Last modified: 2016-09-01
Abstract
With a growing number of developing cities, the capacities of roads cannot meet the rapidly growing demands of cars, causing congestion. Understanding the spatial-temporal process of traffic flow and detecting traffic congestion are important issues associated with developing urban policies to resolve congestion. The topological structure of a street network influences the turning probabilities between streets and the moving speeds of automobiles on those streets. Moreover, the connectivity of road segments reflects the degree of the road system facilitating people to their destination. Therefore, the objective of this study is to propose an innovative analytical procedure for investigating the traffic demands in terms of the traffic flow concentration and complexity of the road network based on turning probability. First, we proposed a flow-based ranking algorithm (Flow-based PageRank, FBPR) to determine the traffic flow concentration. Second, we analyzed the real volumes of vehicle movements to calibrate the turning probability. Finally, we measured the topological complexity in terms of outgoing entropy. Congested segments are defined as the street segments that are prone to traffic congestion. By overlapping the traffic demand in terms of FBPR scores and the topological complexity of street segments, congested segments can be identified. The results show that by relying on the topological characteristics of streets, most congested segments identified in the study successfully included the streets identified as the ten most congested streets or segments with slow moving speeds based on vehicle detector (VD) monitoring. The congested segments might also be sources of traffic congestion. Traffic demands can be determined by FBPR scores, which capture human movements, and street complexity can be measured by the outgoing entropy, which represents the topological complexity in terms of turning probability. We also examined the association of urban land use types with traffic demand and street complexity. Identifying the topological characteristics of traffic congestion provides comprehensive insights for city planners, and these characteristics can be used to further understand congestion spreading.
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References
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