Pinpoint: An Efficient Approach to Traffic State Estimation System Using Mobile Probes

This paper proposes a novel, nicknamed the "Pinpoint", method for an efficient and robust traffic estimation system using mobile phones as traffic probes. In this approach, the real-time traffic data is collected and sent to the server at the right time by the right players. Only the utilized data is reported to the server by the travelling vehicles. The mobile phones from walkers are prevented from sending data thus the data transmission load is controlled, improving efficiency and effectiveness of the system significantly. In additions, this approach consists of a robust vehicle classification method based on only the GPS data. This novel feature improves not only the accuracy in estimating the seriousness of congestions but also the scalability of the system. This proposed approach can be flexibly applied in any traffic system structure and in any country, especially in developing countries where a lot of motorbikes are travelling on the roads. The evaluation shows that our proposed method is more efficient, effective and scalable compared to the existing ones.

[1]  R Zito,et al.  Global positioning systems in the time domain: How useful a tool for intelligent vehicle-highway systems? , 1995 .

[2]  Alexandre M. Bayen,et al.  Virtual trip lines for distributed privacy-preserving traffic monitoring , 2008, MobiSys '08.

[3]  Joy Dahlgren,et al.  Using Vehicles Equipped with Toll Tags as Probes for Providing Travel Times , 2001 .

[4]  Teruo Higashino,et al.  A Method for Sharing Traffic Jam Information using Inter-Vehicle Communication , 2006, 2006 3rd Annual International Conference on Mobile and Ubiquitous Systems - Workshops.

[5]  Antonio F. Gómez-Skarmeta,et al.  Sharing Context-Aware Road and Safety Information , 2009, IEEE Pervasive Computing.

[6]  El Dagless,et al.  Vision-based road-traffic monitoring sensor , 2001 .

[7]  Benjamin Coifman Identifying the Onset of Congestion Rapidly with Existing Traffic Detectors , 1999 .

[8]  Youngbin Yim,et al.  Travel Time Estimation on the San Francisco Bay Area Network Using Cellular Phones as Probes , 2000 .

[9]  Alexander Skabardonis,et al.  Performance Evaluation of Travel Time Methods for Real Time Traffic Applications , 2007 .

[10]  Janusz Gajda,et al.  A vehicle classification based on inductive loop detectors , 2001, IMTC 2001. Proceedings of the 18th IEEE Instrumentation and Measurement Technology Conference. Rediscovering Measurement in the Age of Informatics (Cat. No.01CH 37188).

[11]  Jean Walrand,et al.  Vehicles As Probes , 1995 .

[12]  Carlos F. Daganzo,et al.  A simple detection scheme for delay-inducing freeway incidents , 1997 .

[13]  James E. Marca,et al.  TRACER: In-Vehicle, GPS-Based Wireless Technology for Traffic Surveillance and Management. - eScholarship , 2003 .

[14]  Mingyan Liu,et al.  Surface street traffic estimation , 2007, MobiSys '07.