With the development of big data and deep learning, bus passenger flow prediction considering real-time data becomes possible. Real-time traffic flow prediction helps to grasp real-time passenger flow dynamics, provide early warning for a sudden passenger flow and data support for real-time bus plan changes, and improve the stability of urban transportation systems. To solve the problem of passenger flow prediction considering real-time data, this paper proposes a novel passenger flow prediction network model based on long short-term memory (LSTM) networks. The model includes four parts: feature extraction based on Xgboost model, information coding based on historical data, information coding based on real-time data, and decoding based on a multi-layer neural network. In the feature extraction part, the data dimension is increased by fusing bus data and points of interest to improve the number of parameters and model accuracy. In the historical information coding part, we use the date as the index in the LSTM structure to encode historical data and provide relevant information for prediction; in the real-time data coding part, the daily half-hour time interval is used as the index to encode real-time data and provide real-time prediction information; in the decoding part, the passenger flow data for the next two 30 min interval outputs by decoding all the information. To our best knowledge, it is the first time to real-time information has been taken into consideration in passenger flow prediction based on LSTM. The proposed model can achieve better accuracy compared to the LSTM and other baseline methods.

中文翻译：

---

一种考虑历史和实时数据的基于 LSTM 的客流预测方法

随着大数据和深度学习的发展，考虑实时数据的公交客流预测成为可能。实时交通流预测有助于掌握实时客流动态，为突发客流提供预警，为实时公交计划变化提供数据支持，提高城市交通系统的稳定性。为了解决考虑实时数据的客流预测问题，本文提出了一种基于长短期记忆（LSTM）网络的新型客流预测网络模型。该模型包括四部分：基于Xgboost模型的特征提取、基于历史数据的信息编码、基于实时数据的信息编码、基于多层神经网络的解码。在特征提取部分，通过融合总线数据和兴趣点来增加数据维度，以提高参数数量和模型精度。在历史信息编码部分，我们使用日期作为LSTM结构中的索引，对历史数据进行编码，为预测提供相关信息；在实时数据编码部分，以每天半小时的时间间隔为指标，对实时数据进行编码，提供实时预测信息；在解码部分，通过对所有信息进行解码输出接下来两个30分钟间隔的客流数据。据我们所知，这是第一次在基于 LSTM 的客流预测中考虑实时信息。与 LSTM 和其他基线方法相比，所提出的模型可以实现更好的准确性。