Advertisement

Chinese Geographical Science

, Volume 28, Issue 4, pp 612–623 | Cite as

Walking Access Distance of Metro Passengers and Relationship with Demographic Characteristics: A Case Study of Nanjing Metro

  • Jinliao He
  • Ruozhu Zhang
  • Xianjin Huang
  • Guangliang Xi
Article
  • 38 Downloads

Abstract

In the metropolises of China, the metro plays an increasingly important role in commuting because of its efficiency, affordability, and cleanliness. This paper attempts to explore the relationship between walking access distance to metro stations and the demographic characteristics of passengers, such as age, monthly income, travel frequency, gender, and travel purpose, as well as the influence of the urban context. Nanjing Metro Line 2 is selected as the case study. By using different methods such as a questionnaire survey, spatial decay function, analysis of covariance (ANOVA), network analysis of routes, and K-means cluster analysis, it is suggested that demographic characteristics have a significant impact on the pedestrian walking distance, with the exception of gender. Furthermore, the paper finds a spatial decay effect in walking access distance, the decay rate of which, however, varies across stations. Terminal stations have a larger pedestrian catchment area than in regular and exchange stations. Moreover, the passengers of Nanjing Metro Line 2 can be classified into six groups according to their demographic characteristics, among which education and occupation are vital indicators in determining their willingness to walk to the stations. Middle-class passengers have a higher dependence on the metro and tend to walk longer than other groups do. This study provides an important reference for planners and transport sectors to optimize land-use and transport infrastructures.

