This study examines the safety effects of the improvements made on multi-lane arterials. The improvements were divided into two categories 1) corridor level improvements, and 2) intersection improvements. Empirical Bayes method, which is one of the most accepted approaches for conducting before-after evaluations, has been used to assess the safety effects of the improvement projects. Safety effects are estimated not only in terms of all crashes but also rear-end (most common type) as well as severe crashes (crashes involving incapacitating and/or fatal injuries) and also angle crashes for intersection improvements. The Safety Performance Functions (SPFs) used in this study are negative binomial crash frequency estimation models that use the information on ADT, length of the segments, speed limit, and number of lanes for corridors. And for intersections the explanatory variables used are ADT, number of lanes, speed limit on major road, and number of lanes on the minor road. GENMOD procedure in SAS was used to develop the SPFs. Corridor SPFs are segregated by crash groups (all, rear-end, and severe), length of the segments being evaluated, and land use (urban, suburban and rural). The results of the analysis show that the resulting changes in safety following corridor level improvements vary widely. Although the safety effect of projects involving the same type of improvement varied, the overall effectiveness of each of the corridor level improvements were found to be positive in terms of reduction in crashes of each crash type considered (total, severe, and rear-end) except for resurfacing projects where the total number of crashes slightly increased after the roadway section is resurfaced. Evaluating additional improvements carried out with resurfacing activities showed that all (other than sidewalk improvements for total crashes) of them consistently led to improvements in safety of multilane arterial sections. It leads to the inference that it may be a good idea to take up additional improvements if it is cost effective to do them along with resurfacing. It was also found that the addition of turning lanes (left and/or right) and paving shoulders were two improvements associated with a project�s relative performance in terms of reduction in rear-end crashes. No improvements were found to be associated with a resurfacing project�s relative performance in terms of changes in (i.e., reducing) severe crashes. For intersection improvements also the individual results of each project varied widely. Except for adding turn lane(s) all other improvements showed a positive impact on safety in terms of reducing the number of crashes for all the crash types (total, severe, angle, and rear-end) considered. Indicating that the design guidelines for this work type have to be revisited and safety aspect has to be considered while implementing them. In all it can be concluded that FDOT is doing a good job in selecting the sites for treatment and it is very successful in improving the safety of the sections being treated although the main objective(s) of the treatments are not necessarily safety related.

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