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 objectives of this research were to explore ways to assess the safety performance of (predominantly two-lane) rural highways in New Zealand (NZ) and in particular identify driver/road/environmental factors affecting crashes on rural curves. Following a wide-ranging literature review, the Interactive Highway Safety Design Model (IHSDM) was identified as worthy of further investigation for adaptation to use in NZ. To help with this investigation, a comprehensive database was developed of road, traffic, crash and environmental data for all NZ State Highways, divided into variable-length road elements. A number of tasks were identified and undertaken to adapt IHSDM for general use here, including calibrating the Crash Prediction Module (CPM), developing a Design Policy file based on local agency standards, and developing an importing routine for NZ highway geometry and crash data. To assess the effectiveness of IHSDM for predicting the relative safety of rural road alignments, a series of tests were undertaken to confirm its appropriateness for use in NZ. These included “before and after” design consistency checks of a bridge replacement, a “before and after” crash comparison of a major highway realignment, and checks of actual versus predicted crash numbers along longer lengths of highway in varying terrain. These initial investigations have shown that IHSDM is a promising tool for safety and operational assessment of highway alignments (both existing and proposed) in NZ. Incorporating crash history data generally improves IHSDM’s accuracy in crash numbers, and appears to provide a better level of “local calibration” than by using sub-national (e.g. regional or terrain-specific) calibration parameters. Reported fatal/injury crash data generally provide more robust and precise measures than non-injury crashes. Correct specification of the extreme attributes of sub-standard elements (e.g. minimum radius, maximum roadside hazard) appears to be crucial to getting suitably accurate crash estimates on existing alignments. However, IHSDM’s current lack of consideration for bridges and inconsistent adjacent elements are notable omissions that limit the ability of the CPM to assess sub-standard existing routes with as much accuracy as well-designed newer alignments.

No description available.

Nesta dissertação é feita uma avaliação dos resultados da aplicação do modelo de previsão de acidentes apresentado no Highway Safety Manual - HSM, publicado pela American Association of State Highway and Transportation Officials (AASHTO) no ano de 2010, para o caso de trechos de rodovia de pista simples localizados no interior do estado de São Paulo. São analisados o método original do HSM, o método do HSM calibrado para os trechos de rodovias estudados e o método empírico de Bayes. Os resultados apresentados pelo método original do HSM são bastante distintos dos valores reais observados, o que exclui a possibilidade do seu emprego direto no universo analisado. No entanto, os valores resultantes da aplicação do método do HSM calibrado e do método de Bayes, em todas as análises realizadas, aproximam-se de maneira satisfatória dos valores observados (com resultados melhores no método de Bayes) - o que mostra que esses métodos podem ser utilizados para a previsão de acidentes no universo analisado, bem como em outras situações com condições similares. Ainda que seja recomendada a realização de estudos específicos para a aplicação do modelo em outras regiões do país, na ausência desses estudos, os valores obtidos neste trabalho podem ser empregados como primeira aproximação. / This research conducts an evaluation of the results achieved by means of the application of crash prediction method presented by the Highway Safety Manual - HSM, published by the American Association of State Highway and Transportation Officials (AASHTO) in 2010, for the case of two lane highway segments situated in the state of Sao Paulo. The analyses were aimed at the original method of the HSM, the method calibrated to the studied highways segments and with the use of the empirical Bayes method. The results obtained by application of the HSM original method are quite distinct from the actual crash values observed, which excludes the possibility of its direct employment in the universe analyzed. However, the values resulting from application of the calibrated HSM method and the method of Bayes, in all data analysis, approach satisfactorily the observed values (with better results with the use of Bayes method) - which shows that these methods can be used for the prediction of accidents in the universe analyzed, as well as in other situations with similar conditions. Although it is recommended to perform specific studies for the application of the method in other regions of the country, in the absence of these studies, the values provided by this research study can be used as a first approximation.

This paper offers empirical evidence on the power of Sornette et al's [2001] model of bubbles and crashes regarding the German stock market between 1960 and 2009. We identify relevant time periods and describe them with the function given by Sornette et al's model. Our results show some evidence in predicting crashes with the understanding of logarithmic periodic structures that are hidden in the stock price trajectories. It was shown that for the DAX most of the relevant parameters determining the shape of the logarithmic periodic structures are lying in the expected interval researched by Sornette et al. Further more the paper implicitly shows that the point of time of former crashes can be predicted with the presented formula. We conclude that the concept of financial time series conceived as purely random objects should be generalised as to admit complexity.

