Via, condutor e veículo são os principais componentes de um sistema viário. A segurança viária decorre de como estes agentes se relacionam entre si. Esta relação pode ser avaliada através da velocidade operacional que o condutor aplica em seu veículo segundo a percepção de conforto e segurança repassada pela rodovia. A análise da consistência geométrica é uma ferramenta de análise para rodovias de pista simples que permite identificar o padrão de utilização das mesmas, através de modelos de estimativa da velocidade operacional e de critérios classificatórios. A análise de consistência geométrica vem sendo utilizada como ferramenta para avaliar problemas de projeto e operação de rodovias existentes e em projeto. A Federal Highway Administration – FHWA recomenda a utilização deste tipo de análise para avaliar a segurança viária de rodovias de pista simples. Os modelos utilizados na análise de consistência geométrica estão calibrados para condicionantes culturais do condutor, de veículos e de rodovias dos Estados Unidos. Assim, a aplicação direta do software IHSDM – Interactive Highway Safety Design Model em rodovias de pista simples do Brasil não é recomendada. A presente Tese propõe um método para análise da consistência geométrica de rodovias brasileiras de pista simples. O método utiliza modelos próprios para estimativa da velocidade operacional, critérios de aplicação das taxas de aceleração e desaceleração e propõe um índice para classificação das rodovias por quilômetro e por trecho: o ICG (índice de consistência geométrica). No estudo de caso, 14 trechos rodoviários, totalizando 225 quilômetros, são submetidos à análise pelo método proposto. A classificação obtida é confrontada com quatro índices de acidentes distintos, resultantes de uma base de dados de acidentes de seis anos, com 14.608 ocorrências. O resultado deste confronto apresenta uma correlação entre o índice de consistência geométrica por trecho, proposto, e o índice de acidentes totais e parciais de 0,59. O valor obtido confirma a relação prevista entre a freqüência de acidentes e indicadores de consistência geométrica, verificada pela FHWA, surpreendendo pela sua intensidade. O melhor desempenho do índice proposto é associado a sua composição, contemplando os critérios de segurança I (consistência do projeto) e II (consistência da velocidade operacional) e ao comportamento verificado em condutores brasileiro, menos sensíveis a restrições geométricas. / The highway, the individual users and the vehicle are the main agents of a traffic system. Traffic safety is determined by how these agents interconnect. Such relation may be assessed by the observation of the operational speed the driver uses in the vehicle according to his perception of comfort and safety of the roadways. The design consistency analysis is an assessment tool for rural two-lane highways which allows the identification of their usage patterns by using operational speed estimate models and sorting criteria. The design consistency analysis has been used as a tool to assess project and operation flaws of existing highways and projects to construct new highways. The Federal Highway Administration – FHWA recommends the use of this kind of analysis to assess the traffic safety of rural two-lane highways. The models used for the design consistency analysis are calibrated to the conditional cultural aspects of United States of America’s drivers, vehicles and highways. Therefore, the straight application of IHSDM – Interactive Highway Safety Design Model software in Brazilian rural two-lane highways is not recommended. This doctoral thesis aims at presenting a method of design consistency analysis of Brazilian rural two-lane highways. Such method uses original models for estimating the operational speed, new criteria for application of acceleration vehicle dynamics rate and introduces a new index to classify highways by kilometer and by segment: the ICG or Design Consistency Index. In this case study, 14 roadway segments – of a total of 225 kilometers – were analyzed by the proposed method. The resulting classification was compared to four other accident indexes from a database of 14,608 official records collected in six years. The result of this comparison leads to a correlation between the proposed design consistency index by highways segment and the total and partial accident index of 0.59. The result confirms the foreseen relation between the accident frequency and the design consistency indicators verified by FHWA – and its intensity is astonishing. The best performance of the proposed index is associated to its composition, which involves the safety criteria I (design consistency) and II (operational speed consistency), and to the behavior observed in Brazilian drivers, less sensitive to geometric restrictions.

