What Effect Does Driver Maneuvers Have on The Safety of Pedestrians and Cyclists? An In-Depth Descriptive Analysis of Vulnerable User Crashes and Near-Misses

Vulnerable Road user safety is a leading issue in the effort to create safer driving environment and reduce the instances of crashes on the roadways. The research approach here is to conduct an in-depth descriptive analysis of pedestrian and bicyclist pre-incident behaviors and evasive maneuvers in near-miss or crash-like situations and to seek an understanding of how different driving behaviors put these road users at risk. By analysing naturalistic driving data from the 2nd Strategic Highway Research Program (SHRP-2), the pre-incident maneuvers of both drivers are analysed to determine the risk factors of each maneuver to other road users, in comparison to a baseline situation where no crashes were involved. Regarding the analysis, two event scenarios of vehicle-to-vehicle situations and, pedestrians and cyclists involved situations, were identified as main categories of interest to create a more in-depth representation of the risk factors of specific driving maneuvers. These two categories were compared to a baseline scenario where no crashes or near-misses occurred. From the observed descriptive statistics, it can be inferred that unsafe and/or illegal maneuvers increase the instance of crash like events, these values increased from a baseline proportion, of a combined total of 7%, to making-up 17% of PedBike involved events, and 26% of vehicle-to-vehicle events. The proportions can further be broken down for the baseline as 2% safe but illegal, 4% unsafe and illegal, and 1% unsafe but legal. For PedBike involved events we have a breakdown of 1% safe but illegal, 11% unsafe and illegal, and 5% unsafe but legal. Finally, in the instance of vehicle only involved events the breakdown of the proportions is represented as 1% safe but illegal, 16% unsafe and illegal, and 9% unsafe but legal. What the findings suggests is that each driving maneuver requires a certain level of awareness in response to many environmental factors to ensure a safe outcome at the end of the maneuver. This study therefore stresses the importance of driver awareness in successfully initiating and executing all driving maneuvers for the safest possible outcome for pedestrians, cyclists and other drivers

AI-Based Framework for Understanding Car Following Behaviors of Drivers in A Naturalistic Driving Environment

The most common type of accident on the road is a rear-end crash. These crashes have a significant negative impact on traffic flow and are frequently fatal. To gain a more practical understanding of these scenarios, it is necessary to accurately model car following behaviors that result in rear-end crashes. Numerous studies have been carried out to model drivers' car-following behaviors; however, the majority of these studies have relied on simulated data, which may not accurately represent real-world incidents. Furthermore, most studies are restricted to modeling the ego vehicle's acceleration, which is insufficient to explain the behavior of the ego vehicle. As a result, the current study attempts to address these issues by developing an artificial intelligence framework for extracting features relevant to understanding driver behavior in a naturalistic environment. Furthermore, the study modeled the acceleration of both the ego vehicle and the leading vehicle using extracted information from NDS videos. According to the study's findings, young people are more likely to be aggressive drivers than elderly people. In addition, when modeling the ego vehicle's acceleration, it was discovered that the relative velocity between the ego vehicle and the leading vehicle was more important than the distance between the two vehicles

Machine Learning Framework for Real-Time Assessment of Traffic Safety Utilizing Connected Vehicle Data

Assessment of roadway safety in real-time is a necessary component for providing proactive safety countermeasures to ensure the continued safety and efficiency of roadways. A framework for utilizing data from connected vehicles and other probe sources is proposed in this study. Connected vehicles present an opportunity to provide live fingerprinting and activity monitoring on roadways. Taking advantage of high-resolution trajectory data streaming directly from connected vehicles, variables are extracted and the relationship with crashes are explored utilizing statistical and machine learning models. Hard acceleration events, in conjunction with segment miles are shown to have strong positive correlations with historical crash outcomes as proven by OLS, Poisson and Gradient Booster regression models. An XGBoost classification model is then trained to predict the real-time instances of crash outcomes at 5 min temporal bins with high levels of accuracy when trained with data including the real-time segment speed, reference speed, segment miles, a segment crash risk factor and other variables related to the difference in speeds between consecutive segments as well as the hour of the day. A weighted ensemble model achieved the best performance with an accuracy of 0.95. The results present evidence that the framework can capitalize on the richness of data available via connected vehicles and is implementable as a component in Advanced Traffic Management Systems for the analysis of safety critical situations in real-time

MIMIC \u2014 Multidisciplinary Initiative on Methods to Integrate and Create Realistic Artificial Data

693JJ31950023Traditional safety modeling efforts primarily focus on accurately estimating crash frequencies or rates. The true relationships between crashes and potential causal factors are not always easily discernible from safety models. While a model consisting of multiple causal factors may produce accurate estimates of crash measures, it may not accurately explain all causal relationships. Knowing the true cause-and-effect relationships is important while choosing countermeasures to address safety problems. This Exploratory Advanced Research Program project developed a framework to generate realistic artificial data (RAD) datasets that mimic the known causal relationships between contributing factors and crashes. The proposed framework is generic and can be used to generate RAD for other facilities, such as work zones, bicycle/pedestrian facilities, innovative geometric designs, etc. The framework was applied to generate RAD for ramp terminals and speed change lane facilities at diamond interchanges. A web-based software was developed to provide easy access to the RAD dataset. The software provides 196 pregenerated datasets and the option to request custom datasets. Sample RAD datasets were used to test negative binomial and a suite of machine learning models. A model evaluation rubric was developed to evaluate and compare the performance of different models. Additionally, this project developed a second type of RAD dataset\u2014the virtual reality (VR) simulation testbeds for crashes and near-crashes occurring at interchanges. Driving simulator studies offer another source of RAD for evaluating new behavioral and roadway countermeasures. The testbeds were developed using safety critical events recorded in the Strategic Highway Research Program 2 Naturalistic Driving Study data. VR offers an engaging visualization platform to educate the public about interchange crashes and to evaluate different countermeasures. These interventions are well aligned with the USDOT\u2019s National Roadway Safety Strategy\u2019s Safe System Approach of considering an overlapping set of safety measures\u2014roadway countermeasures, behavioral interventions, enforcement, vehicle safety features, and emergency medical care\u2014to achieve zero roadway fatalities