This document summarizes research on the interactions between land use and transportation. It begins with an overview of the theoretical framework showing how land use and transportation influence each other. It then provides examples at different scales, from macro/metropolitan down to micro/neighborhood. At the macro scale, it examines trends in urbanization and suburbanization globally. At the meso/intra-metropolitan scale, it analyzes the effects of planning policies on land use and travel in the Netherlands. At the micro/neighborhood scale, it studies the impact of built environment factors on transit use and walkability near BRT stations in Boston and Jinan, China. The document concludes by discussing implications for both "transportation as a function of land use
Land Use-Transport Interactions: Evidence from and Implications for Urban Public Transportation Systems
1. Land Use-Transport Interactions:
Evidence from and Implications
for Urban Public Transportation
Systems
26 April, 2013
Professor Christopher Zegras
Department of Urban Studies & Planning
Massachusetts Institute of Technology
2. Content
• Built Environment (BE) = f (Transport) and
Transport = f (BE)
– Background and basic theory
• Transport = f (BE)
– theory, evidence, policy implications.
• BE = f (Transport)
– theory, evidence, policy implications.
• Conclusions and Questions
3. Land Use-Transport Interaction:
Theoretical Framework
Land Use
Land Uses (Activities)
Land, Floor Space
Prices Demand
Transportation
Travel (Activities)
Transportation System
Time
Costs
Demand
Connectivity
Spatial
Distribution
Accessibility
4. The Metropolis in Development
– Two Core Phenomena
1950
1955
1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
2025
2030
2035
2040
2045
2050
—
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
10,000
Population(Millions)
“Less Developed”
Urban
“Developed” Urban
Total World
Source: United Nations, Department of Economic and Social Affairs (DESA)
10. Transport = f (LU)?
Something new?
Meyer, et al, 1965 (from Kain, 1999)
Howard’s “Garden City”
11. 11
The Built Environment and Mobility: A
Question of Scale
Scale Refers To Built Environment
Concepts/Indicators
Metropolitan Urban Structure Overall City Size,
population, gross density,
“skeletal” forms (e.g, radial)
Intra-
Metropolitan
(meso)
Urban Form Dispersion, concentration,
mixes, grain, access
networks
Micro Scale:
(neighborhood)
Urban Design “Internal Texture”, Density,
Mixes of Uses, Street
Networks, etc.
12. Ingram, 1998, p. 1027.
Urban Density (persons/hectare)
15,000
10,000
5,000
100 200 300 400
PerCapitaCarKms
Hong Kong
Sacramento, CA
?
?
xSantiago
13 US Cities
7 Canadian Cities
3 Wealthy Asian Cities
11 European Cities
6 “Developing” Asian Cities
6 Australian Cities
Urban Density (persons/hectare)
15,000
10,000
5,000
100 200 300 400
PerCapitaCarKms
Hong Kong
Sacramento, CA
?
?
xSantiago
13 US Cities
7 Canadian Cities
3 Wealthy Asian Cities
11 European Cities
6 “Developing” Asian Cities
6 Australian Cities
Kenworthy & Laube, 1999.
Newman & Kenworthy…
17. 17
A Trip-Based (Cost-Based)
Framework
Auto Travel
Demand
Indicator
Grid Street
(shorter trips)
Traffic
Calming
(slower trips)
Mixed Uses &
Densification
(one trip, more
purposes,
slower speed
All Three
Car Trips
Increase (for
all modes,
likely)
Decrease
Increase or
Decrease
Increase or
Decrease
Vehicle Miles
Traveled
(VMT)
Increase or
Decrease
Decrease
Increase or
Decrease
Increase or
Decrease
Car Mode
Choice
Increase or
Decrease
Decrease
Increase or
Decrease
Increase or
Decrease
Crane, 1996
18. 18
To Better Understand Possible
Effects…
We need to know
• Elasticities of trip demand with respect to
speed and distance
• Cross-elasticities among modes
– How changes for one mode (eg in distance)
affects demand for other modes
• Differentiate by trip purpose
19. Net Utility Approach
• Extending beyond Crane…
• The Built Environment influences disutility
and utility
Maat et al, 2005
22. 22
Net Utility Framework
• Land uses influence net utility:
– Positive utility = activity realization
– Negative utility (disutility) = travel cost
• Extends beyond Crane
– Reveals a dual ambiguity of land use’s influences
• Uncertain influence on trip costs (disutility), thus travel
• Uncertain influence on activities (utility), thus travel
• What happens with saved time?
