Prediction and Factor Identification for Crash Severity: Comparison of Discrete Choice and Tree-Based Models

2019 ◽  
Vol 2673 (9) ◽  
pp. 640-653 ◽  
Author(s):  
Xinyi Wang ◽  
Sung Hoo Kim
Keyword(s):  
Sensitivity Analysis ◽  
Discrete Choice ◽  
Traffic Safety ◽  
Crash Severity ◽  
Continuous Variables ◽  
Contributing Factors ◽  
Explanatory Variables ◽  
Crash Data ◽  
Collision Type ◽  
Mnl Model

Crash severity is one of the most widely studied topics in traffic safety area. Scholars have studied crash severity through various types of models. Using the publicly available 2017 Maryland crash data from the Department of Maryland State Police, the authors develop a multinomial logit (MNL) model and a random forest (RF) model, which belong to discrete choice and tree-based models, respectively, to (1) identify factors contributing to crash severity and (2) compare prediction performances and interpretation abilities between the two models. Based on the model results, major contributing factors of crash severity are identified, including collision type, occupant age, and speed limit. For the given dataset, RF has a higher prediction accuracy than MNL based on multiple measures (precision, recall, and F1 score), even though the differences are not dramatic. Sensitivity analysis results show that RF is less sensitive than MNL. RF can automatically capture the non-linear effects of continuous variables and reduce the influence of collinearity relationships existing among explanatory variables. This study shows the possibility of conducting sensitivity analysis to enhance understanding of MNL and RF results, and uncovers unique characteristics of the discrete choice and tree-based models.

scholarly journals Road traffic accidents on Senai-Desaru expressway

2018 ◽  
Vol 250 ◽  
pp. 02002 ◽  
Author(s):  
Nordiana Mashros ◽  
SittiAsmah Hassan ◽  
Yaacob Haryati ◽  
Mohd Shahrir Amin Ahmad ◽  
Ismail Samat ◽  
...  
Keyword(s):  
Traffic Safety ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Contributing Factors ◽  
Property Damage ◽  
Crash Frequency ◽  
Crash Data ◽  
Run Off ◽  
Accident Data ◽  
Road Segments

Understanding and prioritising crash contributing factors is important for improving traffic safety on the expressway. This paper aims to identify the possible contributory factors that were based on findings obtained from crash data at Senai-Desaru Expressway (SDE), which is the main connector between the western and eastern parts of Johor, Malaysia. Using reported accident data, the mishaps that had occurred along the 77.2 km road were used to identify crash patterns and their possible related segment conditions. The Average Crash Frequency and Equivalent Property Damage Only Average Crash Frequency Methods had been used to identify and rank accident-prone road segments as well as to propose for appropriate simple and inexpensive countermeasures. The results show that the dominant crash type along the road stretches of SDE had consisted of run-off-road collision and property damage only crashes. All types of accidents were more likely to occur during daytime. Out of the 154 segments, the 4 most accident-prone road segments had been determined and analysed. The results obtained from the analyses suggest that accident types are necessary for identifying the possible causes of accidents and the appropriate strategies for countermeasures. Therefore, this accident analysis could be helpful to relevant authorities in reducing the number of road accidents and the level of accident severity along the SDE.

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.

scholarly journals Negative Binomial Regression Model for Road Crash Severity Prediction

2018 ◽  
Vol 12 (4) ◽  
pp. 38 ◽  
Author(s):  
Hana Naghawi
Keyword(s):  
Prediction Model ◽  
Negative Binomial ◽  
Descriptive Analysis ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Crash Severity ◽  
Contributing Factors ◽  
Factors Affecting ◽  
Crash Data ◽  
Severity Prediction

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, then the NBR technique using R-statistic software was used for the development of the crash prediction model that linked crash severities to the identified factors. The NBR model results indicated that severe crashes decreased significantly as the age of both male and female drivers increased. They significantly decreased as the environmental conditions improved. In addition, sever crashes were significantly higher during weekdays than weekends and in the morning than in the evening. The results also indicated that sever crashes significantly increased as drivers have faults while driving. In addition, mirror and brake deficits were found to be the only factors among all possible vehicle deficits factors that contributed significantly to severe crashes. Finally, it was found that the results of the NBR model are in agreement with the descriptive analysis of the crash contributing factors.

scholarly journals Analysis of Factors Affecting the Severity of Automated Vehicle Crashes Using XGBoost Model Combining POI Data

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Hengrui Chen ◽  
Hong Chen ◽  
Zhizhen Liu ◽  
Xiaoke Sun ◽  
Ruiyu Zhou
Keyword(s):  
Classification And Regression Tree ◽  
Motor Vehicles ◽  
Gradient Boosting ◽  
Crash Severity ◽  
Contributing Factors ◽  
Land Use Patterns ◽  
California Department ◽  
Model Combining ◽  
Collision Type ◽  
Extreme Gradient Boosting

