The Impact of Floods on the Mobility of Automobile Commuters in Shanghai Under Climate Change

Qian Yao , Xinmeng Shan , Mengya Li , Jun Wang

International Journal of Disaster Risk Science ›› 2024, Vol. 15 ›› Issue (6) : 986 -1000.

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International Journal of Disaster Risk Science ›› 2024, Vol. 15 ›› Issue (6) : 986 -1000. DOI: 10.1007/s13753-024-00604-3
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The Impact of Floods on the Mobility of Automobile Commuters in Shanghai Under Climate Change

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Abstract

As sea level rises, low-lying coastal cites face increasing threat of flood disruption, particularly in terms of human mobility. Commuters are vulnerable to bad weather, as it is difficult to cancel trips even in extreme weather conditions. Using Shanghai’s automobile commuting population as an example, we categorized commuters by travel distance and income level to assess disruptions and delays due to floods, considering future sea level rise. The results show that local flooding disrupts commuting patterns by affecting roadways, with disruption decreasing with distance from the flooded area. This offers a mobility perspective on the indirect impacts of floods. During baseline flood events, long-distance commuters and the low-income group are most affected, while short-distance commuters and the high-income group are less impacted. As sea level rises, floods will threaten all commuting groups, especially the high-income group. Using inaccessibility-commuting delay bivariate maps, this study revealed how socioeconomic differences impact mobility recovery after floods under climate change. The research highlights the differential impacts of floods on various socioeconomic groups in the context of climate change, offering insights for future urban planning and disaster mitigation strategies.

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Qian Yao, Xinmeng Shan, Mengya Li, Jun Wang. The Impact of Floods on the Mobility of Automobile Commuters in Shanghai Under Climate Change. International Journal of Disaster Risk Science, 2024, 15(6): 986-1000 DOI:10.1007/s13753-024-00604-3

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References

[1]

AlabbadY, MountJ, CampbellAM, DemirI. Assessment of transportation system disruption and accessibility to critical amenities during flooding: Iowa case study. Science of the Total Environment, 2021, 793: Article 148476

[2]

BeckerJS, TaylorHL, DoodyBJ, WrightKC, GruntfestE, WebberD. A review of people’s behavior in and around floodwater. Weather, Climate, and Society, 2015, 7(4): 321-332

[3]

CoolsM, CreemersL. The dual role of weather forecasts on changes in activity-travel behavior. Journal of Transport Geography, 2013, 28: 167-175

[4]

DebionneS, RuinI, ShabouS, LutoffC, CreutinJ. Assessment of commuters’ daily exposure to flash flooding over the roads of the Gard region, France. Journal of Hydrology, 2016, 541: 636-648

[5]

DingW, WuJD. Interregional economic impacts of an extreme storm flood scenario considering transportation interruption: a case study of Shanghai China. Sustainable Cities and Society, 2023, 88: Article 104296

[6]

DottoriF, SzewczykW, CiscarJ, ZhaoF, AlfieriL, HirabayashiY, BianchiA, MongelliI, et al. . Increased human and economic losses from river flooding with anthropogenic warming. Nature Climate Change, 2018, 8(9): 781-786

[7]

DuSQ, ScussoliniP, WardPJ, ZhangM, WenJH, WangLY, KoksE, Diaz-LoaizaA, et al. . Hard or soft flood adaptation? Advantages of a hybrid strategy for Shanghai. Global Environmental Change, 2020, 61: Article 102037

[8]

HauerM, MuellerV, SheriffG, ZhongQ. More than a nuisance: Measuring how sea level rise delays commuters in Miami. FL. Environmental Research Letters, 2021, 16(6): Article 064041

[9]

HeYY, ThiesS, AvnerP, RentschlerJ. Flood impacts on urban transit and accessibility—a case study of Kinshasa. Transportation Research Part D: Transport and Environment, 2021, 96: Article 102889

[10]

