基于交通指数云图的多尺度路网交通流分析

Common methods of traffic flow analysis on road networks take road sections as research objects. These methods divide the speed range of the road sections into some segments, and then map these segments to different colors for traffic state publications in minutes. This is a qualitative evaluation way. This way has three drawbacks. Firstly, among the road sections with the same color, it cannot recognize which road section has the best traffic state. Secondly, it cannot analysis the traffic flow from the areas, which have bigger spatial scale than the road sections. Thirdly, it cannot analysis the traffic flow in larger temporal scale, such as clock, day, month, year, and so on. Among traffic flow analysis, it is a rare method, which analyzes the traffic flow from both the large spatial and temporal scales. Besides that, there are few methods, which can analyze the traffic flow both on variable spatial and temporal scales. This project adopts the traffic index to evaluate the traffic state of the road networks. A novel conception of Traffic Index Cloud Photograph (TICP) is proposed. Also, the method for generating TICPs is presented. Based on TICPs, this project analyzes the traffic flow on large spatial and temporal scales, and mines the mutative patterns of the traffic flow on the spatial and temporal dimensions. Simultaneously, this project employs the traffic index curves of the road sections to analyze the traffic flow on the small scale. Finally, this project completes the goal that multi-scale traffic flow analyzing by uniting the TICPs and traffic index curves.

常见的路网交通流分析方法大多以路段这一路网最微观的组成单元作为研究对象，以分钟作为时间单位，通过将速度分段映射为不同的颜色进行发布。这一定性评价方式对相同颜色的路段无法区分其交通状态的优劣，而且无法对具有宏观空间尺度的路网区域进行分析，也无法从小时、日、月、年等更宏观的时间尺度进行分析与发布。在路网交通流分析中，同时从宏观空间和时间尺度对路网交通流进行分析的研究成果尚少，尤其是实现可变空间尺度和时间尺度路网交通流分析的研究成果几乎没有。本项目采用交通指数这一定量评价指标，首次提出了交通指数云图的概念和自动生成方法，并基于交通指数云图从宏观的空间和时间尺度对路网交通流进行分析，深入挖掘路网交通流在空间和时间维度上的变化特性；同时，还将使用路段交通指数曲线从微观尺度分析路网交通流的特性。最终通过联合交通指数云图和交通指数曲线实现可变空间和时间尺度的路网交通流分析，即多尺度的路网交通流分析。

经典的交通状态预报的方法在进行交通状态预报时，空间尺度和时间尺度固定在较小的尺度。然而，宏观空间尺度和时间尺度的交通流状态的预报和分析也非常重要。例如，选择一个交通状态最好的区去举办展览，或者，选择在一年的时间跨度内交通状态最差的路段去做维护。因此本课题主要聚焦于宏观尺度的交通流分析。本课题主要基于交通指数云图对城市路网的交通流进行多尺度的分析，着重解决了如下几个重要问题：（1）根据交通指数模型计算交通指数；（2）提出了一种新的交通指数的可视化方法，即交通指数云图；（3）解决了交通指数云图中的最优空间尺度计算问题；（4）解决了交通指数云图的最优时间尺度计算问题。