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Journal articles on the topic 'Severity of hypothetical crash'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 February 2022

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1

Freeman, Michael D. "Principles and Methods for Evidence-Based Quantification of the Effect of Seat Belt Non-Use in Crash-Related Litigation." International Journal of Environmental Research and Public Health 18, no. 18 (2021): 9455. http://dx.doi.org/10.3390/ijerph18189455.

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Traffic crashes are a common cause of injury and death, and often result from the negligent actions of an inattentive, speeding, or impaired driver. In such cases, a civil legal action may be brought by an injured claimant for compensation for injuries resulting from a crash. Crash-related litigation is defended on various theories, one of which is to raise the issue of contributory negligence when the claimant was not using an available seat belt at the time of the crash, based on the assertion that the claimed injuries would have been avoided or minimized to some degree if the claimant had b

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2

Ray, Malcolm H., Christine E. Carrigan, and Chuck A. Plaxico. "Method for Modeling Crash Severity with Observable Crash Data." Transportation Research Record: Journal of the Transportation Research Board 2437, no. 1 (2014): 1–9. http://dx.doi.org/10.3141/2437-01.

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Long, Kejun, Zhibo Gao, Quan Yuan, Wang Xiang, and Wei Hao. "Safety evaluation for roadside crashes by vehicle–object collision simulation." Advances in Mechanical Engineering 10, no. 10 (2018): 168781401880558. http://dx.doi.org/10.1177/1687814018805581.

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In order to evaluate roadside crash severity and help making decision on roadside safety improvement alternatives, this article proposes a roadside crash severity evaluation method based on vehicle kinematics metric during the crash: Acceleration Severity Index. Based on the field investigation on 1917 km of representative roads, roadside crash test standards and parameters were determined. A total of 59 crash scenarios, involving 5 typical roadside obstacles, 2 types of guardrails, 15 embankment slopes, and 3 types of vehicles (car, bus, and truck), were designed for simulated crash testing w

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Vangi, Dario, Michelangelo-Santo Gulino, Anita Fiorentino, and Antonio Virga. "Crash momentum index and closing velocity as crash severity index." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 233, no. 13 (2019): 3318–26. http://dx.doi.org/10.1177/0954407018823658.

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The velocity change Δ V of a vehicle subject to a collision, widely recognized as an efficient crash severity indicator, is a typical ‘a posteriori’ parameter, not generally known until the crash phase has been reconstructed. Δ V is the result of a combination of factors, regarding the impact velocities of the colliding vehicles and the geometry of the impact (as eccentricity, etc.): for this reason, its value alone gives no clear indications on the actions which can be undertaken to reduce crash severity. This feature is particularly critical in some application fields, for example, in case o

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Shea, M. Scott, Thanh Q. Le, and Richard J. Porter. "Combined Crash Frequency–Crash Severity Evaluation of Geometric Design Decisions." Transportation Research Record: Journal of the Transportation Research Board 2521, no. 1 (2015): 54–63. http://dx.doi.org/10.3141/2521-06.

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This paper quantified the effects of freeway ramp spacing and auxiliary lane presence on crash frequency and crash severity. Crash frequencies were predicted with a safety performance function, and crash severities were estimated with what was termed a “severity distribution function.” The paper then demonstrated how to combine quantitative knowledge related to the effects of ramp spacing and auxiliary lane presence on both crash frequency and severity into a framework for assessing the overall crash cost for different ramp configurations. Geometric features, traffic characteristics, and crash

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Wu, Biao, Xingyu Wang, Tuo Liu, Naibao Dong, and Yun Li. "Exploring Factors Contributing to Crash Injury Severity in the Rural-Urban Fringe of the Central City." Journal of Advanced Transportation 2021 (August 30, 2021): 1–10. http://dx.doi.org/10.1155/2021/8453465.

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To analyze the risk factors influencing the crash injury severity in rural-urban fringes, crash data in rural-urban fringes were collected from Harbin, China. Four risk factors, namely, time of day, vehicle type, road feature, and crash type, were investigated associated with the severity of rural-urban fringe crashes. The crash injury severity was divided into two categories, including fatal and nonfatal crash. The logistic regression was applied to explore the relationships between the severity outcomes and time of day, vehicle type, road feature, and crash type. The test methods of goodness

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Cao, Yi, Shiwen Li, and Chuanyun Fu. "An Assessment Method of Urban Traffic Crash Severity Considering Traveling Delay and Non-Essential Fuel Consumption of Third Parties." Sustainability 12, no. 17 (2020): 6806. http://dx.doi.org/10.3390/su12176806.

