This study proposes a new method to describe, compare, and classify the traffic congestion states in 23 Chinese cities using the online map data and further reveals the influential factors that may affect them. First, the real-time traffic congestion information is obtained from the online map of AutoNavi in a 15-minute interval. Next, a new measuring index is introduced to describe the overall characterization of congestion patterns in each city based on online map data, which is named as the congestion ratio. The next analysis is the cluster analysis based on the temporal distribution of the congestion ratio, which helps to identify groups of the selected cities with similar traffic congestion states. These cities are categorized as four groups according to the severity of traffic congestion: severely congested, less severely congested, amble, and smooth cities. Lastly, multiple linear regression models are developed to identify the primary factors that affect the congestion ratio. The result shows that the influences of per capita road area, car ownership, and vehicle miles traveled (VMT) on the congestion ratio are significant. Sensitivity analyses are also implemented in order to reveal more effective policy measures in mitigating traffic congestion in urban areas.

Discrete network design is an important part of urban transportation planning. The purpose of this paper is to present a bilevel model for discrete network design. The upper-level model aims to minimize the total travel time under a stochastic demand to design a discrete network. In the lower-level model, demands are assigned to the network through a multiuser traffic equilibrium assignment. Generally, discrete network could affect path selections of demands, while the results of the multiuser traffic equilibrium assignment need to reconstruct a new discrete network. An iterative approach including an improved genetic algorithm and Frank-Wolfe algorithm is used to solve the bi-level model. The numerical results on Nguyen Dupuis network show that the model and the related algorithms were effective for discrete network design.

In order to explore the speed-flow relationship under complicated roadway characteristics and heavy traffic volume, this paper established a new practical model based on one month serial data collected from loop detectors on different sites of the expressway in Beijing. Different from the traditional model, the relationship formulated here is composed by free flow segment, stable flow segment and congested-evacuation segment. We start with a comprehensive review on classical speed-flow models. The limitation of general used model when analyzing the mixed traffic flow is pointed out. Then a cluster of new speed-flow models are established considering the site characteristics expressed with the distance from the bottleneck which appears at the entrance or exit ramp. Actual observed data shows fundamental parameters of speed-flow relationship model of urban expressway are affected by the entrance or exit ramp.

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