Deciphering human activities in complex urban systems : mining big data for sustainable urban future

Abstract

"Big Data" is in vogue, and the explosion of urban sensors, mobile phone traces, and other windows onto urban activities has generated much hype about the advent of a new 'urban science.' However, translating such Big Data into a planning-relevant understanding of activity patterns and travel behavior presents a number of obstacles. This dissertation examines some of these obstacles and develops data processing pipelines and urban activity modeling techniques that can complement traditional travel surveys and facilitate the development of richer models of activity patterns and land use-transportation interactions. This study develops methods and tests their usefulness by using Singapore metropolitan area as an example, and employing data mining and statistical learning methods to distill useful spatiotemporal information on human activities by people and by place from traditional travel survey data, semantically enriched GIS data, massive and passive call detail records (CDR) data, and Wi-Fi augmented mobile positioning data. I illustrate that regularity and heterogeneity exist in individuals' daily activity patterns in the metropolitan area. I test the hypothesis that by characterizing and clustering individuals' activity profiles, and incorporating them into household decision choice models, we can characterize household lifestyles in ways that enhance our understanding and enable us to predict important decision-making processes within the urban system. I also demonstrate ways of integrating Big Data with traditional data sources in order to identify human mobility patterns, urban structures, and semantic themes of places reflected by human activities. Finally, I discuss how the enriched understanding about cities, human mobility, activity, and behavior choices derived from Big Data can make a difference in land use planning, urban growth management, and transportation policies.