Town trip forecasting based on data mining techniques
Subject Areas : Mathematical OptimizationMohammad Fili 1 , Majid Khedmati 2
1 - Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran
2 - Department of Industrial Engineering, Sharif University of Technology
Keywords:
Abstract :
Turner, S. M., Eisele, W. L., Benz, R. J., & Douglas, J. (1998). Travel time data collection handbook. In Federal Highway Administration, USA.
Wu, C. H., Ho, J. M., & Lee, D. T. (2004). Travel-time prediction with support vector regression. IEEE Transactions on Intelligent Transportation Systems, 5(4), 276-281. https://doi.org/10.1109/TITS.2004.837813.
Cho, Y., Kwac, J. (2007). A Travel Time Prediction with Machine Learning Algorithms. http://cs229.stanford.edu/proj2007.
Kwon, J., & Petty, K. (2005). Travel time prediction algorithm scalable to freeway networks with many nodes with arbitrary travel routes. Transportation Research Record. https://doi.org/10.3141/1935-17
Kwon, J., Mauch, M., & Varaiya, P. (2006). Components of congestion: Delay from incidents, special events, lane closures, weather, potential ramp metering gain, and excess demand. Transportation Research Record, 1959, 84-91. https://doi.org/10.3141/1959-10
Zhan, X., Hasan, S., Ukkusuri, S. V., & Kamga, C. (2013). Urban link travel time estimation using large-scale taxi data with partial information. Transportation Research Part C: Emerging Technologies, 33, 37-49. https://doi.org/10.1016/j.trc.2013.04.001
Wang, J., Tsapakis, I., & Zhong, C. (2016). A space-time delay neural network model for travel time prediction. Engineering Applications of Artificial Intelligence, 52, 145-160. https://doi.org/10.1016/j.engappai.2016.02.012
Li, C. Sen, & Chen, M. C. (2014). A data mining based approach for travel time prediction in freeway with non-recurrent congestion. Neurocomputing, 133, 74-83. https://doi.org/10.1016/j.neucom.2013.11.029
Zhang, Y., & Haghani, A. (2015). A gradient boosting method to improve travel time prediction. Transportation Research Part C: Emerging Technologies, 58, 308-324. https://doi.org/10.1016/j.trc.2015.02.019
Friedman, J. H. (2001). Greedy function approximation: A gradient
boosting machine. Annals of Statistics, 29, 1189-1232. https://doi.org/10.1214/aos/1013203451
Antoniades, C., Fadavi, D., Amon, A. F. J. (2016). Fare and Duration Prediction: A Study of New York City Taxi Rides. http://cs229.stanford.edu/proj2016/report
Jaiwal, H., Bansal, T., Jakate, P., Saxena, T. (2016). NYC Taxi Rides:
Fare and Duration Prediction. https://cseweb.ucsd.edu/classes/wi17/cse258-a/reports/a077.pdf
Niaki, S. T. A., & Hoseinzade, S. (2013). Forecasting S&P 500 index using artificial neural networks and design of experiments. Journal of Industrial Engineering International, 9, 1-9. https://doi.org/10.1186/2251-712X-9-1
Zolghadr, M., Niaki, S. A. A., & Niaki, S. T. A. (2018). Modeling and forecasting US presidential election using learning algorithms. Journal of Industrial Engineering International, 14, 491-500. https://doi.org/10.1007/s40092-017-0238-2
Maleki, M. R., Amiri, A., & Mousavi, S. M. (2015). Step change point estimation in the multivariate-attribute process variability using artificial neural networks and maximum likelihood estimation. Journal of Industrial Engineering International, 11, 505-515. https://doi.org/10.1007/s40092-015-0117-7
2016 Yellow Taxi-Trip Data. (n.d.). https://data.cityofnewyork.us/Transportation/2016-Yellow-Taxi-Trip-Data/k67s-dv2t
O’Rourke, J. (1998). Computational Geometry in C. 2nd edition,
Cambridge. Weather data. (n.d.). www.wunderground.com
Han, J., Kamber, M., & Pei, J. (2012). Data Mining: Concepts and Techniques. 3rd edition, Elsevier.
Montgomery, D. C. (2012). Design and Analysis of Experiments. 8th Edition, John Wiley.