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Author: Thaar S. Alqahtani
Publisher:
ISBN:
Category : Traffic accidents
Languages : en
Pages : 58
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Book Description
Truck safety is a very crucial aspect of the overall safety of the transportation system. Statewide there has been a significant increase in the probability of trucks being involved in crashes, primarily due to the fact that the total number of registered trucks, as well as the truck vehicle-miles traveled, have both increased within the last 10 years. Recognizing the substantial impact of truck-related crashes in the overall transportation safety, this study attempted to identify the contributing factors that influence the increase in truck-related crash severity, using truck-related crash data for the last two and half years (July 2013-December 2015) that were obtained from the Ohio Department of Public Safety Traffic (ODPS). This thesis study used the classification tree model to investigate the important factors affecting injury and fatality related to truck crashes in Ohio. Eighteen independent variables that represent various driver, roadway, environmental and crash characteristics were tested in the classification tree model of truck-related crash model. The dependent variable, crash severity was coded as a binary variable, with no injury and injury/fatal as its two crash severity levels. The classification tree model selected five independent variables as the only most significant factors influencing truck-related crash severity. These variables are crash type, posted speed limit, collision event, speed-related and road contour. Their significance is also in that order, with the crash type being the most significant, contributing about 55.8% to the model, posted speed limit contributing about 18.5%, collision event about 17.7%, speed-related about 6.0% and lastly road contour about 2.0%
Author: Thaar S. Alqahtani
Publisher:
ISBN:
Category : Traffic accidents
Languages : en
Pages : 58
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Book Description
Truck safety is a very crucial aspect of the overall safety of the transportation system. Statewide there has been a significant increase in the probability of trucks being involved in crashes, primarily due to the fact that the total number of registered trucks, as well as the truck vehicle-miles traveled, have both increased within the last 10 years. Recognizing the substantial impact of truck-related crashes in the overall transportation safety, this study attempted to identify the contributing factors that influence the increase in truck-related crash severity, using truck-related crash data for the last two and half years (July 2013-December 2015) that were obtained from the Ohio Department of Public Safety Traffic (ODPS). This thesis study used the classification tree model to investigate the important factors affecting injury and fatality related to truck crashes in Ohio. Eighteen independent variables that represent various driver, roadway, environmental and crash characteristics were tested in the classification tree model of truck-related crash model. The dependent variable, crash severity was coded as a binary variable, with no injury and injury/fatal as its two crash severity levels. The classification tree model selected five independent variables as the only most significant factors influencing truck-related crash severity. These variables are crash type, posted speed limit, collision event, speed-related and road contour. Their significance is also in that order, with the crash type being the most significant, contributing about 55.8% to the model, posted speed limit contributing about 18.5%, collision event about 17.7%, speed-related about 6.0% and lastly road contour about 2.0%
Author: Hasna Fawzi Elmagri
Publisher:
ISBN:
Category : Crash injuries
Languages : en
Pages : 38
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Book Description
The purpose of this study was to examine the contributing factors and characteristics associated with fatality and injuries sustained by drivers of emergency vehicles (EVs) involved in traffic crashes in the state of Ohio. Emergence vehicle drivers considered in this study include drivers of firefighter truck, drivers of ambulances (emergency medical services), and law enforcement officers. Some few research efforts recently conducted using Ohio0́9s crash data have shown that emergency vehicles are significant factors in increasing crash and injury severity levels. The current study investigated the injury risk factors of crashes involving EVs by using Ohio crash data for 2011-2015. A binary logistic regression model was developed to identify statistically significant factors related to fatalities and injuries of EV drivers. The logistic regression model identified fourteen factors. Significant factors identified include type of crash, collision type, speed related, traffic control type, alcohol related, type of emergency vehicle, emergency related trip, female driver, light condition, teen-related, not using seatbelt, curved and grade segment. Educational and enforcement strategies can be used to reduce EV related crashes and injuries.
