Mobile GPS based Traffic Anomaly Detection System for Vehicular Network
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International Journal of Computer Trends and Technology (IJCTT) |  |
| © 2019 by IJCTT Journal | ||
| Volume-67 Issue-6 |
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| Year of Publication : 2019 | ||
| Authors : Farrukh Arslan, Bilal Wajid, Haroon Shafique | ||
| DOI : 10.14445/22312803/IJCTT-V67I6P104 |
MLA Style:Farrukh Arslan, Bilal Wajid, Haroon Shafique"Mobile GPS based Traffic Anomaly Detection System for Vehicular Network" International Journal of Computer Trends and Technology 67.6 (2019): 31-36.
APA Style Farrukh Arslan, Bilal Wajid, Haroon Shafique. Mobile GPS based Traffic Anomaly Detection System for Vehicular NetworkInternational Journal of Computer Trends and Technology, 67(6), 31-36.
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.
Reference
[1] Hadoop MapReduce, http://hadoop.apache.org/docs/r1.2.1/mapred_tutorial.html
[2] Bei Pan, Yu Zheng, David Wilkie, Cyrus Shahabi. Crowd Sensing ofTraffic Anomalies based on Human Mobility and Social Media. InSIGSPATIAL GIS 13. [
3] Google Directions API, https://developers.google.com/maps/documentation/directions/start
[4] WenQiang Wang, Xiaoming Zhang, Jiangwei Zhang, Hock BengLim.Smart Traffic Cloud: An Infrastructure for Traffic Applications. In2012 IEEE 18th International Conference on Parallel and DistributedSystems.
[5] Dong Wang, Tarek Abdelzaher, Lance Kaplan, Charu Aggarwal. Recursive Fact-finding: A Streaming Approach to Truth Estimation inCrowdsourcing Applications. In ICDCS ’13 Proceedings of the 2013IEEE 33rd International Conference on Distributed Computing Systems,Pages 530-539.
[6] Android Development, https://developer.android.com/guide/index.html
[7] Rectangle Method for Map Matching, https://github.com/gglnx/google-maps-utility-library-v3/blob/master/routeboxer/docs/examples.html
[8] D. Wang, L. Kaplan, H. Le, and T. Abdelzaher. On truth discovering social sensing: A maximum likelihood estimation approach. In The 11th ACM/IEEE Conference on Information Processing in Sensor Networks (IPSN 12), April 2012.
[9] D.M. Titterington. Recursive parameter estimation using incompletedata. Journal of the Royal Statistical Society. Series B (Methodological), 46(2):pp. 257267, 1984.
[10] D. Wang, L. Kaplan, T. Abdelzaher, and C. C. Aggarwal. On Scalabilityand robustness limitations of real and asymptotic confidence bounds insocial sensing. In The 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON 12), June 2012.
Keywords
Traffic Anomaly Detection, Truth Estimation,MapReduce, Crowd Sensing, Map-Matching