Geographic Information System–Based Community-Level Method to Evaluate the Influence of Built Environment on Traffic Crashes

Yu, Ouyang; Bejleri, Ilir

doi:10.3141/2432-15

Cited by 16 publications

(23 citation statements)

References 21 publications

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“…Compared with residential areas, the likelihood of crashes occurring in commercial areas [ [16] and OUYANG and BEJLERI [17], we find that the presence of additional overpasses may account for the crash frequency reductions in commercial and mixed-land areas of Hong Kong. As shown in Fig.…”

Section: Resultsmentioning

confidence: 89%

Role of street patterns in zone-based traffic safety analysis

Guo

Pei

Yao

et al. 2015

J. Cent. South Univ.

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Although extensive analyses of road segments and intersections located in urban road networks have examined the role of many factors that contribute to the frequency and severity of crashes, the explicit relationship between street pattern characteristics and traffic safety remains underexplored. Based on a zone-based Hong Kong database, the Space Syntax was used to quantify the topological characteristics of street patterns and investigate the role of street patterns and zone-related factors in zone-based traffic safety analysis. A joint probability model was adopted to analyze crash frequency and severity in an integrated modeling framework and the maximum likelihood estimation method was used to estimate the parameters. In addition to the characteristics of street patterns, speed, road geometry, land-use patterns, and temporal factors were considered. The vehicle hours was also included as an exposure proxy in the model to make crash frequency predictions. The results indicate that the joint probability model can reveal the relationship between zone-based traffic safety and various other factors, and that street pattern characteristics play an important role in crash frequency prediction.

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Section: Resultsmentioning

confidence: 89%

Role of street patterns in zone-based traffic safety analysis

Guo

Pei

Yao

et al. 2015

J. Cent. South Univ.

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“…Furthermore, applying more advanced methods like propensity score matching (PSM) and empirical Bayes (EB) could provide reliable findings for a before-and-after comparison. Moreover, several researchers incorporated land use variables and assigning their contributions to the various types of crashes [78][79][80], so further investigation is required to assess how land use correlates with crash density patterns during the COVID-19 pandemic. Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.…”

Section: Limitations and Future Workmentioning

confidence: 99%

Crash Patterns in the COVID-19 Pandemic: The Tale of Four Florida Counties

Koloushani

Ghorbanzadeh

Özgüven

et al. 2021

Future Transportation

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This study investigates the impacts of the noticeable change in mobility during the COVID-19 pandemic with analyzing its impact on the spatiotemporal patterns of crashes in four demographically different counties in Florida. We employed three methods: (1) a Geographic Information System (GIS)-based method to visualize the spatial differences in crash density patterns, (2) a non-parametric method (Kruskal–Wallis) to examine whether the changes in crash densities are statistically significant, and (3) a negative binomial regression-based approach to identify the significant socio-demographic and transportation-related factors contributing to crash count decrease during COVID-19. Results confirm significant differences in crash densities during the pandemic. This may be due to maintaining social distancing protocols and curfew imposement in all four counties regardless of their sociodemographic dissimilarities. Negative binomial regression results reveal that the presence of youth populations in Leon County are highly correlated with the crash count decrease during COVID-19. Moreover, less crash count decrease in Hillsborough County U.S. Census blocks, mostly populated by the elderly, indicate that this certain age group maintained their mobility patterns, even during the pandemic. Findings have the potential to provide critical insights in dealing with safety concerns of the above-mentioned shifts in mobility patterns for demographically different areas.

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“…Various studies have considered neighborhood environmental characteristics that may affect the severity of pedestrian injuries, such as demographic factors, road features, land use, and land development indicators [46,[51][52][53][54][55][56].…”

Section: Neighborhood Environmental Characteristics Of Pedestrian-vehmentioning

confidence: 99%

A Multilevel Model Approach for Investigating Individual Accident Characteristics and Neighborhood Environment Characteristics Affecting Pedestrian-Vehicle Crashes

Park

2020

IJERPH

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Walking is the most basic movement of humans and the most fundamental mode of transportation. To promote walking, it is necessary to create a safe environment for pedestrians. However, pedestrian-vehicle crashes still remain relatively high in South Korea. This study employs a multilevel model to examine the differences between the lower-level individual characteristics of pedestrian crashes and the upper-level neighborhood environmental characteristics in Seoul, South Korea. The main results of this study are as follows. The individual characteristics of pedestrian-vehicle crashes are better at explaining pedestrian injury severity than built environment characteristics at the neighborhood level. Older pedestrians and drivers suffer more severe pedestrian injuries. Larger vehicles such as trucks and vans are more likely to result in a high severity of pedestrian injuries. Pedestrian injuries increase during inclement weather and at night. The severity of pedestrian injuries is lower at intersections and crosswalks without traffic signals than at crosswalks and intersections with traffic signals. Finally, school zones and silver zones, which are representative policies for pedestrian safety in South Korea, fail to play a significant role in reducing the severity of pedestrian injuries. The results of this study can guide policymakers and planners when making decisions on how to build neighborhoods that are safer for pedestrians.

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Geographic Information System–Based Community-Level Method to Evaluate the Influence of Built Environment on Traffic Crashes

Cited by 16 publications

References 21 publications

Role of street patterns in zone-based traffic safety analysis

Role of street patterns in zone-based traffic safety analysis

Crash Patterns in the COVID-19 Pandemic: The Tale of Four Florida Counties

A Multilevel Model Approach for Investigating Individual Accident Characteristics and Neighborhood Environment Characteristics Affecting Pedestrian-Vehicle Crashes

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