Descriptive Analysis of Crashes Involving Pedestrians in Florida, 1990–1994

1998 ◽  
Vol 1636 (1) ◽  
pp. 138-145 ◽  
Author(s):  
Michael R. Baltes
Keyword(s):  
Injury Severity ◽  
Descriptive Analysis ◽  
Weather Condition ◽  
Time Of Day ◽  
Specific Sequence ◽  
Traffic Crashes ◽  
Crash Data ◽  
Sequence Of Events ◽  
Gender And Age ◽  
Pedestrian Crashes

A descriptive analysis was conducted of pedestrian crash data used to categorize pedestrian crashes according to a variety of factors, including pedestrian gender and age, time of day, pedestrian’s contributing cause of crash, injury severity, weather condition, road system identifier, and so forth, to the specific sequence of events perceived to influence the crash. The results reported are based on 5 years (1990–1994) of pedestrian crash data in Florida. The database contained 44,541 or 100 percent of the pedestrian crashes that were reported to law enforcement that occurred in Florida during this period. The process of categorizing pedestrian crashes in the manner described provides a valuable analytical tool for developing effective and practical countermeasures to reduce the deaths and injuries incurred by pedestrians involved in traffic crashes in Florida and elsewhere. Analysis of the pedestrian crash data can provide information about to whom and where, when, and how crashes occur in Florida.

Pedestrian Crash Types: 1990s Update

1996 ◽  
Vol 1538 (1) ◽  
pp. 68-74 ◽  
Author(s):  
Jane C. Stutts ◽  
William W. Hunter ◽  
Wayne E. Pein
Keyword(s):  
Motor Vehicle ◽  
Local Level ◽  
Specific Sequence ◽  
Traffic Crashes ◽  
Sequence Of Events ◽  
Typing System ◽  
Recent Sample ◽  
State And Local ◽  
Pedestrian Crashes ◽  
Crash Types

A report is given on an application of the NHTSA pedestrian crash-typing system for categorizing pedestrian–motor-vehicle crashes according to the specific sequence of events leading up to individual crashes. Results are based on a recent sample of over 5,000 pedestrian crashes drawn from six states and reported by police. Over 80 percent of the pedestrian crashes fell into the following crash type categories: vehicle turn or merge (9.8 percent), intersection dash (7.2 percent), driver violation at intersection (5.1 percent), other intersection (10.1 percent), midblock dart or dash (13.3 percent), other midblock (13.1 percent), not in roadway and waiting to cross (8.6 percent), walking along roadway (7.9 percent), and backing vehicle (6.9 percent). These crash types were found to vary according to the characteristics of the pedestrian and factors of the location, environment, and roadway. The process of typing pedestrian crashes can be a valuable tool at both the state and local level for developing more highly effective countermeasures to reduce the annual toll of nearly 100,000 pedestrians killed and injured in traffic crashes.

Pedestrian Injury Severity vs. Vehicle Impact Speed: Uncertainty Quantification and Calibration to Local Conditions

2019 ◽  
Vol 2673 (11) ◽  
pp. 583-592 ◽  
Author(s):  
Gary A. Davis ◽  
Christopher Cheong
Keyword(s):  
Uncertainty Quantification ◽  
Injury Severity ◽  
Likelihood Estimation ◽  
Monte Carlo Sampling ◽  
National Sample ◽  
Local Conditions ◽  
Impact Speed ◽  
Crash Data ◽  
Vehicle Impact ◽  
Pedestrian Crashes

This paper describes a method for fitting predictive models that relate vehicle impact speeds to pedestrian injuries, in which results from a national sample are calibrated to reflect local injury statistics. Three methodological issues identified in the literature, outcome-based sampling, uncertainty regarding estimated impact speeds, and uncertainty quantification, are addressed by (i) implementing Bayesian inference using Markov Chain Monte Carlo sampling and (ii) applying multiple imputation to conditional maximum likelihood estimation. The methods are illustrated using crash data from the NHTSA Pedestrian Crash Data Study coupled with an exogenous sample of pedestrian crashes from Minnesota’s Twin Cities. The two approaches produced similar results and, given a reliable characterization of impact speed uncertainty, either approach can be applied in a jurisdiction having an exogenous sample of pedestrian crash severities.

