Semiparametric Bayesian models for evaluating time‐variant driving risk factors using naturalistic driving data and case‐crossover approach

Identifying the causal effects of driver distraction on hazardous actions at intersections based on propensity score weighting

Distracted driving is a threat to traffic safety that can result in more traffic crashes. Although previous studies have been conducted to explore the relationship between driver distraction and hazardous driving actions, few studies are available to identify the causation between them. Thus, the study intended to evaluate the causal effects of distraction on hazardous driving actions at intersections based on the crash data extracted from the Crash Report Sampling System (2021-2022). The multinomial logit model was employed to reveal the factors contributing to driver distraction. Then, propensity score weighting was adopted to balance the factor distributions between distraction and non-distraction cases to identify the causal effects on hazardous actions. Results indicated that 1) the propensity of distraction is relevant to factors such as the driver's age, gender, vehicle type, speed limit, area, weather, and light condition, 2) driver distraction can significantly increase the probability of risky actions including speeding, running red lights, failing to obey stop signs, failing to yield, following too closely, and 3) the causal effects show great diversity for different distraction types. The findings serve to understand the influence mechanism of distraction on specific crash risks and develop countermeasures to reduce distraction and hazardous driving actions.

Critical safety management driver identification based upon temporal variation characteristics of driving behavior

Identifying critical safety management drivers with high driver-level risks is essential for traffic safety improvement. Previous studies commonly evaluated driver-level risks based upon aggregated statistical characteristics (e.g., driving exposure and driving behavior), which were obtained from long-period driving monitoring data. However, given the great advancements of the connected vehicle and in-vehicle data instrumentation technologies, there has been a notable increase in the collection of short-period driving data, which has emerged as a prominent data source for analysis. In this data environment, traditionally employed aggregated behavior characteristics are unstable due to the time-varying feature of driving behavior coupled with insufficient data sampling periods. Thus, traditional modeling methods based upon aggregated statistical characteristics are no longer feasible. Instead of utilizing such unreliable statistical information to represent driver-level risks, this study employed temporal variation characteristics of driving behavior to identify critical safety management drivers in the short-period driving data environment. Specifically, the relationships between driving behavior temporal variation characteristics and individual crash occurrence probability were developed. To eliminate the impacts of drivers' driving behavior heterogeneity on model performance, "traffic entropy" index that could quantify the abnormal degrees of driving behavior was proposed. Deep learning models including convolutional neural network (CNN) and long short-term memory (LSTM) were employed to conduct the temporal variation feature mining. Empirical analyses were conducted using data obtained from online ride-hailing services. Experiment results showed that temporal variation characteristics based models outperformed traditional aggregated statistical characteristics based models. The area under the curve (AUC) index was improved by 4.1%. And the proposed traffic entropy index further enhanced the model performance by 5.3%. The best model achieved an AUC of 0.754, comparable to existing approaches utilizing long-period driving data. Finally, applications of the proposed method in driver management program development and its further investigations have been discussed.

Application of naturalistic driving data: A systematic review and bibliometric analysis

The application of naturalistic driving data (NDD) has the potential to answer critical research questions in the area of driving behavior assessment, as well as the impact of exogenous and endogenous factors on driver safety. However, the presence of a large number of research domains and analysis foci makes a systematic review of NDD applications challenging in terms of information density and complexity. While previous research has focused on the execution of naturalistic driving studies and on specific analysis techniques, a multifaceted aggregation of NDD applications in Intelligent Transportation System (ITS) research is still unavailable. In spite of the current body of work being regularly updated with new findings, evolutionary nuances in this field remain relatively unknown. To address these deficits, the evolutionary trend of NDD applications was assessed using research performance analysis and science mapping. Subsequently, a systematic review was conducted using the keywords "naturalistic driving data" and "naturalistic driving study data". As a result, a set of 393 papers, Published between January 2002-March 2022, was thematically clustered based on the most common application areas utilizing NDD. the results highlighted the relationship between the most crucial research domains in ITS, where NDD had been incorporated, and application areas, modeling objectives, and analysis techniques involving naturalistic databases.

Optimization method to reduce the risky driving behaviors of ride-hailing drivers

INTRODUCTION

To promote the safety level of ride-hailing services, this study develops the Targeted and Differentiated Optimization Method of Risky Driving Behavior Education and Training (TDOM-RDBET) founded on driver type classification of high-risk drivers.

METHOD

Based on value and goal orientations, 689 drivers were classified into four driver types and were assigned to three groups, including an experimental group, a blank control group, and a general control group. This research preliminarily analyzes the effectiveness of the TDOM-RDBET to reduce mobile phone use while driving by assessing the main effects of the group and test session on the risk value ranking of mobile phone use while driving (AR), the frequency per 100 km of mobile phone use while driving (AF), and the frequency per 100 km of risky driving behaviors (AFR), as well as the interactive effects of the two factors on AR, AF, and AFR, based on a two-way analysis of variance (two-way ANOVA).

RESULTS

The results demonstrate an overall significant reduction in AR (F = 8.653, p = 0.003), AF (F = 11.027, p = 0.001), and AFR (F = 8.072, p = 0.005) for the experimental group after training. Moreover, significant interactive effects of the driver group × test session on AR (F = 7.481, p = 0.001) and AF (F = 15.217, p < 0.001) were found. AR was significantly lower for the experimental group than for the blank control group (p < 0.05) in the post-training condition. Moreover, AF was also significantly lower for the experimental group than for the blank control group (p < 0.05) and general control group (p < 0.05) in the post-training condition.

