Analysis of Factors Affecting Crash Severity of Pedestrian and Bicycle Crashes Involving Vehicles at Intersections   

Investigators: Abdulaziz Alshehri and Dr. Deogratias Eustace

Status: Completed

Sponsor: School of Engineering, University of Dayton

Abstract:

Vulnerable road users (VRUs) such as pedestrians and bicyclists, also known as non-motorists, are vulnerable due to lack of protection in traffic. They are even more vulnerable at intersections due to increased exposure and conflicts with motor vehicles whose paths have to cross each other. The main objective of this thesis study was to determine factors that contribute significantly to the crash severity of intersection-related crashes involving motor vehicles and the vulnerable road users. When a motor vehicle crashes with a non-motorist road user, the non-motorist road user sustains the higher injury levels. Based on the objectives of this study, a three-year crash database from January 2013 to December 2015 acquired from the Ohio Department of Public Safety was utilized for this analysis. The logistic stepwise selection procedure was applied to estimate statistically significant predictor variables that contribute in increasing bicyclist and pedestrian-related crash severity levels. The logistic regression model identified five statistically significant predictor variables out of fourteen independent variables considered in the current research. The predictors that increase crash severity of crashes involving VRUs who collide with vehicles at intersections are pedestrian-related, road contour, gender, light condition, and unit in error. The other factors that are usually significant such as posted speed limits, alcohol-related, gender, age, etc., were not significant in the current study. However, speed[1]related was not tested in the current study due to lack of enough cases where speeding was reported as contributing factor in the data set used.

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Analysis of Factors Affecting Crash Severity of Pedestrian and Bicycle Crashes Involving Vehicles at Intersections   

Investigators: Abdulaziz Alshehri and Dr. Deogratias Eustace

Status: Completed

Sponsor: School of Engineering, University of Dayton

Abstract:

Vulnerable road users (VRUs) such as pedestrians and bicyclists, also known as non-motorists, are vulnerable due to lack of protection in traffic. They are even more vulnerable at intersections due to increased exposure and conflicts with motor vehicles whose paths have to cross each other. The main objective of this thesis study was to determine factors that contribute significantly to the crash severity of intersection-related crashes involving motor vehicles and the vulnerable road users. When a motor vehicle crashes with a non-motorist road user, the non-motorist road user sustains the higher injury levels. Based on the objectives of this study, a three-year crash database from January 2013 to December 2015 acquired from the Ohio Department of Public Safety was utilized for this analysis. The logistic stepwise selection procedure was applied to estimate statistically significant predictor variables that contribute in increasing bicyclist and pedestrian-related crash severity levels. The logistic regression model identified five statistically significant predictor variables out of fourteen independent variables considered in the current research. The predictors that increase crash severity of crashes involving VRUs who collide with vehicles at intersections are pedestrian-related, road contour, gender, light condition, and unit in error. The other factors that are usually significant such as posted speed limits, alcohol-related, gender, age, etc., were not significant in the current study. However, speed[1]related was not tested in the current study due to lack of enough cases where speeding was reported as contributing factor in the data set used.

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Spatial Analysis of Alcohol-Related Injury and Fatal Traffic Crashes in Ohio  

Investigators: Hesham Razzaghi and Dr. Deogratias Eustace

Status: Completed

Sponsor: School of Engineering, University of Dayton

Abstract:

Traffic crashes are considered alcohol-related if blood alcohol concentration (BAC) of a driver or non-motorist is 0.08 grams per deciliter (g/dl) or above. This thesis analyzed various socioeconomic factors that may influence alcohol-related fatal and injury crashes in Ohio at the county level in order to determine counties that may need heightened attention in terms of enforcement and education campaigns. This study used three years of Ohio traffic crash data at the county level from 2012 to 2014 obtained from the Ohio Department of Public Safety (ODPS). The objective of this study was to explore the use of an Ordinary Least Squares (OLS) regression method in identifying factors affecting alcohol-related fatal and injury crashes in Ohio at the county level. This study was done by using Geographic Information System (GIS) in order to utilize its spatial capabilities. The model of alcohol-related fatal and injury traffic crashes was initially built with 15 independent variables that may affect alcohol-related traffic crashes such as population density and household income. The variables were divided into four groups namely crash response variables, road network variables, traffic variables, and socio-demographic variables. The Moran’s I index for residuals was almost equal to zero demonstrating that there was little evidence of any autocorrelation between each other, then OLS model was deemed adequate in modeling the data used in this study. After removing highly correlated variables, only four variables were found to be significantly affect the rates of alcohol-related traffic crashes at the county level at a 90% confidence level. The variables found significant include percent of males in the population in the county, percent of trucks in the vehicles registered in the county, percent of licensed drivers per population in the county, and elevation range in the county. 