Author: Siddhartha Kotikalapudi
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
In order to improve safety of the overall surface transportation system, each of the critical areas needs to be addressed separately with more focused attention. Statistics clearly show that large-truck crashes contribute significantly to an increased percentage of high-severity crashes. It is therefore important for the highway safety community to identify characteristics and contributory causes related to large-truck crashes. During the first phase of this study, fatal crash data from the Fatality Analysis Reporting System (FARS) database were studied to achieve that objective. In this second phase, truck-crashes of all severity levels were analyzed with the intention of understanding characteristics and contributory causes, and identifying factors contributing to increased severity of truck-crashes, which could not be achieved by analyzing fatal crashes alone. Various statistical methodologies such as cross-classification analysis and severity models were developed using Kansas crash data. Various driver-, road-, environment- and vehicle- related characteristics were identified and contributory causes were analyzed. From the cross-classification analysis, severity of truck-crashes was found to be related with variables such as road surface (type, character and condition), accident class, collision type, driver- and environment-related contributory causes, traffic-control type, truck-maneuver, crash location, speed limit, light and weather conditions, time of day, functional class, lane class, and Average Annual Daily Traffic (AADT). Other variables such as age of truck driver, day of the week, gender of truck-driver, pedestrian- and truck-related contributory causes were found to have no relationship with crash severity of large trucks. Furthermore, driver-related contributory causes were found to be more common than any other type of contributory cause for the occurrence of truck-crashes. Failing to give time and attention, being too fast for existing conditions, and failing to yield right of way were the most dominant truck-driver-related contributory causes, among many others. Through the severity modeling, factors such as truck-driver-related contributory cause, accident class, manner of collision, truck-driver under the influence of alcohol, truck maneuver, traffic control device, surface condition, truck-driver being too fast for existing conditions, truck-driver being trapped, damage to the truck, light conditions, etc. were found to be significantly related with increased severity of truck-crashes. Truck-driver being trapped had the highest odds of contributing to a more severe crash with a value of 82.81 followed by the collision resulting in damage to the truck, which had 3.05 times higher odds of increasing the severity of truck-crashes. Truck-driver under the influence of alcohol had 2.66 times higher odds of contributing to a more severe crash. Besides traditional practices like providing adequate traffic signs, ensuring proper lane markings, provision of rumble strips and elevated medians, use of technology to develop and implement intelligent countermeasures were recommended. These include Automated Truck Rollover Warning System to mitigate truck-crashes involving rollovers, Lane Drift Warning Systems (LDWS) to prevent run-off-road collisions, Speed Limiters (SLs) to control the speed of the truck, connecting vehicle technologies like Vehicle-to-Vehicle (V2V) integration system to prevent head-on collisions etc., among many others. Proper development and implementation of these countermeasures in a cost effective manner will help mitigate the number and severity of truck-crashes, thereby improving the overall safety of the transportation system.
Author: Nayabtigungu Hendrix Ouedraogo
Publisher:
ISBN:
Category :
Languages : en
Pages : 90
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Book Description
Large trucks play a key role in the overall safety of the highway transportation system. Previous studies have shown that in Ohio large trucks are over-represented in crashes that lead to serious and fatal injuries. A previous study that analyzed factors affecting truck-related crashes in Ohio found that posted speed limit and speed-related factors were among the significant factors impacting crash severity of truck-related crashes. Several studies have shown that increasing speed limits on roadways has a significant impact both on safety and operating speeds. On July 1, 2013, the Ohio0́9s legislature raised the speed limits on rural freeways from 65 mph to 70 mph for passenger vehicles, buses and trucks and to date the safety impact of this speed limit raise has not been evaluated. The current study investigated the impact of raising the speed limit on crash severity specifically with interest in large trucks and buses on rural freeways in Ohio. Statewide crash data from January 1, 2010 to December 31, 2018 were obtained from the Ohio Department of Public Safety (ODPS). Given that the numbers of rural freeway segments located all over the state and traffic volumes for each segment over the study period are not easily obtainable; therefore, the use of standard observational before/after study Empirical Bayes (EB) method was not feasible for the current study. Because of the model requirement for stationarity on a response series, this study utilized the Autoregressive Integrated Moving Average (ARIMA) time series intervention analysis method using monthly and seasonal crash data. A time series statistical method takes care of differences in crashes occurring in different years and recognizes trends in different periods of times. Time series analysis is a statistical technique that deals with time series data or trend analysis. Time series data means that data is in a series of time periods or intervals. Results of the current study show that the increase of speed in the selected segments of Ohio rural freeways has an impact on the frequency and severity of crashes associated with large trucks and buses. Moreover, the study found that weather conditions such as a bad winter has a significant impact on the frequency and severity of crashes associated with large trucks and buses as they happen to be higher during winter seasons as compared to other times of the year.