PRACTICAL APPLICATIONS

On the whole, it was preliminarily verified that the TDOM-RDBET is more effective than the general training method at modifying the risky driving behavior.

Risk of Accidents or Chronic Disorders From Improper Use of Mobile Phones: A Systematic Review and Meta-analysis

Background

Mobile phone use has brought convenience, but the long or improper use of mobile phones can cause harm to the human body.

Objective

We aimed to assess the impact of improper mobile phone use on the risks of accidents and chronic disorders.

Methods

We systematically searched in PubMed, EMBASE, Cochrane, and Web of Science databases for studies published prior to April 5, 2019; relevant reviews were also searched to identify additional studies. A random-effects model was used to calculate the overall pooled estimates.

Results

Mobile phone users had a higher risk of accidents (relative risk [RR] 1.37, 95% CI 1.22 to 1.55). Long-term use of mobile phones increased accident risk relative to nonuse or short-term use (RR 2.10, 95% CI 1.63 to 2.70). Compared with nonuse, mobile phone use resulted in a higher risk for neoplasms (RR 1.07, 95% CI 1.01 to 1.14), eye diseases (RR 2.03, 95% CI 1.27 to 3.23), mental health disorders (RR 1.16, 95% CI 1.02 to 1.32), and headaches (RR 1.25, 95% CI 1.18 to 1.32); the pooled risk of other chronic disorders was 1.20 (95% CI 0.90 to 1.59). Subgroup analyses also confirmed the increased risk of accidents and chronic disorders.

Conclusions

Improper use of mobile phones can harm the human body. While enjoying the convenience brought by mobile phones, people have to use mobile phones properly and reasonably.

Driving safety assessment for ride-hailing drivers

Ride-hailing services, which have become increasingly prevalent in the last decade, provide an efficient travel mode by matching drivers and travelers via smartphone apps. Ride-hailing services enable millions of non-traditional taxi drivers to provide travel services, but may also raise safety concerns due to heterogeneity in the driver population. This study evaluated crash risk factors for ride-hailing drivers, including driving history and ride-hailing operational characteristics, using a sample of 189,815 drivers. We utilized the Poisson generalized additive model to accommodate for the potential nonlinear relationship between crash rate and risk factors. Results showed that crash history, the percentage of long-shift bookings, driving distance, operations during peak hours, years of being a ride-hailing driver, and passenger rating were significantly associated with crash risk. Several factors showed nonlinear relationships with crash risk. We adopted the SHapley Additive exPlanation (SHAP) method to assess and visualize the impact of each risk factor. The results indicated that passenger average rating, total driving distance, and crash history were the leading contributing factors. The findings of this study provide critical information for the development of safety countermeasures, driver education programs, as well as safety regulations for the ride-hailing industry.

Flexible derivative time-varying model in matched case-crossover studies for a small number of geographical locations among the participants

In matched case-crossover studies, any stratum effect is removed by conditioning on the fixed number of case-control sets in the stratum, and hence, the conditional logistic regression model is not able to detect any effects associated with matching covariates. However, some matching covariates such as time and location often modify the effect of covariates, making the estimations obtained by conditional logistic regression incorrect. Therefore, in this paper, we propose a flexible derivative time-varying coefficient model to evaluate effect modification by time and location, in order to make correct statistical inference, when the number of locations is small. Our proposed model is developed under the Bayesian hierarchical model framework and allows us to simultaneously detect relationships between the predictor and binary outcome and between the predictor and time. Inference is proposed based on the derivative function of the estimated function to determine whether there is an effect modification due to time and/or location, for a small number of locations among the participants. We demonstrate the accuracy of the estimation using a simulation study and an epidemiological example of a 1-4 bidirectional case-crossover study of childhood aseptic meningitis with drinking water turbidity.

Assessing the impact of sleep time on truck driver performance using a recurrent event model

Driver fatigue is a major safety concern for commercial truck drivers and is directly related to the total hours of sleep prior to a working shift. To evaluate changes in driving performance over a long on-duty driving period, we propose a mixed Poisson process recurrent-event model with time-varying coefficients. We use data from 96 commercial truck drivers whose trucks were instrumented with an advanced in situ data acquisition system. The driving performance is measured by unintentional lane deviation events, a known performance deterioration related to fatigue. Driver sleep time and other activities are extracted from a detailed activity register. The time-varying coefficients are used to model the baseline intensity and difference among three cohorts of shifts in which the driver slept less than 7 hours, between 7 to 9 hours, and more than 9 hours prior to driving. We use the penalized B-splines approach to model the time-varying coefficients and an expectation-maximization algorithm with embedded penalized quasi-likelihood approximation for parameter estimation. Simulation studies show that the proposed model fits low and high event rate data well. The results show a significantly higher intensity after 8 hours of on-duty driving for shifts with less than 7 hours of sleep prior to work. The study also shows drivers tend to self-adjust sleep duration, total driving hours, and breaks. This study provides crucial insight into the impact of sleep time on driving performance for commercial truck drivers and highlights the on-road safety implications of insufficient sleep and breaks while driving.