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Investigating the Effect of Vehicle Color in Crash Risk  

Investigators: Fayez Alanazi and Dr. Deogratias Eustace

Status: Completed

Sponsor: School of Engineering, University of Dayton

Abstract:

Many studies have been conducted to understand the root causes of traffic crashes so as to put in place measures to prevent them. Most of the studies have attributed vehicle crashes to careless driving, un-roadworthy vehicles, poor road conditions, drunk driving, driving under the influence of banned substances and speeding. Nevertheless, few researchers have shown curious about the relationship between traffic crashes and vehicle color. This thesis study sought to establish whether there is a relationship between vehicle color and a risk of a crash and if there is a relationship, which vehicle color is the safest? Data used in this study were obtained from the Ohio Department of Public Safety (ODPS); these are traffic crashes that occurred in the state of Ohio from 2011 to 2015. The induced exposure method was used where the data were divided into two groups: Color prone group, which puts together different types of crashes where the visibility of a vehicle in terms of color may contribute to the occurrence of a crash. Induced exposure group, this group includes vehicle crashes that occur for other reasons other than vehicle visibility such as vehicles hitting a tree or other fixed objects or overturning. These are single-vehicle crashes. Both the negative binomial (NB) and Poisson distributions were used to fit generalized linear models to the data. Model goodness-of-fit tests were utilized to check which model fits better to the data. Model goodness-of-fit tests indicate that the NB model reflected a better fit to the data due to over-dispersion. Results from the negative binomial model confirm that statistically, besides random variations, no vehicle color was found to be safer or riskier than white, the vehicle color used as a baseline color. 

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Exploring and Identifying Contributing Factors of Injury Severity of Drivers of Emergency Vehicles in Ohio 

Investigators: Hasna Elmagri and Dr. Deogratias Eustace

Status: Completed

Sponsor: School of Engineering, University of Dayton

Abstract:

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 Ohio’s 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. 

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Investigation of Characteristics and Assessment of Crash Severity Factors Associated with Truck-Related Crashes in Ohio  

Investigators: Thaar Alqahtani and Dr. Deogratias Eustace

Status: Completed

Sponsor: School of Engineering, University of Dayton

Abstract:

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%.

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Analysis of Crash Location and Crash Severity Related to Road Work Zones in Ohio

Investigators: Ibrahim Alfallaj and Dr. Deogratias Eustace

Status: Completed

Sponsor: School of Engineering, University of Dayton

Abstract:

Due to growth of vehicle travel using street and highway systems in the state of Ohio, pavement repair and rehabilitation projects have increased over time. 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(s) within the work zone locations. The work zone segment area is made of : (1) termination area (TA), (2) before the first work zone warning sign area (BWS), (3) advance warning area (AWA), (4) transition area (TSA), and (5) 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 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 crash type, posted speed limit, vehicle type, speed related, alcohol related, semi-truck related, youth related and road condition. In the 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.

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Analysis of Factors Affecting Motorcycle-Motor Vehicle Crash Characteristics

Investigators: Di Zhu and Dr. Deogratias Eustace

Status: Completed

Sponsor: School of Engineering, University of Dayton

Abstract:

Worldwide, motor vehicle crashes lead to death and disability as well as financial costs to both society and the individuals involved. Motor vehicle crashes may result in injury, death, and property damage. A number of factors contribute to the risk of a motor vehicle crash, including vehicle design, speed of operation, road design, road environment, driver skill and/or impairment, and driver behavior. The objective of this study was to analyze crash data of motorcycle-motor vehicle collisions to identify possibly influential factors that cause these crashes and to study the magnitude of influence of each factor to these crashes. This study tested appropriate regression models to accurately model the factors that significantly influence motorcycle-motor vehicle crashes. A nominal multinomial logistic regression model was built. From stepwise selection procedure, the influential factors included age, time of crash, number of units, vehicle in error, road contour, collision type, alcohol used, posted speed, and helmet used. Number of units involved in a crash impacts the crash severity level, such as two units mostly result in injury and three or more units mostly result in fatal. If the driver of the motor vehicle causes the crash it will more likely result into injury than if the driver of the motorcycle causes the crash. Driver of motorcycle or vehicle that uses alcohol will certainly increase the chance of a fatality or injury. Crashes that occur on highways or freeways with higher speed limits are more likely to result in injuries and fatalities. The occupants of motorcycle use helmet will significantly be protected in the crash. These factors can be applied to reduce the severity of motorcycle-motor vehicle crashes.

