scholarly journals Analysis of Pedestrian–Motor Vehicle Crashes in San Antonio, Texas

2021 ◽  
Vol 13 (12) ◽  
pp. 6610
Author(s):  
Khondoker Billah ◽  
Hatim O. Sharif ◽  
Samer Dessouky
Keyword(s):  
High Risk ◽  
San Antonio ◽  
Speed Limit ◽  
Vehicle Crashes ◽  
Contributing Factors ◽  
Hotspot Analysis ◽  
Lighting Condition ◽  
Downtown Area ◽  
Pedestrian Injury ◽  
Pedestrian Crashes

Pedestrian safety is becoming a global concern and an understanding of the contributing factors to severe pedestrian crashes is crucial. This study analyzed crash data for San Antonio, TX, over a six-year period to understand the effects of pedestrian–vehicle crash-related variables on pedestrian injury severity based on the party at fault and to identify high-risk locations. Bivariate analysis and logistic regression were used to identify the most significant predictors of severe pedestrian crashes. High-risk locations were identified through heat maps and hotspot analysis. A failure to yield the right of way and driver inattention were the primary contributing factors to pedestrian–vehicle crashes. Fatal and incapacitating injury risk increased substantially when the pedestrian was at fault. The strongest predictors of severe pedestrian injury include the lighting condition, the road class, the speed limit, traffic control, collision type, the age of the pedestrian, and the gender of the pedestrian. The downtown area had the highest crash density, but crash severity hotspots were identified outside of the downtown area. Resource allocation to high-risk locations, a reduction in the speed limit, an upgrade of the lighting facilities in high pedestrian activity areas, educational campaigns for targeted audiences, the implementation of more crosswalks, pedestrian refuge islands, raised medians, and the use of leading pedestrian interval and hybrid beacons are recommended.

scholarly journals Analysis of Bicycle-Motor Vehicle Crashes in San Antonio, Texas

2021 ◽  
Vol 18 (17) ◽  
pp. 9220
Author(s):  
Khondoker Billah ◽  
Hatim O. Sharif ◽  
Samer Dessouky
Keyword(s):  
High Risk ◽  
Injury Risk ◽  
Injury Severity ◽  
Motor Vehicle ◽  
San Antonio ◽  
Bivariate Analysis ◽  
Vehicle Crashes ◽  
Lighting Condition ◽  
Downtown Area ◽  
Stop Sign

Bicycling is inexpensive, environmentally friendly, and healthful; however, bicyclist safety is a rising concern. This study investigates bicycle crash-related key variables that might substantially differ in terms of the party at fault and bicycle facility presence. Employing 5 year (2014–2018) data from the Texas Crash Record and Information System database, the effect of these variables on bicyclist injury severity was assessed for San Antonio, Texas, using bivariate analysis and binary logistic regression. Severe injury risk based on the party at fault and bicycle facility presence varied significantly for different crash-related variables. The strongest predictors of severe bicycle injury include bicyclist age and ethnicity, lighting condition, road class, time of occurrence, and period of week. Driver inattention and disregard of stop sign/light were the primary contributing factors to bicycle-vehicle crashes. Crash density heatmap and hotspot analyses were used to identify high-risk locations. The downtown area experienced the highest crash density, while severity hotspots were located at intersections outside of the downtown area. This study recommends the introduction of more dedicated/protected bicycle lanes, separation of bicycle lanes from the roadway, mandatory helmet use ordinance, reduction in speed limit, prioritization of resources at high-risk locations, and implementation of bike-activated signal detection at signalized intersections.

scholarly journals Exploring the Determinants of the Severity of Pedestrian Injuries by Pedestrian Age: A Case Study of Daegu Metropolitan City, South Korea

2020 ◽  
Vol 17 (7) ◽  
pp. 2358 ◽  
Author(s):  
Seung-Hoon Park ◽  
Min-Kyung Bae
Keyword(s):  
Land Use ◽  
Injury Severity ◽  
Binary Logistic Regression ◽  
Vehicle Crashes ◽  
Pedestrian Injury ◽  
Moving Vehicles ◽  
Pedestrian Injuries ◽  
Serious Injury ◽  
Pedestrian Crashes ◽  
Age Range

