摘要
随着经济社会的快速发展,城市规模不断扩大,城市交通面临着巨大的压力。通过智能交通系统建设,提高交通管理与服务的信息化水平和决策支持能力,是减少交通事故,解决交通拥堵,促进城市环保,提高人民生活质量的根本途径。纵观国内外智能交通系统的建设情况,道路、信号灯、摄像头等交通基础设施不断完善,传感、无线通信、智能终端、互联网、云计算等信息技术得到综合利用,交通信息平台中积累了越来越多的交通数据,交通实时监控、定位导航、城市应急管理等新型应用不断涌现。智能交通产业迎来了极大的发展机遇,并且在较长一段时间内都将继续呈现高速增长的态势。
对具有时空特征的交通数据进行智能分析,可获取丰富的、有价值的知识,如时空分布、时空关联规则、时空变化趋势等,这些知识能够为交通调度、路径规划、目标跟踪等提供决策支持。本文面向动态交通流和交通路网拥堵状态分析需求,分析时空数据组织和管理策略,并基于运动轨迹、交通流量、道路拥堵状态等不同数据元素研究时空相似性、时空相关性和时空关联性的度量和表达方法,在此基础上进行时空聚类、时空预测、关联规则挖掘、异常检测等操作,并应用于交通热点、路网拥堵趋势、短时交通流量预测、异常轨迹检测等分析。
本文面向动态交通数据分析需求,主要研究工作包括以下几个方面：
(1)研究多粒度动态交通网络的数据组织与管理策略,适应不同用户兴趣、不同时空范围的动态交通数据分析需求。(第2章)
基于“多粒度动态交通数据集成管理”的核心思想,突破传统空间数据库管理动态交通信息的瓶颈,解决交通数据在多时空粒度下统一组织与有效管理的难题,在减少数据冗余的同时,支持多时空粒度的数据访问和分级存储,增强系统普适性。同时,引入流数据管理技术,在提供空间分析的同时,支持静态数据查询、连续查询和混合查询,提高系统实时分析能力。
(2)以位置序列(轨迹)为数据元素,综合考虑轨迹的时空特征和语义,研究时空相似性分析方法,并与轨迹聚类相结合,分析用户移动模式及其时空分布状态。(第3章)
该方法首先依据路网约束,利用道路交叉点、停留点等语义提取轨迹特征点并进行轨迹划分,从而大大减小后续处理的数据量。之后,分别计算轨迹间的时间相似度和空间相似度并进行归一化操作,以此计算轨迹间的时空距离,并进行轨迹聚类。试验表明,聚类所得的轨迹簇能够直观、准确地反映用户运动模式、热点路径等信息。
(3)以流量序列为数据元素,综合考虑序列间的时空特征和语义,研究时空相关性分析方法,并结合预测模型预测区域内的短时交通流量。(第4章)
交通流量序列的产生与路网的空间可达性密切相关,而且空间相关的道路之间其流量序列必然存在着时间差异关系。为此,引入了空间权重矩阵与时间延迟以表达各流量序列间的时空相关性,并以时空相关系数为依据,快速选取与预测点相关的预测因子,最后采用支持向量机进行短时交通流量预测。试验表明,该方法具有较高的短时交通流量预测精度。
(4)以拥堵状态为数据元素,综合拥堵状态间的时空特征和语义,研究时空关联性分析和表达方法,并结合时空关联规则挖掘分析交通拥堵的趋势和成因。(第5章)
同时考虑时间和空间约束,能够在分析过程中及时过滤时空不相关的数据,提高时空关联规则的获取效率。基于这一思路,在频繁项集的产生过程中同时分析数据的时间有效性和空间关联性,首先对时空数据进行时间段划分和空间关联性分析并形成事务表,然后对空间关联的项集进行连接并产生时空关联规则。实验表明,该方法具有较高的处理性能,可以利用路段间拥堵状态的时空关联规则进行交通拥堵趋势分析与预测。
(5)以实时运动轨迹和预定义路线为数据元素,研究动态时空序列距离计算方法,并应用于异常轨迹实时检测。(第6章)
在公共交通、物流运输等应用领域,移动对象的运动轨迹受路网约束且大多被预先设定,偏离预先设定的正常轨迹可能预示着某种异常。异常轨迹实时检测方法在事先设定的检测时间内,采用流数据连续查询模式获取用户的实际轨迹,并动态调整参与计算的正常轨迹段,最后利用改进的有向Hausdorff距离反映实际轨迹的偏离程度。实验表明,该方法能够及时有效地检测异常。
本文的主要贡献在于结合交通领域约束,对时空数据集成管理、时空语义扩展、时空相似性分析、时空相关性分析、时空关联性分析等关键技术进行研究和应用实践,对提升智能交通管理和出行者服务水平具有一定的借鉴意义和参考价值。
With the rapid development of economy and society, the city scale continues to expand while transportation is under great stress. Constructing the intelligent transportation system is an effective solution to improve the level of transportation management and decision support. It can alleviate the traffic accident and congestion influence, and improve the urban environment and living quality of residents. Throughout the construction of intelligent transportation system in the world, the transportation infrastructure such as road, signal lamp and camera are developed and progressed greatly. With the development of information technologies such as sensor, wireless communication, smart device, Internet and cloud computing, massive traffic data are collected. Many applications such as real-time monitor, navigation and urban emergency management are emerging, which makes a new opportunity and challenge for intelligent transportation industry.
