It has been well established in previous research that driving during rainfall is associated with increased risk of traffic collision involvement. Of particular concern are heavy rain events, which result in elevated risks up to three times higher than those for light rainfalls. As the global climate changes in the coming century, altered precipitation patterns are likely. The primary objective of this thesis is to estimate the potential impacts of climate change on traffic safety in two large Canadian urban regions: the Greater Toronto Area and Greater Vancouver. A secondary objective is to provide a framework or methodology for exploring this question. In accomplishing the primary objective, daily collision and climate records are utilized to establish an empirical estimate of present-day rainfall-related crash risk. This estimate is combined with results of a climate modelling exercise to arrive at a possible traffic safety future for urban Canada over the next 40 years. For the second objective, several important decisions related to data acquisition, compatibility, and completeness are considered, and the tradeoffs are mapped out and discussed, in order to provide guidance for future studies. Results indicate that over the next 40 years, Toronto is likely to see a mean annual increase in rain days of all intensities, resulting in marginally more collisions and casualties each year. Substantially more rainfall days are projected for Vancouver by mid-century, resulting in a small increase in annual incident counts. In both study regions, the greatest adverse safety impact is likely to be associated with moderate to heavy rainfall days (≥ 10 mm); this estimate is consistent with the greater risk increases associated with these conditions today, and suggests that attention should be paid to future changes in the frequency and intensity of extreme rainfall events. Indeed, heavy rain days are likely to account for approximately half of all additional collision and casualty incidents.

Background: Most traffic safety research projects require accurate real world data which is collected in different databases around the world. This is especially important since the results of these projects form the basis for new crash test procedures and standards. In many of these databases the involvement of the frontal structures of the car in frontal crashes is coded by using the SAE J224 practice (Society of Automobile Engineers). There were indications that by using this practice the database would contain an overestimate of the car frontal structure involvement in real world crashes. One purpose of this thesis is therefore to develop a new method for real world crash investigations to better address this issue. One purpose was also to adopt this method in a data collection of fatal crashes in Sweden and examine injury causation mechanisms. Studies shows that the commonly used Hybrid III dummy is not fully reproducing the kinematical behavior observed in frontal sled test with belted PMHS (Post Mortem Human Subject). A human FE-model (Finite Element) might be able to reproduce the behavior evidenced with the PMHS in order to study upper body kinematics in certain types of frontal collision events. Method: A new data collection method was developed with the purpose to examine actual load paths active in the car front during a frontal crash. An important purpose was to examine if there was a relation between these load paths and injury producing mechanisms. This was done in an examination and analysis of 61 fatally injured occupants in 53 car frontal crashes in a sample area covering 40 % of the population of Sweden. Sample period was one year (1st October 2000 to 30th September 2001). An existing human FE-model was developed and validated with respect to upper body kinematics by using existing frontal belted PMHS tests. This was done by building a FE-model of the seat and seat belt used in the PMHS tests. Results: A generic car structure was developed which was used in the data collection methodology. By adopting this new method, Small Overlap (SO) crashes emerged as the most common crash configuration (48 %) among belted frontal fatalities. The injury producing mechanism in SO crashes is characterized by occupant upper body impacts in the side structure (door, a-pillar) of the car. This upper body kinematics is induced by both the crash pulse and the asymmetrical three point belt system. Current crash test procedures are not designed to fully estimate the performance of neither car structures nor restraints in SO crashes. In order to develop a better tool for reproducing this kinematical behavior a FE-model of a human body was refined and validated for belted conditions. This validation was performed with satisfying result. Conclusions: This study showed that by adopting new methods of data collecting new areas of traffic safety could be considered. In this study SO (48 %) crashes emerged as the most common crash configuration for belted frontal fatalities. Approximately ¼ of the fatalities occurred in a crash configuration comparable to current barrier crash test procedures. The body kinematics of PMHS in the SO crashes can be replicated and studied by using a FE-model of a human body in the collision load case model. With this tool possible collision counter measures could be evaluated for the SO crash configuration.

Time series involving count data are present in a wide variety of applications. In many applications, the observed counts are usually small and dependent. Failure to take these facts into account can lead to misleading inferences and may detect false relationships. To tackle such issues, a Poisson parameter-driven model is assumed for the time series at hand. This model can account for the time dependence between observations through introducing an autoregressive latent process. In this thesis, we consider Bayesian approaches for estimating the Poisson parameter-driven model. The main challenge is that the likelihood function for the observed counts involves a high dimensional integral after integrating out the latent variables. The main contributions of this thesis are threefold. First, I develop a new single-move (SM) Markov chain Monte Carlo (MCMC) method to sample the latent variables one by one. Second, I adopt the idea of the particle Gibbs sampler (PGS) method \citep{andrieu} into our model setting and compare its performance with the SM method. Third, I consider Bayesian composite likelihood methods and compare three different adjustment methods with the unadjusted method and the SM method. The comparisons provide a practical guide to what method to use. We conduct simulation studies to compare the latter two methods with the SM method. We conclude that the SM method outperforms the PGS method for small sample size, while they perform almost the same for large sample size. However, the SM method is much faster than the PGS method. The adjusted Bayesian composite methods provide closer results to the SM than the unadjusted one. The PGS and the selected adjustment method from simulation studies are compared with the SM method via a real data example. Similar results are obtained: first, the PGS method provides results very close to those of the SM method. Second, the adjusted composite likelihood methods provide closer results to the SM than the unadjusted one.