Pedestrian-vehicle crashes can result in serious injury to pedestrians, who are exposed to danger when in close proximity to moving vehicles. Furthermore, these injuries can be considerably serious and even lead to death in a manner that varies depending on the pedestrian’s age. This is because the pedestrian’s physical characteristics and behaviors, particularly in relation to roads with moving vehicles, differ depending on the pedestrian’s age. This study examines the determinants of pedestrian injury severity by pedestrian age using binary logistic regression. Factors in the built environment, such as road characteristics and land use of the places where pedestrian crashes occurred, were considered, as were the accident characteristics of the pedestrians and drivers. The analysis determined that the accident characteristics of drivers and pedestrians are more influential in pedestrian-vehicle crashes than the factors of the built environmental characteristics. However, there are substantial differences in injury severity relative to the pedestrian’s age. Young pedestrians (aged under 20 years old) are more likely to suffer serious injury in school zones; however, no association between silver zones and injury severity is found for elderly pedestrians. For people in the age range of 20–39 years old, the severity of pedestrian injuries is lower in areas with more crosswalks and speed cameras. People in the age range of 40–64 years old are more likely to be injured in areas with more neighborhood streets and industrial land use. Elderly pedestrians are likely to suffer fatal injuries in areas with more traffic signals. This study finds that there are differences in the factors of pedestrian injury severity according to the age of pedestrians. Therefore, it is suggested that concrete and efficient policies related to pedestrian age are required to improve pedestrian safety and reduce pedestrian-vehicle crashes.

Modeling Pedestrian Injury Severity in Pedestrian-Vehicle Crashes in Rural and Urban Areas: Mixed Logit Model Approach

2019 ◽  
Vol 2673 (4) ◽  
pp. 1023-1034 ◽  
Author(s):  
Zhen Chen ◽  
Wei (David) Fan
Keyword(s):  
Urban Areas ◽  
Injury Severity ◽  
Mixed Logit ◽  
Speed Limit ◽  
Vehicle Crashes ◽  
Dark Light ◽  
Rural And Urban Areas ◽  
Rural And Urban ◽  
Pedestrian Injury ◽  
Injury Severities

Pedestrian-vehicle crashes are more likely to result in severe pedestrian incapacitating injuries and fatalities than other types of crashes. In this study, mixed logit models are developed to investigate and identify significant contributing factors to the pedestrian injury severity in pedestrian-vehicle crashes in both rural and urban areas in North Carolina, United States. Pedestrian-vehicle crash data from the Highway Safety Information System database from 2005 to 2012 are collected and used in this study. Crash injury severities are classified into five categories: fatality; injury class 1 (disabling injury); injury class 2 (evident injury); injury class 3 (possible injury); and no injury (property damage only). The estimation results show that factors such as a bad driver’s physical condition, heavy trucks, dark light condition, speed limit between 35 and 50 mph and speed limit above 50 mph will significantly increase pedestrian injury severities in both rural and urban areas. The developed model and analysis results provide insights on developing effective countermeasures to reduce pedestrian injury severities in pedestrian-vehicle crashes and improve traffic system safety performance.

scholarly journals Analysis of Intersection Traffic Safety in the City of San Antonio, 2013–2017

2021 ◽  
Vol 13 (9) ◽  
pp. 5296
Author(s):  
Khondoker Billah ◽  
Qasim Adegbite ◽  
Hatim O. Sharif ◽  
Samer Dessouky ◽  
Lauren Simcic
Keyword(s):  
High Risk ◽  
Traffic Safety ◽  
Traffic Control ◽  
Injury Severity ◽  
San Antonio ◽  
Binary Logistic Regression ◽  
Bivariate Analysis ◽  
Crash Severity ◽  
Contributing Factors ◽  
Late Night

An understanding of the contributing factors to severe intersection crashes is crucial for developing countermeasures to reduce crash numbers and severity at high-risk crash locations. This study examined the variables affecting crash incidence and crash severity at intersections in San Antonio over a five-year period (2013–2017) and identified high-risk locations based on crash frequency and injury severity using data from the Texas Crash Record and Information System database. Bivariate analysis and binary logistic regression, along with respective odds ratios, were used to identify the most significant variables contributing to severe intersection crashes by quantifying their association with crash severity. Intersection crashes were predominantly clustered in the downtown area with relatively less severe crashes. Males and older drivers, weekend driving, nighttime driving, dark lighting conditions, grade and hillcrest road alignment, and crosswalk, divider and marked lanes used as traffic control significantly increased crash severity risk at intersections. Prioritizing resource allocation to high-risk intersections, separating bicycle lanes and sidewalks from the roadway, improving lighting facilities, increasing law enforcement activity during the late night hours of weekend, and introducing roundabouts at intersections with stops and signals as traffic controls are recommended countermeasures.

