A novel passenger flow prediction model using deep learning methods
A novel passenger flow prediction model using deep learning methods
复制标题
使用深度学习方法的新型客流预测模型
DOI：
10.1016/j.trc.2017.08.001
复制
发表时间：
2017-11-01
影响因子：
8.3
通讯作者：
Chen, Rung-Ching
中科院分区：
文献类型：
--
作者：
Liu, Lijuan;Chen, Rung-Ching
目前，深度学习已经在很多领域得到了成功的应用，并取得了惊人的效果。与此同时，在过去的几年里，大数据已经彻底改变了交通运输行业。这两个热点问题促使我们重新思考客流预测这一传统问题。作为深度神经网络（DNN）的一种特殊结构，自编码器可以深度抽象地提取嵌入在输入中的非线性特征，而不需要任何标记。利用其卓越的能力，本文提出了一种基于深度学习方法的小时客流预测模型。输入特征定义为一周中的某一天、某一天的某一时刻、某一节假日等时间特征，入境出境、票证卡等场景特征，以及以往平均客流、实时客流等客流特征。在第一阶段，将这些特征组合并训练为不同的堆叠自编码器（SAE）。然后，在第二阶段，进一步使用预训练的SAE来初始化以实时客流作为标签数据的监督DNN。以厦门市4个快速公交（BRT）站点三期客流预测为例，对混合模型（SAE-DNN）进行应用和评价。实验结果表明，该方法能够为具有不同客流分布的不同BRT站点提供更加准确和通用的客流预测模型。(C) 2017 Elsevier Ltd.版权所有。
Currently, deep learning has been successfully applied in many fields and achieved amazing results. Meanwhile, big data has revolutionized the transportation industry over the past several years. These two hot topics have inspired us to reconsider the traditional issue of passenger flow prediction. As a special structure of deep neural network (DNN), an autoencoder can deeply and abstractly extract the nonlinear features embedded in the input without any labels. By exploiting its remarkable capabilities, a novel hourly passenger flow prediction model using deep learning methods is proposed in this paper. Temporal features including the day of a week, the hour of a day, and holidays, the scenario features including inbound and outbound, and tickets and cards, and the passenger flow features including the previous average passenger flow and real-time passenger flow, are defined as the input features. These features are combined and trained as different stacked autoencoders (SAE) in the first stage. Then, the pre-trained SAE are further used to initialize the supervised DNN with the real-time passenger flow as the label data in the second stage. The hybrid model (SAE-DNN) is applied and evaluated with a case study of passenger flow prediction for four bus rapid transit (BRT) stations of Xiamen in the third stage. The experimental results show that the proposed method has the capability to provide a more accurate and universal passenger flow prediction model for different BRT stations with different passenger flow profiles. (C) 2017 Elsevier Ltd. All rights reserved.