A Demand Estimator Based on a Nested Logit Model

Published Online:https://doi.org/10.1287/trsc.2016.0671

References

  • Abed SY, Ba-Fail AO, Jasimuddin SM (2001) An econometric analysis of international air travel demand in Saudi Arabia. J. Air Transport Management 7(3):143–148.CrossrefGoogle Scholar
  • Air Transport Action Group (2012) Air Transport Action Group Report. Technical report, Air Transport Action Group, Geneva. http://www.atag.org/.Google Scholar
  • Barbour R, Fricker JD (1994) Estimating an origin-destination table using a method based on shortest augmenting paths. Transportation Res. Part B: Methodological 28(2):77–89.CrossrefGoogle Scholar
  • Bazaraa MS, Sherali HD, Shetty CM (2006) Nonlinear Programming: Theory and Algorithms (Wiley-Interscience, Hoboken, NJ).CrossrefGoogle Scholar
  • Bekhor S, Prashker JN (2001) Stochastic user equilibrium formulation for generalized nested logit model. Transportation Res. Record: J. Transportation Res. Board 1752(1):84–90.CrossrefGoogle Scholar
  • Bekhor S, Lena R, Tomer T (2007) Application of cross-nested logit route choice model in stochastic user equilibrium traffic assignment. Transportation Res. Record: J. Transportation Res. Board 2003(1):41–49.CrossrefGoogle Scholar
  • Bekhor S, Toledo T, Reznikova L (2009) A path-based algorithm for the cross-nested logit stochastic user equilibrium traffic assignment. Comput.-Aided Civil Infrastructure Engrg. 24(1):15–25.CrossrefGoogle Scholar
  • Bell MG (1983) The estimation of an origin-destination matrix from traffic counts. Transportation Sci. 17(4):198–217.LinkGoogle Scholar
  • Bell MG (1991) The estimation of origin-destination matrices by constrained generalised least squares. Transportation Res. Part B: Methodological 25(1):13–22.CrossrefGoogle Scholar
  • Bell MG, Shield CM (1996) Log-linear model for path flow estimation. Stephanedes YJ, Filippi F, eds. Proc. 4th Internat. Conf. Appl. Advanced Tech. Transportation Engrg. (American Society of Civil Engineers, New York), 695–699.Google Scholar
  • Bell MG, Shield CM, Busch F, Kruse G (1997) A stochastic user equilibrium path flow estimator. Transportation Res. Part C: Emerging Tech. 5(34):197–210.CrossrefGoogle Scholar
  • Ben-Akiva M (1973) Structure of passenger travel demand models. Unpublished doctoral thesis, Department of Civil Engineering, Massachusetts Institute of Technology, Cambridge, MA. https://dspace.mit.edu/handle/1721.1/14790.Google Scholar
  • Ben-Akiva M, Bierlaire M (1999) Discrete choice methods and their applications to short term travel decisions. Hall RW, ed. Handbook of Transportation Science, Internat. Series Oper. Res. Management Sci., Vol. 23 (Springer Science+Business Media, New York), 5–33.CrossrefGoogle Scholar
  • Bierlaire M (1997) Discrete choice models. Labbé M, Laporte G, Tanczos K, Toint P, eds. Operations Research and Decision Aid Methodologies in Traffic and Transportation Management, NATO ASI Series, Vol. 166 (Springer-Verlag, Berlin Heidelberg),203–227.Google Scholar
  • Bierlaire M, Toint P (1995) MEUSE: An origin-destination matrix estimator that exploits structure. Transportation Res. Part B: Methodological 29(1):47–60.CrossrefGoogle Scholar
  • Cascetta E (1984) Estimation of trip matrices from traffic counts and survey data: A generalized least squares estimator. Transportation Res. Part B: Methodological 18(45):289–299.CrossrefGoogle Scholar
  • Chen A, Chootinan P, Recker W (2005) Examining the quality of synthetic origin-destination trip table estimated by path flow estimator. J. Transportation Engrg. 131(7):506–513.CrossrefGoogle Scholar
  • Chen A, Chootinan P, Recker W (2009) Norm approximation method for handling traffic count inconsistencies in path flow estimator. Transportation Res. Part B: Methodological 43(89):852–872.CrossrefGoogle Scholar
