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.

Many urban regions in the U.S. are planning to build extensive networks of new high-occupancy 3ehicle (HOV) freeway lanes. Past modelling 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 alternatives. Recommendations are made for better travel demand modelling methods for such evaluations.

Many regional agencies model travel demand without feeding assigned travel times back to the trip distribution step. This method saves time and money but is likely to be biased in favor of build alternatives because it would underproject trip lengthening induced by the added capacity. Emissions and travel costs for the new roadway projects would be consequently underprojected. We wanted to compare outcomes under the two simulation methods. Our methods of modeling travel demand are outlined and then the results are presented and discussed. With full feedback, building new freeway carpool lanes appears less favorable than doing nothing or than expanding light-rail transit, in terms of induced travel. Light-rail-system expansion has lower emissions than does building new carpool lanes, with partial feedback, and full feedback increases these differences substantially. Better modeling methods, to be used in extensions of this research, are outlined.