Job Accessibility Analysis Considering Travel Cost

doi:10.16097/j.cnki.1009-6744.2022.02.004

Abstract

Abstract: Good job accessibility contributes to the innovation of the job-living interface, which is a key issue in building sustainable cities. Existing accessibility studies have mostly considered travel costs limited to travel distance or travel time, without fully considering travel costs and their effects on different travel modes. Based on POI (Point of Interest) and route planning data from Internet maps and business data platforms, this paper obtains fine-grained employment and travel data and uses an improved two-step floating catchment area model to propose a job accessibility measure that takes into account travel costs, to study the job accessibility of both public transport and private cars and to evaluate the impact of adding travel costs on job accessibility. The case study in Beijing shows that the average travel cost of private cars changes from 54% to 6% higher than that of public transportation after considering travel costs. And job accessibility is sensitive to travel costs, and the interaction between commuting and travel costs cannot be fully captured by considering travel time only; the impact of travel cost is reflected in an overall average decrease of 7.3% and 4.8% for public transportation and private car accessibility, and without considering travel cost, the job accessibility of subdistricts along with the fifth to sixth ring subway will be underestimated; there is a boundary effect of the travel cost on accessibility, and the higher the threshold, the smaller the impact. This paper provides insights for planners and policymakers making job accessibility-oriented adjustment strategies for the balance between jobs and workers.

Key words: urban traffic, job accessibility, two-step floating catchment area, travel cost, multi-source big data

摘要： 良好的就业可达性有助于创新职住对接机制，是建设可持续城市的关键问题。既有可达性研究对于出行成本的考虑多局限于出行距离或出行时间，未充分考虑出行费用并研究其对不同出行方式的影响。本文基于互联网地图和工商数据平台的POI(Point of Interest)数据和实时路径规划数据，获取细粒度的就业和出行成本信息，采用改进的两步移动搜索算法，提出考虑出行费用的就业可达性度量方法，研究公共交通和小汽车两种出行方式的就业可达性并评估出行费用对就业可达性的影响。针对北京的案例研究表明：考虑出行费用后，小汽车平均出行成本由仅为公共交通54%变化至高于公共交通6%。就业可达性对出行费用敏感，仅考虑出行时间无法完全体现就业选择与出行成本之间复杂的互动机制。出行费用的影响体现在公共交通与小汽车可达性平均降低7.3%和4.8%；若不考虑出行费用，五环至六环地铁沿线街道的就业可达性会被低估。出行成本对可达性的影响存在阈值效应，阈值越高，影响越小。本文有助于规划者和政策制定者形成就业可达性引领的职住平衡调整策略。

关键词: 城市交通, 就业可达性, 两步移动搜索算法, 出行费用, 多源大数据

CLC Number:

U491

XU Qi, CHEN Yue , HUANG Jing-ru , GAO Shun-xiang , ZHANG Zhi-jian. Job Accessibility Analysis Considering Travel Cost[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(2): 37-44.

许奇, 陈越, 黄靖茹, 高顺祥, 张志健. 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.

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URL: http://www.tseit.org.cn/EN/10.16097/j.cnki.1009-6744.2022.02.004

http://www.tseit.org.cn/EN/Y2022/V22/I2/37

References

[1] HUANG R H. Transit-based job accessibility and urban spatial structure[J]. Journal of Transport Geography, 2020, 86: 102748.

[2] DEBOOSERE R, EL-GENEIDY A M, LEVINSON D. Accessibility-oriented development[J]. Journal of Transport Geography, 2018, 70: 11-20.

[3] FAN Y L, ALLEN R, SUN T S. Spatial mismatch in Beijing, China: Implications of job accessibility for Chinese low-wage workers[J]. Habitat International, 2014, 44: 202-210.

[4] 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.

[5] 于文涛, 张可, 李静, 等. 基于出行数据的城市公交网络可达性研究[J]. 交通运输系统工程与信息, 2020, 20 (4): 106-112. [YU W T, ZHANG K, LI J, et al. Urban public transport network accessibility based travel data [J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(4): 106-112.]

[6] LUO W, WANG F. Measures of spatial accessibility to health care in a GIS environment: Synthesis and a case study in the Chicago region[J]. Environment & Planning B, 2003, 30(6): 865-884.

[7] WEI L, YI Q. An enhanced two-step floating catchment area (E2SFCA) method for measuring spatial accessibility to primary care physicians[J]. Health & Place, 2009, 15(4): 1100-1107.

[8] 王绮, 修春亮, 魏冶, 等. 基于高斯两步移动搜索法的沈阳市就业可达性评价[J]. 人文地理, 2015, 30(2): 78- 82. [WANG Q, XIU C L, WEI Y, et al. Evaluation of job accessibility of Shenyang using gaussian based 2-step floating catchment area method[J]. Human Geography, 2015, 30(2): 78-82.]

[9] XIAO W, WEI Y D, WAN N. Modeling job accessibility using online map data: An extended two-step floating catchment area method with multiple travel modes[J]. Journal of Transport Geography, 2021, 93(16): 103065.

[10] EL-GENEIDY A, LEVINSON D, DIAB E, et al. The cost of equity: Assessing transit accessibility and social disparity using total travel cost[J]. Transportation Research Part A: Policy and Practice, 2016, 91: 302- 316.

[11] CUI M Y, LEVINSON D. Full cost accessibility[J]. Journal of Transport and Land Use, 2018, 11: 661-679.

[12] 朱宇婷, 刘莹, 许奇, 等. 交通可达性与城市经济活动的空间特征分析: 以北京市为例[J]. 交通运输系统工程与信息, 2020, 20(5): 226-233. [ZHU Y T, LIU Y, XU Q, et al. Spatial characteristics analysis of traffic accessibility and city economic activity: A case study of Beijing[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(5): 226-233.]

[13] 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. [China Academy of Urban Planning & Design. Commuting report of major cities in China[R]. Beijing: China Academy of Urban Planning & Design, 2020.]

[14] 中国科学院资源环境科学数据中心数据注册与出版系统. 中国 GDP 空间分布公里网格数据集[DB/OL]. (2017- 12- 11) [2022- 04- 01]. http: //www. resdc. cn/ DOI. [Data registration and publishing system in resource and environment science and data center of Chinese academy of sciences. The gird dataset of spatial distribution of GDP in China[DB/OL]. (2017- 12- 11) [2022-04-01]. http://www.resdc.cn/DOI.]

[15] 顾涛, 刘泽, 刘跃军, 等. 考虑交通拥堵效应的大城市小汽车出行成本分析[J]. 山东科学, 2019, 32(1): 76- 82. [GU T, LIU Z, LIU Y J, et al. Travel cost of private car in big cities considering traffic congestion[J]. Shandong Science, 2019, 32(1): 76-82.]

[16] 北京统计局. 北京统计年鉴 2020[R]. 北京: 北京统计局, 2020. [Beijing Municipal Bureau Statistics. Beijing statistical yearbook 2020[R]. Beijing: Beijing Municipal Bureau Statistics, 2020.]

[17] DODSON J, GLEESON B, EVANS R, et al. Investigating the social dimensions of transport disadvantage II: From concepts to methods through an empirical case study[J]. Urban Policy and Research, 2007, 25: 63-89.