Tarek Sayed

Inclusion of bicycle traffic in microsimulation tools is essential for evaluating bicycle-accessible infrastructure projects. However, the representation of bicycles in microsimulation models is still at an early stage of development. A better understanding of cyclist behaviour during various interactions is needed to enhance bicycle microsimulation models, which is a pre-requisite for accurate microscopic modeling of bicycle traffic operations. Due to the limited availability of detailed data, the inherent complexity of cyclist decision-making, and the substantial heterogeneity in cycling behaviour, modeling cyclist operation behaviour requires novel methods and techniques. This thesis aims first to characterize cyclist maneuvers in following and overtaking interactions using multivariate finite mixture model-based clustering. Second, an agent-based bicycle simulation method is proposed to model cyclists as intelligent agents making operational and tactical decisions based on their observations of the operating environment. Cyclist position data associated with time stamps are used to infer state and future decisions. The data are extracted from videos collected in Vancouver, BC, Canada using computer vision techniques. For segmenting behavioural states, observations of cyclists in following interactions are clustered into constrained and unconstrained states. Observations of overtaking cyclists are clustered into initiation, merging and post-overtaking states. Generative adversarial imitation learning (GAIL) is used to infer the uncertain intentions and preferences of cyclists from observational data. The model is validated by comparing multivariate distributions of variables such as speed, direction, and spacing of observed and simulated cyclist trajectories. The model performs well in comparison to two other cyclist simulation models from the literature. The proposed approach to miscrosimulation is a significant advancement in agent-based modeling methods, with continuous, non-linear, and stochastic representation of states, decisions, and actions. By modeling cyclist heterogeneity, the proposed approach can enhance applications in bicycle facility planning and design, safety modeling, and energy modeling with consideration of the full diversity of cyclists. Such an advancement is necessary for developing bicycle networks for all ages and abilities of riders.

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Several studies have developed pedestrian-vehicle interaction models. However, these studies failed to consider pedestrian distraction, which considerably influences the safety of these interactions. Utilizing data from two intersections in Vancouver, Canada, this research uses the Multi-agent Adversarial Inverse Reinforcement Learning (MA-AIRL) framework to make inferences about the behavioral dynamics of distracted and non-distracted pedestrians while interacting with vehicles. Results showed that distracted pedestrians maintained closer proximity to vehicles, moved at reduced speeds, and rarely yielded to oncoming vehicles. In addition, they rarely changed their interaction angles, indicating that they mostly remain unaware of the surrounding environment and have decreased navigational efficiency. Conversely, non-distracted pedestrians executed safer maneuvers, kept greater distances from vehicles, yielded more frequently, and adjusted their speeds accordingly. For example, non-distracted pedestrian-vehicle interactions showed a 46.5 % decrease in traffic conflicts severity (as measured by the average Time-to-Collision (TTC) values) and an average 30.2% increase in minimum distances when compared to distracted pedestrian-vehicle interactions. Vehicle drivers also demonstrated different behaviors in response to distracted pedestrians. They often opted to decelerate around distracted pedestrians, indicating recognition of potential risks. Furthermore, the MA-AIRL framework provided different results depending on the type of interactions. The performance of the distracted vehicle-pedestrian model was lower than the non-distracted model, suggesting that predicting non-distracted behavior might be relatively easier. These findings emphasize the importance of refining pedestrian simulation models to include the unique behavioral patterns from pedestrian distractions. This should assist in further examining the safety impacts of pedestrian distraction on the road environment.

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There is growing research interest in evaluating the safety of motorcyclists because of the increasing motorcycling global population coupled with the risks motorcyclists are exposed to as vulnerable road users. An important safety concern for motorcyclists is their lateral interactions with vehicles where a collision avoidance maneuver is needed because of the small lateral separation between vehicles and motorcycles. This study investigates the lateral interaction between motorcycles and vehicles by modeling the critical lateral distance (CLD) between them. The analysis utilized a dataset of motorcycle and vehicle trajectories collected from an urban road network in Athens, Greece. To model the CLD and relate it to various dynamic behavioral and traffic variables (e.g., speed, acceleration, volume, yaw rate), four approaches of survival models were applied and compared. These approaches include fully parametric, fully parametric with Gamma frailty, semi-parametric and machine learning (DeepHit) survival models. The results showed that the DeepHit model outperforms the other three models in terms of the model’s goodness of fit. However, the fully parametric with Gamma frailty model can provide more insights, as it considers the distribution of the CLD and quantifies the influence of behavioral and traffic exposure variables on the probability of lateral interaction. For example, the fully- parametric with Gamma frailty model indicates that the lateral interaction probability increases at higher motorcycle speeds, higher vehicle speeds, higher motorcycle volumes, lower motorcycle yaw rates, and lower relative motorcycle-vehicle decelerations. The results of this study can help quantify and hence mitigate some of the risks that motorcyclists are exposed to, with the overall goal of improving their safety.

