海口城市建成环境对高峰期网约车通勤出行的影响——基于滴滴出行数据

doi:10.13959/j.issn.1003-2398.2022.05.016

Abstract
Figure/Table
References (0)
Related Citation (13)

Download: PDF (11885 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract The popularity of online car-hailing has reshaped residents' commuting behavior, and the urban built environment profoundly affects residents' commuting travel. However, the current academic circles pay more attention to the impact of the urban built environment on traditional commuting modes such as bus, subway, and car. To address this gap in the literature, this paper depicts the spatial differentiation pattern of residents' commuting time and commuting distance on a grid-scale in Haikou and uses Geodetector to analyze the influence of the urban built environment factors and their interaction on residents' commuting travel. The results show that the average commuting time in the morning and evening peaks is 9.01 minutes and 8.79 minutes. Secondly, different built environment factors have distinct effects on commuting time and distance. Thirdly, from the perspective of impact effect, the influence of distance, diversity, design, destination accessibility, and density on commuting time decrease in turn. Finally, superior transportation location, the balance of jobs and housing, high road proximity, and good job accessibility can guide the coordinated development of online car-hailing and diversified transportation.

Key words： built environment commuting time commuting distance didi travel data Haikou

Received: 05 November 2021

PACS:

K901

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	SHAO Hai-yan
	JIN Cheng
	ZHONG Ye-xi
	MAO Wei-sheng

Cite this article:

SHAO Hai-yan,JIN Cheng,ZHONG Ye-xi等. THE IMPACT OF HAIKOU’S URBAN BUILT ENVIRONMENT ON ONLINE CAR-HAILING COMMUTING DURING PEAK HOURS: BASED ON DIDI TRAVEL DATA[J]. HUMAN GEOGRAPHY, 2022, 37(5): 130-139.

URL:

http://rwdl.xisu.edu.cn/EN/10.13959/j.issn.1003-2398.2022.05.016 OR http://rwdl.xisu.edu.cn/EN/Y2022/V37/I5/130

