Integrating Crime and Traffic Crash Data in Nashville

2010 ◽  
Author(s):  
Jason Wyatt ◽  
Michael Alexander
Keyword(s):  
Traffic Crash ◽  
Crash Data

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.

Safety of Freeway Service Patrol Operations: Case Study of Florida Road Rangers

2018 ◽  
Vol 2672 (33) ◽  
pp. 31-41 ◽  
Author(s):  
Grady Carrick ◽  
Sivaramakrishnan Srinivasan ◽  
Khajonsak Jermprapai
Keyword(s):  
Drug Use ◽  
Incident Management ◽  
Work Zones ◽  
Traffic Crashes ◽  
Traffic Incidents ◽  
Traffic Crash ◽  
Crash Data ◽  
Time Period ◽  
Suitable Case

Safety service patrols are a proven strategy to mitigate the effects of traffic incidents through quick clearance, incident management, and assistance to other incident responders like police, fire, emergency medical services, and towing. As encountered by other responders, working on or near roadways presents unique hazards for safety service patrol vehicles and operators. Road Rangers are Florida’s branded safety service patrols and, as a mature program with over 100 beats, a suitable case study for safety. This research combined an analysis of Road Ranger traffic crash data for 3 years with a comprehensive safety survey of more than 200 operators to determine safety characteristics related to service patrols. Comparing 200 Road Ranger traffic crashes from 2014 through 2016 with all Florida freeway crashes for the same time period revealed that Road Ranger crashes are five times more likely to involve a parked vehicle, and involve two or more vehicles 95% of the time. Pedestrian involvement, nighttime, shoulder locations, and work zones have higher representation for Road Ranger crashes, but weather is not a factor. Alcohol is three times more likely, drug use five times more likely, and distraction slightly higher when Road Ranger vehicles are struck. A survey of 217 Road Ranger drivers revealed that they are keenly aware of important safety topics like high-visibility safety apparel, non-traffic side vehicle approaches, and the dangers of working where there is limited lateral buffer space. Drivers overwhelmingly believe that they have the training and equipment necessary to do their jobs safely.

Making traffic crash data useful through rapid record entry and analysis

1997 ◽  
Vol 28 (4) ◽  
pp. 221-231
Author(s):  
John R. Mattox ◽  
William O. Dwyer ◽  
Frank C. Leeming
Keyword(s):  
Traffic Crash ◽  
Crash Data

scholarly journals Validation of Simulated Safety Indicators with Traffic Crash Data

2020 ◽  
Vol 12 (3) ◽  
pp. 925 ◽  
Author(s):  
Borja Alonso ◽  
Vittorio Astarita ◽  
Luigi Dell’Olio ◽  
Vincenzo Pasquale Giofrè ◽  
Giuseppe Guido ◽  
...  
Keyword(s):  
Road Safety ◽  
Simulated Data ◽  
Real Data ◽  
Road Accidents ◽  
Traffic Crash ◽  
Crash Data ◽  
Statistical Comparison ◽  
Accident Data ◽  
Driver Distractions ◽  
The City

The purpose of this document is to validate a new methodology useful for the estimation of road accidents resulting from possible driver distractions. This was possible through a statistical comparison made between real accident data between 2016 and 2018 in the city of Santander (Spain) and simulated data resulting from the application of the methodology on two areas of study. The methodology allows us to evaluate possible collisions starting from the knowledge of vehicular trajectories extrapolated from microsimulation. Studies show that there are good correlations between the real data and the simulated data. The results obtained show that the proposed methodology can be considered reliable and, therefore, it could be of fundamental importance for designers, since it would simplify the choice between different possible intervention scenarios, determining which is the least risky in terms of road safety.

