Doctor of Philosophy / Civil Engineering / Sunanda Dissanayake / Rural roads account for 90.3% of the 140,476 total centerline miles of roadways in Kansas. In recent years, rural fatal crashes have accounted for about 66% of all fatal crashes. The Highway Safety Manual (HSM) provides models and methodologies for analyzing the safety of various types of highways. Predictive methods in the HSM were developed based on national trends and data from few states throughout the United States. However, these methodologies are of limited use if they are not calibrated for individual jurisdictions or local conditions. The objective of this study was to analyze the HSM calibration procedures for rural multilane segments and intersections in Kansas. The HSM categorizes rural multilane segments as four-lane divided (4D) and four-lane undivided (4U) segments and rural multilane intersections as three-legged intersections with minor-road stop control (3ST), four-legged intersections with minor-road stop control (4ST), and four-leg signalized intersections (4SG). The number of predicted crashes at each segment was obtained according to the HSM calibration process. Results from calibration of rural segments indicated that the HSM overpredicts fatal and injury crashes by 50% and 65% and underpredicts total crashes by 48% and 64% on rural 4D and 4U segments, respectively. The HSM-given safety performance function (SPF) regression coefficients were then modified to capture variation in crash prediction. The adjusted models for 4D and 4U multilane segments indicated significant improvement in crash prediction for rural Kansas. Furthermore, Kansas-specific safety performance functions (SPF)s were developed following the HSM recommendations. In order to develop Kansas-specific SPF, Negative Binomial regression was applied to obtain the most suitable model. Several additional variables were considered and tested in the new SPFs, followed by model validation on various sets of locations. The Kansas-specific SPFs are capable of more accurately predicting total and fatal and injury crashes on multilane segments compared to the HSM and the modified HSM models. In addition to multilane segments, rural intersections on multilane highways were also calibrated according to the HSM methodology. Using crash modification factors for corresponding variables, SPFs were adjusted to obtain final predicted crash frequency at intersections. Obtained calibration factors indicated that the HSM is capable of predicting crashes at intersections at satisfactory level. Findings of this study can be used for improving safety of rural multilane highways.

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.

Evaluation of crash count data as a function of roadway characteristics allows Departments of Transportation to predict expected average crash risks in order to assist in identifying segments that could benefit from various treatments. Currently, the evaluation is performed using negative binomial regression, as a function of average annual daily traffic (AADT) and other variables. For this thesis, a crash study was carried out for the interstate, primary and secondary routes, in the Salem District of Virginia. The data used in the study included the following information obtained from Virginia Department of Transportation (VDOT) records: 2010 to 2012 crash data, 2010 to 2012 AADT, and horizontal radius of curvature (CV). Additionally, tire-pavement friction or skid resistance was measured using a continuous friction measurement, fixed-slip device called a Grip Tester. In keeping with the current practice, negative binomial regression was used to relate the crash data to the AADT, skid resistance and CV. To determine which of the variables to include in the final models, the Akaike Information Criterion (AIC) and Log-Likelihood Ratio Tests were performed. By mathematically combining the information acquired from the negative binomial regression models and the information contained in the crash counts, the parameters of each network's true average crash risks were empirically estimated using the Empirical Bayes (EB) approach. The new estimated average crash risks were then used to rank segments according to their empirically estimated crash risk and to prioritize segments according to their expected crash reduction if a friction treatment were applied. / Master of Science

