Overuse of the Interstate and National Highway Systems has led many urban freeways to suffer from recurrent congestion and high crash rates. One method of ameliorating these problems is through the use of Active Traffic Management (ATM). Within ATM, the practice of speed harmonization is well suited to improving safety and reducing delay. In this study, speed harmonization is applied to a segment of Interstate Highway 35, just south of downtown Austin, Texas. First, the need for congestion and safety improvements will be established. Then, the framework of a speed harmonization system will be developed through a synthesis of speed harmonization best practice. Next, the speed harmonization framework will be evaluated for its impact on efficiency through the development of before and after micro-simulation models. Finally, the trajectory files generated from simulation will be analyzed using surrogate safety measures to assess the safety impact of the proposed speed harmonization system. / text

Using microsimulation software, with a focus on VISSIM, the analysis of different applications of merge concepts through delay and safety is presented in this thesis. In order to appropriately draw conclusions and usage trends of different merge concepts from the microsimulation software, early merge, late merge, and signal merge were first explored in a thorough literature review. While focusing primarily on delay, queues, and safety, this thesis essentially provides an introduction to determining the ideal merge concept on freeway work zones for varying roadway configurations, roadway conditions, and user demands, among other factors. In addition to delay and queuing analysis completed using VISSIM, the Federal Highway Administration’s Surrogate Safety Assessment Model (SSAM) was used to address the effects of implementing signal merge on rear-end and lane-change conflicts. Compiling the VISSIM microsimulation outputs and SSAM signal merge safety outputs, general conclusions and decisions were provided. While this thesis provides determinations of ideal merge concepts for a variety of cases, it is important for the next researcher to assess some of the assumptions that were made, to ensure that they would not significantly affect the results and analysis. / text

The increasingly urbanized world needs a solution to solve one of the most difficult problems – traffic congestion and safety. Researchers, consultants, and local officials are all attempting to solve these problems with different methods. However, it is apparent that understanding the driving behaviors on the roadway network and implementing roadway configurations accordingly is one of the great solutions. Therefore, the modeling of driving behavior would be the focus of this two-part thesis. Chapter two of this thesis will elaborate on the modeling of various driving behavior types in the microsimulation software by providing an easier-to-calibrate alternative for the driver behavior model in the microsimulation. The calibration method would leverage VISSIM, its highly customizable External driver model (EDM) API, JMP Pro's experiment design and sensitivity analysis, and SSAM's trajectory analysis. Then a set of driver model parameters are produced through sensitivity analysis, which is effective in producing a set of traffic conflicts that matches a preset target. Chapter three of this thesis focuses on simulating aggressive driving behaviors in a microsimulation and driving simulator co-simulation environment. Two co-simulation platforms are demonstrated, and the data collection are done in the VISSIM-Unity platform to collect microscopic driving data and trajectory data from the aggressive driver. Data analysis are performed on both datasets and determine the aggressive driver's safety impact. / Master of Science / The increasingly urbanized world needs a solution to solve one of the most difficult problems – traffic jams and safety. Researchers, engineers, and local officials are all attempting to solve these problems with different methods. However, it is apparent that understanding people's driving behavior on the road and designing the roads and policies to cater to these driving behaviors is one of the great solutions. Therefore, the modeling of driving behavior would be the focus of this two-part thesis. Chapter two of this thesis will experiment with a traffic simulator (which is a tool used for designing and simulating different road configurations like roundabouts and numbers of lanes) and provide an easier and more accurate way to represent various driving styles in the traffic simulator. The calibration method would leverage a driving simulator called VISSIM, an adjustable driver behavior model, a vehicle route tracker, and a vehicle route conflict analysis tool. Then a set of driving behavior parameters would be produced to match the possible traffic accident count in the traffic simulator. Chapter three of this thesis focuses on simulating aggressive driving behaviors in a traffic simulator and driving simulator (like that of those with a steering). Two driving simulator platforms are tested, and the data collection are done in one of the platforms to collect driving data and vehicle route tracker data from the aggressive driver. Data analysis are performed on both types of data and determine the aggressive driver's safety impact.

