中小城市公共自行车出行模式与驱动机制研究
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摘要
公共自行车在促进交通可持续发展、方便居民出行等方面意义重大。本文以广东省惠州市惠城区和惠阳区、韶关市区公共自行车为研究对象,利用时间序列数据分析、层次聚类、地理可视化等方法探究出行模式,利用随机森林算法分析出行行为的影响因素及其重要性程度。研究发现,惠州市惠城区和韶关市的公共自行车出行行为有规律、成规模,站点呈现生活居住、工作就业等类型;惠州市惠城区自行车使用目的多元,包括通勤、短途办事、公交接驳等,韶关市中,通勤是主要出行目的。惠州市惠阳区受到骑行道路限制,公共自行车使用率低。本研究可以为提高公共自行车系统运营效率、引导慢行交通政策制定、评估城市用地布局提供参考和建议,也能为其他区域公共自行车的研究提供借鉴作用。
Given the importance of environment-friendly cities,the development of public bicycle systems(PBSs) has become more popular in recent years around the world.The purpose of this study was to explore the usage patterns of PBSs in small and medium-sized cities in Guangdong province,China,and to infer the driving mechanisms of system attributes and the built environment.The research applied time series analysis of global activity patterns,hierarchical clustering algorithm using Dynamic Time Warping distances as features and spatial data visualization on station-based data,and then compared different systems by employing a random forest algorithm to evaluate the influencing factors.The study objective was to better understand the relationship between public bicycle usage activity and underlying built environment characteristics.In Huicheng District of Huizhou City and Shaoguan City,the public bicycle usage patterns are regular,and bicycle stations are grouped into several clusters based on usage patterns of "morning destination,night origin" "morning origin,night destination" and "steady throughout the day".The PBS in Huicheng District plays various roles by helping users commute to and from jobs and schools,and to make short distance trips.The PBS also is a complementary tool for bus transit facilities.The PBS in Shaoguan City mostly serves as a mode for commuting.The PBS is inefficiently used in Huiyang District of Huizhou City owing to the poor road conditions.This research provides a study framework that can be reproduced in other areas,and offers a way of optimizing PBSs,thereby assisting urban transportation planning and urban land use allocation.
Given the importance of environment-friendly cities,the development of public bicycle systems(PBSs) has become more popular in recent years around the world.The purpose of this study was to explore the usage patterns of PBSs in small and medium-sized cities in Guangdong province,China,and to infer the driving mechanisms of system attributes and the built environment.The research applied time series analysis of global activity patterns,hierarchical clustering algorithm using Dynamic Time Warping distances as features and spatial data visualization on station-based data,and then compared different systems by employing a random forest algorithm to evaluate the influencing factors.The study objective was to better understand the relationship between public bicycle usage activity and underlying built environment characteristics.In Huicheng District of Huizhou City and Shaoguan City,the public bicycle usage patterns are regular,and bicycle stations are grouped into several clusters based on usage patterns of "morning destination,night origin" "morning origin,night destination" and "steady throughout the day".The PBS in Huicheng District plays various roles by helping users commute to and from jobs and schools,and to make short distance trips.The PBS also is a complementary tool for bus transit facilities.The PBS in Shaoguan City mostly serves as a mode for commuting.The PBS is inefficiently used in Huiyang District of Huizhou City owing to the poor road conditions.This research provides a study framework that can be reproduced in other areas,and offers a way of optimizing PBSs,thereby assisting urban transportation planning and urban land use allocation.
引文
[1]Duran A C,Anaya-boig E,Shake J D,et al.Bicycle-sharing system socio-spatial inequalities in Brazil[J].Journa of Transport&Health,2018,8:262-270.
[2]朱玮,庞宇琦,王德.自行车出行行为和决策研究进展[J]国际城市规划,2013,28(1):54-59.[Zhu W,Pang Y Q,Wang D.Progress of research on bicycle travel behavior and decisions[J].Urban Planning International,2013,28(1):54-59.]
[3]Froehlich J,Neumann J,Oliver N.Sensing and predicting the pulse of the city through shared bicycling.[C].Pasadena:Proceedings of the International Joint Conference on Artificial Intelligence,2009:1420-1426.
[4]Burden A M,Barth R.Bike-share opportunities in New York City[R].New York:Department of City Planning2009:11-19.
[5]O'brien O,Cheshire J,Batty M.Mining bicycle sharing data for generating insights into sustainable transport systems[J].Journal of Transport Geography,2014,34(219)262-273.
