1. Understanding the Concept of Latent Demand in Traffic
Prof. Patricia L. Mokhtarian
Civil & Environmental Engineering, UC Davis
(530)

2. 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

3. 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
Note: Not just capacity increases, but any improvements that increase travel speeds (decrease travel times) can be expected to induce demand. So too would improvements in transit.

4. 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:

5. 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

6. 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

7. 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
For each, will present approach, typical results, advantages and disadvantages.

8. 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

9. 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 doesn’t guarantee causality; can’t identify long-term impacts

10. 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

11. Time Series Aggregate Modeling
Decomposed VMT growth (Milwaukee, ) 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

12. 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 > short-run)
Advantages:
Covariates help capture background influences
If area large enough, demand shifts accounted for
Temporal precedence can be established

13. Cross-sectional/Time Series Aggregate Modeling (cont’d)
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 doesn’t guarantee causality
Effectiveness of lagged variables depends on whether planning horizon is longer than the lag

14. 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:

15. Time Series Link/Facility Level Analysis (cont’d)
Disadvantages:
Difficult to find suitable controls
Doesn’t 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

16. Recent Work: Cervero/Hansen
Cross-sectional/time series aggregate
state hwys, 34 CA counties,
Simultaneous equations:
Lane-miles → VMT
VMT → lane-miles
Both directions of causality significant, lane-miles → VMT the stronger direction

17. Cervero/Hansen (cont’d)
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?

18. Recent Work: Choo/Mokhtarian
Time series aggregate (USwide, )
Comprehensive structural model

19. Transport.Sys. Infrastructure (lane-mi)
Sociodemo-graphics
Economic
Activity
Transport.Sys. Infrastructure (lane-mi)
Travel Demand (VMT)
Telecom
Demand
Telecom System
Infrastructure
Telecom Costs
Travel Costs
Land Use
Endogenous Variable Category
Exogenous Variable Category

20. Choo/Mokhtarian (cont’d)
Time series aggregate (USwide, )
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

21. Summary It’s 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

22. Concluding Thoughts Transportation demand will continue to grow
Thus, can’t 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