[1]	周素红,闫小培. 基于居民通勤行为分析的城市空间解读——以广州市典型街区为案例[J]. 地理学报,2006,61(2):179-189. [Zhou Suhong, Yan Xiaopei. The impact of commuters' travel pattern on urban structure: A case study in some typical communities in Guangzhou[J]. Acta Geographica Sinica, 2006,61(2):179-189.]
[2]	张雪,柴彦威. 基于结构方程模型的西宁城市居民通勤行为及其影响因素[J]. 地理研究,2018,37(11):2331-2343. [Zhang Xue, Chai Yanwei. Influencing factors of residents' commuting behavior in Xining: A SEM analysis using activity-travel diary survey data[J]. Geographical Research, 2018,37(11):2331-2343.]
[3]	韩会然,杨成凤,宋金平.公共交通与私家车出行的通勤效率差异及影响因素——以北京都市区为例[J].地理研究,2017,36(2):253-266. [Han Huiran, Yang Chengfeng, Song Jinping. Impact factors and differences in commuting efficiency between public transit and private automobile travel: A case study on the Beijing metropolitan area[J]. Geographical Research, 2017,36(2):253-266.]
[4]	孙斌栋,但波.上海城市建成环境对居民通勤方式选择的影响[J]. 地理学报,2015,70(10):1664-1674. [Sun Bindong, Dan Bo. Impact of urban built environment on residential choice of commuting mode in Shanghai[J]. Acta Geographica Sinica, 2015,70(10):1664-1674.]
[5]	柴彦威,谭一洺. 中国西部城市居民时空间行为特征研究——以西宁市为例[J].人文地理, 2017, 32(4): 37-44. [Chai Yanwei, Tan Yiming. Examining the space-time behavior patterns of residents in cities of the western China: Evidence from Xining[J]. Human Geography, 2017,32(4):37-44.]
[6]	田金玲,王德,谢栋灿,等.上海市典型就业区的通勤特征分析与模式总结——张江、金桥和陆家嘴的案例比较[J]. 地理研究,2017, 36(1):134-148. [Tian Jinling, Wang De, Xie Dongcan, et al. Identifying the commuting features and patterns of typical employment areas in Shanghai using cellphone signaling data: A case study in Zhangjiang, Jinqiao and Lujiazui[J]. Geographical Research, 2017, 36(1):134-148.]
[7]	Wong C, Zheng W, Qiao M. Urban expansion and neighbourhood commuting patterns in the Beijing metropolitan region: A multilevel analysis[J]. Urban Studies, 2020,57(13):2773-2793.
[8]	曹晨,甄峰,汪侠,等.基于结构方程模型的南京市就业者通勤行为特征对健康的影响研究[J]. 地理科学进展, 2020, 39(12): 2043-2053. [Cao Chen, Zhen Feng, Wang Xia, et al. Study on the influence of commuting behavior characteristics of urban residents on health in Nanjing city based on structural equation model[J]. Progress in Geography, 2020,39(12):2043-2053.]
[9]	刘耀林,陈龙,安子豪,等.基于公交刷卡数据的武汉市职住通勤特征研究[J]. 经济地理,2019,39(2):93-102. [Liu Yaolin, Chen Long, An Zihao, et al. Research on job-housing and commuting in Wuhan based on bus smart card data[J]. Economic Geography, 2019,39(2): 93-102.]
[10]	李峥嵘,柴彦威.大连市民通勤特征研究[J].人文地理,2000,15(6): 67-72,59. [Li Zhengrong, Chai Yanwei. A study on the commuting features of Dalian citizens[J]. Human Geography, 2000,15(6):67-72, 59.]
[11]	Li T, Jing P, Li L C, et al. Revealing the varying impact of urban built environment on online car-hailing travel in spatio-temporal dimension: An exploratory analysis in Chengdu, China[J/OL]. Sustainability, 2019,11(5):1336. https://doi.org/10.3390/su11051336.
[12]	Shi C Y, Li Q Q, Lu S W, et al. Exploring temporal intra-urban travel patterns: An online car-hailing trajectory data perspective[J/OL]. Remote Sensing, 2021, 13(9): 1825. https://doi.org/10.3390/rs13091825.
[13]	刘望保,侯长营.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,2014,34(3):272-279. [Liu Wangbao, Hou Changying. Urban residents' home-work space and commuting behavior in Guangzhou[J]. Scientia Geographica Sinica, 2014,34(3):272-279.]
[14]	朱玮,梁雪媚,桂朝,等.上海职住优化效应的代际差异[J].地理学报,2020,75(10):2192-2205. [Zhu Wei, Liang Xuemei, Gui Zhao, et al. The inter-generational differences in the effects of job-housing optimization in Shanghai[J]. Acta Geographica Sinica, 2020,75(10): 2192-2205.]
[15]	Ahrens A, Lyons S. Do rising rents lead to longer commutes? A gravity model of commuting flows in Ireland[J]. Urban Studies, 2021,58(2):264-279.
[16]	杨文越,梁斐雯,曹小曙.多尺度建成环境对居民通勤出行碳排放的影响——来自广州的实证研究[J].地理研究,2020,39(7):1625-1639. [Yang Wenyue, Liang Feiwen, Cao Xiaoshu. Examining the effects of the multi-scale built environment on residents' CO2 emissions from commuting: An empirical study of Guangzhou[J]. Geographical Research, 2020,39(7):1625-1639.]
