Urban commuting dynamics in response to public transit upgrades: A big data approach
Autoři:
Qi-Li Gao aff001; Qing-Quan Li aff001; Yan Zhuang aff001; Yang Yue aff002; Zhen-Zhen Liu aff004; Shui-Quan Li aff004; Daniel Sui aff005
Působiště autorů:
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, P.R. China
aff001; Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Department of Urban Informatics, School of Architecture and Urban Planning, Shenzhen University, Shenzhen, P.R. China
aff002; Guangdong Key Laboratory of Urban Informatics, Shenzhen University, Shenzhen, P.R. China
aff003; College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, P.R. China
aff004; Department of Geosciences, University of Arkansas, Fayetteville, AR, United States of America
aff005
Vyšlo v časopise:
PLoS ONE 14(10)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0223650
Souhrn
Public transit, especially urban rail systems, plays a vital role in shaping commuting patterns. Compared with census data and survey data, large-scale and real-time big data can track the impacts of urban policy implementations at finer spatial and temporal scales. Therefore, this study proposed a multi-level analytical framework using transit smartcard data to examine urban commuting dynamics in response to rail transit upgrades. The study area was Shenzhen, one of the most highly urbanized and densely populated cities in China, which provides the opportunity to examine the effects of rail transit upgrades on commuting patterns in a rapidly developing urban context. Changes in commuting patterns were examined at three levels: city, region, and individual. At the city level, we considered the average commuting time, commuting speed, and commuting distance across the whole city. At the region level, we analyzed changes in the job accessibility of residential zones. Finally, this study evaluated the potential effects of rail transit upgrades on the jobs-housing relationship at the individual level. Difference-in-difference models were used for causal inference between rail transit upgrades and commuting patterns. In the very short term, the opening of new rail transit lines resulted in no significant changes in overall commuting patterns across the whole city; however, two effects of rail transit upgrades on commuting patterns were identified. First, rail transit upgrades enhanced regional connectivity between residential zones and employment centers, thus improving job accessibility. Second, rail transit improvement increased the commuting distances of individuals and contributed to the separation of workplaces and residences. This study provides meaningful insights into the effects of rail transit upgrades on commuting patterns.
Klíčová slova:
Census – Employment – Housing – Human mobility – Jobs – Transportation infrastructure – Urban areas – Dynamic response
Zdroje
1. Mayer T, Trevien C. The impact of urban public transportation evidence from the Paris region. Journal Urban Economics. 2017;102:1–21.
2. Baum-Snow N, Kahn ME. The effects of new public projects to expand urban rail transit. Journal of Public Economics. 2000;77(2):241–63.
3. Loo BPY, Chen C, Chan ETH. Rail-based transit-oriented development: Lessons from New York City and Hong Kong. Landscape and Urban Planning. 2010;97(3):202–12.
4. Calimente J. Rail integrated communities in Tokyo. Journal of Transport and Land Use. 2012;5(1):14.
5. Lo HK, Tang S, Wang DZW. Managing the accessibility on mass public transit: the case of Hong Kong. Journal of Transport and Land Use. 2008;1(2):23–49.
6. Bao X. Urban rail transit present situation and future development trends in China: Overall analysis based on national policies and strategic plans in 2016–2020. Urban Rail Transit. 2018;4(1):1–12.
7. Newman PWG, Kenworthy JR. Gasoline consumption and cities: a comparison of US cities with a global survey. Journal of the American Planning Association. 1989;55(1):24–37.
8. Baum-Snow N, Kahn ME, Voith R. Effects of urban rail transit expansions: Evidence from sixteen cities, 1970–2000 [with Comment]. Brookings-Wharton Papers on Urban Affairs. 2005;147–206.
9. Cervero R, Day J. Suburbanization and transit-oriented development in China. Transport Policy. 2008;15(5):315–23.
10. Gonzalez-Navarro M, Turner MA. Subways and urban growth: Evidence from earth. Journal Urban Economics. 2018;108:85–106.
11. Wachs M, Taylor BD. Can transportation strategies help meet the welfare challenge? Journal of the American Planning Association. 1998;64(1):15–9.
12. Sanchez TW. The connection between public transit and employment: the cases of Portland and Atlanta. Journal of the American Planning Association. 1999;65(3):284–96.
13. Wu W, Hong J. Does public transit improvement affect commuting behavior in Beijing, China? A spatial multilevel approach. Transportation Research Part D: Transport and Environment. 2017;52:471–9.
14. Korsu E, Le Néchet F. Would fewer people drive to work in a city without excess commuting? Explorations in the Paris metropolitan area. Transportation Research Part A: Policy and Practice. 2017;95:259–74.
15. Pagliara F, Papa E. Urban rail systems investments: an analysis of the impacts on property values and residents’ location. Journal of Transport Geography. 2011;19(2):200–11.
16. Baum-Snow N. Changes in transportation infrastructure and commuting patterns in U.S. metropolitan areas, 1960–2000. American Economic Review. 2010;100(2):378–82.
