In this thesis an incident prediction model is formulated and calibrated. The primary idea of the model developed is to correlate the expected number of crashes on any section of a freeway to a set of traffic stream characteristics, so that a reliable estimation of likelihood of crashes can be provided on a real-time basis. Traffic stream variables used as explanatory variables in this model are termed as “incident precursors”. The most promising incident precursors for the model formulation for this research were determined by reviewing past research. The statistical model employed is the categorical log-linear model with coefficient of speed variation and occupancy as the precursors. Peak-hour indicators and roadway-type indicators were additional categorical variables used in the model. The model was calibrated using historical loop detector data and crash reports, both of which were available from test beds in Austin, Texas. An examination of the calibrated model indicated that the model distinguished different levels of crash rate for different precursor values and hence could be a useful tool in estimating the likelihood of incidents for real-time freeway incident management systems.

In this thesis an incident prediction model is formulated and calibrated. The primary idea of the model developed is to correlate the expected number of crashes on any section of a freeway to a set of traffic stream characteristics, so that a reliable estimation of likelihood of crashes can be provided on a real-time basis. Traffic stream variables used as explanatory variables in this model are termed as “incident precursors”. The most promising incident precursors for the model formulation for this research were determined by reviewing past research. The statistical model employed is the categorical log-linear model with coefficient of speed variation and occupancy as the precursors. Peak-hour indicators and roadway-type indicators were additional categorical variables used in the model. The model was calibrated using historical loop detector data and crash reports, both of which were available from test beds in Austin, Texas. An examination of the calibrated model indicated that the model distinguished different levels of crash rate for different precursor values and hence could be a useful tool in estimating the likelihood of incidents for real-time freeway incident management systems.

The newly published Highway Safety Manual (HSM) has defined six different driveway types based on land use and parking lot size, including major commercial, minor commercial, major industrial/institutional, minor industrial/institutional, major residential, and minor residential. Major driveways are defined as having more than 50 parking spaces, while minor driveways are defined as those having less than 50 parking spaces. The HSM crash prediction models require the inputs of driveway type and density. However, the actual impacts of different types of driveways on the number of crashes are not clear. The crash modification factors were provided based on few past studies, with high standards of error. The purpose of this research is to develop a method to quantify the impact of driveway types and density on traffic crash frequencies, types, and severities. The different driveway types were collected in the State of Illinois and crashes occurring in the function area of each driveway were identified from the IDOT crash database (from 2005 to 2009). A cross section comparison was conducted to compare the mean crash frequency among different driveways. A further statistical analysis was used to develop a relationship between the density of each driveway type and crash frequency and rates.

The purpose of this thesis is to quantify the economic costs associated with traffic crashes for 83 of the largest metropolitan areas in the United States and compare those costs to that of congestion. This was done by collecting injury and fatality data for each area and multiplying those by economic cost estimates for each developed by the FHWA. The findings of this analysis show that the economic cost of traffic crashes exceeds the economic costs of congestion in every metropolitan area studied. These results indicate that transportation safety deserves similar consideration to that of traffic congestion when allocation transportation funds.

Measured by the level of transportation safety, the Commonwealth of Virginia stands out from all the States because despite increased need for mobility, it manages to maintain its safety indices at exceptionally good levels. In many respects we can attribute this success to the comprehensive Strategic Highway Safety Plan of Virginia (SHSP), which is, as concluded from the analysis of its analogues, among the best in the US. The programs and policies described in this document embrace all aspects of transportation safety and create a harmonious system. To assess the effectiveness of the SHSP, this thesis used correlation and regression analysis based on statistical data from the years 2004 – 2011 in Virginia Department of Transportation (VDOT), and Virginia Department of Motor Vehicles (DMV) documents. The performed calculations showed very positive trends with gradual reduction, in crash and death rates. It was also found that citizens would use private vehicles more often in the future without making conditions worse on the highways. Instead, drivers tend to be more careful and responsible. Analysis also reveals a rising level of drunken driving incidents, a finding substantiated by literature review, chiefly planning reports and economic analysis. The current transportation policy I does not adequately address this issue. The correlation between allocation of funds and performance indicators showed it would be more effective to invest in research projects on safety rather than in “safety” itself (i.e. construction of roads). Unfortunately, in difficult times, governments usually cut research projects. Finally, Virginia is on the verge of a new transportation era, when the structure of driving cohorts will change, and decisions about building new highways will have to be balanced between technical and ecological considerations.