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Characteristics of Drivers Who Cause Run-Off-Road-Crashes on Ohio Roadways

Investigators: Abdullah Alruwaished and Dr. Deogratias Eustace

Status: Completed

Sponsor: School of Engineering, University of Dayton

Abstract:

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 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 their 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.

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Evaluation of Relationship of Seat Belt Use between Front Seat Passengers and Their Drivers in Dayton, Ohio

Investigators: Fawaz Alharbi and Dr. Deogratias Eustace

Status: Completed

Sponsor: School of Engineering, University of Dayton

Abstract:

Several studies have determined the use of seat belts to be one of the major contributing factors in the reduction of fatalities and injury severities associated with motor vehicle crashes. Some studies have found that there is a relationship between drivers and their front passengers in terms of seat belts usage. The objective of this study was to evaluate the seat belts usage rates in Dayton, Ohio based on vehicle type, gender, age, day of the week, time of observation, and person type, i.e.,. driver or passenger. Data for this study was collected from thirteen sites in Greater Dayton, Ohio by direct observations at interchange ramps and intersections. The binary logistic regression model was used to investigate some independent variables of seat belt usage rates of drivers and their outboard (front seat) passengers. That is, the binary logistic regression model was used to identify factors that may play a role in relation to seat belt usage. The results from the binary logistic regression modeling show that the person type and vehicle type are significant factors affecting the likelihood of seat belt usage. There were no significant interactions identified between the factors studied. The odds of using seat belt by drivers are higher than the odds of using seat belt by their passengers. Also, the odds of occupants of passenger cars and sport utility vehicles to be belted are higher than the odds of using seat belt by pickup truck occupants. There is no statistically significant difference between van and pickup truck occupants in terms of their seat belt use. Moreover, the pickup truck and van occupants have the lowest seat belt usage rates. In order to increase seat belt usage rates, this study recommends for enforcement officials to pay more attention with pickup truck and van occupants when checking out unbelted vehicle occupants. This persistence will make them increase their seat belt usage, which eventually will increase their chances of saving their lives in case they get involved in severe crashes. The drivers should be encouraged to persuade their passengers to use seat belts. The seat belt law should be upgraded to a primary law from the current ineffective (i.e., difficult to enforce) secondary law if the state of Ohio seriously wants to increase the seat belt usage in the state.

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Characteristics of Injury and Fatality of Run-Off-Road Crashes on Ohio Roadways

Investigators: Omar Almutairi and Dr. Deogratias Eustace

Status: Completed

Sponsor: School of Engineering, University of Dayton

Abstract:

A run-off-road (ROR) crash or a roadway departure crash is a non-intersection crash which occurs after a vehicle crosses an edge line or a center line (i.e., leaves its designated traveled way lane and in the process the vehicle collides with a non-traversable obstacle or another vehicle travelling in the opposite direction or hits a pedestrian, or the vehicle overturns. The main objective of this study study was to determine the factors that contribute significantly to the levels of injury severity when ROR crashes occur. This study used a 5-year crash data for years 2008-2012 obtained from the Ohio Department of Public Safety. The decision tree model in conjunction with generalized ordered logit model was used to investigate characteristics of injury and fatality of run-off-road crashes in Ohio. The decision tree modeling was used for exploratory data analysis and it identified eight factors that explain a large amount of the variation in the response variable, i.e., injury severity. These important predictors for injury severity include road condition, run-off-road (ROR) crash type, posted speed limit, vehicle type, gender, alcohol-related, road contour, and drug-related. Also, complex interactions between parameters were identified. The parameter estimate results from the generalized ordered logit regression model show that the following are significant factors in increasing the likelihood of ROR injury severity levels: alcohol and drugs use, curves and grades, female vehicle occupants, overturn/rollover crashes, ROR crashes occurring on roadway with dry surface conditions. Additionally, buses, trucks, and emergency vehicles, and ROR crashes on roadways with posted speed limits of 40 mph or higher increase the probability of injury severity.

