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Betweenness Centrality Measures as Potential Predictors of Crash Frequency
https://orcid.org/http://orcid.org/0000-0001-7993-6861
Road network patterns and configurations affect traffic and safety. Network structural measures such as edge betweenness centrality (EBC) give useful insights into the vulnerability of different routes in the road network. This study investigates and compares the association of different EBCs with the crash occurrence at a zonal level. For this, macroscopic crash modeling using crash data specific to major roads in the network with 740 Local Self-Governing divisions as the spatial units is performed. The network structure within each spatial unit was measured as three metrics; global edge betweenness centrality (GEBC), local edge betweenness centrality (LEBC), and experiential hierarchy-based edge betweenness centrality (EHBC). Results indicate that all three network structural measures have a significant impact on safety. LEBC represents a better association with crash occurrence in comparison to the other two. This implies the importance of defining the extent of the network which would better illustrate the share of traffic than when considering the entire network. In addition, the crash model results highlight the importance of various network features and demographic characteristics of the spatial unit as well as neighboring spatial units on major road crash frequency.
Betweenness Centrality Measures as Potential Predictors of Crash Frequency
https://orcid.org/http://orcid.org/0000-0001-7993-6861
Road network patterns and configurations affect traffic and safety. Network structural measures such as edge betweenness centrality (EBC) give useful insights into the vulnerability of different routes in the road network. This study investigates and compares the association of different EBCs with the crash occurrence at a zonal level. For this, macroscopic crash modeling using crash data specific to major roads in the network with 740 Local Self-Governing divisions as the spatial units is performed. The network structure within each spatial unit was measured as three metrics; global edge betweenness centrality (GEBC), local edge betweenness centrality (LEBC), and experiential hierarchy-based edge betweenness centrality (EHBC). Results indicate that all three network structural measures have a significant impact on safety. LEBC represents a better association with crash occurrence in comparison to the other two. This implies the importance of defining the extent of the network which would better illustrate the share of traffic than when considering the entire network. In addition, the crash model results highlight the importance of various network features and demographic characteristics of the spatial unit as well as neighboring spatial units on major road crash frequency.
Betweenness Centrality Measures as Potential Predictors of Crash Frequency
https://orcid.org/http://orcid.org/0000-0001-7993-6861
Road network patterns and configurations affect traffic and safety. Network structural measures such as edge betweenness centrality (EBC) give useful insights into the vulnerability of different routes in the road network. This study investigates and compares the association of different EBCs with the crash occurrence at a zonal level. For this, macroscopic crash modeling using crash data specific to major roads in the network with 740 Local Self-Governing divisions as the spatial units is performed. The network structure within each spatial unit was measured as three metrics; global edge betweenness centrality (GEBC), local edge betweenness centrality (LEBC), and experiential hierarchy-based edge betweenness centrality (EHBC). Results indicate that all three network structural measures have a significant impact on safety. LEBC represents a better association with crash occurrence in comparison to the other two. This implies the importance of defining the extent of the network which would better illustrate the share of traffic than when considering the entire network. In addition, the crash model results highlight the importance of various network features and demographic characteristics of the spatial unit as well as neighboring spatial units on major road crash frequency.
Betweenness Centrality Measures as Potential Predictors of Crash Frequency
Transp. in Dev. Econ.
Nair, Shilpa R. (author) / Abhiram, Manoj K. V. (author) / Bhavathrathan, B. K. (author)
2024-10-01
Article (Journal)
Electronic Resource
English
Betweenness Centrality Measures as Potential Predictors of Crash Frequency
| Springer Verlag | 2024
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