How Do Land Use, Built Environment and Transportation Facilities Affect Bike-Sharing Trip Destinations?

Ahmed Jaber; Bálint Csonka

doi:10.7307/ptt.v35i1.67

Authors

Ahmed Jaber Faculty of Transportation Engineering and Vehicle Engineering, Budapest University of Technology and Economics https://orcid.org/0000-0001-5493-2530
Bálint Csonka Faculty of Transportation Engineering and Vehicle Engineering, Budapest University of Technology and Economics

DOI:

https://doi.org/10.7307/ptt.v35i1.67

Keywords:

bike-sharing, public transportation, land use, spatial analysis, trip destination

Abstract

The purpose of this research is to investigate the effect of land use, built environment and public transportation facilities’ locations on destinations of bike-sharing trips in an urban setting. Several methods have been applied to determine the relationship between predicting variables and trip destinations, such as ordinary least squares regression, spatial regression and geographically weighted regression. Additionally, a comparison between the proposed models, count models and random forest has been conducted. The data were collected in Budapest, Hungary. It has been found that touristic points of interest, and healthcare and educational points have a positive impact on bike-sharing destinations. Public transportation stops for buses, trains and trams attract bike-sharing users, which has a potential for the bike-and-ride system. Land use has different effects on bike-sharing trip destinations; mostly as a circular shape variation within the urban structure of the city, such as residential, industrial, commercial and educational zones. Other variables, such as road length and water areas, form as constraints to bike-sharing trip destinations. Geographically weighted and spatial regression performs better than count models and random forest. This study helps decision-makers in predicting the origin-destination matrix of bike-sharing trips based on the transportation network and land use.

References

Nielsena TAS, Skov-Petersen H, Carstensen TA. Urban planning practices for bikeable cities – The case of Copenhagen. Urban Research & Practice. 2013;6(1):110-115. DOI: 10.1080/17535069.2013.765108.

Jaber A, Juhász J, Csonka B. An analysis of factors affecting the severity of cycling crashes using binary regression model. Sustainability. 2021;13(12):6945. DOI: 10.3390/su13126945.

Gao J, et al. Evaluating the cycling comfort on urban roads based on cyclists' perception of vibration. Journal of Cleaner Production. 2018;192(August 2018):531-541. DOI: 10.1016/j.jclepro.2018.04.275.

Si H, et al. Mapping the bike sharing research published from 2010 to 2018: A scientometric review. Journal of Cleaner Production. 2019;213(March 2019):415-427. DOI: 10.1016/j.jclepro.2018.12.157.

El-Assi W, Mahmoud MS, Habib KN. Effects of built environment and weather on bike sharing demand: A station level analysis of commercial bike sharing in Toronto. Transportation. 2017;44:589-613. DOI: 10.1007/s11116-015-9669-z.

Gu T, Kim I, Currie G. To be or not to be dockless: Empirical analysis of dockless bikeshare development in China. Transportation Research Part A: Policy and Practice. 2019;119(January 2019):122-147. DOI: 10.1016/j.tra.2018.11.007.

Pal A, Zhang Y. Free-floating bike sharing: Solving real-life large-scale static rebalancing problems. Transportation Research Part C: Emerging Technologies. 2017;80(July 2017):92-116. DOI: 10.1016/j.trc.2017.03.016.

Lazarus J, et al. Micromobility evolution and expansion: Understanding how docked and dockless bikesharing models complement and compete – A case study of San Francisco. Journal of Transport Geography. 2020;84(April 2020):102620. DOI: 10.1016/j.jtrangeo.2019.102620.

Faghih-Imani A, Elurub N. Incorporating the impact of spatio-temporal interactions on bicycle sharing system demand: A case study of New York CitiBike system. Journal of Transport Geography. 2016;54(June 2016):218-227. DOI: 10.1016/j.jtrangeo.2016.06.008.

Giot R, Cherrier R. Predicting bikeshare system usage up to one day ahead. 2014 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (CIVTS). 2014. p. 22-29. DOI: 10.1109/CIVTS.2014.7009473.

Ashqar HI, et al. Network and station-level bike-sharing system prediction: A San Francisco bay area case study. Journal of Intelligent Transportation Systems. 2021. DOI: 10.1080/15472450.2021.1948412.

Guidon S, Reck DJ, Axhausen K. Expanding a(n) (electric) bicycle-sharing system to a new city: Prediction of demand with spatial regression and random forests. Journal of Transport Geography. 2020;84(April 2020):102692. DOI: 10.1016/j.jtrangeo.2020.102692.

