Passive safety systems try to lessen the effects of an accident. Airbags are a passive safety feature. They are designed to protect occupants of a vehicle during a crash. These systems have to be configured correctly in order to deploy airbags at the right time in case of a collision. Airbag application tools are used to simulate and interpret crashes. Some factors influence when an airbag should deploy. Based on different parameters, the logic for firing airbags is also different. Under every circumstance, an airbag has to be deployed at the right time in order to prevent injuries and fatalities. During the process of simulation, the data which is simulated is written to a database. During interpretation, this data is extracted from the database. Then, the required information can be analyzed and interpreted for further use. This data contains crash related information. For example, the type of crash, crash code and crash channel assignments. For every crash present in the airbag project, crash channels are assigned to the sensors. Each sensor present has a crash channel assigned to it. This is called the crash channel assignment. An airbag application tool is developed to show the crash channel assignments. This tool should handle the information extraction, and visualization of crash channel assignments. The final output should be in a tabular format, which includes user specific customizations.

<p dir="ltr">The emergence of time-dependent data provides researchers with unparalleled opportunities to investigate disaggregated levels of safety performance on roadway infrastructures. A disaggregated crash risk analysis uses both time-dependent data (e.g., hourly traffic, speed, weather conditions and signal controls) and fixed data (e.g., geometry) to estimate hourly crash probability. Despite abundant research on crash risk analysis, coordinated signalized intersections continue to require further investigation due to both the complexity of the safety problem and the relatively small number of past studies that investigated the risk factors of coordinated signalized intersections. This dissertation aimed to develop robust crash risk prediction models to better understand the risk factors of coordinated signalized intersections and to identify practical safety countermeasures. The crashes first were categorized into three types (same-direction, opposite-direction, and right-angle) within several crash-generating scenarios. The data needed were organized in hourly observations and included the following factors: road geometric features, traffic movement volumes, speeds, weather precipitation and temperature, and signal control settings. Assembling hourly observations for modeling crash risk was achieved by synchronizing and linking data sources organized at different time resolutions. Three different non-crash sampling strategies were applied to the following three statistical models (Conditional Logit, Firth Logit, and Mixed Logit) and two machine learning models (Random Forest and Penalized Support Vector Machine). Important risk factors, such as the presence of light rain, traffic volume, speed variability, and vehicle arrival pattern of downstream, were identified. The Firth Logit model was selected for implementation to signal coordination practice. This model turned out to be most robust based on its out-of-sample prediction performance and its inclusion of important risk factors. The implementation examples of the recommended crash risk model to building daily risk profiles and to estimating the safety benefits of improved coordination plans demonstrated the model’s practicality and usefulness in improving safety at coordinated signals by practicing engineers.</p>

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Collision avoidance systems (CASs) are being developed and fielded to reduce the number and severity of rear-end crashes. Kinematic algorithms within CASs evaluate sensor input and apply assumptions describing human-response timing and deceleration to determine when an alert should be presented. This dissertation presents an analytic assessment of dynamic function and performance CASs and associated driver performance for preventing automotive rear-end crashes. A method for using naturalistic data in the evaluation of CAS algorithms is described and applied to three algorithms. Time-series parametric data collected during 13 rear-end crashes and 70 near-crashes are input into models of collision avoidance algorithms to determine when the alerts would have occurred. Algorithm performance is measured by estimating how much of the driving population would be able to respond in the time available between when an alert would occur and when braking was needed. A sensitivity analysis was performed to consider the effect of alternative inputs into the assessment method. The algorithms were found to warn in sufficient time to permit 50–70% of the population to avoid collision in similar scenarios. However, the accuracy of this estimate was limited because the tested algorithms were found to alert too frequently to be feasible. The response of the assessment method was most sensitive to differences in assumed response-time distributions and assumed driver braking levels. Low-speed crashes were not addressed by two of the algorithms. Analysis of the events revealed that the necessary avoidance deceleration based on kinematics was generally less than 2 s in duration. At the time of driver response, the time remaining to avoid collision using a 0.5g average deceleration ranged from â 1.1 s to 2.1 s. In 10 of 13 crashes, no driver response deceleration was present. Mean deceleration for the 70 near-crashes was 0.37g and maximum was 0.72g. A set of the events was developed to measure driver response time. The mean driver response time was 0.7 s to begin braking and 1.1 s to reach maximum deceleration. Implications for collision countermeasures are considered, response-time results are compared to previous distributions and future work is discussed. / Ph. D.

