The 'Safety in Numbers' (SiN) effect proposes that when the volume of cycling traffic increases, the number of crashes increases less (relative to the cycling volume). A recent meta-analysis supported the general idea of a SiN effect, but also highlighted the heterogeneity of its strength, also see). The authors of this meta-study conclude that the SiN effect is strenger at the macro-level than at the micro-level, but bears no clear relationship to the quality of the cycling infrastructure. The mechanisms producing the SiN effect are still unknown. Possible explanations are (i) that safer street regulations and designs are more likely to ex.ist in societies with more wallcing and bicycling; (ii) changes in the behavior of people walking or bicycling; or (iii) changes in behavior of drivers. However, all of these explanations have their shortcomings. Additionally, some authors have argued that an increase in the number of crashes cannot be ruled out due to the increasing numbe:r of inexpe:rienced or particularly risk-taking cyclists. The:re appears to be little research on the question whether and how the SiN effect may be linked to specific road types featuring different combinations of speed zones and cycling infrastructures. Furthermore, the base rate of cyclists (i.e. the cycling volume) is a highly relevant factor when investigating the distribution of crashes throughout different road types [6]. In our research, we thus use a GIS-based approach aimed at testing the relation between the cycling volume and the number of crashes involving cyclists for roads featuring different speed zones and cycling infrastructures.

Since 2014, a distinction between Pedelec (electrical support up to 25 km/h) and bicycle crashes is made in official police reported accidents with personal injuries in Germany. Yet, no comparative analysis using national data is available, moreover some estimation was done how Pedelec crashes may look like based on bicycle crashes. Hence, the present study aims to compare real-world crashes with personal injuries with both vehicle types - Pedelec and bicycle and show similarities and differences of the vehicle classes. Nearly a decade of reporting allows furthermore to have a closer look at the accident figures in a time series and to estimate possible trends.

The Utah Department of Transportation (UDOT) Traffic and Safety Division continues to advance the safety of roadway sections throughout the state. In an effort to aid UDOT in meeting their goal, the Department of Civil and Environmental Engineering at Brigham Young University (BYU) has worked with the Statistics Department in developing analysis tools for safety. The most recent of these tools has been the development of a hierarchical Bayesian Poisson Mixture Model (PMM) of traffic crashes known as the Utah Crash Prediction Model (UCPM), a hierarchical Bayesian Binomial statistical model known as the Utah Crash Severity Model (UCSM), and a Bayesian Horseshoe selection method. The UCPM and UCSM models helped with the analysis of safety on UDOT roadways statewide and the integration of the results of these models was applied to Geographic Information System (GIS) framework. This research focuses on the addition of roadway attributes in the selection and analysis of “hot spots.” This is in conjunction with the framework for highway safety mitigation migration in Utah with its six primary steps: network screening, diagnosis, countermeasure selection, economic appraisal, project prioritization, and effectiveness evaluation. The addition of roadway attributes was included as part of the network screening, diagnosis, and countermeasure selection, which are included in the methodology titled “Hot Spot Identification and Analysis.” Included in this research was the documentation of the steps and process for data preparation and model use for the step of network screening and the creation of one of the report forms for the steps of diagnosis and countermeasure selection. The addition of roadway attributes is required at numerous points in the process. Methods were developed to locate and evaluate the usefulness of available data. Procedures and systemization were created to convert raw data into new roadway attributes, such as grade and sag/crest curve location. For the roadway attributes to be useful in selection and analysis, methods were developed to combine and associate the attributes to crashes on problem segments and problem spots. The methodology for “Hot Spot Identification and Analysis” was enhanced to include steps for the inclusion and defining of the roadway attributes. These methods and procedures were used to help in the identification of safety hot spots so that they can be analyzed and countermeasures selected. Examples of how the methods are to function are given with sites from Utah’s state roadway network.

