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Webinar: Land Use-Transport Interactions: Evidence from and Implications for Urban Public Transportation Systems

Webinar: Land Use-Transport Interactions: Evidence from and Implications for Urban Public Transportation Systems






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    Webinar: Land Use-Transport Interactions: Evidence from and Implications for Urban Public Transportation Systems Webinar: Land Use-Transport Interactions: Evidence from and Implications for Urban Public Transportation Systems Presentation Transcript

    • Land Use-Transport Interactions:Evidence from and Implicationsfor Urban Public TransportationSystems26 April, 2013Professor Christopher ZegrasDepartment of Urban Studies & PlanningMassachusetts Institute of Technology
    • Content• Built Environment (BE) = f (Transport) andTransport = 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 FrameworkLand UseLand Uses (Activities)Land, Floor SpacePrices DemandTransportationTravel (Activities)Transportation SystemTimeCostsDemandConnectivitySpatialDistributionAccessibility
    • The Metropolis in Development– Two Core Phenomena195019551960196519701975198019851990199520002005201020152020202520302035204020452050—1,0002,0003,0004,0005,0006,0007,0008,0009,00010,000Population(Millions)“Less Developed”Urban“Developed” UrbanTotal WorldSource: United Nations, Department of Economic and Social Affairs (DESA)
    • % Change Population by Census Tract (2000-10)USCensus2012
    • ParisAngel et al, 2011
    • Bandung,IndonesiaAngel et al, 2011
    • Average Tract Density: 20 US Metro AreasAngel et al., 2011
    • World “Suburbanization” TrendsAngel et al., 2011
    • Transport = f (LU)?Something new?Meyer, et al, 1965 (from Kain, 1999)Howard’s “Garden City”
    • 11The Built Environment and Mobility: AQuestion of ScaleScale Refers To Built EnvironmentConcepts/IndicatorsMetropolitan Urban Structure Overall City Size,population, gross density,“skeletal” forms (e.g, radial)Intra-Metropolitan(meso)Urban Form Dispersion, concentration,mixes, grain, accessnetworksMicro Scale:(neighborhood)Urban Design “Internal Texture”, Density,Mixes of Uses, StreetNetworks, etc.
    • Ingram, 1998, p. 1027.Urban Density (persons/hectare)15,00010,0005,000100 200 300 400PerCapitaCarKmsHong KongSacramento, CA??xSantiago13 US Cities7 Canadian Cities3 Wealthy Asian Cities11 European Cities6 “Developing” Asian Cities6 Australian CitiesUrban Density (persons/hectare)15,00010,0005,000100 200 300 400PerCapitaCarKmsHong KongSacramento, CA??xSantiago13 US Cities7 Canadian Cities3 Wealthy Asian Cities11 European Cities6 “Developing” Asian Cities6 Australian CitiesKenworthy & Laube, 1999.Newman & Kenworthy…
    • Macro-ScaleForm &FunctionBertaud, 2004
    • 14Micro Scale Built EnvironmentCrane, 1996
    • 15Formalizing the TheoreticalFramework
    • 16Crane’s Trip-Based (Time/Cost-Based)FrameworkCrane, 1996
    • 17A Trip-Based (Cost-Based)FrameworkAuto TravelDemandIndicatorGrid Street(shorter trips)TrafficCalming(slower trips)Mixed Uses &Densification(one trip, morepurposes,slower speedAll ThreeCar TripsIncrease (forall modes,likely)DecreaseIncrease orDecreaseIncrease orDecreaseVehicle MilesTraveled(VMT)Increase orDecreaseDecreaseIncrease orDecreaseIncrease orDecreaseCar ModeChoiceIncrease orDecreaseDecreaseIncrease orDecreaseIncrease orDecreaseCrane, 1996
    • 18To Better Understand PossibleEffects…We need to know• Elasticities of trip demand with respect tospeed 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 disutilityand utilityMaat et al, 2005
    • Stylized Effects of Travel TimeChangesMaat et al, 2005
    • Stylized Effects of Mode ChangesMaat et al, 2005
    • 22Net 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 destinationsB. Carry out more activitiesC. 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: Unclearpathways of effectsTransport-EfficientNeighborhoodTransport-EfficientBehaviorTransport-EfficientPreferencesSpatial cognition, etc…
    • A “Macro-Level” ExampleNetherlandsPolicy  Land Use  Behavior(Schawen et al, 2004)
    • National-Level Planning PoliciesNetherlands• 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 timeSchwanen et al, 2004.
    • Netherlands: Conclusions & RecsSchwanen et al, 2004.
