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Transcript of 0 The Co-Evolution of Land Use and Transport: Theory and Application to London David Levinson...
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The Co-Evolution of Land Use and Transport: Theory and
Application to London
David Levinson
University of MinnesotaImperial College, London
February 21, 2007
With Bhanu Yerra, Feng Xie, Shanjiang Zhu, Ahmed El-Geneidy
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General Points• Transportation and Land use are
interdependent shapers of urban form– Concentration of commercial activities
(suburbanization of residences) and prosperity of public transport at the first half of 20th century.
– Decentralization of both commercial and residential activities accompanied by expanding automobile/highway system in second half of 20th century.
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Organization of Talk
• Movie: http://rational.ce.umn.edu (go to SIMS)
• Orderliness in London• SIGNAL - A Simulation Model of Co-
evolution• Twin Cities & Next steps
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The Sequence of Development?
This extension of the railway system by means of feeder lines means that in many ways the early development of the system can be viewed, not in terms of booms and slumps, but in rational steps. By the end of 1833, three of the five English provincial towns with a population of more than 100,000 had railway links with London under construction; by the end of 1836 only Portsmouth remained among English towns of over 50,000 population without a line authorized; and by the end of 1837 most towns of more than 20,000 inhabitants were on or close to the route of an authorized railway. - M.C. Reed
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Orderliness Hypothesis
• H: Places will be connected to the network roughly in order of their population density (density is used to control for area). The places that have the highest population per unit area (or population density) will get the network first.
• Tested in London
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Background
• 1836 London and Greenwich Railway• 1846 Royal Commission on Railway Termini• 1854 Metropolitan Railway chartered• 1863 Metropolitan Railway opens• 1884 “Circle” closed• 1890 City and South London Railway (first
tube)• Railways not permitted to be developers
except Metropolitan Railway --> Metro-Land• Greenbelt
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Surface Rail & PopulationCorrelation between Population Density Rank and Train Station Density Rank
of London Boroughs (Local Administrations)
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
1820 1840 1860 1880 1900 1920 1940 1960 1980 2000 2020
Correlation
Correlation between Station DensityRank and Population Density Rank(including City of London)
Correlation between Station DensityRank and Population Density Rank(excluding City of London)
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Underground & PopulationCorrelation between Population Density Rank and Tube Station Density Rank
of London Boroughs (Local Administrations)
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
1860 1880 1900 1920 1940 1960 1980 2000 2020
Correlation
0
5
10
15
20
25
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Number of Boroughs with Stations
Correlation between Station DensityRank and Population Density Rank(including City of London) (left axis)
Correlation between Station DensityRank and Population Density Rank(excluding City of London) (left axis)
Number of Boroughs with Stations (rightaxis)
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Boroughs without Underground Service
Year
Boroughs with
Stations (N)
Notable Boroughs (in top N of density) without service at time which eventually get service.
1871 8 Tower Hamlets (2), Southwark(7), Hackney (8)
1881 14 Tower Hamlets (1) , Southwark (6), Hackney (7), Lambeth (9), Lewisham (13), Wandsworth (11)
1891 23 Hackney (6), Greenwich (15), Waltham Forest (16)
1901 23 Hackney (6), Greenwich (16), Waltham Forest (15) missing
1911 23 Hackney (5), Waltham Forest (15), Greenwich (16), Redbridge (22)
1921 23 Hackney (4), Waltham Forest (15), Greenwich (17), Redbridge (20)
1931 23 Hackney (4), Waltham Forest (15), Greenwich (17), Redbridge (20)
1941 25 Greenwich (16), Redbridge (20), Waltham Forest (14)
1951 27 Greenwich (19)
1961 27 Greenwich (18)
1971 27 Greenwich (18)
1981 27 Greenwich (17)
1991 27 Greenwich (18)
2001 28
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City of London
0
20000
40000
60000
80000
100000
120000
140000
160000
180000
200000
1750 1800 1850 1900 1950 2000 2050
Year
Density (Employment, Population)
0
5
10
15
20
25
30
35
Rank of Population Density
Population Density of City ofLondon
Employment Density of City ofLondon
Rank of Population Density of Cityof London
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Leads and Lags
Transport Leads Land Use
Transport Follows Land Use
Developed Area (B) Development densifies in urban area after construction of new transport infrastructure
(A) Constructing new (higher speed) mode in existing urbanized area (e.g. London Transport in early years)
Undeveloped Area (C) Constructing new (higher speed) mode in greenfields, to promote development
Still waiting …
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Qualitative Model
• Rail first inter-city: connects outside -> in• Underground first connects termini, then other
points in developed area, and finally connects to new suburbs: inside -> out
• Has a decentralizing effect for residences, lowering population density in center, increasing it in suburbs.
• Other factors; entrepreneurs, construction costs, South vs. North (income, rail embeddedness, geology, competition, south already more local than north (London in south of England)
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SIGNAL• Simulator of Integrated Growth of Network and
Land use– Simulate the co-evolution of land use and road
networks.– Implement a bottom-up process that incorporates
independent route choices of travelers, location decisions of businesses (jobs) and residents (workers), as well as investment decisions of autonomous roads.
– Kept as simple as possible to capture salient components, while enabling us to display and analyze the emergent patterns of land use and network on a large scale.