A. Invest in going to higher utility destinations
B. Carry out more activities
C. Dedicate more time per activity
– Travel demand increases with?
– A and B
– Consistent with…. constant travel time budgets (e.g., Schafer, 2000).
23. TB = f (BE)?
Empirical Challenges: Unclear
pathways of effects
Transport-Efficient
Neighborhood
Transport-Efficient
Behavior
Transport-Efficient
Preferences
Spatial cognition, etc…
48. Potentially confounding factors
Trip Maker
• Age
• Gender
• Car Ownership
• Household Income
• Occupation
• Frequent BRT User or not
Trip
• Purpose
• Time
• Alternative Mode Availability
• In Group or not
System
• Level of Service
• Transit Fare
Station Context
• Station Function (terminal, transfer?)
• Distance to City Center
• Density Gradient
• Connectivity (Feeder road length)
• Level of Feeder-bus Service
No need control because BRT riders are granted free transfer
between BRT lines and thus using the same system per se.
49. Catchment Area Density Gradient: Hill/ Valley/ Flat
Hill Pattern (convex) Valley Pattern (concave)
BRT
BRT
Station 3 Station 8
STATION CONTEXT
Source: http://jinan.edushi.com/
50. E(Walk Distance)
= 600
+ 150 *(Integrated_Boulevard_Corridor)
+ 400 *(Terminal_Station)
- 100 *(Transfer_Station)
- 150 *(Density_Hill)
+ 150 *(Density_Valley)
+ 50 *(Distance_to_Center in km)
Radial Distance Guidelines for Pedestrian Zones around
BRT Stations AND RRT Stations
Radial Distance (meters)
Corridor Type Terminal Station Non-terminal Station
BRT Arterial-Edge 600-1000 300-600
BRT Integrated-Boulevard 1000-1500 600-1000
BRT Below-Express 800-1200 400-800
RRT Underground 1200 700-900
RRT Elevated 1300 800-1000
Jiang et al, 2012; Zhao & Deng, 2013
E(Walk Distance)
= 900*(Underground typical sta.)
+ 300 *(Terminal_Station)
+ 100 *(Elevated Station)
- 100 *(if Transfer station)
+ 10 *(Distance_to_Center in km)
51. Terminal station presents a unique opportunity
for large transit-oriented development…
RECOMMENDATIONS
(Jiang 2010)
56. A “Demand Side” Example:
Location Efficient Mortgage
• Also known as “Smart Commute
Mortgage”
• Basic Theory:
– Driving less increases household disposable
income
– Can qualify for better mortgage characteristics
(higher mortgage-to-income qualifying ratio)
– Basically attempt to capitalize on the location-
transport cost trade-off
57. Decision Process
1. Household relocating (potentially in the
market)
2. Interested in buying (in the market)
3. Attracted to “location efficient” areas
4. Qualified to buy
5. Interested in LEM
58. Hypothetical Example
Item Without LEM With LEM
Applicant Income
(per month)
$2,100 $2,100
Available for down
payment
$6,000 $6,000
Housing to Income
Ratio Limit
28% 28%
Transport Savings
(per month)
n.a. $653
Mortgage Available $76,000 $115,611
59. Major Risks…
• LEM has the effect of reducing the down
payment as share of property value
• Assumes household will
– Reduce vehicle ownership
– Reduce transport expenses
60. “Testing the Rhetoric”
• Basic hypothesis
– Location efficiency reduces mortgage risk
• How to test?
