Understanding the Concept of Latent Demand in Traffic Prof. Patricia L. Mokhtarian Civil & Environmental Engineering, UC Davis plmokhtarian@ucdavis.edu www.its.ucdavis.edu/telecom/ (530) 752-7062

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

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.

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:

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

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

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.

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

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

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

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

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

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

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:

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

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

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?

Recent Work: Choo/Mokhtarian Time series aggregate (USwide, 1951-2000) Comprehensive structural model

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

Choo/Mokhtarian (cont’d) 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

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

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