A SOFT COMPUTING APPROACH TO TRIP GENERATION ESTIMATION IN LAGOS METROPOLIS, NIGERIA

Authors

  • Olanrewaju Oluwafemi Akinfala Department of Geography and Planning, University of Lagos, 101245, Lagos, Nigeria
  • Folorunso Oladimeji Ogunwolu Department of Systems Engineering, University of Lagos, 101245, Lagos, Nigeria
  • Chidi Onyedikam Department of Systems Engineering, University of Lagos, 101245, Lagos, Nigeria

DOI:

https://doi.org/10.33736/jcest.3821.2022

Keywords:

Linear regression, model performance, trip rates, fuzzy expert system, artificial neural network

Abstract

Trip generation is an indispensable component of the four-stage transportation planning process because the subsequent three stages are predicated on its results.  Linear regression has been widely adopted to predict trips due to its simplicity and its outperformance of more sophisticated count models and in some cases, soft computing models. The efficacy of regression for estimating trip generation alongside Artificial Neural Networks (ANN) and Fuzzy Expert System (FES) was examined. The performance of each model was evaluated using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Coefficient of Determination (R2) and the capability of predicting average trips. The R2 for Regression, ANN and FES were all 0.71. The MAE for Regression, FES and ANN were 0.56, 0.55 and 0.49 respectively. The MSE for Regression, ANN and FES were 1.15, 1.16 and 1.15 respectively. Finally, FES and ANN resulted in average trips of 4.5 in comparison to actual average trips of 4.51 per household,  while regression produced average trips of 4.51. ANN and FES are not superior alternatives to the linear regression model for trip generation modelling. The performance increments gained from adopting these models are marginal and the extra development and computational effort required to apply such sophisticated approaches may not be justified.

References

Cubucku, K. M. (2001). Factors affecting shopping trip generation rates in metropolitan areas. Studies in Regional & Urban Planning, 9, 51–67.

Chang, J. S., Jung, D., Kim, J., & Kang, T. (2014). Comparative analysis of trip generation models: results using home-based work trips in the Seoul metropolitan area. Transportation Letter: The International Journal of Transportation Research, 6(2), 78–88. https://doi.org/10.1179/1942787514Y.0000000011.

Shafahi,Y. & Abrishami, E. S., (2005). School trip attraction modeling using neural & fuzzy-neural approaches. Vienna, Austria. https://doi.org/10.1109/ITSC.2005.1520199.

Rassafi, A. A., Rezaei, R., & Hajizamani, M. (2012). Predicting urban trip generation using a fuzzy expert system. Iranian Journal of Fuzzy Systems. Iranian Journal of Fuzzy Systems, 9(3), 127–146. https://dx.doi.org/10.22111/ijfs.2012.151

Berki, Z. & Monigl, J. H. (2017). Trip generation and distribution modelling in Budapest. Transportation Research Procedia, 27, 172–179. https://doi.org/10.1016/j.trpro.2017.12.023.

Bwambale, A., Choudhury, C. F., & Hess, S. (2019). Modelling trip generation using mobile phone data: A latent demographics approach. Journal of Transport Geography, 76, 276-286. https://doi.org/10.1016/j.jtrangeo.2017.08.020.

Lim, K.K. & Srinivasan, S. (2011). Comparative analysis of alternate econometric structures for trip generation models. Transportation Research Record: Journal of the Transportation Research Board, 2254(1), 68–78. https://doi.org/10.3141/2254-08.

McCarthy, G. M. (1969). Multiple-Regression Analysis of Household Trip Generation-A Critique. Highway Research Record, 297, 31-43.

Badoe, D. A. (2007). Forecasting travel demand with alternatively structured models of trip frequency. Transportation Planning and Technology, 30(5), 455–475. https://doi.org/10.1080/03081060701599938.

Jang, T. Y. (2005). Count data models for trip generation. Journal of Transportation Engineering, 131(6), 444–450. https://doi.org/10.1061/(asce)0733-947x(2005)131:6(444).

