Tarek Sayed


Relevant Thesis-Based Degree Programs

Affiliations to Research Centres, Institutes & Clusters


Graduate Student Supervision

Doctoral Student Supervision

Dissertations completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest dissertations.

Development of a multi-agent based simulation model for cyclist-pedestrian interactions in shared spaces (2022)

Understanding and modeling cyclist and pedestrian dynamics and their microscopic interaction behaviour in shared space facilities are crucial for several applications, including safety and performance evaluations. Recently, a few studies have developed models to simulate road user interactions in shared space facilities. However, existing models suffer from several shortcomings and show significant discrepancies with real-world behaviour. As such, this thesis presents a novel microsimulation-oriented framework for modeling cyclist and pedestrian interactions in such facilities. Advanced Artificial-Intelligent techniques were used to model road users' behaviour and their interactions as reward-based intelligent agents. This thesis bridges the gap in modeling road user interactions by accounting for their rationality, intelligence, and sequential decision-making process by implementing the Markov Decision Process (MDP) modeling framework. Furthermore, this thesis proposes a multi-agent modeling framework to model cyclist and pedestrian interactions in shared spaces. Unlike the traditional game-theoretic framework that models multi-agent systems as a single time-step payoff, the proposed approach is based on Markov Games (MG), which models road users' sequential decisions concurrently. Moreover, this thesis investigates the ability of different equilibrium behavioral theories (i.e., Nash-Equilibrium (NE) and Logistic-Stochastic-Best-Response-Equilibrium (LSBRE)) in predicting road user operational-level decisions and evasive-action mechanisms. Road user trajectories from three shared space facilities located in Vancouver, Canada, and New York City, USA, were extracted by means of computer vision algorithms. Single and multi-agent inverse reinforcement learning approaches were utilized to estimate road user reward functions using examples of their demonstration (i.e., trajectories). Reward function weights infer road users' goals and preferences and can form the key component in developing agent-based microsimulation models. Single-agent and multi-agent simulation platforms were developed, relying on deep reinforcement learning approaches, to emulate and validate road user interactions in shared spaces. The utilized multi-agent modeling approaches led to a significantly more accurate prediction of road user behaviour and their evasive action mechanisms. Moreover, the recovered reward functions based on the single-agent modeling approach failed to capture the equilibrium solution concept compared to the multi-agent approach. This thesis determines a behavior-based consistent paradigm to model equilibrium in multi-agent transportation systems, such as road user interactions in shared space facilities.

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Novel statistical insights into cyclist safety and bike ridership (2022)

Many cities are promoting biking to develop more sustainable and livable communities and improve public health. However, cyclists are vulnerable road users with elevated injury and fatality risks compared to vehicles' drivers and passengers. Although some studies have investigated biking ridership and cyclist-vehicle crash risk on the macro-level, these studies suffer from several limitations and research gaps that need to be addressed. Therefore, this research focuses on addressing key issues related to the development of macro-level models for evaluating biking attractiveness and safety, including 1) assessing the effects of zonal characteristics (e.g., bike network and land use) on cyclist-vehicle crashes while accounting for their effects on bike exposure, 2) developing novel network variables and assess their impact on Bike Kilometers Travelled (BKT) and cyclist-vehicle crashes, 3) developing safety models that account for the measurement error in traffic exposure measures, 4) accounting for the effect of seasonality on cyclist-vehicle crashes, and 5) developing a comprehensive zone-based index to represent both biking attractiveness and cyclists’ crash risk. The models and indices developed in this research are based on 134 Traffic Analysis Zones in Vancouver, Canada. The results show that 1) several zonal characteristics have opposite direct effects and total effects (direct effect plus effects through bike exposure) on cyclist-vehicle crashes, 2) BKT and cyclist-vehicle crash models showed significant associations with novel bike network variables such as centrality, assortativity, and complexity, 3) accounting for the measurement error of the traffic exposure measures in cyclist-vehicle crash models significantly enhanced model fit, 4) accounting for seasonality improves the fit of cyclist-vehicle crash models and captures the change of zonal characteristics’ impact on crashes through different seasons, and 5) the correlation between the developed Bike Attractiveness Index and Bike Safety Index is low, which highlights the need for a comprehensive index that includes biking attractiveness and safety.