[6]刘冰,曹娟娟,周于杰,等.城市公共自行车使用活动的时空间特征研究--以杭州为例[J].城市规划学刊,2016(3)77-84.[Liu B,Cao J J,Zhou Y J,et al.A study on the temporal-spatial features of bicycle-sharing activities:A case of Hangzhou[J].Urban Planning Forum,2016(3):77-84.]
[7]郭素萍,甄峰,尹秋怡.城市公共自行车出行时空间特征分析[J].规划师,2017,33(6):112-118.[Guo S P,Zhen F,Yin Q Y.Analysis on space-temporal characters of public bicycles[J].Planners,2017,33(6):112-118.]
[8]邓力凡,谢永红,黄鼎曦.基于骑行时空数据的共享单车设施规划研究[J].规划师,2017,33(10):82-88.[Deng L F,Xie Y H,Huang D X.Bicycle-sharing facility planning base on riding spatio-temporal data[J].Planners,2017,33(10):82-88.]
[9]朱玮,庞宇琦,王德,等.上海市闵行区公共自行车出行特征研究[J].上海城市规划,2012(6):102-107.[Zhu W,Pang Y Q,Wang D,et al.Research on the travel characteristics of public bicycle in Minhang District,Shanghai[J].Shanghai Urban Planning Review,2012(6):102-107.]
[10]朱玮,庞宇琦,王德,等.公共自行车系统影响下居民出行的变化与机制研究--以上海闵行区为例[J].城市规划学刊,2012(5):76-81.[Zhu W,Pang Y Q,Wang D,et al.Travel behavior change after the introduction of public bicycle systems:A case study of Minhang District,Shanghai[J].Urban Planning Forum,2012(5):76-81.]
[11]Zhang Y,Thomas T,Brussel M,et al.Exploring the impact of built environment factors on the use of public bikes at bike stations:Case study in Zhongshan,China[J].Journal of Transport Geography,2017,58(SupC):59-70.
[12]Wang X,Lindsey G,Schoner J E,et al.Modeling bike share station activity:Effects of nearby businesses and jobs on trips to and from stations[J].Journal of Urban Planning and Development,2015,142(1):4015001.
[13]Faghih-Imani A,Eluru N,El-Geneidy A M,et al.How land-use and urban form impact bicycle flows:Evidence from the bicycle-sharing system(BIXI)in Montreal[J].Journal of Transport Geography,2014,41:306-314.
[14]广东惠民运营股份有限公司.2017.http://hz.2773456.com/,http://sg.2773456.com/.
[15]欧彦婵.韶关主城区自行车交通系统规划研究[J].建筑知识:学术刊,2014(B02):93-93.[Ou Y C.Research on the planning of bicycle transportation system in main urban areas of Shaoguan[J].Building Knowledge:Academic Journal,2014(B02):93-93.]
[16]“2013 LandScan data for population distribution from the Oak Ridge National Laboratory in USA.”[EB/OL].Oak Ridge National Laboratory.https://landscan.ornl.gov/.
[17]OpenStreetMap contributors[EB/OL].“OpenStreetMap.”https://www.openstreetmap.org.
[18]Long Y.Redefining Chinese city system with emerging new data[J].Applied Geography,2016,75:36-48.
[19]Jin X,Long Y,Sun W,et al.Evaluating cities'vitality and identifying ghost cities in China with emerging geographical data[J].Cities,2017,63:98-109.
[20]Lathia N,Ahmed S,Capra L.Measuring the impact of opening the London shared bicycle scheme to casual users[J].Transportation Research Part C:Emerging Technologies,2012,22:88-102.
[21]Berndt D J,Clifford J.Using dynamic time warping to find patterns in time series[C].Working notes of the knowledge discovery in databases workshop,1994:359-370.
[22]Kate R J.Using dynamic time warping distances as features for improved time series classification[J].Data Mining and Knowledge Discovery,2016,30(2):283-312.
[23]El-Assi W,Mahmoud M S,Habib K N.Effects of built environment and weather on bike sharing demand:A station level analysis of commercial bike sharing in Toronto[J].Transportation,2017,44(3):589-613.
[24]Etienne C,Latifa O.Model-based count series clustering for bike sharing system usage mining:A case study with the Vélib'System of Paris[J].Acm Transactions on Intelligent Systems and Technology,2014,5(3):1-21.
[25]Gutierrez J,Cardozo O D,Garciapalomares J C.Transit ridership forecasting at station level:An approach based on distance-decay weighted regression[J].Journal of Transport Geography,2011,19(6):1081-1092.
[26]李航.统计学习方法[M].北京:清华大学出版社,2012:55-56.[Li H.Statistical learning method[M].Beijing:Tsinghua University Press,2012:55-56.]
