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Visual analytics of urban transportation from a bike-sharing and taxi perspective

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Abstract

The urban transportation system is the footstone of a city’s infrastructure, and the booming bike-sharing system has become a vital part of urban transportation. Understanding the bike-sharing system and traditional taxi system as well as their similarities and differences are essential for bike-sharing rebalancing, taxi dispatching, and urban planning. However, due to the sparseness of record data and the difference in service regions, the relationship between them is indeed obscure, and previous solutions mostly focus only on a single system. In this paper, we propose a visual analytics system to investigate the similarities and differences between bike-sharing and taxi systems. The service region for each bike station is created to fuse bike-sharing data and taxi data. We harness two three-order tensors to represent them in a unified framework to generate potential patterns by tensor decomposition. The visual analytics system integrates two spatiotemporal data sources by analyzing the patterns that are typical of each data source and the patterns that are common to both data sources to assist users in better discovering the relationships between the taxi system and the bike-sharing system. We demonstrate the effectiveness of our system through real-world case studies. The urban transportation system is the footstone of a city’s infrastructure, and the booming bike-sharing system has become a vital part of urban transportation. Understanding the bike-sharing system and traditional taxi system as well as their similarities and differences are essential for bike-sharing rebalancing, taxi dispatching, and urban planning. However, due to the sparseness of record data and the difference in service regions, the relationship between them is indeed obscure, and previous solutions mostly focus only on a single system. In this paper, we propose a visual analytics system to investigate the similarities and differences between bike-sharing and taxi systems. The service region for each bike station is created to fuse bike-sharing data and taxi data. We harness two three-order tensors to represent them in a unified framework to generate potential patterns by tensor decomposition. The visual analytics system integrates two spatiotemporal data sources by analyzing the patterns that are typical of each data source and the patterns that are common to both data sources to assist users in better discovering the relationships between the taxi system and the bike-sharing system. We demonstrate the effectiveness of our system through real-world case studies.

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Notes

  1. https://www.citibikenyc.com/how-it-works.

  2. https://www1.nyc.gov/site/tlc/vehicles/get-a-vehicle-license.page.

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Acknowledgements

This work was supported by the National Key Research & Development Program of China (2017YFB0202203), National Natural Science Foundation of China (61672452, 61890954, and 61972343), and NSFC-Guangdong Joint Fund (U1611263).

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  1. State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China

    Haoran Dai, Yubo Tao & Hai Lin

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  1. Haoran Dai
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  2. Yubo Tao
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  3. Hai Lin
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Correspondence to Yubo Tao.

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Dai, H., Tao, Y. & Lin, H. Visual analytics of urban transportation from a bike-sharing and taxi perspective. J Vis 23, 1053–1070 (2020). https://doi.org/10.1007/s12650-020-00673-8

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