Uncertainty Propagation and Sensitivity Analysis During Calibration of an Integrated Land Use and Transport Model
In this work, propagation of uncertainty during calibration
process of TRANUS, an integrated land use and transport model
(ILUTM), has been investigated. It has also been examined, through a
sensitivity analysis, which input parameters affect the variation of the
outputs the most. Moreover, a probabilistic verification methodology
of calibration process, which equates the observed and calculated
production, has been proposed. The model chosen as an application is
the model of the city of Grenoble, France. For sensitivity analysis and
uncertainty propagation, Monte Carlo method was employed, and a
statistical hypothesis test was used for verification. The parameters of
the induced demand function in TRANUS, were assumed as uncertain
in the present case. It was found that, if during calibration, TRANUS
converges, then with a high probability the calibration process is
verified. Moreover, a weak correlation was found between the inputs
and the outputs of the calibration process. The total effect of the
inputs on outputs was investigated, and the output variation was found
to be dictated by only a few input parameters.
[1] M. Wegener. Overview of land use transport models. Handbook of
transport geography and spatial systems, 5:127-146, 2004.
[2] M. Echenique. An integrated land use and transport model. Martin Centre
Transactions URBAN STUDIES, UNIVERSITY OF CAMBRIDGE, 2,
1977.
[3] S. Krishnamurthy and K.M. Kockelman. Propagation of uncertainty in
transportation land use models: Investigation of dram-empal and utpp
predictions in austin, texas. Transportation Research Record: Journal
of the Transportation Research Board, 1831(-1):219-229, 2003.
[4] M. Iacono, D. Levinson, and A. El-Geneidy. Models of transportation
and land use change: a guide to the territory. Journal of Planning
Literature, 22(4):323, 2008.
[5] J. Sullivan, L. Aultman-Hall, A. Troy, D. Azaria, and S. Lawe. Integrated
land-use, transportation and environmental modeling: Validation case
studies. Technical report, 2010.
[6] A. Saltelli, M. Ratto, T. Andres, F. Campolongo, J. Cariboni, D. Gatelli,
M. Saisana, and S. Tarantola. Global sensitivity analysis: the primer.
Wiley Online Library, 2008.
[7] F. Campolongo, A. Saltelli, and J. Cariboni. From screening to
quantitative sensitivity analysis. a unified approach. Computer Physics
Communications, 2011.
[8] H.A. Martens and P. Dardenne. Validation and verification of regression
in small data sets. Chemometrics and intelligent laboratory systems,
44(1-2):99-121, 1998.
[9] H.S. Mahmassani. Uncertainty in transportation systems evaluation:
issues and approaches. Transportation planning and technology, 9(1):1-
12, 1984.
[10] Y. Zhao and K.M. Kockelman. The propagation of uncertainty through
travel demand models: An exploratory analysis. The Annals of regional
science, 36(1):145-163, 2002.
[11] P. Waddell. Integrated land use and transportation planning and
modelling: addressing challenges in research and practice. Transport
Reviews, 31(2):209-229, 2011.
[12] A. Pradhan and K.M. Kockelman. Uncertainty propagation in an
integrated land use-transportation modeling framework: output variation
via urbansim. Transportation Research Record: Journal of the Transportation
Research Board, 1805(-1):128-135, 2002.
[13] P. Waddell. Modeling urban development for land use, transportation,
and environmental planning. Journal of the American Planning Association,
68(3), 2002.
[14] J. Duthie, A. Voruganti, K.M. Kockelman, and S.T. Waller. Highway
improvement project rankings due to uncertain model inputs: Application
of traditional transportation and land use models. Journal of Urban
Planning and Development, 136:294, 2010.
[15] H. Sevc'─▒kov'a, A.E. Raftery, and P.A. Waddell. Assessing uncertainty
in urban simulations using bayesian melding. Transportation Research
Part B: Methodological, 41(6):652-669, 2007.
[16] H. Sevc'─▒kov'a, A.E. Raftery, and P.A. Waddell. Uncertain benefits:
Application of bayesian melding to the alaskan way viaduct in seattle.
Transportation Research Part A: Policy and Practice, 45(6):540-553,
2011.
[17] J.D. Hunt and D.C. Simmonds. Theory and application of an integrated
land use and transport modelling framework. Environment and Planning
B, 20:221-221, 1993.
