Driving the wrong way on freeways has been a nagging traffic safety problem since the interstate highway system was founded in the 1950s. Despite four decades of highway striping and sign improvements at freeway interchanges, the problem persists. This paper is to determine the contributing factors to wrong-way driving on freeways and to develop promising, cost-conscious countermeasures to reduce this driving errors and related crashes. Wrong-way crash data from Illinois Department of Transportation (IDOT) crash database were collected with 632 possible wrong-way crashes. The real wrong-way crashes were further identified by reviewing the wrong-way crash reports hardcopies and information from other resources. Characteristics of wrong-way driving behaviors were analyzed and statistical analyses were conducted to identify the contributing factors of wrong-way crashes on freeway. The state-of-the-art roadway safety management process recommended by the Highway Safety Manual (HSM) was adopted to diagnose the wrong-way driving behavior on Illinois freeway and develop the specific wrong-way crashes management procedures. The first three steps, network screening, diagnosis and countermeasure selection was developed in details. The whole procedure developed could be used to guide the management of freeway wrong-way crashes in the future. The specific procedure of transportation network review, candidate location identification and site ranking for freeway wrong-way crashes was established firstly. Based on the collected wrong-way crash data, the safety performance function (SPF) for wrong-way crashes on freeway was developed with the annual average daily traffic (AADT) and segment length being the independent variables. The procedures for candidate wrong-way crash sites diagnoses with crash data, historic site data, field condition and other information were described step by step. The methods for contributing factors identification were proposed and the Haddon matrix for wrong-way crashes on freeway was constructed finally. Methods for selecting wrong-way crash countermeasures from the perspective of "four E's" based on crash analysis finding, site-specific contributing factors and geographical characteristics were discussed, and research needs on wrong-way crash management in the future were recommended finally.

Master of Science / Department of Civil Engineering / Sunanda Dissanayake / Kansas experienced about 60,000 crashes annually from 2013 to 2016, 25% of which occurred at urban intersections. Hence, urban intersections in Kansas are one of the most critical locations in terms of frequency of crashes. Therefore, an accurate prediction of crashes at these locations would help identify critical intersections with a higher probability of an occurrence of crash, which would help in selecting appropriate countermeasures to reduce those crashes. The crash prediction models provided in the Highway Safety Manual (HSM) predict crashes using traffic and geometric data for various roadway facilities, which are incorporated through Safety Performance Functions (SPFs) and Crash Modification Factors. The primary objective of this study was to estimate calibration factors for different types of urban intersection in Kansas. This study followed the crash prediction method and calibration procedure provided in the HSM to estimate calibration factors for four different urban intersection types in Kansas: 3-leg unsignalized intersections with stop control on the minor approach (3ST), 3-leg signalized intersections (3SG), 4-leg unsignalized intersections with stop control on the minor approach (4ST), and 4-leg signalized intersections (4SG). Following the HSM methodology, the required data elements were collected from various sources. The Annual Average Daily Traffic (AADT) data were extracted from Kansas Crash Analysis & Reporting System (KCARS) database and GIS Shapefiles downloaded from Federal Highway Administration website. For some of 3ST and 3SG intersections, minor-street AADT was not available. Hence, multiple linear regression models were developed for the estimation of minor-street AADT. Crash data were extracted from the Kansas Crash Analysis and Reporting System database, and other geometric data were extracted using Google Earth. The HSM requirement for sample size is 30 to 50 sites, with at least 100 crashes per year for the study period for the combined set of sites. In this study, the study period for 3ST, 3SG, and 4SG intersections were taken as 2013 to 2015, and 2014 to 2016 for 4ST, based on the availability of recent crash data at the beginning of the calibration procedure for each facility type. The sample size considered for calibration was 234 for 3ST, 89 for 3SG, 167 for 4ST, and 198 for 4SG intersections. Out of the 234 3ST intersections, minor-street AADT was estimated using multiple linear regression models for 106 intersections. For 3SG intersections, minor-street AADT was estimated for 21 out of the 89 intersections. The calibration factors for these facility types were estimated to be 0.64 for 3SG, 0.51 for 3ST, 1.17 for 4SG, and 0.61 for 4ST when considering crashes of all severities. Considering only the fatal and injury crashes, the calibration factors were estimated as 0.52 for 3SG, 0.40 for 3ST, 2.00 for 4SG, and 0.73 for 4ST. The calibration factors show that the HSM methodology underpredicted crashes for 4SG, and overpredicted crashes for other three intersection types. The reliability of the calibration factors was assessed with the help of Cumulative Residual plots and coefficient of variation. The results from the goodness-of-fit tests showed that the calibration factors were not reliable and showed bias in the prediction of crashes. Hence, calibration functions were developed, and their reliability were examined. The results showed that calibration functions had better reliability as compared to calibration factors, with more accuracy in crash prediction. The findings from this study can be used to identify intersections with a higher probability of having crashes in the future. Suitable countermeasures can be applied at critical locations which would help reduce the number of crashes at urban intersections in Kansas; thus increasing the safety.