  • Chen A, Ryu S, Chootinan P (2010) l∞ path flow estimator for handling traffic count inconsistencies: Formulation and solution algorithm. J. Transportation Engrg. 136(6):565–575.CrossrefGoogle Scholar
  • Chen A, Chootinan P, Ryu S, Lee M, Recker W (2012) An intersection turning movement estimation procedure based on path flow estimator. J. Advanced Transportation 46(2):161–176.CrossrefGoogle Scholar
  • Chootinan P, Chen A (2011) Confidence interval estimation for path flow estimator. Transportation Res. Part B: Methodological 45(10):1680–1698.CrossrefGoogle Scholar
  • Chootinan P, Chen A, Recker W (2005) Improved path flow estimator for origin-destination trip tables. Transportation Res. Record: J. Transportation Res. Board 1923(1):9–17.CrossrefGoogle Scholar
  • Codina E, Barcel J (2004) Adjustment of O-D trip matrices from observed volumes: An algorithmic approach based on conjugate directions. Eur. J. Oper. Res. 155(3):535–557.CrossrefGoogle Scholar
  • Coldren GM, Koppelman FS (2005) Modeling the competition among air-travel itinerary shares: GEV model development. Transportation Res. Part A: Policy Practice 39(4):345–365.CrossrefGoogle Scholar
  • Coldren GM, Koppelman FS, Kasturirangan K, Mukherjee A (2003) Modeling aggregate air-travel itinerary shares: Logit model development at a major U.S. airline. J. Air Transport Management 9(6):361–369.CrossrefGoogle Scholar
  • Doblas J, 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 Res. Part B: Methodological 39(7):565–591.CrossrefGoogle Scholar
  • Fisk C (1988) On combining maximum entropy trip matrix estimation with user optimal assignment. Transportation Res. Part B: Methodological 22(1):69–73.CrossrefGoogle Scholar
  • Florian M, Chen Y (1992) A successive linear approximation method for the OD matrix adjustment problem. Technical report, Centre de Recherche sur les Transports, Université de Montréal, Montréal.Google Scholar
  • Florian M, Chen Y (1995) A coordinate descent method for the bi-level O–D matrix adjustment problem. Internat. Trans. Oper. Res. 2(2):165–179.CrossrefGoogle Scholar
  • Flötteröd G, Bierlaire M, Nagel K (2011) Bayesian demand calibration for dynamic traffic simulations. Transportation Sci. 45(4):541–561.LinkGoogle Scholar
  • Forinash C, Koppelman F (1993) Application and interpretation of nested logit models of intercity mode choice. Transportation Res. Record: J. Transportation Res. Board 1413(1):98–106.Google Scholar
  • Ghobrial A, Soliman S (1992) An assessment of some factors influencing the competitive strategies of airlines in domestic markets. Internat. J. Transport Econom. 19(3):247–258.Google Scholar
  • Grosche T, Rothlauf F, Heinzl A (2007) Gravity models for airline passenger volume estimation. J. Air Transport Management 13(4):175–183.CrossrefGoogle Scholar
  • Gur J, Turnquist M, Schneiderm M, Leblanc L, Kurth D (1980) Estimation of an origin destination trip table on observed link volumes and turning movements, Vol. 1. Technical report, FHWA, FHWA/RD-80/034, Washington, DC.Google Scholar
  • Hess S, Adler T, Polak JW (2007) Modelling airport and airline choice behaviour with the use of stated preference survey data. Transportation Res. Part E: Logist. Transportation Rev. 43(3):221–233.CrossrefGoogle Scholar
  • Hsu C-I, Wen Y-H (2003) Determining flight frequencies on an airline network with demand supply interactions. Transportation Res. Part E: Logist. Transportation Rev. 39(6):417–441.CrossrefGoogle Scholar
  • Jörnsten K, Nguyen S (1979) On the estimation of a trip matrix from network data, Vol 1. Technical report, Linköping Institute of Technology, Linköping, and Centre de Recherche sur les Transports, Université de Montréal, Montréal.Google Scholar
  • Kopsch F (2012) A demand model for domestic air travel in Sweden. J. Air Transport Management 20:46–48.CrossrefGoogle Scholar
  • Lam WHK, Xu G (1999) A traffic flow simulator for network reliability assessment. J. Advanced Transportation 33(2):159–182.CrossrefGoogle Scholar
