Abstract
This paper proposes a subnetwork origin-destination (OD) matrix estimation model under travel demand constraints (SME-DC) that explicitly considers both internal-external subnetwork connections and OD demand consistency between the subnetwork and full network. This new model uses the maximum entropy of OD demands as the objective function and uses the total traffic generations (attractions) along with some fixed OD demands of the subnetwork OD nodes as the constraints. The total traffic generations and attractions along with the fixed OD demands of the subnetwork OD nodes are obtained through an OD node transformation and subnetwork topology analysis. For solving the proposed model, a convex combination method is used to convert the nonlinear SME-DC to the classical linear transportation problem, and a tabular method is used to solve the transportation problem. The Sioux Falls network and Kunshan network were provided to illustrate the essential ideas of the proposed model and the applicability of the proposed solution algorithm.
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References
Bar-Gera H, Boyce SD, Nie YM (2012) User-equilibrium route flows and the condition of proportionality. Transp Res B Methodol 46(3):440–462
Bazaraa MS, Sherali HD, Shetty CM (2013) Nonlinear programming: theory and algorithms, 3rd edn. Wiley, Hoboken
Bekhor S, Toledo T, Prashker JN (2008) Effects of choice set size and route choice models on path-based traffic assignment. Transportmetrica 4(2):117–133
Bell MGH (1983) The estimation of an origin-destination matrix from traffic counts. Transp Sci 17(2):198–217
Bell MGH (1991) The estimation of origin-destination matrices by constrained generalised least squares. Transp Res B Methodol 25(1):13–22
Boyles SD (2012) Bush-based sensitivity analysis for approximating subnetwork diversion. Transp Res B Methodol 46(1):139–155
Bruton MJ (1975) Introduction to transportation planning. Hutchinson, London
Chen A, Kasikitwiwat P, Yang C (2013) Alternate capacity reliability measures for transportation networks. J Adv Transp 47(1):79–104
De Grange L, Fernández E, de Cea J (2010) A consolidated model of trip distribution. Transport Res E-Log 46(1):61–75
Dijkstra EW (1959) A note on two problems in connexion with graphs. Numer Math 1(1):269–271
Facchinei F, Pang JS (2007) Finite-dimensional variational inequalities and complementarity problems. Springer Science & Business Media, Berlin
Fisk CS (1988) On combining maximum entropy trip matrix estimation with user optimal assignment. Transp Res B Methodol 22(1):69–73
Frank M, Wolfe P (1956) An algorithm for quadratic programming. Nav Res Logist 3(1–2):95–110
Ge Q, Fukuda D (2016) Updating origin-destination matrices with aggregated data of GPS traces. Transp Res C Emerg Technol 69:291–312
Gemar M, Bringardner J, Boyles S, Machemehl R (2014) Subnetwork analysis for dynamic traffic assignment models: strategy for estimating demand at subnetwork boundaries. Transp Res Rec 2466:153–161
Grange L, González F, Bekhor S (2016) Path flow and trip matrix estimation using link flow density. Netw Spat Econ 2017, 17(1): 173-195
Haghani AE, Daskin MS (1983) Network design application of an extraction algorithm for network aggregation. Transp Res Rec 944: 37-46
Hendrickson C, McNeil S (1984) Matrix entry estimation errors. Proceedings of the 9th International Symposium on Transportation and Traffic Theory. Delft University The Netherlands, p 413–430
Iqbal MS, Choudhury CF, Wang P, González MC (2014) Development of origin-destination matrices using mobile phone call data. Transp Res C Emerg Technol 40:63–74
Jafari E, Boyles SD (2016) Improved bush-based methods for network contraction. Transp Res B Methodol 83:298–313
Kuwahara M, Sullivan EC (1987) Estimating origin-destination matrices from roadside survey data. Transp Res B Methodol 21(3):233–248
