Background on Induced Travel

Attempts to address traffic congestion commonly rely on increasing roadway capacity, e.g. by building new roadways or adding lanes to existing facilities. But studies examining that approach indicate it is only a temporary fix. They consistently show that adding roadway capacity in congested areas actually increases network-wide vehicle miles traveled (VMT) by a nearly equivalent proportion within a few years, reducing or negating the initial congestion relief. That increase in VMT is called “induced travel.”

The induced travel concept is not new (see the “fundamental law of highway congestion” Anthony Downs suggested in 1962). Indeed, it is explained by the bedrock economic principles of supply and demand: “adding capacity decreases travel time, in effect lowering the ‘price’ of driving; when prices go down, the quantity of driving goes up” (Handy and Boarnet, 2014a).

The magnitude of the induced travel effect is commonly measured as the elasticity of VMT with respect to lane miles:

The greater the elasticity, the greater the increase in VMT from a given increase in roadway capacity. An elasticity of 1.0 indicates that a given percent increase in lane miles will cause the same percent increase in VMT.

Most recent studies have estimated elasticities in the same ballpark, despite using a range of methods to control for other VMT-inducing factors and the bi-directional relationship between VMT and capacity expansion. In general, the studies show that a 10-percent increase in roadway capacity is likely to increase network-wide VMT by 6 to 10 percent (an elasticity of 0.6 to 1.0) in the long run (5 to 10 years), with greater elasticities for expansions of major highways (e.g. interstates) than for capacity increases on other roadways. These longer-term elasticities account for short-run shifts in travel (as people take advantage of the increased capacity and travel speed by driving more), as well as longer-run dispersion of residential and business location and development.

For more information on the induced travel and other impacts of highway expansion, see Handy (2015) and Handy and Boarnet (2014a). For a summary of key studies estimating the elasticity of VMT with respect to lane miles, see Handy and Boarnet (2014b). Please also see the References section below for a list of relevant studies.

Calculator Purpose and Scope

This calculator allows users to estimate the VMT induced annually as a result of expanding the capacity of publicly owned roadways, like those managed by the California Department of Transportation (Caltrans), in one of California’s urbanized counties (counties within a metropolitan statistical area (MSA)). The calculator applies only to publicly owned facilities with Federal Highway Administration (FHWA) functional classifications of 1, 2 or 3. That corresponds to interstate highways (class 1), other freeways and expressways (class 2), and other principal arterials (class 3).

Caltrans now recommends in its Transportation Analysis Framework (TAF) that the calculator be used where possible to estimate – or at least benchmark – induced VMT: “In cases where the NCST Calculator can be directly used, it should either be used exclusively or used to benchmark results from a [travel demand model]” (Caltrans, 2020 , p. 14). Caltrans’ recommendation is supported by the report of a panel of experts convened by Caltrans to guide its choice of induced travel estimation methods for the TAF (Deakin et al., 2020).