  • Lee M, Chen A, Chootinan P, Laabs W, Recker W (2006) Modeling network traffic for planning applications in a small community. J. Urban Planning Development 132(3):156–159.CrossrefGoogle Scholar
  • Li T (2015a) A bi-level model to estimate the U.S. air travel demand. Asia-Pacific J. Oper. Res. 32(2):1550009-1–1550009-34.CrossrefGoogle Scholar
  • Li T (2015b) Heuristics to improve efficiency of solution algorithm of path flow estimator. Transportation Res. Record: J. Transportation Res. Board 2497:12–22.CrossrefGoogle Scholar
  • Li T, Baik H, Trani AA (2013) A method to estimate the historical U.S. air travel demand. J. Advanced Transportation 47(3):249–265.CrossrefGoogle Scholar
  • Li T, Trani A (2013) Modeling the impact of fuel price on the utilization of piston engine aircraft. Rossetti T, ed. Integrated Comm., Navigation Surveillance Conf. (ICNS) (IEEE, Herndon, VA), 1–12.CrossrefGoogle Scholar
  • Li T, Trani AA (2014) A model to forecast airport-level general aviation demand. J. Air Transport Management 40:192–206.CrossrefGoogle Scholar
  • Lo H, Zhang N, Lam W (1996) Estimation of an origin-destination matrix with random link choice proportions: A statistical approach. Transportation Res. Part B: Methodological 30(4):309–324.CrossrefGoogle Scholar
  • Lundgren JT, Peterson A (2008) A heuristic for the bi-level origin-destination-matrix estimation problem. Transportation Res. Part B: Methodological 42(4):339–354.CrossrefGoogle Scholar
  • Maher M (1983) Inferences on trip matrices from observations on link volumes: A Bayesian statistical approach. Transportation Res. Part B: Methodological 17(6):435–447.CrossrefGoogle Scholar
  • Maher MJ, Zhang X, Vliet DV (2001) A bi-level programming approach for trip matrix estimation and traffic control problems with stochastic user equilibrium link flows. Transportation Res. Part B: Methodological 35(1):23–40.CrossrefGoogle Scholar
  • Marazzo M, Scherre R, Fernandes E (2010) Air transport demand and economic growth in Brazil: A time series analysis. Transportation Res. Part E: Logist. Transportation Rev. 46(2):261–269.CrossrefGoogle Scholar
  • McNeil S, Hendrickson C (1985) A regression formulation of the matrix estimation problem. Transportation Sci. 19(3):278–292.LinkGoogle Scholar
  • Nako SM (1992) Frequent flyer programs and business travellers: An empirical investigation. Logist. Transportation Rev. 28(4):395–414.Google Scholar
  • Nguyen S (1977) Estimating an OD matrix from network data: A network equilibrium approach. Technical report, Centre de Recherche sur les Transports, Université de Montréal, Montréal.Google Scholar
  • Nie Y, Lee D-H (2002) Uncoupled method for equilibrium-based linear path flow estimator for origin-destination trip matrices. Transportation Res. Record: J. Transportation Res. Board 1783(1):72–79.CrossrefGoogle Scholar
  • Nie Y, Zhang H, Recker W (2005) Inferring origin-destination trip matrices with a decoupled GLS path flow estimator. Transportation Res. Part B: Methodological 39(6):497–518.CrossrefGoogle Scholar
  • Peter V, Bekhor S (1998) Link-nested logit model of route choice: Overcoming route overlapping problem. Transportation Res. Record: J. Transportation Res. Board 1645(1):133–142.CrossrefGoogle Scholar
  • Pinjari A, Bhat C (2006) Nonlinearity of response to level-of-service variables in travel mode choice models. Transportation Res. Record: J. Transportation Res. Board 1977:67–74.CrossrefGoogle Scholar
  • Pitfield D, Caves R, Quddus M (2010) Airline strategies for aircraft size and airline frequency with changing demand and competition: A simultaneous-equations approach for traffic on the North Atlantic. J. Air Transport Management 16(3):151–158.CrossrefGoogle Scholar
  • Proussaloglou K, Koppelman F (1995) Air carrier demand. Transportation 22(4):371–388.CrossrefGoogle Scholar