Author: Ibrahim Alfallaj
Publisher:
ISBN:
Category : Road work zones
Languages : en
Pages : 73
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Book Description
Due to growth of vehicle travel using streets and highway systems in the United States, pavement repair and rehabilitation projects have increased. As a result, the presence of work zones has created traffic congestion and has increased the crash risk. The main object of this study was to identify significant factors that contribute to an increase in crash severity in the state of Ohio and recognize the most risk segment within the work zone locations. The work zone segment area is made of : (a) termination area (TA), (b) before the first work zone warning sign area (BWS), (c) advance warning area (AWA), (d) transition area (TSA), (e) activity area (AA). This study used a 5-year crash data from Ohio Department of Public Safety (ODPS) database from 2008 to 2012. In this study, classification tree modeling was used to investigate significant predictor variables of crash severity of work zone related crashes and recognize the most significant crash location within work zone areas in the state of Ohio. Classification tree modeling identified ten important variables (factors) that explain a large amount of the variation in the response variable, crash severity. These predictor variables of work zone crash severity identified include collision type, motorcycle related, work zone crashes type, posted speed limit, vehicle type, speed related, alcohol related, semi-truck related, youth related and road condition. In case of work zone location analysis results, this study identified six significant factors, which include collision type, work zone crash type, posted speed limit, vehicle type, workers present, and age of driver. Collision type is the most significant factor that affects crash severity in a work zone. Likewise, for work zone location, the work-zone crash type was the most significant factor that contributed in increasing the probability of work zone location crashes.
Author: Aemal Khattak
Publisher:
ISBN:
Category : Crash injuries
Languages : en
Pages : 25
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Book Description
Author: Abdullah Faleh Alruwaished
Publisher:
ISBN:
Category : Automobile drivers
Languages : en
Pages : 70
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Book Description
A vehicle that leaves its travel lane at a non-intersection location and collides with another vehicle or with a fixed object or overturns is considered to be involved in a run-off-road (ROR) crash. ROR crashes also known as roadway departure crashes, and these include head-on crashes, crashes that occur due to lane shifts, and crashes where the vehicle leaves its designated travel lane. The main objective of this study was to identify the significant factors that lead to these types of crashes. Crash data used in this study were obtained from the Ohio Department of Public Safety for a five-year period from 2008 to 2012. The classification tree modeling was used in this study to investigate the significant predictor variables of crash severity of ROR crashes. In addition, this thesis study developed two models, the ROR crashes model and the non-run-off-road (NROR) crashes model. The NROR crashes model used crash data for drivers who were at fault when their crash incidents occurred and for ROR crashes; it was assumed that all drivers in this category were at fault of causing the crashes. The ROR model identified nine variables, which include road condition, collision type, alcohol related, posted speed limit, speed related, crash type, vehicle type, gender, and age. The NROR crashes model has six significant predictor variables including collision type, posted speed limit, speed related, road condition, alcohol related, and vehicle type.