– “Efficient” locations should be negatively correlated with
mortgage default rates, ceteris paribus
• Data
– 8,000 mortgages from 1,000 census tracts in Chicago
• Analytic Approach
– Probability of Default = f (Sociodemographic and other
controls, location efficient characteristics)
• Findings
– Location factors have no influence on default rates
Blackman, 2002; Blackman & Krupnick, 2001
61. LEM: Interpretations &
ImplicationsPossible Explanations
• Savings not large enough to influence
– Counter-factual (location inefficient location) is
inaccurate
– VMT and ownership model wrong
• Or, real estate market already capitalizing
financial benefits.
– i.e., value already “captured”
Implications
• Might still have other benefits
• But, must be weighed relative to costs
63. Rail Transit Effects
(Baum-Snow & Kahn, 2000)
Aims
1. How new rail transit attracts commute
trips to transit
2. Which demographic groups benefit most
from rail improvements
3. Rail transit influence on land values
64. Approach
• Case Studies
– Expansions
• Boston, Chicago
– Comprehensive New Networks
• Atlanta, Washington, DC
– Incremental Expansion
• Portland, OR
65. Possible Rail Transit Effects
• Existing Residents Switch to Rail
• New Residents Move into Transit Tracts
• Property Values Increase
66. Data
• Census Tract Data
• Public Use Microdata Sample (PUMS)
– 1% sample, micro data
• Constructed Transit Coverages to
represent system changes (1980-1990)
– Show declines in mean tract distance from
transit (all cities): 5 km to 3 km
67. Analytical Approach
• Transit Use: 3 models
1. Use = f (Tract Distance)
2. Change in use = f (Change in Tract Distance)
3. Change in use = f (Change in Tract Distance,
Migration)
• Transit Capitalization
– “Hedonic” home price capitalization
– Change in home price = f (change in distance)
• Transit Beneficiaries
– Change in Distance to Transit = f (demographics)
68. Results: Transit Use
• There is some Tiebout migration of transit users
to tracts
– i.e., “self-selection”
– Migration rates are higher in tracts with increased
transit access
• Induced transit-oriented development
• Also, transit-shifting by existing residents
– In fact, most mode shift due to this effect
• Overall effects…
– Small 1.4% increase in transit with a 2 km decrease
in distance to transit (from 3 to 1 km)
69. Results: Transit Capitalization &
User Groups
• 3 km to 1 km decrease in transit distance
increases rents by $19/month, house
value by $5,000
– More gain in travel time savings: $1,200/year
• College educated and home-owners more
likely to be in census tracts closer to
transit
71. Some Problems with Baum-Snow & Kahn
• City fixed effects
– Transit markets/service very local
• Ignore other investments/policies occurring at
same time
– E.g., highway investments
– And their expansionary effects
• Rail transit almost certainly retains central city
vitality
– Not captured in their model
– No employment effects captured in model
• Commute trips only
• Possible issues with using census tract…
See, e.g., Voith, 2005.
79. Accessibility: How Measured?
• Local
– Shortest walking time on road network from
location of each property to closest BRT
• Regional
– Line-haul travel time from closest BRT station to
Financial District
– Line-haul travel time from closest BRT station to
Financial District Downtown
– Weighted index of travel time to all BRT stations
• Weighted by the number of passengers travelling
between each pairs
80. Other Variables
• Proximity effects
– Straight line distance to corridor
– To capture possible negative externalities
• Control variables
– Apartment: Size, # bedrooms, age, etc.
– Location: buffer with spatial average of zone
attributes
• Crime, socioeconomic, demographic, land uses,
etc.
81. Results
• Elasticity of rent with
respect to BRT stop dist.
