Brian J. Morton Center for Urban and Regional Studies University of North Carolina - Chapel Hill June 8, 2010
1. TRANUS Highway vehicle technology Trips Mode choice Link-level traffic volumes and average speeds 2. Emission factors model Locations of employment and residences Emissions from highway vehicles, rail transit vehicles, and power plants Assess scenarios using integrated land usetransportation model 3. CMAQ Built environment Residential locational choice models Design future scenarios Ozone concentrations and population exposures Travel mode choice models Input-output model of Mecklenburg County Translate demand models and county s economic structure into model parameters Populate model s databases with baseline land use, employment, households, and transportation supply Calibrate land and travel demand parameters 2
Construction Manufacturing Wholesale Trade Retail TWU Information FIRE etc. Professional etc. Educational Services etc. Arts, Entertain, etc. Other Services Public Admin. High-Income HHs Medium-Income HHs Low-Income HHs Commercial Land Other Business Sector Land Residential Land Central Business District Downtown Mixed Use Neighborhood Mixed Use Low-Intensity Mixed Use Single-Family Residential Large-Lot Residential Forest/Limited Development Large-Lot Industrial/ Undeveloped 3
Construction Manufacturing Wholesale Trade Retail Trade TWU Information FIRE etc. Professional etc. Educational Services etc. Arts, Entertain, etc. Households (by income) I-I HBW trips Other Services Public Admin. External HBW trips are specified in an exogenous trip matrix 4
Construction Manufacturing Wholesale Trade Retail Trade TWU Information FIRE etc. Professional etc. Educational Services etc. Arts, Entertain, etc. Households (by income) I-I HBO trips Other Services Public Admin. NHB trips are specified in an exogenous trip matrix 5
34 raw variables characterize built environment in census block groups Development, employment, population, housing, open space, tree canopy, floodplain Roads, intersections, bus stops Distances to supermarket, gas station, park, school Parcel value, ratio of building value to parcel value 5 factors: walkability, local/regional accessibility, property values, agglomeration (mixture of economic sectors), and industrial areas 8 neighborhood types 6
Neighborhood Types CBD Downtown Mixed Use Neighborhood Mixed Use Low-Intensity Mixed Use Single Family Residential Large-Lot Residential Forest/Limited Development Large-Lot Industrial/Undeveloped
Construction Manufacturing Wholesale Trade Retail Trade TWU Other Business Sector Land Information FIRE etc. Professional etc. Educational Services etc. Commercial Land Arts, Entertain, etc. Other Services Public Admin. Households Neighborhood Types (TRANUS attractors) Residential Land 8
Trip categories Home-based work H0me-based other Non-home based Modes Walk SOV Carpool Local bus and express bus Light rail, commuter rail, and bus rapid transit Next step: expressing the influence of neighborhood type on mode choice 9
< 9,500 links 21 link types 10
A link type may have mode-specific penalties Penalties amplify disutility of travel time. Penalties are greatest for SOV and carpool, and least for walk. This type of connector is walkfriendly (and transit supportive). 11
Unique zone (black color) Intense mix of office, retail, and entertainment High local/regional accessibility via all modes Bus transit center Walk friendly 12
Yellow zones Relatively small parcels Mixed land uses Downtown areas and older neighborhoods, including streetcar suburbs Walk friendly 13
Magenta zones Residential development Forested Inexpensive land Limited local/regional accessibility Not walk friendly 14
Many walk-friendly areas in and south of historical CBD 15
Far fewer walk-friendly areas in southern Mecklenburg 16
Land Use & Scenarios Brian Eun Joo Cho Tracy Hadden-Loh Daniel Rodriguez Shaopeng Zhong Transportation Elizabeth Shay Asad Khattak Brian and Daniel * Includes students and former students Neighborhoods Yan Song Bev Wilson Transportation Validation Asad Xin Wang Brian and Daniel Vehicular Emissions Chris Frey Nagui Rouphail Haibo Zhai 17
EPA STAR Grant R831835 (Bryan Bloomer) NCSU s ITRE & UNC for matching funds Joe Huegy, Mei Ingram, & Bing Mei at ITRE for travel behavior survey Twyla McDermott, Anna Gallup, & others with City of Charlotte & Mecklenburg County for data Vicki Bott (UNC-C) & Rebecca Yarbrough (CCOG) for help with neighborhood types TRANUS (Tomás de la Barra & Juancarlo Añez) Lincoln Institute for Land Policy research fellowship