Disturbances to Urban Mobility and Comprehensive Estimation of Economic Losses

Polytechnica - Tập 1 - Trang 48-60 - 2018
Fang Wei1, Eyuphan Koc2, Lucio Soibelman2,3, Nan Li1
1Department of Construction Management, Tsinghua University, Beijing, China
2Department of Civil and Environmental Engineering, University of Southern California, Los Angeles, USA
3Thousand Talents Professor, Department of Construction Management, Tsinghua University, Beijing, China

Tóm tắt

Civil infrastructure systems are disturbed by natural or man-made hazards at an increasing frequency and severity. Among these systems, transportation systems are especially vulnerable due to their nature and are of significant importance to urban built environments as they maintain the mobility of urban dwellers and goods. Mobility disturbances are significant not only due to the direct losses associated but also due to the greater economic impacts driven by indirect losses stemming from the economic interactions of regions and sectors. Therefore, understanding the economic impacts of urban mobility disturbances is critical. To achieve a better understanding of the status quo of the research on transportation disturbances and economic impact analysis, a literature review was conducted. The review indicates that most of the articles fail to leverage realistic hazard impact information and explicit network modeling, consequently jeopardizing the credibility of the results. To begin addressing the gaps in the field, an interdisciplinary framework was designed to investigate the economic impacts of mobility disturbances. To validate the framework, a case study was conducted to estimate the economic impacts of commuting-based mobility disturbances resulting from a potential earthquake scenario in the Greater Los Angeles Area. The direct and indirect economic losses were estimated to be 285.49 and 93.48 million dollars, respectively. The results indicated that the economic losses could vary significantly among regions as well as industries. Among the five counties in the study region, Los Angeles County suffered the most. In addition, industries related to finance, education and scientific services, etc. were estimated to experience larger losses.