Wang X, Cheng Z, Trépanier M, Sun L. Modeling bike-sharing demand using a regression model with spatially varying coefficients. Journal of Transport Geography. 2021;93(May 2021):103059. DOI: 10.1016/j.jtrangeo.2021.103059.

Rixey RA. Station-level forecasting of bikesharing ridership: Station network effects in three U.S. Systems. Transportation Research Record. 2013;2387(1):46-55. DOI: 10.3141/2387-06.

Faghih-Imania A, et al. How land-use and urban form impact bicycle flows: Evidence from the bicycle-sharing system (BIXI) in Montreal. Journal of Transport Geography. 2014;41(December 2014):306-314. DOI: 10.1016/j.jtrangeo.2014.01.013.

Mateo-Babiano I, Bean R, Corcoran J, Pojani D. How does our natural and built environment affect the use of bicycle sharing? Transportation Research Part A: Policy and Practice. 2016;94(December 2016):295-307. DOI: 10.1016/j.tra.2016.09.015.

Noland RB, Smart MJ, Guo Z. Bikeshare trip generation in New York City. Transportation Research Part A: Policy and Practice. 2016;94(December 2016):164-181. DOI: doi.org/10.1016/j.tra.2016.08.030.

Scott DM, Ciuro C. What factors influence bike share ridership? An investigation of Hamilton, Ontario’s bike share hubs. Travel Behaviour and Society. 2019;16(July 2019):50-58. DOI: 10.1016/j.tbs.2019.04.003.

Shen Y, Zhang X, Zhao J. Understanding the usage of dockless bike sharing in Singapore. International Journal of Sustainable Transportation. 2018; p. 1-15. DOI: 10.1080/15568318.2018.1429696.

Bao J, Shi X, Zhang H. Spatial analysis of bikeshare ridership with smart card and POI data using geographically weighted regression method. IEEE Access. 2018;6:76049-76059. DOI: 10.1109/ACCESS.2018.2883462.

Munira S, Sener IN. A geographically weighted regression model to examine the spatial variation of the socioeconomic and land-use factors associated with Strava bike activity in Austin, Texas. Journal of Transport Geography. 2020;88(October 2020):102865. DOI: 10.1016/j.jtrangeo.2020.102865.

Yang H, et al. Exploring spatial variation of bike sharing trip production and attraction: A study based on Chicago’s Divvy system. Applied Geography. 2020;115(February 2020):102130. DOI: 10.1016/j.apgeog.2019.102130.

Yang F, Ding F, Qu X, Ran B. Estimating urban shared-bike trips with location-based social networking data. Sustainability. 2019;11(11):3220. DOI: 10.3390/su11113220.

Zhao D, Ong GP, Wang W, Hu XJ. Effect of built environment on shared bicycle reallocation: A case study on Nanjing, China. Transportation Research Part A: Policy and Practice. 2019;128(October 2019):73-88. DOI: 10.1016/j.tra.2019.07.018.

Xu Y, et al. Unravel the landscape and pulses of cycling activities from a dockless bike-sharing system. Computers, Environment and Urban Systems. 2019;75(May 2019):184-203. DOI: 10.1016/j.compenvurbsys.2019.02.002.

Zhang Y, Thomas T, Brussel M, van Maarseveen M. 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. 2017;58(January 2017):59-70. DOI: 10.1016/j.jtrangeo.2016.11.014.

Li B, et al. Exploring urban taxi ridership and local associated factors using GPS data and geographically weighted regression. Cities. 2019;87(April 2019):68-86. DOI: 10.1016/j.cities.2018.12.033.

Cardozo OD, García-Palomares JC, Gutiérrez J. Application of geographically weighted regression to the direct forecasting of transit ridership at station-level. Applied Geography. 2012;34(May 2012):548-558. DOI: 10.1016/j.apgeog.2012.01.005.

Chiou YC, Jou RC, Yang CH. Factors affecting public transportation usage rate: Geographically weighted regression. Transportation Research Part A: Policy and Practice. 2015;78(August 2015):161-177. DOI: 10.1016/j.tra.2015.05.016.

Jaber A, Baker LA, Csonka B. The influence of public transportation stops on bike-sharing destination trips: Spatial analysis of Budapest City. Future Transportation. 2022;2(3):688-697. DOI: 10.3390/futuretransp2030038.

