A Modelling Framework to Support Planning for Low-Carbon Urban Growth
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 the 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
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.
Coupled land-use change – transportation model
• Transportation plays a significant role in carbon dioxide (CO2) emissions (i.e. it accounts for approximately a third of the United States’ inventory).
• In order to reduce CO2 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, CO2 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
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 CO2 flux maps (e.g. monthly-averaged values);
• ACASA is a Soil-Vegetation-Atmosphere Transfer model able to incorporate CO2 exchange processes (both natural and anthopogenic) into the WRF meteorological model;
The WRF-ACASA Coupled Model
• Note that due to the nonlinearity of the involved processes, making the CO2 budget at the urban level is not easy;
– for example at midday fluxes in the summer months may be negative because vegetation sequesters CO2 from the atmosphere via photosynthesis at rates greater than those of emissions
– however this effect depends on the air CO2 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 CO2, effects of turbulence etc.) in order to provide with high detail the spatio-temporal data about CO2 net fluxes
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.
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;
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 (Zi, Zj) the number xij of trips;
• After the CA simulation, the entries xij 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;
source
destinationZ1 Z2 Z3
Z4 Z5 Z6
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 mfor each couple of zone (Zi, Zj)
a) a path is assigned to a fraction ij=xij/m of the trips on the basis of the minimum travel cost (accounting for the current traffic congestion);
b) 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 qk
(m) of the load is available for each link li.
for each link li
a) the current traffic load is updated as qk(k)= qk
(k-1)+ q(k)
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 lk a generalised cost expressed as:
[Dendrinos and Sonis, 1990]
link lenght
traffic load
link capacity
parameters
Dynamic traffic assignment: moderate congestibility
hypothetical land use which is source and destination of vehicular trips
Dynamic traffic assignment: high congestibility
Dynamic traffic assignment: land use change
new development
contraction
An in-Progress Application Example: Florence
Cellular Automata Input GIS layers
Terrain slopeLand cover
Accessibility Street network
Zoning/regulations Accessibility of industrial/commercial areas
accessibility of residential areas
Example of future land uses projections (Florence)
-20%
-15%
-10%
-5%
0%
5%
10%
15%
20%
25%
30%
0 2 4 6 8 10 12 14 16 18 20
Urban fabric
Industrial/Commercial
Example of WRF-ACASA output for Florence (Baseline scenario)