  • Proussaloglou K, Koppelman FS (1999) The choice of air carrier, flight, and fare class. J. Air Transport Management 5(4):193–201.CrossrefGoogle Scholar
  • Sarawut J, Anthony C, Ryu S (2012) Alternative planning tool for small metropolitan planning organization in Utah. Transportation Res. Record: J. Transportation Res. Board 2307(1):68–79.CrossrefGoogle Scholar
  • Shen G (2004) Reverse-fitting the gravity model to inter-city airline passenger flows by an algebraic simplification. J. Transport Geography 12(3):219–234.CrossrefGoogle Scholar
  • Sherali HD, Sivanandan R, Hobeika AG (1994) A linear programming approach for synthesizing origin-destination trip tables from link traffic volumes. Transportation Res. Part B: Methodological 28(3):213–233.CrossrefGoogle Scholar
  • Shihsien L, Fricker JD (1996) Estimation of a trip table and the θ parameter in a stochastic network. Transportation Res. Part A: Policy Practice 30(4):287–305.CrossrefGoogle Scholar
  • Spiess H (1987) A maximum likelihood model for estimating origin-destination matrices. Transportation Res. Part B: Methodological 21(5):395–412.CrossrefGoogle Scholar
  • Spiess H (1990) A gradient approach for the O–D matrix adjustment problem. Technical report, Centre de Recherche sur les Transports, Université de Montréal, Montréal. http://emme2.spiess.ch/demadj/demadj.html.Google Scholar
  • Tang S, Zhang HM (2013) Primal-dual heuristic for path flow estimation in medium to large networks. Transportation Res. Record: J. Transportation Res. Board 2333(1):91–99.CrossrefGoogle Scholar
  • Teichert T, Shehu E, Von Wartburg I (2008) Customer segmentation revisited: The case of the airline industry. Transportation Res. Part A: Policy Practice 42(1):227–242.CrossrefGoogle Scholar
  • Theis G, Adler T, Clarke J-P, Ben-Akiva M (2006) Risk aversion to short connections in airline itinerary choice. Transportation Res. Record: J. Transportation Res. Board 1951:28–36.CrossrefGoogle Scholar
  • Train K (2009) Discrete Choice Methods with Simulation (Cambridge University Press, Cambridge, UK).CrossrefGoogle Scholar
  • Warburg V, Bhat C, Adler T (2006) Modeling demographic and unobserved heterogeneity in air passengers’ sensitivity to service attributes in itinerary choice. Transportation Res. Record: J. Transportation Res. Board 1951:7–16.CrossrefGoogle Scholar
  • Wei W, Hansen M (2005) Impact of aircraft size and seat availability on airlines’ demand and market share in duopoly markets. Transportation Res. Part E: Logist. Transportation Rev. 41(4):315–327.CrossrefGoogle Scholar
  • Wei W, Hansen M (2006) An aggregate demand model for air passenger traffic in the hub-and-spoke network. Transportation Res. Part A: Policy Practice 40(10):841–851.CrossrefGoogle Scholar
  • Wen C-H, Lai S-C (2010) Latent class models of international air carrier choice. Transportation Res. Part E: Logist. Transportation Rev. 46(2):211–221.CrossrefGoogle Scholar
  • Yang H (1995) Heuristic algorithms for the bilevel origin-destination matrix estimation problem. Transportation Res. Part B: Methodological 29(4):231–242.CrossrefGoogle Scholar
  • Yang H, Meng Q, Bell MGH (2001) Simultaneous estimation of the origin-destination matrices and travel-cost coefficient for congested networks in a stochastic user equilibrium. Transportation Sci. 35(2):107–123.LinkGoogle Scholar
  • Yang H, Sasaki T, Iida Y, Asakura Y (1992) Estimation of origin-destination matrices from link traffic counts on congested networks. Transportation Res. Part B: Methodological 26(6):417–434.CrossrefGoogle Scholar
  • Yoo K, Ashford N (1996) Carrier choices of air passengers in Pacific Rim: Using comparative analysis and complementary interpretation of revealed preference and stated preference data. Transportation Res. Record: J. Transportation Res. Board 1562:1–7.CrossrefGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.