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Full bayesian models to assess the impacts of mobile automated enforcement on road safety and crime (2021)

The success of road safety programs is highly dependent on using accurate and precise safety models. Traditionally, these safety models were developed at a micro-level and lack understanding of how safety is prioritized at a planning-level. This dissertation bridges this gap by developing macro-level models to enhance the decision-making processes by providing opportunities for planners and designers to become better informed on issues related to road safety and criminology. The contributions of this dissertation were to develop Full Bayesian models to explore new applications for macro-level modeling, which focused on mobile automated enforcement (MAE). This type of enforcement is one of the tools that agencies use when manned enforcement is too costly or not feasible. It consists of units that are installed in vehicles that rotate between sites to improve compliance to the speed limit and to enhance safety. The first application showed that increasing the number of tickets issued for vehicles exceeding the speed limit resulted in a decrease in all collision severities. The results also showed that collision reductions were associated with an extended time enforcing a site. Decision support tools were also created to help agencies make informed decisions regarding how to optimize their enforcement strategy.The second application explored the impact of MAE on both collisions and crime. Previous work suggested that collision and crime hotspots overlapped. It was, therefore, crucial to quantify the degree of correlation between both events. The results of the models confirmed this relationship and showed that increased MAE presence resulted in reductions in both events. This demonstrates how a single deployment can achieve multiple objectives, and allows agencies to optimize their deployment strategy to achieve more with less. Understanding how changing the deployment strategy at a macro-level affects safety provides enforcement agencies with the opportunity to maximize the efficiency of their existing resources. Future work would include using the results in this dissertation to build an optimization tool which accounts for the safety impacts, constraints surrounding a deployment, and the cost of a deployment to allow agencies to maximize the use of their resources to achieve the highest safety benefit.

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Real-time safety and mobility optimization of traffic signals in a connected-vehicle environment (2021)

Adaptive traffic signal control (ATSC) strategies are a promising approach to improving the efficiency of signalized intersections, especially in the era of connected vehicles (CVs) where real-time information on vehicle positions and trajectories is available. Recently, numerous ATSC algorithms have been proposed to accommodate real-time traffic conditions and optimize traffic efficiency. The common objective of these algorithms is to minimize total delays or maximize vehicle throughputs. Despite their positive impacts on traffic mobility, existing ATSC algorithms do not consider optimizing traffic safety. This is most likely due to the lack of tools to evaluate the safety of signalized intersections in real time. This thesis presents several advances toward the real-time safety and mobility optimization of traffic signals in a connected-vehicle environment. First, new models for the real-time safety evaluation of signalized intersections were developed and validated, using traffic video-data of six locations in two Canadian cities. The developed models relate the number of rear-end traffic conflicts, as a surrogate safety measure, to dynamic traffic parameters at the signal cycle level. Several traffic conflict indicators and multiple conflict severity levels were considered. The transferability of the developed models was also investigated and confirmed using additional traffic datasets for two corridors in the United States. Second, a new procedure to integrate the developed real-time safety models with traffic microsimulation models was proposed. The procedure was validated using real-world traffic video data of two signalized intersections in British Columbia. The results showed that the proposed models can predict traffic conflicts from traffic simulation with reasonable accuracy and subsequently can be used to investigate the safety impact of various CVs-based applications before field implementation. Third, a novel self-learning ATSC algorithm to optimize traffic safety using real-time CVs data was proposed. The algorithm was developed using the Reinforcement Learning approach, trained using a microsimulation model, and validated using real-world traffic data of two signalized intersections in British Columbia. Superior to the traditional actuated signal control system, the proposed algorithm showed positive safety and mobility impacts. The proposed ATSC algorithm was also found to be effective and feasible even under low market penetration rates of CVs.

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New insights into active transportation safety: a macro-level analysis framework (2018)

City councils worldwide have shown an increasing interest in active transportation (AT) due to its health, environmental, and economical benefits. However, active commuters are vulnerable to severe crash risk, which is a deterrent to active travel. Therefore, there is a need for developing systematic approaches to improve AT safety. This dissertation introduces a comprehensive framework for identifying, diagnosing and remedying the macro-level AT safety issues. It provides original insights into AT networks, crash models (CM), crash hot zones identification (HZID), and policy recommendations. Data were collected from 134 traffic analysis zones (TAZs) in the City of Vancouver. Cyclist and pedestrian crash data, traffic exposure and large GIS data were incorporated in the analysis. The GIS data integrated various land use, built environment, socioeconomic, and road facility features. Moreover, bike and pedestrian network indicators, developed using graph-theory and representing connectivity, continuity, and topography of the networks, were incorporated. The state of the practice empirical Bayesian (EB) method and the state of the art full Bayesian (FB) methods were adopted for the CMs’ development and HZID. Various FB model forms were investigated, and the Spatial Poisson-Lognormal model performed the best. Cyclist and pedestrian crashes were found positively associated with various attributes of network-connectivity, socio-demographics, built environment, arterial-collector roads, and commercial areas. Conversely, the crashes were negatively associated with various attributes of network-directness, network-topography, residential areas, recreational areas, local roads, separated paths, and actuated signals. Most of the safety correlates had similar effects for the pedestrian and cyclist crashes. Accordingly, mixed multi-response FB CMs were developed and the correlation between pedestrian and cyclist crashes was found significant. The univariate/multivariate CMs with spatial effects consistently outperformed those without, and the multivariate CMs generally outperformed the univariate ones. AT crash hot-zones were then identified using the novel Mahalanobis distance and the conventional potential for safety improvement (PSI) methods, and consistency tests were applied to compare both. Afterwards, trigger variables were statistically identified for the crash hot and safe zones. Lastly, remedies regarding land use, traffic demand, and traffic supply management were proposed based on the trigger variables’ analysis, field studies, and literature consultation.

