Intersection Advance Driver Assist Systems (I-ADAS) may prevent 25 to 93% of intersection crashes. The effectiveness of I-ADAS will be limited by driver's pre-crash behavior and other environmental factors. This study will characterize real-world intersection traversals to evaluate the effectiveness of I-ADAS while accounting for driver behavior in crash and near-crash scenarios. This study characterized real-world intersection traversals using naturalistic driving datasets: the Second Strategic Highway Research Program (SHRP-2) and the Virginia Traffic Cameras for Advanced Safety Technologies (VT-CAST) 2020. A step-by-step approach was taken to create an algorithm that can identify three different intersection traversal trajectories: straight crossing path (SCP); left turn across path opposite direction (LTAP/OD); and left turn across path lateral direction (LTAP/LD). About 140,000 intersection traversals were characterized and used to train a unique driver behavior model. The median average speed for all encounter types was about 7.2 m/s. The driver behavior model was a Markov Model with a multinomial regression that achieved an average 90.5% accuracy across the three crash modes. The model used over 124,000 total intersection encounters including 301 crash and near-crash scenarios. I-ADAS effectiveness was evaluated with realistic driver behavior in simulations of intersection traversal scenarios based on proposed US New Car Assessment Program I-ADAS test protocols. All near-crashes were avoided. The driver with I-ADAS overall helped avoid more crashes. For SCP and LTAP the collisions avoided increased as the field of view of the sensor increased in I-ADAS only simulations. There were 18% crash scenarios that were not avoided with I-ADAS with driver. Among near-crash scenarios, where NHTSA expects no I-ADAS activation, there were fewer I-ADAS activations (58.5%) due to driver input compared to the I-ADAS only simulations (0%). / Master of Science / Intersection Advance Driver Assist Systems (I-ADAS) may prevent 25-93% of intersection crashes. I-ADAS can assist drivers in preventing or mitigating these crashes using a collision warning system or automatically applying the brakes for the driver. One way I-ADAS may assist in crash prevention is with automatic emergency braking (AEB), which will automatically apply braking without driver input if the vehicle detects that a crash is imminent. The United States New Car Assessment Program (US-NCAP) has also proposed adding I-ADAS with AEB tests into its standard test matrix. The US-NCAP has proposed three different scenarios. All the tests have two crash-imminent configurations where the vehicles are set up to collide if no deceleration occurs and a near-miss configuration where the vehicles are set up to barely miss each other. This study will use intersection traversals from naturalistic driving data in the US to build a driver behavior model. The intersection travels will be characterized by their speed, acceleration, deceleration, and estimated time to collision. The driver behavior model was able to predict the longitudinal and lateral movements for the driver. The proposed US-NCAP test protocols were then simulated with varied sensors parameters where one vehicle was equipped with I-ADAS and a driver. The vehicle with I-ADAS with a driver was more successful than a vehicle only equipped with I-ADAS at preventing a crash.

In the recent years, pedelecs (pedal electric cycles) have seen a massive growth. in ridership. In 2013, around 1.3 million e-bilces were on German roads, while in 2020, this number was already at 8.5 million (with about 99% of the e-bikes being pedelecs). The rapid spread of pedelecs has given rise to concerns for road safety, especially due to the fact that riders of electric bicycles reach higher speeds. Indeed, some studies have reported that pedelec riders suffer from more severe crashes than users of conventional bikes. However, the highly dynamic development in pedelec ownership and use might cast some doubts on the long term validity of investigations of pedelec accidents and their characteristics that have to rely on data collected over shorter periods of time. Therefore, the aim of this study was to investigate pedelec accidents and their characterutics over several years in a longitudinal fashion. and compare them to accidents involving cyclists, tobe able to identify trends, and to clarify whether such trends are specifiic to pedelecs. [From: Introduction]

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Crash prediction models are used to estimate the number of crashes using a set of explanatory variables. The highway safety community has used modeling techniques to predict vehicle-to-vehicle crashes for decades. Specifically, generalized linear models (GLMs) are commonly used because they can model non-linear count data such as motor vehicle crashes. Regression models such as the Poisson, Zero-inflated Poisson (ZIP), and the Negative Binomial are commonly used to model crashes. Until recently very little research has been conducted on crash prediction modeling for pedestrian-motor vehicle crashes. This thesis considers several candidate crash prediction models using a variety of explanatory variables and regression functions. The goal of this thesis is to develop a pedestrian crash prediction model to contribute to the field of pedestrian safety prediction research. Additionally, the thesis contributes to the work done by the Federal Highway Administration to estimate pedestrian exposure in urban areas. The results of the crash prediction analyses indicate the pedestrian-vehicle crash model is similar to models from previous work. An analysis of two pedestrian volume estimation methods indicates that using a scaling technique will produce volume estimates highly correlated to observed volumes. The ratio of crash and exposure estimates gives a crash rate estimation that is useful for traffic engineers and transportation policy makers to evaluate pedestrian safety at signalized intersections in an urban environment. / Master of Science

