Publication Cover
Transportation Letters
The International Journal of Transportation Research
Volume 15, 2023 - Issue 9
196
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Testing and enhancing spatial transferability of artificial neural networks based travel behavior models

, ORCID Icon, &

References

  • Abdelwahab, W. M. 1991. “Transferability of Intercity Disaggregate Mode Choice Models in Canada.” Canadian Journal of Civil Engineering 18 (1): 20–26. doi:10.1139/l91-003.
  • Arentze, T., F. Hofman, H. Van Mourik, and H. Timmermans. 2002. “Spatial Transferability of the Albatross Model System: Empirical Evidence from Two Case Studies.” Transportation Research Record 1805 (1): 1–7. doi:10.3141/1805-01.
  • Assi, K. J., K. M. Nahiduzzaman, N. T. Ratrout, and A. S. Aldosary. 2018. “Mode Choice Behavior of High School Goers: Evaluating Logistic Regression and MLP Neural Networks.” Case Studies on Transport Policy 6 (2): 225–230. doi:10.1016/j.cstp.2018.04.006.
  • Atherton, T., and M. Ben-Akiva.1976.“Transferability and Updating Disaggregate Travel Demand Models” Transportation Research Record 610 12–18. Retrieved from: https://trid.trb.org/view/53666
  • Bishop, C. M. 2006. Pattern Recognition and Machine Learning. New York: Springer.
  • Bowman, J. L., and M. Bradley. 2017. “Testing Spatial Transferability of Activity-based Travel Forecasting Models.” Transportation Research Record 2669 (2669): 62–71. doi:10.3141/2669-07.
  • Bowman, J. L., M. Bradley, and J. Castiglione (2013). “Making Advanced Travel Forecasting Models Affordable through Model Transferability.” In A Research Project Sponsored by FHWA under the Broad Agency Announcement DTFH61-10-R-00013. Retrieved from http://jbowman.net
  • Cantarella, G. E., and S. de Luca. 2005. “Multilayer Feedforward Networks for Transportation Mode Choice Analysis: An Analysis and a Comparison with Random Utility Models.” Transportation Research Part C: Emerging Technologies 13 (2): 121–155. doi:10.1016/j.trc.2005.04.002.
  • Centraal Bureau voor de Statistiek (CBS), Rijkswaterstaat (RWS-WVL). 2020. Onderzoek Onderweg in Nederland - ODiN 2019. DANS https://doi.org/10.17026/dans-xpv-mwpg. doi:10.17026/dans-xpv-mwpg.
  • Galbraith, R. A., and D. Hensher. 1982. “Intra Metropolitan Transferability of Mode Choice Models.” Journal of Transport Economics and Policy 16 (1): 7–29.
  • Géron, A. 2017. Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow 1 (CA 95472: O’Reilly) . doi:10.1017/CBO9781107415324.004.
  • Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep Learning. Cambridge: MIT Press.
  • Gunn, H. F., M. E. Ben-Akiva, and M. A. Bradley. 1985. “Tests of the Scaling Approach to Transferring Disaggregate Travel Demand Models.” Transportation Research Record 1037:21–30. Retrieved from. https://trid.trb.org/view/272230
  • Hensher, D. A., and T. T. Ton. 2000. “A Comparison of the Predictive Potential of Artificial Neural Networks and Nested Logit Models for Commuter Mode Choice.” Transportation Research Part E: Logistics and Transportation Review 36 (3): 155–172. doi:10.1016/S1366-5545(99)00030-7.
  • Karasmaa, N. 2007. “Evaluation of Transfer Methods for Spatial Travel Demand Models.” Transportation Research Part A: Policy and Practice 41 (5): 411–427. doi:10.1016/j.tra.2006.09.009.
  • Kato, K., S. Matsumoto, and K. Sano. 2002. “Microsimulation for Commuters’ Mode and Discretionary Activities by Using Neural Networks.” Traffic And Transportation Studies, no. 2002: 1290–1297. doi:10.1061/40630(255)178.