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This research evaluated and compared the operational and the safety performance of five types of unconventional intersections, including the conventional median U-turn (MUT), unconventional MUT, upstream signalized crossover (USC), double crossover intersection (DXI) and crossover displaced left-turn (XDL). The traffic simulation model VISSIM was used to model the intersections and assess the operational performance based on delay and capacity. Vehicle trajectory data were extracted from VISSIM and analyzed in the Surrogate Safety Assessment Model (SSAM) to produce conflict results based on conflict indicators TTC and PET. For the operational performance, the results were generated based on default driving behavior parameters. For safety performance, the results were generated based on both default and safety-oriented calibrated driving behavior parameters. Three methods were applied to determine the signal phases and cycle length, including trial-and-error, simple progression and using the signal optimization software Synchro. Overall, the unconventional MUT has the lowest capacity among the five designs, around 950 veh/hr. Compared to this value, the capacity of the conventional MUT is 47% higher; the capacities of the USC and DXI are 111% and 89% higher; the capacity of the XDL is about 174% higher. By comparing the delay and the conflict results, it can be found that, in general, higher delays will cause a higher number of conflicts. In balanced volume conditions, the unconventional MUT is most favored in terms of both delays and conflicts when the approach volumes are low. For intermediate approach volumes, the best choice is the XDL, followed by the USC. For high approach volumes, the XDL is the most recommended design. However, the DXI and conventional MUT are not recommended in balanced volume conditions. In unbalanced volume conditions, the unconventional MUT has both the lowest delays and the lowest number of conflicts when the minor-to-major road volume ratio is low. For an intermediate volume ratio, the DXI is the most recommended design. When the ratio is relatively high but still in unbalanced conditions, the XDL is most recommended. In addition, the conflict results generated by calibrated models are more consistent and accurate than those generated by default parameter models.

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The proliferation of Connected Vehicles and their ability to collect a large amount of data present an opportunity for real-time safety optimization of traffic networks. At intersections, Adaptive Traffic Signal Control (ATSC) systems and dynamic speed advisories are among the proactive real-time safety interventions that can assist in preventing rear-end collisions. This thesis proposes two systems that utilize connected vehicle data to optimize traffic safety in real-time using reinforcement learning approaches. The first system utilizes a Deep Deterministic Policy Gradient (DDPG) reinforcement learning agent in conjunction with a dynamic programming approach to optimize vehicle trajectories and issue speed advisories. The second proposed system is a Signal-Vehicle Coupled Control (SVCC) system incorporating ATSC and speed advisories to optimize safety in real-time. By applying a rule-based approach in conjunction with a Soft-Actor Critic Reinforcement Learning framework, the system assigns speed advisories to platoons of vehicles on each approach and extends the current signal time accordingly. Dynamic traffic parameters are collected in real time and used to estimate the current conflict rate at the intersection, which is then processed and input into the respective models. The systems were tested on two different intersections modelled using real-world data through the simulation platform VISSIM. Significant reductions in traffic conflicts and delay were observed, with the simple speed advisory system yielding a 9-23% reduction in traffic conflicts and the SVCC system yielding a 41-55% reduction. Similarly, delay reductions of about 24% were observed. Both systems function at lower levels of market penetration, with diminishing returns beyond 50% Market Penetration Ratio (MPR). The thesis thus proposes and demonstrates the effectiveness of two unique CV-based systems that are low in computational intensity and applicable in the near future.

