Strategic Options for Integrating Transportation Innovations and Urban Revitalization (SOTUR) Stakeholder Scenario Building: Imagining Urban Futures Simulation of future scenarios and policies affecting urban regeneration: The Lisbon Case Eng. L. Martínez Prof. C. Zegras

Contents Modeling Background Agent-based Model Approach UrbanSim Model Approach “Discovering” Scenarios via application of multiple model runs Achievements and Limitations

Scenario narratives (3 scenarios) Simulation model Evaluation 100 scenarios generated to span the space of possible futures

Explore the range of interactions between the driving forces affecting urban regeneration and the local impacts Capture the effects of policy options under three different scenarios for 2021 (Lisbon Metro) Aim of simulation

1.A purpose-built, rule-based model (aka ABM) – AnyLogic, commercial agent-based software platform – Behavioral model for agents at household and firm level – Developed specifically for the Lisbon Metropolitan Area 2.OPUS/UrbanSim model – Open-source architecture for microsimulation of land-use and transportation – Behavioral model for agents at household and firm level – Tested in many metropolitan areas throughout the world for operational use – Implemented in Lisbon Metropolitan Area Two modeling tools

Main differences between ABM and UrbanSim  ABM  Greater refinement in the household and firms decisions and lifecycle  Rule based lifecycle approach  Internal household changes: leave home, have children, marriages, etc  Non commuting activities  UrbanSim  Greater degree of spatial resolution  Attempting to represent all the agents of the study area  Larger number of spatial units (BGRIs versus zones)  Each has its strengths and weaknesses  And share common elements  Potential value to be gained from comparing results across the two

Socio-economic Control Total Demographic Control Total INPUTS Transportation Plan Land use Plan Land Development Policy Land Use Pattern New Development Land Price OUTPUT Macroeconomic Model Travel Data and Accessibility Travel Demand Model Policy Economic Demographic Transition Model Real Estate Development Model Location Choice Model Vacant Real Estate Relocation Model Land Price Model Relocated Jobs and Households New Jobs & Households Model Data

Translating scenario narratives into the models Population grows (1% yoy) Low-income group grows fastest (3% yoy) Employment declines by 2% yoy Construction cost: 650 €/m2 Demolition cost: 450 € /m2 Preference for newer apartments (coefficient for dwelling age is twice base value) Population grows (1% yoy) High-income group grows fastest (3% yoy) Employment grows (0.2% yoy) Employment in services grows fastest (2% yoy ) Jobs in the service sector have a relocation probability of 20% (10% for others) New airport as a generator of employment New bridge and rail (in Model 1 only) Construction cost: 510 €/m 2 Demolition cost: 200 € /m 2 Population declines (2% yoy) Employment declines (2% yoy) Construction cost: 300 €/m 2 Demolition cost: 200 € /m 2 New travel time becomes 70% of its base value Scenario 2: Nova DinâmicaScenario 1: Crise Social Scenario 3: Desenvolvimento através da Tecnologia

ABM Application Main Results to Date

Selected policy options P1: Congestion pricing on the entrance to Lisbon municipality (2.5 euros per day)

Scenario 1: Crise Social Without any policy Congestion pricing on the entrance to Lisbon municipality Jobs  population in center –  High and mid-income groups –  Low-income hhlds – Average income in center  No effect on job location  vacancy rate in center No new constructions  average dwelling price in center Overall decrease of private car use (-16%) Households

Scenario 2: Nova Dinâmica Without any policy Congestion pricing on the entrance to Lisbon municipality  population in center –  High and mid-income groups –  Low-income hhlds – Average income in center  jobs in center stagnate and increase more outside Lisbon  vacancy rate in center No new constructions  average dwelling price in center Overall decrease of private car use (-12%) Households Jobs

UrbanSim Application Main Results to Date

(Accessibility) Scenario 3 - (Accessibility) Scenario 2

Selected policy options P1: 20% housing subsidy in city center for entire population P2: Subsidy for renovation in the Lisbon area Costs of construction and demolition decrease by 30% from specified value in each scenario P3: Subsidy for the construction of small (high-rise) units Construction cost for small units in high-rise buildings decreases by 30% P4: 20% housing subsidy in city center for low-income groups

