scholarly journals Effects of a Towaway Reporting Threshold on Crash Analysis Results

1998 ◽  
Vol 1635 (1) ◽  
pp. 49-57 ◽  
Author(s):  
Charles V. Zegeer ◽  
Herman F. Huang ◽  
J. Richard Stewart ◽  
Ron Pfefer ◽  
Jun Wang
Keyword(s):  
North Carolina ◽  
Crash Analysis ◽  
Organizational Strategies ◽  
Rural Roads ◽  
Crash Data ◽  
Urban Streets ◽  
Injury Threshold ◽  
Run Off ◽  
Future Data ◽  
Crash Types

The effects on future data analysis capabilities and results should states convert to a towaway and above crash-reporting threshold are quantified. The results from the four states used in the analysis (Illinois, Michigan, Minnesota, and North Carolina) revealed that only 51.7 percent of the crash data would be included using a towaway threshold. Only 33.7 percent would be included using an injury threshold. In general, a towaway threshold would exclude more crashes on urban streets than on rural roads. For most road classes, 40 to 60 percent of crashes would be excluded. A towaway threshold would result in greatly underestimating the occurrence of certain crash types, particularly rear-end, sideswipe, parking, and animal crashes. Run-off-road and angle/turning crashes would also be affected considerably. Using a towaway criterion will seriously affect researchers’ ability to conduct meaningful evaluations of roadside appurtenances, such as guardrail, breakaway signs and poles, crash cushions, and various median treatments. For most vehicle types, only 30 to 60 percent of crashes would be included under a towaway threshold. Technological, institutional, and organizational strategies for improving crash reporting thresholds are suggested.

Evaluation of Roundabout-Related Single-Vehicle Crashes

2017 ◽  
Vol 2637 (1) ◽  
pp. 17-26 ◽  
Author(s):  
Beau Burdett ◽  
Andrea R. Bill ◽  
David A. Noyce
Keyword(s):  
Negative Binomial ◽  
Vehicle Crashes ◽  
Impaired Driving ◽  
Crash Data ◽  
Road Crashes ◽  
Run Off ◽  
Traffic Volumes ◽  
Single Vehicle ◽  
Total Proportion ◽  
Crash Types

Roundabouts reduce fatal and injury crashes at intersections when converted from other intersection control types. In Wisconsin, roundabouts have been linked to a 38% decrease in fatal and injury crashes. Part of this reduction can be attributed to crash types that result in the mitigation of more serious injuries. However, the reduction comes at a cost because other crash types, such as single-vehicle collisions, may increase. Six years of crash data on 53 roundabouts in Wisconsin were examined for crash causes and geometric characteristics that affected single-vehicle crashes. Weather and impaired driving, particularly by younger drivers, were primary causes for more than half of all single-vehicle crashes at the study roundabouts. Younger drivers (18 to 24 years of age) were involved in a significantly higher proportion of single-vehicle crashes than the total proportion of licensed drivers in that age group. Younger drivers were involved in approximately one-third of all crashes that involved impaired driving and in two-thirds of all speed-related single-vehicle crashes. A negative binomial model was constructed to estimate run-off-road crashes at approaches. It was found that roundabouts with higher approach speeds and higher traffic volumes experienced more run-off-road crashes. Landscaped central islands experienced significantly lower frequencies of run-off-road crashes.

scholarly journals Examination of Fault, Unsafe Driving Acts, and Total Harm in Car-Truck Collisions

2003 ◽  
Vol 1830 (1) ◽  
pp. 63-71 ◽  
Author(s):  
Forrest M. Council ◽  
David L. Harkey ◽  
Daniel T. Nabors ◽  
Asad J. Khattak ◽  
Yusuf M. Mohamedshah
Keyword(s):  
North Carolina ◽  
Information System ◽  
Safety Information ◽  
Highway Safety ◽  
Passenger Cars ◽  
Crash Data ◽  
Fatal Crash ◽  
Fatal Crashes ◽  
Facility Type ◽  
Crash Types

Crashes involving large trucks and passenger cars are important topics for research and countermeasure development since they represent more than 60% of all fatal truck crashes and because the passenger car occupant is much more likely to be killed. This study ( a) examined “fault” in total car–truck crashes using North Carolina Highway Safety Information System (HSIS) data for comparison with fault analyzed in previous studies of fatal crashes, ( b) used general estimates system (GES) crash data to verify unsafe driving acts (UDAs) identified by expert panels in past studies, and ( c) used North Carolina HSIS data to identify critical combinations of roadway facility type, roadway location, and crash type based on “total harm”—a measure combining both the frequency and severity of the crash. Fault in total North Carolina car–truck crashes was found to differ significantly from past fatal crash studies, with the truck driver being at fault more often than the car driver both overall and in certain crash types. Car drivers continue to be at fault much more often in head-on and angle crashes. While it was not possible to analyze all UDAs identified in prior studies, when possible, the current analyses revealed differences between the GES crash data results and the expert-based results, pointing to the need for better UDA methods if they are to be used to target treatments. Finally, using the total-harm analysis with North Carolina car–truck crashes indicated that undivided rural arterials and collectors should be primary targets for further investigation and for treatment.