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Using simulation models to conduct safety assessments can have several advantages as it enables the evaluation of the safety of various design and traffic management options before actually making changes. However, limited studies have developed microsimulation models for the safety evaluation of active road users such as pedestrians. This can be attributed to the limited ability of the existing simulation models to capture the heterogeneity in pedestrian behavior and their complex collision avoidance mechanisms. Therefore, the objective of this thesis is to develop an agent-based framework to realistically model pedestrian behavior in near misses and to improve the understanding of pedestrian evasive action mechanisms in interactions with vehicles. Pedestrian-vehicle conflicts are modeled using single-agent and multi-agent approaches under the Markov Decision Process (MDP) and Markov Games (MG) frameworks, respectively. A continuous Gaussian Process Inverse Reinforcement Learning (GPIRL) approach is implemented to recover pedestrians’ single-agent reward functions and infer their collision avoidance mechanisms in conflict situations. In the multi-agent framework, pedestrian-vehicle conflicts are modeled utilizing the Multi-Agent Adversarial Inverse Reinforcement Learning (MA-AIRL). Video data from a congested intersection in Shanghai, China is used as a case study. Trajectories of pedestrians and vehicles involved in traffic conflicts were extracted with computer vision algorithms. A Deep Reinforcement Learning (DRL) model is used to estimate optimal pedestrian single-agent policies in traffic conflicts. Moreover, the adversarial multi-agent IRL approach simulates road users’ optimum evasive actions with an implementation of the multi-agent actor-critic using Kronecker-factored trust region (MACK). This algorithm enables multi-agent policy estimation using the rewards recovered from the discriminator of the adversarial neural network. The results show that the developed models predicted pedestrian trajectories and their evasive action mechanisms (i.e., swerving maneuver and speed changing) in conflict situations with high accuracy. Moreover, the highly nonlinear structure of the reward function in the multi-agent framework enabled capturing more complex behavior of the road users in near misses and their collision avoidance mechanisms. This study is a crucial step in developing a safety-oriented microsimulation tool for pedestrians in mixed traffic conditions.

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Traffic simulation models have been used recently for road safety evaluation using traffic conflict indicators from simulated road user trajectories. However, this approach has many shortcomings: 1) microsimulation traffic models are developed based on rules that tend to avoid collisions, and 2) they do not realistically model road users’ behaviour and their collision avoidance mechanisms. This research models the interactions between motorcyclists and pedestrians using two inverse reinforcement learning (IRL) frameworks: single-agent IRL and multi-agent IRL. Road users are modelled in a Markov Game setting as intelligent decision-makers that attempt to maximize their utilities over time. The utility is expressed by the reward function, which provides insights into road users’ behaviour in conflict interactions and can be recovered from real road user trajectories. For this study, video data from a busy and congested intersection in Shanghai, China is used. Trajectories of motorcyclists and pedestrians involved in conflict interactions were extracted using computer vision algorithms. For the single-agent model, the Gaussian Process IRL is used to obtain the motorcyclists’ reward function, and the reward function is then utilized to infer motorcyclists’ preferences in conflict situations. In addition, the Deep Reinforcement Learning Actor-Critic framework is used to estimate motorcyclists' optimal policies (sequences of decisions) and simulate their trajectories. For the multi-agent model, Adversarial IRL is used to recover the reward function from the trajectories. The multi-agent model accounts for the equilibrium concept between road users by modelling their intentions in a Markov Game framework. Furthermore, the algorithm applies the Multi-agent Actor-Critic model with Kronecker factors to obtain the road users’ optimal policies. Finally, simulation tools were developed to predict motorcyclist and pedestrian trajectories using the optimal policies. In the single-agent model, the motorcyclist was modelled and the pedestrian had policies that were assumed to be known over time, whereas both road users were modelled as intelligent agents in the multi-agent model. The multi-agent model outperformed the single-agent model in terms of predicting the road users’ trajectories and their evasive action mechanisms. Furthermore, both models provided reasonably accurate predictions for the Post-Encroachment Time (PET) conflict indicator, which correlates well with corresponding field-measured conflicts.

