Integrated Spatio-Temporal Data Mining for Quantitative Assessment of Road Network Performance

伦敦最近的交通调查和道路网性能分析显示，交通流量下降，速度下降，拥堵加剧。据信，拥堵的增加反映了旅行者对争夺道路网络容量的竞争的反应，无论是临时的还是长期的。为追求市长交通优先事项而不断调整网络容量，例如，改善安全和便利设施，以及增加公共汽车、出租车、行人和骑自行车的优先事项，导致私家车交通的延误增加。目前伦敦主要道路拥堵的年成本估计在18亿至30亿英镑之间。道路网络性能的分析是复杂的。这是因为公路网基本上是一个有许多因素的开放系统，旅行者可以通过以许多不同的方式修改他们的选择来作出反应，这将影响到监测的绩效结果。这些因素的形式、它们的因果关系方向、其中一些因素相互作用强烈的事实以及它们的绝对数量都导致了复杂性。这些因素在时间和空间上都有不同的影响模式，而影响的非线性使不同的因果模式的分析变得复杂，包括一旦发生拥堵就可能突然增加。建立各因素的时空相关性模型是分析网络性能的瓶颈。挑战在于如何同时无缝地对空间和时间上的依赖关系进行建模，以便提高分析的准确性。另一个挑战是在分析中充分考虑真实道路网络的拓扑(链接和层次)和几何图形(距离和方向)。这些也是建模其他类型网络复杂性的根本挑战。本研究将解决这些挑战。它将通过两种新的机器学习方法(动态递归神经网络和支持向量机)与最先进的统计时空序列分析(时空自回归综合移动平均-STARIMA)和地理加权回归-GWR的创新组合来实现。之所以选择这些方法，是因为与传统的统计方法相比，它们在交通研究中的应用相对较新，更重要的是，它们具有改善网络复杂性表示的潜力。DRNN和支持向量机可以对STARIMA不能完全容纳的大部分时空数据中存在的非线性和非平稳性进行建模。STARIMA具有DRNN和支持向量机所不具备的解释能力。GWR可以对网络的异构性进行建模，并提高对网络规模的理解。它们的结合使用将提高分析的敏感性和解释力，使各因素的影响能够被单独评估(可隔离)。这些方法还将根据本研究的经验进行探索、提炼和进一步发展。本研究的成果将推进在社会经济现象的时空分析中广泛涉及的主体模拟、动态网络分析以及人工神经网络的计算模型和体系结构等新的和新兴的基础研究。它将为伦敦交通局提供更好的工具和技术，以更有效地管理道路空间和缓解拥堵，从而改善人员旅行时间和整体旅行可靠性，并通过这样做为伦敦带来巨大的经济效益。这项研究的好处将广泛惠及公共和私人交通工具使用者。这里开发的方法将可用于了解世界其他大城市的拥堵情况，并带来经济、货币、社会和环境效益。

Recent traffic surveys and analysis of road network performance in London show a decline in traffic flows and perversely a decline in speeds and increase in congestion. It is believed that the increases in congestion reflect travellers' responses, both temporary and longer-term, to competition for road network capacity. Continuing adjustments to network capacity in pursuit of mayoral transport priorities, for example, improved safety and amenity, and increased priority for buses, taxis, pedestrians and cyclists, has led to increasing delays for private vehicular traffic. The current annual cost of congestion on London's main roads is estimated to be in the range of 1.8 to 3 billion.Analysis of road network performance is intricate. This is because the road network is essentially an open system with many factors and in which travellers can respond by modifying their choices in many different ways that will affect monitored performance outcomes. The form of these factors, their direction of causality, the fact that some of them interact strongly, and their sheer numbers all contribute to the complexity. These factors have different patterns of influence in both time and space, and analysis of the distinct cause-effect patterns is complicated by the non-linearity of the effects, including the possibility of abrupt growth in congestion once it sets in. Modelling spatial-temporal dependency of the factors is the bottleneck in analysis of the network performance. The challenge is to model dependency in both space and time seamlessly and simultaneously so that the accuracy of analysis can be improved. Another challenge is to fully consider the topology (links and hierarchies) and geometry (distances and directions) of real road networks in the analysis. These are also fundamental challenges in modelling complexity of other types of networks.This research will tackle these challenges. It will be achieved by innovative combination of two chosen novel machine learning methods (Dynamic Recurrent Neural Networks - DRNN and Support Vector Machines - SVM) with the most advanced statistical space-time series analysis (Spatio-Temporal Auto-Regressive Integrated Moving Average - STARIMA) and Geographically Weighted Regression - GWR. These methods are selected because their applications in transport studies are relatively new compared with conventional statistical methods, and, more importantly, they have the potential to improve the representation of the network complexity. The DRNN and SVM can model the non-linearity and non-stationarity existing in most spatio-temporal data which may not be fully accommodated by STARIMA. The STARIMA has the explanatory capability which is missing in DRNN and SVM. The GWR can model the heterogeneity of the networks and improve the understanding of the scales of the networks. Their use in combination will improve the sensitivity and explanatory power of the analysis, to enable the effects of the factors to be assessed separately (isolatable). These methods will also be explored, refined and further developed in the light of experience in this study.The outcome of this research will advance the new and emerging fundamental researches in agent simulations, dynamic network analysis, and computational models and architectures of artificial neural networks, which are widely involved in space-time analysis of social-economic phenomena. It will offer TfL better tools and techniques to manage the road space and mitigate congestion more effectively thereby improving person journey times and overall journey reliability, and in doing so also deliver large economic benefits to London. The benefits of the research will accrue widely to both public and private transport users. The methodology developed here will be transferable to understand the congestion in other big cities around the world with economic, monetary, social and environmental benefits.