IslamMR, SaphoresJM. An L.A. story: the impact of housing costs on commuting. Journal of Transport Geography, 2022, 98: Article 103266

[11]

JaroszweskiD, ChapmanL, PettsJ. Assessing the potential impact of climate change on transportation: the need for an interdisciplinary approach. Journal of Transport Geography, 2010, 18(2): 331-335

[12]

JonkmanSN, KelmanI. An analysis of the causes and circumstances of flood disaster deaths. Disasters, 2005, 29(1): 75-97

[13]

KasmalkarIG, SerafinKA, MiaoY, BickIA, OrtolanoL, OuyangD, SuckaleJ. When floods hit the road: resilience to flood-related traffic disruption in the san francisco bay area and beyond. Science Advances, 2020, 6(32): Article a2423

[14]

LiSN, DragicevicS, CastroFA, SesterM, WinterS, ColtekinA, PettitC, JiangB, et al. . Geospatial big data handling theory and methods: a review and research challenges. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 115: 119-133

[15]

LiMY, HuangQY, WangL, YinJ, WangJ. Modeling the traffic disruption caused by pluvial flash flood on intra-urban road network. Transactions in GIS, 2018, 22(1): 311-322

[16]

LiMY, KwanM, HuWY, LiR, WangJ. Examining the effects of station-level factors on metro ridership using multiscale geographically weighted regression. Journal of Transport Geography, 2023, 113: Article 103720

[17]

LiMY, KwanM, WangFH, WangJ. Using points-of-interest data to estimate commuting patterns in central Shanghai, China. Journal of Transport Geography, 2018, 72: 201-210

[18]

LiMY, KwanM, YinJ, YuDP, WangJ. The potential effect of a 100-year pluvial flood event on metro accessibility and ridership: a case study of central Shanghai, China. Applied Geography, 2018, 100: 21-29

[19]

LiuXY, YangSN, YeT, AnR, ChenCZ. A new approach to estimating flood-affected populations by combining mobility patterns with multi-source data: a case study of Wuhan, China. International Journal of Disaster Risk Reduction, 2021, 55: Article 102106

[20]

LyuHM, ShenSL, ZhouAN, YangJ. Perspectives for flood risk assessment and management for mega-city metro system. Tunnelling and Underground Space Technology, 2019, 84: 31-44

[21]

Pahl-WostlC. Transitions towards adaptive management of water facing climate and global change. Water Resources Management, 2007, 21(1): 49-62

[22]

PregnolatoM, FordA, WilkinsonSM, DawsonRJ. The impact of flooding on road transport: a depth-disruption function. Transportation Research Part D: Transport and Environment, 2017, 55: 67-81

[23]

QiangY, XuJW. Empirical assessment of road network resilience in natural hazards using crowdsourced traffic data. International Journal of Geographical Information Science, 2020, 34(12): 2434-2450

[24]

RajputAA, LiuCY, LiuZW, MostafaviA. Human-centric characterization of life activity flood exposure shifts focus from places to people. Nature Cities, 2024, 1(4): 264-274

[25]

RuinI, GaillardJ, LutoffC. How to get there? Assessing motorists’ flash flood risk perception on daily itineraries. Environmental Hazards, 2007, 7(3): 235-244

[26]

SeneviratneS, NichollsN, EasterlingD, GoodessC, KanaeS, KossinJ, LuoYL, MarengoJ, et al. . Changes in climate extremes and their impacts on the natural physical environment, 2012 Cambridge Cambridge University Press

[27]

Shanghai Communications Commission. 2022. The results of the sixth Shanghai comprehensive traffic survey were released. https://jtw.sh.gov.cn/ysqgkzzdgk/20220914/060668206fde43a48fd903dc49efe2c6.html. Accessed 6 Nov 2022.

