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Role of road network features in the evaluation of incident impacts on urban traffic mobility

机译:道路网特征在评估事故对城市交通流动性的影响中的作用

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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.
机译:在本文中,我们试图研究交通事故对城市道路网络的时空影响。复杂网络的理论视角使我们期望事件影响与交叉口在网络中起作用的功能以及事件站点的位置有关。通过结合事件记录挖掘大规模交通流数据,可以在时间和空间维度上测量事件影响。在复杂的网络环境中,城市道路网络可以转换为加权的直接图,其中以交叉点为节点,以路段为边,并结合其地理信息。将四个网络功能(即中间性,加权PageRank,集线器和K-shell)分配给每个路口以衡量其功能。通过应用基于危害的模型,我们暂时发现了事件延迟与两个网络特征之间的显着相关性。在空间上,通过分别估计贝叶斯负二项式条件自回归模型和广义线性模型,发现微观影响和宏观影响与三个网络特征密切相关。我们的研究为利用城市道路网络环境评估事故影响提供了基础,并提供了一些解释,见解和可能的扩展,这些将有助于交通管理部门指导事故后的应变能力和应急管理。

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