There are many valuable spatio-temporal knowledge are implied in massive traffic data, such as spatio-temporal distribution, association rule, variation trend, etc. Spatio-temporal knowledge is useful for decision supporting in traffic scheduling, road planning, target tracking, etc. Facing to the requirement of dynamic analysis of traffic data and status, this dissertation studies multi-granularity and dynamic model of transportation network, and analyzes the spatio-temporal data organization and management strategy. Measure and expression methods of similarity, correlation and association are studied from different data elements such as trajectory, traffic flow and congestion status. These methods are applied for analyzing hotspot distribution, congestion trend, short-time traffic forecasting and anomaly detection.
Facing to the requirements of dynamic transportation analysis, the main research works of this dissertation are listed as follows.
(1) The data organization and management strategy of multi-granularity and dynamic transportation network is studied. It adapts to the requirement of knowledge acquisition for different user interests and spatio-temporal area.(Chapter2)
Based on the core idea of "integrated management of multi-granularity and dynamic transportation data", the spatio-temporal model breaks through the bottleneck of traditional spatial data management and provides a solution for dynamic transportation data organization and management. It reduces the data redundancy and supports multi-granularity data access and multi-level storage. It is useful for constructing a ubiquitous system. On the other hand, this model introduces the data stream technology to support the static, continuous and hybrid query. It helps to improve the capability of real-time processing.
(2) Considering the trajectory data and combining with their spatio-temporal features and semantics, the spatio-temporal similarity measure method is studied. Trajectory clustering based on spatio-temporal similarity analysis can be used for mining user moving pattern and spatio-temporal distribution.(Chapter3)
This method firstly extracts characteristic points according to the road constraints, such as crossings and stops, to partition the trajectories. This step sharply reduces the data size. Then, the temporal similarity and spatial similarity are measured separately. The spatio-temporal distance of trajectories is calculated after normalization and is applied for clustering. Experiment results show that this method gains good performance. The clusters reflect the knowledge of moving pattern, hot path, etc.
(3) Considering the traffic flow data and combining with their spatio-temporal features and semantics, a spatio-temporal correlation analysis method is studied. It can be used for short-term traffic flow forecasting.(Chapter4)
In this method, spatio-temporal correlation coefficient is defined firstly to reveal the relationship of different traffic flow series, a quick calculation method of spatio-temporal correlation coefficient is proposed after analyzing the corresponding properties. Secondly, a spatio-temporal analysis algorithm based on spatio-temporal correlation coefficient matrix is proposed to choose the proper predictor. At last, a forecasting model is presented based on support vector machine due to the nonlinear characteristics of traffic flow. Experiment results show that this method can gain higher prediction precision.
(4) Considering the congestion states data and combining with their spatio-temporal features and semantics, the spatio-temporal association analysis and expression methods are proposed. It can be used for spatio-temporal association rule mining to analyze the congestion trend and reason.(Chapter5)
This method considers the spatial and temporal constraints simultaneously, which filters irrelevant data in advance and improves the efficiency of spatio-temporal association rule discovering. Based on the idea, this method analyzes the time validity and spatial relativity simultaneously during the generation of frequency item sets. It classifies the time duration of spatio-temporal data and considers the spatial relationship firstly and generates the transaction table, then performs join operation on spatial-related item sets. Experiments illuminate that the algorithm is well performed. The algorithm is applied in intelligent transportation system to analyze the trend of traffic congestion by identifying spatio-temporal association between road segments.
(5) Considering the real-time moving trajectory and predefined route, the dynamic distance calculation method of spatio-temporal sequence is studied. It can be integrated in real-time detection of trajectory anomaly.(Chapter6)
In many fields such as public transportation and logistics service, the trajectories of moving objects are constrained by road network and mostly predefined. Deviating from the normal trajectory might imply some problems. During the detection period, partial sequence of the real trajectory is selected dynamically based on continuous query model of stream data, and the scope of normal trajectory is adjusted correspondingly. The improved Hausdorff Distance is used to reflect the degree of deviation finally. Experiments show that, comparing with the conventional map matching method, the proposed algorithm is more efficient for anomaly detection.
The main contribution of this dissertation is the research and application of spatio-temporal data analysis methods with the domain constrains. The research results of key techniques such as spatio-temporal data integration and management, spatio-temporal semantic expansion, spatio-temporal similarity measure, spatio-temporal correlation measure and spatio-temporal association rule are useful for improving the management and service quality of intelligent transportation system.
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