文章编号： 1672-5328（2019）03-0053-14

Cuauhtemoc Anda1, Alexander Erath1, Pieter Jacobus Fourie1 著，宗晶2 译
(1.苏黎世联邦理工大学未来城市实验室，新加坡ETH中心，新加坡138602，新加坡；2.中国城市规划设计研究 院，北京100037)

摘要： 通过新的大数据来源诸如手机通信记录、智能卡数据以及社交媒体地理编码记录，可以前所 未有地观察和了解出行行为的细节。尽管有如此庞大的大数据来源，但在规划实践中使用的交通需 求模型，其数据源仍大多来自交通调查和人口普查等传统方法。对近期利用大数据研究交通行为， 以及使交通规划师可以进行假设情景分析的交通需求模型的最新进展进行梳理。从出行识别到出行 活动推理，回顾和分析现有数据分析方法，这些传统方法使收集到的出行轨迹信息能响应交通需求 模型。未来的研究应该侧重将概率模型和机器学习技术应用于数据科学。设计这些数据挖掘方法是 为了处理由手机移动追踪数据衍生的零散和掺杂偏差的数据的不确定性。此外，这些方法还适用于 将不同的相关数据组整合到一个数据融合方案中，以便用出行日志信息丰富大数据。总之，建模知 识已经在交通运输领域发展成熟，因此强烈建议在交通规划方面应用数据驱动方法时应建立相应领 域专业知识的基础。这些新的挑战呼吁交通模型师和数据科学家之间的多学科协作。

关键词： 大数据；交通规划；出行需求建模；基于个体仿真；智能公交卡；手机网络数据

中图分类号： U491.1+2

文献标识码：A

Transport Modelling in the Age of Big Data

Written by Cuauhtemoc Anda1, Alexander Erath1, Pieter Jacobus Fourie1, Translated by Zong Jing2
(1.ETH Zurich, Future Cities Laboratory, Singapore-ETH Centre, Singapore 138602, Singapore; 2.China Academy of Urban Planning & Design, Beijing 100037, China)

Abstract： New Big Data sources such as mobile phone call data records, smart card data and geo-coded social media records allow to observe and understand mobility behaviour on an unprecedented level of detail. Despite the availability of such new Big Data sources, transport demand models used in planning practice still, almost exclusively, are based on conventional data such as travel diary surveys and population census. This literature review brings together recent advances in harnessing Big Data sources to understand travel behaviour and inform travel demand models that allow transport planners to compute what- if scenarios. From trip identification to activity inference, we review and analyse the existing data-mining methods that enable these opportunistically collected mobility traces inform transport demand models. We identify that future research should tap on the potential of probabilistic models and machine learning techniques as commonly used in data science. Those data-mining approaches are designed to handle the uncertainty of sparse and noisy data as it is the case for mobility traces derived from mobile phone data. In addition, they are suitable to integrate different related data sets in a data fusion scheme so as to enrich Big Data with information from travel diaries. In any case, we also acknowledge that sophisticated modelling knowledge has developed in the domain of transport planning and therefore we strongly advise that still, domain expert knowledge should build the fundament when applying data-driven approaches in transport planning. These new challenges call for a multidisciplinary collaboration between transport modellers and data scientists.

Keywords： Big Data; transport planning; travel demand modelling; agent-based simulation; public transport smart card; mobile phone network data