Ahmadpour, M., Yue, W. L., & Mohammadzaheri, M. (2009). Neuro-Fuzzy Modelling of Workers Trip Production. In 32nd Australasian Transport Research Forum, ATRF 2009.

Avineri, E. (2005). Soft Computing Applications in Traffic and Transport Systems: A Review, Soft Computing: Methodologies and Applications, 32(1), 17–25. https://doi.org/10.1007/3-540-32400-3_2.

Seyedabrishami, S. & Shafahi, Y. (2011). Expert knowledge-guided travel demand estimation: Neuro-fuzzy approach. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, 15(1), 13–27. http://dx.doi.org/10.1080/15472450.2011.544576.

Abu-Eisheh, S. & Irshaid, M. (2020). Modelling trip generation using adaptive neuro-fuzzy inference system in comparison with traditional multiple linear regression approach. International Journal of Simulation--Systems, Science & Technology, 21(2), 1–6. https://doi.org/10.5013/IJSSST.a.21.02.17.

Faghri, A. & Aneja, S. (1996). Artificial neural network–based approach to modeling trip production. Transportation Research Record: Journal of the Transportation Research Board, 1556(1), 131–136. https://doi.org/10.1177/0361198196155600115.

Arliansyah, J. & Hartono, Y. (2015). Trip attraction model using radial basis function neural networks. Procedia Engineering, 125, 445–451. https://doi.org/10.1016/j.proeng.2015.11.117.

Etu , J. E. & Oyedepo, J. O. (2018). Forecasting trip generation for high density residential zones of Akure, Nigeria: Comparability of artificial neural network and regression models. Journal of Civil Engineering, Science and Technology, 9(2), 76–86. https://doi.org/10.33736/jcest.988.2018.

Tillema, F., van Zuilekom, K. M., & van Maarseveen, M. F. A. M. (2004). Trip generation: Comparison of neural networks and regression models. Urban Transport X: Urban transport and the environment in the 21st century, 89, Southampton, UK: WIT Press, 121–130.

Ortuzar, J. de D. & Willumsen, L. G. (2011). Modelling Transport, 4th ed. John Wiley & Sons, Inc. https://doi.org/10.1002/9781119993308

Seabold, S. & Perktold, J. (2010). Statsmodels: Econometric and statistical modeling with python. Proceedings of the 9th Python in Science Conference, Austin, TX, 57, 61. https://doi.org/10.25080/Majora-92bf1922-011

Zilouchan, A. & Jamshidi, A. (2000). Intelligent Control Systems Using Soft Computing Methodologies. Florida: CRC Press. https://doi.org/10.1201/9781420058147

Huang, Y., Lan, Y., Thomson, S., Fang, J. A., Hoffman, W. C., & Lacey, R. E. (2010). Development of soft computing and applications in agricultural and biological engineering. Computers and Electronics in Agriculture, 71, 107–127. https://doi.org/10.1016/j.compag.2010.01.001.

Ibrahim, D. (2016). An overview of soft computing. Procedia Computer Science, 102, 34–38. https://doi.org/10.1016/j.procs.2016.09.366.

Goh, A. T. C. (1995). Back-propagation neural networks for modeling complex systems. Artificial Intelligence in Engineering, 9(3), 143–151 https://doi.org/10.1016/0954-1810(94)00011-S

Chollet, F. (2015). Keras. https://keras.io (accessed Oct. 03, 2021).

Zadeh, L. A. (1965). Fuzzy set. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X.

Singh, H. & Lone, Y. A. (2020). Deep Neuro-Fuzzy Systems with Python: With Case Studies and Applications from the Industry, APRESS. Accessed: Mar. 29, 2021. [Online]. https://doi.org/10.1007/978-1-4842-5361-8

Sivanandam, S. N., Deepa, S. N., & Sumathi, S. (2007). Introduction to Fuzzy Logic using MATLAB, 1. Berlin: Springer. https://doi.org/10.1007/978-3-540-35781-0

Scikit-Fuzzy, (2016). https://pythonhosted.org/scikit-fuzzy/overview.html (accessed Mar. 29, 2021).

