ABSTRACT
Railway passenger transport is essential to modern transportation in China. The prediction of railway passenger capacity is of vital importance for ensuring the safety of railway transportation. This paper introduces social network text data into the prediction of railway passenger capacity. In the process of analyzing social network text data, text mining methods are used to analyze the text data, and the information related to railway passenger flow is extracted from the text and added to the prediction model. Meanwhile, in order to obtain better prediction results, this paper applies deep learning method on the data. The combination of text mining and deep learning method has greatly improved the accuracy of our prediction model. Experimental results show that a good accuracy rate has been achieved.
- Cox, T., Houdmont, J., & Griffiths, A. (2006). Rail passenger crowding, stress, health and safety in Britain. Transportation Research Part A: Policy and Practice, 40(3), 244--258.Google ScholarCross Ref
- Dalkey, N., & Helmer, O. (1963). An experimental application of the Delphi method to the use of experts. Management science, 9(3), 458--467.Google Scholar
- Liu, Y., Zhou, B., Feng, C., & Pu, S. (2012). Application of comprehensive evaluation method integrated by Delphi and GAHP in optimal siting of electric vehicle charging station. 2012 International Conference on In Control Engineering and Communication Technology (ICCECT), pp. 88--91. Google ScholarDigital Library
- Sun, Y., Leng, B., & Guan, W. (2015). A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system. Neurocomputing, 166, 109--121. Google ScholarDigital Library
- Zhuo, W., Li-Min, J., Yong, Q., & Yan-Hui, W. (2007). Railway passenger traffic volume prediction based on neural network. Applied Artificial Intelligence, 21(1), 1--10. Google ScholarDigital Library
- Tsai, T. H., Lee, C. K., & Wei, C. H. (2009). Neural network based temporal feature models for short-term railway passenger demand forecasting. Expert Systems with Applications, 36(2), 3728--3736. Google ScholarDigital Library
- Qi, F., Liu, X., & Ma, Y. (2009). Prediction of railway passenger traffic volume based on neural tree model. 2009 ICICTA'09. Second International Conference on Intelligent Computation Technology and Automation. Vol. 1, pp. 370--373. Google ScholarDigital Library
- Deng, W., Li, W., & Yang, X. H. (2011). A novel hybrid optimization algorithm of computational intelligence techniques for highway passenger volume prediction. Expert Systems with Applications, 38(4), 4198--4205. Google ScholarDigital Library
- Wang, Y., Xu, W., & Jiang, H. (2015). Using text mining and clustering to group research proposals for research project selection. In 2015 48th Hawaii International Conference on System Sciences (HICSS). pp. 1256--1263. Google ScholarDigital Library
- Wang, Y., & Xu, W. (2018). Leveraging deep learning with LDA-based text analytics to detect automobile insurance fraud. Decision Support Systems, 105, 87--95.Google ScholarCross Ref
- Wang, Y., Wang, M., & Xu, W. (2018). A Sentiment-Enhanced Hybrid Recommender System for Movie Recommendation: A Big Data Analytics Framework. Wireless Communications and Mobile Computing, 2018. Google ScholarDigital Library
- Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. nature, 323(6088), 533.Google Scholar
- LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436.Google Scholar
- Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85--117. Google ScholarDigital Library
- Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., & Ng, A. Y. (2011). Multimodal deep learning. In Proceedings of the 28th international conference on machine learning. pp. 689--696. Google ScholarDigital Library
- Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural computation, 18(7), 1527--1554. Google ScholarDigital Library
Index Terms
Social Networks and Railway Passenger Capacity: An Empirical Study Based on Text Mining and Deep Learning
Recommendations
Flexible Connections in PESP Models for Cyclic Passenger Railway Timetabling
<P>In this paper we describe how rolling stock and passenger connections in a cyclic railway timetable can be modeled in a flexible way within the model for the Periodic Event-Scheduling Problem (PESP). Usually, PESP models assume that the constraints ...
Optimize train capacity allocation for the high-speed railway mixed transportation of passenger and freight
Highlights- We study the train capacity allocation problem for mixed transportation in high-speed rail systems.
AbstractThe collaborative transportation strategy of passengers and freights can improve the efficiency and revenue of the high-speed railway (HSR) system. This paper focuses on the train capacity allocation problem for the mixed ...
Modeling the Operation of Passenger Flow on Railway Passenger Dedicated Line Based on Multi-agents
AICI '10: Proceedings of the 2010 International Conference on Artificial Intelligence and Computational Intelligence - Volume 03Passenger flow operation is an urgent issue to be solved for the segmental construction of passenger dedicated lines in China. In essence, passenger flow operation is the mergence and splitting of passenger flows. Therefore, this study establishes a ...
Comments