Estimating time-dependent origin–destination demand from traffic counts: extended gradient method

References

• Alibabai H and Mahmassani HS. 2008. Dynamic origin–destination demand estimation using turning movement counts. Transportation Research Record: Journal of the Transportation Research Board, 2085, pp.39–48.
   Web of Science ®Google Scholar
• Ashok K and Ben-Akiva ME. 1993. Dynamic origin–destination matrix estimation and prediction for real-time traffic management systems. In: 12th International Symposium on the Theory of Traffic Flow and Transportation., 1993, Berkeley, CA: Transportation and traffic theory.
   Google Scholar
• Balakrishna R. Ben-Akiva M and Koutsopoulos HN. 2007. Offline calibration of dynamic traffic assignment: simultaneous demand-and-supply estimation. Transportation Research Record: Journal of the Transportation Research Board, 2003, pp.50–58.
   Web of Science ®Google Scholar
• Cascetta E. 2009. Transportation systems analysis: models and applications. New York: Springer.
   Google Scholar
• Cascetta E. Inaudi D and Marquis G. 1993. Dynamic estimators of origin–destination matrices using traffic counts. Transportation Science, 27, pp.363–73.
   Web of Science ®Google Scholar
• Cascetta E. Papola A. Marzano V. Simonelli F and Vitiello I. 2013. Quasi-dynamic estimation of o–d flows from traffic counts: formulation, statistical validation and performance analysis on real data. Transportation Research Part B: Methodological, 55, pp.171–187.
   Web of Science ®Google Scholar
• Chi H. Zhao F and Sun X. 2010. An efficient dynamic origin–destination matrix estimation framework. In: Transportation Research Board 89th Annual Meeting. Washington, DC.
   Google Scholar
• Cipriani E. Florian M. Mahut M and Nigro M. 2011. A gradient approximation approach for adjusting temporal origin–destination matrices. Transportation Research Part C: Emerging Technologies, 19, pp.270–282.
   Web of Science ®Google Scholar
• Cremer M. 1983. Determining the time dependent trip distribution in a complex intersection for traffic responsive control. In: Preprint of the 4th International IFAC/IFIP/IFORS Conference on Control in Transportation Systems, Baden-Baden, Germany.
   Google Scholar
• Cremer M and Keller H. 1984. A systems dynamics approach to the estimation of entry and exit OD flows. In: Ninth International Symposium on Transportation and Traffic Theory. Utrecht, The Netherlands: VNU, pp. 431–50.
   Google Scholar
• Cremer M and Keller H. 1987. A new class of dynamic methods for the identification of origin–destination flows. Transportation Research Part B: Methodological, 21, pp.117–32.
   Web of Science ®Google Scholar
• Doblas J and Benitez FG. 2005. An approach to estimating and updating origin–destination matrices based upon traffic counts preserving the prior structure of a survey matrix. Transportation Research Part B: Methodological, 39, pp.565–91.
   Web of Science ®Google Scholar
• Florian M and Noriega Y. 2010. O–D matrix adjustment: using a reference matrix and multiclass adjustments. In: Transportation Research Board 89th Annual Meeting. Washington DC.
   Google Scholar
• Kang Y. 1999. Estimation and prediction of dynamic origin–destination (OD) demand and system consistency control for real-time dynamic traffic assignment operation. PhD. University of Texas at Austin.
   Google Scholar
• Kim H. Baek S and Lim Y. 2001. Origin–destination matrices estimated with a genetic algorithm from link traffic counts. Transportation Research Record: Journal of the Transportation Research Board, 1771, pp.156–63.
   Google Scholar
• Lu C.-C. Zhou X and Zhang K. 2013. Dynamic origin–destination demand flow estimation under congested traffic conditions. Transportation Research Part C: Emerging Technologies, 34, pp.16–37.
   Web of Science ®Google Scholar
• Pindyck RS and Rubinfeld DL. 1998. Econometric models and economic forecasts. Boston: Irwin/McGraw-Hill.
   Google Scholar
• Sherali HD and Park T. 2001. Estimation of dynamic origin–destination trip tables for a general network. Transportation Research Part B: Methodological, 35, pp.217–35.
   Web of Science ®Google Scholar
• Spall JC. 1998. An overview of the simultaneous perturbation method for efficient optimization. Johns Hopkins APL Technical Digest, 19, pp.482–92.
   Google Scholar
• Spiess H. 1990. A gradient approach for the OD matrix adjustment problem. Montreal: University of Montreal, Publication No. 693.
   Google Scholar
• Tavana H. 2001. Internally-consistent estimation of dynamic network origin–destination flows from intelligent transportation systems data using bi-level optimization. Doctor of Philosophy. The University of Texas at Austin.
   Google Scholar
• Toledo T and Koutsopoulos HN. 2004. Statistical validation of traffic simulation models. Transportation Research Record: Journal of the Transportation Research Board, 1876, pp.142–50.
   Google Scholar
• Tsekeris T. Dimitriou L and Stathopoulos A. 2007. Simultaneous origin–destination matrix estimation in dynamic traffic networks with evolutionary computing. In: Giacobini M, ed. Applications of evolutionary computing. New York: Springer, 668–77.
   Google Scholar
• Verbas İÖ. Mahmassani H.S and Zhang K. 2011. Time-dependent origin–destination demand estimation. Transportation Research Record: Journal of the Transportation Research Board, 2263, pp.45–56.
   Google Scholar