It has been well established in previous research that driving during rainfall is associated with increased risk of traffic collision involvement. Of particular concern are heavy rain events, which result in elevated risks up to three times higher than those for light rainfalls. As the global climate changes in the coming century, altered precipitation patterns are likely. The primary objective of this thesis is to estimate the potential impacts of climate change on traffic safety in two large Canadian urban regions: the Greater Toronto Area and Greater Vancouver. A secondary objective is to provide a framework or methodology for exploring this question. In accomplishing the primary objective, daily collision and climate records are utilized to establish an empirical estimate of present-day rainfall-related crash risk. This estimate is combined with results of a climate modelling exercise to arrive at a possible traffic safety future for urban Canada over the next 40 years. For the second objective, several important decisions related to data acquisition, compatibility, and completeness are considered, and the tradeoffs are mapped out and discussed, in order to provide guidance for future studies. Results indicate that over the next 40 years, Toronto is likely to see a mean annual increase in rain days of all intensities, resulting in marginally more collisions and casualties each year. Substantially more rainfall days are projected for Vancouver by mid-century, resulting in a small increase in annual incident counts. In both study regions, the greatest adverse safety impact is likely to be associated with moderate to heavy rainfall days (≥ 10 mm); this estimate is consistent with the greater risk increases associated with these conditions today, and suggests that attention should be paid to future changes in the frequency and intensity of extreme rainfall events. Indeed, heavy rain days are likely to account for approximately half of all additional collision and casualty incidents.

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No description available.

The last few years have brought about a series of substantial changes for mobility on two wheels, especially if the impact of the COVID-19 pandemic is considered as a relevant fact for transportation dynamics [1,2]. Social distancing recommendations have promoted the use of individual transportation systems instead of massive transportations means. Consequently, riding a bike for urban trips has become increasingly prevalent in many countries [3-5]. Besides an opportunity to make urban mobility more active and sustainable, this panorama poses the challenge to prevent that, along with its growing use, bicycle crashes ---and their consequences-might continue to increase. In this regard, recent studies have emphasized the role of individual differences and personality-related factors as potential issues influencing both cycling behaviors and traffic crashes suffered while riding [6,7].

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.

Intersection related crashes form a significant proportion of the crashes occurring on roadways. Many organizations such as the Federal Highway Administration (FHWA) and American Association of State Highway and Transportation Officials (AASHTO) are considering intersection safety improvement as one of their top priority areas. This study contributes to the area of safety of signalized intersections by identifying the traffic and geometric characteristics that affect the different types of crashes. The first phase of this thesis was to classify the crashes occurring at signalized intersections into rear-end, angle, turn and sideswipe crash types based on the traffic and geometric properties of the intersections and the conditions at the time of the crashes. This was achieved by using an innovative approach developed in this thesis "Neural Network Trees". The first neural network model built in the Neural Network tree classified the crashes either into rear end and sideswipe or into angle and turn crashes. The next models further classified the crashes into their individual types. Two different neural network methods (MLP and PNN) were used in classification, and the neural network with a better performance was selected for each model. For these models, the significant variables were identified using the forward sequential selection method. Then a large simulation database was built that contained all possible combinations of intersections subjected to various crash conditions. The collision type of crashes was predicted for this simulation database and the output obtained was plotted along with the input variables to obtain a relationship between the input and output variables. For example, the analysis showed that the number of rear end and sideswipe crashes increase relative to the angle and turn crashes when there is an increase in the major and minor roadways' AADT and speed limits, surface conditions, total left turning lanes, channelized right turning lanes for the major roadway and the protected left turning lanes for the minor roadway, but decrease when the light conditions are dark. The next phase in this study was to predict the frequency of different types of crashes at signalized intersections by using the geometric and traffic characteristics of the intersections. A high accuracy in predicting the crash frequencies was obtained by using another innovative method where the intersections were first classified into two different types named the "safe" and "unsafe" intersections based on the total number of lanes at the intersections and then the frequency of crashes was predicted for each type of intersections separately. This method consisted of identifying the best neural network for each step of the analysis, selecting significant variables, using a different simulation database that contained all possible combinations of intersections and then plotting each input variable with the average output to obtain the pattern in which the frequency of crashes will vary based on the changes in the geometric and traffic characteristics of the intersections. The patterns indicated that an increase in the number of lanes of the major roadway, lanes of the minor roadway and the AADT on the major roadway leads to an increased crashes of all types, whereas an increase in protected left turning lanes on the major road increases the rear end and sideswipe crashes but decreases the angle, turning and overall crash frequencies. The analyses performed in this thesis were possible due to a diligent data collection effort. Traffic and geometric characteristics were obtained from multiple sources for 1562 signalized intersections in Brevard, Hillsborough, Miami-Dade, Seminole and Orange counties and the city of Orlando in Florida. The crash database for these intersections contained 27,044 crashes. This research sheds a light on the characteristics of different types of crashes. The method used in classifying crashes into their respective collision types provides a deeper insight on the characteristics of each type of crash and can be helpful in mitigating a particular type of crash at an intersection. The second analysis carried out has a three fold advantage. First, it identifies if an intersection can be considered safe for different crash types. Second, it accurately predicts the frequencies of total, rear end, angle, sideswipe and turn crashes. Lastly, it identifies the traffic and geometric characteristics of signalized intersections that affect each of these crash types. Thus the models developed in this thesis can be used to identify the specific problems at an intersection, and identify the factors that should be changed to improve its safety

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.