scholarly journals Factor Identification and Prediction for Teen Driver Crash Severity Using Machine Learning: A Case Study

2020 ◽  
Vol 10 (5) ◽  
pp. 1675 ◽  
Author(s):  
Ciyun Lin ◽  
Dayong Wu ◽  
Hongchao Liu ◽  
Xueting Xia ◽  
Nischal Bhattarai
Keyword(s):  
Machine Learning ◽  
Traffic Safety ◽  
Rural Areas ◽  
Computational Cost ◽  
The United States ◽  
Crash Severity ◽  
Rural Roads ◽  
Factors Affecting ◽  
West Texas ◽  
Teen Drivers

Crashes among young and inexperienced drives are a major safety problem in the United States, especially in an area with large rural road networks, such as West Texas. Rural roads present many unique safety concerns that are not fully explored. This study presents a complete machine leaning pipeline to find the patterns of crashes involved with teen drivers no older than 20 on rural roads in West Texas, identify factors that affect injury levels, and build four machine learning predictive models on crash severity. The analysis indicates that the major causes of teen driver crashes in West Texas are teen drivers who failed to control speed or travel at an unsafe speed when they merged from rural roads to highways or approached intersections. They also failed to yield on the undivided roads with four or more lanes, leading to serious injuries. Road class, speed limit, and the first harmful event are the top three factors affecting crash severity. The predictive machine learning model, based on Label Encoder and XGBoost, seems the best option when considering both accuracy and computational cost. The results of this work should be useful to improve rural teen driver traffic safety in West Texas and other rural areas with similar issues.

scholarly journals Evaluation of Contributing Factors Affecting Number of Vehicles Involved in Crashes Using Machine Learning Techniques in Rural Roads of Cosenza, Italy

2021 ◽  
Author(s):  
Giuseppe Guido ◽  
Sina Shaffiee Haghshenas ◽  
Sami Shaffiee Haghshenas ◽  
Alessandro Vitale ◽  
Vittorio Astarita ◽  
...  
Keyword(s):  
Machine Learning ◽  
Rural Areas ◽  
Road Safety ◽  
Safety Management ◽  
Effective Parameters ◽  
Road Transportation ◽  
Contributing Factors ◽  
Rural Roads ◽  
Factors Affecting ◽  
Average Speed

Evaluation of road safety is a critical issue having to be conducted for successful safety management in road transport systems, whereas safety management is considered in road transportation systems as a challenging task according to the dynamic of this issue and the presence of a large number of effective parameters on road safety. Therefore, evaluation and analysis of important contributing factors affecting the number of crashes play a key role in increasing the efficiency of road safety. For this purpose, in this research work, two machine learning algorithms including the group method of data handling (GMDH)-type neural network and a combination of support vector machine (SVM) and the grasshopper optimization algorithm (GOA) are employed for evaluating the number of vehicles involved in the accident based on the seven factors affecting transport safety including the Daylight (DL), Weekday (W), Type of accident (TA), Location (L), Speed limit (SL), Average speed (AS) and Annual average daily traffic (AADT) of rural roads of Cosenza in southern Italy. In this study, 564 data sets of rural areas were investigated and relevant effective parameters were measured. In the next stage, several models were developed to investigate the parameters affecting the safety management of road transportation for rural areas. The results obtained demonstrated that "Average speed" has the highest level and "Weekday" has the lowest level of importance in the investigated rural area. Finally, although the results of both algorithms were the same, the GOA-SVM model showed a better degree of accuracy and robustness than the GMDH model.

scholarly journals Comparison of Crash Severity Risk Factors at Signalized and Stop-Controlled Intersections in Urban and Rural Areas in Alabama

2018 ◽  
Vol 7 (5) ◽  
pp. 50
Author(s):  
Sumalatha Kesavareddy ◽  
Kirolos Haleem ◽  
Mehrnaz Doustmohammadi ◽  
Michael Anderson
Keyword(s):  
Traffic Control ◽  
Rural Areas ◽  
Signalized Intersections ◽  
Crash Severity ◽  
Factors Affecting ◽  
Side Impacts ◽  
Binary Logit ◽  
Urban And Rural Areas ◽  
New Variables ◽  
Significant Factors

