Performance Comparison of a Roundabout Versus Two-Way Stop Controlled (TWSC) Intersections

The superior safety record of modern roundabouts is well known in Western Europe, Australia and in most British-influenced countries around the world. The experience from a single modern roundabout installed in the City of Manhattan, Kansas, some three years ago, also shares the same experience. A major study of the performance of Manhattan, Kansas, modern roundabout was conducted at Kansas State University (KSU) and was co-sponsored by Mac-Blackwell National Rural Transportation Study Center, Kansas State University, and the City of Manhattan. The study examined three intersections, one modern roundabout and two Two-Way Stop Controlled (TWSC) intersections with similar traffic conditions. The aim of this paper is to compare how a roundabout functions with regard with one type of the traditional intersection traffic controls, i.e., the TWSC

Urban Transportation Planning Decision Making: A Robustness Analysis Approach

Travel demand modeling remains a cornerstone of the transportation planning process. Although current transportation planning procedures are mostly performed by an interdisciplinary team approach, there are still a number of institutional issues which may hinder the process from providing the best results. According to Marshment (2001), the most notable setback is a lack of coordination between land use and transportation planning. Most of the time, land use plans do not reflect the influence of the accessibility to the alternative transportation facilities. The lack of interaction between land use and transportation planning may play a major role in using unrealistic and unattainable land use and socioeconomic and demographic assumptions in the projection of future traffic levels with travel demand models. ... Robustness analysis can provide an approach to the structuring of transportation problems where uncertainty is high and where sequential, time-phased decision making is necessary

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

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

Pedestrian Reaction to Crossing Signal Delay

Pedestrians with experience at a certain intersection learn the length of signal cycle, the phase change pattern of the signal, and the order in which traffic flow is released. These pedestrians have a higher chance of noncompliance with traffic signals. Most of the noncomplying pedestrians save a small amount of time, less than 18 seconds, by crossing the road shortly before the onset of the WALK signal indication. The study agrees with other studies done in the past that pedestrians ignore the traffic signals. They do so in order to reduce their own time delays

Modeling Socio-Economic Determinants of Traffic Fatalities

Over several decades, traffic growth has caused an increased number of traffic crashes, which are associated with economic losses and human sufferings. According to the National Highway Traffic Safety Administration [1] in 2016 there were a total of 34,247 fatal traffic crashes in the United States that resulted into 37,133 fatalities. Risk factors relating to the occurrence of fatal and injury severity of motor vehicle crashes have been extensively studied. Most studies [2-7] that have attempted to model the occurrences of traffic crashes and fatalities have been mainly confined to factors related to driver characteristics, roadway geometry characteristics, traffic characteristics, crash characteristics, and environmental characteristics. The driver characteristics usually modeled include driver age, gender, alcohol use, and drug impairment involvement. Roadway geometry factors mostly include horizontal and vertical alignments, roadway and shoulder widths, presence of work zone construction, and number of lanes. Traffic characteristics mainly include average daily traffic volume (ADT) and percent of trucks. For crash characteristics, factors usually considered include type of crash, manner of collision, and location where the crash occurred. The environmental characteristics include light condition, weather condition, day of the week and time when the crash occurred. However, some few studies [8-10] have attempted to model other factors such as socioeconomic factors that may play role in occurrences of traffic crashes and fatalities. Kirk et al. [8] explored the impacts of socio-economic factors and safety regulations have on statewide traffic crash rates in the state of Kentucky. Their study indicates that at the national level, socioeconomic factors such as poverty, income and education have a significant impact on traffic crash rates but when analyzed at the state level, they found that high school education attainment was the most significant indicator for elevated crash crashes. Recently, many authors [11-13] proposed full Bayes (FB) hierarchical model to study traffic crashes over space and time. Although FB approaches accounts for the sources of uncertainties, but in some cases, the variables may not be converged after many iterations. In contrast, linear regression models have much lower running time and less space complexity. The objective of this paper was to model socio-economic determinants of traffic fatalities across all U.S States. This goal was accomplished by employing the Geographically Weighted Regression (GWR) and global ordinary least squares model (OLS). The results demonstrated that the GWR model outperformed OLS model in terms of accuracy. Furthermore, it was found that population with travel time to wok less than 20 minutes, population with no high school diploma, median income, population with age over 65 in labor force and high school graduates between 18-24 significantly contributed to traffic fatality rate