    • “Micro-Scale” EffectsBoston, Jinan
    • Land Use in Boston Work TripMode Choice ModelZhang, 2004
    • Micro-level Example: BE and BRT PedestrianCatchment 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)
    • Approach𝐷𝐼𝑆𝑇𝑖 = 𝑓(𝑇𝑀𝑖, 𝑇𝑅𝑖, 𝑆𝑖, 𝐶𝑖; 𝛽) + 𝜀𝑖• Station area user survey• Built Environment Analysis• Regression
    • CORRIDOR WALKABILITYA BRT Users’ Perspective29%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 WALKABILITYA BRT Users’ Perspective69%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 WALKABILITYA BRT Users’ Perspective48%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
    • 4756475823295014590100200300400500600700Avg Walking DistanceAvg Straight-line Distance(m)DetourFactor 1.59 1.36 1.33CORRIDOR WALKABILITYDirectnessWalkingdistanceStraight-linedistance
    • 0.00%10.00%20.00%30.00%40.00%50.00%60.00%70.00%80.00%90.00%100.00% 015030045060075090010501200135015001650180019502100225024002550270028503000315033003450360037503900PercentageofBRTridersAccess/Egress Walking Distance (m)Terminal StationTransfer StationTypical StationStation Function vs. Access/Egress Walking DistanceWalking Distance (m) Typical Station Transfer Station Terminal StationMean 547 587 1365Median 435 458 1311Maximum 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%015030045060075090010501200135015001650180019502100225024002550270028503000315033003450360037503900PercentageofBRTridersAccess/Egress Walking Distance (m)Arterial-EdgeIntegrated-BoulevardBelow-ExpresswayCorridor Type vs. Access/Egress Walking Distance(non-terminal stations only)Walking Distance (m) Arterial-Edge Integrated-Boulevard Below-ExpresswayMean 475 649 580Median 412 520 458Maximum 1635 2023 2738
    • Potentially confounding factorsTrip Maker• Age• Gender• Car Ownership• Household Income• Occupation• Frequent BRT User or notTrip• Purpose• Time• Alternative Mode Availability• In Group or notSystem• Level of Service• Transit FareStation Context• Station Function (terminal, transfer?)• Distance to City Center• Density Gradient• Connectivity (Feeder road length)• Level of Feeder-bus ServiceNo need control because BRT riders are granted free transferbetween BRT lines and thus using the same system per se.
    • Catchment Area Density Gradient: Hill/ Valley/ FlatHill Pattern (convex) Valley Pattern (concave)BRTBRTStation 3 Station 8STATION CONTEXTSource: 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 aroundBRT Stations AND RRT StationsRadial Distance (meters)Corridor Type Terminal Station Non-terminal StationBRT Arterial-Edge 600-1000 300-600BRT Integrated-Boulevard 1000-1500 600-1000BRT Below-Express 800-1200 400-800RRT Underground 1200 700-900RRT Elevated 1300 800-1000Jiang et al, 2012; Zhao & Deng, 2013E(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 opportunityfor 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 sidewalkin an appropriate way…(Jiang 2010)
    • T = f (BE)An Example Policy Implication
    • A “Demand Side” Example:Location Efficient Mortgage• Also known as “Smart CommuteMortgage”• Basic Theory:– Driving less increases household disposableincome– Can qualify for better mortgage characteristics(higher mortgage-to-income qualifying ratio)– Basically attempt to capitalize on the location-transport cost trade-off
    • Decision Process1. Household relocating (potentially in themarket)2. Interested in buying (in the market)3. Attracted to “location efficient” areas4. Qualified to buy5. Interested in LEM
    • Hypothetical ExampleItem Without LEM With LEMApplicant Income(per month)$2,100 $2,100Available for downpayment$6,000 $6,000Housing to IncomeRatio Limit28% 28%Transport Savings(per month)n.a. $653Mortgage Available $76,000 $115,611
    • Major Risks…• LEM has the effect of reducing the downpayment 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 withmortgage default rates, ceteris paribus• Data– 8,000 mortgages from 1,000 census tracts in Chicago• Analytic Approach– Probability of Default = f (Sociodemographic and othercontrols, location efficient characteristics)• Findings– Location factors have no influence on default ratesBlackman, 2002; Blackman & Krupnick, 2001
    • LEM: Interpretations &ImplicationsPossible Explanations• Savings not large enough to influence– Counter-factual (location inefficient location) isinaccurate– VMT and ownership model wrong• Or, real estate market already capitalizingfinancial 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)Aims1. How new rail transit attracts commutetrips to transit2. Which demographic groups benefit mostfrom rail improvements3. 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 torepresent system changes (1980-1990)– Show declines in mean tract distance fromtransit (all cities): 5 km to 3 km
    • Analytical Approach• Transit Use: 3 models1. 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 usersto tracts– i.e., “self-selection”– Migration rates are higher in tracts with increasedtransit 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 decreasein distance to transit (from 3 to 1 km)
    • Results: Transit Capitalization &User Groups• 3 km to 1 km decrease in transit distanceincreases rents by $19/month, housevalue by $5,000– More gain in travel time savings: $1,200/year• College educated and home-owners morelikely to be in census tracts closer totransit
    • Relative Suburban Benefits fromRail TransitBaum-Snow & Kahn, 2005.PublicTransitUsebyDecadefor16CitiesthatExpandedRailTransit(1970-2000)
    • Some Problems with Baum-Snow & Kahn• City fixed effects– Transit markets/service very local• Ignore other investments/policies occurring atsame time– E.g., highway investments– And their expansionary effects• Rail transit almost certainly retains central cityvitality– 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 EffectsTransmilenio Case
    • ~Current Network114 Stations; 84 Kms; 1263 vehicles; 27 km/h; 200K peak hour passengers83 Feeder routes; 516 feeder buses
    • Hidalgo, 2006.Calle 13 – Av. Caracas
    • VehiclesGraftieaux, 2005.
