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Transport modelling in the age of big data
Cuauhtemoc Anda,Alexander Erath,Pieter Jacobus Fourie 서울시립대학교 도시과학연구원 2017 도시과학국제저널 Vol.21 No.2
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.
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