[27]Breiman L.Random forests[J].Machine Learning,2001,45(1):5-32.
[28]Liu Y,Liu X,Gao S,et al.Social sensing:A new approach to understanding our socioeconomic environments[J].Annals of the Association of American Geographers,2015,105(3):512-530.
[29]牟乃夏,张恒才,陈洁,等.轨迹数据挖掘城市应用研究综述[J].地球信息科学学报,2015,17(10):1136-1142.[Mou N X,Zhang H C,Chen J,et al.A review on the application research of trajectory data mining in urban cities[J].Journal of Geo-information Science,2015,17(10):1136-1142.]
[30]Han H,Guo X,Yu H.Variable selection using mean decrease accuracy and mean decrease gini based on random forest[C].IEEE International Conference on Software Engineering and Service Science,2017:219-224.
[31]Zhang L,Zhang J,Duan Z Y,et al.Sustainable bike-sharing systems:Characteristics and commonalities across cases in urban China[J].Journal of Cleaner Production,2015,97:124-133.
[32]Shaheen S A,Zhang H,Martin E.Hangzhou public bicycle:Understanding early adoption and behavioral response to bikesharing in Hangzhou,China[J].Transportation Research Record,2011,2247(2247):34-41.
[33]Campbell A A,Cherry C R,Ryerson M S,et al.Factors influencing the choice of shared bicycles and shared electric bikes in Beijing[J].Transportation Research Part C,2016,67:399-414.
[34]吴健生,李博,黄秀兰.小城市居民出行行为时空动态及驱动机制研究[J].地球信息科学学报,2017,19(2):176-184.[Wu J S,Li B,Huang X L.Spatio-temporal dynamics and driving mechanisms of resident trip in small cities[J].Journal of Geo-information Science,2017,19(2):176-184.]
[2]朱玮,庞宇琦,王德.自行车出行行为和决策研究进展[J]国际城市规划,2013,28(1):54-59.[Zhu W,Pang Y Q,Wang D.Progress of research on bicycle travel behavior and decisions[J].Urban Planning International,2013,28(1):54-59.]
[3]Froehlich J,Neumann J,Oliver N.Sensing and predicting the pulse of the city through shared bicycling.[C].Pasadena:Proceedings of the International Joint Conference on Artificial Intelligence,2009:1420-1426.
[4]Burden A M,Barth R.Bike-share opportunities in New York City[R].New York:Department of City Planning2009:11-19.
[5]O'brien O,Cheshire J,Batty M.Mining bicycle sharing data for generating insights into sustainable transport systems[J].Journal of Transport Geography,2014,34(219)262-273.
[6]刘冰,曹娟娟,周于杰,等.城市公共自行车使用活动的时空间特征研究--以杭州为例[J].城市规划学刊,2016(3)77-84.[Liu B,Cao J J,Zhou Y J,et al.A study on the temporal-spatial features of bicycle-sharing activities:A case of Hangzhou[J].Urban Planning Forum,2016(3):77-84.]
[7]郭素萍,甄峰,尹秋怡.城市公共自行车出行时空间特征分析[J].规划师,2017,33(6):112-118.[Guo S P,Zhen F,Yin Q Y.Analysis on space-temporal characters of public bicycles[J].Planners,2017,33(6):112-118.]
[8]邓力凡,谢永红,黄鼎曦.基于骑行时空数据的共享单车设施规划研究[J].规划师,2017,33(10):82-88.[Deng L F,Xie Y H,Huang D X.Bicycle-sharing facility planning base on riding spatio-temporal data[J].Planners,2017,33(10):82-88.]
[9]朱玮,庞宇琦,王德,等.上海市闵行区公共自行车出行特征研究[J].上海城市规划,2012(6):102-107.[Zhu W,Pang Y Q,Wang D,et al.Research on the travel characteristics of public bicycle in Minhang District,Shanghai[J].Shanghai Urban Planning Review,2012(6):102-107.]
[10]朱玮,庞宇琦,王德,等.公共自行车系统影响下居民出行的变化与机制研究--以上海闵行区为例[J].城市规划学刊,2012(5):76-81.[Zhu W,Pang Y Q,Wang D,et al.Travel behavior change after the introduction of public bicycle systems:A case study of Minhang District,Shanghai[J].Urban Planning Forum,2012(5):76-81.]
[11]Zhang Y,Thomas T,Brussel M,et al.Exploring the impact of built environment factors on the use of public bikes at bike stations:Case study in Zhongshan,China[J].Journal of Transport Geography,2017,58(SupC):59-70.
