References
[1] 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.
[2] 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.
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] 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.
[8] 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.
[9] 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.
[10] 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.
[11] 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.
[12] 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.
[13] 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.
[14] 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.
[15] 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.
[16] 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.
[17] 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.
[18] 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.
[19] 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.
[20] 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.
[21] 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.
[22] 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.
[23] 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.
[24] 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.
[25] 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.
[26] 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.
[27] 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.
[28] 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.
[29] 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.
[30] 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.
[31] 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.
[32] 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.
[33] 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.
[34] KSH. Data of population by main characteristics of education by region from Microcensus 2016. https://statinfo.ksh.hu/Statinfo/haViewer.jsp [Cited Apr. 2022].
[35] Obaid M, Torok A. Macroscopic traffic simulation of autonomous vehicle effects. Vehicles. 2021;3(2):187-196. DOI: 10.3390/vehicles3020012.
[36] 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.
[37] 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.
[38] Mátrai T, Tóth J. Cluster analysis of public bike sharing systems for categorization. Sustainability. 2020;12(14):5501. DOI: 10.3390/su12145501.
[39] 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.
[40] 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.
[41] 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.
[42] 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.
[43] 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.
[44] 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.
[45] 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.
[46] 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.
[47] 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.
[48] 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.
[49] 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.
