Tomography of the London Underground: a Scalable Model for Origin-Destination Data
Tomography of the London Underground: a Scalable Model for Origin-Destination Data
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发表时间：
2017
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通讯作者：
Nicolò Colombo;Ricardo Silva;Soong Moon Kang
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作者：
Nicolò Colombo;Ricardo Silva;Soong Moon Kang
本文解决了经典的网络断层扫描问题，推断本地流量给定的起点-目的地的意见。针对大型复杂的公共交通系统，我们建立了一个可扩展的模型，利用输入输出信息来估计未观察到的链路/站点负载和用户的路径偏好。该模型通过对用户出行时间分布的重构，能够灵活地捕捉到用户在相似时间、相似路径上可能的不同路径选择策略以及用户之间的相关性。相应的似然函数是棘手的中型或大型网络，我们提出了两种不同的策略，即精确的最大似然推理的近似，但易处理的模型和变分推理的原始棘手的模型。作为我们的方法的应用，我们考虑了伦敦地铁网络的象征性案例，其中一个自来水/自来水系统跟踪开始/退出时间和一天中所有行程的位置。一组合成模拟和伦敦交通提供的真实的数据被用来验证和测试模型的可观测和不可观测量的预测。
The paper addresses the classical network tomography problem of inferring local traffic given origin-destination observations. Focussing on large complex public transportation systems, we build a scalable model that exploits input-output information to estimate the unobserved link/station loads and the users path preferences. Based on the reconstruction of the users' travel time distribution, the model is flexible enough to capture possible different path-choice strategies and correlations between users travelling on similar paths at similar times. The corresponding likelihood function is intractable for medium or large-scale networks and we propose two distinct strategies, namely the exact maximum-likelihood inference of an approximate but tractable model and the variational inference of the original intractable model. As an application of our approach, we consider the emblematic case of the London Underground network, where a tap-in/tap-out system tracks the start/exit time and location of all journeys in a day. A set of synthetic simulations and real data provided by Transport For London are used to validate and test the model on the predictions of observable and unobservable quantities.