Safety reviews of existing roads are becoming a popular practice of many agencies nationally and internationally. Knowing road safety information is of great importance to both policymakers in addressing safety concerns and travelers in managing their trips. There have been various efforts in developing methodologies to measure and assess road safety in an effective manner. However, the existing research and practices are still constrained by their subjective and reactive nature. The goal of this research is to develop a framework of Road Risk Indices (RRIs) to assess road risks of existing highway infrastructure for both road users and agencies based on road geometrics, traffic conditions, and historical crash data. The proposed RRIs are intended to give a comprehensive and objective view of road safety, so that safety problems can be identified at an early stage before they rise in the form of accidents. A methodological framework of formulating RRIs that integrates results from crash prediction models and historical crash data is proposed, and Linear Referencing tools in the ArcGIS software are used to develop digital maps to publish estimated RRIs. These maps provide basic Geographic Information System (GIS) functions, including viewing and querying RRIs, and performing spatial analysis tasks. A semi-parameter count model and quantile regression based estimation are proposed to capture the specific characteristics of crash data and provide more robust and accurate predictions on crash counts. Crash data collected on Interstate Highways in Washington State for the year 2002 was extracted from the Highway Safety Information System (HSIS) and used for the case study. The results from the case study show that the proposed framework is capable of capturing statistical correlations between traffic crashes and influencing factors, leading to the effective integration of safety information in composite indices. / text

Nesta dissertação é feita uma avaliação dos resultados da aplicação do modelo de previsão de acidentes apresentado no Highway Safety Manual - HSM, publicado pela American Association of State Highway and Transportation Officials (AASHTO) no ano de 2010, para o caso de trechos de rodovia de pista simples localizados no interior do estado de São Paulo. São analisados o método original do HSM, o método do HSM calibrado para os trechos de rodovias estudados e o método empírico de Bayes. Os resultados apresentados pelo método original do HSM são bastante distintos dos valores reais observados, o que exclui a possibilidade do seu emprego direto no universo analisado. No entanto, os valores resultantes da aplicação do método do HSM calibrado e do método de Bayes, em todas as análises realizadas, aproximam-se de maneira satisfatória dos valores observados (com resultados melhores no método de Bayes) - o que mostra que esses métodos podem ser utilizados para a previsão de acidentes no universo analisado, bem como em outras situações com condições similares. Ainda que seja recomendada a realização de estudos específicos para a aplicação do modelo em outras regiões do país, na ausência desses estudos, os valores obtidos neste trabalho podem ser empregados como primeira aproximação. / This research conducts an evaluation of the results achieved by means of the application of crash prediction method presented by the Highway Safety Manual - HSM, published by the American Association of State Highway and Transportation Officials (AASHTO) in 2010, for the case of two lane highway segments situated in the state of Sao Paulo. The analyses were aimed at the original method of the HSM, the method calibrated to the studied highways segments and with the use of the empirical Bayes method. The results obtained by application of the HSM original method are quite distinct from the actual crash values observed, which excludes the possibility of its direct employment in the universe analyzed. However, the values resulting from application of the calibrated HSM method and the method of Bayes, in all data analysis, approach satisfactorily the observed values (with better results with the use of Bayes method) - which shows that these methods can be used for the prediction of accidents in the universe analyzed, as well as in other situations with similar conditions. Although it is recommended to perform specific studies for the application of the method in other regions of the country, in the absence of these studies, the values provided by this research study can be used as a first approximation.