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Model Framework
•
User-defined Land uses
User-defined Network
Travel Demand Models
Road Investment
Models
Access and Land use Models
Trip Generation
Trip Distribution
Traffic Assignment
Revenue Model
Cost Model
Investment Model
Accessibility
Employment Location
Population Location
SIGNAL Interface
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Travel Demand Models1.Trip Generation
2.Trip Distribution
4. Traffic Assignment
Calibrated Stochastic User Equilibrium (SUE)
3. Mode Choice
Single mode is assumed
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Road Investment Models1. Revenue Model
2. Cost Model
3. Investment Model
ab
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SONG (fixed Land Use)Base Case: Uniform Initial Speeds and Land use
(U/U) (left) Spatial distribution of uniform speed for the initial
network; (right) Spatial distribution of speed for the network at equilibrium reached after 8 iterations.
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Uniform initial speeds and random initial land use (U/R)
Spatial distribution of speed for experiment U/R after reaching equilibrium;
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Random initial speeds and random initial land use (R/R)
(left) Spatial distribution of initial speed for experiment R/R (random initial speeds and random initial land use);
(right) Spatial distribution of speeds for the network after reaching equilibrium; The color and thickness of the link
shows its relative speed or flow.
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Uniform initial speeds and bell-shaped initial land use (U/B)
Spatial distribution of final speeds for experiment U/B (uniform initial speeds and bell shaped land use)
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Trips Produced
Trips Attracted
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Access and Land use Models•Assumptions
–Residence and employment are the only types of activities, and their totals are kept equal and constant.
–Accessibility to population and to employment are the only factors that affect location decisions.
–People want to live near jobs, but far from other people to maximize available space and to avoid potential competitors for jobs, while employment wants to be accessible both to other employment and to people (who are their suppliers of labor and customers).
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Access & Land use Models
1. Accessibility
2. Potential
3. Redistribution
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Measures• Gini
€
rE=Ejdj
2j=1
n∑Ej
j=1
n∑
Equivalent radius (r)
Where
Ej= employment of zone j
dj= distance of zone j to the center of a region
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Numerical Example • A hypothetical metropolitan area where:
– both the population and employment are distributed over a two-dimensional grid, stretching 10 km in both dimensions, divided into a 20X20 grid lattice of land use cells (400 zones).
– A total of 400,000 people are living in the city, which is equivalent to an average of 1,000 residents in each zone. Total employment equals 400,000 as well (and each resident holds a job).
– Two-way roads connect the centroids of each pair of adjacent zones, thus forming a 20X20 grid of road network as well, comprising 400 nodes and 1520 links.
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Experiments and Hypotheses Experiments
HypothesesInitially flat land uses become more concentrated, and initially concentrated become less so.
The degree to which land uses are concentrated is reinforced when road networks are allowed to vary rather than remain constant.
€
0 < ΔGini(Ex.1) < ΔGini(Ex.2)
Δr(Ex.2) < Δr(Ex.1) < 0
ΔGini(Ex.3) < ΔGini(Ex.4) < 0
0 < Δr(Ex.4) < Δr(Ex.2)
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Result: activity density
Evolution of employment without network dynamics
Evolution of employment with network dynamics
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Results: Gini
0 more equitable
1 less equitable
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Results: Equivalent Radius
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Sensitivity
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Sensitivity
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Summary• A simple co-evolution model of transportation and
land use which incorporates independent route choices of travelers, location decisions of businesses (jobs) and residents (workers), as well as investment decisions of autonomous roads.
• Experimental results show that the degree of both employment and population concentration is reinforced when road networks are allowed to vary rather than remain constant.
• Contemporary integrated transportation and land use models that neglect road dynamics could underestimate the concentration of land uses.
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Next Steps
• Application to Twin Cities• Estimation & Calibration of Models
(Markov Chain, Cellular Automata, MCCA, SIGNAL) using detailed empirical dataset.
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Future Models …
• Modeling– Explicitly consider co-evolution– Dynamic transportation network instead of
static network– Dynamic land use rather than fixed land use– Variable rather than static demand– Bottom-up process (Cellular
Automata/Agent-Based) instead of top-down (Allocation, Equilibrium, Optimization)
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Orderliness
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Random initial speeds and bell-shaped initial land use (R/B)(top) Spatial distribution of initial speed for experiments R/B (random
initial speeds and bell shaped land use); (bottom) Spatial distribution of speeds for the network after reaching equilibrium
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Gravity model parameter variations with uniform network and land use (U/U) Spatial
distribution of relative speeds at equilibrium for (top) w = 0.02 (less sensitive to travel cost);
(bottom) w = 0.8 (more sensitive to travel cost).
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Variation of Average Traffic Flow with Travel Behavior
50
250
450
650
850
1050
1250
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
w
Average Traffic Flow
10X10
11X11
15X15
Variation of Average Link Speed with Travel Behavior
5
5.5
6
6.5
7
7.5
8
8.5
9
9.5
10
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1w
Average Link Speed
10X1011X1115X15
Variation of average traffic flow (left) speed (right) with w for 10X10, 11X11
and 15X15 networks.
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Slice of a bell-shaped downtown land use
pattern
Trips Produced
Trips Attracted