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

Xie, Kun; Özbay, Kaan; Yang, Hong

doi:10.1016/j.aap.2014.10.015

Cited by 43 publications

(32 citation statements)

References 37 publications

(42 reference statements)

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“…More increase in the log likelihood of spatial regression model than that of the OLS model suggest an improvement of fit of the data [38]. A spatial lag model, which is a spatial autoregressive model, assumes that the spatial autoregressive process occurs only in the dependent variable [39] and is expressed in matrix notation as follows [39,40]:…”

Section: Regression Modelsmentioning

confidence: 99%

Investigating the Influences of Tree Coverage and Road Density on Property Crime

Chen

2018

IJGI

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With the development of Geographic Information Systems (GIS), crime mapping has become an effective approach for investigating the spatial pattern of crime in a defined area. Understanding the relationship between crime and its surrounding environment reveals possible strategies for reducing crime in a neighborhood. The relationship between vegetation density and crime has long been under debate. The convenience of a road network is another important factor that can influence a criminal's selection of locations. This research is conducted to investigate the correlations between tree coverage and property crime, and road density and property crime in the City of Vancouver. High spatial resolution airborne LiDAR data and road network data collected in 2013 were used to extract tree covered areas for cross-sectional analysis. The independent variables were inserted into Ordinary Least-Squares (OLS) regression, Spatial Lag regression, and Geographically Weighted Regression (GWR) models to examine their relationships to property crime rates. The results of the cross-sectional analysis provide statistical evidence that there are negative correlations between property crime rates and both tree coverage and road density, with the stronger correlations occurring around Downtown Vancouver.

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Section: Regression Modelsmentioning

confidence: 99%

Investigating the Influences of Tree Coverage and Road Density on Property Crime

Chen

2018

IJGI

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“…This distinct feature of hazard-based duration models has inspired many studies to apply same method, but use different or mixture probability distributions that specify hazard functions or unobserved heterogeneity (Qi and Teng, 2008;Qi et al, 2009;Chung, 2010;Chung et al, 2010;Al Kaabi et al, 2011;Al Kaabi et al, 2012;Li et al, 2015). Hazard-based duration models have also the capability to account for spatial dependence of incident duration (Xie et al, 2015) and include random effects of explanatory variables (Tavassoli Hojati et al, 2014).…”

Section: Accident Durationmentioning

confidence: 97%

Simultaneous equation modeling of freeway accident duration and lanes blocked

Chung

Chiou

Lin

2015

Analytic Methods in Accident Research

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“…Tasic and Porter built an areawide model for Chicago to evaluate the spatial association of safety issues and multimodel transportation infrastructure and found a strong relationship between crashes and the availability of transportation service (19). Xie et al developed an incident duration model for Hurricane Sandy and confirmed spatial dependencies of durations of neighboring incidents (20). Spatial error and spatial lag models were further developed to indicate factors that affect the duration of an incident.…”

Section: Spatial Analysis Of Transportation Networkmentioning

confidence: 99%

Data-Driven Spatial Modeling for Quantifying Networkwide Resilience in the Aftermath of Hurricanes Irene and Sandy

Zhu

Xie²,

Özbay³

et al. 2017

Transportation Research Record

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In recent years, the New York City metropolitan area was hit by two major hurricanes, Irene and Sandy. These extreme weather events dis rupted and devastated the transportation infrastructure, including road and subway networks. As an extension of the authors' recent research on this topic, this study explored the spatial patterns of infrastructure resilience in New York City with the use of taxi and subway ridership data. Neighborhood tabulation areas were used as the units of analysis. The recovery curve of each neighborhood tabulation area was modeled with the logistic function to quantify the resilience of road and subway systems. Moran's I tests confirmed the spatial correlation of recovery patterns for taxi and subway ridership. To account for this spatial correlation, citywide spatial models were estimated and found to out perform linear models. Factors such as the percentage of area influenced by storm surges, the distance to the coast, and the average elevation are found to affect the infrastructure resilience. The findings in this study provide insights into the vulnerability of transportation networks and can be used for more efficient emergency planning and management.

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

Cited by 43 publications

References 37 publications

Investigating the Influences of Tree Coverage and Road Density on Property Crime

Investigating the Influences of Tree Coverage and Road Density on Property Crime

Simultaneous equation modeling of freeway accident duration and lanes blocked

Data-Driven Spatial Modeling for Quantifying Networkwide Resilience in the Aftermath of Hurricanes Irene and Sandy

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