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基于平行数据的交通预测和社会交通信息提取方法研究
陈圆圆
学位类型工学博士
导师王飞跃
2018-05-23
学位授予单位中国科学院大学
学位授予地点北京
关键词平行数据智能 平行交通 交通知识自动化 深度学习 智能交通
摘要智能交通系统 (Intelligent Transportation System, ITS) 是交通管理与控制由工业化时代进入信息化时代的标志,相关理论研究和技术应用极大地提高了系统的运行效率和安全性。然而随着交通系统规模和结构的不断变化,ITS由于缺乏智能性而进入技术发展瓶颈,因此需引入新一代智能化交通管控理论和技术体系。在此背景下,平行交通 (Parallel Transportation or Transportation 5.0) 被正式提出。作为基于软件定义的交通系统、线上线下的计算实验和知识自动化的闭环平行控制于一体的智能化系统管控体系,平行交通将引领交通系统的管控由信息化时代跨入智能化时代。
交通数据的智能化处理与分析是平行交通系统研究的重要组成部分,具有重要的理论研究意义和实践应用价值。ITS的研究对象主要集中在物理空间,平行交通系统在此基础上通过空间扩展实现智能化。具体地,已有研究工作主要面向基于物理传感器采集的结构化数据,考虑到采集该类型数据在经济和时间等方面的成本较高,论文以人工交通数据生成为基础,开展基于平行数据的交通数据智能化处理与分析研究。进一步,论文将研究工作扩展到基于社会传感器采集的非结构化数据,开展基于社交媒体文本的交通相关信息的提取研究。论文的主要工作及创新点包括以下几个方面:
1. 提出了一种基于对抗网络的交通数据生成方法。相比于现有方法中利用随机变量作为生成器的输入隐编码 (latent code),考虑交通数据的时空关联特性,提出利用真实数据或加入噪声的真实数据作为生成器的输入隐编码。根据人工交通数据生成目的,提出利用重建误差限制生成数据偏离其对应的真实数据的程度。设计了交通数据生成模型的网络结构和参数训练算法,并通过实验评估了所提出方法的可行性和有效性。
2. 提出了一种基于平行数据的交通数据弥补方法。基于小规模真实交通数据,利用生成对抗网络模型合成大规模人工交通数据,设计了基于平行数据的交通数据弥补实现机制及对应的模型参数训练算法。最后,论文针对离散和连续缺损的交通流量弥补问题分别设计了计算实验,并从检测器、时间段等多个维度比较了交通流量弥补方法的性能,实验结果表明,与仅利用真实数据的交通数据弥补方法相比,基于平行数据的交通数据弥补方法是有效的,且显著地提高了交通弥补的性能指标。
3. 提出了一种基于平行数据的交通预测方法。针对交通数据序列中的动态特性,提出了基于长短记忆 (Long Short-Term Memory, LSTM) 模型的交通预测方法。基于小规模真实交通数据,利用生成对抗网络模型合成大规模人工交通数据,设计了基于平行数据的交通预测实现机制及对应的模型参数训练算法。最后,设计了交通状态预测实验,验证了基于 Stacked LSTM 模型的交通预测方法的有效性和优越性,并设计了基于平行数据的交通流量的预测实验,实验结果表明,与仅利用真实数据的交通流量预测方法相比,基于平行数据的交通预测方法是有效的,且显著地提高了交通流量预测的性能指标。
4. 提出了一种社交媒体文本中交通相关信息的提取方法。针对社会交通信息的非结构化特征,将交通相关信息的提取任务转换为分类问题。考虑到用户自主产生的社交媒体短文本内容中话题分布广泛又包含大量噪声的特点,提出了利用在大规模未标注数据集上训练得到的语义词向量表示社交媒体文本,并运用卷积神经网络 (Convolutional Neural Network, CNN) 模型、 LSTM 模型以及基于 LSTM 和 CNN 的集成模型 LSTM-CNN 提取深层特征,然后利用特征分类方法实现对交通相关信息的提取。最后,设计了基于新浪微博文本的交通相关信息提取实验,验证了论文方法的有效性和优越性。
其他摘要

