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Public Seminar by Alex Anas (Professor of Economics, State University of New York at Buffalo) 18.02.2013, NES
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What do Chicago, Paris and Los Angeles have in common?
Alex Anas
Professor of Economics
State University of New York at Buffalo
Public Lecture
New Economic School
Moscow, Russia
February 18, 2013
• Chicago, Paris, L.A.
have developed differently
because of history and initial
conditions
• But they are shaped by the
same economic processes
• The same economic model can be
used to study all three places
• A Computable General Equilibrium model
based on theory and econometrics can be
applied to any metropolitan area
• To use these models, we just have to
recognize that the different areas are
shaped by the same processes
The RELU-TRAN Model (Regional Economy Land Use
and Transportation)
• There is a working version for the Chicago, MSA and the Greater Paris Region
• There is a current project underway that will
apply the model to the Greater Los Angeles Region.
RELU RELU LOOPS CONVERGED
RELU TRIPS
TRAN TRAN ITERATIONS CONVERGED
STARTING POINT
p, w, R,V,S G, g
Update
G and g
for next
cycle
RELU-TRAN CYCLE
Cyclical linking of the RELU and TRAN
algorithms in RELU-TRAN
G and g converged?
p, w, R, V converged?
Excess demands, profits
converged?
YES
RELU-TRAN CYCLES
CONVERGED
PRICES, p
( w, R ) p
OUTPUTS, X
( p, w, R, S,V ) X
WAGES, w
( p, X, R,S,V )w
RENTS, R
(p, X, w, S,V) R
VALUES, V
RV
STOCKS, S
VS
START POINT
p, w, R, V, S, G, g
The RELU algorithm
RELU LOOP
p, w, R, V converged?
Excess demands converged?
Economic profits converged?
RELU loops converged
NO YES
Update
p, w, R, V
for next loop
RELU TRIPS
ZONE-TO-ZONE
EXPECTED TIMES & COSTS
G and g
ROUTE CHOICE & NETWORK
EQUILIBRIUM FLOW
The TRAN Algorithm
ITERATIONS CONVERGED
AUTO MODE CHOICE
PROBABILITIES
CONGESTED HIGHWAY LINK
TRAVEL TIMES
TRAN
Decisions
Workplace- residence
locations Voluntary
unemployment
• Labor supply / leisure
• Commuting mode choice / vehicle choice
• Housing (quantity / type)
• Vehicle ownership (quantity / type)
• Discretionary travel pattern to obtain goods and services
Where to go ? Where not to go ?
How many trips per period ?
How much to spend ?
Mode choice / vehicle choice on each trip
Decisions are hierarchically linked and involve discrete as well as continuous choices
All choices on left
apply except those
in red
Consumer
Enter labor market Stay out of labor market
Discrete choice of Working/not working
(i,j,k)
Discrete choice of triplet: i: residence zone j: workplace zone k: type of housing
Auto Transit
Discrete choice of mode for commuting
Continuous variables •Floor space of type k in residence zone I •Labor hours of work supplied to place of work at j • Number of non-work trips and their destinations and modes • Quantity of goods purchased on non-work trips
A mix of discrete and continuous choices
FIRMS
PRODUCTION FUNCTION OUTPUT
LABOR TYPES
BUILDING
TYPES
INTERMEDIATE INPUTS FROM OTHER INDUSTRIES
AGRICULTURE MANUFACTURING
BUSINESS SERVICES
RETAIL TRADE
CONSUMER
INTER-INDUSTRY STRUCTURE
Congestion and urban development
• Many issues can be studied using a model such as RELU-TRAN
• Today I will focus on one issue mainly:
• How does urban development respond to increases in traffic congestion in Chicago,
Paris, Los Angeles
Congestion’s effect
on urban development depends strongly on:
• 1. How much public transit is available
• 2. How spread out geographically are
the jobs in the urban arae
• 3. How decentralized trips are
1. Use of public transit varies
• Los Angeles 2%
•USA (average) 4.9%
•Chicago 13%
•Greater Paris 50%
2. Job concentration varies
• Greater Paris 50% in the core
(City of Paris & CDTs)
• Chicago 30% in 4 job centers
• Los Angeles 30% in 30 job centers
3. Trips are decentralized and getting more so
• Most travel occurs in the suburbs
• Suburban to suburban travel is the most rapidly increasing
• This effect is bigger when public transit is less available
United States Canada
Residence Workplace 2000 Census (%) 2001