Calculator use is not appropriate in all circumstances or for all purposes, however. The following caveats apply:
  • The calculator is limited to use for capacity expansions (lane additions, roadway lengthening, and new facility construction). It cannot be used to estimate VMT effects of capacity reductions or lane type conversions.
  • The calculator is conservatively limited to use for additions of general-purpose, high-occupancy vehicle (HOV), and high-occupancy toll (HOT) lanes. It should not be used for additions of pure toll lanes (where all users, even HOVs, must pay a toll). Hundreds of both general-purpose and HOV lane mile additions were included in the two principal studies used to derive the elasticities for the calculator (Duranton and Turner, 2011Cervero and Hansen, 2002Legislative Analyst’s Office, 2000). While few HOT lanes had been added to publicly owned roadways before the end of the data collection periods for those two studies, studies using data from more recent periods (after more HOT lanes had been opened) have estimated similar induced travel elasticities (e.g. Hymel, 2019; Graham et al., 2014; Melo et al., 2012). Furthermore, because HOT lanes allow more vehicles than HOV lanes (high-occupancy vehicles plus drivers willing to pay to use the lane), they would logically have at least as large induced travel effects as HOV lanes.
  • The calculator is limited to use for capacity expansions on or construction of new publicly owned roadways with FHWA functional classifications of 1, 2, or 3. See Caltrans’ California highway system map with functional class delineations.
  • The calculator is limited to use in California’s 37 urbanized counties (counties within MSAs). The calculator cannot be used to assess the VMT effects of roadway expansions in California counties outside of MSAs, or in any geography outside California. However, the method could be adapted for use outside California, assuming the necessary lane mile and VMT data is available and an appropriate evidence-backed elasticity can be identified. For example, all of the 49 other states should have similar lane mile and VMT data in their respective Highway Performance Monitoring System databases. And an elasticity of 1.0 could similarly be appropriate for calculating induced travel from additions to interstate highways in MSAs outside of California. Duranton and Turner (2011) used data from all MSAs in the United States that had nonzero interstate lane mileage in the study years. Subsequent studies estimating similar elasticities have also used regional data from across the United States (e.g. Hymel, 2019; Graham et al., 2014; Melo et al., 2012).
  • The calculator is also limited to use in California geographies that already contain roadways with FHWA classifications matching the classification of the proposed expanded or new roadway. There are 10 MSAs in which there are no interstate highways. In addition, sufficient data are not available on baseline VMT for interstate lane miles in the Napa MSA to calculate induced VMT from interstate capacity expansions there. After you have run it, the calculator will alert you in the “Results” box if it cannot estimate the induced VMT of the selected facility type in the selected geography due to the aforementioned limitations.
  • The calculator produces long-run estimates of induced VMT, the additional annual VMT that could be expected 5 to 10 years after facility installation.
  • All estimates account for the possibility that some of increased VMT on the expanded facility is traffic diverted from other types of roads in the network. In general, the studies show that “capacity expansion leads to a net increase in VMT, not simply a shifting of VMT from one road to another” (Handy and Boarnet, 2014a).
  • The calculator currently uses lane mileage and VMT data from 2016, 2017, 2018, and 2019. The data will be updated periodically as new data become available.
  • Knowledge of local conditions can help contextualize the calculator’s estimates.

Data and Elasticities

To estimate induced VMT for user-specified capacity expansion projects, the calculator simply solves this equation:

%∆ Lane Miles x Existing VMT x Elasticity = Project-Induced VMT

Data sources and specifications for the equation inputs are as follows:

Lane Miles. The calculator uses lane mileage data from Caltrans’ Transportation System Network (TSN) database (similarly reported in the Highway Performance Monitoring System [HPMS]). The user has the option to use baseline data from 2016, 2017, 2018, or 2019. The percent change in lane miles is calculated by dividing the number of project-added lane miles (input by the user) by the total lane miles of the same facility type in the same geography. For interstate highways (FHWA functional class 1), lane mileage is calculated at the MSA level. For other freeways, expressways and major arterials (classes 2 and 3) managed by public agencies, lane mileage is calculated at the county level. The choice of geographies is discussed further below, in conjunction with elasticities. The data is available here.

Existing VMT. The calculator uses VMT data retrieved using Caltrans’ TSN and HPMS database. The user has the option to use baseline data from 2016, 2017, 2018, or 2019. The VMT is tallied for each county and each FHWA functional classification. Existing VMT on interstate highways (FHWA functional class 1) is calculated at the MSA level, and existing VMT on other publicly owned freeways, expressways and major arterials (classes 2 and 3) is calculated at the county level. The data is available here.

Elasticities. The calculator uses an elasticity of 1.0 for lane additions to interstate highways, and an elasticity of 0.75 for lane additions to class 2 or 3 facilities.

For interstate highways, the 1.0 elasticity derives from Duranton and Turner (2011) -- perhaps the most thorough and stringently vetted induced travel study to date – as well as subsequent studies that also use robust statistical methods to address the simultaneity bias (the fact that increasing VMT an spur roadway expansion in addition to being caused by it). Duranton and Turner’s study used data from 1983, 1993 and 2003 for all MSAs in the United States that had nonzero interstate lane mileage in all three years. Among other modeling, the study used a two-stage least squares regression with three instrumental variables to estimate the elasticity of vehicle kilometers traveled (VKT) on interstate highways in the 228 studied MSAs with respect to interstate lane kilometers in those MSAs. The authors concluded that their estimation method better controls for the possible bi-directional relationship between VMT and lane miles than the methods used in previous studies. Using that “preferred estimation method,” their “preferred estimate” was a long-run (10-year) elasticity of 1.03. However, while the authors concluded that “diversion of traffic from other road networks does not appear to play a large role,” they cautioned that they could not “rule out the absence of a substitution effect.” They estimated that the “diversion of traffic from other classes of roads accounts for between 0 and 10 percent of the total [induced] interstate VKT.”

