Estimating Urban Shared-Bike Trips with Location-Based Social Networking Data

Fan Ding · Xu Qu · Bin Ran

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Urban Transport and Accessibility Transportation and Mobility Innovations Human Mobility and Location-Based Analysis

Abstract

Dockless shared-bikes have become a new transportation mode in major urban cities in China. Excessive number of shared-bikes can occupy a significant amount of roadway surface and cause trouble for pedestrians and auto vehicle drivers. Understanding the trip pattern of shared-bikes is essential in estimating the reasonable size of shared-bike fleet. This paper proposed a methodology to estimate the shared-bike trip using location-based social network data and conducted a case study in Nanjing, China. The ordinary least square, geographically weighted regression (GWR) and semiparametric geographically weighted regression (SGWR) methods are used to establish the relationship among shared-bike trip, distance to the subway station and check ins in different categories of the point of interest (POI). This method could be applied to determine the reasonable number of shared-bikes to be launched in new places and economically benefit in shared-bike management.

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References

2018The station-free sharing bike demand forecasting with a deep learning approach and large-scale datasets 2014Urban activity pattern classification using topic models from online geo-location data 2019Active travel for active ageing in China: The role of built environment 2018To be or not to be dockless: Empirical analysis of dockless bikeshare development in China 2018Understanding bike sharing travel patterns: An analysis of trip data from eight cities 2018Identifying tourists and analyzing spatial patterns of their destinations from location-based social media data 2014Inferring Urban Land Use Using Large-Scale Social Media Check-in Data 2017Tweeting Transit: An examination of social media strategies for transport information management during a large event

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