New and emerging data forms in transportation planning and policy: Opportunities and challenges for “Track and Trace” data

Abstract High quality, reliable data and robust models are central to the development and appraisal of transportation planning and policy. Although conventional data may offer good ‘content’, it is widely observed that it lacks context i.e. who and why people are travelling. Transportation modelling has developed within these boundaries, with implications for the planning, design and management of transportation systems and policy-making. This paper establishes the potential of passively collected GPS-based “Track & Trace” (T&T) datasets of individual mobility profiles towards enhancing transportation modelling and policy-making. T&T is a type of New and Emerging Data Form (NEDF), lying within the broader ‘Big Data’ paradigm, and is typically collected using mobile phone sensors and related technologies. These capture highly grained mobility content and can be linked to the phone owner/user behavioural choices and other individual context. Our meta-analysis of existing literature related to spatio-temporal mobile phone data demonstrates that NEDF’s, and in particular T&T data, have had little mention to date within an applied transportation planning and policy context. We thus establish there is an opportunity for policy-makers, transportation modellers, researchers and a wide range of stakeholders to collaborate in developing new analytic approaches, revise existing models and build the skills and related capacity needed to lever greatest value from the data, as well as to adopt new business models that could revolutionise citizen participation in policy-making. This is of particular importance due to the growing awareness in many countries for a need to develop and monitor efficient cross-sectoral policies to deliver sustainable communities.