[12]Wang X,Lindsey G,Schoner J E,et al.Modeling bike share station activity:Effects of nearby businesses and jobs on trips to and from stations[J].Journal of Urban Planning and Development,2015,142(1):4015001.
[13]Faghih-Imani A,Eluru N,El-Geneidy A M,et al.How land-use and urban form impact bicycle flows:Evidence from the bicycle-sharing system(BIXI)in Montreal[J].Journal of Transport Geography,2014,41:306-314.
[14]广东惠民运营股份有限公司.2017.http://hz.2773456.com/,http://sg.2773456.com/.
[15]欧彦婵.韶关主城区自行车交通系统规划研究[J].建筑知识:学术刊,2014(B02):93-93.[Ou Y C.Research on the planning of bicycle transportation system in main urban areas of Shaoguan[J].Building Knowledge:Academic Journal,2014(B02):93-93.]
[16]“2013 LandScan data for population distribution from the Oak Ridge National Laboratory in USA.”[EB/OL].Oak Ridge National Laboratory.https://landscan.ornl.gov/.
[17]OpenStreetMap contributors[EB/OL].“OpenStreetMap.”https://www.openstreetmap.org.
[18]Long Y.Redefining Chinese city system with emerging new data[J].Applied Geography,2016,75:36-48.
[19]Jin X,Long Y,Sun W,et al.Evaluating cities'vitality and identifying ghost cities in China with emerging geographical data[J].Cities,2017,63:98-109.
[20]Lathia N,Ahmed S,Capra L.Measuring the impact of opening the London shared bicycle scheme to casual users[J].Transportation Research Part C:Emerging Technologies,2012,22:88-102.
[21]Berndt D J,Clifford J.Using dynamic time warping to find patterns in time series[C].Working notes of the knowledge discovery in databases workshop,1994:359-370.
[22]Kate R J.Using dynamic time warping distances as features for improved time series classification[J].Data Mining and Knowledge Discovery,2016,30(2):283-312.
[23]El-Assi W,Mahmoud M S,Habib K N.Effects of built environment and weather on bike sharing demand:A station level analysis of commercial bike sharing in Toronto[J].Transportation,2017,44(3):589-613.
[24]Etienne C,Latifa O.Model-based count series clustering for bike sharing system usage mining:A case study with the Vélib'System of Paris[J].Acm Transactions on Intelligent Systems and Technology,2014,5(3):1-21.
[25]Gutierrez J,Cardozo O D,Garciapalomares J C.Transit ridership forecasting at station level:An approach based on distance-decay weighted regression[J].Journal of Transport Geography,2011,19(6):1081-1092.
[26]李航.统计学习方法[M].北京:清华大学出版社,2012:55-56.[Li H.Statistical learning method[M].Beijing:Tsinghua University Press,2012:55-56.]
[27]Breiman L.Random forests[J].Machine Learning,2001,45(1):5-32.
[28]Liu Y,Liu X,Gao S,et al.Social sensing:A new approach to understanding our socioeconomic environments[J].Annals of the Association of American Geographers,2015,105(3):512-530.
[29]牟乃夏,张恒才,陈洁,等.轨迹数据挖掘城市应用研究综述[J].地球信息科学学报,2015,17(10):1136-1142.[Mou N X,Zhang H C,Chen J,et al.A review on the application research of trajectory data mining in urban cities[J].Journal of Geo-information Science,2015,17(10):1136-1142.]
[30]Han H,Guo X,Yu H.Variable selection using mean decrease accuracy and mean decrease gini based on random forest[C].IEEE International Conference on Software Engineering and Service Science,2017:219-224.
[31]Zhang L,Zhang J,Duan Z Y,et al.Sustainable bike-sharing systems:Characteristics and commonalities across cases in urban China[J].Journal of Cleaner Production,2015,97:124-133.
[32]Shaheen S A,Zhang H,Martin E.Hangzhou public bicycle:Understanding early adoption and behavioral response to bikesharing in Hangzhou,China[J].Transportation Research Record,2011,2247(2247):34-41.
[33]Campbell A A,Cherry C R,Ryerson M S,et al.Factors influencing the choice of shared bicycles and shared electric bikes in Beijing[J].Transportation Research Part C,2016,67:399-414.
[34]吴健生,李博,黄秀兰.小城市居民出行行为时空动态及驱动机制研究[J].地球信息科学学报,2017,19(2):176-184.[Wu J S,Li B,Huang X L.Spatio-temporal dynamics and driving mechanisms of resident trip in small cities[J].Journal of Geo-information Science,2017,19(2):176-184.]