期刊
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
卷 117, 期 -, 页码 101-116出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.trb.2018.08.013
关键词
Traffic mobility; Incident impacts; Network features; Hazard-based model; Bayesian Negative-binomial CAR model; Generalized linear model
类别
资金
- National Natural Science Foundation of China [71671100]
- Joint Research Scheme of the National Natural Science Foundation of China/Research Grants Council of Hong Kong [71561167001, N_HKU707/15]
- Research Funds of Tsinghua University [20151080412]
- Francis S Y Bong Endowed Professorship in Engineering
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, Flub, 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. (C) 2018 Elsevier Ltd. All rights reserved.
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