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The development of behavior-based traffic conflict indicators through automated traffic safety analysis (2018)

Traffic collisions are a severe epidemic that causes the loss of 1.25 million lives worldwide every year, the majority of which are in developing and emerging countries. Traditionally, road safety analysis has been conducted by relying on collision records as the primary source of data. This reactive approach has several shortcomings such as the poor quality of collision data, the long observation periods, the subjectivity of evaluation, and the difficulty in understanding the mechanisms that lead to collisions. These limitations have led to the growing interest in using surrogate safety measures, such as traffic conflicts (i.e., near misses), as a proactive approach to analyzing safety from a broader perspective than collision data alone. The analysis of traffic conflicts is typically performed using a number of conflict severity measures such as Time-To-Collision and Post-Encroachment-Time. These measures rely on road-users getting within specific spatial and temporal proximity from each other and, therefore, assume that proximity is the indicator of conflict severity. However, this assumption may not be valid in all driving cultures where road-users are less organized and traffic rules are weakly enforced. In these environments, close interactions between road-users are very common and sudden evasive actions are the primary collision-avoidance mechanism. The objective of this research is to investigate the applicability of existing time-proximity measures in less-organized traffic environments and to propose evasive action-based conflict indicators as complementary measures of conflict severity. The mechanisms by which road-users perform evasive actions are studied and used to recommend new behavior-based conflict indicators. Time-proximity and evasive action conflict indicators are then compared to evaluate conflict severity at locations from five major cities with different traffic environments; Shanghai, New Delhi, New York, Doha, and Vancouver. Ordered-response models were utilized to relate both indicators to conflict severity, taking into account the unobserved heterogeneity in conflicts. The findings reveal that evasive action-based indicators are most effective in less-organized traffic environments such as Shanghai and New Delhi, with less potential in more structured environments such as Vancouver, where time-proximity measures are more effective. The results emphasize the need to select the proper conflict indicators depending on the studied traffic environment.

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Development of an agent based simulation model for pedestrian interactions (2017)

Developing a solid understanding of pedestrian behavior is important for promoting walking as an active mode of transportation and enhancing pedestrian safety. Computer simulation of pedestrian dynamics has gained recent interest as an important tool in analyzing pedestrian behavior in many applications. As such, this thesis presents the details of the development of a microscopic simulation model that is capable of modeling detailed pedestrian interactions. The model was developed based on the agent-based modeling approach, which outperforms other existing modeling approaches in accounting for the heterogeneity of the pedestrian population and considering the pedestrian intelligence. Key rules that control pedestrian interactions in the model were extracted from a detailed pedestrian behavior study that was conducted using an automated computer vision platform, developed at UBC. The model addressed both uni-directional and bi-directional pedestrian interactions. A comprehensive methodology for calibrating model parameters and validating its results was proposed in the thesis. Model parameters that could be measured from the data were directly calibrated from actual pedestrian trajectories, acquired by means of computer vision. Other parameters were indirectly calibrated using a Genetic Algorithm that aimed at minimizing the error between actual and simulated trajectories. The validation showed that the average error between actual and simulated trajectories was 0.35 meters. Detailed validation of the accuracy of simulating pedestrian behavior during different interactions showed that the model successfully reproduced the actual behavior taken by pedestrians in the actual data in 95% of the cases. The simulation model was then applied to analyze pedestrian behavior in two case studies in Vancouver and Oakland. The two case studies addressed different pedestrian flow conditions and different walking environments. The average errors between actual and simulated trajectories for the two studies were found to be 0.28 m and 0.49 m, respectively. The average speed errors were 0.06 m/s and 0.04 m/s in the two studies, correspondingly. The accuracy of reproducing the actual behavior of pedestrians exceeded 87% for most of interactions considered in the two studies. The accuracy of simulating group behavior during different interactions was found to be 96% and 92% in the two studies, respectively.

