1 Crash modification factors (CMFs) can be used to capture the safety effects of countermeasures 2 and play significant roles in traffic safety management. As an alternative to the before-after 3 study, the regression model method has been widely used for estimating CMFs. Although before4 after studies are considered to be superior, the use of regression models for estimating CMFs has 5 never b...

This paper presents a driver performance map of braking and steering in response to three driving scenarios that lead to rear-end crashes. This map encompasses low risk, conflict, near-crash, and crash imminent driving states that correspond to advisory warning, crash imminent warning, and crash mitigation functionalities for intelligent vehicle rear-end crash countermeasures. Specifically, thi...

Motor vehicle crashes are one of the most common causes fatalities on roads. Real-time severity prediction such may contribute towards reducing rate fatality. In this study, fundamental goal is to develop machine learning models that predict whether outcome a collision will be fatal or not. A Canadian road crash dataset containing 5.8 million records utilized in research. ensemble have been dev...

When a software system crashes, crash information from user's machine is sent to the developers of the system for repair. For software systems with a large client base (such as Eclipse, Web browsers, etc.), the number of reports that are submitted every day can be quite high. Managing these reports is known to be a tedious and a time consuming task. Fortunately, not all crashes are caused by ne...

We study the impact of firms’ abnormal business operations on their future crash risk in stock prices. Computed based on real earnings management (REM) models, firms' deviation in real operations from industry norms (DRO) is shown to be positively associated with their future crash risk. This association is incremental to that between discretionary accruals (DA) and crash risk found by prior st...

This paper proposes a machine learning based methodology for predicting the buckling response of tubular structures. An extensive dataset force-time curves is generated using calibrated finite element model within parametric space where highly non-linear. Based on fully connected neural network template, hyper-parameters are determined and resulting evaluated separate test set, with regard to m...

Research into the application of freeway loop detector data for traffic safety has gained momentum in recent years. The incompleteness of data from loop detectors has been a common problem in both the development and the implementation of models. The effect of individual crash precursors, obtained one at a time from a series of loop detectors, on relative risk of crash occurrence was examined t...

A critical part of any risk assessment is identifying how to represent exposure to the risk involved. Recent research shows that the relationship between crash count and traffic volume is non-linear; consequently, a simple crash rate computed as the ratio of crash count to volume is not proper for comparing the safety of sites with different traffic volumes. To solve this problem, we describe a...