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Estimating Public Bicycle Trip Characteristics with Consideration of Built Environment Data

Author

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  • De Zhao

    (Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing 210096, China
    Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 210096, China
    School of Transportation, 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
    Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 210096, China
    School of Transportation, Southeast University, Nanjing 210096, China)

  • Wei Zhou

    (Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing 210096, China
    Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 210096, China
    School of Transportation, Southeast University, Nanjing 210096, China)

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.

Suggested Citation

  • De Zhao & Ghim Ping Ong & Wei Wang & Wei Zhou, 2021. "Estimating Public Bicycle Trip Characteristics with Consideration of Built Environment Data," Sustainability, MDPI, vol. 13(2), pages 1-13, January.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:2:p:500-:d:476104
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    References listed on IDEAS

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    1. Faghih-Imani, Ahmadreza & Eluru, Naveen, 2016. "Incorporating the impact of spatio-temporal interactions on bicycle sharing system demand: A case study of New York CitiBike system," Journal of Transport Geography, Elsevier, vol. 54(C), pages 218-227.
    2. Médard de Chardon, Cyrille & Caruso, Geoffrey, 2015. "Estimating bike-share trips using station level data," Transportation Research Part B: Methodological, Elsevier, vol. 78(C), pages 260-279.
    3. Jiaoe Wang & Jie Huang & Michael Dunford, 2019. "Rethinking the Utility of Public Bicycles: The Development and Challenges of Station-Less Bike Sharing in China," Sustainability, MDPI, vol. 11(6), pages 1-20, March.
    4. Jinyi Zhou & Changyuan Jing & Xiangjun Hong & Tian Wu, 2019. "Winter Sabotage: The Three-Way Interactive Effect of Gender, Age, and Season on Public Bikesharing Usage," Sustainability, MDPI, vol. 11(11), pages 1-14, June.
    5. Faghih-Imani, Ahmadreza & Eluru, Naveen, 2015. "Analysing bicycle-sharing system user destination choice preferences: Chicago’s Divvy system," Journal of Transport Geography, Elsevier, vol. 44(C), pages 53-64.
    6. Yi Yao & Yifang Zhang & Lixin Tian & Nianxing Zhou & Zhilin Li & Minggang Wang, 2019. "Analysis of Network Structure of Urban Bike-Sharing System: A Case Study Based on Real-Time Data of a Public Bicycle System," Sustainability, MDPI, vol. 11(19), pages 1-17, September.
    7. Fishman, Elliot & Washington, Simon & Haworth, Narelle & Watson, Angela, 2015. "Factors influencing bike share membership: An analysis of Melbourne and Brisbane," Transportation Research Part A: Policy and Practice, Elsevier, vol. 71(C), pages 17-30.
    8. Wafic El-Assi & Mohamed Salah Mahmoud & Khandker Nurul Habib, 2017. "Effects of built environment and weather on bike sharing demand: a station level analysis of commercial bike sharing in Toronto," Transportation, Springer, vol. 44(3), pages 589-613, May.
    9. Caulfield, Brian & O'Mahony, Margaret & Brazil, William & Weldon, Peter, 2017. "Examining usage patterns of a bike-sharing scheme in a medium sized city," Transportation Research Part A: Policy and Practice, Elsevier, vol. 100(C), pages 152-161.
    10. Zhao, De & Ong, Ghim Ping & Wang, Wei & Hu, Xiao Jian, 2019. "Effect of built environment on shared bicycle reallocation: A case study on Nanjing, China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 128(C), pages 73-88.
    11. Corcoran, Jonathan & Li, Tiebei & Rohde, David & Charles-Edwards, Elin & Mateo-Babiano, Derlie, 2014. "Spatio-temporal patterns of a Public Bicycle Sharing Program: the effect of weather and calendar events," Journal of Transport Geography, Elsevier, vol. 41(C), pages 292-305.
    12. Kyle Gebhart & Robert Noland, 2014. "The impact of weather conditions on bikeshare trips in Washington, DC," Transportation, Springer, vol. 41(6), pages 1205-1225, November.
    13. Zhang, Ying & Thomas, Tom & Brussel, Mark & van Maarseveen, Martin, 2017. "Exploring the impact of built environment factors on the use of public bikes at bike stations: Case study in Zhongshan, China," Journal of Transport Geography, Elsevier, vol. 58(C), pages 59-70.
    14. Advait Sarkar & Neal Lathia & Cecilia Mascolo, 2015. "Comparing cities’ cycling patterns using online shared bicycle maps," Transportation, Springer, vol. 42(4), pages 541-559, July.
    15. Noland, Robert B. & Smart, Michael J. & Guo, Ziye, 2016. "Bikeshare trip generation in New York City," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 164-181.
    16. Faghih-Imani, Ahmadreza & Eluru, Naveen & El-Geneidy, Ahmed M. & Rabbat, Michael & Haq, Usama, 2014. "How land-use and urban form impact bicycle flows: evidence from the bicycle-sharing system (BIXI) in Montreal," Journal of Transport Geography, Elsevier, vol. 41(C), pages 306-314.
    17. Mingzhu Song & Kaiping Wang & Yi Zhang & Meng Li & He Qi & Yi Zhang, 2020. "Impact Evaluation of Bike-Sharing on Bicycling Accessibility," Sustainability, MDPI, vol. 12(15), pages 1-16, July.
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    2. Mohiuddin, Hossain & Fitch-Polse, Dillon T. & Handy, Susan L., 2023. "Does bike-share enhance transport equity? Evidence from the Sacramento, California region," Journal of Transport Geography, Elsevier, vol. 109(C).

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