Safety Evaluation and Improvements for Highway Intersections

2008 ◽  
Vol 2060 (1) ◽  
pp. 46-52 ◽  
Author(s):  
Li Yuan ◽  
Jian Lu
Keyword(s):  
Traffic Control ◽  
Cost Benefit Analysis ◽  
Safety Evaluation ◽  
Cost Benefit ◽  
Safety Performance ◽  
Crash Analysis ◽  
Traffic Crash ◽  
Crash Data ◽  
Intersection Safety ◽  
Traffic Signal Timing

Intersection safety is one of the most important issues in transportation. Traffic crash analysis—the most popular method to evaluate or assess the safety performance of an intersection—has been used for a long time. However, it is based on a lot of crash data, which need to be accumulated over a long period. In addition, traffic crashes sometimes occur randomly as a result of human driving behavior. Therefore, without sufficient data and crash history, traffic crash analysis may not give an overall evaluation of an intersection's safety performance. This paper introduces an approach to evaluating highway intersection safety performance. It is fully based on the existing conditions of the intersection, including geometrics, sight distance, pavement surface conditions, traffic control devices, traffic signal timing, and phasing. The non-accident-based approach is based on field surveys under the conditions mentioned previously. The approach will also result in a safety index to indicate the safety performance of the intersection. Corresponding countermeasures are ranked and recommended based on cost–benefit analysis. This paper is based on research results from part of a project (entitled Safety Design of Highway Intersections) sponsored by the China Department of Transportation. In this paper, the approach (called a diagnostic approach) is practically applied to evaluate the safety performance of some intersections in Shan Dong Province. Results from the real application indicate that the approach has good applicability and can be used by field safety engineers in real applications.

scholarly journals Investigating the Relationship between Traffic Violations and Crashes at Signalized Intersections: An Empirical Study in China

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Ze-Hao Jiang ◽  
Xiao-Guang Yang ◽  
Tuo Sun ◽  
Tao Wang ◽  
Zheng Yang
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Multiple Linear Regression Model ◽  
Signalized Intersections ◽  
Traffic Light ◽  
Traffic Crashes ◽  
Traffic Crash ◽  
Crash Data ◽  
Traffic Violations ◽  
The Relationship

About 90% of traffic crashes are caused by human factors, within which traffic violations are one of the most typical and common causes. In order to investigate the relationship between traffic violations and traffic crashes, this research targets signalized intersections in two Chinese cities: Yinchuan and Suqian. Thirty-one intersections are selected as the research sites, and additionally, the traffic volume, traffic violation, and traffic crash data of each intersection are collected for one year. A White’s test is conducted to test the homoscedasticity of the data and a multiple linear regression model is employed to investigate the relationship between traffic crashes and violations. The results show the following: (1) although the research sites are located in two different cities, the data is homoscedastic, which suggests that the above result may be statistically stable between different cities. (2) There is a significant multiple linear regression relationship (R2 = 0.782, adjusted R2 = 0.716) between the total number of traffic crashes and traffic violations. Among the chosen 7 independent variables, four are significantly related to the dependent variable, namely, driving commercial vehicle during internship, wrong-way entry, speeding, and traffic-light violation. (3) With the increase of annual average daily traffic (AADT), the number of total crashes goes up; however, the injury-or-fatality rate decreases, which means that intersections with smaller traffic volumes tend to have higher traffic crash severity. Based on the above conclusions, it is possible to conduct more targeted enforcement to improve the safety of intersections.

Children in Alcohol-Related Motor Vehicle Crashes

PEDIATRICS ◽  
1986 ◽  
Vol 77 (6) ◽  
pp. 870-872
Author(s):  
Lewis H. Margolis ◽  
Jonathan Kotch ◽  
John H. Lacey
Keyword(s):  
North Carolina ◽  
Health Care Providers ◽  
Motor Vehicle ◽  
Motor Vehicle Crashes ◽  
Vehicle Crashes ◽  
Drinking And Driving ◽  
Care Providers ◽  
Traffic Crash ◽  
Crash Data ◽  
Alcohol Control

Review of North Carolina traffic crash data revealed that alcohol use, although associated with 7.9% of motor vehicle crashes involving children, accounted for 15.4% of the motor vehicle-related deaths and 10.4% of the injuries. The largest proportion of these deaths were child passengers in a vehicle in which the driver had been drinking, followed by child passengers in multiple-vehicle crashes in which the other driver had been drinking. The smallest proportion of deaths were child pedestrians. These findings suggest that, in addition to supporting more stringent alcohol control legislation, health care providers should be admonishing parents about the deadly hazards of drinking and driving to the children in their care.

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