scholarly journals The Impact of Aggressive Driving Behavior on Driver-Injury Severity at Highway-Rail Grade Crossings Accidents

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Changxi Ma ◽  
Wei Hao ◽  
Wang Xiang ◽  
Wei Yan
Keyword(s):  
Open Space ◽  
Injury Severity ◽  
Mixed Logit ◽  
Weather Conditions ◽  
Driving Behavior ◽  
Aggressive Driving ◽  
Driving Behaviors ◽  
Grade Crossings ◽  
High Level ◽  
The Impact

The effect of aggressive driving behavior on driver’s injury severity is analyzed by considering a comprehensive set of variables at highway-rail grade crossings in the US. In doing so, we are able to use a mixed logit modelling approach; the study explores the determinants of driver-injury severity with and without aggressive driving behaviors at highway-rail grade crossings. Significant differences exist between drivers’ injury severity with and without aggressive driving behaviors at highway-rail grade crossings. The level of injury for younger male drivers increases a lot if they are with aggressive driving behavior. In addition, driving during peak-hour is found to be a statistically significant predictor of high level injury severity with aggressive driving behavior. Moreover, environmental factors are also found to be statistically significant. The increased level of injury severity accidents happened for drivers with aggressive driving behavior in the morning peak (6-9 am), and the probability of fatality increases in both snow and fog condition. Driving in open space area is also found to be a significant factor of high level injury severity with aggressive driving behaviors. Bad weather conditions are found to increase the probability of drivers’ high level injury severity for drivers with aggressive driving behaviors.

scholarly journals Analyzing Severity of Vehicle Crashes at Highway-Rail Grade Crossings: Multinomial Logit Modeling

2015 ◽  
Author(s):  
Wei (David) Fan ◽  
Martin R. Kane ◽  
Elias Haile
Keyword(s):  
Logit Model ◽  
High Speed ◽  
Injury Severity ◽  
Weather Conditions ◽  
Multinomial Logit Model ◽  
The United States ◽  
Multinomial Logit ◽  
Vehicle Crashes ◽  
Grade Crossings ◽  
The Impact

The purpose of this paper is to develop a nominal response multinomial logit model (MNLM) to identify factors that are important in making an injury severity difference and to explore the impact of such explanatory variables on three different severity levels of vehicle-related crashes at highway-rail grade crossings (HRGCs) in the United States. Vehicle-rail and pedestrian-rail crash data on USDOT highway-rail crossing inventory and public crossing sites from 2005 to 2012 are used in this study. A multinomial logit model is developed using SAS PROC LOGISTICS procedure and marginal effects are also calculated. The MNLM results indicate that when rail equipment with high speed struck a vehicle, the chance of a fatality resulting increased. The study also reveals that vehicle pick-up trucks, concrete, and rubber surfaces were more likely to be involved in more severe crashes. On the other hand, truck-trailer vehicles in snow and foggy weather conditions, development area types (residential, commercial, industrial, and institutional), and higher daily traffic volumes were more likely to be involved in less severe crashes. Educating and equipping drivers with good driving habits and short-term law enforcement actions, can potentially minimize the chance of severe vehicle crashes at HRGCs.

scholarly journals Electric Bicyclist Injury Severity during Peak Traffic Periods: A Random-Parameters Approach with Heterogeneity in Means and Variances

2021 ◽  
Vol 18 (21) ◽  
pp. 11131
Author(s):  
Tong Zhu ◽  
Zishuo Zhu ◽  
Jie Zhang ◽  
Chenxuan Yang
Keyword(s):  
Traffic Control ◽  
Injury Severity ◽  
Safety Management ◽  
Management Strategies ◽  
Mixed Logit ◽  
Random Parameters ◽  
Severe Injuries ◽  
Crash Data ◽  
Electric Bicycle ◽  
The Impact

Accidents involving electric bicycles, a popular means of transportation in China during peak traffic periods, have increased. However, studies have seldom attempted to detect the unique crash consequences during this period. This study aims to explore the factors influencing injury severity in electric bicyclists during peak traffic periods and provide recommendations to help devise specific management strategies. The random-parameters logit or mixed logit model is used to identify the relationship between different factors and injury severity. The injury severity is divided into four categories. The analysis uses automobile and electric bicycle crash data of Xi’an, China, between 2014 and 2019. During the peak traffic periods, the impact of low visibility significantly varies with factors such as areas with traffic control or without streetlights. Furthermore, compared with traveling in a straight line, three different turnings before the crash reduce the likelihood of severe injuries. Roadside protection trees are the most crucial measure guaranteeing riders’ safety during peak traffic periods. This study reveals the direction, magnitude, and randomness of factors that contribute to electric bicycle crashes. The results can help safety authorities devise targeted transportation safety management and planning strategies for peak traffic periods.

