Performance monitoring is an issue of growing concern both nationally and in the state of Washington. Travel times and speeds always have been of interest to traveler information researchers, but there is limited infrastructure with which to collect such data on a continuous basis. Transit vehicles were used as probes, and a framework was developed for modeling the time series that arise from irregularly sampled transit vehicle locations. These samples of vehicle location were obtained from the King County Department of Metropolitan Services automatic vehicle location system. An optimal filter method that estimates speed as a function of space and time was developed. An optimal solution for the state vector, containing the variables speed and position, was made at each time step by using a Kalman filter.
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References
1.
El FaouziN. E., and LesortJ. B.. Travel Time Estimation on Urban Networks from Traffic Data and On-Board Trip Characteristics. Second World Congress on Intelligent Transport Systems, Yokohama, Japan, 1995, pp. 88–93.
2.
DaileyD. J.Travel Time Estimation Using Cross Correlation Techniques. Transportation Research, Vol. 27B, No. 2, April1993, pp. 97–107.
3.
PettyK. F., BickelP., JiangJ., OstlandM., RiceJ., RitovY., and SchoenbergF.. Accurate Estimation of Travel Times from Single-loop Detectors. Transportation Research, Vol. 32A, No. 1, Jan.1998, pp. 1–7.
4.
DaileyD. J.A Statistical Algorithm for Estimating Speed from Single Loop Volume and Occupancy Measurements. Transportation Research, Vol. 33B, No. 5, June1999, pp. 313–322.
5.
HallF. L., and PersaudB. N.. Estimating Speeds from Freeway Traffic Management Systems. In ITE 1988 Compendium of Technical Papers, ITE, Washington, D.C., 1988, pp. 166–171.
6.
PersaudB. N., and HurdleV. F.. Some New Data That Challenge Some Old Ideas About Speed-Flow Relationships. In Transportation Research Record 1194, TRB, National Research Council, Washington, D.C., 1988, pp. 191–198.
7.
HallF. L., and GunterM. A.. Further Analysis of the Flow-Concentration Relationship. In Transportation Research Record 1091, TRB, National Research Council, Washington, D.C., 1986, pp. 1–9.
8.
PersaudB. N., and HallF. L.. Catastrophe Theory and Patterns in 30-Second Freeway Traffic Data—Implications for Incident Detection. Transportation Research, Vol. 23A, No. 2, March1989, pp. 103–113.
9.
HallF. L.An Interpretation of Speed-Flow-Concentration Relationships. Using Catastrophe Theory. Transportation Research, Vol. 21A, No. 3, May1987, pp. 191–201.
10.
GunterM. A., and HallF. L.. Transitions in the Speed-Flow Relationship (Abridgment). In Transportation Research Record 1091, TRB, National Research Council, Washington, D.C., 1986, pp. 18–21.
11.
DaileyD. J., and LiL.. Video Image Processing to Create a Speed Sensor. Washington State Department of Transportation, Olympia, Wash., 1999.
12.
SohJ., ChunB. T., and WangM.. Analysis of Road Image Sequences for Vehicle Counting. 1995 IEEE International Conference on Systems, Man and Cybernetics, Vancouver, British Columbia, Canada, Vol. 1, 1995, pp. 679–683.
13.
KilgerM.Video Based Traffic Monitoring. International Conference on Image Processing and Its Applications, Maastricht, Netherlands, IEE UK, 1992, pp. 89–92.
14.
TakabaS., SakauchiM., KanekoT., HwangB. W., and SekineT.. Measurement of Traffic Flow Using Real Time Processing of Moving Pictures. 32nd IEEE Vehicular Technology Conference, San Diego, Calif., 1982, pp. 488–494.
15.
GilS., MilanesesR., and PunT.. Comparing Features for Target Tracking in Traffic Scenes. Pattern Recognition, Vol. 29, No. 8, August1996, pp. 1285–1296.
16.
WestermanM., LitjensR., and LinnartzJ. P.. Integration of Probe Vehicle and Induction Loop Data—Estimation of Travel Times and Automatic Incident Detection. Report UCB-ITS-PRR-96-13. California PATH, University of California, Berkeley, 1996.
17.
LinnartzJ. P., WestermanM., and HamerslagR.. Monitoring the San Francisco Bay Area Freeway Network Using Probe Vehicles and Random Access Radio Channel. Research Report UCB-ITS-PRR-94-23. California PATH, University of California, Berkeley, 1994.
DaileyD. J., HaselkornM. P., GuibersonK., and LinP. J.. Automatic Transit Location System. Technical Report WA-RD 394.1. Washington State Department of Transportation, Olympia, Wash., 1996.
22.
PressW. H., TeukolskyS. A., VetterlingW. T., and FlanneryB. P.. Numerical Recipes in C, 2nd ed.Cambridge University Press, New York, 1988.
23.
SanwalK. K., and WalrandJ.. Vehicles as Probes. Report UCB-ITS-PWP-95-11. California PATH, University of California, Berkeley, 1995.
24.
NATO Advisory Group for Aerospace Research and conf-procDevelopment Group. Kalman Filter Integration of Modern Guidance and Navigation Systems. Presented at NATO AGARD Lecture Series 166, Neuilly sur Seine, France, 1989.
25.
ChuiC. K., and ChenG.. Kalman Filtering with Real-Time Applications, 2nd ed.Springer-Verlag, Berlin, New York, 1991.
26.
BrownR. G., and HwangP. Y. C.. Introduction to Random Signals and Applied Kalman Filtering, 3rd ed.John Wiley and Sons, Inc., New York, 1992.
27.
BozicS. M.Digital and Kalman Filtering: An Introduction to Discrete-Time Filtering and Optimum Linear Estimation. Halsted Press, New York, 1994.
28.
BellB. M.The Iterated Kalman Smoother as a Gauss-Newton Method. SIAM Journal on Optimization, Vol. 4, No. 3, 1994, pp. 626–636.