Tài liệu tham khảo

Aloughareh IR, Ashtiany MG, Nasserasadi K (2016) An integrated methodology for regional macroeconomic loss estimation of earthquake: a case study of Tehran. Singap Econ Rev 61. https://doi.org/10.1142/S0217590815500253 Ashrafi Z, Shahrokhi Shahraki H, Bachmann C et al (2017) Quantifying the criticality of highway infrastructure for freight transportation. Transp Res Rec J Transp Res Board 2610:10–18. https://doi.org/10.3141/2610-02 Cho JK, Gordon P, Moore JE et al (2015) TransNIEMO: economic impact analysis using a model of consistent inter-regional economic and network equilibria. Transp Plan Technol 38:483–502. https://doi.org/10.1080/03081060.2015.1039230 Cho S, Gordon P, Moore JE et al (2001) Integrating transportation network and regional economic models to estimate the costs of a large urban earthquake. J Reg Sci 41:39–65. https://doi.org/10.1111/0022-4146.00206 Cochrane HC (1974) Predicting the economic impacts of earthquakes Crowther KG, Haimes YY, Taub G (2007) Systemic valuation of strategic preparedness through application of the inoperability input-output model with lessons learned from hurricane Katrina. Risk Anal 27:1345–1364. https://doi.org/10.1111/j.1539-6924.2007.00965.x Dietzenbacher E (1997) In vindication of the Ghosh model: a reinterpretation as a price model. J Reg Sci 37:629–651. https://doi.org/10.1111/0022-4146.00073 Fallis A. (2014) The value of travel time savings: departmental guidance for conducting economic evaluations revision 2 (2014 update). Washington Faturechi R, Miller-Hooks E (2014) Measuring the performance of transportation infrastructure systems in disasters: a comprehensive review. ASCE J Infrastruct Syst 21:1–15. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000212. Ghosh A (1958) Input-output approach in an allocation system. Economica XXV:58–64 Federal Emergency Management Agency (2018) Hazus program and related news updates page. https://www.fema.gov/hazus/. Accessed 25 May 2018 Gordon P, Moore JE II, Richardson HW et al (2004) Earthquake disaster mitigation for urban transportation systems: an integrated methodology that builds on the Kobe and Northridge experiences. Model Spat Econ Impacts Disasters:205–232. https://doi.org/10.1007/978-3-540-24787-6_11 Greenberg M, Lioy P, Ozbas B et al (2013) Passenger rail security, planning, and resilience: application of network, plume, and economic simulation models as decision support tools. Risk Anal 33:1969–1986. https://doi.org/10.1111/risa.12073 Gueler CU, Johnson AW, Cooper M (2012) Case study: energy industry economic impacts from Ohio River transportation disruption. Eng Econ 57:77–100. https://doi.org/10.1080/0013791X.2012.677114 Hallegatte S (2008) An adaptive regional input-output model and its application to the assessment of the economic cost of Katrina. Risk Anal 28:779–799. https://doi.org/10.1111/j.1539-6924.2008.01046.x Irimoto H, Shibusawa H, Miyata Y (2017) Evaluating the economic damages of transport disruptions using a transnational and interregional input-output model for Japan, China, and South Korea. In: AIP Conference Proceedings. AIP - American Institute of Physics, USA, p 110002 (6 pp) Jaiswal P, Van Westen CJ, Jetten V (2010) Quantitative assessment of direct and indirect landslide risk along transportation lines in southern India. Nat Hazards Earth Syst Sci 10:1253–1267. https://doi.org/10.5194/nhess-10-1253-2010 Kajitani Y, Chang SE, Tatano H (2013) Economic impacts of the 2011 Tohoku-oki earthquake and tsunami. Earthquake Spectra 29:S457–S478. https://doi.org/10.1193/1.4000108 Kim E, Kwon YJ (2016) Indirect impact of nuclear power plant accidents using an integrated spatial computable general equilibrium model with a microsimulation module on the Korean transportation network. In: Kim E, Kim B (eds) Quantitative regional economic and environmental analysis for sustainability in Korea. Springer, Singapore, pp 141–152 Koks EE, Carrera L, Jonkeren O et al (2016) Regional disaster impact analysis: comparing input-output and computable general equilibrium models. Nat Hazards Earth Syst Sci 16:1911–1924. https://doi.org/10.5194/nhess-16-1911-2016 Koks EE, Thissen M (2016) A multiregional impact assessment model for disaster analysis. Econ Syst Res 28:429–449. https://doi.org/10.1080/09535314.2016.1232701 Leontief W (1936) Quantitative input and output relations in the economic systems of the United States. Rev Econ Stat 18:105–125. https://doi.org/10.2307/1927837 Li J, Crawford-Brown D, Syddall M, Guan D (2013) Modeling imbalanced economic recovery following a natural disaster using input-output analysis. Risk Anal 33:1908–1923. https://doi.org/10.1111/risa.12040 Lian C, Halmes YY (2006) Managing the risk of terrorism to interdependent infrastructure systems through the dynamic inoperability input-output model. Syst Eng 9:241–258. https://doi.org/10.1002/sys.20051 MacKenzie C, Santos J, Barker K (2012) Measuring changes in international production from a disruption: case study of the Japanese earthquake and tsunami. Int J Prod Econ 138:293–302. https://doi.org/10.1016/j.ijpe.2012.03.032 Mesa-Arango R, Zhan X, Ukkusuri SV, Mitra A (2016) Direct transportation economic impacts of highway networks disruptions using public data from the United States. J Transp Saf Secur 8:36–55. https://doi.org/10.1080/19439962.2014.978962 Minnesota IMPLAN Group (MIG) (2018) Impact Analysis for Planning (IMPLAN) system homepage. http://www.implan.com. Accessed 19 March 2018 Omer M, Mostashari A, Nilchiani R (2013) Assessing resilience in a regional road-based transportation network. Int J Ind Syst Eng 13:389–408 Oosterhaven J (1988) On the plausibility of the supply-driven input-output model. 