骑行替代步行后公共交通可达性改善效果评估方法

doi:10.12082/dqxxkx.2023.220495

地球信息科学学报 ›› 2023, Vol. 25 ›› Issue (3): 439-449.doi: 10.12082/dqxxkx.2023.220495

• 地球信息科学理论与方法 • 下一篇

骑行替代步行后公共交通可达性改善效果评估方法

高顺祥¹(), 陈珍¹, 张志健¹, 陈越¹, 肖中圣¹, 邓进¹^,², 许奇¹^,³^,^*()

1.北京交通大学综合交通运输大数据应用技术交通运输行业重点实验室，北京 100044
2.北京城建交通设计研究院有限公司，北京 100037
3.北京交通大学中国综合交通研究中心，北京 100044

收稿日期:2022-07-08 修回日期:2022-09-13 出版日期:2023-03-25 发布日期:2023-04-19
通讯作者: * 许奇（1982— ），男，云南普洱人，博士，副教授，研究方向为轨道交通与城市可持续发展。 E-mail: xuqi@bjtu.edu.cn
作者简介:高顺祥（1999— ），男，安徽亳州人，硕士研究生，主要从事时空大数据挖掘研究。E-mail: 20120807@bjtu.edu.cn
基金资助:
国家自然科学基金项目(71621001);国家自然科学基金项目(71971021)

Evaluation of Improvement of Public Transport Accessibility Considering Riding Instead of Walking

GAO Shunxiang¹(), CHEN Zhen¹, ZHANG Zhijian¹, CHEN Yue¹, XIAO Zhongsheng¹, DENG Jin¹^,², XU Qi¹^,³^,^*()

1. Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University, Beijing 100044, China
2. Beijing Urban Construction Transport Planning & Design Institute Co. Limited, Beijing 100037, China
3. Integrated Transportation Research Centre, Beijing Jiaotong University, Beijing 100044, China

Received:2022-07-08 Revised:2022-09-13 Online:2023-03-25 Published:2023-04-19
Contact: XU Qi
Supported by:
National Natural Science Foundation of China(71621001);National Natural Science Foundation of China(71971021)

摘要/Abstract

摘要：

改善末端的慢行环境是提升绿色交通出行竞争力的关键问题。既有研究多针对出行效率问题分析地面公交末端出行的改善效果，未充分考虑城市公共交通系统与土地利用的关系。本文融合多源交通大数据构建“门到门”精细尺度的公共交通出行链，提出步行和骑行等两种方式下公共交通全过程出行时间的计算方法，据此构建基于累计机会模型的末端出行改善效果的评估模型，研究骑行替代步行后公共交通可达性改善效果。该方法两步计算具有计算量较小和数据更新机制灵活的特点，适应于大空间尺度公共交通可达性研究。基于2020年北京的案例研究表明：骑行替代的公共交通出行时间平均减少315 s，降低幅度达12.8%；末端出行效率的提高改善将进一步提升就业、医疗、餐饮、绿地、购物和休闲等城市居民活动的公共交通可达性，其改善幅度达90%、74%、94%、33%、107%和77%，且改善的区域聚集于中心城区和外围居住组团。另外，公共交通可达性改善效果呈圈层径向递减的空间特征，城市轨道交通作为公共交通的主干网络，就业、医疗、餐饮、绿地、购物和休闲活动提升效果分别为地面公交的1.43、1.43、1.70、1.42、1.70、1.71倍。

关键词: 城市公共交通, 绿色交通, “最后一公里”问题, 末端衔接, 可达性分析, 空间异质性, 全出行链, 多源大数据

Abstract:

