Urban Land Use and Transportation Center
Johnston, Robert A. and Caroline J. Rodier (1997) Consumer Welfare Effects and Other Impacts of Travel Demand Management Measures. Institute of Transportation Studies, University of California, Davis, Research Report UCD-ITS-RR-97-25
The California Department of Transportation's Direct Travel Impact Model 2 (DTIM2) and the California Air Resources Board's model EMFAC7F were used in the emissions analysis in this study. The outputs from the travel demand model used in the emissions analysis included the results of assignment for each trip purpose by each time period (A.M. peak, P.M. peak, and off-peak).
To estimate traveler net benefits, we applied the Kenneth Small and Harvey Rosen (1981) method for obtaining consumer welfare measures from discrete choice models to the SACMET 94 mode choice models. Our review of the published literature suggests that Small and Rosen's method has not yet been applied to regional travel demand models in the United States. We conducted an analysis of traveler net benefits rather than a full social welfare analysis, and thus capital costs, operation and maintenance costs, accident costs, and externalities of new projects are not included in our analysis.
As part of this report, we conducted a literature review on travel demand management measures was conducted. With respect to land use intensification policies, we were able to conclude, based on our review, that (1) jobs-housing balance or land-use mix does not seem to be very effective, unless it is part of a density policy and (2) density increases near to transit lines seems to be effective in reducing vehicle miles of travel, emissions, and energy use, especially in conjunction with travel pricing, not building more freeways, and major improvements to transit. With respect to pricing policies, we concluded that pricing is effective, except in very large urban areas with excellent transit service where pricing auto use at peak periods per se may not reduce vehicle miles traveled, because of pent-up demand for auto travel. We also found some studies that indicated that pricing polices may benefit all income groups.
Seventeen travel demand management scenarios were examined for this project. The scenarios included various combinations of transit, new HOV lanes, land use intensification, pricing policies, and automated freeways.
Based on our analysis, the following general conclusions can be drawn from this study:
- (1) Pricing policies, with and without transit and roadway capacity expansion, reduce travel delay and emissions and increase total consumer welfare.
- (2) Pricing policies may be combined with significantly expanded transit and roadway capacity to reduce travel delay and emissions and increase consumer welfare for all income classes.
- (3) Transit investment and supportive land use intensification provides comparatively modest reductions in travel delay and emissions and increases consumer welfare for all income classes.
- (4) Freeway automation significantly reduces travel delay; however, it increases emissions.
- (5) Freeway automation can increase total consumer welfare as long as gains in travel time savings resulting from reduced travel delay are greater than the full private automobile operating costs of additional travel; although, only the highest income groups may reap these gains.
The travel results indicate, generally, that vehicle trips, vehicle miles traveled, and drive alone mode share increase with expansion of roadway capacity. The addition of land use intensification centers to automation scenarios tended to mitigate this effect somewhat by increasing walk and bike trips. Capacity expansion, particularly automated freeways at 80 miles per hour (mph), were very effective in reducing vehicles hours of delay and levels of service E and F (high levels of congestion) on freeways. However, congestion in centers can reduce this benefit. Overall, the pricing policies (fuel tax, peak period tolls, and parking pricing) were effective in reducing vehicle trips, vehicle miles of travel, and vehicle hours of delay and in increasing shared ride, transit, walk, and bike mode shares. The combination of pricing policies and expanded single occupant vehicle roadway capacity tended to lessen this effect, whereas the combination of pricing policies and transit expansion tended to increase this effect. Transit and shared ride mode shares did not tend to increase significantly in the presence of expanded capacity for those modes without also employing pricing policies. Similarly, reductions in vehicle trips, vehicle miles of travel, and vehicle hours of delay were modest for the transit and high occupancy vehicle (HOV) lane expansion scenarios without pricing.
The emissions modeling results show that roadway capacity expansion projects tend to increase emissions over the no-build scenario: the more capacity the roadway projects added, the greater the increase in emissions. The automation scenarios had the highest increase in emissions. The addition of centers to automation scenarios tended to mitigate increases in emissions and, in the case of total organic gases, actually to reduce emissions over the no-build scenario. Pricing policies generally resulted in significant decreases in emissions over the no-build scenario. The super light rail with centers scenario and light rail scenario also tended to reduce emissions. Our review of Barth and Norbeck's work on emission correction factors for automated highway systems (AHS), indicated that AHS may or may not result in emission reductions per vehicle mile. A good case cannot be made either way. And thus, we did not factor emissions down in our automation scenarios.
The aggregate consumer welfare results suggest that pricing policies result in comparatively high consumer welfare benefits. When pricing is combined with additional transportation capacity, the highest welfare benefits were achieved. Additional transit capacity and supportive land use intensification without pricing policies also provided relatively large welfare benefits. The full automation scenarios (60 mph) with and without centers also produced consumer welfare benefits. It appears that the moderate time savings in these automation scenarios offset the additional automobile operating cost associated with driving somewhat farther. In contrast, the partial automation, automated HOV, full automation (80 mph), and HOV scenarios do not appear to generate enough time savings to offset the operating costs of the additional auto travel. Land use intensification centers for automation scenarios increased consumer benefits when travel volumes could be accommodated by the centers; however, when centers could not accommodate additional volumes because of automation, consumer benefits were reduced.
Thus, it appears that the pricing policy scenarios resulted in more efficient use of existing and added roadway capacity because perceived auto operating costs begin to approach the actual costs. When the perceived cost of travel does not match the actual cost, new roadway capacity induces additional auto travel, the full private cost of which exceeds the reductions in time costs resulting from the improvements.
The results of our equity analysis indicate that the economically efficient transportation pricing policies may be inequitable without compensatory spending or investment programs. For example, the pricing and no-build, pricing and light rail, and pricing and HOV lane scenarios all resulted in losses to the lowest income class. Capacity improvements are one way to offset losses because of these pricing policies. One example is the pricing and automated HOV lane scenario. In addition, automation scenarios that yield high total welfare benefits may result in losses (because of greater auto travel) to all but the highest income class. Transit investment policies with and without supportive land use intensification increased consumer welfare for all income groups.
A social welfare analysis that included capital, operation and maintenance, and external costs for each scenario would reduce the net benefits of the capacity-adding scenarios. Our future research will examine this issue. We will also incorporate land development models in our work, to capture the welfare effects of locational behavior resulting from changes in accessibility.