Bicycle Rider Behavior and Crash Involvement in Australia

This research investigated how behaviours and attitudes of bicycle riders influence crash frequency and severity. The study recruited 1102 Australian bicycle riders for an online survey. The survey comprised questions on demographics, frequency of riding and the number and severity of traffic crashes during the last five years. The survey included the Cycling Behaviour Questionnaire and the Cyclist Risk Perception and Regulation Scale. Overall, there were low levels of errors and violations reported by participants indicating that these behaviours were on average never or rarely exhibited while riding a bicycle. Conversely, participants reported high levels of engagement in positive behaviours and reported high levels of traffic rule knowledge and risk perception. Higher rates of violations and errors were associated with increased crash likelihood, while higher rates of positive behaviours were associated with reduced rates of crash involvement in a period of 5 years. The findings highlight the relationship between errors, total crashes and crash severity Further promotion of positive behaviours amongst riders may also help to reduce the risk of crashes.

A Gradient Boosting Crash Prediction Approach for Highway-Rail Grade Crossing Crash Analysis

Highway-rail grade crossing (HRGC) crashes continue to be the major contributors to rail causalities in the United States and have been intensively researched in the past. Data-mining models focus on prediction while dominant general linear models focus on model and data fitness. Decision makers and traffic engineers rely on prediction models to examine at-grade crash frequency and make safety improvement. The gradient boosting (GB) model has gained popularity in many research areas. In this study, to fully understand the model performance on HRGC accident prediction performance, the GB model with functional gradient descent algorithm is selected to analyze crashes at highway-rail grade crossings (HRGCs) and to identify contributor factors. Moreover, contributors’ importance and partial-dependent relations are generated to further understand the relationship of identified contributors and HRGC crash likelihood to concur “black box” issues that most machine learning methods face. Furthermore, to fully demonstrate the model’s prediction performance, a comprehensive model prediction power assessment based on six measurements is conducted, and the prediction performance of the GB model is verified and compared with a decision tree model as a reference due to their popularity and comparable data availability. It is demonstrated that the GB model produces better prediction accuracy and reveals nonlinear relationships among contributors and crash likelihood. In general, HRGC crash likelihood is significantly impacted by several traffic exposure factors: highway traffic volume, railway traffic volume, and train travel speed and others.

A Predictive Framework of Speed Camera Locations for Road Safety

Road traffic crashes are a public health issue due to their terrible impact on individuals, communities, and countries. Studies affirmed that vehicle speed is a major contributor to crash likelihood and severity. At the same time, they identified Automated Speed Enforcement (ASE) systems, namely speed cameras, as a highly effective measure to reduce excessive and inappropriate speed, and thus improving road safety. However, identifying optimum sites for fixed speed camera placement stays an open issue in the literature, although it is a key factor that guarantees the efficiency of such ASE systems. This paper describes a predictive framework of speed camera locations using a classification algorithm that can predict, for each section of a given road network, its pertinence as a speed camera location. First, we identify a set of features as predictors of the classification algorithm, that we have argued their goodness through correlation tests. Second, for training our algorithm, data from road controlled sections, corresponding to existing speed cameras, is exploited. Each section class reflects the contribution level of the ASE system (good, neutral, or bad) to road safety. Third, as a proofof-concept, the framework has been implemented and deployed on the Moroccan road network. The results showed that Random Forest classifier is the best performing model attaining an accuracy of 95% and a precision of 88%. Further, a tool was developed to visualize updated classification results on a Moroccan road network map to support authorities in their decision making process.

Does Fixed Asset Revaluation Build Trust between Management and Investors?

In this paper, we examine whether fixed asset revaluation has an impact on the timeliness and relevance of information disclosed in financial reporting. Using firms listed in the Korea Stock Exchange market during 2007–2017, this study investigates the change in transparency of the information disclosure environment as proxied by stock price crash risk. We find that, on average, fixed asset revaluation has a positive effect on sustainability by improving timeliness and relevance of disclosed information, thereby decreasing stock price crash risk. In contrast, firms with unhealthy financial conditions and a high degree of information asymmetry show an increase in crash likelihood after fixed asset revaluation. These findings suggest that the relationship between fixed asset revaluation and stock price cash risk is dependent upon management’s motivation for honesty during the revaluation process.

Evaluation and Validation of Distraction Detection Algorithms on Multiple Data Sources

Many researchers have developed algorithms to detect distraction, but they have yet to be validated on multiple data sources. This study aims to evaluate these algorithms by comparing their ability to detect distraction and predict event likelihood. Four algorithms that use measures of cumulative glance, past glance behavior, and glance eccentricity were used to understand the distracted state of the driver and were validated on two separate data sources: naturalistic and experimental data. Results showed that there was a higher likelihood of event detection when cumulative glances were considered. Glance eccentricity was best for predicting distraction. Future research can use these findings to design mitigation systems that give drivers feedback in instances of high crash likelihood.