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Spatial analysis of highway incident durations in the context of Hurricane Sandy

Accid Anal Prev. 2015 Jan:74:77-86. doi: 10.1016/j.aap.2014.10.015. Epub 2014 Oct 28.

Abstract

The objectives of this study are (1) to develop an incident duration model which can account for the spatial dependence of duration observations, and (2) to investigate the impacts of a hurricane on incident duration. Highway incident data from New York City and its surrounding regions before and after Hurricane Sandy was used for the study. Moran's I statistics confirmed that durations of the neighboring incidents were spatially correlated. Moreover, Lagrange Multiplier tests suggested that the spatial dependence should be captured in a spatial lag specification. A spatial error model, a spatial lag model and a standard model without consideration of spatial effects were developed. The spatial lag model is found to outperform the others by capturing the spatial dependence of incident durations via a spatially lagged dependent variable. It was further used to assess the effects of hurricane-related variables on incident duration. The results show that the incidents during and post the hurricane are expected to have 116.3% and 79.8% longer durations than those that occurred in the regular time. However, no significant increase in incident duration is observed in the evacuation period before Sandy's landfall. Results of temporal stability tests further confirm the existence of the significant changes in incident duration patterns during and post the hurricane. Those findings can provide insights to aid in the development of hurricane evacuation plans and emergency management strategies.

Keywords: Hurricane Sandy; Incident duration; Spatial dependence; Spatial lag model.

MeSH terms

  • Accidents, Traffic / statistics & numerical data*
  • Cyclonic Storms / statistics & numerical data*
  • Disasters / statistics & numerical data*
  • Incidence
  • Models, Statistical
  • New York / epidemiology
  • New York City
  • Spatial Analysis
  • Time Factors