Big data applications in urban transport research in Chinese cities: an overview

Sui Tao, Min Zhang and Jiangyue Wu

Chapter 15 in Big Data Applications in Geography and Planning, 2021, pp 220-244 from Edward Elgar Publishing

Abstract: Big data have become one of the most important information sources for urban transport research in Chinese cities, where rapid economic development and motorisation are drastically shaping people’s travel behaviour. Yet, a comprehensive summary and assessment of this body of research is still lacking. This chapter addresses this deficit by providing an overview of the big data applications in transport research in Chinese cities. Research is grouped into five thematic areas, namely, data enrichment and travel-behaviour mining, understanding variability of travel behaviour, transport system assessment and planning, evaluating urban structure and function, and environmental and social implications of transport and mobility. Overall, it is argued that the existing literature has substantially extended our understanding of the urban system and mobility in Chinese cities. Methodological advancement concerning big data manipulation and analysis has also been observed. However, certain important limitations, including data validation and sample representativeness, remain largely unresolved. Furthermore, theoretical contributions have been relatively limited. A series of avenues have been identified as a starting point for developing future big-data transport research in China and beyond, including more research in medium and small-sized Chinese cities, multimodal transport system and mobility, pedestrian travel behaviour, linkage between residential and daily travel behaviour, and finally, attitudinal dimensions of travel behaviour.

Keywords: Economics and Finance; Geography; Innovations and Technology; Research Methods; Urban and Regional Studies (search for similar items in EconPapers)
Date: 2021
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