Improved Vehicle Reidentification and Travel Time Measurement on Congested Freeways
Publication: Journal of Transportation Engineering
Volume 129, Issue 5
Abstract
This paper presents an improved algorithm for matching individual vehicle measurements at a freeway detector station with the vehicles’ corresponding measurements taken at another detector station located upstream. The improvements consist of four new tests to identify incorrect matches and either replace them with the correct matches or discard the errors. The result is a higher number of reidentified vehicles with a lower frequency of errors compared to our earlier work. Although this algorithm is potentially compatible with many vehicle detector technologies, the paper illustrates the method using existing dual loop detectors to measure vehicle lengths. Due to the limited accuracy of the existing loop detector measurements under free flow conditions, this presentation is restricted to matching vehicles during congested traffic conditions. The algorithm is applied to individual lanes and exploits information about the sequence that vehicles pass the detectors. As such, the effect of vehicles changing lanes between stations is also considered in this paper. Of course once a vehicle has been matched across neighboring detector stations, the difference in its arrival time at each station defines the vehicle’s travel time on the intervening segment. Thus, the algorithm extracts travel time data without requiring the deployment of new detector technologies. The approach described herein has been extensively tested on I-80 in the Berkeley Highway Laboratory (BHL). This paper presents some of the results obtained over two different segments of the BHL and demonstrates that the algorithm matches a sufficient number of vehicles for the purpose of travel time measurement.
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References
Balke, K., Ullman, G., McCasland, W., Mountain, C., and Dudek, C. (1995). Benefits of real-time travel information in Houston, Texas, Southwest Region Univ. Transportation Center, Texas Transportation Institute, College Station, Tex.
Coifman, B. (1998). “Vehicle reidentification and travel time measurement using loop detector speed traps.” Dissertation, Univ. of California, Berkeley, Calif.
Coifman, B. (1999). “Using dual loop speed traps to identify detector errors,” Transportation Research Record No. 1683, Transportation Research Board, 47–58.
Coifman, B.(2003). “Identifying the onset of congestion rapidly with existing traffic detectors.” Transp. Res., Part A: Policy Pract., 37(3), 277–291.
Coifman, B., and Cassidy, M.(2002). “Vehicle reidentification and travel time measurement on congested freeways.” Transp. Res., Part A: Policy Pract., 36(10), 899–917.
Coifman, B., Lyddy, D., and Skabardonis, A. (2000). “The Berkeley highway laboratory-building on the I-880 field experiment.” Proc., IEEE ITS Council Annual Meeting, IEEE, Dearborn, Mich., 5–10.
Dailey, D.(1993). “Travel time estimation using cross correlation techniques.” Transp. Res., Part B: Methodol., 27(2), 97–107.
Holdener, D., and Turner, S. (1996). “Probe vehicle sample sizes for real-time information: The Houston experience.” Intelligent Transportation: Realizing the Benefits-Proc. of the 1996 Annual Meeting of ITS America, Vol. 1, ITS America, Houston, 287–295.
Huang, T., and Russell, S. (1997). “Object identification in a Bayesian context.” Proc., 15th Int. Joint Conf. on Artificial Intelligence (IJCAI-97), International Joint Conferences on Artificial Intelligence, Nagoya, Japan.
Kuhne, R., and Immes, S. (1993). “Freeway control systems for using section-related traffic variable detection.” Pacific Rim TransTech Conf. Proc., Vol. 1, ASCE, New York, 56–62.
Larson, J., Van Katwyk, K., Liu, C., Cheng, H., Shaw, B., and Palen, J. (1998). A real-time laser-based prototype detection system for measurement of delineations of moving vehicles. UCB-ITS-PWP-98-20, PATH, Univ. of California, Berkeley, Calif.
MacCarley, C. A. (1998). Videobased vehicle signature analysis and tracking phase 1: Verification of concept and preliminary testing. UCB-ITS-PWP-98-10, PATH, Univ. of California, Berkeley, Calif.
Palen, J. (1997). “The need for surveillance in intelligent transportation systems.” Intellimotion, Vol. 6, No. 1, Univ. of California PATH, Berkeley, Calif., 1–3, 10.
Petty, K., Bickel, P., Ostland, M., Rice, J., Schoenberg, F., Jiang, J., and Ritov, Y.(1997). “Accurate estimation of travel times from single loop detectors.” Transp. Res., Part A: Policy Pract., 32(1), 1–17.
Pfannerstill, E. (1984). “A pattern recognition system for the re-identification of motor vehicles.” Proc., 7th Int. Conf. on Pattern Recognition, IEEE, Montreal, N.J., 553–555.
Reijmers, J. (1979). “On-line vehicle classification.” Proc., Int. Symposium on Traffic Control Systems, Vol. 2B, Institute of Transportation Studies, Univ. of California at Berkeley, Calif., 87–102.
Van Aerde, M., Hellinga, B., Yu, L., and Rakha, H. (1993). “Vehicle probes as real-time ATMS sources of dynamic O-D and travel time data.” Large Urban Systems-Proc. of the Advanced Traffic Management Conf., Federal Highway Adminstration, Washington, D.C., 207–230.
Westerman, M., and Immers, L. (1992). “A method for determining real-time travel times on motorways.” Road transport informatics/intelligent vehicle highways systems, International Symposium on Automotive Technology and Automation, Florence, Italy, 221–228.
Westerman, M., Litjens, R., and Linnartz, J. (1996). Integration of probe vehicle and induction loop data-estimation of travel times and automatic incident detection. PATH, Univ. of California at Berkeley, Calif.
Windover, J. (1998). “Empirical studies of the dynamic features of freeway traffic.” Dissertation, Univ. of California, Berkeley, Calif.
Worrall, R., and Bullen, A.(1970). “An empirical analysis of lane changing on multilane highways.” Highw. Res. Rec., 303, 30–43.
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Copyright © 2003 American Society of Civil Engineers.
History
Received: Aug 7, 2001
Accepted: Jul 2, 2002
Published online: Aug 15, 2003
Published in print: Sep 2003
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