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Towards a Planning Decision Support System for Low-Carbon Urban Development
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Towards a Planning Decision Support System for Low-Carbon Urban Development

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Towards a Planning Decision Support System for Low-Carbon Urban Development …

Towards a Planning Decision Support System for Low-Carbon Urban Development
Ivan Blečić, Arnaldo Cecchini, Giuseppe A. Trunfio - Department of Architecture, Planning and Design, University of Sassari, Alghero
Serena Marras, Donatella Spano - Department of Economics and Woody Plant Ecosystems,University of Sassari
Matthias Falk, David R. Pyles - Land, Air and Water Resources, University of California

Published in Technology , Real Estate
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  • 1. A Modelling Framework to Support Planning for Low-Carbon Urban Growth
  • 2. Main objective
    • Developing a software framework able to estimate the carbon exchanges accounting for alternative scenarios which can influence urban development;
    • This reasearch is part of t he BRIDGE project (VII FP)
    • http://www.bridge-fp7.eu/
      • is a joint effort of 14 European Organizations aiming at incorporating sustainability aspects in urban planning processes, accounting for some well recognised relations between  urban metabolism  and  urban structure . 
      • BRIDGE  was launched in order to  assist urban planners to present and evaluate planning alternatives towards a sustainable city
  • 3. Overview
    • The modeling system is based on four main components:
      • ( i ) a Constrained Cellular Automata model for the simulation of the urban land-use dynamics;
      • ( ii ) a transportation model , able to estimate the variation of the transportation network load and
      • ( iii ) the ACASA (Advanced Canopy-Atmosphere-Soil Algorithm) model tightly coupled with the
      • ( iv ) mesoscale weather model  WRF for the estimation of the relevant urban metabolism components.
  • 4. Coupled land-use change – transportation model
    • Transportation plays a significant role in carbon dioxide (CO 2 ) emissions (i.e. it accounts for approximately a third of the United States’ inventory).
    • In order to reduce CO 2 emissions in the future, transportation policy makers are looking to make vehicles more efficient and increasing the use of carbon-neutral alternative fuels.
    • In addition, CO 2 emissions can be lowered through the reduction of traffic congestion.
    Spatial dynamic model for simulating future urban development + Transportation model for estimating the traffic load related to the future land uses scenarios Spatialized emission inventory
  • 5. The WRF-ACASA Coupled Model
    • In the proposed framework, the data about land use change and traffic congestion are used by the couple WRF-ACASA to produce CO 2 flux maps (e.g. monthly-averaged values) ;
    • ACASA is a Soil-Vegetation-Atmosphere Transfer model able to incorporate CO 2 exchange processes ( both natural and anthopogenic ) into the WRF meteorological model;
  • 6. The WRF-ACASA Coupled Model
    • Note that due to the nonlinearity of the involved processes, making the CO 2 budget at the urban level is not easy;
      • for example at midday fluxes in the summer months may be negative because vegetation sequesters CO 2 from the atmosphere via photosynthesis at rates greater than those of emissions
      • however this effect depends on the air CO 2 concentration in the vegetated areas under consideration which depend on the wind conditions, turbulence, position and intensity of CO2 antropogenic sources etc;
    • ACASA and WRF model all the relevant processes (e.g. vegetation emission and uptake of CO 2 , effects of turbulence etc.) in order to provide with high detail the spatio-temporal data about CO 2 net fluxes
  • 7. Constrained CA simulation of land use change neighbourhood effect (interactions between urban functions) Transition potentials Land use at time t Land use at time t+1 Land use requests in the area Planning regulation, Accessibility, suitability, etc.
  • 8. The Transportation Model
    • Provides an estimate of the vehicular traffic load related to the future land uses scenarios
      • it is a dynamic formulation of the well-known gravity model of trips distribution
      • It is based on an origin-destination (OD) trip matrix , expressing the spatial distribution of trip demand;
  • 9. The OD Matrix
    • The entire urban area is partitioned in zones including sorces and destinations of vehicular trips;
    • The OD matrix provides for each couple of zones (Z i , Z j ) the number x ij of trips;
    • After the CA simulation, the entries x ij of the OD matrix are computed on the basis of the mix of land uses in both the source and destination zones (i.e. the ones modelled in the CA model) considered as trip sources and attractors;
    Z 1 Z 2 Z 3 Z 4 Z 5 Z 6 source destination
  • 10. Dynamic assignment of vehicular trips
    • A dynamic assignment of the path of each trip is carried out using an m -steps simulation procedure ;
    for k = 1 to m for each couple of zone (Z i , Z j )
      • a path is assigned to a fraction  ij = x ij /m of the trips on the basis of the minimum travel cost (accounting for the current traffic congestion) ;
      • the value  ij is added to the current traffic load increment  q (k) of each link included in the path ;
    • At the end of the m steps, the total amount of trips are allocated and the final estimate q k (m) of the load is available for each link l i .
    for each link l i
      • the current traffic load is updated as q k (k) = q k (k-1) +  q (k)
  • 11. Dynamic assignment of vehicular trips
    • The network is considered as congestible (i.e. every traveller imposes a certain amount of delay on every other traveller);
    • At the end of the r-th step, to each link l k a generalised cost expressed as:
    [Dendrinos and Sonis, 1990] link lenght traffic load link capacity parameters
  • 12. Dynamic traffic assignment: moderate congestibility hypothetical land use which is source and destination of vehicular trips
  • 13. Dynamic traffic assignment: h igh congestibility
  • 14. Dynamic traffic assignment: land use change new development contraction
  • 15. An in-Progress Application Example: Florence
  • 16. Cellular Automata Input GIS layers Terrain slope Land cover Accessibility Street network Zoning/regulations Accessibility of industrial/commercial areas accessibility of residential areas
  • 17. Example of future land uses projections (Florence)
  • 18. Example of WRF-ACASA output for Florence (Baseline scenario)