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Urban traffic crashes may lead to only a few casualties, but may generate severe negative impacts on the surrounding traffic, such as evidently increasing traveling delay and non-essential fuel consumption of third parties (i.e., vehicles not involved in the crash). Such detrimental consequences of urban traffic crashes are usually ignored by the traditional crash severity evaluation approaches. Therefore, this study attempts to classify urban traffic crash severity by considering the traveling delay and non-essential fuel consumption of third parties in addition to casualties and property dam

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Qin, Xiao, Most Afia Sultana, Madhav V. Chitturi, and David A. Noyce. "Developing Truck Corridor Crash Severity Index." Transportation Research Record: Journal of the Transportation Research Board 2386, no. 1 (2013): 103–11. http://dx.doi.org/10.3141/2386-12.

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Harvey, Chester, and Lisa Aultman-Hall. "Urban Streetscape Design and Crash Severity." Transportation Research Record: Journal of the Transportation Research Board 2500, no. 1 (2015): 1–8. http://dx.doi.org/10.3141/2500-01.

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Streetscape design is increasingly acknowledged as a tool for improving traffic safety and livability in urban settings. While traditional highway safety engineering promotes removing obstacles from and widening roadside clear zones to reduce collision potential, a contrasting framework proposes that smaller, more enclosed streetscapes may encourage slower, less risky driving and therefore improve both livability and safety. Such a strategy may have particular relevance along urban arterials, where large clear zones may be impractical because of complex adjacent land uses and where the promoti

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10

Nguyen, Thanh Chuong, Minh Hieu Nguyen, Jimmy Armoogum, and Thanh Tung Ha. "Bus Crash Severity in Hanoi, Vietnam." Safety 7, no. 3 (2021): 65. http://dx.doi.org/10.3390/safety7030065.

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Recently, there has been an increasing interest in targeting the safety of bus operations worldwide; however, little is known about the determinants of the bus crash severity in developing countries. By estimating an ordered logit model using the bus-involved collision data in Hanoi (Vietnam), spanning the period from 2015 to 2019, this study investigates various factors associated with the crash severity. The results reveal that the severity risk increases for (1) large buses, (2) raining conditions, (3) evening or night, (4) sparse traffic, (5) non-urban areas, (6) roads with at least three

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Kaplan, Sigal, and Carlo Giacomo Prato. "Observed and unobserved correlation between crash avoidance manoeuvers and crash severity." International Journal of Injury Control and Safety Promotion 23, no. 4 (2015): 413–26. http://dx.doi.org/10.1080/17457300.2015.1056806.

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Sequeira, Gerald Joy, Akshay Patel, Shahabaz Afraj, Robert Lugner, and Thomas Brandmeier. "FEM-based Methodology for Crash Severity Estimation in Frontal Crash Scenarios." IOP Conference Series: Materials Science and Engineering 831 (June 6, 2020): 012019. http://dx.doi.org/10.1088/1757-899x/831/1/012019.

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Blackman, Ross A., and Narelle L. Haworth. "Comparison of moped, scooter and motorcycle crash risk and crash severity." Accident Analysis & Prevention 57 (August 2013): 1–9. http://dx.doi.org/10.1016/j.aap.2013.03.026.

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14

Meng, Fanyu, Pengpeng Xu, Cancan Song, Kun Gao, Zichu Zhou, and Lili Yang. "Influential Factors Associated with Consecutive Crash Severity: A Two-Level Logistic Modeling Approach." International Journal of Environmental Research and Public Health 17, no. 15 (2020): 5623. http://dx.doi.org/10.3390/ijerph17155623.