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Modeling adaptation behavior to driving simulators and effect of experimental practice on research validity (2011)

Driving simulators provide a safe and controllable environment, where different aspects of driving can be analyzed without risking other road users’ safety. However, as simulators cannot precisely replicate real-life scenarios, there has been an ongoing debate about how well the results of simulator studies can be generalized to the actual world. Many studies have compared the outcomes of field experiments and those involving their simulated counterparts in order to test the validity of the research on driving simulators. In nearly all cases, however, the researchers made comparisons without analyzing the underlying psychological explanations behind potential differences. This thesis will discuss why adaptation, or the process by which participants learn how to interact with a simulator, is an important precondition of validity in simulator experiments. Data collected from several experiments revealed that adaptation can distract participants from performing the main task and can systematically bias the results of the experiments. The current study demonstrated that although most researchers provide a practice session before the main scenario, there is no unified approach to determine the characteristics of practice scenarios. The practice sessions vary greatly both in duration and form; and no method has been formulated to verify that a participant has in fact adapted at the end of the practice session. To address these shortcomings, this thesis provides a methodology that mathematically models the learning pattern of subjects to steering and pedals, which can also help identify the adapted and non-adapted subjects at the conclusion of practice scenarios. A comparison of the results of two groups of subjects (control and experiment) showed that adaptation to a driving simulator is largely task-independent. This study analyzed the effect of the practice scenario design on the performance of participants in the main task, which led to the observation that during the main scenario participants tend to continue focusing on the subskills they learned during the practice scenario. Based on the results of these experiments, the thesis provides recommendations on how to measure adaptation and also how to improve the quality of the practice scenario design to minimize any unwanted impact on the main scenario.

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New techniques for developing safety performance functions (2011)

While motorized travel provides many benefits, it can also do serious harm in the form of road-related collisions. The problem affects millions of human lives and costs billions of dollars in economic and social impacts every year. The problem could be addressed thorough several approaches with engineering initiatives being recognized as the most sustainable and cost effective. However, the success of the engineering approaches in reducing collision occurrences hinges upon the existence of reliable methods that provide accurate estimates of road safety. Currently, Safety Performance Functions (SPFs) are considered by many as the main tool in estimating the safety levels associated with different road entities. Therefore, the research in this thesis focuses on addressing key issues related to the development of SPFs for i) collision data analysis and ii) collision intervention analysis. Some of the key issues addressed include: 1) adding spatial effects to SPFs thereby recognizing the evident spatial nature of road collisions, 2) fitting hierarchical models to allow inference to be made on more than one level, 3) recognizing the multivariate nature of collisions as most data are available by collision type or severity and modeling the data as such, 4) identifying and accounting for outliers in the development of SPFs, 5) developing a novel evaluation methodology to estimate the effectiveness of safety countermeasures when subject to data limitations, and 6) compare different tools for investigating the safety change in treated sites due to the implementation of safety countermeasures. The applications of the various models have been demonstrated using several collision datasets and/or safety programs. The results provide strong evidence for (i) incorporating spatial effects in SPFs, (ii) clustering road segments or intersections into homogeneous groups (e.g., corridors, zones, districts, municipalities, etc.) and incorporating random cluster parameters in SPFs, (iii) developing robust multivariate models with multiple covariates for modeling collisions by severity and/or type concurrently, and (iv) the effectiveness of the proposed full Bayes safety assessment methods that account for several theoretical and practical issues concurrently. In addition to the improvement in goodness of fit, the proposed models have also improved inference and precision of expected collision frequency.

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Application of Computer Vision Techniques for Automated Road Safety Analysis and Traffic Data Collection (2010)

Safety and sustainability are the two main themes of this thesis. They are also the two main pillars of a functional transportation system. Recent studies showed that the cost of road collisions in Canada exceeds the cost of traffic congestion by almost tenfold. The reliance on collision statistics alone to enhance road safety is challenged by qualitative and quantitative limitations of collision data. Traffic conflict techniques have been advocated as a proactive and supplementary approach to collision-based road safety analysis. However, the cost of field observation of traffic conflicts coupled with observer subjectivity have inhibited the widespread acceptance of these techniques. This thesis advocates the use of computer vision for conducting automated, resource-efficient, and objective traffic conflict analysis. Video data in this thesis was collected at several national and international locations. Real-world coordinates of road users' positions were extracted by tracking moving features visible on road users from a calibrated camera. Subsequently, road users were classified into pedestrians and non-pedestrians, not differentiating between other road users' classes. Classification was based on automatically-learned and manually-annotated motion patterns. Subsequent to road user tracking, various spatiotemporal proximity measures were implemented to measure the severity of traffic events. The following contributions were achieved in this thesis: i) co-development of a methodology for tracking and classifying road users, ii) development of a methodology for measuring real-world coordinates of road users' positions which appear in video sequences, iii) automated measurement of pedestrian walking speed, iv) investigation of the effect of different factors on pedestrian walking speed, v) development and validation of a methodology for automated detection of pedestrian-vehicle conflicts, vi) investigation of the application of the developed methodology in a before-and-after evaluation of a pedestrian scramble treatment, vii) development of a methodology for aggregating event-level severity measurements into a safety index, viii) development and validation of two methodologies for automated detection of spatial traffic violations. Another contribution of this thesis was the creation of a video library collected from several locations around the world which can significantly aid in future developments in this field.

