Citation

Sun C, Pei X, Hao J, Wang Y, Zhang Z, Wong SC. Transp. Res. B Methodol. 2018; 117: 101-116.

Copyright

(Copyright © 2018, Elsevier Publishing)

DOI

10.1016/j.trb.2018.08.013

PMID

unavailable

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.

Language: en

Keywords

Bayesian Negative-binomial CAR model; Generalized linear model; Hazard-based model; Incident impacts; Network features; Traffic mobility