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A consecutive crash series is composed by a primary crash and one or more subsequent secondary crashes that occur immediately within a certain distance. The crash mechanism of a consecutive crash series is distinctive, as it is different from common primary and secondary crashes mainly caused by queuing effects and chain-reaction crashes that involve multiple collisions in one crash. It commonly affects a large area of road space and possibly causes congestions and significant delays in evacuation and clearance. This study identified the influential factors determining the severity of primary

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15

Wu, Peijie, Xianghai Meng, Li Song, and Wenze Zuo. "Crash Risk Evaluation and Crash Severity Pattern Analysis for Different Types of Urban Junctions: Fault Tree Analysis and Association Rules Approaches." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 1 (2019): 403–16. http://dx.doi.org/10.1177/0361198118822817.

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Urban junctions usually present significant safety concerns, and the majority of all crashes in urban areas occur in or near junctions. Factors contributing to crash severity at junctions have been explored, but crash risk levels and crash severity patterns of different junction types have hardly been investigated. In order to fill this gap, this study analyzed the safety performance of six junction types and the factors contributing to crash severity, in order to assist city transportation authorities to implement effective countermeasures. Fault tree analysis (FTA) was applied for the risk e

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Komol, Md Mostafizur Rahman, Md Mahmudul Hasan, Mohammed Elhenawy, Shamsunnahar Yasmin, Mahmoud Masoud, and Andry Rakotonirainy. "Crash severity analysis of vulnerable road users using machine learning." PLOS ONE 16, no. 8 (2021): e0255828. http://dx.doi.org/10.1371/journal.pone.0255828.

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Road crash fatality is a universal problem of the transportation system. A massive death toll caused annually due to road crash incidents, and among them, vulnerable road users (VRU) are endangered with high crash severity. This paper focuses on employing machine learning-based classification approaches for modelling injury severity of vulnerable road users—pedestrian, bicyclist, and motorcyclist. Specifically, this study aims to analyse critical features associated with different VRU groups—for pedestrian, bicyclist, motorcyclist and all VRU groups together. The critical factor of crash sever

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17

Naghawi, Hana. "Negative Binomial Regression Model for Road Crash Severity Prediction." Modern Applied Science 12, no. 4 (2018): 38. http://dx.doi.org/10.5539/mas.v12n4p38.

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In this paper, the Negative Binominal Regression (NBR) technique was used to develop crash severity prediction model in Jordan. The primary crash data needed were obtained from Jordan Traffic Institute for the year 2014. The collected data included number and severity of crashes. The data were organized into eight crash contributing factors including: age, age and gender, drivers’ faults, environmental factors, crash time, roadway defects and vehicle defects. First of all, descriptive analysis of the crash contributing factors was done to identify and quantify factors affecting crash severity,

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Adegbite, Qasim, Khondoker Billah, Hatim Sharif, and Samer Dessouky. "Urban Intersections and Traffic Safety in the City of San Antonio." MATEC Web of Conferences 271 (2019): 06003. http://dx.doi.org/10.1051/matecconf/201927106003.

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Intersections are high-risk locations on roadways and often experience high incidence of crashes. Better understanding of the factors contributing to crashes and deaths at intersections is crucial. This study analyzed the factors related to crash incidence and crash severity at intersections in San Antonio for crashes from 2013 to 2017 and identified hotspot locations based on crash frequency and crash rates. Binary logistic regression model was considered for the analysis using crash severity as the response variable. Factors found to be significantly associated with the severity of intersect

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Billah, Khondoker, Qasim Adegbite, Hatim O. Sharif, Samer Dessouky, and Lauren Simcic. "Analysis of Intersection Traffic Safety in the City of San Antonio, 2013–2017." Sustainability 13, no. 9 (2021): 5296. http://dx.doi.org/10.3390/su13095296.

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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

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Song, Xiuguang, Jianqing Wu, Hongbo Zhang, and Rendong Pi. "Analysis of Crash Severity for Hazard Material Transportation Using Highway Safety Information System Data." SAGE Open 10, no. 3 (2020): 215824402093992. http://dx.doi.org/10.1177/2158244020939924.

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Crash severity, as a major concern in the routing and scheduling of hazardous material shipments, has caused great loss of lives and property damage every year. Although abundant studies have been conducted to identify the relationship between different factors on crash severity, the analysis of the severity of hazard material transportation (HMT) crashes is very limited. Factors including road, vehicle, driver, and environment are not well considered in previous studies. This article analyzed the influence of various factors on HMT crash severity using Highway Safety Information System data.

