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 J.J.Laird@its.leeds.ac.uk

Flooding in York

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

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?

Workington Northside Bridge Collapse 2009© Andy V Byers. http://en.wikipedia.org/wiki/2009_Workington_floods

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)

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)

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

Snow at Heathrow © Caroline Cook. http://www.airportsinternational.com/2010/01/snow-patrol/snow-heathrow-2

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

Case Study 2 – York Floods

Snow in Kent in 2009: http://www.wilmingtonpc.kentparishes.gov.uk/default.cfm?pid=3873

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)

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)

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

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

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

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

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

Thank you for your attention