Potential long-term effects of Covid-19 on telecommuting and environment: An Italian case-study.

To contain the sudden spread of SARS-CoV-2, many governments encouraged people to work from home, generating an unprecedented diffusion of this activity. Furthermore, Covid-19 has induced drastic changes in everyday life and travel habits, which might persist in the future. This paper aims to understand and estimate the potential long-term impacts of telework on the environment due to the pandemic, by analyzing factors affecting the frequency of telecommuting, the mode choice for traveling to work, and pollutant emissions generated by these trips. Data from a mobility survey administered in Padova (Italy) was used. Results indicate that Covid-19 could cause a rebound effect reversing the positive impacts of working from home, since, even if the number of trips could be reduced, many shifts towards non-sustainable travel modes could occur. The promotion of telework should be combined with measures fostering sustainable travel habits to pave the way towards a future green mobility.

The Effect of Partial Sleep Deprivation and Time-on-Task on Young Drivers' Subjective and Objective Sleepiness.

Despite sleepiness being considered one of the main factors contributing to road crashes, and even though extensive efforts have been made in the identification of techniques able to detect it, the assessment of fitness-to-drive regarding driving fatigue and sleepiness is still an open issue. In the literature on driver sleepiness, both vehicle-based measures and behavioral measures are used. Concerning the former, the one considered more reliable is the Standard Deviation of Lateral Position (SDLP) while the PERcent of eye CLOSure over a defined period of time (PERCLOS) seems to be the most informative behavioral measure. In the present study, using a within-subject design, we assessed the effect of a single night of partial sleep deprivation (PSD, less than 5 h sleeping time) compared to a control condition (full night of sleep, 8 h sleeping time) on SDLP and PERCLOS, in young adults driving in a dynamic car simulator. Results show that time-on-task and PSD affect both subjective and objective sleepiness measures. Moreover, our data confirm that both objective and subjective sleepiness increase through a monotonous driving scenario. Considering that SDLP and PERCLOS were often used separately in studies on driver sleepiness and fatigue detection, the present results have potential implications for fitness-to-drive assessment in that they provide useful information allowing to combine the advantages of the two measures for drowsiness detection while driving.

A conflict-based approach for real-time road safety analysis: Comparative evaluation with crash-based models.

An innovative approach for real-time road safety analysis is presented in this work. Unlike traditional real-time crash prediction models (RTCPMs), in which crash data are used in the training phase, a real-time conflict prediction model (RTConfPM) is proposed. This model can be trained using surrogate measures of safety, and can therefore be applied even in situations in which highly spatial/temporal-accurate crash data are unavailable or unreliable. The application of an RTConfPM consists of using a set of input variables recorded during a given time interval, to predict whether there will be an increased risk of unsafe situations in the following interval. This paper presents an RTConfPM to predict rear-end crashes, using time-to-collision values recorded with radar sensors on multiple motorway cross-sections to define unsafe situations, and traffic conditions recorded on the same sections as input to the model. The RTConfPM is compared to a traditional RTCPM, trained with a dataset of crashes located on the same motorway, and using the same traffic data as input. In both approaches, variable selection is performed with Pearson's correlation test and random forest; synthetic minority oversampling technique (SMOTE) is used to balance the classes in the training dataset, support vector machine (SVM) is used as classifier, and Monte Carlo cross-validation is adopted for robustness. The two approaches are evaluated considering accuracy, recall, specificity/false alarm rate, and area under the curve (AUC). As shown by the results of this paper, the conflict-based approach appears promising, and is able to predict the occurrence of unsafe situations within 5 min with more than 93% accuracy, recall and specificity, significantly outperforming the RTCPM.

Visual Feedback Effectiveness in Reducing Over Speeding of Moped-Riders.

The use of assistance systems aimed at reducing road fatalities is spreading, especially for car drivers, but less effort has been devoted to developing and testing similar systems for powered two-wheelers (PTWs). Considering that over speeding represents one of the main causal factors in road crashes and that riders are more vulnerable than drivers, in the present study we investigated the effectiveness of an assistance system which signaled speed limit violations during a simulated moped-driving task, in optimal and poor visibility conditions. Participants performed four conditions of simulated riding: one baseline condition without Feedback, one Fog condition in which visual feedback was provided so as to indicate to the participants when a speed limit (lower than that indicated by the traffic signals) was exceeded, and two post-Feedback conditions with and without Fog, respectively, in which no feedback was delivered. Results showed that participants make fewer speeding violations when the feedback is not provided, after 1 month, and regardless of the visibility condition. Finally, the feedback has been proven effective in reducing speed violations in participants with an aggressive riding style, as measured in the baseline session.

Precision teaching to improve drivers' lane maintenance.

INTRODUCTION: This study investigates the effect of precision teaching signals on lane maintenance. METHODS: In experiment 1, the control group drove a simulator with no signals. In experiment 2, drivers were presented with auditory signals depending on their position within or outside the lane. In experiment 3, visual signals were presented in addition to auditory signals to examine the effect of redundancy on drivers' lane maintenance. RESULTS: Results showed an improvement in lane maintenance in experiment 2. Cross-experiment analysis indicated this effect not to be the result of learning. Data from experiment 3 also showed that presenting redundant signals did not further reduce lane variability or help drivers maintain a more central position within the lane. CONCLUSIONS: Taken together, data suggest precision teaching be effective as an educational tool to improve lane maintenance. Practical Applications: Our study shows the potential for precision teaching to serve as a valuable tool in driver training.

Advanced driver assistance systems: Using multimodal redundant warnings to enhance road safety.

This study investigated whether multimodal redundant warnings presented by advanced assistance systems reduce brake response times. Warnings presented by assistance systems are designed to assist drivers by informing them that evasive driving maneuvers are needed in order to avoid a potential accident. If these warnings are poorly designed, they may distract drivers, slow their responses, and reduce road safety. In two experiments, participants drove a simulated vehicle equipped with a forward collision avoidance system. Auditory, vibrotactile, and multimodal warnings were presented when the time to collision was shorter than five seconds. The effects of these warnings were investigated with participants performing a concurrent cell phone conversation (Exp. 1) or driving in high-density traffic (Exp. 2). Braking times and subjective workload were measured. Multimodal redundant warnings elicited faster braking reaction times. These warnings were found to be effective even when talking on a cell phone (Exp. 1) or driving in dense traffic (Exp. 2). Multimodal warnings produced higher ratings of urgency, but ratings of frustration did not increase compared to other warnings. Findings obtained in these two experiments are important given that faster braking responses may reduce the potential for a collision.