A Machine Learning-based Approach for Vehicular Tracking in Low Power Wide Area Networks

Abstract

This paper addresses the issue of monitoring and tracking people and vehicles within smart cities. The actors in this work jointly cooperate in sensing, sensible data processing, anonymized data delivery, and data processing, with the final goal of providing real-time mapping of vehicular and pedestrian concentration conditions. The classification of conditions can bring out critical situations that can be communicated in realtime to citizens. Tests were conducted in the city of Cagliari, Italy.

Improving the practical skills of STEM students at a historically black college and university (HBCU)

“…In transportation applications, the IoT can be used to count the number of cyclists and pedestrians on roads and highways. Understanding the travel behavior of pedestrians and cyclists on roadways is critical in evaluating safety outcomes relative to rates of exposure, identifying appropriate, context-sensitive complete street infrastructure interventions, and understanding overall state-wide and location-specific transportation trends (Bertolusso et al, 2022).…”