L&D Manual Turn Lane Storage Validation/Update

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

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

This study compares the age and gender of at-fault drivers who were involved in fatal crashes and the corresponding driving errors that contributed to these crashes. This comparison provides insights that may help traffic engineers devise countermeasures to lessen the number of these unnecessary deaths. Data from the Fatality Analysis Reporting System (FARS) for the years 2001 through 2003 were used in this study. The analysis included passenger vehicles (automobiles, utility vehicles, minivans, and pickup trucks) involved in either single or two vehicle crashes. The driver responsible in each crash was identified through the driver error variable codes as listed in the FARS databases. Younger drivers (16 through 19 years of age) and the elderly (those 75 and older) were responsible for a disproportionate amount of fatality-related crashes. When combined these two groups accounted for only 6.4% of the total miles driven in 2001 but they were responsible for 83.1% of the fatal crashes attributed to driver-related errors. Driver operating error was listed as the contributing factor in 73% of fatal motor vehicle crashes when the driver was male and 83% of the crashes when the driver was female. The youngest drivers tended to be carrying the highest number of passengers when they were involved in fatal crashes. Failing to stay in the proper lane and driving too fast for road conditions were the two most frequent driver operating errors contributing to fatal crashes for both male and female drivers

Risk and Protective Factors Associated with Motorcycle Injury Severity in the United States

According to the Centers for Disease Control and Prevention, road crashes are one of the major causes of morbidity and mortality in the United States. The Healthy People 2020 has indicated that accidents are a major public health issue. Most motor-vehicle related events resulting in injury, disability, or death are predictable and preventable. Injuries are the leading cause of death for Americans aged 1 to 44 years and a leading cause of disability for all ages, regardless of sex, race/ethnicity, or socioeconomic status. Although motorcycles represent approximately 3% of all registered vehicles in the United States, motorcycling accounts for more than 13% of highway traffic fatalities. While fatalities normally represent a small percent of other motor vehicle occupants, fatalities can be as high as 40% for the motorcyclists when involved in accidents (or traffic crashes as typically referred to by transportation safety professionals). Motorcyclists are more vulnerable in crashes due to their lack of protection like enclosed vehicles do to motor vehicle occupants, so they are more likely to be severely injured or killed. Therefore, there is a motivation among stakeholders to decrease the injury severity of motorcyclists. A clear understanding of the factors influencing injury severity levels due to motorcycle crashes and the related evidence prevention strategies is of paramount importance. When examining a topic of motorcycle injury severity, it is important to keep into consideration of different issues that include the definition and concept of injury severity, trends in motorcycle crashes, motorcycle-related policies and laws, knowledge of risk and protective factors. The main purpose of this chapter is to discuss the risk factors and protective factors related to injury severity of motorcycle crashes in the US. Important aspects related to motorcycle crashes’ injury severity such as methodological challenges related to conceptual clarity and measurement are discussed. In addition, implications for education, research, practice and policy including laws and enforcement are highlighted so that the overall motorcycle safety situation could be improved

L&D Manual Turn Lane Storage Validation/Update

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

A Simplified Method for Analyzing Factors Contributing to Motorcyclists’ Fatal Injuries in Ohio

The aim of this paper was to analyze traffic crash data by using a simplified method in determining significant factors that increase the risk of a motorcyclist being fatally injured once involved in a motorcycle crash in Ohio. The concept of overrepresentation, which is similar to relative risk, was used in identifying significant variables associated with the elevated risk of a motorcycle crash resulting into a fatality. The overrepresentation factor (ORF) was calculated for each variable of interest. The ORF offers a simple but powerful procedure of determining whether a certain factor significantly occurs more or less frequently in one subset than in its complementary subset. The procedure involved determining the magnitude of the overrepresentation by computing the ORF values and determining whether or not the overrepresentation is statistically significant by computing the confidence intervals over ORF values at a 5% alpha level. The results show that being age 65 and over, riding while speeding, riding while under the influence of alcohol or drugs, riding without a helmet, riding at nighttime, being male, and being the operator were statistically significant motorcyclist-related characteristics that elevated the risk of being fatally injured. The significant roadway characteristics included crashes occurring on major roads, on horizontal curves (bends), on graded road segments, and on open roadway segments (non-intersections). Environment conditions such as dark with light or dark with no light and when the weather condition was cloudy had significant effects of increasing the chances of motorcyclists’ fatal injuries once they get involved in traffic crashes. Moreover, crashes occurring on Saturdays pose significantly higher risks of fatalities when compared with other days of the week. In terms of crash type characteristics, a motorcyclist had elevated risks of being fatally injured when involved in single-vehicle crashes especially running off-the-road, crossing the median/centerline, and hitting a curb. Head-on and angle collisions in multivehicle crashes were significant causes of fatal injuries. The overrepresentation method was able to correctly identify similar fatal risk factors that were identified in previous studies that used more advanced and rigorous methods