A spatio-temporal prediction method of short-term OD in urban rail transit with sparse data

Accurate and rapid acquisition of short-term Origin-Destination (OD) demand is critical for urban rail transit managers to catch passenger travel demand changes in a timely manner and make scientific decisions. Due to the high dimensional and sparse characteristics of OD data, short-term OD prediction has issues with low prediction accuracy and slow calculation. To improve the prediction accuracy and timeliness, an OD predication model was proposed based on spatio-temporal decomposition with dynamic mode decomposition (STDMD) by considering the spatio-temporal characteristics and matrix decomposability of OD demand. First, the spatiotemporal decomposition module that incorporates time series decomposition and discrete wavelet transform was utilized to decompose the original data into several spatio-temporal components and capture the space-time aspects. Meanwhile, the eigenvalues of the data matrix were truncated by using singular value decomposition and the dynamic mode decomposition prediction module. The data dimensionality was reduced and denoised, and the prediction results of each component were integrated to realize the fast and accurate prediction of urban rail OD. To verify the validity of the model, the Beijing subway data was used to illustrate the effectiveness of the proposed model. The results show that, the STDMD has higher prediction accuracy and shorter prediction time that improves accuracy by 5.0%, 15.3% and 17.9% than vector autoregression, convolution long and short memory networks, and time regularized matrix factorization. It also reduced prediction time by 95.7% and 37.6% as compared to vector autoregression and convolution long and short memory networks, respectively. Each module in STDMD model can effectively improve the prediction accuracy. The STDMD model has strong robustness on a variety of metro data sets. The suggested STDMD provides a new idea and method for OD prediction with sparse data, and it has both research and practical value. © 2023, Central South University Press. All rights reserved.