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 Identifying Locations with High Rates of Alcohol Related Traffic Crashes in Ohio

Investigators: Sowjanya Ponnada and Dr. Deogratias Eustace

Status: Completed

Sponsor: University of Dayton Research Council

Abstract:

On average, every day in the United States 32 people die in motor vehicle crashes involving alcohol-impaired drivers. This is equivalent to one death every 45 minutes. The objectives of this study were three-fold: (1) to identify counties in the state of Ohio with relative high alcohol-related traffic crash rates; (2) to develop a visual presentation of results by using Geographic Information System (GIS on the Ohio map of counties); (3) to recommend areas or locations needing elevated and targeted alcohol-related driving reinforcement and educational efforts. Ohio traffic crash, number of population, number of registered vehicles, the number of licensed drivers and average daily vehicle miles of travel data at the county level for 2007-2010 were used to analyze the alcohol-related traffic crash rates in counties. The results indicate that the most urbanized counties of Franklin, Cuyahoga, Hamilton, Summit, Montgomery, Lucas and their surrounding counties which are highly populated and also with high traffic volumes are the locations where most of the alcohol related traffic crashes occurred. The interesting results, however, were obtained from analysis of the risk of alcohol-related traffic crashes when the population and other exposure metrics were factored in order to determine the relative risk rates, which enable us to compare the counties fairly. Population density, daily vehicle miles of travel, the number of licensed drivers and the number of registered vehicles enabled us to capture counties with high risk rates. Generally, rural southern counties in the Appalachian areas and in the eastern parts of the state are the ones that appeared on the top of the list in almost all risk rate methods used in this study. In the northeastern area, the county of Ashtabula was the only county in that area which was ranked among the most risk counties in almost all the risk traffic crash rates methods used in this study. Counties that were highly ranked include Carroll, Harrison, Guernsey, Perry, Belmont, and Muskingum counties in the eastern part of the state and counties in the southern part highly ranked include Vinton, Ross, Pike, Adams, and Lawrence.

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 Design of an Intelligent Traffic Management System

Investigators: Amin Azimian and Dr. Deogratias Eustace

Status: Completed

Sponsor: University of Dayton Research Council

Abstract:

Due to present-day significant increases in population and consequently in traffic congestion in most metropolitan cities in the world, designing of an intelligent traffic management system (ITMS) in order to detect the path with the shortest travel time is critical for emergency, health, and courier services. The aim of this study study was to develop a theoretical traffic detection system and capable of estimating the travel time associated with each street segment based on the traffic data updated every 20 seconds, which successively finds the path with the shortest travel time in the network by using a dynamic programming technique. Furthermore, in this study we model the travel time associated with each street segment based on the historical and real time data considering that the traffic speed on each road segment is piecewise constant. It would be useful to implement such algorithms in GIS systems such as Google map in such a way that the service delivery drivers can avoid congested routes by receiving real time traffic information.

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 L&D Manual Turn Lane Storage Validation/Update

Investigators: Sowjanya Ponnada and Dr. Deogratias Eustace

Status: Completed

Sponsor: Ohio Department of Transportation (ODOT)

Abstract:

Queuing occurs at intersections mostly due to overflow or inadequacy of turn bays. The ODOT L&D Manual Volume 1 has storage requirements for both signalized and unsignalized intersections. Figures 401-9E and 401-10E of the L&D Manual provide the required turn lane storage lengths which should be compared with the real world conditions to check for adequacy of these lengths as a measure of ensuring that accesses to the left turn lanes are not blocked. In addition to the projected turn lane volume, ODOT’s methodology incorporates both deceleration (based on the speed of the roadway) and potential blockage from the adjacent through lane. Currently, however, there are no records whether these storage lengths computed by the methodology put forth in this manual are valid and accurately represent the actual conditions at intersections in Ohio. This study used real world traffic and queue storage data at some intersections and analyzed these data to validate the model ODOT is currently using. This study used the observed field data to evaluate the ODOT’s model of storage length at intersections. In addition, the queue storage lengths observed from field data were compared with the prediction results of HCS and SYNCHRO computer packages. The model evaluation task evaluated the level of precision of each of the three models (ODOT, HCS, and SYNCHRO) with respect to the field data observation. L&D Manual lead the way by accurately predicting the observed queues by about 81.6% and closely followed by HCS, which also had a 79.2% prediction accuracy. SYNCHRO was by far the lowest with a 46.0% prediction accuracy.