Improving the slow travel environment at public transport stations is a key issue to enhance the competitiveness of green transportation. It is an important measure to improve the service level of public transportation to further coordinate public transportation, especially the end connection between urban rail transit and slow traffic, and to open up the ' last mile '. Existing studies mostly analyze the improvement of travel efficiency at bus stations, and do not fully consider the interaction between urban public transport system and land use. Achieving good accessibility is the main goal of building a livable city, and the spatial analysis of public transport accessibility provides a core indicator to measure the integration of public transport and urban development. As a location-based accessibility evaluation method, the cumulative opportunity method has advantages in understanding the relationship between transportation and land use and is easy to use. To this end, we construct a “door-to-door” fine-scale public transport trip chain based on multi-source traffic big data and develop a two-step calculation method to compute travel time of public transport under two modes of walking and cycling. The two-step calculation of this method has the characteristics of less calculation and flexible data update mechanism, which is suitable for the study of public transport accessibility at large spatial scale. A case study based on Beijing in 2020 shows that the average travel time of public transport via cycling is reduced by 315 seconds and 12.8%. Improvement of travel efficiency at stations improves the public transport accessibility for urban activities such as employment, health care, catering, green space, shopping, and leisure. The improvement range is 90%, 74%, 94%, 33%, 107%, and 77%, respectively, and the improved areas are concentrated in the central urban area and the surrounding residential areas. In addition, the improvement effect of accessibility of public transport shows a spatial feature of a radial-decreasing circular structure. As the main network of public transport, urban rail transit presents an improvement in employment, medical treatment, catering, green space, shopping, and leisure activities by 1.43, 1.43, 1.70, 1.42, 1.70, and 1.71 times, respectively, compared to ground bus. The results show that compared with the ground bus system, cycling substitution will significantly improve the integration of rail transit and city. According to the co-opetition relationship between rail transit and ground bus, rational allocation of bicycle facilities and optimization of shared bicycles will further increase the competitiveness of green transportation.

Key words: urban public transport, green transportation, "first-and-last mile" problem of transit, terminal connection, accessibility analysis, full trip chain, spatial heterogeneity, multi-source big data

高顺祥, 陈珍, 张志健, 陈越, 肖中圣, 邓进, 许奇. 骑行替代步行后公共交通可达性改善效果评估方法[J]. 地球信息科学学报, 2023, 25(3): 439-449.DOI:10.12082/dqxxkx.2023.220495

GAO Shunxiang, CHEN Zhen, ZHANG Zhijian, CHEN Yue, XIAO Zhongsheng, DENG Jin, XU Qi. Evaluation of Improvement of Public Transport Accessibility Considering Riding Instead of Walking[J]. Journal of Geo-information Science, 2023, 25(3): 439-449.DOI:10.12082/dqxxkx.2023.220495

