A Vehicle Tracking System Using Greedy Forwarding Algorithms for Public Transportation in Urban Arterial

Jimoh, Onemayin David; Ajao, Lukman Adewale; Adeleke, Oluwafemi Oyetunde; Kolo, Stephen Sunday · 2020 · Crossref

DOI: 10.1109/access.2020.3031488

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Summary

This paper addresses the inefficiency and chaos of public transportation systems in developing nations, specifically focusing on urban arterials in Nigeria. The authors aim to improve commuter experience and fleet management by developing a real-time vehicle tracking system. The motivation stems from the lack of scheduled, reliable transport between the two campuses of the Federal University of Technology, Minna (Gidan-Kwano and Bosso), where captive commuters face unpredictable service. The study proposes a component of an Advanced Public Transportation System that provides real-time location data and arrival time estimates to users. The system integrates hardware and software components to track vehicles using Global Positioning System (GPS) and Global System for Mobile Communication (GSM) technologies. The hardware architecture utilizes an Atmega328P controller chip as the logical control unit, programmed in C-language via Arduino IDE, alongside a Ublox NEO6MV2 GPS module and a Sim900 GSM module for data transmission. A Raspberry Pi ARM Cortex A53 processor serves as the base station for data fusion and dissemination. The software employs a GPS-based greedy forwarding algorithm (GFA) for route optimization and satellite selection, using the minimum inversion matrix method for Geometric Dilution of Precision (GDoP). Distance calculations are performed using a pseudo-range mathematical model based on the Haversine formula, while a radar range sensor aids in detecting distance and vehicle state (dynamic or stationary) using discrete Kalman filters. The system was deployed and tested on the 28 km route between the university’s two campuses. During testing, the system tracked 11 satellites and recorded minor dilution errors, specifically a Position Dilution of Precision (PDoP) of 1.9, Horizontal Dilution of Precision (HDoP) of 0.9, and Vertical Dilution of Precision (VDOP) of 1.7. The results demonstrated that the system efficiently and accurately displayed distance and time information with only minor delays. The greedy forwarding algorithm successfully identified optimum routes, and the integration of GPS and GSM provided reliable real-time updates to remote displays, including Google Maps interfaces. The significance of this work lies in its potential to enhance fleet management schemes and provide reliable information to commuters in urban areas with chaotic transport systems. By offering real-time tracking and accurate arrival predictions, the system addresses the economic and logistical challenges of unscheduled public transport. The authors conclude that the system is efficient, accurate, and capable of universal adoption, serving as a foundational step toward more organized and effective public transportation infrastructure in developing regions.

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