Examining spatial relationships between crashes and the built environment: A geographically weighted regression approach

Huang, Yu‐An; Wang, Xiaoguang; Patton, David K.

doi:10.1016/j.jtrangeo.2018.04.027

Cited by 66 publications

(35 citation statements)

References 52 publications

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“…Several studies have analyzed the traffic crash characteristics from spatial perspectives, and primarily by analyzing the road network density through kernel density estimation in 2D space [12][13][14][15]. In addition, numerous studies have also used spatial autocorrelation functions based on GIS to identify the traffic crash hotspot [16][17][18][19][20].…”

Section: Two-dimensional Spatial Analysismentioning

confidence: 99%

Traffic Crash Evolution Characteristic Analysis and Spatiotemporal Hotspot Identification of Urban Road Intersections

Cheng

Lü

2018

Sustainability

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Road traffic safety is a key concern of transport management as it has severely restricted Chinese economic and social development. With the objective to prevent and reduce road traffic crashes, this study proposes a comprehensive spatiotemporal analysis method that integrates the time-space cube analysis, spatial autocorrelation analysis, and emerging hot spot analysis for exploring the traffic crash evolution characteristics and identifying crash hot spots. These analyses are all conducted by the corresponding toolbox of ArcGIS 10.5. Then, a small sized-city of China (i.e., Wujiang) is selected as the case study, and the historical traffic crash data occurring at the road intersections of Wujiang for the year 2016 are analyzed by the proposed method. The analysis process identifies the high incidence locations of traffic crashes, then presents the spatial change trend and statistical significance of the crash locations. Finally, different types of crash hotspots, as well as their evolution patterns over time, are determined. The results illustrate that the traffic crash hotspots of road intersections are primarily distributed in the Northeast area of Wujiang’s major urban area, while the crash cold spots are concentrated in the Southwest of Wujiang, which points out the direction for crash prevention. In addition, the finding has a potential engineering application value, and it is of great significance to the sustainable development of Wujiang.

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Section: Two-dimensional Spatial Analysismentioning

confidence: 99%

Traffic Crash Evolution Characteristic Analysis and Spatiotemporal Hotspot Identification of Urban Road Intersections

Cheng

Lü

2018

Sustainability

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“…The frequency of people conducting commercial activities and the proximity of intersections are key factors that can increase the risk of accidents. 12,17 The historic center and the bus terminal comprise the city's financial district, in which commercial activity and foot traffic are present daily. This sector comprehends five of the primary markets in the city, and the main private schools and university campuses which accommodate thousands of students every day.…”

Section: Rev Bras Epidemiol 2021; 24: E210003mentioning

confidence: 99%

“…8 Geographical information systems (GIS) allow the visualization of elements that are present in roads that may increase the likelihood of road traffic injuries. [11][12][13] For example, areas of high commercial or touristic activity in which there is heavy foot traffic tend to have higher incidence of accidents. [14][15][16] Likewise, studies show that areas near bus stops, colleges and universities, commercial centers, and hospitals increment the likelihood of crashes.…”

Section: Introductionmentioning

confidence: 99%

Predictors and geographic analysis of road traffic accidents in Leon, Nicaragua

Toval-Ruíz

Rojas‐Roque

Hernández-Vasquéz

2021

Rev. bras. epidemiol.

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Objective: To identify environmental factors present in areas with high density of road traffic accidents (RTA) in Leon, Nicaragua. Methods: The analysis included all accidents recorded by the Police Department in León City, from January to June 2017. All crashes were georeferenced, and data were collected from the environment elements within a perimeter of 20 meters from the site in which accidents occurred with a pre-tested data collection instrument. We specified a Poisson regression model to estimate incidence rate ratios (IRR) and 95% confidence intervals (CI) to determine environmental factors associated with the event incidence. For the identification areas with high, medium, and low occurrences of crashes, kernel density around points in which RTA occurred were estimated. Results: Out of 667 recorded crashes, 90% involved men aged 15-40, and motorcycle accidents represented 60% of injuries or deaths. Environmental factors that were positively associated with RTA included good road conditions (adjusted IRR = 1.36, 95%CI 1.13 - 1.63) and the existence of bicycle lanes (adjusted IRR = 1.64, 95%CI 1.29 - 2.10). Environmental characteristics associated with higher speeds and heavier accidents can increase their incidence. Conclusion: We found that high-foot-traffic commercial or touristic centers are three areas with high density of crashes. Local authorities can use these findings to promote road safety measures in high-incidence areas in León City.

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“…Associations of these factors with taxi crashes and fatalities are examined through Poisson and logistic regression models without taking into account spatial and temporal characteristics. Such models, along with multivariate regressions models (e.g., [ 28 ]), seemingly unrelated regressions (e.g., [ 29 ]), and geographically weighted regressions (e.g., [ 30 , 31 ]) are leveraged in a variety of road safety outcomes modeling studies. Note that after systematically searching for evidence, we have not identified studies that examine associations of taxi use and road safety outcomes the way that ridesourcing use relationships have been studied with safety outcomes.…”

Section: Literature Reviewmentioning

confidence: 99%

Associating ridesourcing with road safety outcomes: Insights from Austin, Texas

Kontou

McDonald

2021

PLoS ONE

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Improving road safety and setting targets for reducing traffic-related crashes and deaths are highlighted as part of the United Nations sustainable development goals and worldwide vision zero efforts. The advent of transportation network companies and ridesourcing expands mobility options in cities and may impact road safety outcomes. We analyze the effects of ridesourcing use on road crashes, injuries, fatalities, and driving while intoxicated (DWI) offenses in Travis County, Texas. Our approach leverages real-time ridesourcing volume to explain variation in road safety outcomes. Spatial panel data models with fixed-effects are deployed to examine whether the use of ridesourcing is significantly associated with road crashes and other safety metrics. Our results suggest that for a 10% increase in ridesourcing trips, we expect a 0.12% decrease in road crashes, a 0.25% decrease in road injuries, and a 0.36% decrease in DWI offenses in Travis County. On the other hand, ridesourcing use is not significantly associated with road fatalities. This study augments existing work because it moves beyond binary indicators of ridesourcing availability and analyzes crash and ridesourcing trips patterns within an urbanized area rather than their metropolitan-level variation. Contributions include developing a data-rich approach for assessing the impacts of ridesourcing use on the transportation system’s safety, which may serve as a template for future analyses for other cities. Our findings provide feedback to policymakers by clarifying associations between ridesourcing use and traffic safety and uncover the potential to achieve safer mobility systems with transportation network companies.

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Examining spatial relationships between crashes and the built environment: A geographically weighted regression approach

Cited by 66 publications

References 52 publications

Traffic Crash Evolution Characteristic Analysis and Spatiotemporal Hotspot Identification of Urban Road Intersections

Traffic Crash Evolution Characteristic Analysis and Spatiotemporal Hotspot Identification of Urban Road Intersections

Predictors and geographic analysis of road traffic accidents in Leon, Nicaragua

Associating ridesourcing with road safety outcomes: Insights from Austin, Texas

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