Urban street bicycle flow analysis based on multi-scale geographically weighted regression model

HUANG Yonghao; YANG Xinmiao; YUE Jintao

doi:10.16511/j.cnki.qhdxxb.2022.26.014

PDF(10426 KB)

Journal of Tsinghua University(Science and Technology) ›› 2022, Vol. 62 ›› Issue (7) : 1132-1141. DOI: 10.16511/j.cnki.qhdxxb.2022.26.014

Research Article

Urban street bicycle flow analysis based on multi-scale geographically weighted regression model

{{article.zuoZhe_EN}}

Author information +

History +

Abstract

Residents are demanding better conditions for urban non-motorized traffic. However, the classical forecasting model for non-motorized traffic has mainly focused on large city areas and existing roads, which cannot fully meet planning demands for the construction or extension of urban bicycle paths. This study used an improved multi-scale geographically weighted regression (MGWR) model to predict the flow of non-motorized traffic. The MGWR model includes the influence of land development and utilization, road network structure, and the nearby public transport on the bicycle flows. The area along the Beijing to Zhangjiakou Railway was used as an example for a case study using bike sharing data in this region. The results show that the MGWR model has better predictive ability with the geographically weighted regression model R² increased by 0.087 and the multiple linear regression with R² increased by 0.113. Finally, this paper gives an approach for non-motorized traffic planning and management based on key factors affecting non-motorized traffic with appropriate ranges of the independent variables.

Key words

transportation planning / non-motorized traffic / space syntax / multi-scale geographically weighted regression / traffic flow predictions

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

HUANG Yonghao, YANG Xinmiao, YUE Jintao. Urban street bicycle flow analysis based on multi-scale geographically weighted regression model[J]. Journal of Tsinghua University(Science and Technology). 2022, 62(7): 1132-1141 https://doi.org/10.16511/j.cnki.qhdxxb.2022.26.014