While not accounting for a significant proportion of overall crashes, median crossover crashes in the state of Utah do account for a significant proportion of interstate fatalities. Due to the seriousness of median crossover crashes in the state of Utah, the need exists to evaluate the impact of median crossover crashes in the state, to identify locations where median crossover crashes may be occurring at particularly high rates, and to identify methods to help mitigate these crashes. Previous research has noted that median crossover crash rates appear to increase in the vicinity of interchanges. The purpose of this research, therefore, is to develop a strategy to mitigate median crossover crashes statewide and determine the role that the interchanges play in contributing to median crossover crashes. Fourteen years of crash data spanning the years 1992 through 2005 on Interstates 15, 70, 80, 84, and 215 were used to determine overall characteristics of median crossover crashes in Utah and determine the relationship between median crossover crashes and other types of crashes. Using a chi-square goodness of fit test, the distributions of median crossover crashes and all types of interstate crashes in the vicinity of interchanges were compared. Three-year median crossover crash rates spanning the years 2003 through 2005 for rural and urban areas were then used to identify which sections of Utah interstates are most prone to median crossover crashes. Finally, recommendations were made concerning appropriate median barrier installation for the 37 critical sections as identified by the three-year analysis.

This thesis investigates the price discovery process between the stock and the credit default swap market (CDS). We link the financial theory of efficient markets and the underlying models and conditions involved in CDSs, the stock market and financial crashes. This study uses publicly listed firms and the European market CDS series to construct a matched stock portfolio and uses financial data collected between the years 2019 to 2021. The purpose is to better understand the price discovery process during a potential new type of crisis in modern financial history. It could potentially allow portfolio managers, traders, arbitrageurs and stakeholders who monitor systematic indices to gauge the level of risk in the overall economy. It can also better inform regulators about how the CDS and the stock market reacted to each other during the COVID-19 pandemic. This deductive and quantitative research is based on secondary data gathered from the Eikon financial database. It uses a vector autoregressive model to test a hypothesis regarding the price discovery process between the stock and CDS portfolios.  Our results show that when using only the variables for the CDS and stock market, both variables cause each other, which is to say a feedback effect is present between the CDS Europe index and the matched portfolio of stocks. When adding the three control variables, the stock variable no longer causes the CDS variable, while the CDS variable still causes the stock variable. We conclude that the European credit default swap index leads the matched portfolio of stocks in the price discovery process with our chosen variables.

Till följ av de stora skadorna som riskeras vid frontalkollision mellan personbil och lastbil, utför Scania CV AB kraschtester för att bättre kunna utveckla komponenter med syfte att skydda passagerarna i personbilen. Den typ av test som denna studie bygger på utvärderar den s.k. FUP:en (engelska Front Underrun Protection). I dagsläget görs ett fullskaligt test, där en personbil avfyras in i en lastbil. Syftet med studien är att undersöka möjligheten att utveckla en förenklad test metod där endast de väsentliga komponenterna från lastbilen inkluderas, och en representativ struktur ersätter personbilen. Om möjligt kommer detta minska kostnaderna samt möjliggöra för större repeterbarhet. Tester och utvärderingar görs med hjälp av simulationer i LS-Dyna, ANSA & META, och designkoncept visualiseras i CAD-programmet CATIA V5. Resultat visar att det finns goda förutsättningar för att ersätta personbilen med en barriär av honeycomb struktur samt att lastbilen kan ersättas med en vagn där de väsentliga komponenterna fäst. Diskussioner kring simuleringarna och designen lyfter fram faktorer som visar på goda utvecklingsmöjligheter, men med betoning på det fortsatta arbetet som krävs. / Scania CV AB are developing components to prevent fatal damages during frontal collisions with passenger cars. Therefore, they need to test their assemblies and specifically FUP (Frontal Underrun Protection). Currently, a full-scale test is done in which a passenger car is launched into a truck. The purpose of this study is to examine and develop the possibility of having a simplified test procedure in which only the relevant components of the truck are included, and a representative structure replaces the car. If possible, this would reduce costs and allow for greater repeatability. Analysis and evaluations are done via finite element models using ANSA, LS-Dyna and META. The conceptual design is visualized using CATIA V5. Results show good indication that the passenger car can be replaced by a trolley with deformable barriers mounted on it and the truck can be replaced by a simplified structure with main FUP components mounted onto it. Discussions about the numerical models results and the conceptual design highlight factors that show promising possibilities, but with emphasis on the continued work that is required.