图/表 11

图1

图2

图3

图4

图5

图6

图7

图8

图9

图10

表1

参考文献 33

[1]	毛保华, 王敏, 何天健, 等. 城市公共交通服务水平研究回顾和展望[J]. 交通运输系统工程与信息, 2022, 22(1):2-13.
	[Mao B H, Wang M, He T J, et al. A review and prospect of urban public transit level-of-service research[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(1):2-13.] DOI:10.16097/j.cnki.1009-6744.2022.01.001 doi: 10.16097/j.cnki.1009-6744.2022.01.001
[2]	段德忠, 刘承良, 桂钦昌, 等. 西方城市公共交通空间研究进展:一个地理学的视角[J]. 地理与地理信息科学, 2016, 32(5):87-96.
	[Duan D Z, Liu C L, Gui Q C, et al. Advances in space research of western urban public transport: A geography perspective[J]. Geography and Geo-Information Science, 2016, 32(5):87-96.] DOI:10.3969/j.issn.1672-0504.2016.05.014 doi: 10.3969/j.issn.1672-0504.2016.05.014
[3]	樊晓春, 张雪英, 刘学军, 等. 一种公交换乘优化算法设计[J]. 地球信息科学学报, 2009, 11(2):157-162.
	[Fan X C, Zhang X Y, Liu X J, et al. Design of an algorithm of public traffic transfer based on the least transfer[J]. Journal of Geo-Information Science, 2009, 11(2):157-162.] DOI:10.3969/j.issn.1560-8999.2009.02.004 doi: 10.3969/j.issn.1560-8999.2009.02.004
[4]	Vansteenwegen P, Melis L, Aktaş D, et al. A survey on demand-responsive public bus systems[J]. Transportation Research Part C: Emerging Technologies, 2022, 137:103573. DOI:10.1016/j.trc.2022.103573 doi: 10.1016/j.trc.2022.103573
[5]	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.] DOI:10.13813/j.cn11-5141/u.2020.0004 doi: 10.13813/j.cn11-5141/u.2020.0004
[6]	申犁帆, 王烨, 张纯, 等. 轨道站点合理步行可达范围建成环境与轨道通勤的关系研究——以北京市44个轨道站点为例[J]. 地理学报, 2018, 73(12):2423-2439. doi: 10.11821/dlxb201812011
	[Shen L F, Wang Y, Zhang C, et al. Relationship between built environment of rational pedestrian catchment areas and URT commuting ridership: Evidence from 44 URT stations in Beijing[J]. Acta Geographica Sinica, 2018, 73(12):2423-2439.] DOI:10.11821/dlxb201812011 doi: 10.11821/dlxb201812011
[7]	王家川, 欧阳松寿. 北京市轨道交通站点周边区域共享自行车运行不均衡性研究[J]. 交通运输系统工程与信息, 2019, 19(1):214-221.
	[Wang J C, Ouyang S S. Disequilibrium of bicycle-sharing in rail transit station areas in Beijing[J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(1):214-221.] DOI:10.16097/j.cnki.1009-6744.2019.01.032 doi: 10.16097/j.cnki.1009-6744.2019.01.032
[8]	高楹, 宋辞, 郭思慧, 等. 接驳地铁站的共享单车源汇时空特征及其影响因素[J]. 地球信息科学学报, 2021, 23(1):155-170. doi: 10.12082/dqxxkx.2021.200351
	[Gao Y, Song C, Guo S H, et al. Spatial-temporal characteristics and influencing factors of source and sink of dockless sharing bicycles connected to subway stations[J]. Journal of Geo-Information Science, 2021, 23(1):155-170.] DOI:10.12082/dqxxkx.2021.200351 doi: 10.12082/dqxxkx.2021.200351
[9]	邝嘉恒, 邬群勇. 接驳地铁站的共享单车时空均衡性分析与吸引区域优化[J]. 地球信息科学学报, 2022, 24(7):1337-1348. doi: 10.12082/dqxxkx.2022.210775
	[Kuang J H, Wu Q Y. Spatial-temporal equilibrium analysis and attraction area optimization of dockless sharing bicycles connected to subway stations[J]. Journal of Geo-Information Science, 2022, 24(7):1337-1348.] DOI:10.12082/dqxxkx.2022.210775 doi: 10.12082/dqxxkx.2022.210775
[10]	孙启鹏, 曾开邦, 张锴琦, 等. 北京市共享单车出行的时空规律与需求预测研究[J]. 交通运输系统工程与信息, 2022, 22(1):332-338.
	[Sun Q P, Zeng K B, Zhang K Q, et al. Spatiotemporal travel patterns and demand prediction of shared bikes in Beijing[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(1):332-338.] DOI:10.16097/j.cnki.1009-6744.2022.01.035 doi: 10.16097/j.cnki.1009-6744.2022.01.035
[11]	Jäppinen S, Toivonen T, Salonen M. Modelling the potential effect of shared bicycles on public transport travel times in Greater Helsinki: An open data approach[J]. Applied Geography, 2013, 43:13-24. DOI:10.1016/j.apgeog.2013.05.010 doi: 10.1016/j.apgeog.2013.05.010
[12]	Lee J, Choi K, Leem Y. Bicycle-based transit-oriented development as an alternative to overcome the criticisms of the conventional transit-oriented development[J]. International Journal of Sustainable Transportation, 2016, 10(10):975-984. DOI:10.1080/15568318.2014.923547 doi: 10.1080/15568318.2014.923547
[13]	Griffin G P, Sener I N. Planning for bike share connectivity to rail transit[J]. Journal of Public Transportation, 2016, 19(2):1-22. DOI:10.5038/2375-0901.19.2.1 doi: 10.5038/2375-0901.19.2.1 pmid: 27872554
[14]	Deboosere R, El-Geneidy A M, Levinson D. Accessibility-oriented development[J]. Journal of Transport Geography, 2018, 70:11-20. DOI:10.1016/j.jtrangeo.2018.05.015 doi: 10.1016/j.jtrangeo.2018.05.015