Via, condutor e veículo são os principais componentes de um sistema viário. A segurança viária decorre de como estes agentes se relacionam entre si. Esta relação pode ser avaliada através da velocidade operacional que o condutor aplica em seu veículo segundo a percepção de conforto e segurança repassada pela rodovia. A análise da consistência geométrica é uma ferramenta de análise para rodovias de pista simples que permite identificar o padrão de utilização das mesmas, através de modelos de estimativa da velocidade operacional e de critérios classificatórios. A análise de consistência geométrica vem sendo utilizada como ferramenta para avaliar problemas de projeto e operação de rodovias existentes e em projeto. A Federal Highway Administration – FHWA recomenda a utilização deste tipo de análise para avaliar a segurança viária de rodovias de pista simples. Os modelos utilizados na análise de consistência geométrica estão calibrados para condicionantes culturais do condutor, de veículos e de rodovias dos Estados Unidos. Assim, a aplicação direta do software IHSDM – Interactive Highway Safety Design Model em rodovias de pista simples do Brasil não é recomendada. A presente Tese propõe um método para análise da consistência geométrica de rodovias brasileiras de pista simples. O método utiliza modelos próprios para estimativa da velocidade operacional, critérios de aplicação das taxas de aceleração e desaceleração e propõe um índice para classificação das rodovias por quilômetro e por trecho: o ICG (índice de consistência geométrica). No estudo de caso, 14 trechos rodoviários, totalizando 225 quilômetros, são submetidos à análise pelo método proposto. A classificação obtida é confrontada com quatro índices de acidentes distintos, resultantes de uma base de dados de acidentes de seis anos, com 14.608 ocorrências. O resultado deste confronto apresenta uma correlação entre o índice de consistência geométrica por trecho, proposto, e o índice de acidentes totais e parciais de 0,59. O valor obtido confirma a relação prevista entre a freqüência de acidentes e indicadores de consistência geométrica, verificada pela FHWA, surpreendendo pela sua intensidade. O melhor desempenho do índice proposto é associado a sua composição, contemplando os critérios de segurança I (consistência do projeto) e II (consistência da velocidade operacional) e ao comportamento verificado em condutores brasileiro, menos sensíveis a restrições geométricas. / The highway, the individual users and the vehicle are the main agents of a traffic system. Traffic safety is determined by how these agents interconnect. Such relation may be assessed by the observation of the operational speed the driver uses in the vehicle according to his perception of comfort and safety of the roadways. The design consistency analysis is an assessment tool for rural two-lane highways which allows the identification of their usage patterns by using operational speed estimate models and sorting criteria. The design consistency analysis has been used as a tool to assess project and operation flaws of existing highways and projects to construct new highways. The Federal Highway Administration – FHWA recommends the use of this kind of analysis to assess the traffic safety of rural two-lane highways. The models used for the design consistency analysis are calibrated to the conditional cultural aspects of United States of America’s drivers, vehicles and highways. Therefore, the straight application of IHSDM – Interactive Highway Safety Design Model software in Brazilian rural two-lane highways is not recommended. This doctoral thesis aims at presenting a method of design consistency analysis of Brazilian rural two-lane highways. Such method uses original models for estimating the operational speed, new criteria for application of acceleration vehicle dynamics rate and introduces a new index to classify highways by kilometer and by segment: the ICG or Design Consistency Index. In this case study, 14 roadway segments – of a total of 225 kilometers – were analyzed by the proposed method. The resulting classification was compared to four other accident indexes from a database of 14,608 official records collected in six years. The result of this comparison leads to a correlation between the proposed design consistency index by highways segment and the total and partial accident index of 0.59. The result confirms the foreseen relation between the accident frequency and the design consistency indicators verified by FHWA – and its intensity is astonishing. The best performance of the proposed index is associated to its composition, which involves the safety criteria I (design consistency) and II (operational speed consistency), and to the behavior observed in Brazilian drivers, less sensitive to geometric restrictions.

