An empirical study on the intra-urban goods movement patterns using logistics big data

ABSTRACT

Movement patterns of intra-urban goods/things and the ways they differ from human mobility and traffic flow patterns have seldom been explored due to data access and methodological limitations, especially from systemic and long timescale perspectives. However, urban logistics big data are increasingly available, enabling unprecedented spatial and temporal resolutions to this issue. This research proposes an analytical framework for exploring intra-urban goods movement patterns by integrating spatial analysis, network analysis and spatial interaction analysis. Using daily urban logistics big data (over 10 million orders) provided by the largest online logistics company in Hong Kong (GoGoVan) from 2014 to 2016, we analyzed two spatial characteristics (displacement and direction) of urban goods movement. Results showed that the distribution of goods displaceFower law or exponential distribution of human mobility trends. The origin–destination flows of goods were used to build a spatially embedded network, revealing that Hong Kong became increasingly connected through intra-urban freight movement. Finally, spatial interaction characteristics were revealed using a fitting gravity model. Distance lacked substantial influence on the spatial interaction of goods movement. These findings have policy implications to intra-urban logistics and urban transport planning.

KEYWORDS:

• Goods movement patterns
• logistics big data
• spatial analysis
• network analysis
• spatial interaction

Acknowledgments

The authors thank the GoGoVan Company and Mr. Michal Szczecinski for the logistics big data used in this study, and the anonymous reviewers, who helped to improve this work. Author 2 acknowledges funding support from an Area of Excellence project (1-ZE24) and a startup project (1-ZE6P), both of which are funded by The Hong Kong Polytechnic University.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was financially supported from an Area of Excellence project (1-ZE24) and a startup project (1-ZE6P), both of which are funded by The Hong Kong Polytechnic University.

Notes on contributors

Xintao Liu

Xintao Liu, PhD, currently an Assistant Professor at the Department of Land Surveying and Geo-Informatics in The Hong Kong Polytechnic University. His research interest includes GI services and science, urban computing, and GIS in Transportation, and his research goal is to use state-of-art technologies to advance smart city for a better urban life. He received PhD in Geoinformatics from Royal Institute of Technology, Sweden in 2012, and he is PI and Co-PI of several national projects funded by Sweden, Canada and Hong Kong. He is a reviewer of a series of major international journals such as IJGIS and AAG in his field.