ISTEA requires consistent land use and transportation plans for metropolitan regions and the evaluation of economic efficiency for projects and plans. Policies being examined for reducing travel demand include land use policies. Major capacity increases with automated highway systems are also being examined and such systems will have major effects on land use patterns. The Clean Air Act regulations require the proper simulation of latent demand in travel modeling for conformity analysis. Current models in use by MPOs in the U.S. cannot represent the interactions of land use and transport systems in an economically rigorous way and cannot project changes in economic efficiency (locational and traveler surplus). Regional travel demand models in use in the U.S. can represent latent demand as it affects trip distribution, but cannot represent latent demand for auto ownership and for trip generation. The statewide Intermodal Transportation Management System models to be used in California do not represent latent demand at all. Integrated urban models do exist that can perform all of these functions, for regions and for states. We describe a modeling project that applies such a model to an urban region in California and also makes use of GIS to project environmental impacts of scenarios.

The focus of this project was to examine the potential travel, emissions, and consumer benefits of advanced freeway automation and travel demand management measures. The Sacramento Regional Travel Demand model (SACMET 95) was used to simulate the travel effects of travel demand management measures in the Sacramento region for a twenty year time horizon. The scenarios examined included various combinations of automated freeways, new High Occupancy Vehicle (HOV) lanes, transit, land use intensification, and pricing policies.

U.S. law requires consistent land use and transportation plans for metropolitan regions and the evaluation of economic efficiency for projects and plans. Policies being examined for reducing travel demand include land use policies. The Clean Air Act regulations require the proper simulation of latent demand in travel modeling for conformity analysis. Current models in use by regional agencies in the U.S. cannot represent the interactions of land use and transport systems in an economically rigorous way and cannot project changes in economic efficiency (locational and traveler surplus). Regional travel demand models in use in the U.S. can represent latent demand as it affects trip distribution, but cannot represent latent demand for auto ownership and for trip generation. Integrated urban models do exist that can perform all of these functions, for regions and for states. We describe a modeling project that applies such a model to an urban region in California and also makes use of GIS to project environmental impacts of scenarios. This set of models may be of use for regional and national planning in Slovakia.

With the enactment of a new federal transportation law in 2005, State and regional transportation plans and programs are for the first time required to achieve the objectives of the SAFETEA-LU planning process, which focus on enhancing mobility and supporting economic development, while minimizing conventional emissions and greenhouse gas emissions. In 2007, the U.S. Supreme Court held that greenhouse gases are a pollutant and so are covered by the Clean Air Act and, consequently, the USEPA can regulate them. California and 13 other states are now attempting to regulate the emissions of greenhouse gases from vehicles.

The results from over 40 long-range regional scenario exercises performed in the U.S. and Europe demonstrate that substantial reductions in vehicle-miles of travel (VMT), fuel use, and emissions of both criteria pollutants and greenhouse gases are possible using transportation pricing policies and investment priorities that have been demonstrated as acceptable and effective in a modest but growing number of metropolitan areas and regions around the world.

There is an ongoing debate worldwide about the indicators that policy makers should use to evaluate progress toward sustainable transportation systems. In general, studies of indicators lack an overall normative framework that allows decision makers or the public to make sense of the many overlapping and partial measures. A statewide urban growth model for California was run iteratively with the California statewide travel model to evaluate major transportation scenarios, such as freeway widening and high-speed rail. In addition, transportation and land use policies intended to provide more affordable housing accessible to jobs, widespread habitat protection, and strong reductions in greenhouse gas emissions were evaluated. This model provides many performance measures for travel, economic welfare and equity, rents paid, energy use, greenhouse gas emissions, vehicular air pollution, and habitat loss. A framework for interpreting these data on the basis of recent advances in the theories of well-being for individuals and nations is proposed. This theoretical framework for evaluating the model outputs used in planning also applies to the analysis of the empirical indicators used to track actual outcomes.

The purpose of this study was to demonstrate the travel and emissions impacts of urban freeway automation scenarios and to compare these to travel demand reduction scenarios, such as travel pricing and land use intensification. The Sacramento regional travel demand model set was used and an alternatives analysis was conducted. Two protocols are used to operate the model set. Results are discussed, comparing the alternative scenarios, and methodological findings are discussed, comparing the results from the two protocols for operating the model set. Methodological findings present new ideas of interest to modelers regarding the differential effects of congestion on the various alternatives.

Many urban regions in the U.S. are planning to build extensive networks of new high-occupancy vehicle (HOV) freeway lanes. Past modeling efforts are reviewed and travel demand simulations by the authors are used to demonstrate that new HOV lanes may increase travel (vehicle-miles) and increase emissions when compared to transit alternative. Recommendations are made for better travel demand modeling methods for such evaluations.

The automation of urban freeways is a major feature of the new surface transportation act. Reducing freeway congestion can be presumed to have drawbacks, however, in terms of induced travel and possibly greater travel costs and higher emissions. We test several freeway-automation scenarios for effects on travel demand using a regional four-step model set. A partial-automation scenario has the lowest congestion, but higher vehicle miles than conventional scenarios (transit expansion, new high-occupancy vehicle (HOV) lanes, doing nothing). We projected emissions with the official California models and found that the conventional scenarios produced lower emissions for most pollutants. Using our own traveler-cost model, we found the same general rankings, with all the automation alternatives more costly than transit, HOV lanes, and doing nothing. These results should be seen as tentative and be used to guide further research. Improved automation scenarios and more accurate methods of projecting travel demand, emissions, and costs are outlined.

In this paper, we simulate the effects of policies intended to reduce auto travel, emissions, and fuel use. We review previous studies of auto pricing and land use policies, both the empirical research and simulation studies. We then describe our methods for modeling the Sacramento, California, region, which include the use of a four-step travel demand model and the California emissions models. We find that: 1o land use intensification near to passenger rail stations, 2. strong parking, fuel, and all-day roadway pricing policies, and 3. rail transit expansion together may be able to reduce travel by 10%, fuel use by i4%, and emissions by 8-14% in 20 years. We interpret our findings in terms of the earlier studies and discuss the limitations of our models. Finally, we suggest improvements in modeling to better simulate the effects of these policies.