Models

Baltimore-Washington Region

Maryland Statewide Transportation Model (MSTM)

The MSTM is an advanced trip-based model developed in 2008 by the Maryland State High-way Administration in conjunction with the Parsons Brinkerhoff. It was de-signed to estimate the impacts of transportation investments, changes to land use develop-ment, and impacts from factors beyond state boundaries, particularly freight.

The model Input data include population and employment by model zones, highway and public transit networks, and data on travel behavior. The model outputs report traffic impacts on the overall system, corridors or individual links.

Simple Integrated Land Use Orchestrator (SILO)

Initially developed as a research project by Parsons Brinckerhoff for Minneapolis/St. Paul, SILO has been implemented for the state of Maryland. It micro-simulates household relocation, demographic changes and developers who add, upgrade or demolish dwellings. SILO is designed as a discrete choice model. Thus, every household, person and dwelling is treated as an individual object. Spatial decisions, such as relocation, development of new dwellings, etc., are modeled with Logit models. Other decisions, such as getting married, giving birth to a child, etc., are modeled by Markov models that apply transition probabilities.

SILO uses the Public Use Microdata Sample (PUMS) to create individual households and their dwellings. The MSTM provides the zone-to-zone travel time by auto and public transit. SILO generates a synthetic population with households, persons, dwellings and jobs for the base year 2000 and incrementally updates these dataset in one-year increments through 2040. Every year the MSTM runs, SILO provides updated socio-demographic data.

Mobile Emissions Model (MEM)

The MEM estimates transportation emissions by applying emission of the MOVES 2010 EPA model to MSTM-generated traffic flows (Welch, 2013). The MEM input data includes road network, vehicle trips, temperatures by month and hour for each county in the study area, humidity, average speed distribution, the vehicle miles of travel (VMT) on varied road types, fuel formulation and supply. MEM runs every time the MSTM has run.

Building Energy Consumption & Emissions Model (BEM)

The BEM estimates CO2 emissions and energy consumption from the built environment with-in Maryland. It uses the building, location and climate variables of each proper-ty to determine whether the structure is likely to combust fossil fuels on site. If the probability is greater than 50%, then the model calculates CO2 emissions from local combustion based on a set of related multipliers derived from a regression of the data from US Energy Information Administration’s residential (RECS) and commercial building (CBECS) energy consump-tion surveys. SILO provides the building stock for BEM. CO2 emissions and energy consumption are the primary outputs of the model.

Chesapeake Bay Land Change Model (CBLCM)

The CBLCM was developed by the USGS within the Chesapeake Bay Program. It uses a sto-chastic methodology to emulate residential urban land use development in Maryland over a se-ries of pre-defined time segments. It is as an independent cellular automata model that trans-lates exogenous county-level projections of population and employment to estimates of urban land demand and then spatially allocates that onto 30m-resolution raster cells. The locations of future growth are informed by data on protected lands, zoning, slopes, land cover, proximity to urban centers, and proximity to locations of recent job and housing growth. The model calculates a probability surface for growth locations, and allocates households and dwellings provided by SILO. CBLCM generates fine-grained patterns of residential urban growth across the study area.

 

Greater Dublin Region

MOLAND

The MOLAND land use model has been developed by the Research Institute for Knowledge Systems as part of an initiative of the EC Joint Research Centre. It generates alternative future scenarios informing urban planners and policy makers on the possible implications of their decisions in terms of land use change.

The MOLAND model is based on the GEONAMICA framework; and stands out from other urban models because of its capability of simulating 32 land uses, which adopt the CORINE land cover classification system. It comprises two dynamic sub-models with a common temporal increment of one year but working at different scales. At the macro scale, the model allocates regional population and jobs among the sub-regions. At micro scale, the provision for population and jobs is translated into demand for various land types using constrained cellular automaton (CA), which for the GDR consists of (i) a land use raster grid with 200m cell size and 23 classes, (ii) a set of factors influencing the direction of land use change such as suitability, zoning, accessibility, and neighbouring land uses, and (iii) transition rules determining the attraction and repulsion between land uses. The model also includes a stochastic parameter which insures the generation of realistic land use patterns.

The Source Load Apportionment Model (SLAM)

There are two broad approaches to load apportionment modelling, (i) load-orientated approaches which apportion origin based on measured in-stream loads, and (ii) source-orientated approaches where amounts of diffuse emissions are calculated using models typically based on export coefficients from catchments with similar characteristics. The SLAM framework developed at UCD Doogle Centre for Water Resources Research takes the latter approach which enables estimates of the relative contribution of sources of nitrogen (N) and phosphorus (P) to surface waters in catchments without in-stream monitoring data. In contrast to models such as SWAT, this approach allows the model to be applied throughout Ireland, independently of the availability of measured in-stream calibration data. It uses the best available Irish data and models to quantify annual nutrient losses from both point discharges from urban wastewater, industry and septic tank systems, and diffuse sources including various land uses.

The SLAM framework incorporates multiple national spatial datasets relating to nutrient emissions to surface water, including land use and physical characteristics of the sub-catchments.  The agriculture (pasture & arable) and septic tank systems modules use spatial outputs from the Catchment Characterisation Tool (CCT) and SANICOSE models, respectively. The diffuse nutrient emissions from forestry, peatlands and urban areas were modelled based on export coefficients from the land cover classes, calculated as: Diffuse=Area × Export, where Area is the area of the land cover category (ha) and ?????? is the export coefficient for nitrogen or phosphorus (kg ha-1 yr-1).

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