Understanding the Concept of Latent Demand in Traffic

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Understanding the Concept of Latent Demand in
Traffic

• Prof. Patricia L. Mokhtarian
• Civil Environmental Engineering, UC Davis
• plmokhtarian_at_ucdavis.edu
• www.its.ucdavis.edu/telecom/
• [phone]

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Outline of this Talk

• What are latent and induced demand, and their
  implications?
• Empirical approaches to assessing induced demand
• Typical results
• Limitations
• UC Davis study using matched pairs
• More recent work Cervero/Hansen Choo/Mokh.
• Summary
• Concluding thoughts

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What is Latent Demand?

• Often used interchangeably with induced demand,
  but the two concepts can be technically
  distinguished as follows
• Latent demand Pent-up (dormant) demand for
  travel, travel that is desired but unrealized
  because of constraints
• Induced demand Realized demand that is
  generated (induced, drawn out) because of
  improvements to the transportation system

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Induced Demand

• The increment of new vehicle traffic that would
  not have occurred at all without the capacity
  improvement.
• Clear in theory, but difficult in practice!
• Observed increases in traffic on a
  capacity-enhanced network link can arise from a
  variety of sources

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When is Traffic Growth Induced Demand?

• Shifts in departure time
• Changes in route or destination (no for vehicle
  trips but maybe for VMT)
• Shifts from shared modes to drive alone
• New or longer trips to existing locations
• Background demographic growth (WHOA)
• Trips generated by new development attracted to
  the improved corridor

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Why do we Care about Induced Demand?

• Need to be able to forecast newly-created travel
  (that WNHOA)
• Affects the cost-benefit calculation for the
  improvement
• Affects the assessment of environmental impacts
• Legal/political ramifications
• Sierra Club v. MTC, 1989
• UK abandoned predict and provide policy in mid
  90s

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Empirical Approaches

• Case studies
• Cross-sectional disaggregate modeling
• Cross-sectional aggregate modeling
• Time series aggregate modeling
• Cross-sectional/time series aggregate modeling
• Time series link/facility level analysis with
  controls

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Case Studies

• Change in traffic on single facility measured
• Results mixed, but have generally found observed
  volumes higher than forecasts
• May highlight idiosyncratic circumstances
• Often short-term difficult to distinguish
  induced demand from shifted demand or background
  growth

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Cross-sectional Disaggregate Modeling

• Using 1995 NPTS (travel diary data), analyze
  association of VMT with speed
• Higher speeds associated with greater VMT
• Speed is a more behaviorally-sound influence on
  VMT than capacity
• Association doesnt guarantee causality cant
  identify long-term impacts

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Cross-sectional Aggregate Modeling

• Models impact of lane-miles on VMT for metro
  areas in US
• Increase of 1 in lane-mi leads to 0.8 increase
  in VMT
• Potentially represents long-term equilibrium
• Bi-directional causality impossible to untangle
  with single equation, no dynamic element

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Time Series Aggregate Modeling

• Decomposed VMT growth (Milwaukee, 1963-1991) into
  sources based on assumed relationships
• 6-22 of total VMT growth attributable to new
  capacity
• Regional focus decomposition approach useful
• Still only one direction of causality permitted

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Cross-sectional/Time Series Aggregate Modeling

• Models VMT as function of lane-mi among other
  variables, for multiple areas over time
• 1 increase in ln-mi ? 0.2 0.9 increase in VMT
  (long-run gt short-run)
• Advantages
• Covariates help capture background influences
• If area large enough, demand shifts accounted for
• Temporal precedence can be established

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Cross-sectional/Time Series Aggregate Modeling
(contd)

• Disadvantages
• Not all background influences captured
• Facility/metro-level analyses subject to
  confounding with changes in classification and
  urban boundary over time
• Even temporal precedence doesnt guarantee
  causality
• Effectiveness of lagged variables depends on
  whether planning horizon is longer than the lag

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Time Series Link/Facility Level Analysis with
Controls

• Compares growth in ADT on improved links, to that
  on matched set of unimproved links
• Study of 18 matched prs in CA (UCD faculty) found
  no difference in growth rates
• Controls for causes of growth common to improved
  and comparison segments
• Several disadvantages

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Time Series Link/Facility Level Analysis (contd)

• Disadvantages
• Difficult to find suitable controls
• Doesnt control for spatial shifts from nearby
• Cannot establish a control for an entirely new
  link
• Another possible reason for difference ADT
  v. VMT new capacity may affect trip length more
  than frequency

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Recent Work Cervero/Hansen

• Cross-sectional/time series aggregate
• state hwys, 34 CA counties, 1976-97
• Simultaneous equations
• Lane-miles ? VMT
• VMT ? lane-miles
• Both directions of causality significant,
  lane-miles ? VMT the stronger direction

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Cervero/Hansen (contd)

• Probably the most rigorous published study to
  date
• Issues
• Did facility reclassification, metro area effects
  confound relationships?
• What happened to traffic on lower-classification
  facilities?
• Are the instrumental variables appropriate?
• Is the high goodness-of-fit spurious?
• Were the lags long enough?

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Recent Work Choo/Mokhtarian

• Time series aggregate (USwide, 1951-2000)
• Comprehensive structural model

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Sociodemo-graphics
Economic Activity
Travel Demand (VMT)
Telecom Demand
Transport.Sys. Infrastructure (lane-mi)
Telecom System Infrastructure
Telecom Costs
Travel Costs
Land Use
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Choo/Mokhtarian (contd)

• Time series aggregate (USwide, 1951-2000)
• Comprehensive structural model
• Corrected for high correlations due to similar
  temporal trends
• Also found both directions of causality
  significant, lane-miles ? VMT the stronger
  direction

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Summary

• Its a complex issue!
• Each approach has advantages and disadvantages,
  something to offer but not definitive answers
• To better understand extent to which answer
  depends on method, apply multiple methods to same
  region
• Nevertheless, the most sophisticated analyses
  find evidence for induced demand

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Concluding Thoughts

• Transportation demand will continue to grow
• Thus, cant eliminate all system improvements
  just because demand will increase
• Should rather weigh the costs (increased fuel
  consumption, emissions) against the benefits
  (increased mobility, economic gain)
• Need to continue to improve our measurement and
  modeling of both costs and benefits
• And continue efforts to more appropriately price
  the provision of service