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International Journal of Intelligent Transportation Systems Research
Erschienen in:

10.12.2019

Cross Comparison of Spatial Partitioning Methods for an Urban Transportation Network

verfasst von: Takao Dantsuji, Shin Hirabayashi, Qian Ge, Daisuke Fukuda

Erschienen in: International Journal of Intelligent Transportation Systems Research | Ausgabe 3/2020

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Abstract

Partitioning a heterogeneous road network into homogenous subnetworks is one way of solving the problem of high scattering or a hysteresis loop that may be inherent in the empirics of a macroscopic fundamental diagram. This study conducts cross-comparison analysis of two well-studied partitioning methods—(i) community detection through modularity maximization and (ii) normalized cut graph partitioning—to investigate the applicability of these methods. Through a case study using real traffic data recorded by an enormous number of detectors in the Tokyo central business district, we found that both methods work well for the test transportation network; however, undesirable results may be obtained if there is only one bottleneck in a subnetwork, or if there is a drastic change in traffic conditions between adjacent links.
Vorheriger Artikel Study on Pedestrian Compliance Behavior at Vehicular Traffic Signals and Traffic-Police-Controlled Intersections
Nächster Artikel Early Detection of a Traffic Flow Breakdown in the Freeway Based on Dynamical Network Markers

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1
For example, if we choose a value of 100 as the bandwidth, the average is 0.168 and the standard deviation is 0.313, which is low. In such a case, almost all links are disconnected. Meanwhile, a bandwidth of 10,000 leads to an average of 0.992 and standard deviation of 0.018, which would generate almost the same results as the unweighted network. These values would therefore yield undesirable results.
 
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Metadaten
Titel
Cross Comparison of Spatial Partitioning Methods for an Urban Transportation Network
verfasst von
Takao Dantsuji
Shin Hirabayashi
Qian Ge
Daisuke Fukuda
Publikationsdatum
10.12.2019
Verlag
Springer US
Erschienen in
International Journal of Intelligent Transportation Systems Research / Ausgabe 3/2020
Print ISSN: 1348-8503
Elektronische ISSN: 1868-8659
DOI
https://doi.org/10.1007/s13177-019-00209-x

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