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Ye, Fan, and Yong Wang. "Performance Evaluation of Various Missing Data Treatments in Crash Severity Modeling." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 38 (2018): 149–59. http://dx.doi.org/10.1177/0361198118798485.

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Data quality, including record inaccuracy and missingness (incompletely recorded crashes and crash underreporting), has always been of concern in crash data analysis. Limited efforts have been made to handle some specific aspects of crash data quality problems, such as using weights in estimation to take care of unreported crash data and applying multiple imputation (MI) to fill in missing information of drivers’ status of attention before crashes. Yet, there lacks a general investigation of the performance of different statistical methods to handle missing crash data. This paper is intended t

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22

Jou, Rong-Chang, and Tzu-Ying Chen. "Estimating Productivity Loss Cost according to Severity of Vehicle Crash Injury." Journal of Advanced Transportation 2019 (December 12, 2019): 1–14. http://dx.doi.org/10.1155/2019/7219047.

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This study estimates the productivity loss cost according to the severity of vehicle crash injury. A contingent valuation survey was conducted to estimate the willingness to pay (WTP) of vehicle crash offenders in Taiwan. In addition, a Double-Hurdle model was used to deal with the large number of zero WTP responses. The results show that the estimated productivity loss cost of vehicle crash ranges from 2,000 USD to 47,000 USD. In addition, as expected, the individuals’ WTP is positively related with education, average monthly income, share of vehicle crash responsibility, experience of modera

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Assemi, Behrang, and Mark Hickman. "Relationship between heavy vehicle periodic inspections, crash contributing factors and crash severity." Transportation Research Part A: Policy and Practice 113 (July 2018): 441–59. http://dx.doi.org/10.1016/j.tra.2018.04.018.

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Park, T.-W., H.-Y. Jeong, and S.-W. Park. "A Crash Severity Algorithm for All Frontal Crash Modes Using Compensation Factors." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 220, no. 5 (2006): 531–41. http://dx.doi.org/10.1243/09544070d14904.

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Wang, Kai, Shanshan Zhao, and Eric Jackson. "Multivariate Poisson Lognormal Modeling of Weather-Related Crashes on Freeways." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 38 (2018): 184–98. http://dx.doi.org/10.1177/0361198118776523.

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Adverse weather conditions are one of the primary causes of motor vehicle crashes. To identify the factors contributing to crashes during adverse weather conditions and recommend cost-effective countermeasures, it is necessary to develop reliable crash prediction models to estimate weather-related crash frequencies. To account for the variations in crash count among different adverse weather conditions, crash types, and crash severities for both rain- and snow-related crashes, crash data on freeways was collected from the State of Connecticut, and crash prediction models were developed to esti

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Li, Dawei, and Mustafa F. M. Al-Mahamda. "Collective Risk Ranking of Highway Segments on the Basis of Severity-Weighted Crash Rates." Journal of Advanced Transportation 2020 (October 28, 2020): 1–9. http://dx.doi.org/10.1155/2020/8837762.

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This study is intended to focus on the major factors affecting traffic crash rates and severity levels, in addition to identifying crash-prone locations (i.e., black spots) based on the two indicators. The available crash data for different road segments used for the analysis were obtained from the Washington state database provided by the Highway Safety Information System (HSIS) for the years 2006 to 2011. A Random Forest (RF) classifier was used to predict the outcome level of crash severity, while crash rates were predicted by applying RF regressor. Certain features were selected for each m

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Shiran, Gholamreza, Reza Imaninasab, and Razieh Khayamim. "Crash Severity Analysis of Highways Based on Multinomial Logistic Regression Model, Decision Tree Techniques, and Artificial Neural Network: A Modeling Comparison." Sustainability 13, no. 10 (2021): 5670. http://dx.doi.org/10.3390/su13105670.