Since before the release of the Highway Safety Manual research has been indicating the need to incorporate mobility and control aspects to road safety analysis. The first part of this work developed and implement in an existing computational engine a signal timing optimization method that considers mobility, safety, and emissions measures simultaneously. A sensitivity analysis was conducted to provide insight on the practical effects and order of relevance of 20 key input variables. Mobility improvement performance usually coincides with emissions improvements, but sometimes at the expense of safety. The second part of this work investigated the relationship between hourly traffic density and crash rates on Brazilian expressways with different characteristics, based on a database containing over 20,000 crashes and more than 35 million traffic volume observations and. The resulting curves for urban expressways follow a U shape, with minimum values associated with LOS B to C, while the relationships for rural expressways were found to be continuously increasing, suggesting that low volume rural roads are safer than the higher volume ones. The analysis of other influencing factors revealed that nighttime conditions, weaving segments and urban multilane highways could be related to higher crash rates. The third part of the project extends the analysis to crash severity modeling, using an ordered response choice model. The framework that better fit this database led to the development of two different models: single-vehicle crashes (SV) and multiple-vehicle crashes (MV), since the factors that explain the severity of crashes varies widely between these models. For instance, guardrails and barriers proved to effectively reduce severity for SV crashes, for which run-offs are the most severe crash type. The unique database used in this study also allowed for an investigation of the influence of prevailing traffic conditions on crash severity, while still controlling for all other factors. The results suggested that multiple-vehicle crash severity is negatively related with traffic density, while single-vehicle crashes are more closely related to speed. The findings of this work have implications to policy and design decisions, and the produced equation could be incorporated to active traffic management (ATM) and HCM reliability analysis. / Desde antes da publicação do Highway Safety Manual, vários pesquisadores indicam a necessidade de se incorporar aspectos de operação de tráfego à análise de segurança viária. A primeira parte deste trabalho desenvolveu e implementou em uma ferramenta computacional existente um método de otimização de tempos semafóricos que considera medidas de desempenho de operação, segurança e emissões simultaneamente. Uma análise de sensibilidade foi realizada para produzir conhecimento sobre os efeitos práticos e a ordem de relevância de 20 variáveis de entrada principais. O desempenho da programação semafórica em termos de redução dos atrasos geralmente coincide com redução das emissões, embora às vezes às custas da segurança. A segunda parte deste trabalho investigou a relação entre a densidade horária de tráfego e as taxas de acidentes em autoestradas e rodovias de pista dupla brasileiras com características diversas, com base em um banco de dados contendo mais de 20.000 registros de acidentes e mais de 35 milhões de observações de tráfego. As curvas resultantes para rodovias urbanas seguem um formato em U, com valores mínimos associados aos níveis de serviço B a C, enquanto que as relações para as rodovias rurais são contínuas e crescentes, sugerindo que rodovias rurais de baixo volume são mais seguras do que as de maior volume. A análise de outros fatores revelou que condições noturnas, segmentos de entrelaçamento e rodovias de pista dupla convencionais urbanas estariam relacionadas a maiores taxas de acidentes. A terceira parte deste projeto amplia a análise para modelagem de severidade dos acidentes, usando um modelo de escolha discreta ordenado. A estrutura que melhor se adequa a esse banco de dados levou ao desenvolvimento de dois modelos diferentes: acidentes com um veículo e acidentes com múltiplos veículos, já que os fatores que explicam a severidade dos acidentes variam muito entre esses modelos. Por exemplo, defensas e barreiras se mostraram efetivas para a redução da severidade de acidentes com um veículo, para as quais a saída de pista é o tipo de acidente mais grave. O amplo banco de dados usado neste estudo também permitiu uma investigação da influência das condições de tráfego na severidade do acidente, em comparação com todos os outros fatores. Os resultados sugeriram que a severidade de acidentes de múltiplos veículos está negativamente relacionada com a densidade de tráfego, enquanto colisões com um único veículo estão mais relacionadas à velocidade. As descobertas deste trabalho têm implicações nas decisões sobre políticas e projetos de transportes, e a equação produzida pode ser incorporada à análise de confiabilidade do gerenciamento ativo do tráfego (ATM) e do Highway Capacity Manual.