Prototype of a System for Tracking Transit Service Based on IoV, ITS, and Machine Learning

The transit service in a city should be the most efficient, least polluting, accessible, and sustainable means of transportation for its citizens. However, serious shortcomings have been detected, mainly medium-sized cities developing countries. These are related to lack user information, insecurity, low availability, repeated stops inappropriate and/or unauthorized places. Some these contribute high accident rates traffic congestion. development tools improve characteristics conditions has become an imperative need quality life citizens sustainability. Transit tracking is relevant aspects such as online location information travelers control by transport companies compliance with speed limits, schedules, routes, stops. This research proposes vehicle system based on Internet Vehicles (IoV) Vehicle-to-Roadside (V2R) classification. proposed ideal use electric vehicles due power consumption device. uses Intelligent Transportation Systems (ITS) architecture, Long Range (LoRa) communication technology, LoRa Wide Area Network (LoRaWAN) protocol. Additionally, offers real-time prediction absence position data. IoV device integrates GPS-LoRa module card Inertial Measurement Unit (IMU). A algorithm was implemented train store model previously collected data from devices. To evaluate developed model, case study Popayán (Colombia) implemented, using three routes testing. results implementation were satisfactory, obtaining average coverage 60.4% final field tests through communication. For remaining 39.6% used, accuracy 177 m respect real location. Considering obtained results, one this article can used systems countries fleet control.