The research and development of autonomous vehicle (AV) technology have been gaining ground globally. However, a few studies have performed an in-depth exploration of the contributing factors of crashes involving AVs. This study aims to predict the severity of crashes involving AVs and analyze the effects of the different factors on crash severity. Crash data were obtained from the AV-related crash reports presented to the California Department of Motor Vehicles in 2019 and included 75 uninjured and 18 injured accident cases. The points-of-interest (POI) data were collected from Google Map Application Programming Interface (API). Descriptive statistics analysis was applied to examine the features of crashes involving AVs in terms of collision type, crash severity, vehicle movement preceding the collision, and degree of vehicle damage. To compare the classification performance of different classifiers, we use two different classification models: eXtreme Gradient Boosting (XGBoost) and Classification and Regression Tree (CART). The result shows that the XGBoost model performs better in identifying the injured crashes involving AVs. Compared with the original XGBoost model, the recall and G-mean of the XGBoost model combining POI data improved by 100% and 11.1%, respectively. The main features that contribute to the severity of crashes include weather, degree of vehicle damage, accident location, and collision type. The results indicate that crash severity significantly increases if the AVs collided at an intersection under extreme weather conditions (e.g., fog and snow). Moreover, an accident resulting in injuries also had a higher probability of occurring in areas where land-use patterns are highly diverse. The knowledge gained from this research could ultimately contribute to assessing and improving the safety performance of the current AVs.

scholarly journals Investigating Rural Single-Vehicle Crash Severity by Vehicle Types Using Full Bayesian Spatial Random Parameters Logit Model

2021 ◽  
Vol 11 (17) ◽  
pp. 7819
Author(s):  
Fulu Wei ◽  
Zhenggan Cai ◽  
Zhenyu Wang ◽  
Yongqing Guo ◽  
Xin Li ◽  
...  
Keyword(s):  
Risk Factors ◽  
Spatial Correlation ◽  
Traffic Safety ◽  
Logit Model ◽  
Unobserved Heterogeneity ◽  
Crash Severity ◽  
Random Parameters ◽  
Crash Data ◽  
Random Parameters Logit ◽  
Single Vehicle

The effect of risk factors on crash severity varies across vehicle types. The objective of this study was to explore the risk factors associated with the severity of rural single-vehicle (SV) crashes. Four vehicle types including passenger car, motorcycle, pickup, and truck were considered. To synthetically accommodate unobserved heterogeneity and spatial correlation in crash data, a novel Bayesian spatial random parameters logit (SRP-logit) model is proposed. Rural SV crash data in Shandong Province were extracted to calibrate the model. Three traditional logit approaches—multinomial logit model, random parameter logit model, and random intercept logit model—were also established and compared with the proposed model. The results indicated that the SRP-logit model exhibits the best fit performance compared with other models, highlighting that simultaneously accommodating unobserved heterogeneity and spatial correlation is a promising modeling approach. Further, there is a significant positive correlation between weekend, dark (without street lighting) conditions, and collision with fixed object and severe crashes and a significant negative correlation between collision with pedestrians and severe crashes. The findings can provide valuable information for policy makers to improve traffic safety performance in rural areas.

scholarly journals Analysis on Residents’ Travel Activity Pattern in Historic Urban Areas: A Case Study of Historic Urban Area of Yangzhou, China

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Mao Ye ◽  
Miao Yu ◽  
Xiucheng Guo ◽  
Yingshun Liu ◽  
Zhibin Li
Keyword(s):  
Urban Area ◽  
Activity Pattern ◽  
Urban Areas ◽  
Activity Patterns ◽  
Primary Objective ◽  
Contributing Factors ◽  
Explanatory Variables ◽  
Local Residents ◽  
Mnl Model

Travel behaviors and activity patterns in the historic urban area of a city are expected to be different from the overall situations in the city area. The primary objective of this study is to analyze the residents’ travel activity patterns in historic urban area. Based on survey data conducted in the historic urban area of Yangzhou, the travel activities of local residents in a whole day were classified into five types of patterns. The multinomial logit (MNL) model was developed to evaluate the impacts of explanatory variables on the choices of activity patterns. The results showed that the choice of activity pattern was significantly impacted by five contributing factors including the gender, age, occupation, car ownership, and number of electric bikes in household. The other variables, which were the family population, preschoolers, number of conventional bikes in household, motorcycle ownership, and income, were found to be not significantly related to the choice of activities. The results of this study from historic urban area were compared to findings of previous studies from overall urban area. The comparison showed that the impacts of factors on activity pattern in the historic urban area were different from those in the overall area. Findings of this study provide important suggestions for the policy makings to improve the traffic situations in historic urban areas of cities.

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.

scholarly journals Analysis of Nonsevere Crashes on Two- and Four-Lane Urban and Rural Highways: Effects of Wet Pavement Surface Condition

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Aschalew Kassu ◽  
Michael Anderson
Keyword(s):  
Negative Binomial ◽  
Surface Condition ◽  
Negative Binomial Regression ◽  
Segment Length ◽  
Mean Values ◽  
Rural Highways ◽  
Explanatory Variables ◽  
Crash Data ◽  
Pavement Surface ◽  
Binomial Regression

This study examines the effects of wet pavement surface conditions on the likelihood of occurrences of nonsevere crashes in two- and four-lane urban and rural highways in Alabama. Initially, sixteen major highways traversing across the geographic locations of the state were identified. Among these highways, the homogenous routes with equal mean values, variances, and similar distributions of the crash data were identified and combined to form crash datasets occurring on dry and wet pavements separately. The analysis began with thirteen explanatory variables covering engineering, environmental, and traffic conditions. The principal terms were statistically identified and used in a mathematical crash frequency models developed using Poisson and negative binomial regression models. The results show that the key factors influencing nonsevere crashes on wet pavement surfaces are mainly segment length, traffic volume, and posted speed limits.

Sign in / Sign up

Export Citation Format

Share Document