Accessibility Approach to Estimating Bicycle and Pedestrian Demand

Accessibility Approach to Estimating Bicycle and Pedestrian DemandNCHRP 08-78Renaissance Planning GroupRich KuzmyakChris SinclairAlex BellTRB National Transportation PlanningApplications ConferenceMay 6, 2013Columbus, OhioOutlineResearch contextBasics of the accessibility approachSummary of research findingsApplicationNext stepsResearch ContextNCHRP 8-78: Estimating Bicycle and Pedestrian DemandCreate new methods sensitive to land use and facilitiesProduct: Practitioner Guidebook of scalable techniquesTour-based models (Seattle Bradley & Bowman)Enhanced 4-step (Seattle Kockelman & Kahn)Accessibility approach (Arlington VA)

Basics of the Accessibility ApproachTravel behavior is responsive to accessibilityMode choice can be linked to:Amount of activity reachable from an origin over corresponding modal networkAccount for impedance unique to mode (directness, slope, facility type, etc.)

Study Area: Arlington County, VA Activity and Networks

Study Area: Arlington County, VA Survey Data

Basics of the Accessibility ApproachUse detailed travel networks to model travel times from a given origin to all accessible destinations (by mode):WalkBikeTransitAuto NAVTEQ streets, GTFS transit schedules, bicycle facilities and trails

Comparative Accessiblities

Comparative AccessiblitiesSummary of Research FindingsNon-motorized trip making is associated with high accessibility scores, regardless of modeModal competiveness (in terms of accessibility) influences mode choiceMore transit trips are made to destinations that have high walk and transit accessibility scoresDiscretionary walk trips are highly sensitive to walk accessibility at the trip originWalk to work a unique/limited opportunity

Summary of Research FindingsNumber of Establishments Accessible from OriginChosen ModeAutoTransitWalkBikeTransit13671291091162Drive Alone11956960868Auto Pax11776657840Walk134512498998Bicycle1506120911191Number of Employees Accessible from OriginChosen ModeAutoTransitWalkBikeTransit181101650145713986Drive Alone1509298479710783Auto Pax136588406739254Walk235832962232915778Bicycle195161470123714845Summary of Research FindingsWalk accessibility alone influences mode choice12Spreadsheet Application

Spreadsheet ApplicationSpreadsheet tool (beta version and template for additional tools)

ApplicationModels effects ofLand use changesDevelopment master plansDisaggregated TAZ forecastsFuture land use scenariosNetwork enhancementsAccessibility benefits of improving street connectivityNon-motorized facilitiesApplicationData and processMicrozone residential and employment activityPerson trip generationZone to zone walk skims (microzones)Walk accessibility scoreOutputMicrozone walk trip generationMatrix of walk microzone to microzone walk trip interchangeNext StepsDevelop native GIS tool Community Viz or other scenario planning platformEnable dynamic spatial analysisEvaluate and operationalize relationships among modal accessibilities and socio-demographicsHigh local walk access, low regional transit access vs. High local walk access, high regional transit accessHousehold and individual characteristics

Next StepsIncorporate into NCHRP GuidebookIntegrate with regional travel demand modelIncorporate EPAs Smart Location Database modal accessibilities and add geoprocessing services to support rapid deployment of the tool

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