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The classification of vehicular crashes based on their severity is crucial since not all of them have the same financial and injury values. In addition, avoiding crashes by identifying their influential factors is possible via accurate prediction modeling. In crash severity analysis, accurate and time-saving prediction models are necessary for classifying crashes based on their severity. Moreover, statistical models are incapable of identifying the potential severity of crashes regarding influencing factors incorporated in models. Unlike previous research efforts, which focused on the limited

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Liu, Litao, and Sunanda Dissanayake. "Factors Affecting Crash Severity on Gravel Roads." Journal of Transportation Safety & Security 1, no. 4 (2009): 254–67. http://dx.doi.org/10.1080/19439960903381669.

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Schrum, Kevin D., Francisco Daniel Benicio De Albuquerque, Dean L. Sicking, Ronald K. Falle, and John D. Reid. "Correlation Between Crash Severity and Embankment Geometry." Journal of Transportation Safety & Security 6, no. 4 (2014): 321–34. http://dx.doi.org/10.1080/19439962.2013.877548.

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Toran Pour, Alireza, Sara Moridpour, Richard Tay, and Abbas Rajabifard. "Modelling pedestrian crash severity at mid-blocks." Transportmetrica A: Transport Science 13, no. 3 (2016): 273–97. http://dx.doi.org/10.1080/23249935.2016.1256355.

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Tsoi, Ada H., and Hampton C. Gabler. "Evaluation of Vehicle-Based Crash Severity Metrics." Traffic Injury Prevention 16, sup2 (2015): S132—S139. http://dx.doi.org/10.1080/15389588.2015.1067693.

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Toran Pour, Alireza, Sara Moridpour, Richard Tay, and Abbas Rajabifard. "Neighborhood Influences on Vehicle-Pedestrian Crash Severity." Journal of Urban Health 94, no. 6 (2017): 855–68. http://dx.doi.org/10.1007/s11524-017-0200-z.

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Li, Yingfeng, Chunxiao Liu, and Liang Ding. "Impact of pavement conditions on crash severity." Accident Analysis & Prevention 59 (October 2013): 399–406. http://dx.doi.org/10.1016/j.aap.2013.06.028.

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Hu, Wen, and Eric T. Donnell. "Median barrier crash severity: Some new insights." Accident Analysis & Prevention 42, no. 6 (2010): 1697–704. http://dx.doi.org/10.1016/j.aap.2010.04.009.

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Chen, Xining. "Traffic Crash Severity Prediction with Deep Learning." Journal of Physics: Conference Series 1883, no. 1 (2021): 012141. http://dx.doi.org/10.1088/1742-6596/1883/1/012141.

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Song, Tai-Jin, Jaehyun (Jason) So, Jisun Lee, and Billy M. Williams. "Exploring Vehicle–Pedestrian Crash Severity Factors on the Basis of In-Car Black Box Recording Data." Transportation Research Record: Journal of the Transportation Research Board 2659, no. 1 (2017): 148–54. http://dx.doi.org/10.3141/2659-16.

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This study investigated the main factors affecting the severity of injury to pedestrians in taxi–pedestrian crashes on urban arterial roads. Video data recorded by an in-car black box were used. Because the video data provided direct crash observation, they were more reliable than the crash data, and video images and speed profiles retrieved from the black box were advantageous for safety studies. For analysis of the black box data, this study defined new explanatory variables that affected injury severity; these variables could not have been identified by the conventional method, which was ba

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Zeng, Qiang, Wei Hao, Jaeyoung Lee, and Feng Chen. "Investigating the Impacts of Real-Time Weather Conditions on Freeway Crash Severity: A Bayesian Spatial Analysis." International Journal of Environmental Research and Public Health 17, no. 8 (2020): 2768. http://dx.doi.org/10.3390/ijerph17082768.

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This study presents an empirical investigation of the impacts of real-time weather conditions on the freeway crash severity. A Bayesian spatial generalized ordered logit model was developed for modeling the crash severity using the hourly wind speed, air temperature, precipitation, visibility, and humidity, as well as other observed factors. A total of 1424 crash records from Kaiyang Freeway, China in 2014 and 2015 were collected for the investigation. The proposed model can simultaneously accommodate the ordered nature in severity levels and spatial correlation across adjacent crashes. Its st

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Champahom, Thanapong, Sajjakaj Jomnonkwao, Chinnakrit Banyong, Watanya Nambulee, Ampol Karoonsoontawong, and Vatanavongs Ratanavaraha. "Analysis of Crash Frequency and Crash Severity in Thailand: Hierarchical Structure Models Approach." Sustainability 13, no. 18 (2021): 10086. http://dx.doi.org/10.3390/su131810086.

