OD Mobility Estimation Using Artificial Neural Networks

  1. Turias, I. J. 1
  2. Acosta Sánchez, Luis E.
  3. González-Enrique, Javier 1
  4. Moscoso-López, J. A. 1
  5. Ruiz-Aguilar, J. J. 1
  1. 1 Universidad de Cádiz
    info

    Universidad de Cádiz

    Cádiz, España

    ROR https://ror.org/04mxxkb11

Llibre:
INCREaSE 2019

Editorial: Springer

Any de publicació: 2019

Pàgines: 643-652

Tipus: Capítol de llibre

DOI: 10.1007/978-3-030-30938-1_49 GOOGLE SCHOLAR lock_openAccés obert editor

Referències bibliogràfiques

  • McNally, M.G.: The Four Step Model. Institute of Transportation Studies, Department of Civil and Environmental Engineering (2000)
  • Ahmed, B.: The traditional four steps transportation modeling using simplified transport network: a case of Dhaka city, Bangladesh. Int. J. Adv. Sci. Eng. Technol. Res. 1, 19–40 (2012)
  • Tamin, O.Z., Willumsen, L.G.: Transport demand model estimation from traffic counts. Transportation (Amst) 16, 3–26 (1989). https://doi.org/10.1007/BF00223044
  • Van Zuylen, H.J., Willumsen, L.G.: The most likely trip matrix estimated from traffic counts. Transp. Res. Part B: Methodol. 14, 281–293 (1980). https://doi.org/10.1016/0191-2615(80)90008-9
  • Cascetta, E.: Estimation of trip matrices from traffic counts and survey data: a generalized least squares estimator. Transp. Res. Part B: Methodol. 18, 289–299 (1984). https://doi.org/10.1016/0191-2615(84)90012-2
  • Maher, M.J.: Inferences on trip matrices from observations on link volumes: a Bayesian statistical approach. Transp. Res. Part B: Methodol. 17, 435–447 (1983). https://doi.org/10.1016/0191-2615(83)90030-9
  • Spiess, H.: A maximum likelihood model for estimating origin-destination matrices. Transp. Res. Part B: Methodol. 21B, 395–412 (1987). https://doi.org/10.1016/0191-2615(87)90037-3
  • Dougherty, M.: A review of neural networks applied to transport. Transp. Res. Part C: Emerg. Technol. 3, 247–260 (1995). https://doi.org/10.1016/0968-090X(95)00009-8
  • Gong, Z.: Estimating the urban OD matrix: a neural network approach. Eur. J. Oper. Res. 106, 108–115 (1998). https://doi.org/10.1016/S0377-2217(97)00162-8
  • Kikuchi, S., Nanda, R., Perincherry, V.: A method to estimate trip O-D patterns using a neural network approach. Transp. Plan. Technol. 17, 51–65 (1993). https://doi.org/10.1080/03081069308717499
  • Remya, K.P., Mathew, S.: OD Matrix Estimation from Link Counts Using Artificial Neural Network (2013). http://www.ijser.org
  • Çelikoğlu, H.B., Akad, M.: Estimation of public transport trips by feed forward back propagation artificial neural networks; a case study for Istanbul. In: Hoffmann, F., Köppen, M., Klawonn, F., Roy, R. (eds.) BT - Soft Computing: Methodologies and Applications. Springer, Heidelberg (2005)
  • Faghri, A., Hua, J.: Evaluation of artificial neural network applications in transportation engineering. Transp. Res. Rec. 1358, 71–80 (1992)
  • Wei, Y., Chen, M.C.: Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks. Transp. Res. Part C Emerg. Technol. 21, 148–162 (2012). https://doi.org/10.1016/j.trc.2011.06.009
  • Himanen, V., Nijkamp, P., Reggiani, A.: Neural Networks in Transport Applications. Routledge, Abingdon (2020)
  • Wargelin, L., Stopher, P., Minser, J., Tierney, K., Rhindress, M., O’Connor, S., Investigators, P., Systematics, C., Group, R.S.: GPS-based household interview survey for the Cincinnati, Ohio Region (2012). http://www.dot.state.oh.us/Divisions/Planning/SPR/Research/reportsandplans/Reports/2012/Planning/134421_FR.pdf
  • Perrakis, K., Karlis, D., Cools, M., Janssens, D., Vanhoof, K., Wets, G.: A Bayesian approach for modeling origin-destination matrices. Transp. Res. Part A Policy Pract. 46, 200–212 (2012). https://doi.org/10.1016/j.tra.2011.06.005
  • Avineri, E.: Soft computing applications in traffic and transport systems: a review. In: Hoffmann, F., Köppen, M., Klawonn, F., Roy, R. (eds.) BT - Soft Computing: Methodologies and Applications. Springer, Heidelberg (2005)
  • Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003). https://doi.org/10.1016/j.aca.2011.07.027
  • Kumar, V.: Feature selection: a literature review. Smart Comput. Rev. 4, 211–229 (2014). https://doi.org/10.6029/smartcr.2014.03.007
  • Jovic, A., Brkic, K., Bogunovic, N.: A review of feature selection methods with applications. In: 2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics, pp. 1200–1205 (2015). https://doi.org/10.1109/MIPRO.2015.7160458
  • Hall, M.: Correlation-Based Feature Selection for Machine Learning (1999). http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.62.9584&rep=rep1&type=pdf
  • Kononenko, I.: Estimating attributes: analysis and extensions of RELIEF, pp. 171–182 (1994). https://doi.org/10.1007/3-540-57868-4_57
  • Robnik-Šikonja, M., Kononenko, I.: Theoretical and empirical analysis of RelifF and RReliefF. Mach. Learn. 53, 23–69 (2003)
  • Shannon, C.E.: A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423, 623–656 (1948)
  • Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Netw. 2, 359–366 (1989). https://doi.org/10.1016/0893-6080(89)90020-8
  • Rumelhart, D., Hinton, G., Williams, R.: Learning internal representations by error propagation. In: Rumelhart, D.E., McClelland, J.L. (eds.) Parallel Distributed Processing, pp. 318–362. MIT Press, Cambridge (1986)
  • Marquardt, D.W.: An algorithm for least-squares estimation of nonlinear parameters. J. Soc. Ind. Appl. Math. 11, 431–441 (1963). https://doi.org/10.1137/0111030
  • Sarle, W.S.: Stopped training and other remedies for overfitting. Comput. Sci. Stat. 27, 352–360 (1996)
  • Yao, Y., Rosasco, L., Caponnetto, A.: On early stopping in gradient descent boosting. Constr. Approx. 26, 289–315 (2007)
  • Prechelt, L.: Early stopping-but when? In: Montavon, G., Orr, G.B., Müller, K.R. (eds.) Neural Networks: Tricks of the Trade, pp. 55–69. Springer, Heidelberg (1998)
  • Krstajic, D., Buturovic, L.J., Leahy, D.E., Thomas, S.: Cross-validation pitfalls when selecting and assessing regression and classification models. J. Cheminform. 6, 1–15 (2014). https://doi.org/10.1186/1758-2946-6-10
  • Hastie, T.T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics, 2nd edn. Springer, Heidelberg (2009). https://doi.org/10.1007/b94608