scholarly journals Injury Severity of Bus–Pedestrian Crashes in South Korea Considering the Effects of Regional and Company Factors

2019 ◽  
Vol 11 (11) ◽  
pp. 3169 ◽  
Author(s):  
Ho-Chul Park ◽  
Yang-Jun Joo ◽  
Seung-Young Kho ◽  
Dong-Kyu Kim ◽  
Byung-Jung Park
Keyword(s):  
South Korea ◽  
Injury Severity ◽  
The Elderly ◽  
Type I ◽  
Severe Injuries ◽  
Crash Data ◽  
Level Model ◽  
Multi Level ◽  
Pedestrian Crashes ◽  
Upper Level

Bus–pedestrian crashes typically result in more severe injuries and deaths than any other type of bus crash. Thus, it is important to screen and improve the risk factors that affect bus–pedestrian crashes. However, bus–pedestrian crashes that are affected by a company’s and regional characteristics have a cross-classified hierarchical structure, which is difficult to address properly using a single-level model or even a two-level multi-level model. In this study, we used a cross-classified, multi-level model to consider simultaneously the unobserved heterogeneities at these two distinct levels. Using bus–pedestrian crash data in South Korea from 2011 through to 2015, in this study, we investigated the factors related to the injury severity of the crashes, including crash level, regional and company level factors. The results indicate that the company and regional effects are 16.8% and 5.1%, respectively, which justified the use of a multi-level model. We confirm that type I errors may arise when the effects of upper-level groups are ignored. We also identified the factors that are statistically significant, including three regional-level factors, i.e., the elderly ratio, the ratio of the transportation infrastructure budget, and the number of doctors, and 13 crash-level factors. This study provides useful insights concerning bus–pedestrian crashes, and a safety policy is suggested to enhance bus–pedestrian safety.

scholarly journals Risk Factors for Older Pedestrian Injuries and Fatalities Among Communities in Massachusetts

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 113-113
Author(s):  
Shuangshuang Wang ◽  
Nina Silverstein ◽  
Chae Man Lee ◽  
Frank Porell ◽  
Beth Dugan
Keyword(s):  
Risk Factors ◽  
Healthy Aging ◽  
Health And Safety ◽  
Pedestrian Safety ◽  
The United States ◽  
Time Of Day ◽  
Community Factors ◽  
Crash Data ◽  
The Road ◽  
Pedestrian Crashes

Abstract The number of pedestrian crashes in the United States has increased by 35 percent from 2008 to 2017. Among all pedestrian fatalities in 2017, 48% were pedestrians aged 50 and older, which suggests a disproportionate threat to older residents’ health and safety. Massachusetts has a large older population and is experiencing increased numbers of older pedestrian crashes. This research identified risk factors and community characteristics contributing to older pedestrian crashes and suggests leveraging the state’s age-friendly efforts to speed the implementation of countermeasures. Based on ten-year statewide crash data (2006-2015) and community indicators from the 2018 Massachusetts Healthy Aging Data Report, this study examined 4,472 crashes across Massachusetts that involved pedestrians age 55 and over. The leading reasons for crashes were driver’s inattention, driver’s failure to yield right of way, and driver’s issues with visibility. Older pedestrians were hit while walking in the road, often in crosswalks at intersections. Many factors were found to contribute to older pedestrian crashes: time of day (rush hour), time of year (winter), and community factors (higher rates of disabilities, higher percentage of racial minority residents, higher number of cultural amenities, and lack of dementia-friendly community efforts. Greater awareness of older pedestrian safety risks is needed. Communities highlighted in this research warrant priority attention from planning, health, aging services, and transportation authorities to improve older pedestrian safety.