[15]	许奇, 陈越, 黄靖茹, 等. 考虑出行费用的就业可达性分析[J]. 交通运输系统工程与信息, 2022, 22(2):37-44.
	[Xu Q, Chen Y, Huang J R, et al. Job accessibility analysis considering travel cost[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(2):37-44.] DOI:10.16097/j.cnki.1009-6744.2022.02.004 doi: 10.16097/j.cnki.1009-6744.2022.02.004
[16]	Hansen W G. How accessibility shapes land use[J]. Journal of the American Institute of Planners, 1959, 25(2):73-76. DOI:10.1080/01944365908978307 doi: 10.1080/01944365908978307
[17]	Geurs K T, de Montis A, Reggiani A. Recent advances and applications in accessibility modelling[J]. Computers, Environment and Urban Systems, 2015, 49:82-85. DOI:10.1016/j.compenvurbsys.2014.09.003 doi: 10.1016/j.compenvurbsys.2014.09.003
[18]	Yang L, Ma R, Zhang H M, et al. Driving behavior recognition using EEG data from a simulated car-following experiment[J]. Accident Analysis & Prevention, 2018, 116:30-40. DOI:10.1016/j.aap.2017.11.010 doi: 10.1016/j.aap.2017.11.010
[19]	Horner M W. Spatial dimensions of urban commuting: A review of major issues and their implications for future geographic research[J]. The Professional Geographer, 2004, 56(2):160-173. DOI:10.1111/j.0033-0124.2004.05602002.x doi: 10.1111/j.0033-0124.2004.05602002.x
[20]	Geurs K T, Wee B V. Accessibility evaluation of land-use and transport strategies: Review and research directions[J]. Journal of Transport Geography, 2004, 12(2):127-140. DOI:10.1016/j.jtrangeo.2003.10.005 doi: 10.1016/j.jtrangeo.2003.10.005
[21]	Foda, M. A., & Osman, A. O. A GIS approach to study the bus transit network accessibility, case study: The city of Alexandria. Journal of Arab Academy for Science, Technology & Maritime Transport, 2008, 34(65):32-39.
[22]	Lei T L, Church R L. Mapping transit-based access: Integrating GIS, routes and schedules[J]. International Journal of Geographical Information Science, 2010, 24(2):283-304. DOI:10.1080/13658810902835404 doi: 10.1080/13658810902835404
[23]	Mavoa S, Witten K, et al. GIS based destination accessibility via public transit and walking in Auckland, New Zealand[J]. Journal of Transport Geography, 2012, 20(1):15-22. DOI:10.1016/j.jtrangeo.2011.10.001 doi: 10.1016/j.jtrangeo.2011.10.001
[24]	江世雄. 城市公共交通系统可达性评价与优化方法[D]. 北京: 北京交通大学, 2019.
	[Jiang S X. Urban public transit system accessibility evaluation and optimization method[D]. Beijing: Beijing Jiaotong University, 2019.]
[25]	Ji Y J, Cao Y, Liu Y, et al. Analysis of temporal and spatial usage patterns of dockless bike sharing system around rail transit station area[J]. Journal of Southeast University (English Edition), 2019, 35(2):228-235.
[26]	Cheng C L, Agrawal A. TTSAT: A new approach to mapping transit accessibility[J]. Journal of Public Transportation, 2010, 13(1):55-72. doi: 10.5038/2375-0901
[27]	Zuo T, Wei H, Rohne A. Determining transit service coverage by non-motorized accessibility to transit: Case study of applying GPS data in Cincinnati metropolitan area[J]. Journal of Transport Geography, 2018, 67:1-11. DOI:10.1016/j.jtrangeo.2018.01.002 doi: 10.1016/j.jtrangeo.2018.01.002
[28]	Wang J Y, Kwan M P, et al. Assessing changes in job accessibility and commuting time under bike-sharing scenarios[J]. Transportmetrica A Transport Science, 2022, 20(4):39-50. DOI:10.1080/23249935.2022.2043950 doi: 10.1080/23249935.2022.2043950
[29]	Zuo T, Wei H, Chen N, et al. First-and-last Mile solution via bicycling to improving transit accessibility and advancing transportation equity[J]. Cities, 2020, 99:102614. DOI:10.1016/j.cities.2020.102614 doi: 10.1016/j.cities.2020.102614
[30]	高德开放平台. 路线规划2.0[EB/OL]. (2022-6-15) [2022-6-27] https://lbs.amap.com/api/webservice/guide/api/newroute.
[31]	中国城市规划设计研究院. 全国主要城市通勤检测报告[R]. 北京: 中国城市规划设计研究院, 2020.
[32]	Transit Cooperative Research Program, United States. Federal Transit Administration, Transit Development Corporation, National Research Council. Transit Capacity and Quality of Service Manual, second ed[M].Transportation Research Board, Washington, DC, 2003.
[33]	Hadas Y, Ranjitkar P. Modeling public-transit connectivity with spatial quality-of-transfer measurements[J]. Journal of Transport Geography, 2012, 22:137-147. DOI:10.1016/j.jtrangeo.2011.12.003 doi: 10.1016/j.jtrangeo.2011.12.003