17. Li SM. Evolving residential and employment locations and patterns of commuting under hyper growth: The case of Guangzhou, China. Urban Studies. 2010;47(8):1643–61.
18. Wang E, Song J, Xu T. From “spatial bond” to “spatial mismatch”: An assessment of changing jobs–housing relationship in Beijing. Habitat International. 2011;35(2):398–409.
19. Zhou S, Wu Z, Cheng L. The impact of spatial mismatch on residents in low-income housing neighbourhoods: a study of the Guangzhou metropolis, China. Urban Studies. 2013;50(9):1817–35.
20. Zheng S, Fu Y, Liu H. Housing-choice hindrances and urban spatial structure: Evidence from matched location and location-preference data in Chinese cities. Journal of Urban Economics. 2006;60(3):535–57.
21. Fan Y, Allen R, Sun T. Spatial mismatch in Beijing, China: Implications of job accessibility for Chinese low-wage workers. Habitat International. 2014;44:202–10.
22. Zhang C, Man J. Examining job accessibility of the urban poor by urban metro and bus: A case study of Beijing. Urban Rail Transit. 2015;1(4):183–93.
23. Xu Y, Chan EHW, Yung EHK. Analysis of the mechanisms contributing to spatial mismatch in transitional Chinese cities. Journal of Urban Planning and Development. 2014;140(2): 04013011.
24. Batty M. Big data, smart cities and city planning. Dialogues in Human Geography. 2013;3(3):274–9. doi: 10.1177/2043820613513390 29472982
25. Zhou J, Murphy E, Long Y. Commuting efficiency in the Beijing metropolitan area: an exploration combining smartcard and travel survey data. Journal of Transport Geography. 2014;41:175–83.
26. Long Y, Thill J-C. Combining smart card data and household travel survey to analyze jobs–housing relationships in Beijing. Computers, Environment and Urban Systems. 2015;53:19–35.
27. Jiang J, Li Q, Tu W, Shaw S-L, Yue Y. A simple and direct method to analyse the influences of sampling fractions on modelling intra-city human mobility. International Journal of Geographical Information Science. 2019;33(3):618–44.
28. Lin D, Allan A, Cui J. Exploring differences in commuting behaviour among various income groups during polycentric urban development in China: New evidence and its implications. Sustainability. 2016;8(11):1188.
29. Chin S, Kahn ME, Moon HR. Estimating the gains from new rail transit investment: A machine learning tree approach. Real Estate Economics. 2018;0(0).
30. Giuliano G. Is jobs-housing balance a transportation issue? University of California Transportation Center Working Papers. 1991;1305(1305):305–12.
31. Huang J, Levinson D, Wang J, Zhou J, Wang Z-j. Tracking job and housing dynamics with smartcard data. Proceedings of the National Academy of Sciences. 2018;115(50): 12710–12715.
32. Kim C. Commuting time stability: A test of a co-location hypothesis. Transportation Research Part A: Policy and Practice. 2008;42(3):524–44.
33. Gao Q-L, Li Q-Q, Yue Y, Zhuang Y, Chen Z-P, Kong H. Exploring changes in the spatial distribution of the low-to-moderate income group using transit smart card data. Computers, Environment and Urban Systems. 2018;72:68–77.
34. Glaeser EL. The economics approach to cities. National Bureau of Economic Research; 2007.
35. Ibeas Á, Cordera R, dell’Olio L, Coppola P, Dominguez A. Modelling transport and real-estate values interactions in urban systems. Journal of Transport Geography. 2012;24:370–82.
36. Levinson DM, Kumar A. The rational locator: why travel times have remained stable. Journal of the American Planning Association. 1994;60(3):319–32.
Článek vyšel v časopise
PLOS One
2019 Číslo 10
- Proč se v Česku šíří několik „vymýcených“ infekcí najednou? Ptáme se odborníků
- ADHD jako evoluční výhoda? Výzkum se zaměřil na lovce a sběrače
- Není migréna jako migréna: Co pociťují ženy a co muži?
- Metamizol jako analgetikum první volby: kdy, pro koho, jak a proč?
- Není statin jako statin aneb praktický přehled rozdílů jednotlivých molekul
Nejčtenější v tomto čísle
- Correction: Low dose naltrexone: Effects on medication in rheumatoid and seropositive arthritis. A nationwide register-based controlled quasi-experimental before-after study
- Combining CDK4/6 inhibitors ribociclib and palbociclib with cytotoxic agents does not enhance cytotoxicity
- Efficacy of cotrimoxazole (Sulfamethoxazole-Trimethoprim) as a salvage therapy for the treatment of bone and joint infections (BJIs)
- Risk factors associated with IgA vasculitis with nephritis (Henoch–Schönlein purpura nephritis) progressing to unfavorable outcomes: A meta-analysis
Zvyšte si kvalifikaci online z pohodlí domova
Všechny kurzy