A Real-Time Traffic Information Model Using GPS-Based Probe Car Data

Wan Jia Chen; Chi Hua Chen; Bon Yeh Lin; Chi Chun Lo

doi:10.4028/www.scientific.net/KEM.467-469.1433

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HomeKey Engineering MaterialsKey Engineering Materials Vols. 467-469A Real-Time Traffic Information Model Using...

A Real-Time Traffic Information Model Using GPS-Based Probe Car Data

Abstract:

In recent year, the rise of economic growth and technology advance leads to improve the quality of service of traditional transport system. Intelligent Transportation System (ITS) has become more and more popular. At present, the collection of real-time traffic information is executed in two ways: (1) Stationary Vehicle Detectors (VD) and (2) Global Position System (GPS)-based probe cars reporting. However, VD devices need a large sum of money to build and maintain. Therefore, we propose the linear regression model to infer the equation between vehicle speed and traffic flow. The traffic flow can be estimated from the speed which is obtained from GPS-based probe cars. In experiments, the Speed Error Ratio (SER) and Flow Error Ratio (FER) of linear regression model are 4.60% and 24.63% respectively. The estimated speed and traffic flow by using linear regression model is better than by using linear model, power law model, exponential model, and normal distribution model. Therefore, the linear regression model can be used to estimate traffic information for ITS.

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Periodical:

Key Engineering Materials (Volumes 467-469)

Pages:

1433-1437

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.467-469.1433

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Online since:

February 2011

Authors:

Wan Jia Chen, Chi Hua Chen, Bon Yeh Lin, Chi Chun Lo

Keywords:

GPS-Based Probe Car Data, Intelligent Transportation System (ITS), Linear Regression Model, Real-Time Traffic Information

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References

[1] J.T. Kneafsey, Transportation Economic Analysis, Lexington Books, Lexington, Massachusetts, USA, (1975).

Google Scholar

[2] S. Panwai and H. Dia, Comparative Evaluation of Microscopic Car-Following Behavior, IEEE Transactions on Intelligent Transportation System, Vol. 6, No. 3, pp.314-325, (2005).

DOI: 10.1109/tits.2005.853705

Google Scholar

[3] Y. Wang, M. Papageorgiou, and A. Messmer, Investigation of the Adaptive Features of a Real-time Nonlinear Freeway Traffic State Estimator, Nonlinear Dyn., Vol. 49, No. 4, pp.511-524, (2007).

DOI: 10.1007/s11071-006-9112-2

Google Scholar

[4] Y. Wang, M. Papageorgiou, and A. Messmer, RENAISSANCE – A Unified Macroscopic Model-based Approach to Real-time Freeway Network Traffic Surveillance, Transportation Research Part C, Vol. 14, pp.190-212, (2006).

DOI: 10.1016/j.trc.2006.06.001

Google Scholar

[5] R. Wunderlich, C. Liu, I. Elhanany, and T. Urbanik, A Novel Signal-Scheduling Algorithm With Quality-of-Service Provisioning for an Isolated Intersection, IEEE Transactions on Intelligent Transportation System, Vol. 9, No. 3, pp.536-547, (2008).

DOI: 10.1109/tits.2008.928266

Google Scholar