骑行替代步行后公共交通可达性改善效果评估方法

Evaluation of Improvement of Public Transport Accessibility Considering Riding Instead of Walking

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 11

参考文献 33

相关文章 15

Metrics

本文评价

推荐阅读 0

环路	二环内	二-三环	三-四环	四-五环	五-六环
就业	124	97	110	87	63
医疗	112	87	92	67	50
餐饮	138	114	119	81	64
绿地	58	45	36	24	22
购物	153	120	127	101	75
休闲	112	97	93	80	50

[1]	王钰莹, 王海军, 周新刚, 张彬, 周晓艳. 耦合元胞和斑块尺度分层驱动机制的城镇扩展CA模拟[J]. 地球信息科学学报, 2023, 25(9): 1784-1797.
[2]	董勇, 周亮, 高鸿, 王宝. 黄土高原地形破碎化探测及空间异质性分析[J]. 地球信息科学学报, 2023, 25(8): 1625-1636.
[3]	杨雨, 宋福铁, 张杰. 金融网络中心性对中国城市经济增长的影响机理[J]. 地球信息科学学报, 2023, 25(5): 982-998.
[4]	吴子豪, 刘耀林, 冯向阳, 陈奕云, 闫庆武. 基于多尺度地理加权回归的土壤镉污染局部影响因子分析[J]. 地球信息科学学报, 2023, 25(3): 573-587.
[5]	刘乐, 盛科荣, 王传阳. 中国科技型初创企业的时空格局及影响因素研究——基于创业生态系统视角[J]. 地球信息科学学报, 2023, 25(2): 340-353.
[6]	岳林峰, 薛存金, 王振国, 牛超然. 基于Argo剖面浮标观测的海洋溶解氧空间-质量迭代插值方法[J]. 地球信息科学学报, 2023, 25(10): 2000-2011.
[7]	高歆, 袁胜元, 李京忠, 赵会兵, 许淑娜. 面向稀疏降水站点的套合各向异性贝叶斯地统计估计研究[J]. 地球信息科学学报, 2022, 24(8): 1445-1458.
[8]	赵迪, 陈鹏, 李海成, 苗红斌. 2005—2018年北京市外来人口迁移特征与影响因素分析[J]. 地球信息科学学报, 2022, 24(4): 698-710.
[9]	李军, 刘举庆, 游林, 董恒, 俞艳, 张晓盼, 钟文军, 杨典华. 多源大数据支持的土地储备智能决策模型集研究[J]. 地球信息科学学报, 2022, 24(2): 299-309.
[10]	高书鹏, 刘晓龙, 宋金玲, 史正涛, 杨磊, 郭利彪. 热带山区高时空分辨率NDVI融合精度及其影响因素分析[J]. 地球信息科学学报, 2022, 24(2): 405-419.
[11]	梁立锋, 曾文霞, 宋悦祥, 邵振峰, 刘秀娟. 顾及人群集聚和情绪强度的城市综合活力评价及影响因素[J]. 地球信息科学学报, 2022, 24(10): 1854-1866.
[12]	刘智谦, 吕建军, 姚尧, 张嘉琪, 寇世浩, 关庆锋. 基于街景图像的可解释性城市感知模型研究方法[J]. 地球信息科学学报, 2022, 24(10): 2045-2057.
[13]	韦原原, 江南, 陈云海, 李响, 杨振凯. 顾及时空对象空间相互作用的疫情风险评估建模与应用[J]. 地球信息科学学报, 2021, 23(2): 274-283.
[14]	葛咏, 刘梦晓, 胡姗, 任周鹏. 时空统计学在贫困研究中的应用及展望[J]. 地球信息科学学报, 2021, 23(1): 58-74.
[15]	艾廷华, 雷英哲, 谢鹏, 周校东. 等时线模型支持下的深圳市综合医院空间可达性测度分析[J]. 地球信息科学学报, 2020, 22(1): 113-121.