28:203–217 Oztanriseven F, Nachtmann H (2017) Economic impact analysis of inland waterway disruption response. Eng Econ 62:73–89. https://doi.org/10.1080/0013791X.2016.1163627 Pant R, Barker K, Grant FH, Landers TL (2011) Interdependent impacts of inoperability at multi-modal transportation container terminals. Transport Res E-Log 47:722–737. https://doi.org/10.1016/j.tre.2011.02.009 Park J, Cho J, Gordon P et al (2011) Adding a freight network to a national interstate input-output model: a TransNIEMO application for California. J Transp Geogr 19:1410–1422. https://doi.org/10.1016/j.jtrangeo.2011.07.019 Park J, Gordon P, Ii JEM, Richardson HW (2005a) Simulating the state-by-state effects of terrorist attacks on three major US ports: applying NIEMO (National Interstate Economic Model) Park J, Gordon P, Kim S, et al (2005b) Estimating the State-by-State Economic Impacts of Hurricane Katrina Park JY (2008) The economic impacts of dirty-bomb attacks on the Los Angeles and Long Beach ports: applying the supply-driven NIEMO (National Interstate Economic Model). J Homel Secur Emerg Manag 5:Article 21-Article 21. https://doi.org/10.2202/1547-7355.1312 Park JY, Gordon P, Moore JSE, Richardson HW (2008) The state-by-state economic impacts of the 2002 shutdown of the Los Angeles-Long Beach ports. Growth Change 39:548–572. https://doi.org/10.1111/j.1468-2257.2008.00446.x Postance B, Hillier J, Dijkstra T, Dixon N (2017) Extending natural hazard impacts: an assessment of landslide disruptions on a national road transportation network. Environ Res Lett 12. https://doi.org/10.1088/1748-9326/aa5555 Rose A (2004) Defining and measuring economic resilience to disasters. Disaster Prev Manag An Int J 13:307–314. https://doi.org/10.1108/09653560410556528 Rose A, Liao SY (2005) Modeling regional economic resilience to disasters: a computable general equilibrium analysis of water service disruptions. J Reg Sci 45:75–112. https://doi.org/10.1111/j.0022-4146.2005.00365.x Rose A, Sue Wing I, Wei D, Wein A (2016) Economic impacts of a California tsunami. Nat Hazards Rev 17:4016002. https://doi.org/10.1061/(ASCE)NH.1527-6996.0000212 Rose A, Wei D (2013) Estimating the economic consequences of a port shutdown: the special role of resilience. Econ Syst Res 25:212–232. https://doi.org/10.1080/09535314.2012.731379 Santos JR, Haimes YY (2004) Modeling the demand reduction input-output (I-O) inoperability due to terrorism of interconnected infrastructures. Risk Anal 24:1437–1451. https://doi.org/10.1111/j.0272-4332.2004.00540.x Sohn J, Kim TJ, Hewings GJD et al (2003) Retrofit priority of transport network links under an earthquake. J Urban Plan Dev 129:195–210. https://doi.org/10.1061/(asce)0733-9488(2003)129:4(195) Stergiou E, Kiremidjian AS (2006) Treatment of uncertainties in seismic risk analysis of transportation systems Tan XM, Zhang Y, Lam JSL (2015) Economic impact of port disruptions on industry clusters: a case study of Shenzhen. In: Yan XP, Hu ZZ, Zhong M, Wu CZ, Yang Z (eds) The 3rd International Conference on Transportation Information and Safety (ICTIS 2015). IEEE, pp 617–622 Tatano H, Tsuchiya S (2008) A framework for economic loss estimation due to seismic transportation network disruption: a spatial computable general equilibrium approach. Nat Hazards 44:253–265. https://doi.org/10.1007/s11069-007-9151-0 Thekdi SA, Santos JR (2016) Supply chain vulnerability analysis using scenario-based input-output modeling: application to port operations. Risk Anal 36:1025–1039. https://doi.org/10.1111/risa.12473 Thissen M (2004) The indirect economic effects of a terrorist attack on transport infrastructure: a proposal for a SAGE. Disaster Prev Manag 13:315–322. https://doi.org/10.1108/09653560410556537 Tirasirichai C (2007) An indirect loss estimation methodology to account for regional earthquake damage to highway bridges. ProQuest Diss Theses 147 Tirasirichai C, Enke D (2007) Case study: applying a regional CGE model for estimation of indirect economic losses due to damaged highway bridges. Eng Econ 52:367–401 Tokui J, Kawasaki K, Miyagawa T (2017) The economic impact of supply chain disruptions from the Great East-Japan earthquake. Japan World Econ 41:59–70. https://doi.org/10.1016/j.japwor.2016.12.005 Tsuchiya S, Tatano H, Okada N (2007) Economic loss assessment due to railroad and highway disruptions. Econ Syst Res 19:147–162. https://doi.org/10.1080/09535310701328567 Ueda T, Koike A, Iwakami K (2001) Economic damage assessment of catastrophes in high speed rail network. Proc 1st Work ‘Comparative Study Urban Earthq Disaster Manag 13–19 Vadali S, Chandra S, Shelton J et al (2015) Economic costs of critical infrastructure failure in bi-national regions and implications for resilience building: evidence from El Paso-Ciudad Juarez. Res Transp Bus Manag 16:15–31. https://doi.org/10.1016/j.rtbm.2015.08.001 Wei F, Koc E, Soibelman L, Li N (2018) Disaster economics and networked transportation infrastructures: status quo and a multi-disciplinary framework to estimate economic losses. In: Advanced Computing Strategies for Engineering Wei H, Dong M, Sun S (2010) Inoperability input-output modeling (IIM) of disruptions to supply chain networks. Syst Eng 13:324–339. https://doi.org/10.1002/sys.20153 Xie F, Levinson D (2011) Evaluating the effects of the I-35W bridge collapse on road-users in the twin cities metropolitan region. Transp Plan Technol 34:691–703 Xie W, Li N, Li C et al (2014) Quantifying cascading effects triggered by disrupted transportation due to the Great 2008 Chinese Ice Storm: implications for disaster risk management. Nat Hazards 70:337–352. https://doi.org/10.1007/s11069-013-0813-9 Yu KDS, Tan RR, Santos JR (2013) Impact estimation of flooding in Manila: an inoperability input-output approach. In: 2013 IEEE Systems and Information Engineering Design Symposium (SIEDS). IEEE, 345 E 47TH ST, New York, pp 47–51 Zhang Y, Lam JSL (2015) Estimating the economic losses of port disruption due to extreme wind events. Ocean Coast Manag 116:300–310. https://doi.org/10.1016/j.ocecoaman.2015.08.009 Zhang Y, Lam JSL (2016) Estimating economic losses of industry clusters due to port disruptions. Transp Res A 91:17–33. https://doi.org/10.1016/j.tra.2016.05.017 Zhou Y, Banerjee S, Shinozuka M (2010) Socio-economic effect of seismic retrofit of bridges for highway transportation networks: a pilot study. Struct Infrastruct Eng 6:145–157. https://doi.org/10.1080/15732470802663862