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Article: Role of road network features in the evaluation of incident impacts on urban traffic mobility
Title | Role of road network features in the evaluation of incident impacts on urban traffic mobility |
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Authors | |
Keywords | Bayesian Negative-binomial CAR model Generalized linear model Hazard-based model Incident impacts Network features Traffic mobility |
Issue Date | 2018 |
Publisher | Pergamon. The Journal's web site is located at http://www.elsevier.com/locate/trb |
Citation | Transportation Research Part B: Methodological, 2018, v. 117 n. pt. A, p. 101-116 How to Cite? |
Abstract | In this paper, we seek to investigate the spatiotemporal impacts of traffic incident on urban road networks. The theoretical lens of a complex network leads us to expect that incident impacts are associated with the functionality that an intersection acts in a network, and also, the location of incident sites. Incident impacts are measured in both temporal and spatial dimension through mining the large-scale traffic flow data in conjunction with the incident record. In the complex network context, the urban road network can be converted into a weighted direct graph with intersections as nodes and road segments as edges with their geographic information. Four network features, i.e., Betweenness Centrality, weighted PageRank, Hub, and K-shell are assigned to each intersection to measure its functionality. Temporally, we find out significant correlations between incident delay and two network features by applying hazard-based models. Spatially, the micro impact and the macro impact are found to be strongly associated with three network features through estimating a Bayesian Negative-binomial Conditional Autoregressive model and a generalized linear model, respectively. Our study provides the basis of leveraging urban road network context to evaluate incident impacts, with some explanations, insights and possible extensions that would assist traffic administrations to guide the post-incident resilience and emergency management. |
Persistent Identifier | http://hdl.handle.net/10722/261425 |
ISSN | 2023 Impact Factor: 5.8 2023 SCImago Journal Rankings: 2.660 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Sun, C | - |
dc.contributor.author | Pei, X | - |
dc.contributor.author | Hao, J | - |
dc.contributor.author | Wang, Y | - |
dc.contributor.author | Zhang, Z | - |
dc.contributor.author | Wong, SC | - |
dc.date.accessioned | 2018-09-14T08:57:56Z | - |
dc.date.available | 2018-09-14T08:57:56Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Transportation Research Part B: Methodological, 2018, v. 117 n. pt. A, p. 101-116 | - |
dc.identifier.issn | 0191-2615 | - |
dc.identifier.uri | http://hdl.handle.net/10722/261425 | - |
dc.description.abstract | In this paper, we seek to investigate the spatiotemporal impacts of traffic incident on urban road networks. The theoretical lens of a complex network leads us to expect that incident impacts are associated with the functionality that an intersection acts in a network, and also, the location of incident sites. Incident impacts are measured in both temporal and spatial dimension through mining the large-scale traffic flow data in conjunction with the incident record. In the complex network context, the urban road network can be converted into a weighted direct graph with intersections as nodes and road segments as edges with their geographic information. Four network features, i.e., Betweenness Centrality, weighted PageRank, Hub, and K-shell are assigned to each intersection to measure its functionality. Temporally, we find out significant correlations between incident delay and two network features by applying hazard-based models. Spatially, the micro impact and the macro impact are found to be strongly associated with three network features through estimating a Bayesian Negative-binomial Conditional Autoregressive model and a generalized linear model, respectively. Our study provides the basis of leveraging urban road network context to evaluate incident impacts, with some explanations, insights and possible extensions that would assist traffic administrations to guide the post-incident resilience and emergency management. | - |
dc.language | eng | - |
dc.publisher | Pergamon. The Journal's web site is located at http://www.elsevier.com/locate/trb | - |
dc.relation.ispartof | Transportation Research Part B: Methodological | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | Bayesian Negative-binomial CAR model | - |
dc.subject | Generalized linear model | - |
dc.subject | Hazard-based model | - |
dc.subject | Incident impacts | - |
dc.subject | Network features | - |
dc.subject | Traffic mobility | - |
dc.title | Role of road network features in the evaluation of incident impacts on urban traffic mobility | - |
dc.type | Article | - |
dc.identifier.email | Wong, SC: hhecwsc@hku.hk | - |
dc.identifier.authority | Wong, SC=rp00191 | - |
dc.description.nature | postprint | - |
dc.identifier.doi | 10.1016/j.trb.2018.08.013 | - |
dc.identifier.scopus | eid_2-s2.0-85053035631 | - |
dc.identifier.hkuros | 291229 | - |
dc.identifier.volume | 117 | - |
dc.identifier.issue | pt. A | - |
dc.identifier.spage | 101 | - |
dc.identifier.epage | 116 | - |
dc.identifier.isi | WOS:000455559600006 | - |
dc.publisher.place | United Kingdom | - |
dc.identifier.issnl | 0191-2615 | - |