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Currently, research on the development of crash models in terms of crash frequency on road segments and crash severity applies the principles of spatial analysis and heterogeneity due to the methods’ suitability compared with traditional models. This study focuses on crash severity and frequency in Thailand. Moreover, this study aims to understand crash frequency and fatality. The result of the intra-class correlation coefficient found that the spatial approach should analyze the data. The crash frequency model’s best fit is a spatial zero-inflated negative binomial model (SZINB). The results

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Dissanayake, Sunanda, and John Lu. "Analysis of Severity of Young Driver Crashes: Sequential Binary Logistic Regression Modeling." Transportation Research Record: Journal of the Transportation Research Board 1784, no. 1 (2002): 108–14. http://dx.doi.org/10.3141/1784-14.

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Young drivers have the highest fatality involvement rates of any driver age group within the United States driving population. They also experience a higher percentage of single-vehicle crashes compared with others. When looking at the methods of improving this alarming death rate of young drivers, it is important to identify the determinants of higher crash and injury severity. With that intention, the study developed, using the Florida Traffic Crash Database, a set of sequential binary logistic regression models to predict the crash severity outcome of single-vehicle fixed-object crashes inv

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Sinha, Amolika, Vincent Vu, Sai Chand, Kasun Wijayaratna, and Vinayak Dixit. "A Crash Injury Model Involving Autonomous Vehicle: Investigating of Crash and Disengagement Reports." Sustainability 13, no. 14 (2021): 7938. http://dx.doi.org/10.3390/su13147938.

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Autonomous vehicles (AVs) are being extensively tested on public roads in several states in the USA, such as California, Florida, Nevada, and Texas. AV utilization is expected to increase into the future, given rapid advancement and development in sensing and navigation technologies. This will eventually lead to a decline in human driving. AVs are generally believed to mitigate crash frequency, although the repercussion of AVs on crash severity is ambiguous. For the data-driven and transparent deployment of AVs in California, the California Department of Motor Vehicles (CA DMV) commissioned AV

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Shaon, Mohammad Razaur Rahman, and Xiao Qin. "Crash Data-Based Investigation into How Injury Severity Is Affected by Driver Errors." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 5 (2020): 452–64. http://dx.doi.org/10.1177/0361198120916469.

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Unsafe driving behaviors, driver limitations, and conditions that lead to a crash are usually referred to as driver errors. Even though driver errors are widely cited as a critical reason for crash occurrence in crash reports and safety literature, the discussion on their consequences is limited. This study aims to quantify the effect of driver errors on crash injury severity. To assist this investigation, driver errors were categorized as sequential events in a driving task. Possible combinations of driver error categories were created and ranked based on statistical dependences between error

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Lee, Myungwoo, and Aemal J. Khattak. "Case Study of Crash Severity Spatial Pattern Identification in Hot Spot Analysis." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 9 (2019): 684–95. http://dx.doi.org/10.1177/0361198119845367.

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Traffic crash hot spot analyses allow identification of roadway segments that may be of safety concern. Understanding geographic patterns of existing motor vehicle crashes is one of the primary steps for geostatistical-based hot spot analysis. Much of the current literature, however, has not paid particular attention to differentiating among cluster types based on crash severity levels. This study aims at building a framework for identifying significant spatial clustering patterns characterized by crash severity and analyzing identified clusters quantitatively. A case study using an integrated

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Thomas, Libby, Krista Nordback, and Rebecca Sanders. "Bicyclist Crash Types on National, State, and Local Levels: A New Look." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 6 (2019): 664–76. http://dx.doi.org/10.1177/0361198119849056.

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This paper presents an overview of prevalent bicyclist crash types in the United States, providing insights for practitioners that may be useful in planning safer networks and taking other proactive and risk-based approaches to treatment. The study compares fatal bicyclist crash types from national data with serious injury and all-severity bicyclist collisions from the state of North Carolina (NC) and the city of Boulder, Colorado. Overall, bicyclist fatalities in the United States are more prevalent in urban areas (69%) than rural areas (29%). Though the majority of all-severity crashes are a

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Zhang, Guangnan, Yanyan Li, Mark J. King, and Qiaoting Zhong. "Overloading among crash-involved vehicles in China: identification of factors associated with overloading and crash severity." Injury Prevention 25, no. 1 (2018): 36–46. http://dx.doi.org/10.1136/injuryprev-2017-042599.