Many studies have shown that intersections are among the most dangerous locations of a roadway network. Therefore, there is a need to understand the factors that contribute to traffic crashes at such locations. One approach is to model crash occurrences based on configuration, geometric characteristics and traffic. Instead of combining all variables and crash types to create a single statistical model, this analysis created several models that address the different factors that affect crashes, by type of collision as well as injury level, at signalized intersections. The first objective was to determine if there is a difference between important variables for models based on individual crash types or severity levels and aggregated models. The second objective of this research was to investigate the quality and completeness of the crash data and the effect that incomplete data has on the final results. A detailed and thorough data collection effort was necessary for this research to ensure the quality and completeness of this data. Multiple agencies were contacted and databases were crosschecked (i.e. state and local jurisdictions/agencies). Information (including geometry, configuration and traffic characteristics) was collected for a total of 832 intersections and over 33,500 crashes from Brevard, Hillsborough and Seminole Counties and the City of Orlando. Due to the abundance of data collected, a portion was used as a validation set for the tree-based regression. Hierarchical tree-based regression (HTBR) and ordered probit models were used in the analyses. HTBR was used to create models for the expected number of crashes for collision type as well as injury level. Ordered probit models were only used to predict crash severity levels due to the ordinal nature of this dependent variable. Finally, both types of models were used to predict the expected number of crashes. More specifically, tree-based regression was used to consider the difference in the relative importance of each variable between the different types of collisions. First, regressions were only based on crashes available from state agencies to make the results more comparable to other studies. The main finding was that the models created for angle and left turn crashes change the most compared to the model created from the total number of crashes reported on long forms (restricted data usually available at state agencies). This result shows that aggregating the different crash types by only estimating models based on the total number of crashes will not predict the number of expected crashes as accurately as models based on each type of crash separately. Then, complete datasets (full dataset based on crash reports collected from multiple sources) were used to calibrate the models. There was consistently a difference between models based on the restricted and complete datasets. The results in this section show that it is important to include minor crashes (usually reported on short forms and ignored) in the dataset when modeling the number of angle or head-on crashes and less important to include minor crashes when modeling rear-end, right turn or sideswipe crashes. This research presents in detail the significant geometric and traffic characteristics that affect each type of collision. Ordered probit models were used to estimate crash injury severity levels for three different types of models; the first one based on collision type, the second one based on intersection characteristics and the last one based on a significant combination of factors in both models. Both the restricted and complete datasets were used to create the first two model types and the output was compared. It was determined that the models based on the complete dataset were more accurate. However, when compared to the tree-based regression results, the ordered probit model did not predict as well for the restricted dataset based on intersection characteristics. The final ordered probit model showed that crashes involving a pedestrian/bicyclist have the highest probability of a severe injury. For motor vehicle crashes, left turn, angle, head-on and rear-end crashes cause higher injury severity levels. Division (a median) on the minor road, as well as a higher speed limit on the minor road, was found to lower the expected injury level. This research has shed light on several important topics in crash modeling. First of all, this research demonstrated that variables found to be significant in aggregated crash models may not be the same as the significant variables found in models based on specific crash types. Furthermore, variables found to be significant in crash type models typically changed when minor crashes were added to complete the dataset. Thirdly, ordered probit models based on significant crash-type and intersection characteristic variables have greater crash severity prediction power, especially when based on the complete dataset. Lastly, upon comparison between tree-based regression and ordered probit models, it was found that the tree-based regression models better predicted the crash severity levels.

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%.