Transportation safety has been recognized as a public health issue worldwide, consequently, transportation researchers and practitioners have been attempting to provide adequate safety performance for the various transportation components and facilities to all road users given the usually scarce resources available. Safety engineers have been trying to make decisions affecting safety based on the knowledge extracted from different types of statistical models and/or observational before-after analysis. It is generally recognized that this type of factual knowledge is not easily obtained either statistically or empirically. Despite the intuitive link between road safety and observed crashes, a good understanding of the sequence of events prior to the crash can provide a more rational basis for the development of engineering countermeasures. The development of more comprehensive mechanistic models for safety assessment is heavily dependent on detailed vehicle tracking data that is not readily available. The potential of microscopic simulation in traffic safety and traffic conflict analysis has gained increasing interest mostly due to recent developments in human behaviour modelling and real-time vehicle data acquisition. In this thesis, we present a systematic investigation of the use of existing behavioural microscopic simulation models in short-term road safety studies. Initially, a microscopic framework is introduced to identify potentially unsafe vehicle interactions for different vehicle movements based on three types of traffic behaviour protocols: car-following, lane change and gap acceptance. This microscopic model for safety assessment applies a safety performance measure based on pairwise comparisons of spacing and speed differential between adjacent vehicles and individual braking power in real-time. A calibration/validation procedure using factorial analysis is presented to select best model input parameters for this safety performance measure by using high resolution vehicle tracking data. The ability of the proposed safety performance measure to reflect real-life observed high-risk vehicular interactions is explored in three intuitive tests using observed crash data. Finally, the usefulness of the model is illustrated through its application to investigate the safety implications of two different geometric and operational traffic strategies. The overall results indicate that, notwithstanding the fact that actual behavioural microscopic algorithms have not been developed strictly to model crashes, they are able to replicate several factors directly related to high risk situations that could lead to crashes with reasonable accuracy. With the existing upward trend in computing power, modelling techniques and increasing availability of detailed vehicle tracking data, it is likely that safety studies will be carried out using a more mechanistic and inclusive approach based on disruptive driving behaviour rather than ultimate unpredictable and heavily restrictive crash events.

Transportation safety has been recognized as a public health issue worldwide, consequently, transportation researchers and practitioners have been attempting to provide adequate safety performance for the various transportation components and facilities to all road users given the usually scarce resources available. Safety engineers have been trying to make decisions affecting safety based on the knowledge extracted from different types of statistical models and/or observational before-after analysis. It is generally recognized that this type of factual knowledge is not easily obtained either statistically or empirically. Despite the intuitive link between road safety and observed crashes, a good understanding of the sequence of events prior to the crash can provide a more rational basis for the development of engineering countermeasures. The development of more comprehensive mechanistic models for safety assessment is heavily dependent on detailed vehicle tracking data that is not readily available. The potential of microscopic simulation in traffic safety and traffic conflict analysis has gained increasing interest mostly due to recent developments in human behaviour modelling and real-time vehicle data acquisition. In this thesis, we present a systematic investigation of the use of existing behavioural microscopic simulation models in short-term road safety studies. Initially, a microscopic framework is introduced to identify potentially unsafe vehicle interactions for different vehicle movements based on three types of traffic behaviour protocols: car-following, lane change and gap acceptance. This microscopic model for safety assessment applies a safety performance measure based on pairwise comparisons of spacing and speed differential between adjacent vehicles and individual braking power in real-time. A calibration/validation procedure using factorial analysis is presented to select best model input parameters for this safety performance measure by using high resolution vehicle tracking data. The ability of the proposed safety performance measure to reflect real-life observed high-risk vehicular interactions is explored in three intuitive tests using observed crash data. Finally, the usefulness of the model is illustrated through its application to investigate the safety implications of two different geometric and operational traffic strategies. The overall results indicate that, notwithstanding the fact that actual behavioural microscopic algorithms have not been developed strictly to model crashes, they are able to replicate several factors directly related to high risk situations that could lead to crashes with reasonable accuracy. With the existing upward trend in computing power, modelling techniques and increasing availability of detailed vehicle tracking data, it is likely that safety studies will be carried out using a more mechanistic and inclusive approach based on disruptive driving behaviour rather than ultimate unpredictable and heavily restrictive crash events.

This study adopted the traffic conflict technique, investigated and applied it for evaluation of freeway weaving section safety performance. Conflicts between vehicles were identified based on the state of interactions between vehicles in the traffic stream at microscopic level. The VISSIM microscopic simulation model was employed to simulate traffic operation. Surrogate safety measures were formulated based on deceleration rate required to avoid crash and these simulation-based measures were statistically compared and validated using crash data collected from the same study site. Three study sites located in Houston and Dallas areas were selected. Geometric and traffic data were collected using various technique including the use of traffic surveillance cameras and pneumatic tubes. The study revealed the existence of links between actually observed crashes and the surrogate safety measures. The study findings support the possible the use of microscopic simulation to evaluate safety performance of weaving areas and other transportation facilities.

Traffic conflicts associated to signalized intersections are one of the major contributing factors to crash occurrences. Driver behavior plays an important role in the safety concerns related to signalized intersections. In this research effort, dynamics of driver behavior in relation to the traffic conflicts occurring at the onset of yellow is investigated. The area ahead of intersections in which drivers encounter a dilemma to pass through or stop when the yellow light commences is called Dilemma Zone (DZ). Several DZ-protection algorithms and advance signal settings have been developed to accommodate the DZ-related safety concerns. The focus of this study is on drivers' decision dynamics, human learning, and choice behavior in DZ, and DZ-related safety measures. First, influential factors to drivers' decision in DZ were determined using a driver behavior survey. This information was applied to design an adaptive experiment in a driving simulator study. Scenarios in the experimental design are aimed at capturing drivers learning process while experiencing safe and unsafe signal settings. The result of the experiment revealed that drivers do learn from some of their experience. However, this learning process led into a higher level of risk aversion behavior. Therefore, DZ-protection algorithms, independent of their approach, should not have any concerns regarding drivers learning effect on their protection procedure. Next, the possibility of predicting drivers' decision in different time frames using different datasets was examined. The results showed a promising prediction model if the data collection period is assumed 3 seconds after yellow. The prediction model serves advance signal protection algorithms to make more intelligent decisions. In the next step, a novel Surrogate Safety Number (SSN) was introduced based on the concept of time to collision. This measure is applicable to evaluate different DZ-protection algorithms regardless of their embedded methodology, and it has the potential to be used in developing new DZ-protection algorithms. Last, an agent-based human learning model was developed integrating machine learning and human learning techniques. An abstracted model of human memory and cognitive structure was used to model agent's behavior and learning. The model was applied to DZ decision making process, and agents were trained using the driver simulator data. The human learning model resulted in lower and faster-merging errors in mimicking drivers' behavior comparing to a pure machine learning technique. / Ph. D.