This research focuses on the Crash Analysis Methodology for Segments (CAMS) which provides a way for engineers at the Utah Department of Transportation (UDOT) to prioritize safety improvements on state-owned roadways. Unlike the Utah crash analysis methodologies that come before it, the CAMS focuses exclusively on segment-related crashes. The benefits of such an analysis can be found in identifying locations that have safety concerns unbiased from intersections and their related crashes. The CAMS uses UDOT data to create a spreadsheet of roadway segments and their associated crashes. Each segment is homogeneous with respect to five variables: Annual Average Daily Traffic (AADT), functional class, number of lanes, speed limit, and urban code. In the statistical analyses performed on the data, four years of crash data (2014-2017) are used to predict distributions of crashes for the most recent year of data (2018). Observed crash counts are compared to the predicted distributions and assigned a percentile value within the distributions, and segments are subsequently ranked in order of safety concern according to those percentiles. Two-page technical reports are created for segments that rank high in the state or UDOT Region. These reports consist of concise tables of roadway data and crash trends pertaining to each segment. Research analysts also add observations made in virtual site visits to the reports. In the end, the results and the reports are sent to UDOT where UDOT Region engineers may review and study identified segments in further detail. This research also includes modifications made to the Intersection Safety Analysis Methodology (ISAM) which focuses exclusively on intersection-related crashes. The modifications made to the ISAM mirror the abilities of the CAMS, thus allowing the pair of methodologies to analyze the entire state route network without overlapping any crash data.

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 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.

The objective of this research is to evaluate and introduce a new methodology regarding rural highway safety. Current practices rely on crash prediction models that utilize specific explanatory variables, whereas the depository of knowledge for past research is the Highway Safety Manual (HSM). Most of the prediction models in the HSM identify the effect of individual geometric elements on crash occurrence and consider their combination in a multiplicative manner, where each effect is multiplied with others to determine their combined influence. The concepts of 3-dimesnional (3-D) representation of the roadway surface have also been explored in the past aiming to model the highway structure and optimize the roadway alignment. The use of differential geometry on utilizing the 3-D roadway surface in order to understand how new metrics can be used to identify and express roadway geometric elements has been recently utilized and indicated that this may be a new approach in representing the combined effects of all geometry features into single variables. This research will further explore this potential and examine the possibility to utilize 3-D differential geometry in representing the roadway surface and utilize its associated metrics to consider the combined effect of roadway features on crashes. It is anticipated that a series of single metrics could be used that would combine horizontal and vertical alignment features and eventually predict roadway crashes in a more robust manner. It should be also noted that that the main purpose of this research is not to simply suggest predictive crash models, but to prove in a statistically concrete manner that 3-D metrics of differential geometry, e.g. Gaussian Curvature and Mean Curvature can assist in analyzing highway design and safety. Therefore, the value of this research is oriented towards the proof of concept of the link between 3-D geometry in highway design and safety. This thesis presents the steps and rationale of the procedure that is followed in order to complete the proposed research. Finally, the results of the suggested methodology are compared with the ones that would be derived from the, state-of-the-art, Interactive Highway Safety Design Model (IHSDM), which is essentially the software that is currently used and based on the findings of the HSM.

Southeastern states have about 26 percent of the nations total fatalities, and are about 24 percent above the national mean over recent years. Descriptive statistics, graphs, and figures are used to illustrate and quantify the crash trends, which depict a comprehensive picture of status and trends of the fatal crashes in southeastern states. The severity of crashes is studied as a function of characteristics of the person involved in the crash, vehicle, traffic condition, physical road geometry, and environmental factors. Detailed geometric feature data were collected for this study, which makes it possible to investigate the relationship between geometric features and crash severity. This study identifies causal factors contributing to the high fatality rate in southeastern states, and sheds light on the differences and similarities among these states for reducing the severity of fatal crashes, by developing multinomial logit models to explain the severity and type of fatal crashes.

Improvements to highway safety have become a high priority for highway authorities due to increasing public awareness and concern of the high social and economic costs of accidents. However, satisfying this priority in an environment of limited budgets is difficult. It is therefore important to ensure that the funding available for highway safety improvements is efficiently utilized. In attempt to maximize the overall highway safety benefits, highway professionals usually invoke an optimization process. The objective of this thesis study is to develop a model for the selection of appropriate improvements on a set of black spots which will provide the maximum reduction in the expected number of accidents (total return), subject to the constraint that the amount of money needed for the implementation of these improvements does not exceed the available budget. For this purpose, a computer program, BSAP (Black Spot Analysis Program) is developed. BSAP is comprised of two separate, but integrated programs: the User Interface Program (UIP) and the Main Analysis Program (MAP). The MAP is coded in MATLAB and contains the optimization procedure itself and performs all the necessary calculations by using a Binary Integer Optimization model. The UIP, coded in VISUAL BASIC, was used for monitoring the menu for efficient data preparation and providing a user-friendly environment.

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.