scholarly journals Misconceptions in NFL Injuries: A Study on the Achilles

2019 ◽  
Vol 4 (4) ◽  
pp. 2473011419S0035
Author(s):  
Lauren V. Ready ◽  
Neill Y. Li ◽  
Samantha J. Worobey ◽  
Nicholas J. Lemme ◽  
JaeWon Yang ◽  
...  
Keyword(s):  
National Football League ◽  
Weather Conditions ◽  
Analysis Data ◽  
American Football ◽  
Injury Incidence ◽  
Field Representation ◽  
Career Length ◽  
Significant Difference ◽  
High Level ◽  
The Impact

Category: Sports, Trauma, Ankle, Achilles Introduction/Purpose: Injuries are an ever-present entity in the National Football League, with recent research highlighting American football with the highest injury incidence among all major sports. A torn Achilles can sideline a player for six to twelve months and reduce their power rankings by over fifty percent. Within Achilles tears, there was a focus on comparing rookie rates to the rest of the players, examining tear rates for different game conditions and studying the day of the week the injury occurred. Due to the impact of the injury and limited research, we sought to examine Achilles tears in the NFL from 2009-2016 to identify trends correlating tears with game and player demographics. Methods: NFL players with a diagnosed Achilles tear between 2009 and 2016 were selected as the study population for this retrospective analysis. Data on NFL injury was collected from an established database, previously comprised of publicly available athlete information. NFL player profiles were then employed to determine position, team and game statistics at time of injury. Injury rates were calculated as a percentage of total league games on Thursdays and Sundays. The proportion of rookies in the NFL was approximated by summing the number of draft picks and the number of signed, undrafted free agents and measured against the total number of roster spots before the commencement of the season. Game surface was discerned at time of injury by consulting a timeline of the field surfaces and cross referencing the date of the game. Game conditions, such as weather and temperature, were discerned from the game logs published on the NFL website. Results: There were 101 documented Achilles tears. Sixty-four percent (65/101) occurred before the official season, in training or pre-season games. Only 1% (1/101) of tears occurring during post-season play-offs. Twenty-nine percent (19/65) of the pre- season tears occurred in rookies and 97% (35/36) of the in-season game tears affected non-rookies. Thirty-six percent (36/101) of all documented tears occurred in undrafted free agents. Of players with Achilles tear, 58.41% (59/101) returned to play in the NFL after injury. Despite an average age of 26.7 years, the tear distribution was bimodal with players, ages 24 and 36, exhibited the highest rates of tear. With regard to tears during games, 43.18% occurred on grass and 56.82% occurred on turf. These values mirror their field representation in games. The average game temperature was 67.04 degrees Fahrenheit with wide stratification (range: 1-91 degrees). When examining rate of tears for players during away versus home games, there was not a significant difference of note; of the 45 in-game tears, 21 (46.67%) occurred in home games and 24 (53.33%) during away games. Conclusion: In our focused analysis of the Achilles in NFL athletes, we show no significant difference in tear rates when comparing grass and artificial turf surfaces and in comparing Thursday and Sunday games. When reviewing experience level, a large percent of the tears occurred in rookie players, especially during the pre-season, despite these players making up less than a quarter of the athletes. We also show that tears were not restricted to certain weather conditions. When analyzing career length post tear, most players that returned to play continued to perform at a high level. This challenges the perception of AT tear as a career-ending injury.

Preliminary Investigation of the Effectiveness of High-Visibility Crosswalks on Pedestrian Safety Using Crash Surrogates

2017 ◽  
Vol 2659 (1) ◽  
pp. 182-191 ◽  
Author(s):  
M. Tawfiq Sarwar ◽  
Grigorios Fountas ◽  
Courtney Bentley ◽  
Panagiotis C. Anastasopoulos ◽  
Alan Blatt ◽  
...  
Keyword(s):  
New York ◽  
Unobserved Heterogeneity ◽  
Preliminary Investigation ◽  
Mixed Logit ◽  
Weather Conditions ◽  
Pedestrian Safety ◽  
Test Site ◽  
Driving Behavior ◽  
Preliminary Evaluation ◽  
Linear Regression Models