Roadway surface friction along the West Virginia Division of Highways' roadway network is key to the safety of all traveling motorists. Being geographically located in the rugged Appalachian Mountains, the West Virginia Division of Highways' roadway network is flooded with innumerable geometric and design challenges, causing drivers to have to exercise the most care and attention when navigating the network. This dissertation introduces the concept of roadway surface friction management to this network. For decades, roadway surface friction has only been tested and checked on an as-needed basis at crash sites and intersections, in legal situations, and pavement acceptance on construction projects. It also seeks to use the acquired data through a case study to insure proper methodology of roadway surface friction management, to develop sample safety performance functions and best crash estimates, and to apply this decision-making data to provide assistance and guidance in the selection of projects in the West Virginia Highway Safety Improvement Program. This dissertation follows the manuscript format and is composed of three papers. The first chapter of the dissertation examines the usage of Method 3 of the AASHTO Guide for Pavement Friction and the modifications to this method to collect existing roadway surface friction data along the District Ten portion of the network. The second chapter of the dissertation discusses the development of sample safety performance functions to estimate the average number of crashes along each of the tested roadway categories: Interstate Routes, United States Routes, and West Virginia Routes. It also discussed the development of best crash estimates using the Empirical Bayes Method. This is essential to be able to forecast how crash counts should improve, given the application of various roadway improvements. The third and final chapter of the dissertation develops the case study based on the District Ten portion of the network and shows how to enhance project selection in the West Virginia Highway Safety Improvement Program. This is completed by applying the safety performance functions and best crash estimates from the second chapter to arrive at real friction numbers for the network and their project impacts. / Doctor of Philosophy / Roadway surface friction along the West Virginia Division of Highways' roadway network is key to the safety of all traveling motorists. Being geographically located in the rugged Appalachian Mountains, the West Virginia Division of Highways' roadway network is flooded with innumerable geometric and design challenges, causing drivers to have to exercise the most care and attention when navigating the network. This dissertation introduces the concept of roadway surface friction management to this network. For decades, roadway surface friction has only been tested and checked on an as-needed basis at crash sites and intersections, in legal situations, and pavement acceptance on construction projects. It also seeks to use the acquired data through a case study to insure proper methodology of roadway surface friction management, to develop sample safety performance functions and best crash estimates, and to apply this decision-making data to provide assistance and guidance in the selection of projects in the West Virginia Highway Safety Improvement Program. This dissertation follows the manuscript format and is composed of three papers. The first chapter of the dissertation examines the usage of Method 3 of the AASHTO Guide for Pavement Friction and the modifications to this method to collect existing roadway surface friction data along the District Ten portion of the network. The second chapter of the dissertation discusses the development of sample safety performance functions to estimate the average number of crashes along each of the tested roadway categories: Interstate Routes, United States Routes, and West Virginia Routes. It also discussed the development of best crash estimates using the Empirical Bayes Method. This is essential to be able to forecast how crash counts should improve, given the application of various roadway improvements. The third and final chapter of the dissertation develops the case study based on the District Ten portion of the network and shows how to enhance project selection in the West Virginia Highway Safety Improvement Program. This is completed by applying the safety performance functions and best crash estimates from the second chapter to arrive at real friction numbers for the network and their project impacts.

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This thesis comprises two studies that investigated bike infrastructure safety in North America. The first study conducted a corridor-level analysis to quantify the safety of different bike infrastructure types and assess the influence of corridor-specific characteristics on their performance. Using a Poisson-lognormal Full Bayesian model, the study analyzed fatal and injury bike-vehicle collisions on over 7800 corridors in Toronto, Canada. The findings revealed that bike infrastructure effectively reduces bike collisions, with cycle tracks demonstrating superior safety benefits due to the physical separation of cyclists from vehicular traffic. Cycle tracks were found to be particularly effective on long corridors with fewer intersections as bike-vehicle interactions along the corridor are minimized. Signed routes were safe on low-volume and low-speed roads, while bike lanes are more suited for a short section of high-volume corridors with a high intersection density. The second study assessed the safety of parking-protected bike lanes (PPBL), a new concept that is rapidly growing in North America. Utilizing data from nineteen street sections in Vancouver and Ottawa, the study developed a Full Bayesian Before-and-after model to evaluate the safety impacts of converting traditional painted bike lanes to PPBLs. The results indicated a significant reduction of 31.2% in total collisions after PPBL implementation. However, the effects of PPBLs on cyclist safety were found to be sensitive to factors such as bike path opening density, intersection density, and intersection treatments. In roads where proper intersection treatments and minimal protection of PPBL openings can be provided, painted bike lanes can be converted into PPBLs, and significant safety benefits can be expected. The findings of this thesis offer practical guidance for city planners and policymakers regarding the safety implications of different bike infrastructure types and the most appropriate conditions to implement them, which supports bike safety enhancement initiatives and attracts more people to bike. / Thesis / Master of Applied Science (MASc) / This thesis presents two studies that offer valuable insights to improve bike safety. The first study examined the safety of various bike infrastructure types along with the impact of corridor characteristics on their performance. The findings emphasized the effectiveness of cycle tracks in reducing collisions on long corridors with fewer intersections. Signed routes were found to be effective on low-volume and low-speed roads, while bike lanes were ideal on short sections of high-volume roads with a high intersection density. The second study assessed the impacts of new designed concept, known as the parking-protected bike lanes (PPBLs). The study showed that converting painted bike lanes to PPBLs significantly reduced total collisions. However, proper treatment of intersection and bikeway openings is crucial for enhancing cyclist safety and reducing multi-vehicle rear-end collisions. Where proper intersection treatment and minimal protection of bikeway openings can be provided, bike lanes can be safely converted into PPBLs.