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Traditional road safety analysis is usually conducted using historical collision records. This reactive approach to road safety has been shown to have several shortcomings. Recently, there has been significant interest in using surrogate measures such as traffic conflicts to analyze safety. This interest has been strengthened by the availability of tools to automate the traffic conflict analysis from video data. Using automated computer vision techniques, road users can be tracked, classified, and their interactions determined accurately and reliably. This thesis demonstrates two applications of automated road safety analysis techniques using traffic conflicts. The first application is related to the diagnosis of road safety issues. A case study of safety at a school zone in Edmonton, Alberta is used. 240 video-hours of traffic data were recorded in two different seasons. The data was analyzed to evaluate the current safety performance of the school zone to identify factors that may be contributing to safety concerns and to propose potential safety improvements. The analysis included the automated analysis of traffic conflicts, violations, and traffic speed. Several recommendations were presented that would potentially improve the safety for all road users without affecting the mobility along the intersections. The second application included an evaluation of the safety effectiveness of improving the signal head visibility at two signalized intersections located in the City of Edmonton, Alberta, by conducting an automated before-and-after safety analysis using traffic conflicts. The use of automated conflict analysis in before/after safety evaluation can significantly reduce the time needed to reach conclusions about the effectiveness of safety countermeasures. More than 300 video-hours of traffic data were recorded at the two treated intersections before and after applying the treatment. In addition, traffic data was collected at two other intersections with similar characteristics to be used as comparison sites. A before/after road safety evaluation was performed using the Empirical Bayes method that accounts for the effects of the regression to the mean confounding factor. The methodology employs the use of a calibrated conflict-based safety performance function (SPF). The results showed a statistically-significant reduction (24.5%) in the average hourly conflict due to the improved signal heads.

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Active road users such as cyclists are usually subject to an elevated risk of collision. Therefore, there is a need for efficient techniques for evaluating the safety of active road users. Traditional road safety analysis has often been conducted using historical collision records. However, limitations associated with collision data have motivated the development of complementary proactive techniques for road safety analysis. Recently, there has been significant interest in using traffic conflicts to analyze safety which has been strengthened by the availability of automated traffic conflict analysis tools. This thesis demonstrates two applications of automated road safety analysis techniques using traffic conflicts. The first application is a safety diagnosis of a major intersection in Vancouver, British Columbia, with bicycle and pedestrian safety issues. Automated video-based computer vision techniques are used to extract and analyze data from the video footage. Traffic conflict indicators, such as time to collision and post-encroachment time, are used to assess conflicts along the intersection to identify safety problems based on the frequency and severity of conflicts. Different spatial and temporal non-conforming behavior patterns are also analyzed. The diagnosis revealed that the Burrard Bridge’s access and exit ramps are the main sources of conflicts at the intersection and their design encouraged many non-conforming behavior patterns. It can be expected that removing both ramps will address a significant amount of safety problems. The second application covers detailed analysis of cyclist yielding behavior at the same intersection. Cyclist yielding behavior is evaluated by analyzing vehicle and bicycle yielding rates in two bicycle crossings with different rules of priority. Compliance with traffic regulations is also studied by looking at how intersections actually operate in contrast to the formal traffic rules. Results showed that bicycle yielding rates can change significantly depending on the crossing’s configuration and legal right-of-way. Low bicycle yielding rates in combination with consistent but relatively low vehicle yielding rates can present a safety problem: understanding cyclist yielding behavior can enable engineers to design and build safer intersections which are consistent with road users’ expectations, and to develop more realistic models of traffic behavior, safety, and operations.

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Connected vehicles are on the cutting edge of automotive technology with applications expected to improve mobility and safety. Several studies have evaluated the mobility benefits of connected vehicle technology but there is little research on its impact on safety. The first objective of this study is to investigate the ability to evaluate the safety of a connected vehicle applications using surrogate safety measures through a combination of the micro-simulation model VISSIM and the Surrogate Safety Assessment Model (SSAM). Two connected vehicle applications are reviewed, considering two types of connected vehicle communications, specifically Vehicle-to-Vehicle and Vehicle-to-Infrastructure. The two applications are a cumulative travel time (CTT) intersection control algorithm connected vehicle environment, and a cooperative adaptive cruise control (CACC) application, facilitating vehicle platooning on a freeway. The CACC study investigates the improvement to the freeway segment through a simulated incident. The CTT study investigates the impacts of calibrating the micro-simulation model using real-world vehicle trajectory and conflict data. The CTT algorithm is applied to a signalized intersection and evaluated under three calibration scenarios: uncalibrated, first step calibrated for desired speed and vehicle arrival types, and second step calibrated for conflicts observed in the field. In both studies, a comparison of safety based on the number of conflicts at different time-to-collision thresholds is provided for the varying scenarios. Results show that the combination of VISSIM and SSAM provide an appropriate tool to use in the evaluation of changes in the level of safety of connected vehicle applications, specifically the CACC application and the CTT intersection control application. Calibration of the micro-simulation model has a significant impact on the results of the safety evaluation. However, it is inconclusive whether the results are realistic with the lack of a real-world connected vehicle implementation.

