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

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

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

The Metropolis in Development

– Two Core Phenomena 1

95

0

19

55

19

60

19

65

19

70

19

75

19

80

19

85

19

90

19

95

20

00

20

05

20

10

20

15

20

20

20

25

20

30

20

35

20

40

20

45

20

50

—

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000

Po

pu

lati

on

(M

illio

ns

)

“Less Developed”

Urban

“Developed” Urban

Total World

Source: United Nations, Department of Economic and Social Affairs (DESA)

% Change Population by Census Tract (2000-10)

US

Census

2012

Paris

Angel et al, 2011

Bandung,

Indonesia

Angel et al, 2011

Average Tract Density: 20 US Metro Areas

Angel et al., 2011

World “Suburbanization” Trends

Angel et al., 2011

Transport = f (LU)? Something new?

Meyer, et al, 1965 (from Kain, 1999) Howard’s “Garden City”

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.

Ingram, 1998, p. 1027.

Urban Density (persons/hectare)

15,000

10,000

5,000

100 200 300 400

Per

Cap

ita C

ar

Km

s

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

Per

Cap

ita C

ar

Km

s

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…

Macro-

Scale

Form &

Function

Bertaud, 2004

14

Micro Scale Built Environment

Crane, 1996

15

Formalizing the Theoretical

Framework

16

Crane’s Trip-Based (Time/Cost-Based)

Framework

Crane, 1996

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

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

Net Utility Approach

• Extending beyond Crane…

• The Built Environment influences disutility

and utility

Maat et al, 2005

Stylized Effects of Travel Time

Changes

Maat et al, 2005

Stylized Effects of Mode Changes

Maat et al, 2005

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).

TB = f (BE)?

Empirical Challenges: Unclear

pathways of effects Transport-Efficient

Neighborhood

Transport-Efficient

Behavior

Transport-Efficient

Preferences Spatial cognition, etc…

A “Macro-Level” Example

Netherlands

Policy Land Use Behavior

(Schawen et al, 2004)

National-Level Planning Policies Netherlands

• 1970s-1980s – “concentrated decentralization”

• 1980s – “compact urban growth”

– with urban renewal subsidies

• 1990s – “A-B-C location policy”

• A: centrally located sites

• B: outside CBDs, but still public transport connected

• C: highway-oriented sites

• Challenge: growth in service/office sector

• Retail policy

• Overall: mixed success – Primarily guiding residential and retail development

Schwanen et al, 2004.

Netherlands: Estimated Effects?

• Data

– Travel

• One-day travel survey (NTS)

• Male/female Head of Household

– Land Uses

• Macro: urban structure (mono-, poly-centric)

• Meso: degree of urbanization

• Travel Effects

– Mode Choice

– Distance and time

Schwanen et al, 2004.

Netherlands: Conclusions & Recs

Schwanen et al, 2004.

“Micro-Scale” Effects

Boston, Jinan

Land Use in Boston Work Trip

Mode Choice Model

Zhang, 2004

Micro-level Example: BE and BRT Pedestrian

Catchment Area (PCA) in Jinan China (Jiang et al, 2012)

Arterial- Edge Corridor

(Jingshi St.)

1

(Jiang 2010)

Integrated- Boulevard Corridor

(Lishan Rd.)

2

(Jiang 2010)

Below- Expressway Corridor

(Beiyuan St.)

3

(Jiang 2010)

Approach

𝐷𝐼𝑆𝑇𝑖 = 𝑓(𝑇𝑀𝑖 ,𝑇𝑅𝑖 , 𝑆𝑖 ,𝐶𝑖 ;𝛽) + 𝜀𝑖

• Station area user survey

• Built Environment Analysis

• Regression

CORRIDOR WALKABILITY

A BRT Users’ Perspective

29% 33% 33%

26% 26% 28%

18%

24% 26%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Crossing is safe. Crossing is easy. Walking on sidewalksis safe.

Arterial-edge(n=464)

Integrated-boulevard(n=356)

Below-expressway(n=946)

Unsafe crossing, poor signals…

(Jiang 2010)

Distance… (Jiang 2010)

(Jiang 2010)

CORRIDOR WALKABILITY

A BRT Users’ Perspective

69%

47% 45% 50%

33%

24%

38% 35%

27%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Pavement is good. Streets are clean. Few blockages are onsidewalks.

Arterial-edge(n=464)

Integrated-boulevard(n=356)

Below-expressway(n=946)

CORRIDOR WALKABILITY

A BRT Users’ Perspective

48%

42%

70%

58%

39%

49%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Trees on sidewalks makewalking comfortable.