Neighbourhood Influences on Vehicle-Pedestrian Crash Severity

2019 ◽  
pp. 102-121
Keyword(s):  
High Risk ◽  
Socioeconomic Factors ◽  
Crash Severity ◽  
Relative Importance ◽  
Contributing Factors ◽  
Counter Measures ◽  
Road Users ◽  
Pedestrian Crashes ◽  
Crash Modelling ◽  
Partial Dependence

Socioeconomic factors are known to be contributing factors to vehicle-pedestrian crashes. Although several studies have examined the socioeconomic factors related to the locations of crashes, few studies have considered the socioeconomic factors of the neighbourhoods where road users live in vehicle-pedestrian crash modelling. In vehicle-pedestrian crashes in the Melbourne metropolitan area, 20% of pedestrians, 11% of drivers, and only 6% of both drivers and pedestrians had the same postcode for the crash and residency locations. Therefore, an examination of the influence of socioeconomic factors of their neighbourhoods, and their relative importance will contribute to advancing knowledge in the field, as very limited research has been conducted on the influence of socioeconomic factors of both the neighbourhoods where crashes occur and where pedestrians live. In this chapter, neighbourhood factors associated with road users' residents and location of crash are investigated using BDT model. Furthermore, partial dependence plots are applied to illustrate the interactions between these factors. The authors found that socioeconomic factors account for 60% of the 20 top contributing factors to vehicle-pedestrian crashes. This research reveals that socioeconomic factors of the neighbourhoods where road users live and where crashes occur are important in determining the severity of crashes, with the former having a greater influence. Hence, road safety counter-measures, especially those focussing on road users, should be targeted at these high-risk neighbourhoods.

scholarly journals Examining Injury Severity of Pedestrians in Vehicle–Pedestrian Crashes at Mid-Blocks Using Path Analysis

2020 ◽  
Vol 17 (17) ◽  
pp. 6170 ◽  
Author(s):  
Haorong Peng ◽  
Xiaoxiang Ma ◽  
Feng Chen
Keyword(s):  
Path Analysis ◽  
Indirect Effects ◽  
Injury Severity ◽  
Road Traffic ◽  
Environmental Benefits ◽  
Direct And Indirect Effects ◽  
Contributing Factors ◽  
Explanatory Variables ◽  
Pedestrian Injury ◽  
Pedestrian Crashes

Walking is a sustainable mode of transport which has well established health and environmental benefits. Unfortunately, hundreds of thousands of pedestrians lose their lives each year over the world due to involvement in road traffic crashes, and mid-blocks witness a significant portion of pedestrian fatalities. This study examined the direct and indirect effects of various contributing factors on the pedestrian injury severity in vehicle–pedestrian crashes at mid-blocks. Data of vehicle–pedestrian crashes during 2002–2009 were extracted from the NASS-GES, with pre-crash behaviors and injury severity included. The SEM path analysis method was applied to uncover the inter-relationships between the pedestrian injury severity and various explanatory variables. Both the direct and indirect effects of these explanatory variables on the pedestrian injury severity were calculated based on the marginal effects in the multinomial and ordered logit models. The results indicate some variables including number of road lanes and the age of pedestrian have indirect impacts on the injury severity through influencing the pre-crash behaviors. Although most indirect effects are relatively small compared with the direct effects, the results in this study still provide some valuable information to improve the overall understanding of pedestrian injury severity at mid-blocks.

Are Older Drivers Safe on Interchanges? Analyzing Driving Errors Causing Crashes

2021 ◽  
pp. 036119812110312
Author(s):  
Denis Elia Monyo ◽  
Henrick J. Haule ◽  
Angela E. Kitali ◽  
Thobias Sando
Keyword(s):  
Latent Class ◽  
Time Of Day ◽  
Older Drivers ◽  
Contributing Factors ◽  
Lighting Condition ◽  
Crash Data ◽  
Traffic Characteristics ◽  
Road Users ◽  
Traffic Conflicts ◽  
Penalized Logistic Regression

Older drivers are prone to driving errors that can lead to crashes. The risk of older drivers making errors increases in locations with complex roadway features and higher traffic conflicts. Interchanges are freeway locations with more driving challenges than other basic segments. Because of the growing population of older drivers, it is vital to understand driving errors that can lead to crashes on interchanges. This knowledge can assist in developing countermeasures that will ensure safety for all road users when navigating through interchanges. The goal of this study was to determine driver, environmental, roadway, and traffic characteristics that influence older drivers’ errors resulting in crashes along interchanges. The analysis was based on three years (2016–2018) of crash data from Florida. A two-step approach involving a latent class clustering analysis and the penalized logistic regression was used to investigate factors that influence driving errors made by older drivers on interchanges. This approach accounted for heterogeneity that exists in the crash data and enhanced the identification of contributing factors. The results revealed patterns that are not obvious without a two-step approach, including variables that were not significant in all crashes, but were significant in specific clusters. These factors included driver gender and interchange type. Results also showed that all other factors, including distracted driving, lighting condition, area type, speed limit, time of day, and horizontal alignment, were significant in all crashes and few specific clusters.

