On the spatial partitioning of urban transportation networks

Abstract

a b s t r a c tIt has been recently shown that a macroscopic fundamental diagram (MFD) linking spacemean network 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. This collective behavior concept can also be utilized to introduce simple control strategies to improve mobility in homogeneous city centers without the need for details in individual links. However many real urban transportation networks are heterogeneous with different levels of congestion. In order to study the existence of MFD and the feasibility of simple control strategies to improve network performance in heterogeneously congested networks, this paper focuses on the clustering of transportation networks based on the spatial features of congestion during a specific time period. Insights are provided on how to extend this framework in the dynamic case. The objectives of partitioning are to obtain (i) small variance of link densities within a cluster which increases the network flow for the same average density and (ii) spatial compactness of each cluster which makes feasible the application of perimeter control strategies. Therefore, 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. Firstly, an over segmenting of the network is provided by a sophisticated algorithm (Normalized Cut). Secondly, a merging algorithm is developed based on initial segmenting and a rough partitioning of the network is obtained. Finally, a boundary adjustment algorithm is designed to further improve the quality of partitioning by decreasing the variance of link densities while keeping the spatial compactness of the clusters. In addition, both density variance and shape smoothness metrics are introduced to identify the desired number of clusters and evaluate the partitioning results. These results show that both the objectives of small variance and spatial compactness can be achieved with this partitioning mechanism. A simulation in a real urban transportation network further demonstrates the superiority of the proposed method in effectiveness and robustness compared with other clustering algorithms.

On the distribution of urban road space for multimodal congested networks

“…. , N. Criteria for partitioning a region (approximate size a few hundred links each) are: homogeneous distribution of congestion within each region to obtain a low scatter MFD (see for example Ji and Geroliminis, 2012), similar topological characteristics and similar type of mode usage. Any region i is partitioned into sub-regions, each one containing a specific type of mode usage, e.g.…”

Experienced travel time prediction for congested freeways

“…Otherwise large variations and temporal bias might be experienced by utilizing very heterogeneous data. Clustering techniques have been already used in transportation field to analyze traffic flow patterns, see for example Weijermars and Van Berkum (2005) or Ji and Geroliminis (2012). Since travel times are computed using local velocity measurements in this study, time-dependent speed measurements along the roadway can be used in the clustering step.…”

Analysis of Large-Scale Traffic Dynamics in an Urban Transportation Network Using Non-Negative Tensor Factorization

“…It encodes spatial congestion co-occurrence over entire network. We note it as network-level traffic state, inherited from our previous work [29]. Each frontal slice T :,:,k stores the network-level traffic states within the same period k as its column vectors following their temporal orders.…”

“…Spatial partitioning of network based on MFD, as proposed in [29], is quite promising for designing traffic management strategies at city level. However, our goal is to unveil typical temporal congestion daily evolutions, rather than provide a geographical partition.…”