Keywords

metro traveling mode walking access distance pedestrian catchment area China 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Alshalalfah B W, Shalaby A S, 2007. Case study: relationship of walk access distance to transit with service, travel, and personal characteristics. Journal of Urban Planning and Development, 133(2): 114–118. doi: 10.1061/(ASCE)0733-9488(2007)133:2(114)CrossRefGoogle Scholar
  2. Anderson J E, 1978. Transit Systems Theory, Lexington Books. Lexington, Ky, USA: D.C. Health and Company, Toronto.Google Scholar
  3. Burke M, Brown A L, 2007. Distances people walk for transport. Road and Transport Research, 16(3): 16–29.Google Scholar
  4. Cao Xiaoshu, Xue Desheng, Yan Xiaopei, 2005. A study on the urban accessibility of national trunk highway system in China. Acta Geographica Sinica, 60(6): 903–910. (in Chinese)Google Scholar
  5. Cao Xiaoshu, Lin Qiang, 2011. A SEM-based study on urban community resident’s travel behavior in Guangzhou. Acta Geographica Sinica, 66(2): 167–177. (in Chinese)Google Scholar
  6. Cao Youhui, Li Haijian, Chen Wen, 2004. The spatial structure and the competition pattern of the container port system of China. Acta Geographica Sinica, 59(6): 1020–1027. (in Chinese)Google Scholar
  7. Cervero R, Round A, Goldman T et al., 1995. Rail Access Modes and Catchment Areas for the BART System. Berkeley, CA, USA: University of California Transportation Center.Google Scholar
  8. Chen Yao, Zhao Zhenbin, Zhang Cheng et al., 2015. Landscape value perception and attitude evaluation of community residents on historical protection area: a case study of Han Chang’an City Historical Site. Geographical Research, 34(10): 1971–1980. (in Chinese)Google Scholar
  9. Dueker K, Bianco M, 1999. Light-rail-transit impacts in Portland: the first ten years. Transportation Research Record: Journal of the Transportation Research Board, 1685: 171–180. doi: 10.3141/1685-22CrossRefGoogle Scholar
  10. El-Geneidy A, Grimsrud M, Wasfi R et al., 2014. New evidence on walking distances to transit stops: identifying redundancies and gaps using variable service areas. Transportation, 41(1): 193–210. doi: 10.1007/s11116-013-9508-zCrossRefGoogle Scholar
  11. Feng Yanfen, Liang Xiaosi, Wu Dafang, 2011. Study on housing price along the Guangzhou metro line 1 based on hedonic price model. Journal of Guangzhou University (Natural Science Edition), 10(4): 90–95. (in Chinese)Google Scholar
  12. He Di, Yan Yusong, Guo Shoujing et al., 2007. Optimal routing algorithm for public traffic network based on matrix analysis. Journal of Southwest Jiaotong University, 42(3): 315–319. (in Chinese)Google Scholar
  13. Hsiao S, Lu J, Sterling J et al., 1997. Use of geographic information system for analysis of transit pedestrian access. Transportation Research Record: Journal of the Transportation Research Board, 1604: 50–59. doi: 10.3141/1604-07CrossRefGoogle Scholar
  14. Iacono M, Krizek K J, El-Geneidy A, 2010. Measuring non-motorized accessibility: issues, alternatives, and execution. Journal of Transport Geography, 18(1): 133–140. doi: 10.1016/j.jtrangeo.2009.02.002CrossRefGoogle Scholar
  15. Jiang Y, Zegras P C, Mehndiratta S, 2012. Walk the line: station context, corridor type and bus rapid transit walk access in Jinan, China. Journal of Transport Geography, 20(1): 1–14. doi: 10.1016/j.jtrangeo.2011.09.007CrossRefGoogle Scholar
  16. Jiao J J, Wang J E, Jin F J et al., 2014. Impacts on accessibility of China’s present and future HSR network. Journal of Transport Geography, 40: 123–132. doi: 10.1016/j.jtrangeo.2014.07.004CrossRefGoogle Scholar
  17. Jin F J, Wang C J, Cao Y H et al., 2016. Progress of research on transportation geography in China. Journal of Geographical Sciences, 26(8): 1067–1080. doi: 10.1007/s11442-016-1316-xCrossRefGoogle Scholar
  18. Levinson H S, 1983. Analyzing transit travel time performance. In: Transportation Research Record. Washington, D.C.: TRB, National Research Council, 915: 1–6.Google Scholar
  19. Li Wenling, Xie Yi, 2004. The characteristics of the trips of inhabitants and the urban space structure along the subway in Guangzhou City. Modern Urban Research, 19(4): 61–64. (in Chinese)Google Scholar
  20. Li Zhi, Zhou Shenglu, Wu Shaohua et al., 2014. The impact of metro lines on public transit accessibility and land value capture in Nanjing. Acta Geographica Sinica, 69(2): 255–267. (in Chinese)Google Scholar
  21. Lin Geng, 2009. Effect of subway development on consumption space of big cities. City Planning Review, 33(3): 17–24. (in Chinese)Google Scholar
