Crowdsource Based Vehicle Tracking System

• Published in: Wireless personal communications 2019-06, Vol.106 (4), p.2387-2405
• Main Authors: Metlo, Sundas, Memon, Mahnoor Gul, Shaikh, Faisal Karim, Teevno, Mansoor Ali, Talpur, Anum
• Format: Article
• Language: English
• Subjects: Accidents; Algorithms; Batteries; Communications Engineering; Computer Communication Networks; Crowdsourcing; Developing countries; Energy consumption; Engineering; Global positioning systems; GPS; Intelligent transportation systems; LDCs; Networks; Power consumption; Reliability analysis; Response time; Satellite navigation systems; Scientific papers; Signal,Image and Speech Processing; Smartphones; Tracking devices; Tracking systems; Traffic congestion; Traffic flow; Traffic jams
• ISSN: 0929-6212; 1572-834X
• DOI: 10.1007/s11277-019-06323-z

The transport of developing countries faces lots of problems including heavy traffic jams, the uncertainty of waiting time, increased fuel consumption and the risk of road accidents due to unmanaged traffic flow. These problems are being solved by using Intelligent Transportation System (ITS). In this paper, In this paper, we propose the tracking of vehicles which uses crowdsourced positioning data obtained from GPS of smartphones. Traditional transit tracking is costly and resources limited, thus instead installing a tracking device in each vehicle, the location of each vehicle is tracked by user’s smartphone. The positioning data from GPS of smartphone is used as an input. We have developed an algorithm, that will detect and recognize the specific vehicle based on crowdsourced data. Using this the users waiting for their desired vehicle at various routes can track vehicle’s current location and estimated the time the vehicle will take to reach their location. In this research paper, we have analyzed the usability and reliability of crowdsourcing tracking data in terms of usage of battery charge, power consumption, CPU utilization and response time. It can be seen in the results that proposed work consumes power in between the range of 412 mW to 502 mW only. Whereas, maximum CPU utilization goes up to 0.3%. In the end, the battery life of the proposed system is compared with existing work and it shows proposed system have more battery life than existing systems. As a case study, we have applied our algorithm for tracking of university buses.