  • Koppelman, F. S., and C. G. Wilmot. 1982. “Transferability Analysis of Disaggregate Choice Models.” Transportation Research Record 895: 18–24.
  • Koushik, A. N. P., M. Manoj, and N. Nezamuddin. 2020. “Machine Learning Applications in Activity-travel Behaviour Research: A Review.” Transport Reviews 40 (3): 288–311. doi:10.1080/01441647.2019.1704307.
  • Mittal, V., S. Sasetty, R. Choudhary, and A. Agarwal (2022). “Deep-Learning Spatio-Temporal Prediction Framework for PM under Dynamic Monitoring.” Transportation Research Board (TRB) 101st Annual Meeting Washington, D.C.
  • Mohammadian, A., and E. J. Miller. 2002. “Nested Logit Models and Artificial Neural Networks for Predicting Household Automobile Choices: Comparison of Performance.” Transportation Research Record: Journal of the Transportation Research Board 1807 (1): 92–100. doi:10.3141/1807-12.
  • Mozolin, M., J.-C. Thill, and E. L. Usery. 2000. “Trip Distribution Forecasting with Multilayer Perceptron Neural Networks: A Critical Evaluation.” Transportation Research Part B: Methodological 34 (1): 53–73. doi:10.1016/S0191-2615(99)00014-4.
  • Olah, C. (2015). “Understanding LSTM Networks.” Retrieved 14 July 2019, from https://colah.github.io/posts/2015-08-Understanding-LSTMs/
  • Pan, S. J., and Q. Yang. 2010. “A Survey on Transfer Learning.” IEEE Transactions on Knowledge and Data Engineering 22 (10): 1345–1359. doi:10.1109/TKDE.2009.191.
  • Patil, S., N. Raju, S. S. Arkatkar, and S. Easa. 2021. “Modeling Vehicle Collision Instincts over Road Midblock Using Deep Learning.” Journal of Intelligent Transportation Systems 1–15. doi:10.1080/15472450.2021.2014833.
  • Qiu, J., Q. Wu, G. Ding, Y. Xu, and S. Feng. 2016. “A Survey of Machine Learning for Big Data Processing.” EURASIP Journal on Advances in Signal Processing 2016 (67). doi:10.1186/s13634-016-0355-x.
  • Santoso, D. S., and K. Tsunokawa. 2005. “Spatial Transferability and Updating Analysis of Mode Choice Models in Developing Countries.” Transportation Planning and Technology 28 (5): 341–358. doi:10.1080/03081060500319694.
  • Santoso, D. S., and K. Tsunokawa. 2010. “Comparison of Updating Techniques in Transferability Analysis of Work Trip Mode Choice Models in Developing Countries.” Journal of Advanced Transportation 44 (2): 89–102. doi:10.1002/atr.112.
  • Shmueli, D., I. Salomon, and D. Shefer. 1996. “Neural Network Analysis of Travel Behavior: Evaluating Tools for Prediction.” Transportation Research Part C: Emerging Technologies 4 (3): 151–166. doi:10.1016/S0968-090X(96)00007-1.
  • Sikder, S. 2013 Spatial Transferability of Activity-Based Travel Forecasting Models PhD Dissertation . . University of South Florida.
  • Sikder, S., B. Augustin, A. R. Pinjari, and N. Eluru. 2014. “Spatial Transferability of Tour-based Time-of-day Choice Models: Empirical Assessment.” Transportation Research Record: Journal of the Transportation Research Board 2429 (2429): 99–109. doi:10.3141/2429-11.
  • Sikder, S., and A. R. Pinjari. 2013. “Spatial Transferability of person-level Daily Activity Generation and Time Use Models.” Transportation Research Record 2343 (1): 95–104. doi:10.3141/2343-12.
  • Tang, L., C. Xiong, and L. Zhang. 2018. “Spatial Transferability of Neural Network Models in Travel Demand Modeling.” Journal of Computing in Civil Engineering 32 (3). doi:10.1061/(asce)cp.1943-5487.0000752.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.