Prediction of Occurrence and Severity of Run-off-Roadway Crashes on Rural Two-Lane Roadways Using Bayesian Networks

2021 ◽  
pp. 036119812110513
Author(s):  
Boniphace Kutela ◽  
Raul E. Avelar ◽  
Srinivas R. Geedipally ◽  
Ankit Jhamb
Keyword(s):  
Bayesian Networks ◽  
Graphical Model ◽  
Crash Data ◽  
Factors Associated ◽  
Independent Variables ◽  
The Road ◽  
Run Off ◽  
Study Results ◽  
On The Road ◽  
Crash Types

Run-off-roadway (ROR) crashes are among the most common crash types on rural two-lane roadways. Current methodologies to predict their occurrence and severity by considering conditional nature and interactions between independent variables require complex mathematical procedures. This study employs Bayesian networks (BNs), a non-functional form graphical model, to determine factors associated with the occurrence and severity of ROR crashes. The study used five-year (2014–2018) crash data collected from 397 randomly selected road segments within Texas. Out of 397 segments, 279 did not experience ROR crashes. The first BN model used all 397 segments and explored factors associated with occurrences of ROR crashes. The second BN model used the remaining 118 segments that involved ROR crashes and focused on factors associated with different crash types (guardrail [GR], overturning [OT], and fixed object [FO] crashes) and their associated severity levels. Study results revealed that the presence of horizontal curves and utility poles within the clear zone on the road individually increased the chance of ROR crashes by about 35%. Moreover, FO crashes resulted in 36% more fatal and injury crashes than GR crashes, which showed the effectiveness of guardrails in reducing severity. This study also explored the combined influence of variables on ROR crash occurrence and severity, as well as the interrelation between several independent variables. The proposed methodology can be used to evaluate the effectiveness of countermeasures.

scholarly journals Evaluating the Safety Risk of Rural Roadsides Using a Bayesian Network Method

2019 ◽  
Vol 16 (7) ◽  
pp. 1166 ◽  
Author(s):  
Tianpei Tang ◽  
Senlai Zhu ◽  
Yuntao Guo ◽  
Xizhao Zhou ◽  
Yang Cao
Keyword(s):  
Bayesian Network ◽  
Low Cost ◽  
Road Networks ◽  
Access Point ◽  
Safety Risk ◽  
Crash Data ◽  
Risk Levels ◽  
The Road ◽  
Run Off ◽  
Point Density

Evaluating the safety risk of rural roadsides is critical for achieving reasonable allocation of a limited budget and avoiding excessive installation of safety facilities. To assess the safety risk of rural roadsides when the crash data are unavailable or missing, this study proposed a Bayesian Network (BN) method that uses the experts’ judgments on the conditional probability of different safety risk factors to evaluate the safety risk of rural roadsides. Eight factors were considered, including seven factors identified in the literature and a new factor named access point density. To validate the effectiveness of the proposed method, a case study was conducted using 19.42 km long road networks in the rural area of Nantong, China. By comparing the results of the proposed method and run-off-road (ROR) crash data from 2015–2016 in the study area, the road segments with higher safety risk levels identified by the proposed method were found to be statistically significantly correlated with higher crash severity based on the crash data. In addition, by comparing the respective results evaluated by eight factors and seven factors (a new factor removed), we also found that access point density significantly contributed to the safety risk of rural roadsides. These results show that the proposed method can be considered as a low-cost solution to evaluating the safety risk of rural roadsides with relatively high accuracy, especially for areas with large rural road networks and incomplete ROR crash data due to budget limitation, human errors, negligence, or inconsistent crash recordings.

scholarly journals Detection and molecular characterization of swine hepatitis E virus in North Carolina swine herds and their faecal wastes

2009 ◽  
Vol 7 (2) ◽  
pp. 344-357 ◽  
Author(s):  
Julie A. Kase ◽  
Maria T. Correa ◽  
Mark D. Sobsey
Keyword(s):  
North Carolina ◽  
Hepatitis E Virus ◽  
Liquid Waste ◽  
Pcr Primers ◽  
Hepatitis E ◽  
Land Application ◽  
Run Off ◽  
Management Procedures ◽  
Swine Operations ◽  
Exposure Risks

Recent findings of almost genetically indistinguishable swine and human strains, have suggested swine play a role in the transmission of hepatitis E virus (HEV). The extent to which HEV may be present and persist in the faecal waste generated from intensive swine operations is largely unknown. The fate of swine waste liquid is often land application, possibly resulting in unintentional seepage into groundwater or run-off into surface waters, hence validating concerns of human exposure risks. Freshly passed swine faeces, barn flush liquid waste, and lagoon liquid from production sites in North Carolina were surveyed periodically for HEV using RT-PCR primers located in ORF2. On three farms where HEV RNA was detected in swine faeces, it was also found in stored liquid waste on several occasions. HEV presence was related to swine age but not to animal management and waste management procedures, which varied amongst the farms. Seasonal patterns of HEV prevalence could not be established as viral RNA was isolated at all time points from two farms. Phylogenetic analysis of 212 bases of the genomic RNA indicated that isolates resembled the known US swine and human strains (percentage nucleic acid homology 91 to 94%), with one amino acid substitution.