The standard method for assessing traffic safety is to use the predictive method outlined in the Highway Safety Manual (HSM). This method is site-level, data-intensive, and does not account for interactions between sites, making it difficult to assess larger areas. This dissertation develops a corridor-level approach to traffic safety which uses less data than the HSM predictive method and views roadways holistically rather than combinations of individual, independent sites. First, a corridor definition is developed and applied to 10 urban Florida counties with a history of many crashes, resulting in the identification of 1,048 corridors. These corridors were primarily defined using context classification and lane count, with additional considerations for data availability and minimum length. From 2017–2021, these corridors experienced 459,603 unique crashes. After preliminary modeling and scope refinement, 559 corridors received supplemental data collection. Between the two datasets, a total of 11 models were developed using either negative binomial (NB) or random forest (RF) regression. NB models can be used for network screening purposes or identifying the impacts of potential safety improvements, while RF models can be used to identify variables important to the accuracy of the prediction. Potential safety improvements identified from the NB models include increasing proactive law enforcement patrols for dangerous driving behaviors and installing corridor lighting in corridors without lighting. While both NB and RF models were accurate, NB models were recommended due to resulting in a definite equation and overdispersion parameter that could be used with the empirical Bayes (EB) method to improve prediction accuracy. Overall, the corridor-level NB models outperformed the HSM models in terms of accuracy and statistical reliability. Using a corridor-level approach can help agencies quickly network screen their systems to identify high-risk corridors in need of safety improvements or supplement site-level analyses.

Road safety is one of the most important problems in our society. It causes hundreds of fatalities every year worldwide. A road accident may be caused by several concurrent factors. The most common are human and infrastructure. Their interaction is important too, which has been studied in-depth for years. Therefore, there is a better knowledge about the driving task. In several cases, these advances are still not included in road guidelines. Some of these advances are centered on explaining the underlying cognitive processes of the driving task. Some others are related to the analysis of drivers’ response or a better estimation of road crashes. The concept of design consistency is related to all of them. Road design consistency is the way how road alignment fits drivers’ expectancies. Hence, drivers are surprised at inconsistent roads, presenting a higher crash risk potential. This PhD presents a new, operating speed-based global consistency model. It is based on the analysis of more than 150 two-lane rural homogeneous road segments of the Valencian Region (Spain). The final consistency parameter was selected as the combination of operational parameters that best estimated the number of crashes. Several innovative auxiliary tools were developed for this process. One example is a new tool for recreating the horizontal alignment of two-lane rural roads by means of an analytic-heuristic process. A new procedure for determining road homogeneous segments was also developed, as well as some expressions to accurately determine the most adequate design speed. The consistency model can be integrated into safety performance functions in order to estimate the amount of road crashes. Finally, all innovations are combined into a new road design methodology. This methodology aims to complement the existing guidelines, providing to road safety a continuum approach and giving the engineers tools to estimate how safe are their road designs. / Camacho Torregrosa, FJ. (2015). DEVELOPMENT AND CALIBRATION OF A GLOBAL GEOMETRIC DESIGN CONSISTENCY MODEL FOR TWO-LANE RURAL HIGHWAYS, BASED ON THE USE OF CONTINUOUS OPERATING SPEED PROFILES [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/48543