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Numerous studies have reported the efficacy of high-occupancy vehicle (HOV) lane restriction and truck lane restriction implemented independently, but the simultaneous use of both restrictions on an urban freeway corridor raises numerous operational and safety issues. This research study analyzed the operational and safety experience of an 83-mi corridor of I-95 in South Florida that has both HOV and truck lane restriction. Results of a field-validated VisSim simulation model showed that high-occupancy vehicles and automobiles gained significantly more travel time savings and speeds on the restricted lanes than on the general lanes. Also, vehicle queue lengths around critical merging and diverging areas increased significantly as the percentage of trucks increased. Results indicated that during peak traffic conditions right lanes had higher lane occupancy than left lanes, whereas during off-peak traffic conditions center lanes carried more vehicles per lane than the outermost lanes; these results suggest that congestion on right lanes forces automobiles to use left lanes. Furthermore, results showed that most lane changes occurred during peak traffic flow conditions—about twice that of off peak—and appreciable speed differences existed between restricted and nonrestricted lanes. Simulation results for off-peak traffic conditions did not show any noticeable changes in traffic operating characteristics resulting from lane restriction strategies. On the basis of these results, it can be fairly concluded that on urban freeways significant operational and safety benefits of the combined implementation of HOV and truck lane restrictions accrue during congested traffic conditions rather than during uncongested conditions.

Walking requires paying attention to the surrounding environment to ensure safe maneuvers. Walkers engaging in multitask activities such as texting, calling, listening to music, snacking, and reading while walking have detrimental effects on safety similar to those experienced with distracted drivers. Because this is an emerging problem, few data have been collected to assess the negative impact of distracted walking on safety. The present survey was conducted to collect information on distracted walking by college students. The objectives were to estimate the extent of distracting activities of college pedestrians and to assess the perception of the safety problem of college pedestrians while walking. The survey was sent to the potential student population of 5,000 by Survey Monkey at South Carolina State University in Orangeburg, and 297 surveys were completed. The results indicated that about 55% of respondents always checked their electronic devices while walking. Further, about 4% of the respondents reported that they or their immediate family members sustained injuries resulting from distracted walking. More important, approximately 53% of the respondents had seen near crashes resulting from pedestrians engaging in distracting activities while walking. In this survey, males engaged more in distracting activities than females. About 70% of females perceived that distracted walking had become a problem, compared with 30% of males. The most common proposed interventions by the respondents to curb distracted walking include education, outreach programs, citations, and legislation.

Recent technological developments have attracted the use of machine learning technologies and sensors in various pavement maintenance and rehabilitation studies. To avoid excessive road damages, which cause high road maintenance costs, reduced mobility, vehicle damages, and safety concerns, the periodic maintenance of roads is necessary. As part of maintenance works, road pavement conditions should be monitored continuously. This monitoring is possible using modern distress detection methods that are simple to use, comparatively cheap, less labor-intensive, faster, safer, and able to provide data on a real-time basis. This paper proposed and developed two models: computer vision and sensor-based. The computer vision model was developed using the You Only Look Once (YOLOv5) algorithm for detecting and classifying pavement distresses into nine classes. The sensor-based model combined eight Controller Area Network (CAN) bus sensors available in most new vehicles to predict pavement distress. This research employed an extreme gradient boosting model (XGBoost) to train the sensor-based model. The results showed that the model achieved 98.42% and 97.99% area under the curve (AUC) metrics for training and validation datasets, respectively. The computer vision model attained an accuracy of 81.28% and an F1-score of 76.40%, which agree with past studies. The results indicated that both computer vision and sensor-based models proved highly efficient in predicting pavement distress and can be used to complement each other. Overall, computer vision and sensor-based tools provide cheap and practical road condition monitoring compared to traditional manual instruments.

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