[18] M.J. Clay and R.A. Johnston. Multivariate uncertainty analysis of an
integrated land use and transportation model: Meplan. Transportation
Research Part D: Transport and Environment, 11(3):191-203, 2006.
[19] T. de la Barra. Integrated land use and transport modeling: the
tranus experience. Planning Support Systems: Integrating Geographic
Information Systems, Models and Visualization Tools Eds RK Brail, RE
Klosterman (ESRI Press, Redlands, CA) pp, pages 129-156, 2001.
[20] T. de la Barra, B. P'erez, and N. Vera. Tranus-j: putting large models into
small computers. Environment and Planning B: Planning and Design,
11(1):87-101, 1984.
[21] J. Anez. Development of tranus: An integrated land use and transport
modeling system. Technical report, Mimeo from Oregon DOT), 1994,
1994.
[22] V. Vichiensan, K. Miyamoto, M. Roychansyah, and Y. Tokunaga.
Evaluation system of policy measure alternatives for a metropolis based
on tranus from the view point of sustainability. Journal of the Eastern
Asia Society for Transportation Studies, 6:3803-3818, 2005.
[23] T. de la Barra. The mathematical and algorithmic structure of tranus.
TRANUS manual available at http://www.modelistica.com, 1998.
[24] A. Saltelli, K. Chan, E.M. Scott, et al. Sensitivity analysis, volume 134.
Wiley New York, 2000.
[25] M.E. Magnello. Karl pearson-s gresham lectures: Wfr weldon, speciation
and the origins of pearsonian statistics. Reporter, 1878(79):38, 1996.
[26] A.H.S. Ang and W.H. Tang. Probability concepts in engineering.
Planning, 1(4):1-3, 2004.
[27] M. Saujot and B. Lef'evre. Simuler les dynamiques urbaines avec
tranus: une m'ethodologie d-application comme passerelle entre th'eorie et
territoire. Article under review in Les Cahiers Scientifiques du Transport,
2011.
[28] T. de la Barra. Integrated land use and transport modelling. Decision
chains and hierarchies. Number 12. 1989.
[1] M. Wegener. Overview of land use transport models. Handbook of
transport geography and spatial systems, 5:127-146, 2004.
[2] M. Echenique. An integrated land use and transport model. Martin Centre
Transactions URBAN STUDIES, UNIVERSITY OF CAMBRIDGE, 2,
1977.
[3] S. Krishnamurthy and K.M. Kockelman. Propagation of uncertainty in
transportation land use models: Investigation of dram-empal and utpp
predictions in austin, texas. Transportation Research Record: Journal
of the Transportation Research Board, 1831(-1):219-229, 2003.
[4] M. Iacono, D. Levinson, and A. El-Geneidy. Models of transportation
and land use change: a guide to the territory. Journal of Planning
Literature, 22(4):323, 2008.
[5] J. Sullivan, L. Aultman-Hall, A. Troy, D. Azaria, and S. Lawe. Integrated
land-use, transportation and environmental modeling: Validation case
studies. Technical report, 2010.
[6] A. Saltelli, M. Ratto, T. Andres, F. Campolongo, J. Cariboni, D. Gatelli,
M. Saisana, and S. Tarantola. Global sensitivity analysis: the primer.
Wiley Online Library, 2008.
[7] F. Campolongo, A. Saltelli, and J. Cariboni. From screening to
quantitative sensitivity analysis. a unified approach. Computer Physics
Communications, 2011.
[8] H.A. Martens and P. Dardenne. Validation and verification of regression
in small data sets. Chemometrics and intelligent laboratory systems,
44(1-2):99-121, 1998.
[9] H.S. Mahmassani. Uncertainty in transportation systems evaluation:
issues and approaches. Transportation planning and technology, 9(1):1-
12, 1984.
[10] Y. Zhao and K.M. Kockelman. The propagation of uncertainty through
travel demand models: An exploratory analysis. The Annals of regional
science, 36(1):145-163, 2002.
[11] P. Waddell. Integrated land use and transportation planning and
modelling: addressing challenges in research and practice. Transport
Reviews, 31(2):209-229, 2011.
[12] A. Pradhan and K.M. Kockelman. Uncertainty propagation in an
integrated land use-transportation modeling framework: output variation
via urbansim. Transportation Research Record: Journal of the Transportation
Research Board, 1805(-1):128-135, 2002.
[13] P. Waddell. Modeling urban development for land use, transportation,
and environmental planning. Journal of the American Planning Association,
68(3), 2002.
