An artificial neural network based method to uncover the value-of-travel-time distribution
Tóm tắt
This study proposes a novel Artificial Neural Network (ANN) based method to derive the Value-of-Travel-Time (VTT) distribution. The strength of this method is that it is possible to uncover the VTT distribution (and its moments) without making assumptions about the shape of the distribution or the error terms, while being able to incorporate covariates and taking the panel nature of stated choice data into account. To assess how well the proposed ANN-based method works in terms of being able to recover the VTT distribution, we first conduct a series of Monte Carlo experiments. After having demonstrated that the method works on Monte Carlo data, we apply the method to data from the 2009 Norwegian VTT study. Finally, we extensively cross-validate our method by comparing it with a series of state-of-the-art discrete choice models and nonparametric methods. Based on the promising results we have obtained, we believe that there is a place for ANN-based methods in future VTT studies.
Tài liệu tham khảo
Abrantes, P.A.L., Wardman, M.R.: Meta-analysis of UK values of travel time: an update. Transp. Res. A Policy Practice 45(1), 1–17 (2011)
Allenby, G.M., Rossi, P.E.: Marketing models of consumer heterogeneity. J. Econom. 89(1–2), 57–78 (1998)
Alwosheel, A., van Cranenburgh, S., Chorus, C.G.: Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis. J. Choice Model 28, 167–182 (2018)
Alwosheel, A., van Cranenburgh, S., Chorus, C.G.: ‘Computer says no’ is not enough: using prototypical examples to diagnose artificial neural networks for discrete choice analysis. J. Choice Model. 33, 100186 (2019)
Batley, R., Bates, J., Bliemer, M., Börjesson, M., Bourdon, J., Cabral, M.O., Chintakayala, P.K., Choudhury, C., Daly, A., Dekker, T.: New appraisal values of travel time saving and reliability in Great Britain. Transportation 46, 1–39 (2017)
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)
Börjesson, M., Eliasson, J.: Experiences from the Swedish value of time study. Transp. Res. A Policy Practice 59, 144–158 (2014)
Börjesson, M., Fosgerau, M., Algers, S.: Catching the tail: empirical identification of the distribution of the value of travel time. Transp. Res. A Policy Practice 46(2), 378–391 (2012)
Cameron, T.A., James, M.D.: Efficient estimation methods for" closed-ended" contingent valuation surveys. Rev. Econ. Stat. 69, 269–276 (1987)
Cantarella, G.E., de Luca, S.: Multilayer feedforward networks for transportation mode choice analysis: an analysis and a comparison with random utility models. Transp. Res. C Emerg. Technol. 13(2), 121–155 (2005)
Castelvecchi, D.: Can we open the black box of AI? Nat. News 538(7623), 20 (2016)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)
Chen, C., Ma, J., Susilo, Y., Liu, Y., Wang, M.: The promises of big data and small data for travel behavior (aka human mobility) analysis. Transp. Res. C Emerg. Technol. 68, 285–299 (2016)
Cybenko, G.: Approximation by superpositions of a sigmoidal function. Math. Control Signals Syst. (MCSS) 2(4), 303–314 (1989)
Daly, A., Tsang, F., Rohr, C.: The value of small time savings for non-business travel. J. Transp. Econ. Policy (JTEP) 48(2), 205–218 (2014)
Daly, A., Zachery, S.: Improved multiple choice models. In: Hensher, D. (ed.) Determinants of Travel Choice. Saxon House, Sussex (1978)
Day, B., Pinto Prades, J.-L.: Ordering anomalies in choice experiments. J. Environ. Econ. Manag. 59(3), 271–285 (2010)
De Borger, B., Fosgerau, M.: The trade-off between money and travel time: a test of the theory of reference-dependent preferences. J. Urban Econ. 64(1), 101–115 (2008)
Erdem, T., Srinivasan, K., Amaldoss, W., Bajari, P., Che, H., Ho, T., Hutchinson, W., Katz, M., Keane, M., Meyer, R.: Theory-driven choice models. Mark. Lett. 16(3–4), 225–237 (2005)
Fan, J., Heckman, N.E., Wand, M.P.: Local polynomial kernel regression for generalized linear models and quasi-likelihood functions. J. Am. Stat. Assoc. 90(429), 141–150 (1995)
Fosgerau, M.: Investigating the distribution of the value of travel time savings. Transp. Res. B Methodol. 40(8), 688–707 (2006)
Fosgerau, M.: Using nonparametrics to specify a model to measure the value of travel time. Transp. Res. A Policy Practice 41(9), 842–856 (2007)
Fosgerau, M., Bierlaire, M.: A practical test for the choice of mixing distribution in discrete choice models. Transp. Res. B Methodol. 41(7), 784–794 (2007)
Fosgerau, M., Bierlaire, M.: Discrete choice models with multiplicative error terms. Transp. Res. B Methodol. 43(5), 494–505 (2009)
Fosgerau, M., Hjorth, K. & Lyk-Jensen, S. V. (2007). The Danish value of time study: Final Report.