Census(%)
Central
city
Central
city 27.5 46.1
Central
city Suburb 8.9 7.5
Suburb Central
city 20.2 16.2
Suburb Suburb 43.4 30.2
100.0 100.0
US & Canadian Commuting Patterns
Commute time increases
with city size in the US
Doubling population increases commute time by 10%
URBAN AREA WORKERS AVERAGE COMMUTE
LOUISVILLE 0.5 million 22.7 minutes
PITTSBURG 1.0 million 25.5 minutes
HOUSTON 2.0 million 28.8 minutes
CHICAGO 4.0 million 31.0 minutes
NEW YORK 8.0 million 34.0 minutes
• New York has 16 times more workers than Louisville
but only 50% higher commute time
Avoidance behavior: workers
• Workers avoid congestion by
1) Switching from driving to
public mass transit;
2) Relocating their homes closer
to their jobs;
3) Other
Avoidance behavior: firms
• Firms respond to congestion by
1) Paying higher wages to attract
workers;
2) Relocate closer to workers and
customers;
Combined effects of workers and firms
• Workers’ response increases
housing and job density in the
centers of cities
• Firms’ response spreads jobs to
less congested outlying areas and
makes more urban sprawl
Access to jobs/shops
Access to labor and
customers
Household Firm
Firm Household
Linkages between firms and households
Urban sprawl in the US
• One way in which an urban area reduces
congestion is by sprawling
• Because public transit is not plentiful
and easily accessible
• US urban areas have adjusted to congestion
by jobs moving out to suburbs
How much urban sprawl has happened in the US?
• 1972 to 1996: the U.S. urbanized land has sprawled at a rate of 2.48% per annum (2.5 times the 0.98% growth rate of urbanized population)
• An example follows about the Buffalo-Niagara Falls area in which I live
Chicago’s congestion
• How does congestion affect public transit, urban sprawl and travel behavior in Chicago?
• What would be the effects of a London type or Stockholm type cordon policy in Chicago?
1
Levels of network and zonal aggregation
CHICAGO 14+1 ZONE TEST VERSION Larger Chicago 111+6 zones
CITY
(5 zone)
SUBURBS
(9 zone)
Central Business District
Rest of City of Chicago
Inner ring suburbs
Outer ring suburbs
Exurban areaxban area
The Chicago
MSA
Lake
Michigan
Real Estate Growth (2000-2030)
Single family houses
Other buildings
Land available for
development
Change in Aggregate and Per Capita VMT
(Without Highway Capacity Additions )
Change in Aggregate and Per Capita VMT
(With Highway Capacity Additions)
Aggregate VMT
Per capita VMT
The Constancy of Commuting Time by Car Despite
Population Growth and Increasing Sprawl
Percent commuting
by car
Minutes of two way
commuting
Round-trip commuting by mode
Travel time per day
Driving-related aggregates
Non-work trips
Gasoline
VMT
Fuel Economy. MPG
Per-capita changes in driving-related variables
Non-work trips
Gasoline
VMT all trips
VMT in commuting
Effect of Growth on Job Sprawl
Other results from Chicago
• Do not add any road capacity more congestion, more sprawl, less VMT.
• Improve transit travel times (5% per decade)
less sprawl, less congestion but slightly,
more transit ridership, centralization.
• Stable gasoline prices
more intra-zonal trips, more
non-work trips, more sprawl
• Improve car fuel economy (4%
per decade)
Similar to gas price, but not as
strong.
How will rapid rail investments affect the Grand Paris Region
by 2035?
• The RELU-TRAN model was used for the “Ile de France” to model the effect of an estimated 35 billion Euros in planned rapid rail investments.
2
Population changes after projects
Job changes after projects
Rent increases after projects
What would be the effects of higher congestion in Los Angeles?
• 2% of the trips are by public transit
• 30% of the jobs are in 30 sub-centers
• It is the 2nd largest metro area in the US but has lower than expected travel times
3
Commute time increases
with city size in the US
Job sub-centers in the
Los Angeles MSA
Highways in the
Los Angeles MSA
Highways and job
centers in L.A.
Expected effects of higher congestion in L.A.
• Will new job sub-centers emerge?
• Will existing job sub-centers get bigger or smaller?
• How much will VMT per-capita decrease?