As the management of transportation transitioning from industrial technology to information technology, Intelligent Transportation System (ITS) has made transportation safer and more efficient. However ITS has encountered technology bottlenecks with the development of societies and transportation systems. Therefore, management of transportation systems needs parallel intelligence, which will lead a transition from information technology to intelligence technology. More specifically, the application of parallel intelligence in transportation is called parallel transportation, which is based on software-defined transportation systems, O2O (online to offline and vice versa) computational transportation experiments, and parallel transportation with knowledge automation for closed-loop control and management with society-wide feedback.

Intelligent processing and analyzing of data is an elemental function of parallel transportation, which is of great theoretical significance and practical value to accelerate the researches and applications. Previous work in ITS focus on the entities of physical space, parallel transportation gains intelligence by extending the exploration to artificial space and social space. More specifically, structural data collected from physical detectors are main entities to process in ITS. It is very expensive and even impossible to collect big and accurate data. Therefore, we investigate the alternative artificial data to be used in data processing and data-driven modeling. Further, we investigate the processing and analyzing of the unstructured data generated by social sensors. The major works and achievements consist of four aspects:

1. A method of traffic data generation based on generative adversarial networks (GANs) is proposed. As it is difficult for the standard GAN algorithm to generate time-dependent traffic flow data, we made twofold modifications: (i) Using the real data or the corrupted ones instead of random vectors as latent codes to generator within GANs, and (ii) Introducing a representation loss to measure discrepancy between synthetic data and real data. Experimental results on a real traffic dataset demonstrate that our method can significantly improve the performance of traffic data imputation.

2. An approach of traffic data imputation based on parallel data paradigm is proposed. To develop traffic data imputation models with high accuracy, traffic data must be large and diverse, which is costly. An alternative is to use synthetic traffic data, which is cheap and easy-access. We train GANs model to generate artificial traffic data and design imputation models for the tasks consisting of discrete corrupted points and continuous corrupted points, respectively. We perform extensive experiments on real traffic dataset to investigate the effectiveness of the proposed method. Experimental results show that our method leads to significant improvements.

3. An approach of traffic prediction based on parallel data paradigm is proposed. Taking the dynamic temporal behavior for a traffic data sequence into consideration, we propose to apply long short-term memory (LSTM) model to predict traffic data.  As developing traffic prediction models with high accuracy, traffic data must be large and diverse, which is costly, therefore, we alternatively use synthetic traffic data to train traffic prediction models. For live traffic condition prediction, we compared the performance of the proposed Stacked LSTM model with multilayer perceptron model, decision tree model and support vector machine model. Experiments show the proposed method is superior to the competing methods. For traffic flow prediction, we perform extensive experiments to investigate the parallel data method, and experimental results show that our method leads to significant improvements.

 4. An approach of traffic relevant information extraction is proposed. We focus on a specific problem in social media mining which is to extract traffic relevant microblogs from Sina Weibo, a Chinese microblogging platform. It is transformed into a machine learning problem of short text classification. Firstly, we apply the continuous bag-of-word (CBOW) model to learn word embedding representations based on a dataset of three billion microblogs. Compared to traditional one-hot vector representation of words, word embedding can capture semantic similarity between words and has been proved effective in natural language processing tasks. Next we propose using convolutional neural networks (CNNs), long short-term memory (LSTM) models and their combination LSTM-CNN to extract traffic relevant microblogs with the learned word embeddings as inputs. We compare the proposed methods with competitive approaches, including the support vector machine (SVM) model based on bag of n-gram features, SVM model based on word vector features, and multi-layer perceptron model based on word vector features. Experimental results show the effectiveness of the proposed deep learning approaches.

文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/21024
专题毕业生_博士学位论文
作者单位中国科学院自动化研究所
推荐引用方式
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陈圆圆. 基于平行数据的交通预测和社会交通信息提取方法研究[D]. 北京. 中国科学院大学,2018.
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