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To encourage greener cities while reducing transportation impacts such as climate change, traffic congestion, and road safety issues, governments have been investing in sustainable transportation modes such as cycling. A safe and comfortable cycling environment is critical to encourage bicycle trips, since cyclists are subject to greater safety risks and represent the highest share of severe and fatal road collisions. Traditionally, engineering approaches have addressed road safety in reaction to existing collision histories. For bicycle collisions, which are rare events, a proactive approach is more appropriate. This study described the development of bicycle related macro-level (i.e. neighbourhood or traffic analysis zone level) Collision Prediction Models (CPMs) and tested the models as empirical tools for bicycle road safety evaluation and planning. This study was unique in its usage of the bicycle exposure variable represented by Bicycle Kilometers Travelled (BKT) as a lead exposure variable in the models. The macro-level CPMs that were developed for bicycle-vehicle collisions were applied to a case study of the City of Vancouver at the zonal level. The objectives of the study were to: (1) identify bicycle data safety indicators, (2) develop bicycle macro-level CPMs using generalized linear regression modeling (GLM), (3) demonstrate model use by applying them to a case study of the City of Vancouver through a macro-reactive road safety application, and (4) identify potential safety countermeasures for the highest ranked Collision Prone Zones (CPZs). The models were effective in enhancing traditional road safety initiatives and identifying and ranking dangerous CPZs in the City of Vancouver. The top three collision prone areas were then brought forward for diagnosis and remedy analysis. This case study effectively demonstrated the use of the models to proactively enhance bicycle safety.

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Recently, there has been a growing interest in using microsimulation models for the safety assessment of road facilities by analyzing vehicle trajectories and estimating conflict indicators. Using microsimulation in safety studies can have several advantages. However, concerns have been raised about the ability of these models to realistically represent unsafe vehicle interactions and near misses and the need for a rigorous model calibration. The main objective of this thesis is to investigate the relationship between field-measured traffic conflicts and simulated traffic conflicts at signalized intersections. Automated video-based computer vision techniques were used to extract vehicle trajectories and identify field-measured rear-end conflicts. Conflict measures (e.g. time-to-collision (TTC)) and locations were determined and compared with simulated conflicts from the Surrogate Safety Assessment Model (SSAM) by analyzing the vehicles trajectories extracted from two microsimulation models: VISSIM and PARAMICS. To increase the correlation between simulated and field-measured conflicts, a two-step calibration procedure of the simulation models was proposed and validated. In the first calibration step, the simulation model was calibrated to ensure that the simulation gives reasonable results of average delay times. Then, in the second calibration step, a Genetic Algorithm procedure was used to calibrate the safety-related parameters in the simulation model. The correlation between simulated and field-measured conflicts was investigated at different thresholds of TTC. The results obtained from VISSIM and PARAMICS were compared. Furthermore, the transferability of the calibrated simulation models for safety analysis between different sites was investigated. As well, the spatial distributions of the field-measured and the simulated conflicts were compared through conflict heat maps. Overall, good correlation between field-measured and simulated conflicts was obtained after calibration for both models especially at higher TTC values. Also, the results showed that the simulation model parameters are generally transferable between different locations as the transferred parameters provided better correlation between simulated and field-measured conflicts than using the default parameters. The heat maps showed that there were major differences between field-measured and simulated conflicts spatial distribution for both simulation models. This indicates that despite the good correlation obtained, both PARAMICS and VISSIM do not capture the actual conflict occurrence mechanism.

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Road safety studies attempt to develop solutions to deficiencies by identifying causes and prescribing remedies. Most often, traffic safety engineers use collision information to detect potential problem locations and to provide assessments of treatments. The main short-coming of this technique is that it analyzes past information to determine whether a problem exists. This reactive approach requires an expert tasked to improve safety having to stand by and wait for collisions to occur. Many experts have recognized the need for a more proactive approach in order to reduce the analysis period and provide timely safety improvements.One particularly promising alternative is the use of traffic conflicts as surrogates to actual collisions. Conflict data collection offers many benefits to that of collisions, including their relative frequency, and marginal social cost. Traffic conflict studies can be deployed in any location, need little planning, and do not require a vigilant database maintenance. However, since trained human reviewers are required there are significant costs associated with in-situ conflict observation studies. Furthermore, traffic conflict studies also rely on human judgement, which introduces subjectivity into results. The goal, therefore, is to find a way to harness data-rich traffic conflicts that is both efficient and fundamentally objective. This thesis presents the novel use of an automated traffic conflict detection tool to diagnose safety issues at intersections with known safety deficiencies. Two intersections were analyzed to determine which movement types were over-represented. Once the most dangerous movements were identified, characteristics of the road user, environment, and conflicts themselves were analyzed to provide an educated recommendation for safety improvement. When the treatments had been implemented for some time, additional data was collected and similarly analyzed to determine whether it had achieved the intended goal.The outcomes of this research provide evidence that objective and surrogate safety indicators can effectively be used to identify safety problems at intersections. In addition, the rich data collected using the automated traffic conflict technique can be mined to understand the mechanisms leading to and resulting in offending conflicts. This information can help traffic safety experts make informed decisions for focused countermeasure implementation.

