An Information-Theoretic Sensor Location Model for Traffic Origin-Destination Demand Estimation Applications

References

• Antoniou C., Ben-Akiva M., Koutsopoulos H. N. Incorporating automated vehicle identification data into origin-destination estimation. J. Transportation Res. Board (2004) 1882:37–44Crossref, Google Scholar
• Asakura Y., Hato E., Kashiwadani M. OD matrices estimation model using AVI data and its application to the Han-Shin expressway network. Transportation (2000) 27(4):419–438Crossref, Google Scholar
• Ashok K., Ben-Akiva M. Alternative approaches for real-time estimation and prediction of time-dependent origin-destination flows. Transportation Sci. (2000) 34(1):21–36Link, Google Scholar
• Bell M. G. H. Variances and covariances for origin-destination flows when estimated by log-linear models. Transportation Res. Part B (1985) 19:497–507Crossref, Google Scholar
• Bianco L., Confessore G., Reverberi P. A network based model for traffic sensor location with implications on O/D matrix estimates. Transportation Sci. (2001) 35(1):50–60Link, Google Scholar
• Bierlaire M. The total demand scale: A new measure of quality for static and dynamic origin-destination trip tables. Transportation Res. Part B (2002) 36:755–851Crossref, Google Scholar
• Birge J. R., Louveaux F.Introduction to Stochastic Programming (1997) (Springer-Verlag, London) 137–138Google Scholar
• Bryson A. E., Ho Y.-C.Applied Optimal Control (1975) (Hemisphere, New York) 409–414Google Scholar
• Cascetta E. Estimation of trip matrices from traffic counts and survey data: A generalized least squares estimator. Transportation Res. Part B (1984) 22:289–299Crossref, Google Scholar
• Cascetta E., Nguyen S. A unified framework for estimating or updating origin/destination matrices from traffic counts. Transportation Res. Part B (1988) 22:437–455Crossref, Google Scholar
• Chen A., Chootinan P., Recker W. Examining the quality of synthetic O-D trip table estimated by path flow estimator. J. Transportation Engrg. (2005) 131:506–513Crossref, Google Scholar
• Chung I.-H. An optimum sampling framework for estimating trip matrices from day-to-day traffic counts. (2001) . Unpublished doctoral disseration, University of Leeds, Leeds, UKGoogle Scholar
• Denzler J., Brown C. M. Information theoretic sensor data selection for active object recognition and state estimation. IEEE Trans. Pattern Anal. Machine Intelligence (2002) 24(2):145–157Crossref, Google Scholar
• Dixon M. P. Incorporation of automatic vehicle identification data into synthetic OD estimation process. (2000) . Unpublished doctoral dissertation, Texas A&M University, College Station, TXGoogle Scholar
• Dixon M. P., Rilett L. R. Real-time OD estimation using automatic vehicle identification and traffic count data. J. Comput.-Aided Civil Infrastructure Engrg. (2002) 17(1):7–21Crossref, Google Scholar
• Ehlert A., Bell M. G. H., Grosso S. The optimisation of traffic count locations in road networks. Transportation Res. Part B (2006) 40:460–479Crossref, Google Scholar
• Eisenman S. M., List G. F. Using probe data to estimate OD matrices. Proc. Seventh Internat. IEEE Conf. Intelligent Transportation Systems CD (2004) (IEEE, Washington, DC) Crossref, Google Scholar
• Eisenman S. M., Fei X., Zhou X., Mahmassani H. S. Number and location of sensors for real-time network traffic estimation and prediction: A sensitivity analysis. J. Transportation Res. Board (2006) 1964:260–269Crossref, Google Scholar
• Gelb A.Applied Optimal Estimation (1974) (MIT Press, Cambridge, MA) 107–118Google Scholar
• Hintz K. J., McVey E. S. Multi-process constrained estimation. IEEE Trans. Systems, Man, Cybernetics (1991) 21(1):434–442Crossref, Google Scholar
• Johnson R., Wichern D.Applied Multivariate Statistical Analysis (2002) 5th ed.(Prentice-Hall, New York, 112–130) Google Scholar
• Lam W. H. K., Lo H. P. Accuracy of O-D estimates from traffic counting stations. Traffic Engrg. Control (1990) 31:358–367Google Scholar
• Lee J. Constrained maximum-entropy sampling. Oper. Res. (1998) 46:655–664Link, Google Scholar
• Lewis R.Optimal Estimation with an Introduction to Stochastic Control Theory (1986) (John Wiley & Sons, New York) 67–79Google Scholar
• List G. F., Turnquist M. Estimating truck travel patterns in urban areas. Transportation Res. Record (1994) 1430:1–9Google Scholar
• Maher M. J. Inferences on trip matrices from observations on link volumes: A Bayesian statistical approach. Transportation Res. Part B (1983) 17:435–447Crossref, Google Scholar
• Rice J. R.Numerical Methods, Software, and Analysis (1983) (McGraw-Hill, New York) Google Scholar
• Sherali H. D., Desai J., Rakha H. A discrete optimization approach for locating automatic vehicle identification readers for the provision of roadway travel times. Transportation Res. Part B (2006) 40:857–871Crossref, Google Scholar
• Tavana H. Internally-consistent estimation of dynamic network origin-destination flows from intelligent transportation systems data using bi-level optimization. (2001) . Unpublished doctoral dissertation, University of Texas at Austin, AustinGoogle Scholar
• Tavana H., Mahmassani H. S. Estimation of dynamic origin-destination flows from sensor data using bi-level optimization method. (2001) . Transportation Research Board CD-ROM Paper Preprints (TRB Paper 01-3241), National Research Council, Washington, DCGoogle Scholar
• Van der Zijpp N. J. Dynamic OD-matrix estimation from traffic counts and automated vehicle identification data. Transportation Res. Record (1997) 1607:87–94Crossref, Google Scholar
• Van Zuylen H. J., Willumsen L. G. The most likely trip matrix estimated from traffic counts. Transportation Res. Part B (1980) 14:281–293Crossref, Google Scholar
• Wiki. Kalman filter. (2007) . Anonymous. Accessed on June 16, 2007, http://en.wikipedia.org/wiki/kalman_filterGoogle Scholar
• Yang H. Heuristic algorithms for the bi-level origin-destination matrix estimation algorithm. Transportation Res. Part B (1995) 29:231–242Crossref, Google Scholar
• Yang H., Zhou J. Optimal traffic counting locations for origin destination matrix estimation. Transportation Res. Part B (1998) 32:109–126Crossref, Google Scholar
• Yang H., Iida Y., Sasaki T. An analysis of the reliability of an origin-destination trip matrix estimated from traffic counts. Transportation Res. Part B (1991) 25:351–363Crossref, Google Scholar
• Yang H., Yang C., Gan L. Models and algorithms for the screen line-based traffic-counting location problems. Comput. Oper. Res. (2006) 33(3):836–858Crossref, Google Scholar
• Yim K. N., Lam W. H. K. Evaluation of count location selection methods for estimation of OD matrices. J. Transportation Engrg. (1998) 124(4):376–383Crossref, Google Scholar
• Zhao F., Shin J., Reich J. Information-driven dynamic sensor collaboration. IEEE Signal Processing Magazine (2002) 19:61–72Crossref, Google Scholar
• Zhou X., Mahmassani H. S. Dynamic OD demand estimation using automatic vehicle identification data. IEEE Trans. Intelligent Transportation Systems (2005) 7(1):105–114Crossref, Google Scholar