scholarly journals Analyzing Accident Injury Severity via an Extreme Gradient Boosting (XGBoost) Model

2021 ◽  
Vol 2021 ◽  
pp. 1-11 ◽  
Author(s):  
Shubo Wu ◽  
Quan Yuan ◽  
Zhongwei Yan ◽  
Qing Xu
Keyword(s):  
Risk Factors ◽  
Statistical Analysis ◽  
Injury Severity ◽  
Northern China ◽  
Time Of Day ◽  
Road User ◽  
Gradient Boosting ◽  
Extreme Gradient Boosting ◽  
Pedestrian Crashes ◽  
Crash Injury Severity

Vehicle to vulnerable road user (VRU) crashes occupy a large proportion of traffic crashes in China, and crash injury severity analysis can support traffic managers to understand the implicit rules behind the crashes. Therefore, 554 VRUs-involved crashes are collected from January, 2017, to February, 2021, in a city in northern China, including 322 vehicle-pedestrian crashes and 232 vehicle-bicycle crashes. First, a descriptive statistical analysis is conducted to investigate the characteristics of VRUs-involved crashes. Second, the extreme gradient boosting (XGBoost) model is introduced to identify the importance of risk factors (i.e., time of day, day of week, rushing hour, crash position, weather, and crash involvements) of VRUs-involved crashes. The statistical analysis demonstrates that the risk factors are closely related to VRUs-involved crash injury severity. Moreover, the results of XGBoost reveal that time of day has the greatest impact on VRUs-involved crashes, and crash position shows the minimum importance among these risk factors.

scholarly journals Short-Term Segment-Level Crash Risk Prediction Using Advanced Data Modeling with Proactive and Reactive Crash Data

2022 ◽  
Vol 12 (2) ◽  
pp. 856
Author(s):  
Branislav Dimitrijevic ◽  
Sina Darban Khales ◽  
Roksana Asadi ◽  
Joyoung Lee
Keyword(s):  
Input Data ◽  
Injury Severity ◽  
Nearest Neighbor ◽  
Weather Condition ◽  
Crash Risk ◽  
Gradient Boosting ◽  
K Nearest Neighbor ◽  
Short Term ◽  
Traffic Crashes ◽  
The Impact

Highway crashes, along with the property damage, personal injuries, and fatalities that they cause, continue to present one of the most significant and critical transportation problems. At the same time, provision of safe travel is one of the main goals of any transportation system. For this reason, both in transportation research and practice much attention has been given to the analysis and modeling of traffic crashes, including the development of models that can be applied to predict crash occurrence and crash severity. In general, such models assess short-term crash risks at a given highway facility, thus providing intelligence that can be used to identify and implement traffic operations strategies for crash mitigation and prevention. This paper presents several crash risk and injury severity assessment models applied at a highway segment level, considering the input data that is typically collected or readily available to most transportation agencies in real-time and at a regional network scale, which would render them readily applicable in practice. The input data included roadway geometry characteristics, traffic flow characteristics, and weather condition data. The paper develops, tests, and compares the performance of models that employ Random effects Bayesian Logistics Regression, Gaussian Naïve Bayes, K-Nearest Neighbor, Random Forest, and Gradient Boosting Machine methods. The paper applies random oversampling examples (ROSE) method to deal with the problem of data imbalance associated with the injury severity analysis. The models were trained and tested using a dataset of 10,155 crashes that occurred on two interstate highways in New Jersey over a two-year period. The paper also analyzes the potential improvement in the prediction abilities of the tested models by adding reactive data to the analysis. To that end, traffic crashes were classified in multiple classes based on the driver age and the vehicle age to assess the impact of these attributes on driver injury severity outcomes. The results of this analysis are promising, showing that the simultaneous use of reactive and proactive data can improve the prediction performance of the presented models.

