Context Aware Traffic Congestion Estimation to Compensate Intermittently Available Mobile Sensors

Estimating the degree of traffic congestion at run time is crucially important for Intelligent Transportation Systems (ITS) especially when selecting travel routes. One of the challenges when using a mobile sensor (e.g. mobile phone, GPS) as source of traffic data stems from its mobility. This paper proposes a novel approach to fusing mobile data and an algorithm for traffic congestion estimation to compensate intermittent available mobile sensors. Furthermore, our proposed model is also tolerant to incomplete data and it is open to any type of sensors. This approach differs from other traffic congestion estimation techniques in that it utilizes discoverable context even on sensorless road segments in real time instead of entirely relying on a sensor-based data of observed road segment. Implementation and validation through extensive experimentation with real data confirms feasibility of the proposed approach.

[1]  Bernard Burg,et al.  Verification of uncertain context based on a theory of context spaces , 2007, Int. J. Pervasive Comput. Commun..

[2]  Liang Zou,et al.  Arterial speed studies with taxi equipped with global positioning receivers as probe vehicle , 2005, Proceedings. 2005 International Conference on Wireless Communications, Networking and Mobile Computing, 2005..

[3]  Geoff Rose,et al.  Mobile Phones as Traffic Probes: Practices, Prospects and Issues , 2006 .

[4]  William W. Cohen.  Learning Trees and Rules with Set-Valued Features , 1996, AAAI/IAAI, Vol. 1.

[5]  Roy H. Campbell,et al.  Reasoning about Uncertain Contexts in Pervasive Computing Environments , 2004, IEEE Pervasive Comput..

[6]  W. Hongsakham,et al.  Estimating road traffic congestion from cellular handoff information using cell-based neural networks and K-means clustering , 2008, 2008 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology.

[7]  Mel Siegel,et al.  Sensor fusion for context understanding , 2002, IMTC/2002. Proceedings of the 19th IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.00CH37276).

[8]  Gregory D. Abowd,et al.  Towards a Better Understanding of Context and Context-Awareness , 1999, HUC.

[9]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[10]  S. K. Jason Chang,et al.  Segment based Traffic Information Estimation Method Using Cellular Network Data , 2005 .

[11]  Wasan Pattara-Atikom,et al.  Estimating Road Traffic Congestion using Cell Dwell Time with Simple Threshold and Fuzzy Logic Techniques , 2007, 2007 IEEE Intelligent Transportation Systems Conference.