Understanding the factors that affect crash severity at intersections is essential to develop strategies to alleviate safety deficiencies. This paper identifies and compares the significant factors affecting crash severity at signalized and stop-controlled intersections in urban and rural areas in Alabama using recent five-year crashes. A random forest model was used to rank variable significance and a binary logit model was applied to identify the significant factors at both intersection types in urban and rural areas. Four separate models (urban signalized, urban stop-controlled, rural signalized, and rural stop-controlled) were developed. New variables that were not previously explored were used in this study, such as the roadway type (one-way vs. two-way) and traffic control functioning (yes or no). It was found that one-way roadways were associated with a reduction in crash severity at urban signalized intersections. In all four models, rear-end crashes showed lesser severity than side impacts. Head-on crashes, higher speed limits, and curved sections showed higher severity in urban signalized and stop-controlled intersections. In rural stop-controlled intersections, right-turning maneuvers had a severity reduction. Female drivers showed 15% and 45% higher severity likelihood (compared to males) at urban and rural signalized intersections, respectively. Strategies to alleviate crash severity are proposed.

scholarly journals Estimation of Crash Severity on Mountainous Freeways in Chongqing

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Yunwei Meng
Keyword(s):  
Traffic Safety ◽  
Traffic Accidents ◽  
Weather Conditions ◽  
Crash Severity ◽  
Driving Experience ◽  
Factors Affecting ◽  
The Past ◽  
Accident Severity ◽  
Partial Proportional Odds Model ◽  
The Times

Mountainous freeways always suffer from accidents due to special terrain, weather conditions, driving environment, and so on. Based on the records of 898 accidents that occurred on mountainous freeways in Chongqing during the past 6 years, the partial proportional odds model is used to identify the factors affecting the accident severity. The time of the accident, season, involvement of trucks, accident characteristics, speeding, maximum driving experience of involved drivers, and weather and road conditions are found to be important for the levels of accident severity. Zero to 6 a.m. and 19 to 24 p.m. are the times prone to serious traffic accidents. The probability of serious traffic accidents in summer and autumn is greater than that in spring and winter. Once a truck is involved in an accident, the consequence is often more severe. Turnover and speeding will result in a grave accident. When there is an experienced driver, the probability of serious traffic accidents is low. The fog is extremely unfavorable weather conditions. The probability of serious accident happening in the downgrade, ramp, curve, bridge, and tunnel sections is greater than the others. The results aim to provide valuable reference for traffic safety on mountainous freeways.

scholarly journals Analyzing the Factors Affecting the COVID-19 Risk Level in the US Counties

2021 ◽  
Author(s):  
Samira Ziyadidegan ◽  
Moein Razavi ◽  
Homa Pesarakli ◽  
Amir Hossein Javid
Keyword(s):  
United States ◽  
Rural Areas ◽  
Pearson Correlation ◽  
The United States ◽  
Positive Case ◽  
Risk Level ◽  
Classification Models ◽  
Factors Affecting ◽  
The Us ◽  
Us Counties

The COVID-19 disease spreads swiftly, and nearly three months after the first positive case was confirmed in China, Coronavirus started to spread all over the United States. Some states and counties reported an extremely high number of positive cases and deaths, while some reported too few COVID-19 related cases and mortality. In this paper, the factors that could affect the transmission of COVID-19 and its risk level in different counties have been determined and analyzed. Using Pearson Correlation, K-means clustering, and several classification models, the most critical ones were determined. Results showed that mean temperature, percent of people below poverty, percent of adults with obesity, air pressure, percentage of rural areas, and percent of uninsured people in each county were the most significant and effective attributes.

