Advanced driving assistance systems (ADAS) are designed to reduce potential crash risks and enhance driving safety. However, drivers’ interactions with ADAS may vary depending on their individual driving styles and characteristics. This study proposes a novel approach to classifying driving styles and explores how age and gender affect interactions with ADAS. The study utilized two naturalistic driving data sets comprising 148 drivers from four age groups: teens; younger adults; middle-aged adults; and older adults. Data were collected during two periods: baseline (without ADAS); and treatment (with ADAS). First, the K-means clustering algorithm was employed to divide trips into one conservative and two aggressive groups based on three driving behavior metrics: tailgating; speeding; and lane-changing. The aggressive-trip ratios were then calculated for each driver during each of the two periods. The Bayesian Gaussian mixture model was applied to determine the threshold values of the aggressive-trip ratios to classify drivers as conservative, moderate, or aggressive during each period. This allowed for identifying changes in driving style upon the activation of ADAS. The subsequent multinomial logistic regression model results showed that driving styles vary across age groups, with teens being the most aggressive drivers. Certain changes in driving style were observed, with some conservative drivers becoming aggressive or moderate and some aggressive drivers becoming conservative or moderate, but these differences were statistically non-significant. The findings of this study indicate that warning-based ADAS may not elicit significant changes in driving style, particularly among teenage drivers who are consistently the most aggressive drivers.

The safety of vulnerable road users, including bicyclists, has become an increasing societal concern. Factors characterizing bicyclists’ crashes with motor vehicles may affect bicyclists’ injuries differently depending on the location of these crashes. The purpose of this paper is to provide a comprehensive analysis for identifying significant factors that affect bicyclists’ injury levels from crashes occurring at travel lanes and at non-travel lanes (e.g., crosswalks and bicycle lanes). For this purpose, this study applied Multinomial Logistic Regression on the Crash Report Sampling System data for three consecutive years. Bicyclists’ injuries were categorized into three levels: (1) Possible, (2) Moderate, and (3) Severe. The study found that running a separate model for each location provided better performances than running an aggregated model for both locations. Results showed common factors significantly associated with an increased likelihood of moderate and/or severe injuries at both locations. Five unique factors were associated with higher likelihoods of these moderate and/or severe injuries to bicyclists in the Travel Lane model, whereas two unique factors were found related to increased odds of these injuries to bicyclists in the Non-Travel Lane model. The results of this study contribute to a better understanding of bicyclists’ crash scenarios and the development of potential countermeasures by alternating some circumstances characterizing these crashes, when possible, to reduce potential injuries.

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Pedestrians are the most vulnerable road users and are at risk of severe consequences when involved in traffic accidents. The purpose of this research is to determine the factors that have significant impacts on the increasing likelihood of pedestrians being seriously injured or killed when involved in a collision with a single vehicle at an intersection over a recent 6-year period. Both 2013–2015 General Estimates System (GES) and 2016–2018 Crash Report Sampling System (CRSS) crash data were used in the analysis. Logistic regression models for the two crash datasets showed that there were four common significant variables affecting pedestrians’ injury levels. The following pairwise comparisons of these common significant factors using the Wald chi-square statistic test showed similar log-odds with few exceptions, suggesting that these affecting factors share similar effects from 2013 through 2018. In both datasets, results showed that a high likelihood of pedestrians’ severe injuries was associated with pedestrians older than 25, dark lighting conditions, light trucks and buses, and vehicles’ straight maneuver. Furthermore, the GES data distinguished further factors imposing higher threats on pedestrians as being drivers’ 19–25 age group, speeding, pedestrians’ roadway crossings maneuvers, and rain conditions. Crashes that occurred at intersections with more than two lanes or during summertime had significantly higher odds of resulting in severe injuries for pedestrians than crashes at two-lane intersections or during wintertime, respectively, in the CRSS dataset. Results of this study contribute to a better understanding of the recent changes in pedestrian safety at intersections and potential countermeasure design suggestions.

The main goal of this paper is to apply a user-centered approach to develop a telemedicine solution, Medlly, to support multidisciplinary care delivery. For this purpose, three key tasks for the proper conduction of the remote visit between the various medical specialists and the patient were considered: (1) the scheduling procedure, (2) the online communication meeting, and (3) the conglomerate after-visit summary. The goal was achieved through the application of user requirements analysis methods including task analysis, prototyping, and usability testing which was conducted with actual healthcare providers. Results showed that the developed multidisciplinary telemedicine prototype ensures efficient interaction between the interface and its users.