time period. A partitioning mechanism which consists of three consecutive algorithms, is designed to minimize the variance of link densities while maintaining the spatial compactness of the clusters. Secondly we explore the dynamics of traffic conditions and thus the corresponding dynamic partitioning scheme. We reveal the hidden information during the process of congestion formation by exploring empirical data from large-scale urban networks. Specifically, we aim at studying the spatiotemporal relation of congested links, observing congestion propagation from an macroscopic perspective, and finally identifying critical congestion regimes to aid the design of peripheral control strategies. Finally, we study the formation of traffic congestion by simulation in large scale cities with different network structures. A parsimonious model of congestion propagation is developed and validated by real data in San Francisco in US and Shenzhen in China (20000 taxis with 30 million logs per day). The effect of network structure on congestion propagation is investigated by simulations conducted in a variety of network structures.