Analysis of Factors Affecting Injury Severity in Traffic Crashes on Arizona Tribal Lands

2019 ◽  
Vol 2673 (9) ◽  
pp. 345-354
Author(s):  
Emmanuel James ◽  
Brendan J. Russo
Keyword(s):  
Traffic Safety ◽  
Injury Severity ◽  
The United States ◽  
The State ◽  
Severe Injury ◽  
Federal State ◽  
Severe Injuries ◽  
Traffic Crashes ◽  
Factors Affecting ◽  
Tribal Lands

Reducing fatal and serious injuries sustained in traffic crashes on tribal lands is a priority of federal, state, and local agencies in the United States. In the state of Arizona, the proportion of fatal and severe injury crashes on several areas of tribal land are 4.0% higher compared with statewide statistics. There is a need to investigate why higher proportions of fatal and severe injuries are occurring on tribal lands to plan effective countermeasures aimed at improving traffic safety in these areas. This study presents an analysis of factors affecting injury severity in crashes occurring within five tribal reservations in the state of Arizona. Crash data were obtained from the Arizona Department of Transportation, and the analysis included data for 9,597 persons involved in traffic crashes on these tribal lands for the years 2010–2016. An ordered logit model with random parameters was estimated using this data to identify factors significantly associated with severe injury outcomes in the event of a crash on tribal lands. Several person-, vehicle-, roadway-, and environmental-related variables were found to impact injury severity. For instance, alcohol and safety device usage were significantly associated with severity outcomes. The results of this study have the potential to aid transportation agencies effectively plan strategies to reduce traffic crash injuries and fatalities on tribal lands, and potential countermeasures considering the 4Es of traffic safety (engineering, education, enforcement, and emergency medical services) are discussed.

International Telesupervision: The Clinical Fellowship Experience

2017 ◽  
Vol 2 (11) ◽  
pp. 73-78
Author(s):  
David W. Rule ◽  
Lisa N. Kelchner
Keyword(s):  
Graduate Students ◽  
Clinical Supervision ◽  
Rural Areas ◽  
The United States ◽  
Speech Language Pathology ◽  
Access To Services ◽  
East African ◽  
The World ◽  
Language Pathology ◽  
National Certification

Telepractice technology allows greater access to speech-language pathology services around the world. These technologies extend beyond evaluation and treatment and are shown to be used effectively in clinical supervision including graduate students and clinical fellows. In fact, a clinical fellow from the United States completed the entire supervised clinical fellowship (CF) year internationally at a rural East African hospital, meeting all requirements for state and national certification by employing telesupervision technology. Thus, telesupervision has the potential to be successfully implemented to address a range of needs including supervisory shortages, health disparities worldwide, and access to services in rural areas where speech-language pathology services are not readily available. The telesupervision experience, potential advantages, implications, and possible limitations are discussed. A brief guide for clinical fellows pursuing telesupervision is also provided.

A Commented Synopsis of the Report of the Committee for the National Tire Efficiency Study4

2007 ◽  
Vol 35 (2) ◽  
pp. 70-93
Author(s):  
Marion G. Pottinger ◽  
Joseph D. Walter ◽  
John D. Eagleburger
Keyword(s):  
Traffic Safety ◽  
Rolling Resistance ◽  
The United States ◽  
Final Report ◽  
Highway Traffic ◽  
National Academy Of Sciences ◽  
Scrap Tire ◽  
National Highway Traffic Safety ◽  
Fuel Savings ◽  
Time Required

Abstract The Congress of the United States petitioned the Transportation Research Board of the National Academy of Sciences to study replacement passenger car tire rolling resistance in 2005 with funding from the National Highway Traffic Safety Administration. The study was initiated to assess the potential for reduction in replacement tire rolling resistance to yield fuel savings. The time required to realize these savings is less than the time required for automotive and light truck fleet replacement. Congress recognized that other factors besides fuel savings had to be considered if the committee’s advice was to be a reasonable guide for public policy. Therefore, the study simultaneously considered the effect of potential rolling resistance reductions in replacement tires on fuel consumption, wear life, scrap tire generation, traffic safety, and consumer spending for tires and fuel. This paper summarizes the committee’s report issued in 2006. The authors, who were members of the multidisciplinary committee, also provide comments regarding technical difficulties encountered in the committee’s work and ideas for alleviating these difficulties in further studies of this kind. The authors’ comments are clearly differentiated so that these comments will not be confused with findings, conclusions, and recommendations developed by the committee and contained in its final report.

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