Determining Road Crash Severity from Police First Information Reports

2022 ◽  
Author(s):  
Kamlesh Kumar Ahirwar ◽  
Om Mishra ◽  
Gitakrishnan Ramadurai
Keyword(s):  
Crash Severity ◽  
Road Crash

scholarly journals Investigation of Contributing Factors to Traffic Crash Severity in Southeast Texas Using Multiple Correspondence Analysis

2021 ◽  
Vol 32 (4) ◽  
pp. 15-28
Author(s):  
Guanlong Li ◽  
Yueqing Li ◽  
Yalong Li ◽  
Brian Craig ◽  
Xing Wu
Keyword(s):  
Correspondence Analysis ◽  
Multiple Correspondence Analysis ◽  
Extreme Weather ◽  
Road Surface ◽  
Subtropical Climate ◽  
Crash Severity ◽  
Contributing Factors ◽  
Traffic Crash ◽  
Road Crash ◽  
Southeast Texas

Driving is the essential means of travel in Southeast Texas, a highly urbanized and populous area that serves as an economic powerhouse of the whole state. However, driving in Southeast Texas is subject to many risks as this region features a typical humid subtropical climate with long hot summers and short mild winters. Local drivers would encounter intense precipitation, heavy fog, strong sunlight, standing water, slick road surface, and even frequent extreme weather such as tropical storms, hurricanes and flood during their year-around travels. Meanwhile, research has revealed that the fatality rate per 100 million vehicle miles driven in urban Texas became considerably higher than national average since 2010, and no conclusive study has elucidated the association between Southeast Texas crash severity and potential contributing factors. This study used multiple correspondence analysis (MCA) to examine a group of contributing factors on how their combinatorial influences determine crash severity by creating combination clouds on a factor map. Results revealed numerous significant combinatorial effects. For example, driving in rain and extreme weather on a wet road surface has a higher chance in causing crashes that incur severe or deadly injuries. Besides, other contributing factors involving risky behavioral factors, road designs, and vehicle factors were well discussed. The research outcomes could inspire local traffic administration to take more effective countermeasures to systematically mitigate road crash severity.

Safety Impacts at Intersections on Curved Segments

2005 ◽  
Vol 1908 (1) ◽  
pp. 130-140 ◽  
Author(s):  
Peter T. Savolainen ◽  
Andrew P. Tarko
Keyword(s):  
High Speed ◽  
Negative Binomial ◽  
National Standards ◽  
Crash Severity ◽  
Department Of Transportation ◽  
Crash Frequency ◽  
Lighting Conditions ◽  
Fatal Crashes ◽  
Road Crash ◽  
Research Findings

Indiana geometric design policy, consistent with national standards, allows for the design of intersections on superelevated curves if other solutions are prohibitively expensive. Consequently, the Indiana Department of Transportation (DOT) has built a number of such intersections. Following a series of fatal crashes at one of these intersections, Indiana DOT made a decision to avoid designing intersections on segments with steep superelevation. This design restriction calls for expensive alternatives, such as realigning roads or adding grade separations. This research was done to determine whether superelevated intersections were more hazardous than similar intersections located on tangents and, if so, to determine what combination of factors made this true. The research focused on two-way stop-controlled intersections where the mainline was a high-speed four-lane divided highway located on a superelevated curve. An attempt was made to analyze as many factors as possible by using appropriate comparison techniques. Negative binomial models were developed to determine the statistical relationship between crash occurrence and intersection geometric characteristics, including curvature of the main road. Crash severity and the joint impact of curvature with weather and lighting conditions were examined by using binomial comparisons of proportions. Research findings show significant increases in crash frequency and severity at intersections located on superelevated curves.

scholarly journals Crash Severity Analysis of Highways Based on Multinomial Logistic Regression Model, Decision Tree Techniques and Artificial Neural Network: A Modeling Comparison

2021 ◽  
Vol 13 (10) ◽  
pp. 5670
Author(s):  
Gholamreza Shiran ◽  
Reza Imaninasab ◽  
Razieh Khayamim
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Decision Tree ◽  
Multinomial Logistic Regression ◽  
Crash Severity ◽  
Traffic Crash ◽  
Road Crash ◽  
Mlr Model

