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.

A Method to Evaluate Vehicle Impact Speeds in Passenger Car-Pedestrian Accidents

2016 ◽  
Vol 835 ◽  
pp. 788-792
Author(s):  
Cheng Jian Feng ◽  
Kui Li ◽  
Zhi Yong Yin
Keyword(s):  
Traffic Accidents ◽  
Linear Regression Analysis ◽  
Average Height ◽  
Passenger Car ◽  
Impact Speed ◽  
Pedestrian Injury ◽  
Vehicle Impact ◽  
Pedestrian Crashes ◽  
The Impact ◽  
The Relationship

This paper aimed to research the relationship between the wrap around distance (WAD) to head contact and vehicle impact speed based on real pedestrian traffic accidents with video. A team was established to collect passenger car-pedestrian accident cases occurring between July 2011 and July 2015 in Chongqing, China. A total of 15 pedestrian crashes were selected into the sample. Impact speeds were calculated by a video analysis technology, and the WAD was revised according to the average height of pedestrians involved in the sample. The relationship between the WAD and impact speed was analyzed using linear regression analysis. We propose a method to evaluate the impact speed in passenger car-pedestrian. These results will contribute to the development of judicial identification and research of pedestrian injury.

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.

scholarly journals Exploration of Vehicle Impact Speed – Injury Severity Relationships for Application in Safer Road Design

2016 ◽  
Vol 14 ◽  
pp. 4247-4256 ◽  
Author(s):  
Chris Jurewicz ◽  
Amir Sobhani ◽  
Jeremy Woolley ◽  
Jeff Dutschke ◽  
Bruce Corben
Keyword(s):  
Injury Severity ◽  
Impact Speed ◽  
Road Design ◽  
Vehicle Impact

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.

scholarly journals Exploratory Analysis of Pedestrian Road Trauma in Finland

2021 ◽  
Vol 13 (12) ◽  
pp. 6715
Author(s):  
Steve O’Hern ◽  
Roni Utriainen ◽  
Hanne Tiikkaja ◽  
Markus Pöllänen ◽  
Niina Sihvola
Keyword(s):  
Cluster Analysis ◽  
Motor Vehicle ◽  
Seasonal Effects ◽  
Unique Element ◽  
Driver Behaviour ◽  
Crash Data ◽  
Safety Issues ◽  
Key Issues ◽  
Development Goals ◽  
Pedestrian Crashes

In Finland, all fatal on-road and off-road motor vehicle crashes are subject to an in-depth investigation coordinated by the Finnish Crash Data Institute (OTI). This study presents an exploratory and two-step cluster analysis of fatal pedestrian crashes between 2010 and 2019 that were subject to in-depth investigations. In total, 281 investigations occurred across Finland between 2010 and 2019. The highest number of cases were recorded in the Uusimaa region, including Helsinki, representing 26.4% of cases. Females (48.0%) were involved in fewer cases than males; however, older females represented the most commonly injured demographic. A unique element to the patterns of injury in this study is the seasonal effects, with the highest proportion of crashes investigated in winter and autumn. Cluster analysis identified four unique clusters. Clusters were characterised by crashes involving older pedestrians crossing in low-speed environments, crashes in higher speed environments away from pedestrian crossings, crashes on private roads or in parking facilities, and crashes involving intoxicated pedestrians. The most common recommendations from the investigation teams to improve safety were signalisation and infrastructure upgrades of pedestrian crossings, improvements to street lighting, advanced driver assistance (ADAS) technologies, and increased emphasis on driver behaviour and training. The findings highlight road safety issues that need to be addressed to reduce pedestrian trauma in Finland, including provision of safer crossing facilities for elderly pedestrians, improvements to parking and shared facilities, and addressing issues of intoxicated pedestrians. Efforts to remedy these key issues will further Finland’s progression towards meeting Vision Zero targets while creating a safer and sustainable urban environment in line with the United Nations sustainable development goals.

scholarly journals Analysis of Severe Injuries in Crashes Involving Large Trucks Using K-Prototypes Clustering-Based GBDT Model

Safety ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. 32
Author(s):  
Syed As-Sadeq Tahfim ◽  
Chen Yan
Keyword(s):  
Injury Severity ◽  
Unobserved Heterogeneity ◽  
The United States ◽  
Clustering Methods ◽  
Severe Injuries ◽  
Traffic Crash ◽  
Crash Data ◽  
Boosted Decision Trees ◽  
The Individual ◽  
Crash Characteristics

The unobserved heterogeneity in traffic crash data hides certain relationships between the contributory factors and injury severity. The literature has been limited in exploring different types of clustering methods for the analysis of the injury severity in crashes involving large trucks. Additionally, the variability of data type in traffic crash data has rarely been addressed. This study explored the application of the k-prototypes clustering method to countermeasure the unobserved heterogeneity in large truck-involved crashes that had occurred in the United States between the period of 2016 to 2019. The study segmented the entire dataset (EDS) into three homogeneous clusters. Four gradient boosted decision trees (GBDT) models were developed on the EDS and individual clusters to predict the injury severity in crashes involving large trucks. The list of input features included crash characteristics, truck characteristics, roadway attributes, time and location of the crash, and environmental factors. Each cluster-based GBDT model was compared with the EDS-based model. Two of the three cluster-based models showed significant improvement in their predicting performances. Additionally, feature analysis using the SHAP (Shapley additive explanations) method identified few new important features in each cluster and showed that some features have a different degree of effects on severe injuries in the individual clusters. The current study concluded that the k-prototypes clustering-based GBDT model is a promising approach to reveal hidden insights, which can be used to improve safety measures, roadway conditions and policies for the prevention of severe injuries in crashes involving large trucks.

Examining Driver Injury Severity in Single-Vehicle Road Departure Crashes Involving Large Trucks

2021 ◽  
pp. 036119812110101
Author(s):  
Mehdi Hosseinpour ◽  
Kirolos Haleem
Keyword(s):  
Rural Areas ◽  
Injury Severity ◽  
Factors Affecting ◽  
Crash Data ◽  
Natural Logarithm ◽  
Concrete Barriers ◽  
Local Roads ◽  
Single Vehicle ◽  
Safety Countermeasures ◽  
Injury Severities

Road departure (RD) crashes are among the most severe crashes that can result in fatal or serious injuries, especially when involving large trucks. Most previous studies neglected to incorporate both roadside and median hazards into large-truck RD crash severity analysis. The objective of this study was to identify the significant factors affecting driver injury severity in single-vehicle RD crashes involving large trucks. A random-parameters ordered probit (RPOP) model was developed using extensive crash data collected on roadways in the state of Kentucky between 2015 and 2019. The RPOP model results showed that the effect of local roadways, the natural logarithm of annual average daily traffic (AADT), the presence of median concrete barriers, cable barrier-involved collisions, and dry surfaces were found to be random across the crash observations. The results also showed that older drivers, ejected drivers, and drivers trapped in their truck were more likely to sustain severe single-vehicle RD crashes. Other variables increasing the probability of driver injury severity have included rural areas, dry road surfaces, higher speed limits, single-unit truck types, principal arterials, overturning-consequences, truck fire occurrence, segments with median concrete barriers, and roadside fixed object strikes. On the other hand, wearing seatbelt, local roads and minor collectors, higher AADT, and hitting median cable barriers were associated with lower injury severities. Potential safety countermeasures from the study findings include installing median cable barriers and flattening steep roadside embankments along those roadway stretches with high history of RD large-truck-related crashes.

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