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The safety of a transportation system is a serious concern for transportation agencies and analysts. In Canada, roughly 29% and 43% of fatalities and serious injury collisions, respectively, occur at intersections (Road Safety Directorate, 2007). There has been a growing interest in the construction of roundabouts to improve the safety performance and increase the traffic efficiency at regular intersections. As more roundabouts are installed throughout North America, there will be an increased need for a detailed analysis of their safety performance. Collision data used to evaluate the safety performance of roundabouts is considered a reactive and costly approach. Recently, the Traffic Conflict Technique (TCT) has been used to improve and complement the collision-based safety diagnosis approach. The purpose of this thesis is to demonstrate the use of an automated safety analysis tool, developed at the University of British Columbia (UBC), for the diagnosis of safety issues at roundabouts. Traffic conflicts occurring at a roundabout, located at UBC campus, are automatically identified and analyzed to develop an in-depth understanding of the behaviour of road users and the causes of traffic conflicts. The results from this detailed and low-cost approach are used to propose effective countermeasures to proactively improve the safety of roundabouts, and to ultimately reduce collisions. Based on these results, the following safety concerns have been determined; a confusion of the right-of-way between entering and circulating vehicles; inappropriate negotiation between circulating and exiting vehicles; higher risk of pedestrian-vehicle conflicts at exit lanes than entry lanes and the accommodation of cyclists at mixed traffic roundabouts. Several countermeasures proposed to address these concerns are to add cross hatch markings, narrow down circulating lanes, modify central island markings, provide pedestrian crossing signs, and propose further education for drivers on using roundabouts and accommodating vulnerable road users. This thesis helps to demonstrate the effectiveness of the advanced safety tool in diagnosing safety, and proactively demonstrate safety issues at the roundabout.

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Traditionally, road safety analysis has been undertaken using historical collision records. This approach to road safety analysis is reactive in that the analyst has to wait for collisions to take place before an action can be taken. An alternative approach is to study traffic conflicts or near misses which occur more frequently, can be clearly observed and are related to collisions. However, there are issues of subjectivity, reliability, and cost associated with the use of human observers. The use of computer vision techniques to automate the process of collecting traffic conflicts data can help mitigate these problems. This thesis presents the results of a before-after safety evaluation of a proposed design for channelized right-turn lanes. The evaluation uses an automated safety analysis approach to identify and measure the severity of traffic conflicts. The new design, termed “Smart Channels”, decreases the angle of the channelized right turn to approximately 70 degrees, and is considered to have safety benefits for both vehicle-pedestrian and vehicle-vehicle interaction. Data for three treatment sites and one control site, located in British Columbia, Canada, are evaluated using automated traffic conflict analysis that relies on computer vision for conflict detection. The results of the evaluation show that the implementation of the right-turn treatment has resulted in a considerable reduction in the severity and frequency of merging, rear-end, and total conflicts. The total average hourly conflict was reduced by a statistically significant 51 percent, while the average conflict severity was reduced by a statistically significant 41 percent. Many different traffic conflict indicators have been proposed and studied, but the methods of combining the results has not been well examined. This thesis considers four conflict indicators and examines methods of combining or aggregating the information provided by each indicator in order to better account for all components of risk in traffic conflicts. The four indicators are time-to-collision, gap-time, deceleration-to-safety time, and post-encroachment time. Two primary aggregation methods are studied: time aggregation and road-user aggregation. Time aggregation is appropriate for determining aggregate severity over periods of time, and road-user aggregation is used for normalizing risk to the volume of users.