Master of Science / Department of Civil Engineering / Eric J. Fitzsimmons / Transportation officials continuously seek to prevent and reduce wrong-way crashes on interstate highways in the United States. These crashes typically have a high probability of head-on vehicle crashes, resulting in fatalities or serious injuries due to excessive vehicle speeds, and decreased room to maneuver because of fixed barriers or rough shoulders. This research project studied wrong-way crashes on interstate highways in Kansas in order to determine what, if any, statistically significant variables contribute to wrong-way driving crashes. Although these crashes represented only 0.05 percent of all vehicle crashes in Kansas in 2015, wrong-way crashes were found to have a higher rate of fatalities and injuries. In Kansas, 22.6 percent of all crashes and 56 percent of all wrong-way crashes resulted in fatalities and injuries, even though typical vehicle crashes in Kansas occur at non-intersection locations in daylight or in the presence of streetlights without negative factors of adverse weather conditions or drivers influenced by alcohol or drugs. Using crash data provided by the Kansas Department of Transportation from the years 2005 to 2015, the research team examined 372 wrong-way crashes. A cumulative logit statistical model was developed to identify significant characteristics of variables associated with each wrong-way crash. Results showed that driver not under the influence of alcohol or drugs was a significant characteristic in fatal and injury wrong-way crashes. Additionally, certain days of the week were associated with decreased vehicle crash rates when compared to the reference category.

The demand for in-vehicle information systems (IVIS) has been increasing through the years. There are numerous systems that can be incorporated into vehicles and various ways in which the information can and should be presented to the driver. The way the information is presented to the drivers is extremely important in terms of increasing safety and decreasing driver distraction. The expected outcomes of this research included the development of human factors guidelines for the design and use of in-vehicle information systems. It was a desirable goal to identify the most suitable information presentation formats for certain tasks, since this may influence the drivers' attention and driving performance. This study focused on how the factors of interest may affect drivers' attention and driving performance while performing IVIS secondary tasks related to specific applications. This was accomplished through an on-road within-factors experiment. Sixteen participants performed secondary tasks related to three IVIS applications at two levels of difficulty. The tasks were presented using five types of displays. Data collected from video and in-vehicle sensors were statistically analyzed to determine significant effects between the factors. Driving performance, external reaction time, and perceived mental workload results were compiled into general guidelines for the design and use of IVIS. The findings of this study strongly suggest that visual displays should not be used for the presentation of IVIS. Auditory and multi-modal (i.e. both visual and auditory interface) displays are the most appropriate ways to present IVIS information. A normal speech rate is preferred over a fast speech rate. IVIS tasks should be kept as simple as possible in terms of the number of steps. From the three manipulated factors (type of display, IVIS application, and task level of difficulty), the type of display had the largest number of significant results across the dependent variables measurements. The visual display led to the worst driver performance, while auditory and multi-modal displays yielded significantly better driving performance. / Master of Science

