No abstract

Automated vehicles (AVs) may enter the consumer market with various stages of automation in 10 years or even sooner. Meanwhile, regional planning agencies are envisioning plans for time horizons out to 2040 and beyond. To help decision makers understand the effect of AV technology on regional plans, modeling tools should anticipate its impact on transportation networks and traveler choices. This research uses the Seattle, Washington, region's activity-based travel model to test a range of travel behavior impacts from AV technology development. The existing model was not originally designed with AVs in mind, so some modifications to the model assumptions are described in areas of roadway capacity, user values of time, and parking costs. Larger structural model changes were not yet considered. Results of four scenario tests show that improvements in roadway capacity and in the quality of the driving trip may lead to large increases in vehicle miles traveled, while a shift to per mile usage charges may counteract that trend. Travel models will need to have major improvements in the coming years, especially with regard to shared ride, taxi modes, and the effect of multitasking opportunities, to better anticipate the arrival of this technology.

A methodology was developed to interpret and assess land cover change between 1991 and 1999 in Central Puget Sound, Washington at several scales (landscape, sub-basins, and 90 m grid window) relevant to regional and local decision makers. Land cover data are derived from USGS Landsat (Thematic Mapper and Enhanced Thematic Mapper ϩ) images of Central Puget Sound. Landsat data were registered, intercalibrated, and corrected for atmosphere and topography to ensure accuracy of land cover change assessment. We apply a hybrid classification method to each image to address the spectral heterogeneity of urbanizing regions. The method combines a supervised classification approach with a spectral unmixing approach to produce seven classes: Ͼ75 percent impervious, 15 to 75 percent impervious, forest, grass, clear cut, bare soil, and water. Land cover change is identified using the direct spatial comparison of classified images derived independently for each time period. We assess that the overall accuracy of each classified image was 91 percent for 1991 and 88 percent for 1999 respectively, which produces an accuracy of 85 percent for the change analysis. Our results show that urban growth over the last decade has produced an overall 6.7 percent increase in paved area.

Urbanization and the resulting changes in land cover have myriad impacts on ecological systems. Monitoring these changes across large spatial extents and long time spans requires synoptic remotely sensed data with an appropriate temporal sequence. We developed a multi-temporal land cover dataset for a six-county area surrounding the Seattle, Washington State, USA, metropolitan region. Land cover maps for 1986, 1991, 1995, 1999, and 2002 were developed from Landsat TM images through a combination of spectral unmixing, image segmentation, multi-season imagery, and supervised classification approaches to differentiate an initial nine land cover classes. We then used ancillary GIS layers and temporal information to define trajectories of land cover change through multiple updating and backdating rules and refined our land cover classification for each date into 14 classes. We compared the accuracy of the initial approach with the landscape trajectory modifications and determined that the use of landscape trajectory rules increased our ability to differentiate several classes including bare soil (separated into cleared for development, agriculture, and clearcut forest) and three intensities of urban. Using the temporal dataset, we found that between 1986 and 2002, urban land cover increased from 8 to 18% of our study area, while lowland deciduous and mixed forests decreased from 21 to 14%, and grass and agriculture decreased from 11 to 8%. systems that are present in this region were likely significantly altered by these changes in land cover. Our results suggest that multi-temporal (i.e., multiple years and multiple seasons within years) Landsat data are an economical means to quantify land cover and land cover change across large and highly heterogeneous urbanizing landscapes. Our data, and similar temporal land cover change products, have been used in ecological modeling of past, present, and likely future changes in ecological systems (e.g., avian biodiversity, water quality). Such data are important inputs for ecological modelers, policy makers, and urban planners to manage and plan for future landscape change.