– -0.16 to -0.22
• Every five minutes from
BRT stop, rent declines by
US$15
• Elasticity of rent with
respect to BRT Corridor
– 0.19 to 0.21
• Every 100 meters from
corridor, rent goes up by
US$77
82. Comparing Results
• Results (in terms of % change in property value)
fairly comparable to
– Los Angeles Blue Line
– DC WMATA
• Slightly lower than San Diego (LRT) and UK
Tramlink (Manchester)
• Estimated absolute premium (annualizing rents)
– US$440-650 per 100 meters
– Roughly Double the Baum-Snow & Kahn Effect
(measured from 3 to 1 km change)
83. Other Notes and Commentary
• No apparent Regional Accessibility Benefit
• Short time frame of analysis may mean
conservative estimate
• Cross-sectional analysis
• Corridor effect might be confounded
– By other traffic
• But, station effects might also be confounded
– E.g., urban recovery
• Residential land only
87. BE = f (Transport)
An Example Policy Implication
88. Chicago: Hedonic Model, CTA
Station Access
p = f (I, N, T)
where:
p is the property sales price;
I is a vector of attributes of the improvements on the parcel, such as number of bathrooms, number
of floors, and age, etc.;
N is a vector of attributes of the neighborhood, such as quality of public facilities and services
(including schools) and socioeconomic characteristics; and,
T is a combined vector of attributes of the transportation-related locational accessibility of the
parcel, such as proximity to transportation services (including transit), relative accessibility to
opportunities across the broader metropolitan area, etc.
89.
90.
91. Variation in Elasticity of Property Value with
Respect to Walking Time Based on Properties’
Walk Times to CTA Station
93. Rail Transit Value Capture
Potential: Chicago, Lisbon
Zegras et al 2013b
94. 94
Transit = f (BE): Summary
• Consider the geographical scale of analysis/intervention
– Generally, theory implies same types of effects, operating at
different scales
• Theoretically, impacts are ambiguous
• Complexity of LUT relationships increases with society’s
complexities
– Time routines, age, family cycle, etc.
– Keep in mind the type of potential activities (e.g., trip purpose) and
related spatial and temporal constraints
• Simple consideration: BE influence on walk influence to
station access
95. BE = f (Transit): In Summary
• Public Transit, in right conditions, will influence
urban form
• Land Value effects are consistently seen
• Institutionality is barrier to land value capture
(LVC)
– Including poor transport finance pictures
• LVC not a panacea
• Realistic amount to raise, will be modest, in most
cases
• Ex-ante system in place (before build/expand)
96. BRT Centre May Webinar
Cost Efficiency under Negotiated Performance-Based
Contracts and Benchmarking – Are there gains through
Competitive Tendering in the absence of an Incumbent
Public Monopolist?
Friday, May 24th at 4pm Sydney, Australia time (UTC+10)
Presented by Professor David Hensher
Institute of Transport and Logistics Studies
The University of Sydney
97. References
• Angel, S., J. Parent, D. Civco, A. Blei (2011) Making Room for a Planet of Cities, Policy Focus
Report, Lincoln Institute of Land Policy.
• Baum-Snow, N. and M. Kahn (2000) The effects of new public projects to expand urban rail
transit. Journal of Public Economics, Vol. 77, pp. 241-263.
• Bertaud, A. (2004) The spatial organization of cities: Deliberate outcome or unforeseen
consequence? May: http://alain-
bertaud.com/images/AB_The_spatial_organization_of_cities_Version_3.pdf
• Blackman, A. (2002) Testing the Rhetoric. Regulation (Spring): 34-38.
• Crane, R. (1996) On form versus function: Will the new urbanism reduce traffic, or increase it?
Journal of Planning Education and Research, Vol. 15, pp. 117-126.
• Geurs, K.T. and B. van Wee (2004) Accessibility Evaluation of Land-Use and Transport
Strategies: Review and Research Directions. Journal of Transport Geography Vol. 12: 127-140.