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 Exploring Factors Contributing to Injury Severity at Freeway Merging and Diverging Areas

Investigators: Worku Y. Mergia and Dr. Deogratias Eustace

Status: Completed

Sponsor: University of Dayton Research Council

Abstract:

Identifying factors that affect crash injury severity and understanding how these factors affect injury severity is critical in planning and implementing highway safety improvement programs. Factors which can be categorized in to classes such as driver-related, traffic, environmental, and geometric design, and other factors such as airbag deployment conditions and collision types were considered to develop a statistical model that can be used to predict the effects of these factors on severity of injuries sustained from crashes. Police-reported crash data obtained from the Ohio Department of Public Safety (ODPS) at selected freeway merge and diverge areas in the State of Ohio was used for the development of the model. A generalized ordinal logit model or partial proportional odds model was applied to identify the factors that tend to increase the likelihood of one of five levels of injury severity: no injuries, possible/invisible injuries, non-incapacitating injuries, incapacitating injuries, or fatal injuries. The results of this study showed that semi-truck related crashes, motorcycle related crashes, higher number of lanes on mainlines, higher number lanes on ramps, speed related crashes, and alcohol related crashes to increase the likelihood of sustaining severe injuries. This study showed the dangers of speeding which is found to be fatal in both models developed. Therefore, enforcement of the maintenance of posted speed limits is emphasized.

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 Development of a GIS-Based Tool for Traffic Crash Analysis and Modeling Accident Occurrences

Investigators: Aline R. Aylo, Nikhita Kadaveru and Dr. Deogratias Eustace

Status: Completed

Sponsor: Ohio Transportation Consortium (OTC)

Abstract:

GIS has become one of the most used programs for various project and research areas. It has been used in a variety of applications for planning, presenting, and analyzing data and results. Its application in transportation safety however, has been limited. ArcGIS provides the option of creating new tools by programming with different languages such as Visual Basic. Most of the available GIS-based traffic safety tools developed so far perform simple traffic crash analyses by querying, analyzing crash data and ranking of high crash locations by using crash data only. The objectives of this study are three-fold: (1) to identify the most important feature and method in safety analysis; (2) to develop a safety analysis application using ArcGIS and Visual Basic; (3) to use the application in a case study.

The safety application developed is able to query traffic crashes by accident characteristics which are always reported and included in any traffic accident database such as crash severity, road condition, collision type, light condition, etc. The application also performs analysis both on a micro level and on a macro level. Users have the choice to study an intersection or a roadway segment as well as group of intersections or group of roadway segments that share common characteristics or traits. Eight different safety analysis methods were programmed, which ranged from simple methods such as the crash frequency to the more advanced methods such as the empirical Bayes (EB). A before-after tool is added and it is able to perform a naïve before-after study and an empirical Bayes study. Finally an output tool was added to the application therefore the results can be exported to other formats or maps. The application was tested with a case study using Montgomery County as a study area.

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 Effects of Left-Side Merging and Diverging Ramps on Crash Frequency on Urban Freeway Segments

Investigators: Aline R. Aylo, Worku Y. Mergia and Dr. Deogratias Eustace

Status: Completed

Sponsor: University of Dayton Research Council

Abstract:

This study analyzes the effect of left- and right-side merging and diverging ramps and other variables such as light condition, roadway pavement condition, drivers’ age and presence of construction work zones on the occurrence frequency of crashes. A 6.5-mile section of I-75 that passes through downtown Dayton, Ohio was considered in the study. A statistical modeling technique that assumes a negative binomial distribution on generalized linear models (GLMs) was used to develop two separate models one for merging and another one for diverging ramps. Four year records of crash data from 2005 to 2008 obtained from the Ohio Department of Public Safety (ODPS) crash database was used to build the models. The model results show that left-side on- and off-ramps are critical elements in crash occurrence frequency in the vicinity of ramps on freeways. In addition, roadway pavement condition, light condition, and construction work zones were found to be significant variables in predicting crash occurrence frequency.