Fosgerau, M., McFadden, D., Bierlaire, M.: Choice probability generating functions. J. Choice Modell. 8, 1–18 (2013)
Golshani, N., Shabanpour, R., Mahmoudifard, S.M., Derrible, S., Mohammadian, A.: Modeling travel mode and timing decisions: comparison of artificial neural networks and copula-based joint model. Travel Behav. Soc. 10, 21–32 (2018)
HCG (1998) The second Netherlands' value of time study—final report. Report 6098–1 The Hague
Hess, S., Daly, A., Batley, R.: Revisiting consistency with random utility maximisation: theory and implications for practical work. Theor. Decis. 84(2), 181–204 (2018)
Hess, S., Daly, A., Dekker, T., Cabral, M.O., Batley, R.: A framework for capturing heterogeneity, heteroskedasticity, non-linearity, reference dependence and design artefacts in value of time research. Transp. Res. B Methodol. 96, 126–149 (2017)
Hess, S., Rose, J.M., Polak, J.: Non-trading, lexicographic and inconsistent behaviour in stated choice data. Transp. Res. D Transp. Environ. 15(7), 405–417 (2010)
Karlaftis, M.G., Vlahogianni, E.I.: Statistical methods versus neural networks in transportation research: differences, similarities and some insights. Transp. Res. C Emerg. Technol. 19(3), 387–399 (2011)
Koster, P.R., Koster, H.R.A.: Commuters’ preferences for fast and reliable travel: a semi-parametric estimation approach. Transp. Res. B Methodol. 81, 289–301 (2015)
Kouwenhoven, M., de Jong, G.C., Koster, P., van den Berg, V.A.C., Verhoef, E.T., Bates, J., Warffemius, P.M.J.: New values of time and reliability in passenger transport in The Netherlands. Res. Transp. Econ. 47, 37–49 (2014)
Lancsar, E., Louviere, J.: Deleting ‘irrational’responses from discrete choice experiments: a case of investigating or imposing preferences? Health Econ. 15(8), 797–811 (2006)
Lee, D., Derrible, S., Pereira, F.C.: Comparison of four types of artificial neural network and a multinomial logit model for travel mode choice modeling. Transp. Res. Record. (2018). https://doi.org/10.1177/0361198118796971
Luce, R.D.: Individual Choice Behavior: A Theoretical Analysis. Dover Publications, Mineola (2014)
McFadden, D.: Econometric models of probabilistic choice. In: Structural analysis of discrete data with econometric applications (1981)
McFadden, D.L.: Conditional logic analysis of qualitative choice behavior. In: Zarembka, P. (ed.) Frontiers in Econometrics, pp. 105–142. Academic Press, New York (1974)
Mohammadian, A., Miller, E.: Nested logit models and artificial neural networks for predicting household automobile choices: comparison of performance. Transp. Res. Rec. J. Transp. Res. Board 1807, 92–100 (2002)
Ojeda-Cabral, M., Batley, R., Hess, S.: The value of travel time: random utility versus random valuation. Transportmet. A Transp. Sci. 12(3), 230–248 (2016)
Ojeda-Cabral, M., Chorus, C.G.: Value of travel time changes: theory and simulation to understand the connection between random valuation and random utility methods. Transp. Policy 48, 139–145 (2016)
Ojeda-Cabral, M., Hess, S., Batley, R.: Understanding valuation of travel time changes: are preferences different under different stated choice design settings? Transportation 45(1), 1–21 (2018)
