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This paper examines the impacts of road network density on motor vehicle travel through traffic flow and network analysis of cities in China from three perspectives: multi-city comparison with traffic flow data, multi-city and multi-period comparisons with structural characteristics. When we examine the correlation between road network density and traffic flow characteristics including the travel time and speed of 35 major cities in China, no correlation exists between them which may result from different network structures, dynamic traffic demands, traffic control plans, driving behaviors, and so on. It inspires us to explore the correlation between road network density and static travel indicators. Then structural characteristic of road networks can be treated as a fixed relatively attribute and becomes the next research emphasis of this work. Comparative analysis results of three current cities (Xiamen, Washington D.C., San Jose) indicate that the average travel distance in different cities with the similar land area will not increase significantly as road network density increases. Results of an analysis of road network evolution of Changchun, China show that the increase of road network density shortens the average road segment length as well as travel distance in general. This impact on average travel distance is also related to evolution periods or modes of urban road networks. These findings provide important empirical support for planners and policymakers to understand the impacts of road network density on transport performance and design more effective urban road networks. [ABSTRACT FROM AUTHOR]

The complex network theory is applied to the study of urban road traffic network topology, and we constructed a new measure to characterize an urban road network. It is inspiring to quantify the interaction more appropriately between nodes in complex networks, especially in the field of traffic. The measure takes into account properties of lanes (e.g. number of lanes, width, traffic direction). As much, it is a more comprehensive measure in comparison to previous network measures. It can be used to grasp the features of urban street network more clearly. We applied this measure to the road network in Xiamen, China. Based on a standard method from statistical physics, we examined in more detail the distribution of this new measure and found that (1) due to the limitation of space geographic attributes, traditional research conclusions acquired by using the original definition of degree to study the primal approach modeled urban street network are not very persuasive; (2) both of the direction of the network connection and the degree’s odd or even classifications need to be analyzed specifically; (3) the improved degree distribution presents obvious hierarchy, and hierarchical values conform to the power-law distribution, and correlation of our new measure shows some significant segmentation of the urban road network. [ABSTRACT FROM AUTHOR]