This paper, with the use of data from the SHRP 2 naturalistic driving study, provides a preliminary evaluation of the effectiveness of high-visibility crosswalks (HVCs) in improving pedestrian safety at un-controlled locations. This evaluation was accomplished by analyzing the driving behavior of SHRP 2 participants at three uncontrolled locations at the Erie County, New York, test site. In this context, crash surrogates (i.e., speed, acceleration, throttle pedal actuation, and brake application) were used to evaluate the participants’ driving behavior, primarily on the basis of data from before and after the HVC installation. The before–after analysis allowed the assessment of HVC effectiveness in driver behavior modification. Mixed logit and random parameters linear regression models were estimated, and panel effects and unobserved heterogeneity were accounted for. Several factors were explored and controlled for (e.g., vehicle and driver characteristics, roadside environment, weather conditions), and the preliminary exploratory results show that HVCs can improve pedestrian safety and positively modify driving behavior.

scholarly journals DrivingSense: Dangerous Driving Behavior Identification Based on Smartphone Autocalibration

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Chunmei Ma ◽  
Xili Dai ◽  
Jinqi Zhu ◽  
Nianbo Liu ◽  
Huazhi Sun ◽  
...  
Keyword(s):  
Estimation Error ◽  
Drunk Driving ◽  
Speed Control ◽  
Driving Behavior ◽  
Sensor Reading ◽  
Driving Behaviors ◽  
Direction Change ◽  
Dangerous Driving ◽  
Filter Noise ◽  
The Impact

Since pervasive smartphones own advanced computing capability and are equipped with various sensors, they have been used for dangerous driving behaviors detection, such as drunk driving. However, sensory data gathered by smartphones are noisy, which results in inaccurate driving behaviors estimations. Some existing works try to filter noise from sensor readings, but usually only the outlier data are filtered. The noises caused by hardware of the smartphone cannot be removed from the sensor reading. In this paper, we propose DrivingSense, a reliable dangerous driving behavior identification scheme based on smartphone autocalibration. We first theoretically analyze the impact of the sensor error on the vehicle driving behavior estimation. Then, we propose a smartphone autocalibration algorithm based on sensor noise distribution determination when a vehicle is being driven. DrivingSense leverages the corrected sensor parameters to identify three kinds of dangerous behaviors: speeding, irregular driving direction change, and abnormal speed control. We evaluate the effectiveness of our scheme under realistic environments. The results show that DrivingSense, on average, is able to detect the driving direction change event and abnormal speed control event with 93.95% precision and 90.54% recall, respectively. In addition, the speed estimation error is less than 2.1 m/s, which is an acceptable range.

The impact of aggressive driving behaviors on multi-lane highway traffic flow

2018 ◽  
Vol 29 (07) ◽  
pp. 1850056 ◽  
Author(s):  
H. B. Zhu ◽  
G. Y. Chen ◽  
H. Lin ◽  
Y. J. Zhou
Keyword(s):  
Traffic Flow ◽  
High Speed ◽  
Aggressive Driving ◽  
Highway Traffic ◽  
Lane Changing ◽  
Car Following ◽  
Driving Behaviors ◽  
Evolution Pattern ◽  
Proposed Model ◽  
The Impact

A modified cellular automata traffic model is proposed to simulate four-lane traffic flow, in which drivers are classified into aggressive drivers and cautious drivers and the anticipative velocity of the adjacent vehicles is considered. Analysis from the vehicles’ evolution pattern indicates that vehicles driven by the aggressive drivers are more powerful in behaviors of lane-changing and car-following. The model is refined by using the small cell of one meter long in order to simulate the traffic flow meticulously and realistically. The results indicate that the lane-changing maneuver exhibits different property as the density varies, and it does have a significant impact on the characteristics of the surrounding traffic flow due to their interfering effects on the following vehicles. Furthermore, the phenomenon of high-speed car-following is exhibited, and the results coincide with the empirical data very well. It is shown that the proposed model is reasonable and can partially reflect the real traffic.

