Examples are presented of the use of machine vision technology for reading and processing vehicle license plate images acquired in the field by video camcorders. Applications include travel time and microscale origin-destination pattern analyses for a freeway interchange in Tampa and the road network serving Houston's Intercontinental Airport, and high-occupancy vehicle lane performance measurements in Seattle. It is concluded that video and machine vision analysis of license plates is an effective and efficient means of conducting a wide variety of traffic engineering and traffic management studies.
Get full access to this article
View all access options for this article.
References
1.
RobertsonD. J.Electronics in Managing the Demand for Road Capacity.Symonds Travers Morgan, Ltd., East Grinstead, West Sussex, England, Sept. 1995.
2.
LiuT. K.Field Tests of Travel Time Survey Methodologies and Development of a Standardized Data Processing and Reporting System. Proc., National Traffic Data Acquisition Conference 1994, Vol. 2, Connecticut Department of Transportation and FHWA, Sept. 1994, pp. 383–426.
3.
ShuldinerP. W.Acquisition and Analysis of License Plate Data Using Video and Machine Vision Technology. Proc., National Traffic Data Acquisition Conference 1994, Vol. 2, Connecticut Department of Transportation and FHWA, Sept. 1994, pp. 427–438.
4.
PietrzykM.Application of Advanced Technologies to Congestion Management Systems Being Tested. CUTRlines, Vol. 6, No. 3, Spring 1995, pp. 1–3, 10.
5.
PietrzykM. C.An Application of ITS Technology for Congestion Management Systems. Presented at 65th Meeting of the Institute of Transportation Engineers, Denver, Colo., Aug. 1995.
6.
ITS Field Demonstration of Automated Video-Based Traffic Data Collection: Origin-Destination and Travel Time Surveys.Transfomation Systems, Inc., Houston, Tex., Aug. 1995.
7.
Automated Video-Based Origin-Destination Surveys IAH Through Traffic Study.Transfomation Systems, Inc., Houston, Tex., Aug. 1995.
8.
Video Survey of Travel Times in HOV vs. General Purpose Lanes.Transfomation Systems, Inc., Houston, Tex., Aug. 1995.
9.
HuA.Use of Video/Machine Vision Technology to Estimate Travel Time. Master's thesis. Graduate School of the University of Massachusetts, Amherst, Sept. 1994.
10.
BerryD. S., and GreenF. H.. Techniques for Measuring Overall Speeds in Urban Areas. HRB Proc., Vol. 28, 1949, pp. 311–318.
11.
BerryD. S.Evaluation of Techniques for Determining Overall Travel Time. HRB Proc., Vol. 31, 1952, pp. 429–439.
12.
RickmanT. D., HallenbeckM. E., and SchroederM.. Improved Method for Collecting Travel Time Information. In Transportation Research Record 1271, TRB, National Research Council, Washington, D.C., 1990, pp. 79–88.
13.
TurnerS. M.Advanced Techniques for Travel Time Data Collection. Proc., Pacific Rim Transtech Conference, Seattle, Wash. 1995, pp. 40–47.
14.
LomaxT., TurnerS., ShunkG., LevinsonH., PrattR., BayP., and DouglasG.. Quantifying Congestion: Final Report. NCHRP, TRB, National Research Council, Washington, D.C., Jan. 1995.
