Nodes from the Underground: Causal and Probabilistic Approaches for Complex Transportation Networks

地下节点：复杂交通网络的因果和概率方法

• 批准号: EP/N020723/1
• 负责人: Ricardo Silva
• 金额: $ 50.32万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FN020723%2F1
• 关键词: Nodes; Underground; Causal; Probabilistic; Approaches

An efficient transportation system is vital to the economic and social well-being of large cities. The transport demand implied by economic growth, however, requires transport networks to become more and more complex, making their management difficult. Fortunately, modern systems such as the London Underground generate vast amounts of data that can be analysed to better understand passenger behaviour and needs. Besides understanding the typical daily patterns that we can observe on a regular basis, Data Science methods allows us to look into in the less usual events such as unplanned disruptions that are still important to any user, and to also model individualised behaviour instead of only aggregates. In a large system such as the London Underground, signal failures and disruptive events eventually take place, requiring passengers to change plans in a variety of ways. This research provides advanced statistical modelling and machine learning approaches to learn from past events to examine how passengers adapt themselves when a disruption occurs. When a disruption takes place, the model will provide information of likely changes, such as increased number of passengers leaving a station because they could not reach their destination. These models are important for transport authorities to understand the resilience of the system, different combinations of location and time of a disruption, and unusual responses from passengers that may motivate different communication strategies to inform users of better travel adjustments. This research also opens up conceptual ideas to be exploited in the future using new technologies to monitor and adaptively respond to passenger needs in a more optimised and time-effective way.

高效的交通系统对于大城市的经济和社会福祉至关重要。然而，经济增长带来的交通需求要求交通网络变得越来越复杂，管理难度加大。幸运的是，伦敦地铁等现代系统会生成大量数据，可以对这些数据进行分析，以更好地了解乘客的行为和需求。除了了解我们可以定期观察到的典型日常模式之外，数据科学方法还允许我们研究不太常见的事件，例如对任何用户仍然很重要的计划外中断，并且还可以对个性化行为进行建模，而不仅仅是聚合。在伦敦地铁这样的大型系统中，信号故障和破坏性事件最终会发生，要求乘客以各种方式改变计划。这项研究提供了先进的统计模型和机器学习方法，可以从过去的事件中学习，以检查乘客在发生中断时如何适应。当发生中断时，该模型将提供可能发生变化的信息，例如由于无法到达目的地而离开车站的乘客数量增加。这些模型对于交通当局了解系统的弹性、中断位置和时间的不同组合以及乘客的异常反应非常重要，这些反应可能会激发不同的沟通策略，以通知用户更好的旅行调整。这项研究还开辟了未来可利用的概念思路，利用新技术以更优化、更省时的方式监控和自适应地响应乘客需求。

项目成果

Inferring urban polycentricity from the variability in human mobility patterns.

从人类流动模式的可变性推断城市多中心性。

• DOI: 10.1038/s41598-023-33003-7
• 发表时间: 2023-04-07
• 期刊: SCIENTIFIC REPORTS
• 影响因子: 4.6
• 作者: Cabrera-Arnau, Carmen;Zhong, Chen;Batty, Michael;Silva, Ricardo;Kang, Soong Moon
• 通讯作者: Kang, Soong Moon

Counterfactual Distribution Regression for Structured Inference

结构化推理的反事实分布回归

• DOI: 10.48550/arxiv.1908.07193
• 发表时间: 2019
• 作者: Colombo N

Tomography of the London Underground: a Scalable Model for Origin-Destination Data

• 发表时间: 2017
• 作者: Nicolò Colombo;Ricardo Silva;Soong Moon Kang