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Spatial and Temporal Analysis of Congestion in Urban Transportation Networks

September 2, 2010

Swiss Transport Research Conference, August 2010

Yuxuan Ji and Nikolas Geroliminis

“It has been recently shown that a macroscopic fundamental diagram (MFD) linking space-mean flow, density and speed exists in the urban transportation networks under some conditions. An MFD is further well defined if the network is homogeneous with links of similar properties. However many real urban transportation networks are heterogeneous with different levels of congestion. The objective of this paper is to study the existence of MFD and the feasibility of simple control strategies to alleviate the congestion in the heterogeneous networks, which can be partitioned into homogeneous components. To achieve these goals, this paper focuses on the clustering of transportation networks based on the spatial and temporal features of congestion. A partitioning mechanism, which consists of three consecutive algorithms, is designed to minimize the variance of link densities while maintaining the spatial compactness of the clusters. Small variance of link densities within a cluster increases the aggregated flow for the same average density and spatial compactness makes feasible the application of perimeter control strategies. Firstly, Normalized Cut is applied to over segment the network into several clusters and a new metric is introduced to evaluate the partitioning results. Secondly, a merging algorithm is developed to improve the metric and total variance of link densities, and the optimal number of clusters is estimated and determined. Finally, a boundary adjustment algorithm is designed to further improve the metric and decrease the variances of the clusters while keeping the compactness of the shapes. Both the objectives of smaller variances and spatial compactness can be achieved after this partitioning mechanism. The simulation further demonstrates the superiority of our method in both effectiveness and robustness compared with other clustering algorithms.”

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