Traditional technologies, such as inductive loop detectors, do not usually produce measurements of the quality required by real-time applications. Therefore, one wonders what could be expected from newer information and communication technologies, such as automatic vehicle location, license plate recognition, and detection of mobile devices. The main objectives of this paper are to explore the quality of the data produced by Bluetooth detection of mobile devices that equip vehicles for travel time forecasting and its use in estimating time-dependent origin-destination matrices. Ad hoc procedures based on Kalman filtering have been designed and implemented successfully, and the numerical results of the computational experiments are presented and discussed.
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References
1.
WallerS. T., ChiuY.-C., Ruiz-JuriN., UnikrishnanA., and BustillosB.Short Term Travel Time Prediction on Freeways in Conjunction with Detector Coverage Analysis. Technical Report FHWA/TX-08/0-5141-1. Center for Transportation Research, University of Texas at Austin for Texas Department of Transportation, Austin,2007.
2.
SanfeliuA., PunsolaA., YoshimuraY., LlácerM. R., and GramuntM. D.Legal Challenges for Networking Robots Deployment in European Urban Areas: The Privacy Issue. Workshop on Network Robot Systems. Presented at IEEE International Conference on Robotics and Automation, Kobe, Japan, 2009.
3.
NanthawichitC., NakatsujiT., and SuzukiH.Application of Probe-Vehicle Data for Real-Time Traffic-State Estimation and Short-Term Travel-Time Prediction on a Freeway. In Transportation Research Record: Journal of the Transportation Research Board, No. 1855, Transportation Research Board of the National Academies, Washington, D.C., 2003, pp. 49–59.
4.
KuchipudiC., and ChienS. I. J.Development of a Hybrid Model for Dynamic Travel Time Prediction. In Transportation Research Record: Journal of the Transportation Research Board, No. 1855, Transportation Research Board of the National Academies, Washington, D.C., 2003, pp. 22–31.
5.
ChengP., ZhijunQ., and RanB.Particle Filter-Based Traffic State Estimation Using Cell Phone Network Data.Proc. Intelligent Transportation Systems Conference, Toronto, Ontario, Canada, 2006.
6.
KalmanR. E.A New Approach to Linear Filtering and Prediction Problem.Journal of Basic Engineering, Vol. 82, No. 1, 1960, pp. 35–45.
7.
Van Der ZijppN. J., and HamerslagR.Improved Kalman Filtering Approach for Estimating Origin–Destination Matrices for Freeway Corridors. In Transportation Research Record 1443, TRB, National Research Council, Washington, D.C., 1994, pp. 54–64.
8.
ChangG. L., and WuJ.Recursive Estimation of Time-Varying Origin–Destination Flows from Traffic Counts in Freeway Corridors.Transportation Research B, Vol. 28, No. 2, 1994, pp. 141–160.
9.
ChuL., OhJ.-S., and ReckerW.Adaptive Kalman-Filter-Based Freeway Travel Time Estimation. Presented at 84th Annual Meeting of the Transportation Research Board, Washington, D.C., 2005.
10.
WorkD., ToossavainenO.-P., BlandinS., BayenA., IwuchukwuT., and TractonT.An Ensemble Kalman Filtering Approach to Highway Traffic Estimation Using GPS Enabled Mobile Devices.Proc. 47th IEEE Conference on Decision and Control, Cancun, Mexico, Dec. 9–11, 2008, pp. 5062–5068.
11.
BellM. G. H.The Real Time Estimation of Origin–Destination Flows in the Presence of Platoon Dispersion.Transportation Research Part B, Vol. 25, No. 2-3, 1991, pp. 115–125.
12.
CodinaE., and BarcelöJ.Adjustment of O-D Matrices from Observed Volumes: An Algorithmic Approach Based on Conjugate Gradients.European Journal of Operations Research, Vol. 155, 2004, pp. 535–557.
13.
Ben-AkivaM., BierlaireM., BurtonD., KotsopoulosH. N., and MishalaniR.Network State Estimation and Prediction for Real-Time Traffic Management.Networks and Spatial Economics, Vol. 1, 2001, pp. 293–318.
14.
MahmassaniH. S., and ZhouX.Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework. In Advances in Control, Communication Networks, and Transportation Systems (AbedE., ed.), Birkhauser Boston, Mass.,2005.
