Freeway ramp control has been successfully implemented since the mid-1960s as an efficient and viable freeway management strategy. However, the effectiveness of any ramp control strategy is largely dependent on site-specific customization and calibration, preferably before its deployment. A general methodology for such performance optimization of ramp control strategies is proposed in a microscopic simulation environment as an alternative to trial-and-error field experimentation. The applicability of the methodology is demonstrated by implementation on Minnesota's new stratified zone metering (SZM). Further, the effect of external factors, such as traffic demand variation and incidents, on SZM control and optimization results was also studied. Results show that the optimization methodology is highly effective depending on the optimization objective, test site characteristics, and demand levels.
Get full access to this article
View all access options for this article.
References
1.
PapageorgiouM., Hadj-SaemH., and MiddelhamF.. ALINEA Local Ramp Metering: Summary of Field Test Results. In Transportation Research Record 1603, TRB, National Research Council, Washington, D.C., 1997, pp. 90–98.
2.
ElefteriadouL.Freeway Merging Operations: A Probabilistic Approach.Proc., 8th International Federation of Automatic Control Symposium on Transportation Systems, Chania, Greece, 1997, pp. 1351–1356.
3.
Operation TimeSaver: Intelligent Transportation Infrastructure.FHWA, U.S. Department of Transportation, 1996.
4.
PearceV.What Have We Learned About Freeway Incident and Emergency Management and Electronic Toll Collection? In What Have We Learned About ITS? Technical Reports and Papers, U.S. Department of Transportation, 2000, Chap. 2.
5.
ZhangM., KimT., NieX., JinW., ChuL., and ReckerW.. Evaluation of On-Ramp Control Algorithms.California PATH Research Report UCB-ITS-PRR-2001–36. University of California, Berkeley,2001.
6.
XinW., MichalopoulosP. G., HourdakisJ., and LauD.. Minnesota's New Ramp Control Strategy: Design Overview and Preliminary Assessment. In Transportation Research Record: Journal of the Transportation Research Board, No. 1867, Transportation Research Board of the National Academies, Washington, D.C., 2004, pp. 69–79.
7.
WattleworthJ. A.Peak-Period Analysis and Control of a Freeway System. In Highway Research Record 157, Highway Research Board, National Research Council, Washington, D.C., 1967.
8.
PapageorgiouM.Application of Automatic Control Concepts to Traffic Flow Modeling and Control.Springer, New York, 1983.
9.
StephanedesY., and ChangK. K.. Optimal Control of Freeway Corridors.Journal of Transportation Engineering, ASCE, Vol. 119, 1993, pp. 504–514.
10.
ZhangeH., RitchieS., and ReckerW.. Some General Results on the Optimal Ramp Metering Control Problem.Transportation Research, Vol. 4C, 1996, pp. 51–69.
11.
ChangT. H., and LiZ. Y.Optimization of Mainline Traffic via an Adaptive Coordinated Ramp-Metering Control Model with Dynamic OD Estimation.Transportation Research, Vol. 10C, 2002, pp. 99–120.
12.
BogenbergerK., and MayA. D.. Advanced Coordinated Traffic Responsive Ramp Metering Strategies.California PATH Paper UCB-ITS-PWP-99-19. University of California, Berkeley,1999.
13.
HasanM., JhaM., and Ben-AkivaM. E.Evaluation of Ramp Control Algorithms Using Microscopic Traffic Simulation.Transportation Research, Vol. 10C, 2002, pp. 229–256.
14.
ChuL., and YangX.. Optimization of ALINEA Ramp-Metering Control. Presented at 82nd Annual Meeting of the Transportation Research Board, Washington D.C., 2003.
15.
ParkK., and CarterB.. On the Effectiveness of Genetic Search in Combinatorial Optimization.Proc., 1995 ACM Symposium on Applied Computing, ACM Press, New York, 1995.
16.
GoldbergD. E.Genetic Algorithm in Search Optimization and Machine Learning.Addison-Wesley, Reading, Mass.,1989.
17.
Cambridge Systematics.Twin Cities Ramp Meter Evaluation. Final Report. Minnesota Department of Transportation, St. Paul, 2001.
18.
LauD.Minnesota Department of Transportation: Stratified Metering Algorithm. Internal Report. Minnesota Department of Transportation, St. Paul,2001.
19.
BoxG. E. P., and WilsonK. B.. On the Experimental Attainment of Optimum Conditions.Journal of the Royal Statistical Society, Series B, Vol. 13, No. 1, 1951, pp. 1–38.
20.
BarcelóJ., FerrerJ. L., and GrauR.. AIMSUN2 and the GETRAM Simulation Environment.Internal Report. Departamento de Estadística e Investigación Operativa, Facultad de Informática, Universitat Politèc-nica de Catalunya,1994.
21.
HourdakisJ., and MichalopoulosP. G.. Evaluation of Ramp Control Effectiveness in Two Twin Cities Freeways. In Transportation Research Record: Journal of the Transportation Research Board, No. 1811, Transportation Research Board of the National Academies, Washington, D.C., 2002, pp. 21–29.
22.
CritchfieldG. C., and WillardK. E.. Probabilistic Analysis of Decision Trees Using Monte Carlo Simulation.Medical Decision Making, Vol. 6, No. 1, 1986, pp. 85–92.
23.
GeldermannJ., and RentzO.. Integrated Technique Assessment with Imprecise Information as a Support for the Identification of Best Available Techniques (BAT).OR Spektrum, Vol. 23, No. 1, 2001, pp. 137–157.
24.
McCamleyF., and RudelR. K.. Graphical Sensitivity Analysis for Generalized Stochastic Dominance (Abstract).Journal of Agricultural and Resource Economics, Vol. 20, No. 2, 1995, p. 403.
25.
KleijnenJ. P. C.Experimental Design for Sensitivity Analysis, Optimization, and Validation of Simulation Models. In Handbook of Simulation (BanksJ., ed.), Wiley, New York, 1993.
26.
MontgomeryD. C.Design and Analysis of Experiments.Wiley and Sons Ltd., New York, 1997.
27.
National Bureau of Standards.Fractional Factorial Experiment Designs for Factors at Two and Three Levels. Applied Mathematics Series 54. U.S. Government Printing Office, Washington, D.C., 1959.
28.
FriesA., and HunterW. G.. Minimum Aberration 2k-p Designs.Technometrics, Vol. 22, 1980, pp. 601–608.
29.
BoxG. E. P., and HunterJ. S.. The 2k-p Fractional Factorial Designs.Technometrics, Vol. 3, 1961, pp. 311–351, 449–458.
30.
MyersR. H., and MontgomeryD. C.. Response Surface Methodology: Process and Product Optimization Using Designed Experiments.Wiley Series. Wiley, New York, 1995.
31.
NelderJ. A., and MeadR.. A Simplex Method for Function Minimization.Computer Journal, Vol. 7, 1965, pp. 308–323.