• IBI Group. 2000. Greenhouse Gas Emissions from Urban Travel: Tool for Evaluating
Neighborhood Sustainability. Healthy Housing and Communities Series Research Report,
prepared for Canada Mortgage and Housing Corporation and Natural Resources Canada,
February.
• Graftieux, P. (2005). World Bank, Personal communication.
• Hidalgo, D. (2006). EMBARQ, Personal communication.
• Ingram, G. (1998) Patterns of Metropolitan Development: What Have We Learned? Urban
Studies, Vol. 35, No. 7, June, pp. 1019-1035.
• Jiang, Y. (2010). CSTC, personal communication.
• Jiang, Y., C. Zegras, Mehndiratta, S. (2012). Walk the line: station context, corridor type and bus
rapid transit walk access in Jinan, China.” Journal of Transport Geography, 20(1), 1–14.
98. References (cont’d)
• Kain, J. (1999) The Urban Transportation Problem: A Reexamination and Update. Essays in
Transportation Economics and Policy. Brookings.
• Kenworthy, P. and F. Laube (1999) Patterns of automobile dependence in cities: an international
overview of key physical and economic dimensions with some implications for urban policy.
Transportation Research A, Vol. 33, pp. 691-723.
• Lari, A., Levinson, D., Zhao, Z., Iacono, M., Aultman, S. Das, K.V., Junge, J., Larson, K.,
Scharenbroich, M. (2009) Value Capture for Transportation Finance: Technical Research Report.
Minneapolis: The Center for Transportation Studies, University of Minnesota
• Maat, K., B. van Wee, D. Stead (2005) Land use and travel behaviour: expected effects from the
perspective of utility theory and activity-based theories. Environment and Planning B: Planning
and Design, Vol. 32, pp. 33-46.
• McNally, M. and A. Kulkarni. (1997) Assessment of Influence of Land Use-Transportation System
on Travel Behavior. Transportation Research Record 1607, pp. 105-115.
• Muller, Peter O. Transportation and Urban Form: Stages in the Spatial Evolution of the American
Metropolis. Chapter 3 in The Geography of Urban Transportation, 59-85. S. Hanson, ed. 3rd
edition, Guildford Press, 2004
99. References (cont’d)
• Rodríguez, D. and Targa, F. (2004) Value of Accessibility to Bogotá’s Bus Rapid Transit System.
Transport Reviews, Vol. 24, No. 5 (September): 587-610.
• Schwanen, T., Dijst, M. and Dieleman, F. (2004) Policies for Urban Form and their Impact on
Travel: The Netherlands Experience. Urban Studies Vol. 41, No. 3: 579-603.
• US Census Bureau (2012) Patterns of Metropolitan and Micropolitan Population Change: 2000 to
2010, Census Special Reports, September.
• Voith, R. (2005) Comment on Effects of Urban Rail Transit Expansions: Evidence from Sixteen
Cities, 1970–2000 (Baum-Snow and Kahn). Brookings-Wharton Papers on Urban Affairs: 198-
206.
• Zegras, C., S. Jiang, C. Grillo (2013a) Sustaining Mass Transit through Land Value Taxation?
Prospects for Chicago, Draft Paper prepared for Lincoln Institute of Land Policy.
• Zegras, C., S. Jiang, C. Grillo, L. Martinez (2013b) Capture the Value to Finance Transit
Systems? A Comparative Assessment of Chicago and Lisbon, Draft.
• Zhang, M. (2004) The Role of Land Use in Travel Mode Choice: Evidence from Boston and Hong
Kong. Journal of the American Planning Association, Vol. 70, No. 3, Summer, pp. 344-360.
• Zhao, J. and Deng, W. (2013) Relationship of Walk Access Distance to Rapid Rail Transit
Stations with Personal Characteristics and Station Context. Journal of Urban Planning and
Development (forthcoming).