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 A Survey of Loop Detectors in the United States

Investigators: Aline R. Aylo and Dr. Deogratias Eustace

Status: Completed

Sponsor: Ohio Transportation Consortium (OTC) as a sub-award to University of Cincinnati

Abstract:

The main tasks for this project were to collect loop-detector standards, drawings and guidelines adopted by various agencies all over the country and evaluate the materials collected. Several types of traffic detectors are currently in use including 1) inductive loops, 2) video, 3) microwave, 4) infrared, 5 acoustic, 6) radar, 7) magnetic, 8) radio frequency, 9) global positioning system (GPS). The inductive loop detectors are by far the most commonly used and they have been regarded as the standard traffic detection method by many agencies. A loop detector consists of a wire curved into a loop which may contain one or several turns of wire placed carefully in a saw-cut slot in the road surface in a location where vehicles are supposed to be detected. The loop wire is then connected to an electronic amplifier via a cable. The loop detector has an ability to detect a vehicle passing over or stopped on the loop. A letter soliciting the required information was sent to all Departments of Transportation in the United States. The responses were received from 18 states, namely California, Connecticut, Florida, Illinois, Indiana, Maryland, Massachusetts, Michigan, Mississippi, Montana, New Jersey, New York, North Carolina, Oregon, Pennsylvania, Texas, Utah, and Washington.

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 Identification of Factors Related to Motorcycle Fatal Injuries in Ohio

Investigators: Vamsi K. Indupuru and Dr. Deogratias Eustace

Status: Completed

Sponsor: University of Dayton Research Council

Abstract:

This study attempts to identify the risk factors related to motorcyclist fatalities provided that a motorcycle crash has occurred. The analysis is based on crash data from Ohio Department of Public Safety (ODPS) over the years 2003-2007. The logistic regression analysis was used to estimate the probability of a motorcyclist being fatally injured in a traffic crash by examining the factors affecting motorcycle fatalities in Ohio. Odds ratios were used to estimate the magnitudes of significant predictors on the occurrence of motorcycle fatalities in traffic crashes. A total of seventeen candidate factors related to driver, road, environment, and collision type that were recorded by police officers who investigated the crashes, were used to develop the models. Helmet use did not show up as a significant factor in a multivariate model at a 5% significance level. But, when it was tested in a univariate model, i.e., helmet use as the only variable in the model, it was found to be highly significant. Therefore, three logistics regression models were developed as a result, i.e., a general model, a helmet used model, and a helmet not used model to study the effect of helmet on the chances of motorcycle fatalities and related significant factors for each model. The results show that risk factors for fatal crashes significantly increase when the following circumstances apply: the motorcyclist is less than 25 years of age , being the motorcycle rider, use of excessive speeding, use of alcohol and/or drugs, riding without helmet, being involved in a single-vehicle crash at a non-intersection location, crashing on horizontal curves, on graded segments, and on major roadways. The combination of risk factors, such as nighttime riding at horizontal curves, excessive speeding on major roads, travelling at excessive speed while under the influence of drugs and/or alcohol, and younger riders negotiating horizontal curves, greatly increases the likelihood of fatal injury crashes. In order to reduce the number of fatal crashes this study indicates that the dangers of excessive speed and operating a motorcycle while intoxicated must be fully stressed to the public. The enactment of an Ohio universal helmet law is particularly recommended.

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Investigation of the Relationship between Vehicle Color and Safety

Investigators: Stephen Owusu-Ansah and Dr. Deogratias Eustace

Status: Completed

Sponsor: University of Dayton Research Council

Abstract:

Over the years, the concern of many, consumers and insurance companies alike, has been geared towards the contribution of vehicle color to the risk of crash. Consequently, there is a need to provide sufficient scientific evidence to back consumers in selecting the appropriate vehicle color that enhances their safety on the road. This research was designed to provide results that may help identify the vehicle color(s) that may be associated with better vehicle conspicuity. Previous studies on the relationship between risk of crash and color of vehicles are very few and some of their findings are contradictory. Moreover, some of the methodologies used are questionable. Therefore, the main objective of this study was to determine if there is a significant association between vehicle color and crash risk. This study utilized traffic crash data from the state of Nevada traffic crash database because the Nevada Department of Transportation keeps records of the color of vehicles involved in traffic crashes in their database. The present research utilized the induced exposure study design, which has been demonstrated to be better design when dealing with traffic safety and crash risks. The data was stratified into two groups, namely, color-prone crash group and induced exposure crash group. The color prone crash group generally includes the types of crashes where vehicle color visibility may play a part in crash occurring. It includes two or more vehicles crashing at the intersections and those traveling in the same or opposite directions, or where pedestrians or motor cyclists are struck. The induced exposure crash group generally includes crashes where vehicle visibility is not likely to be a factor in the crash occurring, such as single vehicle crashes and a vehicle crashing into a parked vehicle or other fixed/stationery objects such as trees, utility poles, etc. The GENMOD Procedure in SAS, which allows the specification of negative binomial and Poisson distributions by fitting a generalized linear model to the data by using the maximum likelihood estimation techniques, was utilized. Then model tests were performed to determine which model is more appropriate for the given set of data. The negative binomial model was selected based on its relatively low ratios of deviance and Pearson chi-square to the degree of freedom values (reflecting better model fit to the data) and to the fact that the data was overdispersed. The negative binomial regression model indicated that none of the crash risks for the vehicle colors modeled were statistically significantly different from that of white color. As opposed to previous studies, this study first desired the appropriate model between the mostly used Poisson and the negative binomial models for crash data analysis and modeling. Based on our results, no single vehicle color was found to be significantly safer or riskier than white color. All the differences noted were not supported by a sound statistical analysis performed.

