基于公交刷卡数据的武汉市职住通勤特征研究
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摘要
通勤出行和职住关系对于理解城市空间结构和职住组织模式具有重要意义。基于公交刷卡数据,以武汉市主城区和都市发展区为研究区,通过构建出行模型和职住地识别规则,识别了职住通勤群体,从职住密度、通勤时间与距离、通勤流向和可视化等方面对武汉市的通勤出行和职住平衡进行测度,并识别了武汉市的职住通勤模式。结果表明:①核心城区的职住密度明显高于其它城区,承载着较多的居住和就业人口;②通勤距离与时间具有核心—外围—边缘的递增模式;③城市主要通勤流位于核心城区和外围城区;④职住通勤模式总体可以概括为核心—外围—边缘的单中心向内通勤模式,越往外围区域越呈现职住错位的趋势。
Commuting and the relationship between job and housing are of great significance for understanding the urban spatial structure and the pattern of job-housing organization. Taking Wuhan city center and urban development zone as study area, this paper identifies home and work groups, which are generated by some measures such as building travel models and recognition rules for job-housing place based on bus smart card data. From the aspects of job-housing density,commuting distance and time, dominant commuting flows and visualization, etc., it measures job-housing balance and commuting patterns in Wuhan. Results indicate that: Firstly, the density of job-housing in the core urban area carrying more residential and employed population is significantly higher than other urban areas; Secondly, commuting distance and time show an increasing pattern on core-periphery-edge urban area level. Thirdly, the dominant commuting flows are located in the core and periphery urban areas. Finally, overall job-housing and commuting modes can be summarized as a core-periphery-edge single-center inward pattern, the trend of job-housing spatial mismatch phenomenon is more and more apparent as the direction moves outward from the core urban area.
Commuting and the relationship between job and housing are of great significance for understanding the urban spatial structure and the pattern of job-housing organization. Taking Wuhan city center and urban development zone as study area, this paper identifies home and work groups, which are generated by some measures such as building travel models and recognition rules for job-housing place based on bus smart card data. From the aspects of job-housing density,commuting distance and time, dominant commuting flows and visualization, etc., it measures job-housing balance and commuting patterns in Wuhan. Results indicate that: Firstly, the density of job-housing in the core urban area carrying more residential and employed population is significantly higher than other urban areas; Secondly, commuting distance and time show an increasing pattern on core-periphery-edge urban area level. Thirdly, the dominant commuting flows are located in the core and periphery urban areas. Finally, overall job-housing and commuting modes can be summarized as a core-periphery-edge single-center inward pattern, the trend of job-housing spatial mismatch phenomenon is more and more apparent as the direction moves outward from the core urban area.
引文
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[2] Howard E. Garden cityes of tomorrow[M]. London:S. Sonnenschein&Co.Ltd,1902.
[3] Saarinen E. The city:Its Growth,its decay,its future[M]. NewYork:Reinhold Publishing Corporation,1945.
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[17]刘望保,候长营.转型期广州市城市居民职住空间与通勤行为研究[J].地理科学,2012,34(3):272-279.
[18]周江评,陈晓键,黄伟,等.中国中西部大城市的职住平衡和通勤效率——以西安为例[J].地理学报,2013,68(10):1 316-1 330.
[19]胡娟,朱丽霞,罗静.武汉市职住空间特征及评价[J].人文地理,2014(3):76-82.
[20]丁亮,钮心毅,宋小冬.上海中心城就业中心体系测度——基于手机信令数据的研究[J].地理学报,2016,71(3):484-499.
[21]钮心毅,丁亮.利用手机数据分析上海市域的职住空间关系——若干结论和讨论[J].上海城市规划,2015,2:39-43.
[22] Barry J,Newhouser R,Rahbee A,et al. Origin and destination estimation in New York city with automated fare system data[J]. Transport Reaearch Record,2002,1 817:183-187.
[23] Trépanier M,Tranchant N,Chapleau R. Individual trip destination estimation in a transit smart card automated fare collection system[J]. Journal of Intelligent Transportation Systems,2007,11(1):1-14.