Assessment of Commercial Truck Driver Injury Severity as a Result of Driving Actions

2021 ◽  
pp. 036119812110098
Author(s):  
Muhammad Tahmidul Haq ◽  
Milan Zlatkovic ◽  
Khaled Ksaibati
Keyword(s):  
Injury Severity ◽  
Binary Logistic Regression ◽  
Time Of Day ◽  
Truck Drivers ◽  
Truck Driver ◽  
Lighting Condition ◽  
Crash Data ◽  
Random Intercept ◽  
Gross Vehicle Weight ◽  
And Gender

The disaggregate modeling approach is a new trend in the literature to analyze the injury severity of truck-involved crashes. The assessment of truck driver injury severity based on driver action is still missing in the literature. This paper presents an extensive exploratory analysis that highlights significant variability in the severity of truck drivers’ injuries based on various action types (i.e., aggressive driving, failure to keep proper lane, driving too fast, and no improper driving). Binary logistic regression with the Bayesian random intercept approach was developed to examine the factors contributing to fatal or any injuries of truck drivers using 10 years (2007–2016) of historical crash data in Wyoming. Log-likelihood ratio tests were performed to justify that separate models by various driving action types are warranted. The results demonstrated the effects of various vehicle, driver, crash, and roadway characteristics, combined with truck driver-specific action, on the corresponding severity of driver injury. The gross vehicle weight, age and gender of the driver, time of day, lighting condition, and the presence of junctions were found to have significantly different impacts on the severity of truck driver injury in various driving action-related crashes. With the incorporation of the random intercept in the modeling procedure, the analysis found a strong presence (27%–33%) of intra-crash correlation in driver injury severity within the same crash. Finally, based on the findings of this study, several recommendations are made.

scholarly journals Investigating the Risk Factors Associated with the Severity of the Pedestrians Injured on Spanish Crosstown Roads

2019 ◽  
Vol 11 (19) ◽  
pp. 5194 ◽  
Author(s):  
Natalia Casado-Sanz ◽  
Begoña Guirao ◽  
Antonio Lara Galera ◽  
Maria Attard
Keyword(s):  
Risk Factors ◽  
Injury Severity ◽  
Motor Vehicle ◽  
Weather Conditions ◽  
Crash Data ◽  
Factors Associated ◽  
The Road ◽  
Pedestrian Injury ◽  
On The Road ◽  
Pedestrian Crashes

According to the Spanish General Traffic Accident Directorate, in 2017 a total of 351 pedestrians were killed, and 14,322 pedestrians were injured in motor vehicle crashes in Spain. However, very few studies have been conducted in order to analyse the main factors that contribute to pedestrian injury severity. This study analyses the accidents that involve a single vehicle and a single pedestrian on Spanish crosstown roads from 2006 to 2016 (1535 crashes). The factors that explain these accidents include infractions committed by the pedestrian and the driver, crash profiles, and infrastructure characteristics. As a preliminary tool for the segmentation of 1535 pedestrian crashes, a k-means cluster analysis was applied. In addition, multinomial logit (MNL) models were used for analysing crash data, where possible outcomes were fatalities and severe and minor injured pedestrians. According to the results of these models, the risk factors associated with pedestrian injury severity are as follows: visibility restricted by weather conditions or glare, infractions committed by the pedestrian (such as not using crossings, crossing unlawfully, or walking on the road), infractions committed by the driver (such as distracted driving and not respecting a light or a crossing), and finally, speed infractions committed by drivers (such as inadequate speed). This study proposes the specific safety countermeasures that in turn will improve overall road safety in this particular type of road.

Bicyclist injury severity in traffic crashes: A spatial approach for geo-referenced crash data to uncover non-stationary correlates

2020 ◽  
Vol 73 ◽  
pp. 25-35
Author(s):  
Jun Liu ◽  
Asad J. Khattak ◽  
Xiaobing Li ◽  
Qifan Nie ◽  
Ziwen Ling
Keyword(s):  
Injury Severity ◽  
Traffic Crashes ◽  
Crash Data ◽  
Spatial Approach
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