Age and pedestrian injury severity in motor-vehicle crashes: A heteroskedastic logit analysis

2008 ◽  
Vol 40 (5) ◽  
pp. 1695-1702 ◽  
Author(s):  
Joon-Ki Kim ◽  
Gudmundur F. Ulfarsson ◽  
Venkataraman N. Shankar ◽  
Sungyop Kim
Keyword(s):  
Injury Severity ◽  
Motor Vehicle ◽  
Motor Vehicle Crashes ◽  
Logit Analysis ◽  
Vehicle Crashes ◽  
Pedestrian Injury

scholarly journals School-Aged Pedestrian–Vehicle Crash Vulnerability

2019 ◽  
Vol 11 (4) ◽  
pp. 1214 ◽  
Author(s):  
Kinga Ivan ◽  
József Benedek ◽  
Silviu Ciobanu
Keyword(s):  
Traffic Safety ◽  
Local Level ◽  
Urban Traffic ◽  
Vehicle Speed ◽  
Vehicle Crashes ◽  
Time Intervals ◽  
Local Authorities ◽  
Traffic Crashes ◽  
Triggering Factors ◽  
Pedestrian Crashes

The analysis of pedestrian–vehicle crashes makes a significant contribution to sustainable pedestrian safety. Existing research is based mainly on the statistical analysis of traffic crashes involving pedestrians and their causes, without the identification of areas vulnerable to traffic crashes that involve pedestrians. The main aim of this paper is to identify areas vulnerable to school-aged pedestrian–vehicle crashes at a local level to support the local authorities in implementing new urban traffic safety measures. The vulnerable areas were determined by computing the severity index (SI) based on the number of fatal, serious, and slight casualties throughout the 2011–2016 period in a large urban agglomeration (Bucharest). As well as the vulnerable areas, the triggering factors and the time intervals related to school-aged pedestrian–vehicle crashes were identified. The outcomes of the study showed that the vulnerable areas were concentrated only in districts 2 and 4 of Bucharest, and they were associated with high vehicle speed and pedestrians’ unsafe crossing behavior. The findings revealed that speed and age are triggering factors in generating school-aged pedestrian–vehicle crashes. The identified time peaks with a high number of traffic crashes correspond to the afternoon time intervals, when scholars go home from school. The identification of the areas vulnerable to school-aged pedestrian crashes may help local authorities in identifying and implementing measures to improve traffic safety in large urban agglomerations.

Evaluating Factors Influencing the Severity of Three-Plus Multiple-Vehicle Crashes using Real-Time Traffic Data

2018 ◽  
Vol 2672 (38) ◽  
pp. 128-137 ◽  
Author(s):  
Angela E. Kitali ◽  
Emmanuel Kidando ◽  
Paige Martz ◽  
Priyanka Alluri ◽  
Thobias Sando ◽  
...  
Keyword(s):  
Real Time ◽  
Random Effect ◽  
Random Effect Model ◽  
Probit Model ◽  
Severe Injury ◽  
Traffic Data ◽  
Vehicle Crashes ◽  
Lighting Condition ◽  
Crash Data ◽  
Real Time Traffic

Multiple-vehicle crashes involving at least two vehicles constitute over 70% of fatal and injury crashes in the U.S. Moreover, multiple-vehicle crashes involving three or more vehicles (3+) are usually more severe compared with the crashes involving only two vehicles. This study focuses on developing 3+ multiple-vehicle crash severity models for a freeway section using real-time traffic data and crash data for the years 2014–2016. The study corridor is a 111-mile section on I-4 in Orlando, Florida. Crash injury severity was classified as a binary outcome (fatal/severe injury and minor/no injury crashes). For the purpose of identifying the reliable relationship between the 3+ severe multiple-vehicle crashes and the identified explanatory variables, a binary probit model with Dirichlet random effect parameter was used. More specifically, Dirichlet random effect model was introduced to account for unobserved heterogeneity in the crash data. The probit model was implemented using a Bayesian framework and the ratios of the Monte Carlo errors were monitored to achieve parameter estimation convergence. The following variables were found significant at the 95% Bayesian credible interval: logarithm of average vehicle speed, logarithm of average equivalent 10-minute hourly volume, alcohol involvement, lighting condition, and number of vehicles involved (3, or >3) in multiple-vehicle crashes. Further analysis involved analyzing the posterior probability distributions of these significant variables. The study findings can be used to associate certain traffic conditions with severe injury crashes involving 3+ multiple vehicles, and can help develop effective crash injury reduction strategies based on real-time traffic data.

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