    • StationsGraftieaux, 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 kmbuffer
    • Accessibility: How Measured?• Local– Shortest walking time on road network fromlocation of each property to closest BRT• Regional– Line-haul travel time from closest BRT station toFinancial District– Line-haul travel time from closest BRT station toFinancial District Downtown– Weighted index of travel time to all BRT stations• Weighted by the number of passengers travellingbetween 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 zoneattributes• Crime, socioeconomic, demographic, land uses,etc.
    • Results• Elasticity of rent withrespect to BRT stop dist.– -0.16 to -0.22• Every five minutes fromBRT stop, rent declines byUS$15• Elasticity of rent withrespect to BRT Corridor– 0.19 to 0.21• Every 100 meters fromcorridor, rent goes up byUS$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 UKTramlink (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 meanconservative 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 RecoveryHidalgo, 2006.
    • Commercial DevelopmentHidalgo, 2006.
    • Commercial DevelopmentHidalgo, 2006.
    • BE = f (Transport)An Example Policy Implication
    • Chicago: Hedonic Model, CTAStation Accessp = 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, numberof 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 theparcel, such as proximity to transportation services (including transit), relative accessibility toopportunities across the broader metropolitan area, etc.
    • Variation in Elasticity of Property Value withRespect to Walking Time Based on Properties’Walk Times to CTA Station
    • Land-Based Finance MechanismsDerived from Lari et al, 2009
    • Rail Transit Value CapturePotential: Chicago, LisbonZegras et al 2013b
    • 94Transit = f (BE): Summary• Consider the geographical scale of analysis/intervention– Generally, theory implies same types of effects, operating atdifferent scales• Theoretically, impacts are ambiguous• Complexity of LUT relationships increases with society’scomplexities– Time routines, age, family cycle, etc.– Keep in mind the type of potential activities (e.g., trip purpose) andrelated spatial and temporal constraints• Simple consideration: BE influence on walk influence tostation access
    • BE = f (Transit): In Summary• Public Transit, in right conditions, will influenceurban 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 mostcases• Ex-ante system in place (before build/expand)
    • BRT Centre May WebinarCost Efficiency under Negotiated Performance-BasedContracts and Benchmarking – Are there gains throughCompetitive Tendering in the absence of an IncumbentPublic Monopolist?Friday, May 24th at 4pm Sydney, Australia time (UTC+10)Presented by Professor David HensherInstitute of Transport and Logistics StudiesThe University of Sydney
    • References• Angel, S., J. Parent, D. Civco, A. Blei (2011) Making Room for a Planet of Cities, Policy FocusReport, Lincoln Institute of Land Policy.• Baum-Snow, N. and M. Kahn (2000) The effects of new public projects to expand urban railtransit. Journal of Public Economics, Vol. 77, pp. 241-263.• Bertaud, A. (2004) The spatial organization of cities: Deliberate outcome or unforeseenconsequence? 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 TransportStrategies: Review and Research Directions. Journal of Transport Geography Vol. 12: 127-140.• IBI Group. 2000. Greenhouse Gas Emissions from Urban Travel: Tool for EvaluatingNeighborhood 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? UrbanStudies, 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 busrapid 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 inTransportation Economics and Policy. Brookings.• Kenworthy, P. and F. Laube (1999) Patterns of automobile dependence in cities: an internationaloverview 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 theperspective of utility theory and activity-based theories. Environment and Planning B: Planningand Design, Vol. 32, pp. 33-46.• McNally, M. and A. Kulkarni. (1997) Assessment of Influence of Land Use-Transportation Systemon Travel Behavior. Transportation Research Record 1607, pp. 105-115.• Muller, Peter O. Transportation and Urban Form: Stages in the Spatial Evolution of the AmericanMetropolis. Chapter 3 in The Geography of Urban Transportation, 59-85. S. Hanson, ed. 3rdedition, 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 onTravel: The Netherlands Experience. Urban Studies Vol. 41, No. 3: 579-603.• US Census Bureau (2012) Patterns of Metropolitan and Micropolitan Population Change: 2000 to2010, Census Special Reports, September.• Voith, R. (2005) Comment on Effects of Urban Rail Transit Expansions: Evidence from SixteenCities, 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 TransitSystems? A Comparative Assessment of Chicago and Lisbon, Draft.• Zhang, M. (2004) The Role of Land Use in Travel Mode Choice: Evidence from Boston and HongKong. 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 TransitStations with Personal Characteristics and Station Context. Journal of Urban Planning andDevelopment (forthcoming).