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Traffic congestion is now nearly ubiquitous in many urban areas. The improvement of road infrastructure is an effective way to ease traffic congestion, especially the key road links. So, it is a fundamental and important step to identify the key link for improving transportation efficiency. However, most approaches in the current literature use simulated data and need many assumption conditions. The result shows the low comprehensibility and the bad exactitude. This paper provides a new identification method of key links for urban road traffic network (URTN) based on temporal-spatial distribution of traffic congestion. The method involves identifying congestion state, computing time distribution of congestion state and determining key road link. By the cluster analysis of the history field data of URTN, the threshold to determine the traffic congestion of each link can be obtained. Then the time-interval of the traffic congestion can be computed by median filtering. At last, the time-interval coverage is defined and used to determine the target road link whether it is a key road link or not. The method is validated by a real-world case (Beijing road traffic network, BRTN). The result shows the feasibility and accuracy. [ABSTRACT FROM AUTHOR]

Traffic state evaluation is a basic and critical work in the research on road traffic congestion. It can provide basic data support for the improvement measures and information release in traffic management and service. The aim of this research is to obtain a comprehensive value to describe traffic state accurately based on the evaluation index system. In this paper, it is carried out using fuzzy c-means (FCM) algorithm and fuzzy entropy weight method. In the framework, traffic flow was classified into six different states to determine the fuzzy range of indices using the improved FCM clustering analysis. Besides, fuzzy entropy weight method is proposed to compute the evaluation result of traffic state for section, road and road network, respectively. The experiments based on the traffic information in a subset of Beijing's road network prove that the findings of traffic evaluation are in accordance with the actual situation and people's sense of traffic state. [ABSTRACT FROM AUTHOR]

The spatial heterogeneity of land use patterns and residents' corresponding economic activities give rise to urban mobility's latent structure, which is of great importance for urban planning and transport infrastructure investment but cannot be readily captured using conventional data sources. We developed a methodological framework for detecting urban mobility structure at the transportation analysis zone (TAZ) level in Beijing using mobile phone signal data. First, we derived origin–destination data at the TAZ level from mobile phone data and visualized them in ArcGIS. Next, we improved community detecting algorithms generally used in social networks by reversing distance weight, such as by dividing ODs by 1, and used the results to reveal hidden clustering features of TAZs, according ODs between them. We visualized and analyzed population density, OD spatial distribution at different times, and ratio of daytime to nighttime population using the GIS platform; all showed some spatial cluster features. We then applied a structure detection algorithm using ODs between TAZ pairs to identify the hidden structure of urban mobility extracted from phone data. For Beijing, the identified mobility structure contains 27 clusters, with those in suburban areas tending to match administrative boundaries well but those in the developed center areas showing complex distributions and matching administrative boundaries poorly. Authorities that provide mobility infrastructure can use the resulting insights into urban planning and transportation planning to inform policy decisions at the local and city levels. [ABSTRACT FROM AUTHOR]

Complex network response to node loss is a central question in different fields of science ranging from physics, sociology, biology to ecology. Previous studies considered binary networks where the weight of the links is not accounted for. However, in real-world networks the weights of connections can be widely different. Here, we analyzed the response of real-world road traffic complex network of Beijing, the most prosperous city in China. We produced nodes removal attack simulations using classic binary node features and we introduced weighted ranks for node importance. We measured the network functioning during nodes removal with three different parameters: the size of the largest connected cluster (LCC), the binary network efficiency (Bin EFF) and the weighted network efficiency (Weg EFF). We find that removing nodes according to weighted rank, i.e. considering the weight of the links as a number of taxi flows along the roads, produced in general the highest damage in the system. Our results show that: (i) in order to model Beijing road complex networks response to nodes (intersections) failure, it is necessary to consider the weight of the links; (ii) to discover the best attack strategy, it is important to use nodes rank accounting links weight. [ABSTRACT FROM AUTHOR]