Relevance of reactive traffic management strategies such as freeway incident detection has been diminishing with advancements in mobile phone usage and video surveillance technology. On the other hand, capacity to collect, store, and analyze traffic data from underground loop detectors has witnessed enormous growth in the recent past. These two facts together provide us with motivation as well as the means to shift the focus of freeway traffic management toward proactive strategies that would involve anticipating incidents such as crashes. The primary element of proactive traffic management strategy would be model(s) that can separate 'crash prone' conditions from 'normal' traffic conditions in real-time. The aim in this research is to establish relationship(s) between historical crashes of specific types and corresponding loop detector data, which may be used as the basis for classifying real-time traffic conditions into 'normal' or 'crash prone' in the future. In this regard traffic data in this study were also collected for cases which did not lead to crashes (non-crash cases) so that the problem may be set up as a binary classification. A thorough review of the literature suggested that existing real-time crash 'prediction' models (classification or otherwise) are generic in nature, i.e., a single model has been used to identify all crashes (such as rear-end, sideswipe, or angle), even though traffic conditions preceding crashes are known to differ by type of crash. Moreover, a generic model would yield no information about the collision most likely to occur. To be able to analyze different groups of crashes independently, a large database of crashes reported during the 5-year period from 1999 through 2003 on Interstate-4 corridor in Orlando were collected. The 36.25-mile instrumented corridor is equipped with 69 dual loop detector stations in each direction (eastbound and westbound) located approximately every ½ mile. These stations report speed, volume, and occupancy data every 30-seconds from the three through lanes of the corridor. Geometric design parameters for the freeway were also collected and collated with historical crash and corresponding loop detector data. The first group of crashes to be analyzed were the rear-end crashes, which account to about 51% of the total crashes. Based on preliminary explorations of average traffic speeds; rear-end crashes were grouped into two mutually exclusive groups. First, those occurring under extended congestion (referred to as regime 1 traffic conditions) and the other which occurred with relatively free-flow conditions (referred to as regime 2 traffic conditions) prevailing 5-10 minutes before the crash. Simple rules to separate these two groups of rear-end crashes were formulated based on the classification tree methodology. It was found that the first group of rear-end crashes can be attributed to parameters measurable through loop detectors such as the coefficient of variation in speed and average occupancy at stations in the vicinity of crash location. For the second group of rear-end crashes (referred to as regime 2) traffic parameters such as average speed and occupancy at stations downstream of the crash location were significant along with off-line factors such as the time of day and presence of an on-ramp in the downstream direction. It was found that regime 1 traffic conditions make up only about 6% of the traffic conditions on the freeway. Almost half of rear-end crashes occurred under regime 1 traffic regime even with such little exposure. This observation led to the conclusion that freeway locations operating under regime 1 traffic may be flagged for (rear-end) crashes without any further investigation. MLP (multilayer perceptron) and NRBF (normalized radial basis function) neural network architecture were explored to identify regime 2 rear-end crashes. The performance of individual neural network models was improved by hybridizing their outputs. Individual and hybrid PNN (probabilistic neural network) models were also explored along with matched case control logistic regression. The stepwise selection procedure yielded the matched logistic regression model indicating the difference between average speeds upstream and downstream as significant. Even though the model provided good interpretation, its classification accuracy over the validation dataset was far inferior to the hybrid MLP/NRBF and PNN models. Hybrid neural network models along with classification tree model (developed to identify the traffic regimes) were able to identify about 60% of the regime 2 rear-end crashes in addition to all regime 1 rear-end crashes with a reasonable number of positive decisions (warnings). It translates into identification of more than ¾ (77%) of all rear-end crashes. Classification models were then developed for the next most frequent type, i.e., lane change related crashes. Based on preliminary analysis, it was concluded that the location specific characteristics, such as presence of ramps, mile-post location, etc. were not significantly associated with these crashes. Average difference between occupancies of adjacent lanes and average speeds upstream and downstream of the crash location were found significant. The significant variables were then subjected as inputs to MLP and NRBF based classifiers. The best models in each category were hybridized by averaging their respective outputs. The hybrid model significantly improved on the crash identification achieved through individual models and 57% of the crashes in the validation dataset could be identified with 30% warnings. Although the hybrid models in this research were developed with corresponding data for rear-end and lane-change related crashes only, it was observed that about 60% of the historical single vehicle crashes (other than rollovers) could also be identified using these models. The majority of the identified single vehicle crashes, according to the crash reports, were caused due to evasive actions by the drivers in order to avoid another vehicle in front or in the other lane. Vehicle rollover crashes were found to be associated with speeding and curvature of the freeway section; the established relationship, however, was not sufficient to identify occurrence of these crashes in real-time. Based on the results from modeling procedure, a framework for parallel real-time application of these two sets of models (rear-end and lane-change) in the form of a system was proposed. To identify rear-end crashes, the data are first subjected to classification tree based rules to identify traffic regimes. If traffic patterns belong to regime 1, a rear-end crash warning is issued for the location. If the patterns are identified to be regime 2, then they are subjected to hybrid MLP/NRBF model employing traffic data from five surrounding traffic stations. If the model identifies the patterns as crash prone then the location may be flagged for rear-end crash, otherwise final check for a regime 2 rear-end crash is applied on the data through the hybrid PNN model. If data from five stations are not available due to intermittent loop failures, the system is provided with the flexibility to switch to models with more tolerant data requirements (i.e., model using traffic data from only one station or three stations). To assess the risk of a lane-change related crash, if all three lanes at the immediate upstream station are functioning, the hybrid of the two of the best individual neural network models (NRBF with three hidden neurons and MLP with four hidden neurons) is applied to the input data. A warning for a lane-change related crash may be issued based on its output. The proposed strategy is demonstrated over a complete day of loop data in a virtual real-time application. It was shown that the system of models may be used to continuously assess and update the risk for rear-end and lane-change related crashes. The system developed in this research should be perceived as the primary component of proactive traffic management strategy. Output of the system along with the knowledge of variables critically associated with specific types of crashes identified in this research can be used to formulate ways for avoiding impending crashes. However, specific crash prevention strategies e.g., variable speed limit and warnings to the commuters demand separate attention and should be addressed through thorough future research.

