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

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 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.

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]

<p dir="ltr">Transportation Network Companies (TNCs) have increased significantly over the last decade, changing the urban mobility dynamics by shifting people from other modes of transportation, potentially affecting safety. While TNC companies promised to enhance urban mobility with more convenient end-to-end services, they were found to contribute to externalities like traffic congestion and safety issues. A deeper analysis is required to test the promise of TNC services and their impacts on cities. This study investigated the safety implications of the surge of TNC services in New York City (NYC) from 2017 to 2019. Specifically, we analyzed the changes in traffic safety performances using surrogate safety measures (SSMs) from 2017 to 2019 based on large-scale GPS trajectories generated by TNC vehicles in NYC.</p><p dir="ltr">This research utilized the twenty-eight days of high-quality and large-scale GPS-based trajectories of Uber vehicles to determine the critical surrogate safety measures (SSMs). To determine the potential traffic conflict and safety from SSMs, this research determined the SSMs based on evasive actions. In addition, this research also utilized real-world historical crash events, traffic flow, road conditions, land use, and congestion index to explore the relationship between critical SSMs and accidents. Additionally, this research extends to assess the socioeconomic inequalities from the perspective of increased TNCs and accidents.</p><p dir="ltr">Our findings indicate a significant increase in critical SSM events such as harsh braking and jerking citywide. These increases are particularly pronounced during off-peak hours and in peripheral areas of Manhattan and transportation hubs. Moreover, we observed stronger correlations between SSMs of TNC vehicles and injury/motorist accidents, compared to those involving pedestrians and cyclists. Despite the evident deterioration in SSMs, we noticed that the overall number of accidents in NYC from 2017 to 2019 has remained relatively stable possibly due to the reduction of traffic speeds. As such, a clustering analysis was conducted to unfold the nuanced patterns of SSMs/accident changes. Also, we find the existence of inequality in the increase in accidents and critical SSMs, and Manhattan is higher in inequality, especially in upper Manhattan. Moreover, individuals disadvantaged from low socioeconomic status and those living in deprived areas are experiencing more inequality from accidents and critical SSMs due to increased TNCs and accidents. This research enriches the understanding of how TNC services impact urban traffic safety. The findings of this research may help to get a holistic understanding of the road safety situations due to increased TNCs and accidents and help the policymakers and authorities to make informed decisions to develop a transportation system prioritizing all road users. Additionally, the methodology employed can be adapted for broader traffic safety applications or real-time monitoring of traffic safety performances using anonymous GPS trajectory segments.</p>

Left-turning maneuvers are considered to be the highest risk movements at intersections and two-thirds of the crashes associated with left-turns are reported at signalized intersections. Left-turning vehicles typically encounter conflicts from opposing through traffic. To separate conflicting movements, transportation agencies use a protected-only phase at signalized intersections where each movement is allowed to move alone. However, this could create delays and thus the concept of a protected-permissive phase has been introduced to balance safety and delays. However, the permissive part of this phasing scheme retains the safety concerns and could increase the possibility of conflicts resulting in crashes. This research developed a model that can predict the number of crashes for protected-permissive left-turn phasing, based on traffic volumes and calculated conflicts. A total of 103 intersections with permissive-protected left-turn phasing in Kentucky were simulated and their left-turn related conflicts were obtained from post processing vehicle trajectories through the Surrogate Safety Assessment Model (SSAM). Factors that could affect crash propensity were identified through the Principal Component Analysis in Negative Binomial Regression. Nomographs were developed from the models which can be used by traffic engineers in left-turn phasing decisions with enhanced safety considerations.