The COVID-19 pandemic has had a global impact, disrupting the normal trends of our everyday life. More specifically, the effects of COVID-19 on road safety are still largely unexplored. Hence, this study aims to investigate the change in road safety trends due to COVID-19 using real-time traffic parameters. Results from the extensive analyses of the 2017 to 2020 data of Interstate-4 show that traffic volume decreased by 13.6% in 2020 compared to the average of 2017–2019’s volume, whereas there is a decreasing number of crashes at the higher volume. Average speed increased by 11.3% during the COVID-19 period; however, the increase in average speed during the COVID-19 period has an insignificant relationship with crash severities. Fatal crashes increased, while total crashes decreased, during the COVID-19 period; severe crashes decreased with the total crashes. Alcohol-related crashes decreased by 22% from 2019 to 2020. Thus, the road-safety trend due to the impact of COVID-19 has evidently changed and presents a unique trend. The findings of the study suggest a larger need for a more in-depth study to analyze the impact of COVID-19 on road safety, to minimize fatalities on roads through appropriate policy measures.
As technology is moving rapidly toward automation and connectivity, it is of paramount importance to predict vehicle trajectories ahead of time. This not only enhances safety but also ensures mobility in a connected and automated environment. Previous studies have shown that, given the previous trajectory, the future trajectory can be estimated. But this method suffers from considerable drawbacks in the case of intersections as it cannot predict turning movements. It also requires advanced sensors that are not readily available in most vehicles. A smartphone device can also be used in such scenarios, bringing partial automation to vehicles without these sensors. This paper presents an integrated method of estimating vehicle trajectories for both general roadway segments and intersections by using a smartphone. A lane change detection system is taken as an indicator of intersection turning movement estimation and corresponding vehicle trajectories are estimated accordingly. The system can achieve high penetration rates and can be used to replicate onboard units. Sensor readings are taken periodically which are first filtered with a low-pass filter to zero out any high-frequency noise and then fed into a machine learning model to detect lane changes. The model can successfully capture lane changes with smartphone data with high accuracy (95%). Finally, vehicle trajectory is estimated using Chebyshev’s polynomial. This type of estimation system can find applications in collision prediction at intersections between a turning vehicle and a pedestrian on a crosswalk.
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