Fine-Grained Service-Level Passenger Flow Prediction for Bus Transit Systems Based on Multitask Deep Learning
Dan Luo, Dong Zhao, Qixue Ke, Xiaoyong You, Liang Liu, Desheng Zhang, Huadóng Ma, Xingquan Zuo
Abstract
Bus services play a crucial role in urban transit. It is significant to achieve the fine-grained service-level passenger flow prediction (SPFP), namely to predict the total number of passengers for each service of each bus line passing through each station during the next short-term interval. However, it faces great challenges due to complex factors including inter-station and inter-line spatial dependencies, intra-station and inter-service temporal dependencies, and internal/external influences. To address these challenges, we propose a multitask deep-learning (MDL) approach, called <i>MDL-SPFP</i>, to jointly predict the arriving bus service flow, line-level on-board passenger flow and line-level boarding/alighting passenger flow by leveraging well-designed deep neural networks called <i>ARM</i>. The MDL framework can mutually reinforce the prediction of each type of flow, and finally integrate the outputs to achieve the fine-grained service-level prediction. The ARM network combines three modules, Attention mechanism, Residual block and Multi-scale convolution, to well capture various complex non-linear spatio-temporal dependencies and influence factors. Extensive experiments based on a large-scale realistic bus operation dataset are conducted to confirm that our MDL-SPFP approach outperforms 10 state-of-the-art baselines, and improves 22.39% accuracy than the best baseline.