Traditional crash frequency prediction models cannot capture the temporal effects of traffic characteristics due to the high level of data aggregation. Also, this approach is less suitable to address the crash risk for active traffic management strategies that typically operate for short-time intervals. Hence, this research proposes short-term crash prediction models for traffic management strategies such as Variable Speed Limit (VSL)/Variable Advisory Speed (VAS), and Part-time Shoulder Use (PTSU). By using high-resolution traffic detectors and VSL/VAS operational data, short-term Safety Performance Functions (SPFs) are estimated at weekday hourly and peak period aggregation levels. The results indicate that the short-term SPFs could capture various crash contributing factors and safety aspects of VSL/VAS more effectively than the traditional highly aggregated Average Annual Daily Traffic (AADT)-based approach. The study also investigates the safety effectiveness of VSL/VAS for different types and severity levels of traffic crashes. The results specify that the VSL/VAS system is effective in reducing rear-end crashes in the Multivariate Poisson Lognormal (MVPLN) crash type model as well as Property Damage Only (PDO) and C (non-incapacitating) crashes in the MVPLN crash severity model. Recommendations include deploying the VSL/VAS system combined with other traffic management strategies, strong enforcement policies, and drivers' compliance to increase the effectiveness of this strategy. Further, this research estimates the Random Parameters Negative Binomial-Lindley (RPNB-L) model for PTSU sections and provides valuable insights on potential crash contributing factors related to PTSU operation, design elements, and high-risk areas. Last, the study proposes a novel integrated crash prediction approach for freeway sections with combined traffic management strategies. By incorporating historical safety conditions from SPFs, real-time crash prediction performance could be improved as a part of proactive traffic management systems. The findings could assist transportation agencies, policymakers, and practitioners in taking appropriate countermeasures for preventing and reducing crash occurrence by incorporating safety aspects while implementing traffic management strategies on freeways.

<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>

Intersection crashes are among the most frequent and lethal crash modes in the United States. Intersection Advanced Driver Assistance Systems (I-ADAS) are an emerging active safety technology which aims to help drivers safely navigate through intersections. One primary function of I-ADAS is to detect oncoming vehicles and in the event of an imminent collision can (a) alert the driver and/or (b) autonomously evade the crash. Another function of I-ADAS may be to detect and prevent imminent traffic signal violations (i.e. running a red light or stop sign) earlier in the intersection approach, while the driver still has time to yield for the traffic control device. This dissertation evaluated the capacity of I-ADAS to prevent U.S. intersection crashes and mitigate associated injuries. I-ADAS was estimated to have the potential to prevent up to 64% of crashes and 79% of vehicles with a seriously injured driver. However, I-ADAS effectiveness was found to be highly dependent on driver behavior, system design, and intersection/roadway characteristics. To generate this result, several studies were performed. First, driver behavior at intersections was examined, including typical, non-crash intersection approach and traversal patterns, the acceleration patterns of drivers prior to real-world crashes, and the frequency, timing, and magnitude of any crash avoidance actions. Second, two large simulation case sets of intersection crashes were generated from U.S. national crash databases. Third, the developed simulation case sets were used to examine I-ADAS performance in real-world crash scenarios. This included examining the capacity of a stop sign violation detection algorithm, investigating the sensor detection needs of I-ADAS technology, and quantifying the proportion of crashes and seriously injuries that are potentially preventable by this crash avoidance technology. / Ph. D. / Intersection crashes account for over 5,000 fatalities each year in the U.S., which places them among the most lethal crash modes. Highly automated vehicles are a rapidly emerging technology, which has the potential to greatly reduce all traffic fatalities. This work evaluated the capacity of intersection advanced driver assistance systems (I-ADAS) to prevent U.S. intersection crashes and mitigate associated injuries. I-ADAS is an emerging technology used by highly automated vehicles to help drivers safely navigate intersections. This technology utilizes onboard sensors to detect oncoming vehicles. If an imminent crash is detected, I-ADAS can respond by (a) warning the driver and/or (b) autonomously braking. Another function of I-ADAS may be to prevent intersection violations altogether, such as running a red light or a stop sign. Preventing and/or mitigating crashes and injuries that occur in intersection crashes are among the highest priority for designers, evaluators, and regulatory agencies. This dissertation has three main components. The first aim of this research was to describe how individuals drive through intersections. This included examining how drivers approach, traverse, and take crash avoidance actions at intersections. The second aim was to develop a dataset of intersection crashes that could be used to examine I-ADAS effectiveness. This was completed by extracting crashes that occurred throughout the U.S., and reconstructing vehicle positions before and after impact. The third aim was to use the extracted dataset of intersection crashes, and consider a scenario where one of the vehicles had been equipped with I-ADAS. Estimates of IADAS effectiveness were then generated based on these results.

Through the application of a phenomenological hermeneutic approach, this study seeks toexamine how seven moral dilemmas can be understood through the lenses of utilitarian ethicsand virtue ethics. These dilemmas are derived from Pablo Vierci’s depiction of a real-life1972 plane crash tragedy in the non-fictional book Society of the Snow. By incorporatingprevious research on similar topics, this study situates itself within the broader academicdiscourse on moral ethics, while providing a contemporary interpretation of a significanthistorical event depicted in literature. The goal of this research is to explore the practicalapplications of utilitarianism and virtue ethics in real-life scenarios. The findings indicate thatanalyzing moral dilemmas through these ethical frameworks deepens our understanding ofmoral philosophy, making complex and distressing moral choices more comprehensible. Byengaging with the intricacies of these theories and their practical implications, individuals canunderstand the complexities of moral decision-making with greater insight and sensitivity.