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Travel time estimation in urban areas using neighbour links data (2010)

Travel time is a simple and robust network performance measure that is perceived and well understood by the public and politicians. However, travel time data collection can be costly especially if the analysis area is extensive. This thesis proposes a solution to the problem of limited network sensor coverage caused by insufficient sample size of probe vehicles or inadequate numbers of fixed sensors. The approach makes use of travel time correlation between nearby (neighbour) links to estimate travel times on links with no data using neighbour links travel time data. A framework is proposed that estimates link travel times using available data from neighbouring links. The proposed framework was validated using real-life data from the City of Vancouver, British Columbia. The travel time estimation accuracy was found comparable to the existing literature. The concept of neighbour links travel time estimation was extended and applied at a corridor level. Regression and Non-Parametric (NP) models were developed to estimate travel times of one corridor using data from another corridor. To analyze the impact of the probes’ sample size on the accuracy of the proposed methodology, a case study was undertaken using a VISSIM microsimulation model of downtown Vancouver. The simulation model was calibrated and validated using field traffic volumes and travel time data. The methodology provided reasonable estimation accuracy even using small probe samples. The use of bus travel time data to estimate automobile travel times of neighbour links was explored. The results showed that bus probes data on neighbour links can be useful for estimating link travel times in the absence of vehicle probes. The fusion of vehicle and bus probes data was analyzed. Using transit data for neighbour links travel time estimation was shown to improve the accuracy of estimation at low market penetration levels of passenger probes. However, the significance of transit probe data diminishes with the increase of market penetration level of probe vehicles. Overall, the results of this thesis demonstrate the feasibility of using neighbour links data as an additional source of information that might not have been extensively explored.

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Master's Student Supervision

Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.

Microscopic agent-based modeling and simulation of cyclists on off-street paths (2024)

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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A comparative analysis of distracted and non-distracted pedestrian dynamics in urban intersections : an adversarial inverse reinforcement learning approach (2023)

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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Modeling lateral interactions between motorcycles and vehicles in mixed traffic using four approaches of survival models (2023)

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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Evaluation of the operational and the safety performance of unconventional intersections (2021)

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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Exploring the applications of connected vehicle data to real time safety optimization at isolated intersections (2021)

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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Modeling pedestrian behavior in pedestrian-vehicle near misses using inverse reinforcement learning algorithms (2021)

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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Modelling motorcyclist-pedestrian interactions using inverse reinforcement learning (2021)

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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Application of Computer Vision Techniques in Safety Diagnosis and Evaluation of Safety Treatments (2017)

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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Safety diagnosis of vehicle-bicycle interactions using computer vision systems: a case study in Vancouver, B.C. (2017)

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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Safety Evaluation of Connected Vehicle Applications Using Micro-Simulation (2017)

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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Developing Macro-Level Collision Prediction Models to Enhance Traditional Road Safety Improvement Programs and Evaluate Bicycle Safety in the City of Vancouver (2016)

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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Calibration and Validation of Traffic Microsimulation Models for Safety Evaluation Using Automated Video-Based Conflict Analysis (2015)

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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Automated diagnoses of safety problems at collision prone intersections using computer vision techniques (2013)

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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Automated roundabout safety analysis: diagnosis and remedy of safety problems (2013)

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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Before and after traffic safety evaluations using computer vision techniques (2013)

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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Evaluating traffic safety performance of countries using data envelopment analysis and acident prediction models (2013)

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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Fully Bayesian inference technique for traffic safety treatment before-and-after study (2013)

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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Investigation of Microscopic Pedestrian Walking Behavior (2012)

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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Graph Theory Based Transit Indicators Applied to Ridership and Safety Models (2011)

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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Investigating speed-accident relationship at urban signalized intersections using accident prediction models (2011)

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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Risk-Based Design of Horizontal Curves with Restricted Sight Distance (2011)

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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