References

[1] 中国城市规划设计研究院. 2020年度全国主要城市通勤监测报告[R/OL].[2022-04-18]. http://www.chinautc.com/20200520.pdf. China Academy of Urban Planning and Design. 2020 annual report on major cities' daily commute monitoring in China[R/OL].[2022-04-18]. http://www.chinautc.com/20200520.pdf. (in Chinese)
[2] 姜超. 基于人本化分析的北京慢行空间研究[D]. 北京:北京工业大学, 2016. JIANG C. The research on pedestrian and bicycle space design in Beijing based on people-oriented analysis[D]. Beijing:Beijing University of Technology, 2016. (in Chinese)
[3] LIN Z J, FAN W. Modeling bicycle volume using crowdsourced data from Strava smartphone application[J]. International Journal of Transportation Science and Technology, 2020, 9(4):334-343.
[4] ZIMMERMANN M, MAI T, FREJINGER E. Bike route choice modeling using GPS data without choice sets of paths[J]. Transportation Research Part C:Emerging Technologies, 2017, 75:183-196.
[5] PAZDAN S. The impact of weather on bicycle risk exposure[J]. Archives of Transport, 2020, 56(4):89-105.
[6] 林堉楠, 徐媛, 杨家文. 轨道交通车站周边建成环境对骑行的影响:基于深圳市ofo数据的实证研究[J]. 城市交通, 2020, 18(1):83-94, 58. LIN Y N, XU Y, YANG J W. Built environment on linking bicycle to rail transit:Case study based on ofo data in Shenzhen[J]. Urban Transport of China, 2020, 18(1):83-94, 58. (in Chinese)
[7] CHEN P, ZHOU J P, SUN F Y. Built environment determinants of bicycle volume:A longitudinal analysis[J]. Journal of Transport and Land Use, 2017, 10(1):655-674.
[8] SHEN Y, ZHANG X H, ZHAO J H. Understanding the usage of dockless bike sharing in Singapore[J]. International Journal of Sustainable Transportation, 2018, 12(9):686-700.
[9] YANG H T, ZHANG Y B, ZHONG L Z, et al. Exploring spatial variation of bike sharing trip production and attraction:A study based on Chicago's Divvy system[J]. Applied Geography, 2020, 115:102130.
[10] YANG F, DING F, QU X, et al. Estimating urban shared-bike trips with location-based social networking data[J]. Sustainability, 2019, 11(11):3220.
[11] 胡元. 城市道路慢行交通需求预测研究[D]. 成都:西南交通大学, 2019. HU Y. Research on the non-motorized traffic demand forecasting of the urban road[D]. Chengdu:Southwest Jiaotong University, 2019. (in Chinese)
[12] 许淼, 刘宏飞, 初凯. 基于AM-LSTM模型的共享单车时空需求预测[J]. 湖南大学学报(自然科学版), 2020, 47(12):77-85. XU M, LIU H F, CHU K. Spatiotemporal demand prediction of bike-sharing based on AM-LSTM model[J]. Journal of Hunan University (Natural Sciences), 2020, 47(12):77-85. (in Chinese)
[13] YANG Z D, HU J, SHU Y C, et al. Mobility modeling and prediction in bike-sharing systems[C]//Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services. Singapore:ACM, 2016:165-178.
[14] GAO X H, LEE G M. Moment-based rental prediction for bicycle-sharing transportation systems using a hybrid genetic algorithm and machine learning[J]. Computers & Industrial Engineering, 2019, 128:60-69.
[15] 王凌苏. 共享单车交通量预测与停放点选址研究[D]. 北京:北京交通大学, 2020. WANG L S. Traffic flow prediction and rental location of bike-sharing system[D]. Beijing:Beijing Jiaotong University, 2020. (in Chinese)
[16] 段进, 杨滔, 盛强, 等. 空间句法教程[M]. 北京:中国建筑工业出版社, 2019. DUAN J, YANG T, SHENG Q, et al. Introduction to space syntax[M]. Beijing:China Architecture & Building Press, 2019. (in Chinese)
[17] ZHANG L Z, CHIARADIA A, ZHUANG Y. A configurational accessibility study of road and metro network in Shanghai, China[M]//Recent Developments in Chinese Urban Planning. Guangzhou:Springer, 2015:219-245.
[18] COOPER C H V. Using spatial network analysis to model pedal cycle flows, risk and mode choice[J]. Journal of Transport Geography, 2017, 58:157-165.
[19] MCCAHILL C, GARRICK N W. The applicability of space syntax to bicycle facility planning[J]. Transportation Research Record, 2008, 2074(1):46-51.
[20] 宋小冬, 陶颖, 潘洁雯, 等. 城市街道网络分析方法比较研究:以Space Syntax、sDNA和UNA为例[J]. 城市规划学刊, 2020(2):19-24. SONG X D, TAO Y, PAN J W, et al. A comparison of analytical methods for urban street network:Taking space syntax, sDNA and UNA as examples[J]. Urban Planning Forum, 2020(2):19-24. (in Chinese)
[21] COOPER C. Spatial Design Network Analysis (sDNA) version 4.1 Manual[EB/OL].[2021-03-04]. https://sdna.cardiff.ac.uk/sdna/wp-content/downloads/documentation/manual/sDNA_manual_v4_1_0.
[22] BRUNSDON C, FOTHERINGHAM A S, CHARLTON M E. Geographically weighted regression:A method for exploring spatial nonstationarity[J]. Geographical Analysis, 1996, 28(4):281-298.
[23] 叶显严. 基于地理加权回归的城市出租车客流影响因素分析及建模[D]. 成都:西南交通大学. 2017. YE X Y. The model and facter analysis of effect on the urban taxi ridership based on geographically weighted regression[D]. Chengdu:Southwest Jiaotong University, 2017. (in Chinese)
[24] FOTHERINGHAM A S, YANG W B, KANG W. Multiscale geographically weighted regression (MGWR)[J]. Annals of the American Association of Geographers, 2017, 107(6):1247-1265.
[25] 宋青, 李晓磊, 李猛. 基于OpenStreetMap的城市自行车网建模与多判据路径规划[J]. 交通运输系统工程与信息, 2017, 17(3):143-149. SONG Q, LI X L, LI M. OpenStreetMap-based modeling and multi-criteria routing of urban cycleway network[J]. Journal of Transportation Systems Engineering and Information Technology, 2017, 17(3):143-149. (in Chinese)
[26] 杨逍遥, 王建军, 李国栋, 等. 基于GWR模型的共享单车空间分布影响因素分析[J]. 交通运输研究, 2021, 7(1):81-94. YANG X Y, WANG J J, LI G D, et al. Factors impacting spatial distribution of bike sharing based on GWR model[J]. Transport Research, 2021, 7(1):81-94. (in Chinese)