institute for transport studies faculty of environment evaluating transport and land use...
TRANSCRIPT
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Institute for Transport StudiesFACULTY OF ENVIRONMENT
Evaluating transport and land use interventions in the face of disruption
UTSG January 6th-8th 2014
Newcastle University
James Laird, Greg Marsden, Jeremy Shires [email protected]
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Flooding in York
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Structure of presentation
• Research questions
• State of practice in CBA of disruptive events
• Case studies
– Snow and ice in the UK
– Flooding in York
• Problems with state of practice CBA and disruption
• Conclusions and further research
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Research questions
• Are user costs/benefits truly representative of the socio-economic costs during periods of disruption?
• Are cost benefit analysis methods appropriate for assessing policies/interventions that ameliorate disruption?
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Workington Northside Bridge Collapse 2009© Andy V Byers. http://en.wikipedia.org/wiki/2009_Workington_floods
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State of practice in assessing socio-economic costs of disruption
• 1994 Northridge earthquake (Los Angeles)
– US$1.6million per day (Wesemann et al., 1996)
• 2007 Minneapolis I-35W bridge collapse
– US$71,000 to US$220,000 per day (Xie and Levinson, 2011)
• Road closures in Central North Island
– NZ$8,000 to NZ$23,000 per hour (Dalziell and Nicholson, 2001)
• Retrofitting freeway bridges for seismic resistance (Los Angeles)
– Traveller costs due to disruption necessary to justify investment (Shinozuka et al., 2008)
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Economic theory
Transport costs (TC)
Traffic (volume)
ATC0
TC1
X1X0
Demand0,1
Supply1
Supply0
Network without disruption
Transport costs (TC)
Traffic (volume)
C
TC1-disrupt
TC0-disrupt
X0-disrupt X1-disrupt
Demand0,1
Supply0-disrupt
Supply1-disrupt
Network during disruption
TC0
TC1
X1X0
Use benefits = (1-p). Area A + p. Area CWhere p = probability(disruption)
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Conditions for user benefits to reflect total economic impact
• Measuring user benefits
– Rule of half must hold
– The marginal costs of disruption are known
• Are user benefits all the benefits? Yes if:
– Benefits are certain (i.e. no uncertainty)
– Perfect competition holds everywhere
– Transport is the only ‘market’ affected
– Land uses are not affected
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Snow at Heathrow © Caroline Cook. http://www.airportsinternational.com/2010/01/snow-patrol/snow-heathrow-2
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Case study 1UK snow and ice - 2013
• 18th January 2013
• Disruption for several days
• School closures – more than 5,000 on 21st January
• Cancellation of public transport – including major airports
• Road closures
• Difficulty travelling on roads that were open.
• On-line panel
• N = 2418
• 6 worst affected regions
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Case Study 2 – York Floods
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Snow in Kent in 2009: http://www.wilmingtonpc.kentparishes.gov.uk/default.cfm?pid=3873
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Marginal costs of disruption
• Can standard values of time be used?
• Activity schedules
– Time constraint bites harder as delays build up (Jenelius et al., 2011)
– Evidence from case studies:
• Short term cancellation/postponment possible, but cannot delay indefinitely going to work, etc.
• Tremendous heterogeneity in resilience and impact of disruption (e.g. childcare: stay at home mum vs single working mother vs dual income households)
– Longer term expect activity schedules to adapt (for e.g. longer lasting disruption e.g. bridge collapse)
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Breakdown in rule of half
• Large cost changes
– UK Snow and ice: 41% of commute and business trips cancelled or postponed (indirect evidence of cost change)
– York flooding: reported journey time increases of 1 hour on a ‘normal’ 15min to 20min journeys
– Nellthorp and Hyman (2001) RoH error of >10%, de Jong et al (2007) error up to 32%
• Loss of mode
– York flooding: bus service was cancelled
– RoH cannot be used
• Analytical solution:
– Numeric integration (Nellthorp and Hyman, 2001) or direct integration of demand curve (de Jong et al., 2007)
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Treatment of uncertainty
• In the presence of uncertainty
(i) Expected use benefits are probabilistic (Captured in standard approach)
(ii) There exists a risk premium/option value (not captured)
– Expect households and businesses to adapt behaviour to changes in uncertainty.
• Case study evidence:
– Stress and difficulty of dealing with uncertainty
– Loss of bus service and difficulties that caused
– Benefit of stay-at-home mum is increased resilience (cost is income foregone).
– Households with experience of flooding hold higher stocks
• Analytical solution
– Option values can impact on appraisal (Laird et al., 2009, 2013). Expect option values of increased winter gritting capacity, flood defences, etc.
– Need to model long run shift in supply curve (i.e. supply chain modelling/stock monitoring
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Impacts across markets
• Some disruptive events confined to transport network only BUT:
• Case study evidence:
– Snow and ice: 5,000 schools closed (impacts on education and employment). Premier league etc. football matches postponed.
– Flooding: significant damage at 30 homes and businesses. York dungeon, Grand Opera House, Comedy Club, Badminton Horse Trials and Great Yorkshire Show all cancelled due to flooding.
• Transport market analysis will not pick up all benefits.
– Need a multi-market analysis
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Policies that promote resilience to disruption
• Resilience policies
– New infrastructure (transport and non-transport)
– Softer measures:
• Flexible working/tele-working
• Land use intensification (walking trips least affected)
• Appraisal issues for ‘non-transport’ projects
– Flexible working etc.
• Is ‘non-transport’ & needs to be assessed in a labour market paradigm
– Land use intensification cannot be assessed using rule of half, as attractiveness of land alters through land use policy
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Conclusions and further research
A890 land slide at Loch Carron © Ross-shire Journal http://www.ross-shirejournal.co.uk/News/Strome-ferry-timetable-unveiled-13012012.htm
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Conclusions and further research
• Are user costs/benefits truly representative of the socio-economic costs during periods of disruption?
– No
– Option values/risk premia, multiple market impacts, ‘non-transport’ interventions are missing from that paradigm
• Are cost benefit analysis methods appropriate for assessing policies/interventions that ameliorate disruption?
– Yes
– But measurement challenges exist.
– Further research: marginal costs of disruption, risk premia of resilient infrastructure, multiple market modelling
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Thank you for your attention