Topic:Prof. Liu Xiang's Lecture Notice
Time: 16:30PM – 18:00PM, Nov. 4, 2025
Location: Room A1148, New Main Building
Guest: Li Xiang is a Professor and Doctoral Supervisor at Beijing Institute of Technology, and a National-Level Talent. His research areas include data-driven decision-making and digital-intelligent transportation management. He has presided over 3 key projects of the National Natural Science Foundation of China. He has won 9 provincial and ministerial-level awards and 6 national industry association awards, published 2 English monographs, edited 2 textbooks, authored more than 160 papers, and obtained 40 authorized patents. Currently, he serves as the Editor-in-Chief of the SCI-indexed journal International Journal of General Systems.
Abstract:
This study focuses on the capacity management of ride-hailing services with unstable passenger demand. In the existing parametric approaches, normally a two-stage prediction-then-optimization (PTO) paradigm is used to implement demand prediction and capacity management sequentially, which generally achieves suboptimal performance due to the information loss in demand prediction. To attain optimality, in this study, we integrate these two stages into a prediction & optimization (P&O) paradigm, and formulate a deep learning approach consisting of a weighted sample average approximation (WSAA) module and VMD-CNN-BiLSTM-AM1 (V-CBA) module, where the WSAA module embeds with knearest neighbors, kernel regression or a decision tree to select significant historical samples, and the V-CBA module couples decomposition, convolution, recursion, attention, and optimization together for complex nonlinear mapping. Based on real-world datasets, the deep learning-based P&O paradigm is demonstrated to have superior performance in capacity management of ride-hailing services.