Analyzing the Injury Severity Sustained by Non-Motorists at Mid-Blocks considering Non-Motorists’ Pre-Crash Behavior

2018 ◽  
Vol 2672 (38) ◽  
pp. 138-148 ◽  
Author(s):  
Bowen Dong ◽  
Xiaoxiang Ma ◽  
Feng Chen
Keyword(s):  
Injury Severity ◽  
Mixed Logit ◽  
Night Time ◽  
The Road ◽  
Related Data ◽  
Road Users ◽  
Five Factors ◽  
Injury Severities ◽  
Side Collision ◽  
The Impact

Non-motorized travel is considered as one of the most beneficial transportation modes. Compared with other road users, non-motorists as a whole account for about 13% of all fatal transportation-related accidents, and from 2002 to 2009 nearly 30% of those fatalities occur at mid-blocks. In addition, there are few reported studies that investigated the impact of non-motorists’ pre-crash behavior on injury severities. To examine the risk factors of non-motorist injury severity at mid-blocks, 8-year crash-related data from the General Estimates System were explored, based on the mixed logit model. The data contain various information including time characteristics, crash features, environmental conditions, roadway attributes, non-motorists’ characteristics, and their pre-crash behaviors. The results show that five factors tend to have mixed effects on injury severities, including the speed limit between 30 and 55 mph, night time indicator, right-side collision, and hit-and-run action on the incapacitating injury, as well as no action of motorists on the non-incapacitating injury. Moreover, heavy and light truck, dark not lighted indicator, and age over 65 are found to increase the likelihood of fatal injury, while age below 25 decreases the likelihood of fatality. Other indicators including roadway alignment, number of lanes, and so forth also affected injury severity. After controlling for these factors, non-motorists’ pre-crash behaviors such as darting or running into the road, activities in the roadway, and improper passing are found to have a significant impact on severity outcomes.

scholarly journals A Multimodal Approach for Monitoring Driving Behavior and Emotions

2020 ◽  
Author(s):  
Vahid Balali ◽  
Arash Tavakoli ◽  
Arsalan Heydarian
Keyword(s):  
Environmental Factors ◽  
Autonomous Vehicles ◽  
Weather Conditions ◽  
Driving Behavior ◽  
Area Network ◽  
Data Logger ◽  
Driving Behaviors ◽  
Smart Watch ◽  
Cloudy Weather ◽  
Naturalistic Driving Study

Studies have indicated that emotions can significantly be influenced by environmental factors; these factors can also significantly influence drivers’ emotional state and, accordingly, their driving behavior. Furthermore, as the demand for autonomous vehicles is expected to significantly increase within the next decade, a proper understanding of drivers’/passengers’ emotions, behavior, and preferences will be needed in order to create an acceptable level of trust with humans. This paper proposes a novel semi-automated approach for understanding the effect of environmental factors on drivers’ emotions and behavioral changes through a naturalistic driving study. This setup includes a frontal road and facial camera, a smart watch for tracking physiological measurements, and a Controller Area Network (CAN) serial data logger. The results suggest that the driver’s affect is highly influenced by the type of road and the weather conditions, which have the potential to change driving behaviors. For instance, when the research defines emotional metrics as valence and engagement, results reveal there exist significant differences between human emotion in different weather conditions and road types. Participants’ engagement was higher in rainy and clear weather compared to cloudy weather. More-over, engagement was higher on city streets and highways compared to one-lane roads and two-lane highways.

scholarly journals Identifying the Factors Contributing to Injury Severity in Work Zone Rear-End Crashes

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Kairan Zhang ◽  
Mohamed Hassan
Keyword(s):  
Traffic Safety ◽  
Injury Severity ◽  
Random Parameter ◽  
Probit Model ◽  
Weather Conditions ◽  
Work Zone ◽  
Work Zones ◽  
Road Project ◽  
Key Indicators ◽  
The Impact

Egypt’s National Road Project is a large infrastructure project aiming to upgrade the existing network of 2500 kilometers as well as constructing new roads of 4000 kilometers to meet today’s need. Increasing highway work zones eventually direct the challenges for traffic safety and mobility. Realizing the need for mitigating the impact of such a challenging scenario, this paper aims to investigate and identify the factors of work zone rear-end crash severity. In this regard, a random parameter ordered probit model was applied to analyze data on the Egyptian long-term highway work zone projects during the period of 2010 to 2017. The factors of speeding and foggy weather conditions are found to be the key indicators for modeling the random parameters. Besides, during the weekend and at nighttime, there is a higher risk of rear-end crash in work zones, while heavy and passenger vehicles are at greater risk in this regard. It is anticipated that the findings of this study would facilitate transport agencies in developing effective measures to ensure safe mobility across work zones.

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