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.

The author of the Highway Safety Manual (HSM) Part B developed a predictive method for safety management. A software tool for highway safety system analysis called the SafetyAnalyst is developed basing on HSM Part B. The author describes an effort to evaluate the feasibility of SafetyAnalyst in Oregon. Seven sample highway sections in Oregon are selected to demonstrate the SafetyAnalyst network screening application. The purpose of this research is to assess if the SafetyAnalyst is compatible with current Oregon Department of Transportation (ODOT) databases such as the Highway Inventory Detail Report, Lane Report, etc. The author also presents an effort to identify current data deficiencies and identify a feasible solution for addressing these deficiencies. SafetyAnalyst requires hundreds of input variables. Not all of these variables are included in the current Oregon database. Those input variables that require additional data collection are described as well. This thesis also includes a sensitivity test for input variables to prioritize required variables. Finally, the author determines that the SafetyAnalyst can be used in Oregon. This research also provides a variable priority for the SafetyAnalyst users. / Graduation date: 2012

Tese de Mestrado Integrado. Engenharia Civil (Vias de Comunicação). Faculdade de Engenharia. Universidade do Porto. 2013

This dissertation addresses the relationship between roadway segment length and roadway attributes and their relationship to the efficacy of Safety Performance Function (SPF) models. This research focuses on three aspects of segmentation: segment length, roadway attributes, and combinations of the two. First, it is shown that choice of average roadway segment length can result in markedly different priority lists. This leads to an investigation of the effect of segment length on the development of SPFs and identifies average lengths that produce the best-fitting SPF. Secondly, roadway attributes are filtered to test the effect that homogeneity has on SPF development. Lastly, a combination of segment length and attributes are examined in the same context. In the process of conducting this research a tool was developed that provides objective goodness-of-fit measures as well as visual depictions of the model. This information can be used to avoid things like omitted variable bias by allowing the user to include other variables or filter the database. This dissertation also discusses and offers examples of ways to improve the models by employing alternate model forms. This research revealed that SPF development is sensitive to a variety of factors related to segment length and attributes. It is clear that strict base condition filters based on the most predominant roadway attributes provide the best models. The preferred functional form was shown to be dependent on the segmentation approach (fixed versus variable length). Overall, an important step in SPF development process is evaluation and comparison to determine the ideal length and attributes for the network being analyzed (about 2 miles or 3.2 km for Kentucky parkways). As such, a framework is provided to help safety professionals employ the findings from this research.

