Abstract
Traffic congestion varies spatially and temporally. The observation of the formation, propagation and dispersion of network traffic congestion can lead to insights about the network performance, the bottleneck dynamics etc. While many researchers use the traffic flow data to reconstruct the congestion profile, the data missing problem is bypassed. Current methods either omit the missing data or supplement the missing part by average etc. Great error may be introduced during these processes. Rather than simply discarding the missing data, this research regards the data missing event as a result of either the severe congestion which prevent the floating vehicle from entering the congested area, or a type of feature of the resulting traffic flow time series. Hence a new traffic flow operational index time series similarity measurement is expected to be established as a basis of identifying the dynamic network bottleneck. The method first measures the traffic flow operational similarity between pairs of neighboring links, and then the similarity results are used to cluster the spatial-temporal congestion. In order to get the similarity under missing data condition, the measurement is implemented in a two-stage manner: firstly the so called first order similarity is calculated given that the traffic flow variables are bounded both upside and downside; then the first order similarity is aggregated to generate the second order similarity as the output. We implement the method on part of the real-world road network; the results generated are not only consistent with empirical observation, but also provide useful insights.
Highlights
BackgroundAs the number of vehicles steadily increases and the expansion of roadways is relatively slow, traffic congestion is becoming a central transportation issue in big cities
The traffic congestion is a result of the imbalance between traffic supply and demand
The congestion itself is the result of bottleneck dynamics
Summary
As the number of vehicles steadily increases and the expansion of roadways is relatively slow, traffic congestion is becoming a central transportation issue in big cities. The traffic congestion is a result of the imbalance between traffic supply and demand. The bottleneck is the weak point of the supply-demand structure. Generally congestion first begins from these bottlenecks and propagates around the network. Sometimes the process is called cascading failures [1,2]. Such daily road network operation can be observed experimentally from traffic flow data
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