In 2010, the American Association of State Highway and Transportation Officials (AASHTO) released a safety analysis software system known as SafetyAnalyst. SafetyAnalyst implements the empirical Bayes (EB) method, which requires the use of Safety Performance Functions (SPFs). The system is equipped with a set of national default SPFs, and the software calibrates the default SPFs to represent the agency’s safety performance. However, it is recommended that agencies generate agency-specific SPFs whenever possible. Many investigators support the view that the agency-specific SPFs represent the agency data better than the national default SPFs calibrated to agency data. Furthermore, it is believed that the crash trends in Florida are different from the states whose data were used to develop the national default SPFs. In this dissertation, Florida-specific SPFs were developed using the 2008 Roadway Characteristics Inventory (RCI) data and crash and traffic data from 2007-2010 for both total and fatal and injury (FI) crashes. The data were randomly divided into two sets, one for calibration (70% of the data) and another for validation (30% of the data). The negative binomial (NB) model was used to develop the Florida-specific SPFs for each of the subtypes of roadway segments, intersections and ramps, using the calibration data. Statistical goodness-of-fit tests were performed on the calibrated models, which were then validated using the validation data set. The results were compared in order to assess the transferability of the Florida-specific SPF models. The default SafetyAnalyst SPFs were calibrated to Florida data by adjusting the national default SPFs with local calibration factors. The performance of the Florida-specific SPFs and SafetyAnalyst default SPFs calibrated to Florida data were then compared using a number of methods, including visual plots and statistical goodness-of-fit tests. The plots of SPFs against the observed crash data were used to compare the prediction performance of the two models. Three goodness-of-fit tests, represented by the mean absolute deviance (MAD), the mean square prediction error (MSPE), and Freeman-Tukey R2 (R2FT), were also used for comparison in order to identify the better-fitting model. The results showed that Florida-specific SPFs yielded better prediction performance than the national default SPFs calibrated to Florida data. The performance of Florida-specific SPFs was further compared with that of the full SPFs, which include both traffic and geometric variables, in two major applications of SPFs, i.e., crash prediction and identification of high crash locations. The results showed that both SPF models yielded very similar performance in both applications. These empirical results support the use of the flow-only SPF models adopted in SafetyAnalyst, which require much less effort to develop compared to full SPFs.

Recent research at UCF into defining surrogate measures for identifying crash prone conditions on freeways has led to the introduction of several statistical models which can flag such conditions with a good degree of accuracy. Outputs from these models have the potential to be used as real-time safety measures on freeways. They may also act as the basis for the evaluation of several intervention strategies that might help in the mitigation of risk of crashes. Ramp Metering and Variable Speed Limits are two approaches which have the potential of becoming effective implementation strategies for improving the safety conditions on congested freeways. This research evaluates both these strategies in different configurations and attempts to quantify their effect on risk of crash on a 9-mile section of Interstate-4 in the Orlando metropolitan region. The section consists of 17 Loop Detector stations, 11 On-ramps and 10 off-ramps. PARAMICS micro-simulation is used as the tool for modeling the freeway section. The simulated network is calibrated and validated for 5 minute average flows and speeds using loop detector data. Feedback Ramp Metering algorithm, ALINEA, is used for controlling access from up to 7 on-ramps. Variable Speed Limits are implemented based on real-time speed conditions prevailing in the whole 9-mile section. Both these strategies are tested separately as well as collectively to determine the individual effects of all the parameters involved. The results have been used to formulate and recommend the best possible strategy for minimizing the risk of crashes on the corridor. The study concluded that Ramp Metering improves the conditions on the freeway in terms of safety by decreasing variance in speeds and decreasing average occupancy. A safety benefit index was developed for quantifying the reduction in crash risk and it indicated that an optimal implementation strategy might produce benefits of up to 55%. The condition on the freeway section improved with increase in the number of metered ramps. It was also observed that shorter signal cycles for metered ramps were more suitable for metering multiple ramps. Ramp Metering at multiple locations also decreased the segment wide travel-times by 5% and was even able to offset the delays incurred by drivers at the metered on-ramps. Variable Speed Limits (VSL) were individually not as effective as ramp metering but when implemented along with ramp metering, they were found to further improve the safety on the freeway section under consideration. By means of a detailed experimental design it was observed that the best strategy for introducing speed limit changes was to raise the speed limits downstream of the location of interest by 5 mph and not affecting the speed limits upstream. A coordinated strategy - involving simultaneous application of VSL and Ramp Metering - provided safety benefits of up to 56 % for the study section according to the safety benefit index. It also improved the average speeds on the network besides decreasing the overall network travel time by as much as 21%.