Distracted driving is known to be one of the core contributors to crashes in the U.S., accounting for about 40% of all crashes. Drivers' situational awareness, decision-making, and driving performance are impaired due to temporarily diverting their attention from the primary task of driving to other tasks not related to driving. Detecting driver distraction would help in adapting the most effective countermeasures. To find the best strategies to overcome this problem, we developed a Bayesian Network (BN) distracted driving prediction model using a driving simulator. In this study, we use a Bayesian Network classifier as a powerful machine learning algorithm on our trained data (80%) and tested (20%) with the data collected from a driving simulator, in which the 92 participants drove six scenarios of hand-held calling, hands-free calling, texting, voice command, clothing, and eating/drinking on four different road classes (rural collector, freeway, urban arterial, and local road in a school zone). Various driving performances such as speed, acceleration, throttle, lane changing, brake, collision, and offset from the lane center were investigated. Here we investigated different optimization models to build the best BN in which a Genetic Search Algorithm obtained the best performance. As a result, we achieved a 67.8% prediction accuracy using our model to predict driver distraction. We also achieved 62.6% true positive rate, which demonstrates the ability of our model to correctly predict distractions.

This study investigates travelers’ reactions to different types of information, in deciding their parking choice behavior and its effect on circulation time, through a driving simulator and a stated preference (SP) survey. In the simulator-based driving experiments, we develop a 3.47 mile² network in the Chinatown area of Washington, D.C., with different scenarios of traffic, driving conditions, and information provision. The parking information is provided using a variable message sign (VMS) and mobile application. In all scenarios, participants can choose from three parking options with different prices and different walking distances to the destination (Verizon Garage, 11th St. Garage, and on-street parking). A sample of 76 participants with diverse socio-economic backgrounds who in total conducted 636 experiments is used. We applied a multinomial logistic regression model, linear regression, and t-test to analyze the collected data. We conclude that types of information and age are important determinants of drivers’ parking choice and compliance behaviors. In addition, the results show that the existence of information decreases the circulation time. In addition, the parking choice behavior revealed through the driving simulator is shown to be significantly different from that stated in the survey questionnaire.

This study investigates the potential effect(s) of different dynamic message signs (DMSs) on driver behavior using a full-scale high-fidelity driving simulator. Different DMSs are categorized by their content, structure, and type of messages. A random forest algorithm is used for three separate behavioral analyses—a route diversion analysis, a route choice analysis, and a compliance analysis—to identify the potential and relative influences of different DMSs on these aspects of driver behavior. A total of 390 simulation runs are conducted using a sample of 65 participants from diverse socioeconomic backgrounds. Results obtained suggest that DMSs displaying lane closure and delay information with advisory messages are most influential with regards to diversion, while color-coded DMSs and DMSs with avoid route advice are the top contributors potentially impacting route choice decisions and DMS compliance. In this first-of-a-kind study, based on the responses to the pre- and post-simulation surveys as well as results obtained from the analysis of driving-simulation-session data, the authors found that color-coded DMSs are more effective than alphanumeric DMSs, especially in scenarios that demand high compliance from drivers. The increased effectiveness may be attributed to reduced comprehension time and ease with which such DMSs are understood by a greater percentage of road users.

Learning critical concepts that are centered on the analysis, design, and maintenance of transportation infrastructure systems poses a measure of difficulty for undergraduates in engineering. Therefore, hands-on learning pedagogy should be an excellent precursor to increase understanding of these concepts, since the pedagogy incorporates real-life experience in the delivery. This paper describes how a hands-on learning pedagogy called experiment-centric pedagogy (ECP) has been used to teach these concepts to undergraduate students at a historically Black university. The research questions are as follows: (1) How well can ECP improve students’ understanding of concepts essential to the analysis and design of transportation infrastructure systems ? (2) How has the ECP facilitated the achievement of the learning objectives of these concepts? and (3) Does an ECP increase the engagement of undergraduate students in their transportation infrastructure engineering learning and lead to measurable lasting gains ? To answer these research questions, ECP was implemented and assessed when used to teach the concepts of stress and strain utilized in the analysis of bridges and other transportation infrastructure, sound used in the development and design of noise barriers, moisture content in controlling compaction of highway infrastructure systems, and degradation of infrastructure systems exposed to various environmental settings. Assessment results from 92 undergraduates reveal an increase in students’ motivation and cognitive understanding of the relevant concepts, as well as learning gains and an improved success rate compared to the traditional method of teaching.

Cellphone usage is often considered to be one of the major causes of distracted driving. Similarly, voice messaging is identified as a potential cause of distracted driving but has received limited attention in the literature. Thus, this study aims to develop supervised machine learning (ML) methods to detect distracted driving events caused by texting and voice messaging using vehicle trajectory data. Vehicle trajectory data was collected from 92 participants who drove a simulated network of the Baltimore metropolitan area using a driving simulator. Different key variables were extracted from the data to construct the features for developing the ML methods, including speed, brake usage, throttle, steering velocity, brake light, and offset from the road center. Several methods, including the support vector machine, k -nearest neighbor, decision tree, neural network, and adaptive boosting (AdaBoost), were examined on the data to achieve the best model. In addition, several metrics were used to assess the performance of the ML models, such as accuracy, sensitivity, precision, the Matthews correlation coefficient, and the area under the receiver operating characteristic curve (AUC-ROC). The results indicated that the AdaBoost algorithm had the best yields, with an accuracy of 74.67% and AUC-ROC of 82.5% on the independent test set. The findings of this study can be directly leveraged to develop in-vehicle driver warning systems to alert drivers with respect to distracted behavior, which lead to the reduction of distracted driving events and an improvement in traffic safety.