Like Duranton and Turner (2011), the calculator uses MSAs as the unit of analysis for interstate highway capacity expansions. The calculator also uses a similar VMT elasticity (1.0), albeit rounded down (in part to account for the (small) potential substitution effect). That accords with Handy and Boarnet’s (2014a) conclusion that the best estimate for the long-run VMT elasticity for highway lane additions is close to 1.0. It is also consistent with more recent studies that have estimated long-run induced VMT elasticities using instrumental variables, including Melo et al. (2012) (elasticity of 0.989 for major [class 3] and minor [class 4] arterials in 100 urbanized areas in the U.S.), Graham et al. (2014) (elasticity of 0.772 for class 1, 2, 3, and 4 [minor arterial] facilities in 101 urbanized areas in the U.S.), Hsu and Zhang (2014) (elasticity of between 1.24 and 1.34 for Japan’s national expressways), and Hymel (2019) (elasticity of between 0.892 and 1.063 for freeways and other limited-access roads in Census-defined urban areas in the U.S.). See Volker et al. (2020) for further discussion of the induced travel literature and the analysis of induced travel in the environmental review process.

For other highways, expressways and major arterials managed by public agencies, the 0.75 elasticity derives from Duranton and Turner (2011) and Cervero and Hansen (2002), as well as subsequent studies that also use robust statistical methods to address the simultaneity bias (the fact that increasing VMT an spur roadway expansion in addition to being caused by it). While Duranton and Turner (2011) could not use their preferred method to estimate elasticities for state highways and other “major roads” besides interstates, their elasticity estimates using ordinary least squares regression all fall between 0.67 and 0.89. Cervero and Hansen (2002) similarly estimated an intermediate-run (5-year) VMT elasticity of 0.79 for lane mile additions to state-owned roadways in California’s urbanized counties (then 34, now 37), using three-stage least squares regression. Those elasticities are similar to other longer-term elasticities calculated for combined major road types (not just interstate highways) in California and across the United States (Handy and Boarnet, 2014a).

Like Cervero and Hansen (2002), the calculator uses urbanized counties (those within MSAs) as the unit of analysis for capacity expansions on non-interstate highways, expressways and major arterials managed by public agencies. The calculator also uses a similar VMT elasticity for those facilities (0.75) as Cervero and Hansen (2002) estimated for state-owned roadways (0.79), and within the range Duranton and Turner (2011) found for non-interstate “major roads” in the urbanized areas of MSAs (0.67 to 0.89). The calculator’s 0.75 elasticity is rounded down from the estimates of Cervero and Hansen (2002) and Duranton and Turner (2011), in part to account for the (small) potential substitution effect discussed in Duranton and Turner (2011). The 0.75 elasticity is also similar to the longer-term elasticities calculated in other recent studies for combined major road types (not just interstate highways) in the U.S. (Hymel, 2019; Handy & Boarnet, 2014a; Graham et al., 2014; Melo et al., 2012). See Volker et al. (2020) for further discussion of the induced travel literature and the analysis of induced travel in the environmental review process.

References

California Legislative Analyst’s Office. (2000). HOV Lanes in California: Are They Achieving Their Goals? Retrieved from: https://lao.ca.gov/2000/010700_hov/010700_hov_lanes.html.

Caltrans. (2020). Transportation Analysis Framework: Evaluating Transportation Impacts of State Highway System Projects. Retrieved from: https://dot.ca.gov/-/media/dot-media/programs/sustainability/documents/2020_09_10_1st_edition_taf_fnl_a11y.pdf

Caltrans. (2019). Express Lanes. Retrieved from: https://dot.ca.gov/programs/traffic-operations/express-lanes.

Caltrans. (2018). California Highway System. Retrieved from: https://caltrans.maps.arcgis.com/apps/webappviewer/index.html?id=026e830c914c495797c969a3e5668538.

Cervero, R., & Hansen. (2002). “Induced Travel Demand and Induced Road Investment: A Simultaneous Equation Analysis.” Journal of Transport Economics and Policy, 36(3), 469-490. Available at: https://www.ingentaconnect.com/content/lse/jtep/2002/00000036/00000003/art00005.

