Abstract
With the increasing complexity of subway lines, people’s demand for subway travel is also increasing. Reasonable regulation of vehicles on different zones can not only improve the efficiency of people’s travel but also lay the foundation for future short-time zone passenger flow prediction. The Isomap algorithm is used to represent the high-dimensional data by the low-dimensional method after transformation, and then the low-dimensional data are sorted from small to large, which results in the ordered OD data pairs. The ordered OD data pairs are then sorted in the database one by one for the last month, the corresponding data sets are constructed, and then the data are trained using the recurrent neural network model GRU to derive the passenger flow prediction results for the following week.
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Chen, J., Yu, K., Wu, K., Pan, J. (2022). Deep Learning Short-Time Interval Passenger Flow Prediction Based on Isomap Algorithm. In: Wu, TY., Ni, S., Chu, SC., Chen, CH., Favorskaya, M. (eds) Advances in Smart Vehicular Technology, Transportation, Communication and Applications. Smart Innovation, Systems and Technologies, vol 250. Springer, Singapore. https://doi.org/10.1007/978-981-16-4039-1_1
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DOI: https://doi.org/10.1007/978-981-16-4039-1_1
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