Omrani, H., Charif, O., Gerber, P., Awasthi, A., Trigano, P.: Prediction of individual travel mode with evidential neural network model. Transp. Res. Rec. J. Transp. Res. Board 2399, 1–8 (2013)
Paliwal, M., Kumar, U.A.: Neural networks and statistical techniques: a review of applications. Expert Syst. Appl. 36(1), 2–17 (2009)
Pereira, F.C., Rodrigues, F., Ben-Akiva, M.: Using data from the web to predict public transport arrivals under special events scenarios. J. Intell. Transp. Syst. 19(3), 273–288 (2015)
Prieto, A., Prieto, B., Ortigosa, E.M., Ros, E., Pelayo, F., Ortega, J., Rojas, I.: Neural networks: an overview of early research, current frameworks and new challenges. Neurocomputing 214, 242–268 (2016)
Ramjerdi, F., Flügel, S., Samstad, H., Killi, M. (2010) Value of time, safety and environment in passenger transport-Time, TØI report 1053-B/2010. Institute of Transport Economics (TØI), Oslo
Ramjerdi, F., Lindqvist Dillén, J.: Gap between willingness-to-pay (WTP) and willingness-to-accept (WTA) measures of value of travel time: evidence from Norway and Sweden. Transp. Rev. 27(5), 637–651 (2007)
Ran, Z.-Y., Hu, B.-G.: Parameter identifiability in statistical machine learning: a review. Neural Comput. 29(5), 1151–1203 (2017)
Revelt, D., Train, K.: Customer-Specific Taste Parameters and Mixed Logit. University of California, Berkeley (1999)
Rose, J.M., Bliemer, M.C.J.: Constructing efficient stated choice experimental designs. Transp. Rev. A Trans. Transdiscip. J. 29(5), 587–617 (2009)
Rouwendal, J., de Blaeij, A., Rietveld, P., Verhoef, E.: The information content of a stated choice experiment: a new method and its application to the value of a statistical life. Transp. Res. B Methodol. 44(1), 136–151 (2010)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 533–536 (1986)
Shannon, C.E., Weaver, W.: The Mathematical Theory of Communication. University of Illinois Press, Urbana (1949)
Sifringer, B., Lurkin, V., Alahi, A.: Enhancing discrete choice models with representation learning. Transp. Res. Part B: Methodol. 140, 236–261 (2020)
Small, K.A.: Valuation of travel time. Econ. Transp. 1(1), 2–14 (2012)
Train, K.E.: Discrete Choice Methods with Simulation. Cambridge University Press, New York (2003)
Van Cranenburgh, S., Alwosheel, A.: An artificial neural network based approach to investigate travellers’ decision rules. Transp. Res. C Emerg. Technol. 98, 152–166 (2019)
Van Cranenburgh, S., Kouwenhoven, M.: A logistic regression based method to uncover the Value-of-Travel-Time distribution. Working paper (2019a)
Van Cranenburgh, S., Kouwenhoven, M.: Using artificial neural networks for recovering the value-of-travel-time distribution. In: International Work-Conference on Artificial Neural Networks. Springer (2019b)
Wang, S., Mo, B., Zhao, J.: Deep neural networks for choice analysis: architecture design with alternative-specific utility functions. Transp. Res. Part C: Emerg. Technol. 112, 234–251 (2020)
Wardman, M., Chintakayala, V.P.K., de Jong, G.: Values of travel time in Europe: review and meta-analysis. Transp. Res. A Policy Practice 94, 93–111 (2016)
Wong, M., Farooq, B., Bilodeau, G.-A.: Discriminative conditional restricted Boltzmann machine for discrete choice and latent variable modelling. J. Choice Model. 29, 152–168 (2017)