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Civil e Ambiental, 2016. / Submitted by Albânia Cézar de Melo (albania@bce.unb.br) on 2016-05-02T15:22:29Z No. of bitstreams: 1 2016_DanieleRoewerLagemannSWaihrich.pdf: 22832959 bytes, checksum: 5f8ac52b5af727175e96735f4cc7385d (MD5) / Approved for entry into archive by Raquel Viana(raquelviana@bce.unb.br) on 2016-05-03T21:27:19Z (GMT) No. of bitstreams: 1 2016_DanieleRoewerLagemannSWaihrich.pdf: 22832959 bytes, checksum: 5f8ac52b5af727175e96735f4cc7385d (MD5) / Made available in DSpace on 2016-05-03T21:27:19Z (GMT). No. of bitstreams: 1 2016_DanieleRoewerLagemannSWaihrich.pdf: 22832959 bytes, checksum: 5f8ac52b5af727175e96735f4cc7385d (MD5) / Na promoção da segurança é importante o estabelecimento de recursos para uma avaliação quantitativa da segurança no ambiente viário. Os modelos preditivos de acidentes, a partir de técnicas estatísticas adequadas, estimam o número esperado de acidentes em diferentes momentos de um empreendimento viário, podendo, em especial, atuar preventivamente na segurança. No Brasil, o desenvolvimento de modelos preditivos de acidentes não está ainda bem instituído, havendo uma carência de modelos que quantifiquem a segurança em rodovias brasileiras. O manual americano Highway Safety Manual (HSM) apresenta um método preditivo para diferentes configurações viárias e inclui um procedimento de calibração do método, o que possibilita a sua transferência para outras localidades. Nesta dissertação foi realizada a calibração do método preditivo do HSM em rodovias de pista dupla nas regiões de Minas Gerais e Goiás/ Distrito Federal, tendo por resultado um Fator de Calibração para cada uma das regiões estudadas. Na avaliação da transferibilidade do modelo calibrado foram aplicadas medidas de qualidade de ajuste. Os resultados obtidos não confirmam a transferibilidade do modelo original do HSM calibrado nos cenários estudados, pois, embora tenham sido obtidos resultados satisfatórios na região MG, foram também observados resultados precários das medidas de qualidade de ajuste na região GO/DF. Dessa forma, neste estudo inicial em rodovias rurais de pista dupla no Brasil, a transferência do modelo HSM não se confirmou como uma alternativa efetiva, o que não descarta a possibilidade da obtenção de um modelo calibrado com algum grau de sucesso em outras rodovias e para outras regiões. Os retornos e faixas adicionais são dispositivos frequentes nos segmentos rodoviários estudados. Em função disso, foram delimitadas amostras alternativas que buscaram avaliar o impacto destes dispositivos na segurança, uma vez que os mesmos não são incluídos no modelo preditivo original do HSM. A partir desta análise, foram obtidos, apenas em nível exploratório, “Fatores de Modificação de Acidentes” específicos para estes dispositivos. _______________________________________________________________________________________________ ABSTRACT / When promoting safety, it is important to establish resources to carry out a quantitative assessment of safety in the road environment. Accident predictive models, based on proper statistic technics, estimate the expected number of accidents in different moments of a road system, making it possible to act preventively in regards to safety. In Brazil, the development of accident predictive models has not been well established yet. We lack models that quantify safety in Brazilian highways. The American Highway Safety Manual – HSM – presents a predictive method for different road configurations and includes a procedure for method calibration, which enables its application in other parts of the world. In this dissertation, we have carried out the calibration of the HSM predictive method to multilane highways in the Minas Gerais and Goiás/Federal Ditrict regions, having a calibration factor for each studied region as a result. When validating the capacity of the model of being transferred, we have applied measures to assess the adjustment quality of the calibrated model. The results obtained did not confirm the transferability of the original HSM model, which was calibrated according to the studied scenarios. This occurred because, although we have obtained satisfactory results in the Minas Gerais region, we have observed disappointing results regarding the adjustment quality measures of the calibrated model in the GO/DF region. Thus, this initial study on rural multilane highways in Brazil did not confirm the transference of HSM model as an effective alternative. However, this does not rule out the possibility of obtaining a calibrated model with some degree of success in other highways and other regions. U-turns and Passing Lanes are common features in the studied highway segments. For that reason, we have selected alternative samples for the purpose of assessing the impact they had in safety, since they are not present in the original HSM predictive model. Following this analysis, we have obtained, only at an exploratory level, “accident modification factors” specific for such features.