Analysis of intersection crashes is a significant area in traffic safety research. This study contributes to the area by identifying traffic-geometric characteristics and driver demographics that affect different types of crashes at signalized intersections. A simple methodology to estimate crash frequency at intersections based on the size of the intersection is also developed herein. First phase of this thesis used the crash frequency data from 1,335 signalized intersections obtained from six jurisdictions in Florida, namely, Brevard, Seminole, Dade, Orange, and Hillsborough Counties and the City of Orlando. Using these data a simple methodology has been developed to identify the expected number of crashes by type and severity at signalized intersections. Intersection size, based on the total number of lanes, was used as a factor that was simple to identify and a representative of many geometric and traffic characteristics of an intersection. The results from the analysis showed that crash frequency generally increased with the increased size of intersections but the rates of increase differed for different intersection types (i.e., Four-legged intersection with both streets two-way, Four-legged intersection with at least one street one-way, and T-intersections). The results also showed that the dominant type of crashes differed at these intersection types and severity of crashes was higher at the intersections with more conflict points and larger differential in speed limits between major and minor roads. The analysis may potentially be useful for traffic engineers for evaluating safety at signalized intersections in a simple and efficient manner. The findings in this analysis provide strong evidence that the patterns of crashes by type and severity vary with the size and type of intersections. Thus, in future analysis of crashes at intersections, the size and type of intersections should be considered to account for the effects of intersection characteristics on crash frequency. In the second phase, data (crash and intersection characteristics) obtained from individual jurisdictions are linked to the Department of Highway Safety and Motor Vehicles (DHSMV) database to include characteristics of the at-fault drivers involved in crashes. These crashes are analyzed using contingency tables and binary logistic regression models. This study categorizes crashes into three major types based on relative initial movement direction of the involved vehicles. These crash types are, 1) Initial movement in same direction (IMSD) crashes. This crash type includes rear end and sideswipe crashes because the involved vehicles for these crashes would be traveling in the same direction prior to the crash. 2) Initial movement in opposite direction (IMOD) crashes comprising left-turn and head on crashes. 3) Initial movement in perpendicular direction (IMPD) crashes, which include angle and right-turn crashes. Vehicles involved in these crashes would be traveling on different roadways that constitute the intersection. Using the crash, intersection, and at-fault driver characteristics for all crashes as inputs, three logistic regression models are developed. In the logistic regression analyses total number of through lanes at an intersection is used as a surrogate measure to AADT per lane and also intersection type is introduced as a 'predictor' of crash type. The binary logistic regression analyses indicated, among other results, that at intersections with one-way roads, adverse weather conditions, older drivers and/or female drivers increase the likelihood of being at-fault at IMOD crashes. Similar factors associated with other groups of crashes (i.e., IMSD and IMPD) are also identified. These findings from the study may be used to develop specialized training programs by zooming in onto problematic intersections/maneuvers.