International Journal of Computer
Trends and Technology

Research Article | Open Access | Download PDF

Volume 67 | Issue 6 | Year 2019 | Article Id. IJCTT-V67I6P104 | DOI : https://doi.org/10.14445/22312803/IJCTT-V67I6P104

Mobile GPS based Traffic Anomaly Detection System for Vehicular Network

Farrukh Arslan, Bilal Wajid, Haroon Shafique

Citation :

Farrukh Arslan, Bilal Wajid, Haroon Shafique, "Mobile GPS based Traffic Anomaly Detection System for Vehicular Network," International Journal of Computer Trends and Technology (IJCTT), vol. 67, no. 6, pp. 31-36, 2019. Crossref, https://doi.org/10.14445/22312803/ IJCTT-V67I6P104

Abstract

The quick growth in the number of mobile devices such as smart phones, wearable devices, tablets, sensor enabled vehicles etc. with a large array of sensors like GPS, Accelerometer, Gyroscope, Compass, Magnetometer, Camera etc. enables a new sensing paradigm known as Crowd Sensing. Traffic anomalies can occur due to various events like accidents, functions, celebrations, protests, disasters etc. In this paper we propose an architecture that employs crowd sensing to detect traffic anomalies and uses social media data to determine the authenticity of identified anomalies. Our prototype architecture includes an Android based navigation application for the client and a combination of J2EE application server and Hadoop as the back-end. The client application consists of an interface to report traffic anomalies apart from the basic navigation features. Anyone using this app can report an anomaly that he encounters in his route. Whenever a user reports an incident, a tweet with the exact location and incident details are posted automatically to the twitter account managed by the application. Using Recursive EM Algorithm, the authenticity of the reported anomaly is verified and if it is genuine, all the users in that particular route will get notified in advance. The system will also suggest the best possible alternative route to the same destination. The system also provides a web interface for the traffic authorities to monitor the anomalies in their locality on a real-time basis and can respond to it very immediately. Hadoop based infrastructure which is deployed in the back-end is able to process massive GPS data collected from the users using MapReduce framework. The system has been tested successfully in a simulated environment using Android emulator and GPS Location Spoof application.

Keywords

Traffic Anomaly Detection, Truth Estimation,MapReduce, Crowd Sensing, Map-Matching

References

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