Author: Sreekanth Reddy Akepati
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
In the United States, approximately 1,100 people die and 40,000 people are injured annually as a result of motor vehicle crashes in work zones. These numbers may be a result of interruption to regular traffic flow caused by closed traffic lanes, poor traffic management within work zones, general misunderstanding of problems associated with work zones, or improper usage of traffic control devices. In regard to safety of work zones, this study was conducted to identify characteristics and risk factors associated with work zone crashes in Iowa, Kansas, Missouri, Nebraska and Wisconsin, states currently included in the Smart Work Zone Deployment Initiative (SWZDI) region. The study was conducted in two stages. In the first stage, characteristics and contributory causes related to work zone crashes such as environmental conditions, vehicles, crashes, drivers, and roadways were analyzed for the five states for the period 2002-2006. An analysis of percentage-wise distributions was carried out for each variable based on different conditions. Results showed that most of the work zone crashes occurred under clear environmental conditions as during daylight, no adverse weather, etc. Multiple-vehicle crashes were more predominant than single-vehicle crashes in work zone crashes. Primary driver-contributing factors of work zone crashes were inattentive driving, following too close for conditions, failure to yield right of way, driving too fast for conditions, and exceeding posted speed limits within work zones. A test of independency was performed to find the relation between crash severity and other work zone variables for the combined states. In the second stage, a statistical model was developed to identify risk factors associated with work zone crashes. In order to predict injury severity of work zone crashes, an ordered probit model analysis was carried out using the Iowa work zone crash database. According to findings of the severity model, work zone crashes involving trucks, light duty vehicles, vehicles following too close, sideswipe collisions of same-direction vehicles, nondeployment of airbags, and driver age are some of the contributing factors towards more severe crashes.
Author: Nishitha Naveen Kumar Bezwada
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
One-ninth of all traffic fatalities in the United States (U.S.) in the past five years have involved large trucks, although large trucks contributed to only 3% of registered vehicles and 7% of vehicle miles traveled. This crash overrepresentation indicates that truck crashes in general tend to be more severe than other crashes, though they constitute a smaller portion of vehicles on the road. To study this issue, fatal crash data from the Fatality Analysis Reporting System (FARS) was used to analyze characteristics and factors contributing to truck-involved crashes. Driver, vehicle, and crash-related contributory causes were identified, and as an extension, the likelihood of occurrence of these contributory causes in truck-involved crashes (with respect to non-truck crashes) was evaluated using the Bayesian Statistical approach. Likelihood ratios indicated that factors such as stopped or unattended vehicles and improper following have greater probability of occurrence in truck crashes than in non-truck crashes. Also, Multinomial Logistic Regression was used to model the type of fatal crash (truck vs. non-truck) to compare the relative significance of various factors in truck and non-truck crashes. Factors such as cellular phone usage, failure to yield right of way, inattentiveness, and failure to obey traffic rules also have a greater probability in fatal truck crashes. Among several other factors, inadequate warning signs and poor shoulder conditions were also found to have greater predominance in contributing to truck crashes than non-truck crashes. By addressing these factors through the implementation of appropriate remedial measures, the truck safety experience could be improved, which would eventually help in improving overall safety of the transportation system.
Author: Ishani Madurangi Dias
Publisher:
ISBN:
Category : Roads
Languages : en
Pages : 123
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Book Description
"The safe and efficient flow of traffic through work zones must be established by improving work zone conditions. Therefore, identifying the factors associated with the severity and the frequency of work zone crashes is important. According to current statistics from the Federal Highway Administration, 2,372 fatalities were associated with motor vehicle traffic crashes in work zones in the United States during the four years from 2010 to 2013. From 2002 to 2014, an average of 1,612 work zone crashes occurred in Kansas each year, making it a serious concern in Kansas. The objectives of this study were to analyze work zone crash characteristics, identify the factors associated with crash severity and frequency, and to identify recommendations to improve work zone safety. Work zone crashes in Kansas from 2010 to 2013 were used to develop crash severity models. Ordered probit regression was used to model the crash severities for daytime, nighttime, multi-vehicle and single-vehicle work zone crashes and for work zones crashes in general. Based on severity models, drivers from 26 to 65 years of age were associated with high crash severities during daytime work zone crashes and driver age was not found significant in nighttime work zone crashes. The use of safety equipment was related to reduced crash severities regardless of the time of the crash. Negative binomial regression was used to model the work zone crash frequency using work zones functioned in Kansas in 2013 and 2014. According to results, increased average daily traffic (AADT) was related to higher number of work zone crashes and work zones in operation at nighttime were related to a reduced number of work zone crashes. Findings of this study were used to provide general countermeasure ideas for improving safety of work zones" (page ii).