Davern, M., Holly, R., Beebe, T. J., & Call, K. T. (2005). The effect of income question design in health surveys on family income, poverty and eligibility estimates. HSR Health Service Research, 40(5), 1534–1552. https://doi.org/10.1111/j.1475-6773.2005.00416.x.

Essig, L. & Winter, J. K., (2009). Item non-response to financial questions in household surveys: An experimental study of interviewer and mode effects. Fiscal Studies, 30(3‐4), 367-390. https://doi.org/10.1111/j.1475-5890.2009.00100.x

Riphahn, R. T. & Serfling, O. (2005). Item non-response on income and wealth questions. Empirical Economics, 30(2), 521-538. https://doi.org/10.1007/s00181-005-0247-7

Kitamura, R., Mokhtarian, P. L., & Laidet, L. (1997). A micro-analysis of land use and travel in five neighborhoods in the San Francisco Bay Area. Transportation, 24, 125–158. https://doi.org/10.1023/A:1017959825565

Salau, T. (2015). Public transportation in metropolitan Lagos, Nigeria: analysis of public transport users’ socioeconomic characteristics. Urban, Planning and Transport Research, 3(1), 132–139. https://doi.org/10.1080/21650020.2015.1124247.

LAMATA, https://lamata.lagosstate.gov.ng/ (accessed Feb. 18, 2021).

Masaoe, E. N., Del Mistro, R. F., & Makajuma, G. (2011). Travel behaviour in Cape Town, Dar Es Salaam and Nairobi cities. 30th Annual Southern African Transport Conference, 11-14 July 2011, CSIR International Convention Centre, Pretoria, South Africa.

Xu, Y., Belyi,A., Bojic, I., & Ratti, C. (2018). Human mobility and socioeconomic status: Analysis of Singapore and Boston. Computers, Environment and Urban Systems, 72, 51–67. https://doi.org/10.1016/j.compenvurbsys.2018.04.001.

Kockelman, K. M. (1997). Travel behavior as function of accessibility, land use mixing, and land use balance evidence from San Francisco bay area. Transportation Research Record: Journal of the Transportation Research Board, 1607(1), 116–125. https://doi.org/10.3141%2F1607-16.

Stead, D. (2001). Relationships between land use, socioeconomic factors, and travel patterns in Britain. Environment and Planning B: Urban Analytics and City Science, 28, 499–528. https://doi.org/10.1068/b2677.

Barbosa, H., Hazarie, S., Dickinson, B., Bassolas, A., Frank, A., Kautz, H., Sadilek, A., Ramasco, J. J., & Ghoshal, G. (2021). Uncovering the socioeconomic facets of human mobility, Science Report, 11, Article 8616. https://doi.org/10.1038/s41598-021-87407-4.

Dargay, J. M. & Hanly, M. (2003, October). The impact of land use patterns on travel behaviour. In European Transport Conference, Strasbourg, France.

Hu, D., Jiang, T., & Yu, X. (2020). The construction of non-convex fuzzy sets and its application. Neurocomputing, 393, 175–186. https://doi.org/10.1016/j.neucom.2018.10.111.

Garibaldi, J., Musikasuwan, S., Ozen, T., & John, R. (2004). A Case Study to Illustrate the Use of Non-Convex Membership Functions for Linguistic Terms. IEEE International Conference on Fuzzy Systems, 3, 1403–1408. https://doi.org/10.1109/FUZZY.2004.1375377.

Jenkins, D. & Quintana-Ascencio, P. (2020). A solution to minimum sample size for regressions. PLoS ONE, 15(2), e0229345. https://doi.org/10.1371/journal.pone.0229345

Downloads

Published

2022-04-12

How to Cite

Akinfala, O. O., Ogunwolu, F. O. ., & Onyedikam, C. . (2022). A SOFT COMPUTING APPROACH TO TRIP GENERATION ESTIMATION IN LAGOS METROPOLIS, NIGERIA. Journal of Civil Engineering, Science and Technology, 13(1), 6–22. https://doi.org/10.33736/jcest.3821.2022