This thesis documents the results of the calibration of the Highway Safety Manual (HSM) safety performance function (SPF) for rural two-lane two-way roadway segments in Utah and the development of new SPFs using negative binomial and hierarchical Bayesian modeling techniques. SPFs estimate the safety of a roadway entity, such as a segment or intersection, in terms of number of crashes. The new SPFs were developed for comparison to the calibrated HSM SPF. This research was performed for the Utah Department of Transportation (UDOT).The study area was the state of Utah. Crash data from 2005-2007 on 157 selected study segments provided a 3-year observed crash frequency to obtain a calibration factor for the HSM SPF and develop new SPFs. The calibration factor for the HSM SPF for rural two-lane two-way roads in Utah is 1.16. This indicates that the HSM underpredicts the number of crashes on rural two-lane two-way roads in Utah by sixteen percent. The new SPFs were developed from the same data that were collected for the HSM calibration, with the addition of new data variables that were hypothesized to have a significant effect on crash frequencies. Negative binomial regression was used to develop four new SPFs, and one additional SPF was developed using hierarchical (or full) Bayesian techniques. The empirical Bayes (EB) method can be applied with each negative binomial SPF because the models include an overdispersion parameter used with the EB method. The hierarchical Bayesian technique is a newer, more mathematically-intense method that accounts for high levels of uncertainty often present in crash modeling. Because the hierarchical Bayesian SPF produces a density function of a predicted crash frequency, a comparison of this density function with an observed crash frequency can help identify segments with significant safety concerns. Each SPF has its own strengths and weaknesses, which include its data requirements and predicting capability. This thesis recommends that UDOT use Equation 5-11 (a new negative binomial SPF) for predicting crashes, because it predicts crashes with reasonable accuracy while requiring much less data than other models. The hierarchical Bayesian process should be used for evaluating observed crash frequencies to identify segments that may benefit from roadway safety improvements.

This thesis contains the results of the development of crash prediction models for curved segments of rural two-lane two-way highways in the state of Utah. The modeling effort included the calibration of the predictive model found in the Highway Safety Manual (HSM) as well as the development of Utah-specific models developed using negative binomial regression. The data for these models came from randomly sampled curved segments in Utah, with crash data coming from years 2008-2012. The total number of randomly sampled curved segments was 1,495. The HSM predictive model for rural two-lane two-way highways consists of a safety performance function (SPF), crash modification factors (CMFs), and a jurisdiction-specific calibration factor. For this research, two sample periods were used: a three-year period from 2010 to 2012 and a five-year period from 2008 to 2012. The calibration factor for the HSM predictive model was determined to be 1.50 for the three-year period and 1.60 for the five-year period. These factors are to be used in conjunction with the HSM SPF and all applicable CMFs. A negative binomial model was used to develop Utah-specific crash prediction models based on both the three-year and five-year sample periods. A backward stepwise regression technique was used to isolate the variables that would significantly affect highway safety. The independent variables used for negative binomial regression included the same set of variables used in the HSM predictive model along with other variables such as speed limit and truck traffic that were considered to have a significant effect on potential crash occurrence. The significant variables at the 95 percent confidence level were found to be average annual daily traffic, segment length, total truck percentage, and curve radius. The main benefit of the Utah-specific crash prediction models is that they provide a reasonable level of accuracy for crash prediction yet only require four variables, thus requiring much less effort in data collection compared to using the HSM predictive model.

The Highway Safety Manual (HSM) estimates roadway safety performance based on predictive models that were calibrated using national data. Calibration factors are then used to adjust these predictive models to local conditions for local applications. The HSM recommends that local calibration factors be estimated using 30 to 50 randomly selected sites that experienced at least a total of 100 crashes per year. It also recommends that the factors be updated every two to three years, preferably on an annual basis. However, these recommendations are primarily based on expert opinions rather than data-driven research findings. Furthermore, most agencies do not have data for many of the input variables recommended in the HSM. This dissertation is aimed at determining the best way to meet three major data needs affecting the estimation of calibration factors: (1) the required minimum sample sizes for different roadway facilities, (2) the required frequency for calibration factor updates, and (3) the influential variables affecting calibration factors. In this dissertation, statewide segment and intersection data were first collected for most of the HSM recommended calibration variables using a Google Maps application. In addition, eight years (2005-2012) of traffic and crash data were retrieved from existing databases from the Florida Department of Transportation. With these data, the effect of sample size criterion on calibration factor estimates was first studied using a sensitivity analysis. The results showed that the minimum sample sizes not only vary across different roadway facilities, but they are also significantly higher than those recommended in the HSM. In addition, results from paired sample t-tests showed that calibration factors in Florida need to be updated annually. To identify influential variables affecting the calibration factors for roadway segments, the variables were prioritized by combining the results from three different methods: negative binomial regression, random forests, and boosted regression trees. Only a few variables were found to explain most of the variation in the crash data. Traffic volume was consistently found to be the most influential. In addition, roadside object density, major and minor commercial driveway densities, and minor residential driveway density were also identified as influential variables.