[17]	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):1339-1352. [Long Ying, Zhang Yu, Cui Chengyin. Identifying commuting pattern of Beijing using bus smart card data[J]. Acta Geographica Sinica, 2012, 67(10): 1339-1352.]
[18]	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. [Xu Yuanyuan, Ta Na, Li Xiang. Analysis of commuting behavior and employment center using subway smart card data[J]. Human Geography, 2017, 32(3):93-101.]
[19]	Liu Y L, Fang F G, Jing Y. How urban land use influences commuting flows in Wuhan, Central China: A mobile phone signaling data perspective[J/OL]. Sustainable Cities and Society, 2020, 53: 101914. https://doi.org/10.1016/j.scs.2019.101914.
[20]	张学波,宋金平,于伟,等.基于感知的北京居民职住空间关系生成机理[J]. 地理学报,2021,76(2):383-397. [Zhang Xuebo, Song Jinping, Yu Wei, et al. Formation mechanism of Beijing's jobs-housing spatial relationship based on residents' perception[J]. Acta Geographica Sinica, 2021,76(2):383-397.]
[21]	张纯,程志华,于晓萍,等.乌鲁木齐公共交通基础设施对低收入群体就业的影响研究[J]. 地理科学进展, 2020, 39(1): 111-119. [Zhang Chun, Cheng Zhihua, Yu Xiaoping, et al. Impact of public transportation infrastructure on employment of the low-income group in Urumqi[J]. Progress in Geography, 2020,39(1):111-119.]
[22]	熊丽芳,甄峰,钱前,等.基于CHAID决策树方法的城市居民通勤时间影响因素分析——以南京为例[J].人文地理,2013,28(6):68-73,35. [Xiong Lifang, Zhen Feng, Qian Qian, et al. The influencing factors analysis of city residents' commute time based on CHAID model of decision tree: A case study of Nanjing[J]. Human Geography, 2013,28(6):68-73,35.]
[23]	Mitra S K, Saphores J D M. Why do they live so far from work? Determinants of long-distance commuting in California[J/OL]. Journal of Transport Geography, 2019, 80: 102489. https://doi.org/10.1016/j.jtrangeo.2019.102489.
[24]	朱秋宇,塔娜.职住建成环境对郊区居民通勤方式的影响——以上海市为例[J]. 世界地理研究,2021,30(2):433-442. [Zhu Qiuyu, Ta Na. The impact of built environment in neighborhood and workplace on suburban residents' commuting mode: A case study in Shanghai[J]. World Regional Studies, 2021,30(2):433-442.]
[25]	Zhang B, Chen S Y, Ma Y F, et al. Analysis on spatiotemporal urban mobility based on online car-hailing data[J/OL]. Journal of Transport Geography, 2020, 82: 102568. https://doi.org/10.1016/j.jtrangeo.2019.102568.
[26]	高枫,李少英,吴志峰,等.广州市主城区共享单车骑行目的地时空特征与影响因素[J]. 地理研究, 2019, 38(12): 2859-2872. [Gao Feng, Li Shaoying, Wu Zhifeng, et al. Spatial-temporal characteristics and the influencing factors of the ride destination of bike sharing in Guangzhou city[J]. Geographical Research, 2019, 38(12): 2859-2872.]
[27]	Sun B D, Yin C. Impacts of a multi-scale built environment and its corresponding moderating effects on commute duration in China [J]. Urban Studies, 2019,57(10):2115-2130.
[28]	Ma S, Long Y. Functional urban area delineations of cities on the Chinese mainland using massive Didi ride-hailing records[J/OL]. Cities, 2020, 97: 102532. https://doi.org/10.1016/j.cities.2019.102532.
[29]	Yong J, Zheng L J, Mao X W, et al. Mining metro commuting mobility patterns using massive smart card data[J/OL]. Physica A: Statistical Mechanics and its Applications, 2021, 584: 126351. https://doi.org/10.1016/j.physa.2021.126351.
[30]	王法辉. 基于GIS的数量方法与应用[M]. 姜世国,滕俊华,译. 北京:商务印书馆,2016:98-119. [Wang Fahui. Quantitative Methods and Applications in GIS[M]. Jiang Shiguo, Teng Junhua, trans. Beijing: The Commercial Press, 2016:98-119.]
[31]	Ester M, Kriegel H P, Sander J, et al. A Density-based Algorithm for Discovering Clusters in Large Spatial Databases with Noise[C]. Portland: The AAAI Press, 1996:226-231.
[32]	Boussauw K, Neutens T, Witlox F. Relationship between spatial proximity and travel-to-work distance: The effect of the compact city[J]. Regional Studies, 2012,46(6):687-706.
[33]	王劲峰,徐成东. 地理探测器：原理与展望[J]. 地理学报,2017,72(1):116-134. [Wang Jinfeng, Xu Chengdong. Geodetector: Principle and prospective[J]. Acta Geographica Sinica, 2017,72(1):116-134.]
[34]	Modarres A. Polycentricity, commuting pattern, urban form: The case of southern California[J]. International Journal of Urban and Regional Research, 2011,35(6):1193-1211.
[35]	Zhang M. The role of land use in travel mode choice: Evidence from Boston and Hong Kong[J]. Journal of the American Planning Association, 2004,70(3):344-360.