Deakin, E., et al. (2020). Induced Vehicle Miles of Travel Resulting from Highway Projects: Findings and Recommendations from an Expert Panel. Prepared for Caltrans. Available at: https://dot.ca.gov/-/media/dot-media/programs/transportation-planning/documents/sb-743/2020-09-21-sb-743-expert-panel-rpt-fnl-a11y.pdf

Downs, A. (1962). “The Law of Peak-Hour Expressway Congestion.” Traffic Quarterly, 16(3), 393-409. Available at: https://trid.trb.org/view/694596.

Duranton, G., & Turner, M. A. (2011). “The Fundamental Law of Road Congestions: Evidence from US Cities.” American Economic Review, 101(6), 2616-2652. doi: 10.1257/aer.101.6.2616. Available at: https://www.aeaweb.org/articles?id=10.1257/aer.101.6.2616.

Graham, D. J., McCoy, E. J, & Stephens, D. A. (2014). “Quantifying Causal Effects of Road Network Capacity Expansions on Traffic Volumes and Density via a Mixed Model Propensity Score Estimator.” Journal of the American Statistical Association, 109(508), 1440-1449. doi:10.1080/01621459.2014.956871. Available at: https://www.tandfonline.com/doi/abs/10.1080/01621459.2014.956871.

Handy, S., & Boarnet, M. G. (2014a). Impact of Highway Capacity and Induced Travel on Passenger Vehicle Use and Greenhouse Gas Emissions: Policy Brief. Prepared for the California Air Resources Board. Retrieved from: https://ww3.arb.ca.gov/cc/sb375/policies/hwycapacity/highway_capacity_brief.pdf.

Handy, S., & Boarnet, M. G. (2014b). Impact of Highway Capacity and Induced Travel on Passenger Vehicle Use and Greenhouse Gas Emissions: Technical Background Document. Prepared for the California Air Resources Board. Retrieved from: https://ww3.arb.ca.gov/cc/sb375/policies/hwycapacity/highway_capacity_bkgd.pdf.

Handy, S. (2015). Increasing Highway Capacity Unlikely to Relieve Traffic Congestion: Policy Brief. Prepared for the National Center for Sustainable Transportation. Retrieved from: https://escholarship.org/uc/item/58x8436d.

Hsu, W., & Zhang, H. (2014). “The Fundamental Law of Highway Congestion Revisited: Evidence from National Expressways in Japan.” Journal of Urban Economics, 81: 65-76. doi:10.1016/j.jue.2014.02.002. Available at: https://www.sciencedirect.com/science/article/pii/S0094119014000126?via%3Dihub.

Hymel, K. M. (2019). “If You Build It, They Will Drive: Measuring Induced Demand for Vehicle Travel in Urban Areas.” Transport Policy, 76: 57-66. doi:10.1016/j.tranpol.2018.12.006. Available at: https://www.sciencedirect.com/science/article/abs/pii/S0967070X18301720.

Melo, P. C., Graham, D. J., & Canavan, S. (2012). “Effects of Road Investments on Economic Output and Induced Travel Demand: Evidence from Urbanized Areas in the United States.” Transportation Research Record: Journal of the Transportation Research Board, 2297: 163-171. doi:10.3141/2297-20. Available at: https://journals.sagepub.com/doi/10.3141/2297-20.

Milam, R. T., Birnbaum, M., Ganson, C., Handy, S., & Walters, J. (2017). “Closing the Induced Vehicle Travel Gap between Research and Practice.” Transportation Research Record: Journal of the Transportation Research Board, 2653: 10-16. doi:10.3141/2653-02. Available at: https://journals.sagepub.com/doi/10.3141/2653-02.

Naess, P., Nicolaisen, M. S., & Strand, A. (2012). “Traffic Forecasts Ignoring Induced Demand: A Shaky Fundament for Cost-Benefit Analyses.” European Journal of Transport and Infrastructure Research, 12(3), 291-309. doi:10.18757/ejtir.2012.12.3.2967. Available at: https://pdfs.semanticscholar.org/9e33/14ac9d66c25b2a1db664dcc7747c13c5f3dc.pdf?_ga=2.187485864.1840910524.1573238132-456656367.1573238132.

Volker, J. M. B., Lee, A. E., & Handy, S. (2020). “Induced Vehicle Travel in the Environmental Review Process.” Transportation Research Record: Journal of the Transportation Research Board (in press). doi:10.1177/0361198120923365. Available at: https://sci-hub.tw/10.1177/0361198120923365

Questions?

For questions regarding the induced VMT calculator, please contact:
Jamey Volker (jvolker@ucdavis.edu), or
Susan Handy (slhandy@ucdavis.edu)

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