[28]

ShiY, YaoQ, WenJH, XiJC, LiH, WangQW. A spatial accessibility assessment of urban tourist attractions emergency response in Shanghai. International Journal of Disaster Risk Reduction, 2022, 74: Article 102919

[29]

SunBD, ErmagunA, DanB. Built environmental impacts on commuting mode choice and distance: evidence from Shanghai. Transportation Research Part D: Transport and Environment, 2017, 52: 441-453

[30]

United Nations. Revision of world urbanization prospects, 2018 New York United Nations

[31]

WangX, SunBD. Job accessibility and its impact on income: outcomes from Shanghai metropolitan area. Urban Development Studies, 2020, 27(3): 70-76 in Chinese

[32]

WangJ, GaoW, XuSY, YuLZ. Evaluation of the combined risk of sea level rise, land subsidence, and storm surges on the coastal areas of Shanghai China. Climatic Change, 2012, 115(3–4): 537-558

[33]

WangWP, YangSN, StanleyHE, GaoJX. Local floods induce large-scale abrupt failures of road networks. Nature Communications, 2019, 10(1): Article 2114

[34]

WoodburnA. Rail network resilience and operational responsiveness during unplanned disruption: a rail freight case study. Journal of Transport Geography, 2019, 77: 59-69

[35]

YangYH, SunLF, LiRN, YinJ, YuDP. Linking a storm water management model to a novel two-dimensional model for urban pluvial flood modeling. International Journal of Disaster Risk Science, 2020, 11(4): 508-518

[36]

YinJ, JonkmanS, LinN, YuDP, AertsJ, WilbyR, PanM, WoodE, et al. . Flood risks in sinking delta cities: time for a reevaluation?. Earth’s Future, 2020, 8(8): Article e2020EF001614

[37]

YinJ, LinN, YangYH, PringleWJ, TanJK, WesterinkJJ, YuDP. Hazard assessment for typhoon-induced coastal flooding and inundation in Shanghai China. Journal of Geophysical Research: Oceans, 2021, 126(7): Article e2021JC017319

[38]

YinJ, YuDP, YinZN, LiuM, HeQ. Evaluating the impact and risk of pluvial flash flood on intra-urban road network; a case study in the city center of Shanghai, China. Journal of Hydrology, 2016, 537: 138-145

[39]

YinJ, YuDP, YinZN, WangJ, XuSY. Modelling the combined impacts of sea-level rise and land subsidence on storm tides induced flooding of the Huangpu River in Shanghai China. Climatic Change, 2013, 119(3–4): 919-932

[40]

YuDP, YinJ, WilbyRL, LaneSN, AertsJCJH, LinN, LiuM, YuanHY, et al. . Disruption of emergency response to vulnerable populations during floods. Nature Sustainability, 2020, 3(9): 728-736

[41]

ZhangXY, LiN. Characterizing individual mobility perturbations in cities during extreme weather events. International Journal of Disaster Risk Reduction, 2022, 72: Article 102849

[42]

ZhangFC, LiZS, LiN, FangDP. Assessment of urban human mobility perturbation under extreme weather events: A case study in Nanjing China. Sustainable Cities and Society, 2019, 50: Article 101671

[43]

ZhaoPJ, CaoYS. Commuting inequity and its determinants in Shanghai: new findings from big-data analytics. Transport Policy, 2020, 92: 20-37

[44]

ZhouY, YangC, ChenMY, LiuYH, YuanQ. Commuting versus consumption: the role of core city in a metropolitan area. Cities, 2023, 141: Article 104495

[45]

ZhuZJ, LiZG, LiuY, ChenHS, ZengJ. The impact of urban characteristics and residents’ income on commuting in China. Transportation Research Part D: Transport and Environment, 2017, 57: 474-483

[46]

ZhuY, OzbayK, XieK, YangH. Using big data to study resilience of taxi and subway trips for hurricanes Sandy and Irene. Transportation Research Record: Journal of the Transportation Research Board, 2016, 2599(1): 70-80

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