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)