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

最近的交通调查和对伦敦道路网络性能的分析表明，交通流量下降，速度可能下降，拥堵增加。据信，交通挤塞情况的增加，反映了乘客对道路网络容量竞争的反应，包括暂时和长期的反应。为实现市长的交通优先事项而不断调整网络容量，例如，改善安全和舒适性，以及提高公共汽车、出租车、行人和骑自行车者的优先地位，导致私人车辆交通的延误越来越多。目前，伦敦主要道路每年因拥堵造成的损失估计在18亿至30亿英镑之间。这是因为道路网络基本上是一个开放的系统，有许多因素，旅行者可以通过以许多不同的方式修改他们的选择来做出反应，这将影响监测的性能结果。这些因素的形式、因果关系的方向、其中一些因素相互作用很强的事实，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有不同的影响模式，而对不同的因果模式的分析由于影响的非线性而变得复杂，包括一旦出现拥堵就可能突然加剧。对影响因素的时空相关性建模是网络性能分析的瓶颈。面临的挑战是在空间和时间上无缝地同时对依赖性进行建模，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的道路网络的拓扑（链路和层次）和几何（距离和方向）。这些也是对其他类型网络复杂性建模的基本挑战。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- DRNN和支持向量机- SVM）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。选择这些方法是因为它们在交通研究中的应用与传统的统计方法相比相对较新，更重要的是，它们有潜力提高网络复杂性的表示。DRNN和SVM可以模拟大多数时空数据中存在的非线性和非平稳性，而STARIMA可能无法完全容纳。STARIMA具有DRNN和SVM所缺乏的解释能力。GWR可以模拟网络的异质性，提高对网络规模的理解。它们的组合使用将提高分析的敏感性和解释力，使因素的影响能够单独评估（可隔离）。这些方法也将在本研究中的经验进行探索，完善和进一步发展，本研究的成果将推动新的和新兴的基础研究，在代理模拟，动态网络分析，人工神经网络的计算模型和架构，这是广泛参与时空分析的社会经济现象。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，从而改善人员出行时间和整体出行可靠性，并在此过程中为伦敦带来巨大的经济效益。这项研究的好处将广泛地惠及公共和私人交通工具的使用者。这里开发的方法将被转移到了解在世界各地的其他大城市的拥堵与经济，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.