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 class of crash severity, including property damage only (PDO), fatality, and injury by applying data mining models, the present study sought to predict crash frequency according to five severity levels of PDO, fatality, severe injury, other visible injuries, and complaint of pain. The multinomial logistic regression (MLR) model and data mining approaches, including artificial neural network-multilayer perceptron (ANN-MLP) and two decision tree techniques, (i.e., Chi-square automatic interaction detector (CHAID) and C5.0) are utilized based on traffic crash records for State Highways in California, USA. The comparison of the findings of the relative importance of ten qualitative and ten quantitative independent variables incorporated in CHAID and C5.0 indicated that the cause of the crash (X1) and the number of vehicles (X5) were known as the most influential variables involved in the crash. However, the cause of the crash (X1) and weather (X2) were identified as the most contributing variables by the ANN-MLP model. In addition, the MLR model showed that the driver’s age (X11) accounts for a larger proportion of traffic crash severity. Therefore, the sensitivity analysis demonstrated that C5.0 had the best performance for predicting road crash severity. Not only did C5.0 take a shorter time (0.05 s) compared to CHAID, MLP, and MLR, it also represented the highest accuracy rate for the training set. The overall prediction accuracy based on the training data was approximately 88.09% compared to 77.21 and 70.21% for CHAID and MLP models. In general, the findings of this study revealed that C5.0 can be a promising tool for predicting road crash severity.

scholarly journals The Model of Severity Prediction of Traffic Crash on the Curve

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Jian-feng Xi ◽  
Hai-zhu Liu ◽  
Wei Cheng ◽  
Zhong-hao Zhao ◽  
Tong-qiang Ding
Keyword(s):  
Logistic Regression ◽  
Likelihood Estimation ◽  
Crash Severity ◽  
Traffic Crashes ◽  
Traffic Crash ◽  
Contributory Factors ◽  
Severity Prediction ◽  
Traffic Environment ◽  
Road Crash ◽  
Road Segments

With the study of traffic crashes on curved road segments as the focus of research, a logistic regression based curve road crash severity prediction model was established based on a sample crash database of 20000 entries collected from 4 regions of China and 15 evaluation indicators involving driver, driving environment, and traffic environment factors. Maximum Likelihood Estimation and step-back technique were deployed for data analysis, the conclusion of which is that the three main contributory factors on curve road crash severity are weather, roadside protection facility, and pavement structure. Hosmer and Lemeshow tests were used to verify the reliability of the model, and the model variables were discussed to a certain degree as well.

scholarly journals Crash severity analysis of vulnerable road users using machine learning

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255828
Author(s):  
Md Mostafizur Rahman Komol ◽  
Md Mahmudul Hasan ◽  
Mohammed Elhenawy ◽  
Shamsunnahar Yasmin ◽  
Mahmoud Masoud ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Crash Severity ◽  
Classification Models ◽  
Road Users ◽  
Road Crash ◽  
Vulnerable Road Users ◽  
Severity Levels

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 severity outcomes for these VRU groups is estimated in identifying the similarities and differences across different important features associated with different VRU groups. The crash data for the study is sourced from the state of Queensland in Australia for the years 2013 through 2019. The supervised machine learning algorithms considered for the empirical analysis includes the K-Nearest Neighbour (KNN), Support Vector Machine (SVM) and Random Forest (RF). In these models, 17 distinct road crash parameters are considered as input features to train models, which originate from road user characteristics, weather and environment, vehicle and driver condition, period, road characteristics and regions, traffic, and speed jurisdiction. These classification models are separately trained and tested for individual and unified VRU to assess crash severity levels. Afterwards, model performances are compared with each other to justify the best classifier where Random Forest classification models for all VRU modes are found to be comparatively robust in test accuracy: (motorcyclist: 72.30%, bicyclist: 64.45%, pedestrian: 67.23%, unified VRU: 68.57%). Based on the Random Forest model, the road crash features are ranked and compared according to their impact on crash severity classification. Furthermore, a model-based partial dependency of each road crash parameters on the severity levels is plotted and compared for each individual and unified VRU. This clarifies the tendency of road crash parameters to vary with different VRU crash severity. Based on the outcome of the comparative analysis, motorcyclists are found to be more likely exposed to higher crash severity, followed by pedestrians and bicyclists.

ADHD is a Risk Factor of Road Crashes: A Purposive Study of Finding Relation between ADHD and Road Traffic Accident in Bangladesh

2018 ◽  
Vol 9 (08) ◽  
pp. 20531-20536
Author(s):  
Nusrat Shamima Nur ◽  
M. S. l. Mullick ◽  
Ahmed Hossain
Keyword(s):  
Risk Factor ◽  
Road Traffic Accident ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Fatality Rate ◽  
Road Traffic Accidents ◽  
Road Crashes ◽  
Road Crash ◽  
Day By Day

Background: In Bangladesh fatality rate due to road traffic accidents is rising sharply day by day. At least 2297 people were killed and 5480 were injured in road traffic accidents within 1st six months of 2017.Whereas in the previous year at 2016 at least 1941 people were killed and 4794 were injured within the 1st six months. No survey has been reported in Bangladesh yet correlating ADHD as a reason of impulsive driving which ends up in a road crash.

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