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First published online January 1, 2008

Oversaturated Freeway Flow Algorithm for Use in Next Generation Simulation

Abstract

Existing simulation models have difficulty in accurately modeling oversaturated traffic conditions on freeways. A new behavioral algorithm for oversaturated freeway flow can be used in microscopic simulation models; it was developed as part of the Next Generation Simulation (NGSIM) project, sponsored by FHWA. The proposed algorithm is an integrated car-following and lane-changing modeling framework that is consistent with the kinematic wave theory. The algorithm can explicitly model mandatory and discretionary lane changing, including cooperation during lane changing. As an extension of the algorithm, a new on-ramp merging model was developed and incorporated into the model. The proposed algorithm also accounts for the relaxation process following lane changing. The proposed model includes a small number of parameters that can be readily measured in the field. The proposed model was implemented into a microscopic simulator, and it was validated with both vehicle trajectory data and macroscopic detector data from an NGSIM test site. The results show good agreement with real-world traffic behavior. The study products, including an algorithm description, analysis results, and computer code, are documented and made available to developers and users of traffic simulation tools.

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References

1. Skabardonis A. and Yeo H. NGSIM Oversaturated Freeway Flow Algorithm. University of California, Berkeley, 2006.
2. NGSIM. Next Generation Simulation. FHWA, U.S. Department of Transportation, 2006. ngsim.fhwa.dot.gov/.
3. Brackstone M. and McDonald M. Car-Following: A Historical Review. Transportation Research Part F, Vol. 2, 1999, pp. 181–196.
4. Brockfeld E. Kähne R. D. Skabardonis A. and Wagner P. Toward Benchmarking of Microscopic Traffic Flow Models. In Transportation Research Record: Journal of the Transportation Research Board, No. 1852, Transportation Research Board of the National Academies, Washington, D.C., 2003, pp. 124–129.
5. Daganzo C. F. In Traffic Flow, Cellular Automata = Kinematic Waves. Transportation Research Part B, Vol. 40, 2006, pp. 396–403.
6. Lighthill M. J. and Whitham J. B. On Kinematic Waves. I. Flow Movement in Long Rivers. II. A Theory of Traffic Flow on Long Crowded Road. Proceedings of Royal Society, Vol. A229, 1955, pp. 281–345.
7. Mauch M. and Cassidy M. J. Freeway Traffic Oscillations: Observations and Predictions. Proc., International Symposium of Traffic and Transportation Theory, 2002, pp. 653–674.
8. Newell G. F. A Simplified Car-Following Theory: A Lower Order Model. Transportation Research Part B, Vol. 36, 2002, pp. 195–205.
9. Gipps P. G. A Behavioral Car-Following Model for Computer Simulation. Transportation Research Part B, Vol. 15, 1981, pp. 105–111.
10. Cohen S. L. Application of Relaxation Procedure for Lane Changing in Microscopic Simulation Models. In Transportation Research Record: Journal of the Transportation Research Board, No. 1883, Transportation Research Board of the National Academies, Washington, D.C., 2004, pp. 50–58.
11. Leclercq L. Chiabaut N. Laval J. A. and Buisson C. Relaxation Phenomenon After Changing Lanes: Experimental Validation with NGSIM Data Set. In Transportation Research Record: Journal of the Transportation Research Board, No. 1999, Transportation Research Board of the National Academies, Washington, D.C., 2007, pp. 79–85.
12. Treiterer J. and Meyers J. A. The Hysteresis Phenomenon in Traffic Flow. Proc., Sixth International Symposium on Transportation and Traffic Theory, Sydney, Australia, 1974.
13. Laval A. J. and Daganzo C. F. Lane-Changing in Traffic Streams. Transportation Research Part B, Vol. 40, 2006, pp. 251–264.
14. Highway Capacity Manual. TRB, National Research Council, Washington, D.C., 2000.
15. Lu X.-Y. and Skabardonis A. Freeway Traffic Shockwave Analysis: Exploring NGSIM Trajectory Data. Presented at 86th Annual Meeting of the Transportation Research Board, Washington, D.C., 2007.
16. AIMSUN Microscopic Model SDK: Requirements and Design. Transport Simulation Systems, Barcelona, Spain, 2006.

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Article first published online: January 1, 2008
Issue published: January 2008

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© 2008 National Academy of Sciences.
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Authors

Affiliations

Hwasoo Yeo
Institute of Transportation Studies, University of California, 109 McLaughlin Hall, Berkeley, CA 94720-1720.
Alexander Skabardonis
Institute of Transportation Studies, University of California, 109 McLaughlin Hall, Berkeley, CA 94720-1720.
John Halkias
Office of Operations, FHWA, U.S. Department of Transportation, 1200 New Jersey Avenue Southwest, Washington, D.C., 20590.
James Colyar
Washington Division, FHWA, U.S. Department of Transportation, Suite 501, 711 South Capitol Way, Olympia, Wash., 98501.
Vassili Alexiadis
Cambridge Systematics, 100 Cambridgepark Drive, Cambridge, Mass., 02140.

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