Tarek Sayed

Professor

Relevant Degree Programs

 

Graduate Student Supervision

Doctoral Student Supervision (Jan 2008 - May 2019)
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 (2010 - 2018)
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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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 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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Design-driven quadrangulation of closed 3D curves (2013)

This work presents a novel, design-driven quadrangulating method for closed 3Dcurves. While the quadrangulation of existing surfaces has been well studied for along time, there are few works that can successfully construct a quad-mesh relyingsolely on 3D curves, as the shape of the surface interior is not uniquely defined.I observe that, in most cases, viewers can complete the intended shape by envisioninga dense network of smooth, gradually changing flow-lines across a pair ofinput curve segments with similar orientation and shape. The method proposedhere mimics this behavior.This algorithm begins by segmenting the input closed curves into pairs ofmatching segments. I interpolate the input curves by a network of quadrilateralcycles whose iso-lines define the desired flow line network. I proceed to interpolatethese networks with all-quad meshes that convey designer intents. I evaluatemy results by showing convincing quadrangulations of complex and diverse curvenetworks with concave, non-planar cycles, and validate my approach by comparingmy results to artist generated interpolating meshes.My algorithm is suitable for use in sketch-based modeling systems as well asin other applications where artist curves can be created.

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Evaluating traffic safety performance of countries using data envelopment analysis and accident 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 techniques 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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