Scenario 1: Social crisis P1: Housing subsidy in city center for entire population  population in center – Highest  is in high-income group – Low-income hhlds  slightly – Average income in center   jobs in center – Highest  is in service jobs  vacancy rate in center No new constructions  Average dwelling price in center Minor  average accessibility in center and minor  outside P4: Housing subsidy in city center for low-income group P2: Reduction in cost of renovation in the Lisbon area P3: Subsidy for the construction of small (high-rise) units  population in center but not as much as with P1 –  High and mid-income groups –  Low-income hhlds – Average income in center   jobs in center but not as much as with P1  vacancy rate in center No new constructions Minor  average dwelling price in center Minor  average accessibility in center No policy effect

P1: Housing subsidy in city center for entire population  population in center – Highest  is in high-income group – Low-income hhlds also  – Average income in center  Minor  jobs in center  vacancy rate in center No new constructions  Average dwelling price in center Minor  average accessibility in center and minor  outside P4: Housing subsidy in city center for low-income group P2: Reduction in cost of renovation in the Lisbon area P3: Subsidy for the construction of small (high-rise) units  population in center but not as much as with P1 –  High and mid-income groups –  Low-income hhlds – Average income in center   jobs in center but not as much as with P1  vacancy rate in center No new constructions Minor  average dwelling price in center Minor  average accessibility in center No policy effect Scenario 2: Growth

(Number of low-income hhlds) before P4 - (Number of low-income hhlds) after P4 Number of households increases Small change in household number Number of households decreases

P1: Housing subsidy in city center for entire population Doubles population in center – Highest  is in high-income group – Low-income hhlds also  – Average income in center  Minor  jobs in center Large  vacancy rate in center No new constructions  Average dwelling price in center Minor  average accessibility in center and minor  outside P4: Housing subsidy in city center for low-income group P2: Reduction in cost of renovation in the Lisbon area P3: Subsidy for the construction of small (high-rise) units  population in center but not as much as with P1 –  High and mid-income groups –  Low-income hhlds – Average income in center   jobs in center but not as much as with P1  vacancy rate in center No new constructions Minor  average dwelling price in center Minor  average accessibility in center No policy effect Scenario 3: Will technology save us?

(Number of households) before P1 - (Number of households) after P1 Number of households increases Small change in household number Number of households decreases

IndicatorAreaSc1 P1Sc2P1Sc3P1Sc 1P4Sc 2P4Sc 3P4 Population City center Outside center Jobs City center Outside center Households City center Outside center -30 High income households City center Outside center Medium income households City center Outside center Low income households City center Outside center00-2 Average income City center Outside center Dwelling units City center Outside center Vacancy rate City center Outside center Dwelling price (average) City center Outside center Accessibility City center Outside center Policy effect as a % of scenario value

Scenario narratives (3 scenarios) Simulation model Evaluation 100 scenarios generated to span the space of possible futures Scenario discovery

Are these 3 scenarios bounding? Is there another method to guide choice of urban strategies? For example, can we use the models to assess the possible conditions that would cause a subsidy policy to not achieve its goals? 100 scenarios generated to span the space of possible futures Scenarios simulated in UrbanSim Data-mining used to find the thresholds of failure

Demonstration of concept: the Lisbon UrbanSim application Goal: Increase economic diversity in Lisbon City Center Strategy: 20% economic subsidy for low-income households System inputs, (X): – Population – Employment – Construction costs – Gas tax/subsidy – Output (Y): Increase in low-income households over business-as-usual Discovery of failure scenarios

Scenario narratives (3 scenarios) Simulation model Evaluation 100 scenarios generated to span the space of possible futures

Model output will be aggregated into a set of proposed indicators reflecting sustainable urban regeneration objectives

Scenario – Policy Economic vitality Equity of opportunities Income diversity in center ………. Access to jobsTHEIL index (lower values preferred) HHI Index (lower values preferred) …….... Sc1-P Sc1-P Sc1-P Sc1-P Sc2-P Sc2-P Sc2-P Sc2-P Sc3-P Sc3-P Sc3-P Sc3-P Selected indicators Best income diversity in each scenario is achieved when high-income group migrates to center  but increases inequity! Growth scenario is most inequitable in terms of accessibility

Data Model implementation in the Lisbon Area Model specification Achievements and limitations Achievements Limitations Can be improved with better data and collaboration Model evolution Importance of comparing different models Importance of linking scenario inputs with integrated modeling Explore interrelations between location choices, prices, and travel demand