Pu Z, et al. Evaluation of spatial heterogeneity in the sensitivity of on-street parking occupancy to price change. Transportation Research Part C: Emerging Technologies. 2017;77(April 2017):67-79. DOI: 10.1016/j.trc.2017.01.008.

Pan Y, et al. Investigating the impacts of built environment on traffic states incorporating spatial heterogeneity. Journal of Transport Geography. 2020;83(February 2020):102663. DOI: 10.1016/j.jtrangeo.2020.102663.

Huang Y, Wang X, Patton D. Examining spatial relationships between crashes and the built environment: A geographically weighted regression approach. Journal of Transport Geography. 2018;69(May 2018):221-233. DOI: 10.1016/j.jtrangeo.2018.04.027.

KSH. Data of population by main characteristics of education by region from Microcensus 2016. https://statinfo.ksh.hu/Statinfo/haViewer.jsp [Cited Apr. 2022].

Obaid M, Torok A. Macroscopic traffic simulation of autonomous vehicle effects. Vehicles. 2021;3(2):187-196. DOI: 10.3390/vehicles3020012.

Bucsky P. Modal share changes due to COVID-19: The case of Budapest. Transportation Research Interdisciplinary Perspectives. 2020;8(November 2020):110141. DOI: 10.1016/j.trip.2020.100141.

Fraboni F, et al. A cluster analysis of cyclists in Europe: Common patterns, behaviours, and attitudes. Transportation. 2021. DOI: 10.1007/s11116-021-10187-3.

Mátrai T, Tóth J. Cluster analysis of public bike sharing systems for categorization. Sustainability. 2020;12(14):5501. DOI: 10.3390/su12145501.

Soltani A, Mátrai T, Camporeale R, Allan A. Exploring shared-bike travel patterns using big data: Evidence in Chicago and Budapest. In: Computational Urban Planning and Management for Smart Cities (CUPUM 2019), Lecture Notes in Geoinformation and Cartography. Springer, Cham; 2019. p. 53-68. DOI: 10.1007/978-3-030-19424-6_4.

Koller B, Hegglin D, Schnyder M. A grid-cell based fecal sampling scheme reveals: Land-use and altitude affect prevalence rates of Angiostrongylus vasorum and other parasites of red foxes (Vulpes vulpes). Parasitology Research. 2019;118:2235-2245. DOI: 10.1007/s00436-019-06325-7.

Hou H, Estoque RC, Murayama Y. Spatiotemporal analysis of urban growth in three African capital cities: A grid-cell-based analysis using remote sensing data. Journal of African Earth Sciences. 2016;123(November 2016):381-391. DOI: 10.1016/j.jafrearsci.2016.08.014.

Schimohr K, Scheiner J. Spatial and temporal analysis of bike-sharing use in Cologne taking into account a public transit disruption. Journal of Transport Geography. 2021;92(April 2021):103017. DOI: 10.1016/j.jtrangeo.2021.103017.

Wu C, Kim I, Chung H. The effects of built environment spatial variation on bike-sharing usage: A case study of Suzhou, China. Cities. 2021;110(March 2021):103063. DOI: 10.1016/j.cities.2020.103063.

Radzimski A, Dzięcielski M. Exploring the relationship between bike-sharing and public transport in Poznań, Poland. Transportation Research Part A: Policy and Practice. 2021;145(March 2021):189-202. DOI: 10.1016/j.tra.2021.01.003.

Brunsdon C, Fotheringham AS, Charlton ME. Geographically weighted regression: A method for exploring spatial nonstationarity. Geographical Analysis. 1996;28(4):281-298. DOI: 10.1111/j.1538-4632.1996.tb00936.x.

Ma X, et al. Modeling the factors influencing the activity spaces of bikeshare around metro stations: A spatial regression model. Sustainability. 2018;10(11):3949. DOI: 10.3390/su10113949.

Yang W, et al. The spatial characteristics and influencing factors of modal accessibility gaps: A case study for Guangzhou, China. Journal of Transport Geography. 2017;60(April 2017):21-32. DOI: 10.1016/j.jtrangeo.2017.02.005.

Hurvich CM, Simonoff JS, Tsai CL. Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 2002;60(2):271-293. DOI: 10.1111/1467-9868.00125.

Shen H, et al. Exploring a pricing model for urban rental houses from a geographical perspective. Land. 2022;11(1):4. DOI: 10.3390/land11010004.