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Citation |
Zhou H, Huang H, Xu P, Chang F, Abdel-Aty M. Accid. Anal. Prev. 2019; 132: 105283. |
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Affiliation |
Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL 32816, United States. Electronic address: M.Aty@ucf.edu. |
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Copyright |
(Copyright © 2019, Elsevier Publishing) |
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DOI |
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PMID |
31518765 |
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Abstract |
The rate of road traffic fatalities has long served as a regular indicator to evaluate and compare road safety performance for different administrative divisions. This article introduces a novel method known as the Markov chain spatial model to incorporate the spatial effects into the temporal dynamic of the fatality rates. Compared to the traditional Markov chain model, the proposed spatial Markov chain model can quantify the influence of neighboring sites explicitly in the transition process. A case study using a long duration dataset, from 1975 to 2015 in the 48 lower states of the United Sates, was conducted to illustrate the proposed model. The fatality rates were measured as the number of traffic fatalities per 100 million vehicle miles or per 10,000 residents. The results show that the probability of transition for one state between different levels of traffic fatality risks depends largely on the context of its surrounding neighbors. Another important finding is that relative to the estimates of traditional Markov chain models, states surrounded by neighborhoods with relatively low fatality rates take a longer time to transform to a higher level of fatality risk in the spatial Markov chain model. On the other hand, those with high-risk neighborhoods takes less time to deteriorate. These findings confirm that it is imperative to incorporate spatial effects when modeling the temporal dynamic of safety indicators to assess and monitor the safety trends in the areas of interest. Language: en |
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Keywords |
Spatial Markov chains; Spatial correlation; Temporal dynamics; Traffic fatality risks |