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Road safety is an issue of global importance, receiving both national and international attention. According to the World Health Organization, road traffic injuries are extrapolated to become the fifth leading cause of death in the world by 2030. Studies conducted to gain better insight into how countries can improve their road safety performance levels often use one single variable – the number of fatalities per million inhabitants – and focus predominantly on European countries. This thesis looks to develop and analyze models incorporating a wider range of countries as well as a wider range of road safety performance indicators using data envelopment analysis and accident prediction models. The first method, initially calculate the efficiency scores using three input variables (percentage of seatbelt use in front seat, road density, and total health expenditure as percentage of GDP) and two output variables (number of fatalities per million inhabitants and fatalities per million passenger cars). It was found that the addition of the percentage of seatbelt use in rear seats (fourth input variable) and the percentage of roads paved (fifth input variable) improved the efficiency scores and rankings. Overall, the percentage of seat belt use in front seats and the total health expenditure variables had the greatest importance. The second method developed three accident prediction models using the generalized linear modeling approach with the negative binomial error structure. The elasticity analysis revealed that, for Model 1 and Model 2, the health expenditure variable had the greatest impact on the number of fatalities. For Model 3, the seatbelt wearing rate in front seats and the seatbelt wearing rate in rear seats had the greatest effect on the number of fatalities.

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The importance of improving traffic safety is often understated, partially because it often takes a retrospective approach, garnering little public attention. Nonetheless, from both an economical and societal point of view, traffic safety presents severe and significant problems despite the sizeable benefits that advancements in transportation have brought to society. To further complicate the matter, the net results of most traffic safety interventions are not always straightforward or intuitive. This illustrates the need for sound engineering evaluation of traffic safety interventions that is grounded in statistical analysis. It should be noted that these engineering evaluations can be applied not only to location-specific safety treatments, but can also be used to test the effectiveness of traffic safety-targeted policies such as changes in BAC level or seat belt laws. Previously, a prominent and effective methodology for conducting traffic safety intervention evaluations was known as the Empirical Bayes inference techniques. It was effective in accounting for a number of confounding factors, which threaten the validity of any claims made by simply looking at raw collision data. However, several key drawbacks have been identified, including difficulties to obtain the necessary amount of input data and the statistical discontinuity in the steps where the uncertainties around the input data are not entirely carried through to the final estimates. In theory, the recently-developed Full Bayes technique fully addresses the weaknesses of the Empirical Bayes method; however, there have been hesitations to adopt the methodology because of the increased level of complexity and the previous lack of adequate computational power. The purpose of this thesis to perform a thorough literature on methodologies for conducting traffic safety intervention models particularly with regards to Bayesian inference, devise a standardized methodology using the findings, apply the methodology on a real-world case study in Edmonton, Alberta, and summarize the results to demonstrate the strengths and the feasibility of the Full Bayes methodology. The results indicated that the treatment program was effective in reducing right-turn collisions by 39%. A standardized practical guideline was also developed using the literature review and the results and includes various provisions for flexibility and alterations.

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In sustainable urban planning, non-motorized active modes of travel such as walking are identified as a leading driver for a healthy, liveable, and resource-efficient environment. Walking is also an integral component of most trips. However, walking receives less attention in transportation engineering and planning compared to motorized modes. As the global society is becoming more aware of the benefits of active transportation, there is an increasing demand for designing and shaping the transportation system to put more emphasis on pedestrians. As such, standards and guidelines need to be developed in order to provide practitioners with the tools required to objectively evaluate pedestrian oriented facilities. However, the tools and methods developed and used for modeling pedestrian movement have not yet been developed to a level that can reliably measure pedestrian activity and behavior. To encourage walking, there is a need for a solid understanding of pedestrian walking behavior. This understanding is central to the evaluation of measures of walking conditions such as comfortability and efficiency. The aim of this thesis work is to gain an in-depth understanding of pedestrian walking behavior through the investigation of walking speed and the spatiotemporal gait parameters (step length and step frequency). This microscopic-level analysis provides insight into the pedestrian walking mechanisms and the effect of various attributes such as gender and age. The analysis relies on automated video-based data collection using computer vision techniques. This thesis makes several contributions which include: i) demonstrating the feasibility of using computer vision to capture pedestrian movement, ii) investigation of pedestrian speed variations with respect to design changes to intersection crossings, iii) investigation of the ability of individual pedestrians to change their walking speed as a response to pedestrian signal indications, iv) investigation of pedestrian gait parameters for various pedestrian and design attributes, and v) development of a methodology for classification of pedestrian age and gender using spatiotemporal gait parameters.  