Facilities along streetsmeet my demand.

Arterial-edge(n=464)

Integrated-boulevard(n=356)

Below-expressway(n=946)

Walk next to trees… Arterial-Edge Corridor

Walk under trees… Integrated-Boulevard Corridor

Walk without trees… Below-Expressway Corridor

475

647

582

329

501 459

0

100

200

300

400

500

600

700

Avg Walking Distance

Avg Straight-line Distance

(m)

Detour

Factor 1.59 1.36 1.33

CORRIDOR WALKABILITY

Directness

Walking

distance

Straight-line

distance

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%0

15

0

30

0

45

0

60

0

75

0

90

0

10

50

12

00

13

50

15

00

16

50

18

00

19

50

21

00

22

50

24

00

25

50

27

00

28

50

30

00

31

50

33

00

34

50

36

00

37

50

39

00

Pe

rce

nta

ge o

f B

RT

rid

ers

Access/Egress Walking Distance (m)

Terminal Station

Transfer Station

Typical Station

Station Function vs. Access/Egress Walking Distance

Walking Distance (m) Typical Station Transfer Station Terminal Station

Mean 547 587 1365

Median 435 458 1311

Maximum 2738 2067 5114

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%0

15

0

30

0

45

0

60

0

75

0

90

0

10

50

12

00

13

50

15

00

16

50

18

00

19

50

21

00

22

50

24

00

25

50

27

00

28

50

30

00

31

50

33

00

34

50

36

00

37

50

39

00

Pe

rce

nta

ge o

f B

RT

rid

ers

Access/Egress Walking Distance (m)

Arterial-Edge

Integrated-Boulevard

Below-Expressway

Corridor Type vs. Access/Egress Walking Distance (non-terminal stations only)

Walking Distance (m) Arterial-Edge Integrated-Boulevard Below-Expressway

Mean 475 649 580

Median 412 520 458

Maximum 1635 2023 2738

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.

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/

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)

Terminal station presents a unique opportunity

for large transit-oriented development…

RECOMMENDATIONS

(Jiang 2010)

This probably will NOT work…

(Jiang 2010)

RECOMMENDATIONS

Make crossing safer…

(Jiang 2010)

Put more trees and stores along the sidewalk

in an appropriate way… (Jiang 2010)

T = f (BE)

An Example Policy Implication

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

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

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

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

“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

LEM: Interpretations &

Implications Possible 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

Land Use = f (Transport)?

Muller, 2004

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

Approach

• Case Studies

– Expansions

• Boston, Chicago

– Comprehensive New Networks

• Atlanta, Washington, DC

– Incremental Expansion

• Portland, OR

Possible Rail Transit Effects

• Existing Residents Switch to Rail

• New Residents Move into Transit Tracts

• Property Values Increase

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

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)

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)

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

Relative Suburban Benefits from

Rail Transit

Baum-Snow & Kahn, 2005.

Public

Tra

nsit U

se b

y D

eca

de f

or

16 C

itie

s

tha

t E

xpand

ed R

ail

Tra

nsit (

1970

-20

00

)

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.

Bus Rapid Transit Effects

Transmilenio Case

~Current Network

114 Stations; 84 Kms; 1263 vehicles; 27 km/h; 200K peak hour passengers

83 Feeder routes; 516 feeder buses

Hidalgo, 2006.

Calle 13 – Av. Caracas

Vehicles

Graftieaux, 2005.

Stations

Graftieaux, 2005.

Transmilenio BRT: Land Effects?

Rodriguez and Targa (2004) Approach

• Estimate Effects on Property Values – Hedonic Model

• Rental Properties – Feb-Apr, 2002

– Field visits and newspaper adds

– All properties for rent

– 494 multifamily residential properties

• Dependent variable – Asking price

• Influencing variables (of interest) – Accessibility (local and regional)

1.5 km

buffer

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

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.

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

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)

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

Urban Recovery

Hidalgo, 2006.

Commercial Development

Hidalgo, 2006.

Commercial Development

Hidalgo, 2006.

BE = f (Transport)

An Example Policy Implication

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.

Variation in Elasticity of Property Value with

Respect to Walking Time Based on Properties’

Walk Times to CTA Station

Land-Based Finance Mechanisms

Derived from Lari et al, 2009

Rail Transit Value Capture

Potential: Chicago, Lisbon

Zegras et al 2013b

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

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)

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

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.

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

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).