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Estimating Public Bicycle Trip Characteristics with Consideration of Built Environment Data

Zhao De (), Ghim Ping Ong (), Wei Wang () and Wei Zhou ()
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Zhao De: Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing 210096, China
Ghim Ping Ong: Department of Civil and Environmental Engineering, National University of Singapore, Singapore 117576, Singapore
Wei Wang: Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing 210096, China
Wei Zhou: Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing 210096, China

Sustainability, 2021, vol. 13, issue 2, 1-13

Abstract: A reliable estimation of public bicycle trip characteristics, especially trip distribution and duration, can help decision-makers plan for the relevant transport infrastructures and assist operators in addressing issues related to bicycle imbalance. Past research studies have attempted to understand the relationship between public bicycle trip generation, trip attraction and factors such as built environment, weather, population density, etc. However, these studies typically did not include trip distribution, duration, and detailed information on the built environment. This paper aims to estimate public bicycle daily trip characteristics, i.e., trip generation, trip attraction, trip distribution, and duration using points of interest and smart card data from Nanjing, China. Negative binomial regression models were developed to examine the effect of built environment on public bicycle usage. Totally fifteen types of points of interest (POIs) data are investigated and factors such as residence, employment, entertainment, and metro station are found to be statistically significant. The results showed that 300 m buffer POIs of residence, employment, entertainment, restaurant, bus stop, metro station, amenity, and school have significantly positive effects on public bicycle generation and attraction, while, counterintuitively, 300 m buffer POIs of shopping, parks, attractions, sports, and hospital have significantly negative effects. Specifically, an increase of 1% in the trip distance leads to a 2.36% decrease in the origin-destination (OD) trips or a 0.54% increase of the trip duration. We also found that a 1% increase in the number of other nearby stations can help reduce 0.19% of the OD trips. The results from this paper can offer useful insights to operators in better estimating public bicycle usage and providing reliable services that can improve ridership.

Keywords: public bicycle; negative binomial regression; trip distribution; trip duration; smart card; road traffic engineering (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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