[14] J. Duthie, A. Voruganti, K.M. Kockelman, and S.T. Waller. Highway
improvement project rankings due to uncertain model inputs: Application
of traditional transportation and land use models. Journal of Urban
Planning and Development, 136:294, 2010.
[15] H. Sevc'─▒kov'a, A.E. Raftery, and P.A. Waddell. Assessing uncertainty
in urban simulations using bayesian melding. Transportation Research
Part B: Methodological, 41(6):652-669, 2007.
[16] H. Sevc'─▒kov'a, A.E. Raftery, and P.A. Waddell. Uncertain benefits:
Application of bayesian melding to the alaskan way viaduct in seattle.
Transportation Research Part A: Policy and Practice, 45(6):540-553,
2011.
[17] J.D. Hunt and D.C. Simmonds. Theory and application of an integrated
land use and transport modelling framework. Environment and Planning
B, 20:221-221, 1993.
[18] M.J. Clay and R.A. Johnston. Multivariate uncertainty analysis of an
integrated land use and transportation model: Meplan. Transportation
Research Part D: Transport and Environment, 11(3):191-203, 2006.
[19] T. de la Barra. Integrated land use and transport modeling: the
tranus experience. Planning Support Systems: Integrating Geographic
Information Systems, Models and Visualization Tools Eds RK Brail, RE
Klosterman (ESRI Press, Redlands, CA) pp, pages 129-156, 2001.
[20] T. de la Barra, B. P'erez, and N. Vera. Tranus-j: putting large models into
small computers. Environment and Planning B: Planning and Design,
11(1):87-101, 1984.
[21] J. Anez. Development of tranus: An integrated land use and transport
modeling system. Technical report, Mimeo from Oregon DOT), 1994,
1994.
[22] V. Vichiensan, K. Miyamoto, M. Roychansyah, and Y. Tokunaga.
Evaluation system of policy measure alternatives for a metropolis based
on tranus from the view point of sustainability. Journal of the Eastern
Asia Society for Transportation Studies, 6:3803-3818, 2005.
[23] T. de la Barra. The mathematical and algorithmic structure of tranus.
TRANUS manual available at http://www.modelistica.com, 1998.
[24] A. Saltelli, K. Chan, E.M. Scott, et al. Sensitivity analysis, volume 134.
Wiley New York, 2000.
[25] M.E. Magnello. Karl pearson-s gresham lectures: Wfr weldon, speciation
and the origins of pearsonian statistics. Reporter, 1878(79):38, 1996.
[26] A.H.S. Ang and W.H. Tang. Probability concepts in engineering.
Planning, 1(4):1-3, 2004.
[27] M. Saujot and B. Lef'evre. Simuler les dynamiques urbaines avec
tranus: une m'ethodologie d-application comme passerelle entre th'eorie et
territoire. Article under review in Les Cahiers Scientifiques du Transport,
2011.
[28] T. de la Barra. Integrated land use and transport modelling. Decision
chains and hierarchies. Number 12. 1989.
@article{"International Journal of Business, Human and Social Sciences:61954", author = "Parikshit Dutta and Mathieu Saujot and Elise Arnaud and Benoit Lefevre and Emmanuel Prados", title = "Uncertainty Propagation and Sensitivity Analysis During Calibration of an Integrated Land Use and Transport Model", abstract = "In this work, propagation of uncertainty during calibration
process of TRANUS, an integrated land use and transport model
(ILUTM), has been investigated. It has also been examined, through a
sensitivity analysis, which input parameters affect the variation of the
outputs the most. Moreover, a probabilistic verification methodology
of calibration process, which equates the observed and calculated
production, has been proposed. The model chosen as an application is
the model of the city of Grenoble, France. For sensitivity analysis and
uncertainty propagation, Monte Carlo method was employed, and a
statistical hypothesis test was used for verification. The parameters of
the induced demand function in TRANUS, were assumed as uncertain
in the present case. It was found that, if during calibration, TRANUS
converges, then with a high probability the calibration process is
verified. Moreover, a weak correlation was found between the inputs
and the outputs of the calibration process. The total effect of the
inputs on outputs was investigated, and the output variation was found
to be dictated by only a few input parameters.", keywords = "Uncertainty propagation, sensitivity analysis, calibration
under uncertainty, hypothesis testing, integrated land use and
transport models, TRANUS, Grenoble.", volume = "6", number = "5", pages = "967-9", }