  22. Ma Shuhong, Fu Jianchuan, Yao Zhigang, 2015. Choice characteristics of students travel behavior based on household characteristics and nested logit model. Journal of Chongqing Jiaotong University (Natural Science), 34(4): 122–127. (in Chinese)Google Scholar
  23. Maghelal P K, 2011. Walking to transit: influence of built environment at varying distances. ITE Journal, 81(2): 38–43.Google Scholar
  24. Olszewski P, Wibowo S S, 2005. Using equivalent walking distance to assess pedestrian accessibility to transit stations in Singapore. Transportation Research Record: Journal of the Transportation Research Board, 1927: 38–45. doi: 10.3141/1927-05CrossRefGoogle Scholar
  25. O’Sullivan S, Morrall J, 1996. Walking distances to and from light-rail transit stations. Transportation Research Record: Journal of the Transportation Research Board, 1538: 19–26. doi: 10.3141/1538-03CrossRefGoogle Scholar
  26. Spartz J T, Shaw B R, 2011. Place meanings surrounding an urban natural area: a qualitative inquiry. Journal of Environmental Psychology, 31(4): 344–352. doi: 10.1016/j.jenvp.2011.04.002CrossRefGoogle Scholar
  27. Stringham M G P, 1982. Travel behavior associated with land uses adjacent to rapid transit stations. ITE Journal, 52(4): 18–22.Google Scholar
  28. Wang Chengjin, Ding Jinxue, Yang Wei, 2011. Policy and spatial effect of expressway planning network in China. Acta Geographica Sinica, 66(8): 1076–1088. (in Chinese)Google Scholar
  29. Wang Chengjin, 2012. Evolution and Mechanism of Development of a Container Port System. Beijing: Science Press. (in Chinese)Google Scholar
  30. Wang Jiaoe, Jin Fengjun, Sun Wei et al., 2006. Research on spatial distribution and service level of Chinese airport system. Acta Geographica Sinica, 61(8): 829–838. (in Chinese)Google Scholar
  31. Wu Wei, Cao Youhui, Cao Weidong et al., 2006. Spatial structure and evolution of highway accessibility in the Yangtze River Delta. Acta Geographica Sinica, 61(10): 1065–1074. (in Chinese)Google Scholar
  32. Yang Xiaozhong, Liu Guoming, Feng Lixin, et al., 2011. Spatial economic contact of cross-border tourism region based on network analysis: a case study of Hukou waterfall scenic spot. Geographical Research, 30(7): 1319–1330. (in Chinese)Google Scholar
  33. Yin Z Y, 2014. Study on Relationship Between Catchment and Built Environment of Metro Stations in Hong Kong and Shenzhen. Hong Kong: City University of Hong Kong.Google Scholar
  34. Yu Taofang, Gu Chaolin, Li Zhigang, 2008. China’s urban systems in terms of air passenger and cargo flows since 1995. Geographical Research, 27(6): 1407–1418. (in Chinese)Google Scholar
  35. Zhang Dong, Yang Xiaoguang, 2014. Influences of social norm on urban travel modal choice behavior. Journal of Transportation Systems Engineering and Information Technology, 14(3): 16–21, 42. (in Chinese)Google Scholar
  36. Zhang Yuqing, Zhen Feng, Zhang Yongming, 2016. Characteristics of e-shopping behavior of Nanjing residents: a case of books and clothes. Progress in Geography, 35(4): 476–486. (in Chinese)CrossRefGoogle Scholar
  37. Zhang Zheng, Feng Xujie, Guo Yandong, 2011. Research on the departure time choices of the elders’ daily travels. Journal of Transportation Systems Engineering and Information Technology, 11(S1): 110–115. (in Chinese)Google Scholar
  38. Zhao F, Chow L F, Li M T et al., 2003. Forecasting transit walk accessibility: regression model alternative to buffer method. Transportation Research Record: Journal of the Transportation Research Board, 1835: 34–41. doi: 10.3141/1835-05CrossRefGoogle Scholar
  39. Zhao J B, Deng W, 2013. Relationship of walk access distance to rapid rail transit stations with personal characteristics and station context. Journal of Urban Planning and Development, 139(4): 311–321. doi: 10.1061/(ASCE)UP.1943-5444.0000155CrossRefGoogle Scholar
  40. Zhen Feng, Wei Zongcai, Yang Shan et al., 2009. The impact of information technology on the characteristics of urban resident travel: case of Nanjing. Geographical Research, 28(5): 1307–1317. (in Chinese)Google Scholar

Copyright information

© Science Press, Northeast Institute of Geography and Agricultural Ecology, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Jinliao He
    • 1
  • Ruozhu Zhang
    • 2
  • Xianjin Huang
    • 1
  • Guangliang Xi
    • 3
  1. 1.Research Center of Human GeographyNanjing UniversityNanjingChina
  2. 2.School of Urban and Environmental SciencesPeking UniversityBeijingChina
  3. 3.School of Architecture Design and Urban PlanningNanjing UniversityNanjingChina

Personalised recommendations