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 Analyzing the Safety and Operational Impacts of Installing Roundabouts in Ohio

Investigators: Aline Aylo and Dr. Deogratias Eustace

Status: Completed

Sponsor: University of Dayton Research Council

Abstract:

The pace of designing and constructing roundabouts in the U.S. has been increasing exponentially. The state of Ohio in recent years has also joined the furor for roundabout construction with central Ohio in the lead. The objectives of this study were three-fold: to perform a safety performance comparison using the before-after study design to better estimate the nature and magnitude of crash after installation of roundabouts in Ohio; to perform a crash pattern study to compare the severity of injuries between the before and after situations; and to perform an intersection operational analysis for the before intersection type and the current roundabout using SIDRA software. Due to the lack of traffic volume counts, a Naïve before-after study design was used in this study because it requires traffic crashes only as input data. A total of nine currently operating roundabout intersections in Columbus metropolitan area were included in this study as they met the Naïve before-after study design requirements. The results from the Naïve before-after safety study show that the number of crashes and the severity of injuries have decreased after the installation of roundabouts. Also, the results from SIDRA software indicate that a roundabout improves the intersection’s measures of effectiveness (MOE’s) when using the commonly used criteria: (1) 95% largest back of queue, (2) average intersection delay, (3) largest average movement delay, and (4) degree of saturation. The number of crashes and severity of injuries may be expected to decrease further with time when drivers get more experience with roundabouts. In addition, a public education campaign through fliers and other media advertisements including the addition of roundabout driving materials in the Ohio driver’s manual will be highly helpful. Future before-after safety studies are recommended utilizing a larger number of roundabouts as the number of roundabouts in operation for at least three years increases in Ohio and traffic counts coverage becomes available in order to utilize the empirical Bayes, a more robust modeling method.

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 Evaluation of the Role of Driver’s Knowledge of who has the Right-of-way Contributes to Interstate On-ramp Crashes

Investigators: Vamsi K. Indupuru and Dr. Deogratias Eustace

Status: Completed

Sponsor: Mack-Blackwell Transportation Center (MBTC)

Abstract:

The knowledge of drivers on who has the right-of-way between the one on mainline lanes of a freeway and the one entering the freeway through on-ramp junction lane was evaluated. In addition, drivers’ opinions on how to make the on-ramp junctions safer were collected. A survey instrument with 21 items requesting drivers’ information in regard with demographics, freeway driving experience, knowledge of right-of-way and merging practices was used for data collection. The results show that crashes are relatively rare events and for some reasons, most of them never happen but result into near misses, which can not be reported and documented. Most drivers act correctly when driving on freeway mainlines when they see a vehicle trying to merge from an entrance ramp but act improperly when merging into freeways from on-ramp lanes. Some drivers cannot identify which driver has the right-of-way at the merge area between the mainline and the on-ramp drivers. Yielding problems due to bad drivers’ attitudes have been identified by drivers as the leading cause of freeway-ramp merge area crashes, followed by lack of attention and drivers entering the freeway at low speeds. Most of the drivers believe that they need longer acceleration lanes, better ramp signing and better driver’s education especially in terms of sign meaning and entrance ramp safety in order to make freeway-ramp merge areas safer. Due to sampling problems encountered in this study, one has to be careful when interpreting these results because the sample completely missed teenage drivers and over-sampled older drivers.