scholarly journals Safety Impacts of Converting Two-Way Left-Turn Lanes to Raised Medians and Associated Design Concerns

2016 ◽  
Author(s):  
Priyanka Alluri ◽  
Albert Gan ◽  
Kirolos Haleem
Keyword(s):  
Data Availability ◽  
Left Turn ◽  
Arterial Streets ◽  
Crash Data ◽  
Potential Safety ◽  
Before And After ◽  
Show Evidence ◽  
Crash Types ◽  
Associated Design ◽  
Urban Arterial

Raised medians and two-way left-turn lanes (TWLTLs) are the two most common types of median treatments on arterial streets. This paper aims to conduct a detailed study on the safety impacts of conversion from TWLTLs to raised medians on state roads in Florida. In addition, the study also investigated several potential safety concerns related to raised medians on state roads, including crashes at median openings, vehicles directly hitting the median curb, and median crossover crashes. Based on data availability, 17.51 miles of urban arterial sections in Florida that were converted from TWLTLs to raised medians were analyzed. Police reports of all the crashes before and after median conversion were reviewed to correct miscoded crash types and obtain additional detailed crash information. Overall, a 28.5% reduction in total crash rate was observed after the 10 study locations were converted from TWLTLs to raised medians. The reductions in the proportions of left-turn and right-turn crashes were statistically significant, while the changes in the proportions of other crash types were not statistically significant. Furthermore, the crash data did not show evidence that raised medians are an additional hazard compared with TWLTLs.

scholarly journals Association between Crash Attributes and Drivers’ Crash Involvement: A Study Based on Police-Reported Crash Data

2020 ◽  
Vol 17 (23) ◽  
pp. 9020
Author(s):  
Guofa Li ◽  
Weijian Lai ◽  
Xingda Qu
Keyword(s):  
Logistic Regression ◽  
Age Groups ◽  
Nearest Neighbors ◽  
Older Drivers ◽  
Driving Safety ◽  
Crash Data ◽  
Imbalance Problem ◽  
Different Types ◽  
Crash Types ◽  
Safety Enhancement

Understanding the association between crash attributes and drivers’ crash involvement in different types of crashes can help figure out the causation of crashes. The aim of this study was to examine the involvement in different types of crashes for drivers from different age groups, by using the police-reported crash data from 2014 to 2016 in Shenzhen, China. A synthetic minority oversampling technique (SMOTE) together with edited nearest neighbors (ENN) were used to solve the data imbalance problem caused by the lack of crash records of older drivers. Logistic regression was utilized to estimate the probability of a certain type of crashes, and odds ratios that were calculated based on the logistic regression results were used to quantify the association between crash attributes and drivers’ crash involvement in different types of crashes. Results showed that drivers’ involvement patterns in different crash types were affected by different factors, and the involvement patterns differed among the examined age groups. Knowledge generated from the present study could help improve the development of countermeasures for driving safety enhancement.

Calibration of inertial consistency models on North Carolina two-lane rural roads

2019 ◽  
Vol 127 ◽  
pp. 236-245 ◽  
Author(s):  
David Llopis-Castelló ◽  
Daniel J. Findley ◽  
Francisco Javier Camacho-Torregrosa ◽  
Alfredo García
Keyword(s):  
North Carolina ◽  
Rural Roads ◽  
Consistency Models

scholarly journals Understanding Multi-Vehicle Collision Patterns on Freeways—A Machine Learning Approach

2020 ◽  
Vol 5 (8) ◽  
pp. 62
Author(s):  
Clint Morris ◽  
Jidong J. Yang
Keyword(s):  
Machine Learning ◽  
Statistical Methods ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Learning Approaches ◽  
Crash Analysis ◽  
Suitable Alternative ◽  
Crash Data ◽  
Extreme Gradient Boosting ◽  
Modern Machine

Generating meaningful inferences from crash data is vital to improving highway safety. Classic statistical methods are fundamental to crash data analysis and often regarded for their interpretability. However, given the complexity of crash mechanisms and associated heterogeneity, classic statistical methods, which lack versatility, might not be sufficient for granular crash analysis because of the high dimensional features involved in crash-related data. In contrast, machine learning approaches, which are more flexible in structure and capable of harnessing richer data sources available today, emerges as a suitable alternative. With the aid of new methods for model interpretation, the complex machine learning models, previously considered enigmatic, can be properly interpreted. In this study, two modern machine learning techniques, Linear Discriminate Analysis and eXtreme Gradient Boosting, were explored to classify three major types of multi-vehicle crashes (i.e., rear-end, same-direction sideswipe, and angle) occurred on Interstate 285 in Georgia. The study demonstrated the utility and versatility of modern machine learning methods in the context of crash analysis, particularly in understanding the potential features underlying different crash patterns on freeways.

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.

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