[24] Bagchi M,White P.R. The potential of public transport smart card data[J]. Transport Policy,2005,12(5):464-474.
[25] Ceapa I,Smith C,Capra L. Avoiding the crowds:understanding tube station congestion patterns from trip data. In:Proceeding of the 1st ACM SIGKDD International Workshop on Urban Computing[M]. ACM Press,2012.
[26] Lathia N,Froehlich J,Capra L. Mining Public Transport Usage for Personalised Intelligent Transport Systems[C]//2010IEEE 10th International Conference on Data Mining,2010.
[27] Agard B,Morency C,Trépanier M. Mainning Public transport user behavior from smart card data[C]//The 12th IFAC Symposium on Information Control Problems in Manufacturing(INCOM),2006.
[28]龙瀛,张宇,崔承印.利用公交刷卡数据分析北京职住关系和通勤出行[J].地理学报,2012,67(10):1 339-1 352.
[29] Zhou J,Long Y. Job-housing balance of bus commuters in Beijing exploration with large scale synthesized smart card data[C]//Transportation Research Record 92nd Annual Meeting.Washington DC,2014.
[30]许园园,塔娜,李响.基于地铁刷卡数据的城市通勤与就业中心吸引范围研究[J].人文地理,2017,32(3):93-101.
[31]周春山,叶昌东.中国城市空间结构研究述评[J].地理科学进展,2013,32(7):1 030-1 038.
[32] Cervero R. Jobs-housing balance and regional mobility[J].Journal of the American Planning Association,1989,55(2):136-150.
[33] Thomas R. London's new towns:A study of self-contained and balanced communities[M]. London:PEP,1969.
[34] Joseph XG Zhou,Anthony GO Yeh,Weifeng Li. A commuting spectrum analysis of the job-housing and self containment of employment with mobile phone location big data[J]. Environment and Planning B:Urban Analytics and City Science,2017,45(3):434-451.
[35] Xingjian Liu,Mingshu Wang. How polycentric is urban China and why?A case study of 318 cities[J]. Landscape and Urban Planning,2016,151:10-20.
[36] Bertaud A. The spatial organization of cities:Deliberate outcome or unforeseen consequence?[M]. Berkeley Institute of Urban and Regional Development,2004.
[37]赵晖,杨开忠,魏海涛,等.北京城市职住空间重构及其通勤模式演化研究[J].城市规划,2013,37(8):33-39.
[2] Howard E. Garden cityes of tomorrow[M]. London:S. Sonnenschein&Co.Ltd,1902.
[3] Saarinen E. The city:Its Growth,its decay,its future[M]. NewYork:Reinhold Publishing Corporation,1945.
[4] Cervero R. Jobs-housing balance and regional mobility[J]. Journal of the American Planning Association,1989,55(2):136-150.
[5] Horner M,Murray A. A multi-objective approach to improving regional jobs-housing balance[J]. Regional Studies,2003,37(2):135-146.
[6] Kain J.F. Housing segregation,negro employment,and metropolitan decentralization[J]. The Quarterly Journal of Economics,1968,82(2):175-197.
[7] Duncan B. Factors in work-residence separation:wage and salary workers,chicago,1951[J]. American Sociological Review,1956,21(1):48-56.
[8]柴彦威,张艳,刘志林.职住分离的空间差异性及其影响因素研究[J].地理学报,2011,66(2):157-166.
[9]刘志林,张艳,柴彦威.中国大城市职住分离及其特征——以北京为例[J].城市发展研究,2009,16(9):110-117.
[10]刘志林,王茂军.北京市职住空间错位对居民通勤行为的影响分析——基于就业可达性与通勤时间的讨论[J].地理学报,2011,66(4):457-467.
[11]张艳,柴彦威.基于居住区比较的北京市通勤研究[J].地理研究,2009,28(5):1 327-1 340.
[12]孙斌栋,李南菲,宋洁杰,等.职住平衡对通勤交通的影响分析[J].城市规划学刊,2010(6):55-60.