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Public transportation systems are a fundamental necessity in current times where sustainability and rising safety costs are important concerns to government officials and the general public. Therefore, the design of public transportation systems is an area of great interest for researchers and practitioners. Nonetheless, there is usually little analysis of network properties during transit design and planning. Moreover, due to the lack of empirical tools, there is not much consideration of transit safety at the planning stage . In this research, a study was performed to explore zonal based network properties applied to bus systems. A new technique to measure network connectivity was developed and applied to a real-world transit system, which in addition to the relationship between edges and vertices, incorporated the influence of transit operational factors (i.e. frequency of routes). Additionally, the effect of bus route transfers was analyzed and modeled by adding intermediate walking transfer links between bus stops. The calculated network properties were applied as explanatory variables in the development of macro-level ridership and collision prediction models. The proposed methodology was applied to the Greater Vancouver Regional District (GVRD) public transportation system and its 577 traffic analysis zones. The developed mathematical models include, seven multiple linear regression models which explain transit commuting ridership. The regression models revealed that ridership is positively linked to network characteristics such as coverage, connectivity, complexity and, the local index of transit availability (LITA). In addition, 35 collision prediction models were developed using a Generalized Linear Regression technique, assuming a Negative Binomial error structure. The safety models showed that increased collisions were associated with transit network properties such as: connectivity, coverage, overlapping degree and the LITA. As well, the models revealed a positive relation between collisions and transit physical and operational attributes such as number of routes, frequency of routes, bus density, length of bus route and 3+ priority lanes, among others.

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Motor vehicle speed is a key risk factor contributing to many road accidents. Historical data shows that speed-related accidents account for a significant proportion of all the fatal and serious injury accidents and result in considerable social and economic costs. The objective of this thesis is to understand and quantify the relationship between traffic speed and accident frequency at urban signalized intersections in the city of Edmonton and Vancouver, Canada. This objective is achieved by developing accident prediction models which relate accident frequency to speed variables and other intersection characteristics. Road accident, traffic speed, traffic flow and road geometric data were obtained from the two cities for the purpose of the models development. The generalized linear modelling techniques are used to develop the accident prediction models assuming negative binomial error structure. A total of 15 models are developed relating accident frequency with five speed variables: average speed, mode speed, 85th percentile speed, speed standard deviation and percent of vehicles speeding. The results show that all five speed variables are positively correlated with accident frequency. A quantitative relationship between the change in the value of speed variables and the change in accident frequency is derived from the developed models.

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Current geometric design guides provide deterministic standards where the safety margin of the design output is generally unknown and there is little knowledge on the safety implications of deviating from these standards. Several studies have advocated probabilistic geometric design where reliability analysis can be used to account for the uncertainty in the design parameters and to provide a risk measure of the degree of deviation from design standards. In reliability analysis, this risk is represented by the probability of non-compliance (Pnc) defined as the probability that the supply exceeds the demand. However, there is currently no link between measures of design reliability and the quantification of safety using collision frequency. The analysis presented in this thesis attempts to incorporate a reliability-based quantitative risk measure in the development of Safety Performance Functions (SPFs). The thesis considers the design of horizontal curves, where non-compliance occurs whenever the available sight distance (ASD; supply) falls short of the stopping sight distance (SSD; demand). The inputs of SSD are random variables and appropriate probability distributions were assumed for each. A comprehensive database for the Trans-Canada Highway was used to compute the probability of non-compliance (Pnc) for 100 segments of horizontal curves. Several Negative Binomial (NB) Safety Performance Functions (SPFs) were developed and the predicted collisions were found to increase with risk (Pnc) and that the rate of increase varies by severity level. The likelihood ratio test showed that the inclusion of a risk parameter (Pnc) has generated better predictive models that have significantly outperformed the traditional models. Further, a spatial analysis was carried out which showed that the spatial models were successful in overcoming potential model misspecification resulting from incorporating only exposure and Pnc in the SPFs as relevant covariates might have been omitted.The optimization of cross-section design to minimize the risk associated with restricted sight distance was also considered using a multiple objective function that involves new Collision Modification Factors (CMFs) incorporating Pnc. The results indicated that accounting for the random variations due to drivers’ behavior proactively at the design stage would decrease collisions in addition to achieving an overall risk reduction.

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