This dissertation has contributed to the pedestrian safety literature by assessing and comparing safety benefits and traffic efficiency among midblock Rectangular Rapid Flashing Beacon (RRFB) and Pedestrian Hybrid Beacon (PHB) sites. Video trajectory data were used to calculate pedestrian Surrogate Safety Measures (SSMs) and vehicles' delay. Regression models of SSMs and vehicles' delay revealed that PHB sites offer more safety benefits, at the expense of increased vehicles' delay, compared to RRFB sites. The presence of the PHB, weekday, signal activation, lane count, pedestrian speed, vehicle speed, land use mix, traffic flow, time of day, and pedestrian starting position from the sidewalk have been found to be significant determinants of the SSMs and vehicles' delay. Another avenue of pedestrian safety explored in this dissertation is the lag time. The study investigates survival likelihood and the lag time of non-instant pedestrian fatalities using random parameter Binary Logit and Ordered Logit models. The models were run on a dataset obtained from the Fatality Accident Reporting System (FARS) for the period of 2015-2019. The analysis revealed that weather, driver age groups, drunk/ distracted/ drowsy drivers, hit and run, involvement of large truck, VRU age group, gender, presence of sidewalk, presence of intersection, light condition, and speeding were common significant factors for both models. The factor found to be significant exclusively for the Binary Logit model includes Area type. Factors found to be significant exclusively for the Ordered Logit model include Presence of Crosswalk and Fire station nearby. The results validate the use of lag time as an alternative to crash count and crash severity analysis. The findings of this study pave the way for practitioners and policymakers to evaluate the effectiveness of midblock pedestrian crossing facilities, as well as to use lag time to investigate crashes and corroborate results from traditional crash-based investigations.

We investigated after effects of automation in take-over scenarios in a high-end moving-base driving simulator. Drivers performed evasive manoeuvres encountering a blocked lane in highway driving. We compared the performance of drivers 1) during manual driving, 2) after automated driving with eyes on the road while performing the cognitively demanding n-back task, and 3) after automated driving with eyes off the road performing the visually demanding SuRT task. Both minimum time to collision (TTC) and minimum clearance towards the obstacle disclosed a substantial number of near miss events and are regarded as valuable surrogate safety metrics in evasive manoeuvres. TTC proved highly sensitive to the applied definition of colliding paths, and we prefer robust solutions using lane position while disregarding heading. The extended time to collision (ETTC) which takes into account acceleration was close to the more robust conventional TTC. In line with other publications, the initial steering or braking intervention was delayed after using automation compared to manual driving. This resulted in lower TTC values and stronger steering and braking actions. Using automation, effects of cognitive distraction were similar to visual distraction for the intervention time with effects on the surrogate safety metric TTC being larger with visual distraction. However the precision of the evasive manoeuvres was hardly affected with a similar clearance towards the obstacle, similar overshoots and similar excursions to the hard shoulder. Further research is needed to validate and complement the current simulator based results with human behaviour in real world driving conditions. Experiments with real vehicles can disclose possible systematic differences in behaviour, and naturalistic data can serve to validate surrogate safety measures like TTC and obstacle clearance in evasive manoeuvres.

Cyclist underreporting of lower severity and single cyclist collisions to police results in the underestimation of the societal costs of lower severity and single cyclist collisions [1], [2]. Prevention strategies for these types of collisions are becoming a popular area of research, and video-based approaches have obvious potential for these cases, allowing for detailed analyses of underreported lower severity and single cyclist falls. Video-based studies have been used to investigate site-specific cyclist safety issues such as railway crossings [3 ]. They have also been used for near-collision or near-miss incidents and Surrogate Measures of Safety (SMoS), e.g., [4]. A recent Irish study has identified the most common collision configurations and factors with the inclusion of unreported cases [5]. Findings indicate that falls involving interactions with light rail tram tracks are common in Dublin; they were the most common infrastructural collision partner in this study and a contributing factor in 23% of single cyclist collisions (ibid.), supplementing international findings [6], [7]. Furthermore, along with increasing popularity of cycling, many new light rail systems are being implemented across Europe as part of a broader move towards sustainable transport [8]. Accordingly, further investigation is required to avoid potential conflicts. Therefore, this study aims to use video-based assessment to correlate fall risk with trajectories and crossing angles. [From: Introductiojn]