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ObjectiveMotor vehicle overloading is correlated with the possibility of road crash occurrence and severity. Although overloading of motor vehicles is pervasive in developing nations, few empirical analyses have been performed on factors that might influence the occurrence of overloading. This study aims to address this shortcoming by seeking evidence from several years of crash data from Guangdong province, China.MethodsData on overloading and other factors are extracted for crash-involved vehicles from traffic crash records for 2006–2010 provided by the Traffic Management Bureau in Guangdong

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Traynor, Thomas L. "The impact of driver alcohol use on crash severity: A crash specific analysis." Transportation Research Part E: Logistics and Transportation Review 41, no. 5 (2005): 421–37. http://dx.doi.org/10.1016/j.tre.2005.03.005.

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Liu, Yi, Zongzhi Li, Jingxian Liu, and Harshingar Patel. "Vehicular crash data used to rank intersections by injury crash frequency and severity." Data in Brief 8 (September 2016): 930–33. http://dx.doi.org/10.1016/j.dib.2016.06.046.

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Maio, Ronald F., Paul E. Green, Mark P. Becker, Richard E. Burney, and Charles Compton. "Rural motor vehicle crash mortality: The role of crash severity and medical resources." Accident Analysis & Prevention 24, no. 6 (1992): 631–42. http://dx.doi.org/10.1016/0001-4575(92)90015-b.

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Zhou, Bei, Xinfen Zhang, Shengrui Zhang, Zongzhi Li, and Xin Liu. "Analysis of Factors Affecting Real-Time Ridesharing Vehicle Crash Severity." Sustainability 11, no. 12 (2019): 3334. http://dx.doi.org/10.3390/su11123334.

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The popular real-time ridesharing service has promoted social and environmental sustainability in various ways. Meanwhile, it also brings some traffic safety concerns. This paper aims to analyze factors affecting real-time ridesharing vehicle crash severity based on the classification and regression tree (CART) model. The Chicago police-reported crash data from January to December 2018 is collected. Crash severity in the original dataset is highly imbalanced: only 60 out of 2624 crashes are severe injury crashes. To fix the data imbalance problem, a hybrid data preprocessing approach which com

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Assi, Khaled, Syed Masiur Rahman, Umer Mansoor, and Nedal Ratrout. "Predicting Crash Injury Severity with Machine Learning Algorithm Synergized with Clustering Technique: A Promising Protocol." International Journal of Environmental Research and Public Health 17, no. 15 (2020): 5497. http://dx.doi.org/10.3390/ijerph17155497.

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Predicting crash injury severity is a crucial constituent of reducing the consequences of traffic crashes. This study developed machine learning (ML) models to predict crash injury severity using 15 crash-related parameters. Separate ML models for each cluster were obtained using fuzzy c-means, which enhanced the predicting capability. Finally, four ML models were developed: feed-forward neural networks (FNN), support vector machine (SVM), fuzzy C-means clustering based feed-forward neural network (FNN-FCM), and fuzzy c-means based support vector machine (SVM-FCM). Features that were easily id

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Keramati, Amin, Pan Lu, Xiaoyi Zhou, and Denver Tolliver. "A Simultaneous Safety Analysis of Crash Frequency and Severity for Highway-Rail Grade Crossings: The Competing Risks Method." Journal of Advanced Transportation 2020 (August 3, 2020): 1–13. http://dx.doi.org/10.1155/2020/8878911.

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This paper proposes a mathematical model, the competing risks method, to investigate highway-rail grade crossing (HRGC) crash frequency and crash severity simultaneously over a 30-year period. The proposed competing risks model is a special type of survival analysis to accommodate the competing nature of multiple outcomes from the same event of interest; in this case, the competing multiple outcomes are crash severities, while event of interest is crash occurrence. Knowledge-gain-based benefits to be discovered through the application of this model and 30-year dataset are as follows: (1) a str

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