[13]孙斌栋,涂婷,石巍,等.特大城市多中心空间结构的交通绩效检验[J].城市规划学刊,2013(2):63-69.
[14]孟斌,郑丽敏,于慧丽.北京城市居民通勤时间变化及影响因素[J].地理科学进展,2011,30(10):1 218-1 224.
[15]孟斌.北京城市居民职住分离的空间组织特征[J].地理学报,2009,64(12):1 457-1 466.
[16]周素红,刘玉兰.转型期广州城市居民居住与就业地区位选择的空间关系及其变迁[J].地理学报,2010,65(2):191-201.
[17]刘望保,候长营.转型期广州市城市居民职住空间与通勤行为研究[J].地理科学,2012,34(3):272-279.
[18]周江评,陈晓键,黄伟,等.中国中西部大城市的职住平衡和通勤效率——以西安为例[J].地理学报,2013,68(10):1 316-1 330.
[19]胡娟,朱丽霞,罗静.武汉市职住空间特征及评价[J].人文地理,2014(3):76-82.
[20]丁亮,钮心毅,宋小冬.上海中心城就业中心体系测度——基于手机信令数据的研究[J].地理学报,2016,71(3):484-499.
[21]钮心毅,丁亮.利用手机数据分析上海市域的职住空间关系——若干结论和讨论[J].上海城市规划,2015,2:39-43.
[22] Barry J,Newhouser R,Rahbee A,et al. Origin and destination estimation in New York city with automated fare system data[J]. Transport Reaearch Record,2002,1 817:183-187.
[23] Trépanier M,Tranchant N,Chapleau R. Individual trip destination estimation in a transit smart card automated fare collection system[J]. Journal of Intelligent Transportation Systems,2007,11(1):1-14.
[24] Bagchi M,White P.R. The potential of public transport smart card data[J]. Transport Policy,2005,12(5):464-474.
[25] Ceapa I,Smith C,Capra L. Avoiding the crowds:understanding tube station congestion patterns from trip data. In:Proceeding of the 1st ACM SIGKDD International Workshop on Urban Computing[M]. ACM Press,2012.
[26] Lathia N,Froehlich J,Capra L. Mining Public Transport Usage for Personalised Intelligent Transport Systems[C]//2010IEEE 10th International Conference on Data Mining,2010.
[27] Agard B,Morency C,Trépanier M. Mainning Public transport user behavior from smart card data[C]//The 12th IFAC Symposium on Information Control Problems in Manufacturing(INCOM),2006.
[28]龙瀛,张宇,崔承印.利用公交刷卡数据分析北京职住关系和通勤出行[J].地理学报,2012,67(10):1 339-1 352.
[29] Zhou J,Long Y. Job-housing balance of bus commuters in Beijing exploration with large scale synthesized smart card data[C]//Transportation Research Record 92nd Annual Meeting.Washington DC,2014.
[30]许园园,塔娜,李响.基于地铁刷卡数据的城市通勤与就业中心吸引范围研究[J].人文地理,2017,32(3):93-101.
[31]周春山,叶昌东.中国城市空间结构研究述评[J].地理科学进展,2013,32(7):1 030-1 038.
[32] Cervero R. Jobs-housing balance and regional mobility[J].Journal of the American Planning Association,1989,55(2):136-150.
[33] Thomas R. London's new towns:A study of self-contained and balanced communities[M]. London:PEP,1969.
[34] Joseph XG Zhou,Anthony GO Yeh,Weifeng Li. A commuting spectrum analysis of the job-housing and self containment of employment with mobile phone location big data[J]. Environment and Planning B:Urban Analytics and City Science,2017,45(3):434-451.
[35] Xingjian Liu,Mingshu Wang. How polycentric is urban China and why?A case study of 318 cities[J]. Landscape and Urban Planning,2016,151:10-20.
[36] Bertaud A. The spatial organization of cities:Deliberate outcome or unforeseen consequence?[M]. Berkeley Institute of Urban and Regional Development,2004.
[37]赵晖,杨开忠,魏海涛,等.北京城市职住空间重构及其通勤模式演化研究[J].城市规划,2013,37(8):33-39.