Leblanc LJ (1973) Mathematical programming algorithms for large-scale network equilibrium and network design problems. Unpublished PhD dissertation, Northwestern University, Evanston
Li T (2015) A bi-level model to estimate the us air travel demand. Asia Pac J Oper Res 32(02):1–34
Li T, Wu J, Sun H, Gao Z (2015) Integrated co-evolution model of land use and traffic network design. Netw Spat Econ 2016, 16(2): 579-603
Maher MJ (1983) Inferences on trip matrices from observations on link volumes: a Bayesian statistical approach. Transp Res B Methodol 17(6):435–447
Millard-Ball A (2015) Phantom trips: overestimating the traffic impacts of new development. J Transp Land Use 8(1):31–49
Mishra S, Wang Y, Zhu X, Moeckel R, Mahapatra S (2013) Comparison between gravity and destination choice models for trip distribution in Maryland. In: Transportation Research Board 92nd Annual Meeting
Moya-Gómez B, Salas-Olmedo MH, García-Palomares JC, Gutiérrez J (2018) Dynamic accessibility using big data: the role of the changing conditions of network congestion and destination attractiveness. Netw Spat Econ 18(2):273–290
Nihan NL, Davis GA (1987) Recursive estimation of origin-destination matrices from input/output counts. Transp Res B Methodol 21(2):149–163
Rossi TF, McNeil S, Hendrickson C (1989) Entropy model for consistent impact-fee assessment. J Urban Plan Dev 115(2):51–63
Ruiter E (1967) Towards a better understanding of the intervening opportunities model. Transp Res 1:47–56
Sherali HD, Sivanandan R, Hobeika AG (1994) A linear programming approach for synthesizing origin-destination trip tables from link traffic volumes. Transp Res B Methodol 28(3):213–233
Szeto WY, Jiang Y, Wang DZW, Sumalee A (2015) A sustainable road network design problem with land use transportation interaction over time. Netw Spat Econ 15(3):791–822
Toole JL, Colak S, Sturt B, Alexander LP, Evsukoff A, González MC (2015) The path most traveled: travel demand estimation using big data resources. Transp Res C Emerg Technol 58:162–177
Wei C, Asakura Y (2013) A Bayesian approach to traffic estimation in stochastic user equilibrium networks. Transp Res C Emerg Technol 36:446–459
Winston WL, Goldberg JB (2004) Operations research: applications and algorithms. Duxbury press, Belmont
Xie C, Duthie J (2015) An excess-demand dynamic traffic assignment approach for inferring origin-destination trip matrices. Netw Spat Econ 15(4):947–979
Xie C, Kockelman KM, Waller ST (2010) Maximum entropy method for subnetwork origin-destination trip matrix estimation. Transp Res Rec 2196:111–119
Xie C, Kockelman KM, Waller ST (2011) A maximum entropy-least squares estimator for elastic origin-destination trip matrix estimation. Transp Res B Methodol 45(9):1465–1482
Yang H, Zhou J (1998) Optimal traffic counting locations for origin-destination matrix estimation. Transp Res B Methodol 32(2):109–126
Yang H, Sasaki T, Iida Y, Asakura Y (1992) Estimation of origin-destination matrices from link traffic counts on congested networks. Transp Res B Methodol 26(6):417–434
Yang H, Bell MGH, Meng Q (2000) Modeling the capacity and level of service of urban transportation networks. Transp Res B Methodol 34(4):255–275
Zhou X, Erdogan S, Mahmassani H (2006) Dynamic origin-destination trip demand estimation for subarea analysis. Transp Res Rec 1964:176–184
Acknowledgements
The authors are grateful to two anonymous referees for their constructive comments and suggestions to improve the quality and clarity of the paper. This research is supported by the National Natural Science Foundation of China (No. 71801115) and the Key Research and Development Projects of Zhenjiang City.
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Sun, C., Chang, Y., Shi, Y. et al. Subnetwork Origin-Destination Matrix Estimation Under Travel Demand Constraints. Netw Spat Econ 19, 1123–1142 (2019). https://doi.org/10.1007/s11067-019-09449-6
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DOI: https://doi.org/10.1007/s11067-019-09449-6