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 The Role of Driver Age and Gender in Motor Vehicle Fatal Crashes

Investigators: Dr. Deogratias Eustace

Status: Completed

Sponsor: University of Dayton Research Council

Abstract:

Comparing the age and gender of drivers contributing to fatal crashes provides a better insight into the motor vehicle safety problem that may be more helpful to traffic engineers in devising countermeasures. Data from the Fatality Analysis Reporting System (FARS) for 2001-2003 were used in this study. The young (ages 16-19) and elderly (70+) are more likely to be responsible in fatal vehicle crashes. While younger drivers’ operating errors are more likely to result in deaths of other people, the older drivers are more likely to cause their own deaths. Driver operating errors contribute to about 73% and 83% of males’ and females’ motor vehicle fatal crashes, respectively. Failing to stay in proper lane and driving too fast for road conditions were the two most frequent driver operating errors contributing to fatal crashes for both males and females. In terms of involvement ratio, young males are more likely than young females to be responsible in their fatal crashes, a trend that continues up to around age 60 where older females become more responsible than males of the same age. However, when comparing the involvement in terms of miles driven, there is no noticeable difference between the genders beyond 40 years of age. Countermeasures required for younger and elderly drivers are varied due to the differing causes of their fatal crashes and types of driving errors. Younger drivers need to be restricted in terms of unsupervised passenger carrying and night-time driving. For elderly drivers, the best countermeasures are those that will help them improve their visual recognition and reaction times, especially at intersections.

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 Development of A Problem-Based Learning (PBL) and Cooperative Learning (CL) Transportation Engineering Course for Undergraduate Students

Investigators: Dr. Deogratias Eustace

Status: Completed

Sponsor: Ohio Transportation Consortium (OTC) and Learning Teaching Center (LTC), University of Dayton

Abstract:

This study reports the findings of a project that was done during the implementation of a problem-based learning (PBL) and cooperative learning (CL) elements into an undergraduate transportation engineering course. The study procedure used the student course evaluations, including a survey questionnaire, and university-wide standardized student evaluations. Additionally, student homework, tests, and exam grades were used as part of the evaluation process. Two methods of teaching formats were evaluated: the traditional teaching method of lecturing and using end-of-chapter book questions for homework assignments and the new currently used teaching method of student field data collection, preparation of a lab report for each data collection exercise and use of their data to answer homework questions. The semesters in which data were used in the analyses include, fall 2005, fall 2006, and spring 2007 taught using the traditional format and fall 2007 and spring 2008 taught using the new teaching format. The findings of this study have revealed that students do prefer the current teaching format that incorporates some forms of problem-based leaning (PBL) and cooperative learning (CL) elements over the traditional format of teaching. Students favor this method mainly because they believe that collecting their own data, getting involved in using these data in solving example problems in class and using them as a source of homework assignments improves their learning process.

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 Analyzing the Effects of L.E.D. Traffic Signals on Urban Intersection Safety

Investigators: Valerie E. Griffin and Dr. Deogratias Eustace

Status: Completed

Sponsor: University of Dayton Research Council

Abstract:

The use of light emitting diodes (LED’s) in traffic signals has become widespread over the past decade. Energy efficiency and long service life are the often-cited reasons for converting from incandescent bulbs to LED’s, but could improved safety be another, less obvious benefit? LED’s appear to be more visible than traditional bulbs, possibly providing the driver with more time to appropriately respond to the traffic control devices and avoid a potential collision. The objective of this research is to evaluate crashes at signalized urban intersections to determine whether or not crashes were reduced after the installation of LED traffic signals. A before-and-after analysis was conducted for eight intersections using empirical Bayes estimation. Data used for the “before” period was collected when the intersection operated with conventional incandescent bulbs. Each of the treatment sites were retrofitted with LED fixtures of the same size, and data was collected for the “after” period. Two additional sites which had not received the treatment were chosen as comparison sites in the analysis. The comparison sites were carefully selected based on traffic characteristics, geometry, and driver traits similar to those exhibited at the treatment sites. The empirical Bayes analysis revealed an increase of about 71% in crashes after the installation of LED traffic signals. The study was limited to a small number of intersections, some with atypical traffic trends, and data from only two comparison sites. Additional studies should be conducted using a more broad range of treatment sites and a greater number of comparison sites to determine the long-term safety benefits associated with LED use in traffic signals.