Master of Science / Department of Civil Engineering / Sunanda Dissanayake / Over the past few years, motorcycle fatalities have increased at an alarming rate in the United States. Motorcycle safety issues in Kansas are no different from the national scenario. Accordingly, this study attempted to investigate motorcycle crashes in Kansas to identify critical characteristics and to evaluate the effect of those on motorcycle crash injury outcomes. State-level motorcycle rider fatality rates were investigated while considering various factors including helmet laws, using generalized least squares regression modeling. A detailed characteristic analysis was carried out for motorcycle crashes, using Kansas crash data. Comparisons were made between several aspects of motorcycle crashes and other vehicle crashes. Analysis using Logistic regression was performed on Kansas motorcycle crash data to identify factor affecting fatal motorcycle crashes. In addition, a survey was conducted focusing on identifying motorcycle rider behaviors, helmet usage patterns, perception towards helmet laws in Kansas, potential problems, crash contributory factors, and difficulty levels of different motorcycle maneuvers to execute. Ordered probit modeling was used to identity factors contributing to increased severity of Kansas motorcycle riders involved in crashes. Results from state-level modeling showed statistically significant relationships between motorcycle fatality rates in a given state as well as several other factors. These factors included weather-related conditions, helmet laws, per capita income, highway mileage of rural roads, population density, education, demographic distributions, and motorcycle registrations in the state. The study showed that states with mandatory helmet laws had 5.6% fewer motorcycle fatalities per 10,000 registrations and 7.85% fewer motorcycle fatalities per 100,000 populations. Characteristic analysis of motorcycle crashes in Kansas revealed that motorcycle maneuvers such as overtaking, motorcyclists being older than 40 years, using motorcycle helmets, using motorcycle helmets and eye protection simultaneously, daytime riding, crashes occurring on roadside shoulders, and influence of alcohol among the riders during crashes had higher risk of ending up as a fatal motorcycle crash in Kansas. Results from the survey conducted among motorcycle riders in Kansas revealed that 71% of respondents thought drivers of other vehicles were the single biggest threat to their own safety. Survey results also revealed that 64% of respondents opposed a mandatory law requiring motorcycle riders and passengers to wear helmets in Kansas. Result from the ordered probit modeling of motorcycle rider injury severity showed that overturned and fixed-object motorcycle crashes, helmet use, younger motorcycle riders, speeding, presence of alcohol among motorcycle riders, and good weather contributed to increased severity of injury of motorcycle riders involved in crashes in Kansas.

Typescript (photocopy). / Digitized by Kansas Correctional Industries / Department: Regional and Community Planning.

In 2010, the American Association of State Highway and Transportation Officials (AASHTO) released the first edition of the Highway Safety Manual (HSM). The HSM introduces a six-step safety management process which provides engineers with a systematic and scientific approach to managing road safety. The first step of this process, network screening, aims to identify the locations that will most benefit from a safety improvement program. The output obtained from network screening is simply a list of locations that have a high concentration of collisions, based on their potential for safety improvement. The ranking naturally tends to lead to the assumption that the most highly ranked locations are the obvious target locations where road authorities should allocate their often-limited road safety resources. Though these locations contain the highest frequency of collisions, they are often spatially unrelated, and scattered throughout the roadway network. Allocating safety resources to these locations may not be the most effective method of increasing road safety. The purpose of this research is to investigate and validate a two-step method of post-network screening analysis, which identifies collision hotzones (i.e., groups of neighboring hotspots) on a road network. The first step is the network screening process described in the HSM. The second step is new and involves network-constrained kernel density estimation (KDE), a type of spatial analysis. KDE uses expected collision counts to estimate collision density, and outputs a graphical display that shows areas (referred to here as hotzones) with high collision densities. A particularly interesting area of application is the identification of high-collision corridors that may benefit from a program of systemic safety improvements. The proposed method was tested using five years of collision data (2005-2009) for the City of Regina, Saskatchewan. Three different network screening measures were compared: 1) observed collision counts, 2) observed severity-weighted collision counts, and 3) expected severity-weighted collision counts. The study found that observed severity-weighted collision counts produced a dramatic picture of the City's hotzones, but this picture could be misleading as it could be heavily influenced by a small number of severe collisions. The results obtained from the expected severity-weighted collision counts smoothed the effects of the severity-weighting and successfully reduced regression-to-the-mean bias. A comparison was made between the proposed approach and the results of the HSM’s existing network screening method. As the proposed approach takes the spatial association of roadway segments into account, and is not limited to single roadway segments, the identified hotzones capture a higher number of expected EPDO collisions than the existing HSM methodology. The study concludes that the proposed two-step method can help transportation safety professionals to prioritize hotzones within high-collision corridors more efficiently and scientifically. Jurisdiction-specific safety performance functions (SPFs) were also developed over the course of this research, for both intersections (three-leg unsignalized, four-leg unsignalized, three and four-leg signalized), and roadway segments (major arterials, minor arterials, and collectors). These SPFs were compared to the base SPFs provided in the HSM, as well as calibrated HSM SPFs. To compare the different SPFs and find the best-fitting SPFs for the study region, the study used statistical goodness-of-fit (GOF) tests and cumulative residual (CURE) plots. Based on the results of this research, the jurisdiction-specific SPFs were found to provide the best fit to the data, and would be the best SPFs for predicting collisions at intersections and roadway segments in the City of Regina.