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Integrated Mode Share Estimation Platform (IMSEP): Using ArcGIS and Multiple Regression to Predict Transit Ridership
 

Integrated Mode Share Estimation Platform (IMSEP): Using ArcGIS and Multiple Regression to Predict Transit Ridership

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This was originally presented at the 2013 Esri International User Conference in San Diego, CA, USA by Michael Markieta. The Integrated Mode Share Estimation Platform (IMSEP) assumes the role as an ...

This was originally presented at the 2013 Esri International User Conference in San Diego, CA, USA by Michael Markieta. The Integrated Mode Share Estimation Platform (IMSEP) assumes the role as an embedded tool in the planning workflow for expedited transportation ridership prediction and mode share analysis. The tool was created as an extension for ArcGIS 10.1 using Python Add-Ins. We take advantage of core GIS functions in our analysis and utilize geospatial databases for data storage; while the central analytical engine was built based on leading research on connections between observed travel behavior, land use and urban design. The tool takes into account the direct and derivative attribute or indicator values for any given catchment area around a transportation hub. Through parameter and model adjustments, the IMSEP allows for the modifications to underlying attribute and indicator values, which subsequently produce alternative transit ridership and mode share estimations. The audience will learn about transportation, neighbourhood design attribute indicators and the use of Python to create add-ins for ArcGIS.

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    Integrated Mode Share Estimation Platform (IMSEP): Using ArcGIS and Multiple Regression to Predict Transit Ridership Integrated Mode Share Estimation Platform (IMSEP): Using ArcGIS and Multiple Regression to Predict Transit Ridership Presentation Transcript

    • Esri UC2013 . 2013 Esri International User Conference July 8–12, 2013 | San Diego, California IMSEP: Integrated Mode Share Estimation Platform Using ArcGIS and Multiple Regression to Predict Transit Ridership Michael Markieta Arup & Ryerson University, Toronto, ON, Canada
    • Esri UC2013 . IMSEP: Using ArcGIS and Multiple Regression to Predict Transit Ridership
    • Esri UC2013 . Outline 1. Traditional transportation planning I. Scenario testing II. Assumptions made for mode share/split 2. Transit-oriented development (TOD) planning 3. TOD catchment area and criteria for transit ridership 4. IMSEP: a Python add-in for ArcGIS 5. Implications of IMSEP for transportation planning IMSEP: Using ArcGIS and Multiple Regression to Predict Transit Ridership
    • Esri UC2013 . Traditional Transportation Planning IMSEP: Using ArcGIS and Multiple Regression to Predict Transit Ridership
    • Esri UC2013 . Traditional Transportation Planning Assumptions made for mode share/split • ITE Trip Generation manual • Authoritative & professional experience • Major caveats on model design IMSEP: Using ArcGIS and Multiple Regression to Predict Transit Ridership
    • Esri UC2013 . Traditional Transportation Planning Scenario Testing • Variety of tools for different levels of work - e.g. Agent-based modeling vs. spreadsheet model • Not everyone can be both a transportation planner and GIS analyst • Spend time and money efficiently - Leverage exploratory work done by academics IMSEP: Using ArcGIS and Multiple Regression to Predict Transit Ridership
    • Esri UC2013 . Transit-Oriented Development Planning IMSEP: Using ArcGIS and Multiple Regression to Predict Transit Ridership
    • Esri UC2013 . Transit Oriented Development Planning • Originated in Western cities - Response to low-density urban sprawl • Spatially sensitive planning - Considers residential and commercial districts located around the station, corridor, zone or region. • TOD strategies vary - Transit service, active transportation, walkability, safety, parking management, land use diversification, urban design, local accessibility, decreasing reliance on car ownership, etc. IMSEP: Using ArcGIS and Multiple Regression to Predict Transit Ridership
    • Esri UC2013 . TOD Catchment Areas and Transit Ridership IMSEP: Using ArcGIS and Multiple Regression to Predict Transit Ridership
    • Esri UC2013 . Transit Oriented Development Planning Transit ridership • Transit ridership is influenced by TOD strategies IMSEP: Using ArcGIS and Multiple Regression to Predict Transit Ridership Transit Ridership Reliance on Automobile Transit Ridership Transit Service Transit Ridership Diversified Land Use
    • Esri UC2013 . Transit Oriented Development Planning Transit ridership • Transit ridership a function of the station/catchment area - Measureable criteria in the immediate area around a station - Often spatial in nature (density, distance, etc.) • Many ways to define a catchment area… - Buffers, network distance, distance-decay buffers/network, etc. • Many criteria to look at within a catchment area… - Population density, # of bus routes/stops, average bus headway, etc. IMSEP: Using ArcGIS and Multiple Regression to Predict Transit Ridership
    • Esri UC2013 . Transit Oriented Development Planning Station Area / Catchment Area IMSEP: Using ArcGIS and Multiple Regression to Predict Transit Ridership Gutierrez, J., Cardozo, O., D. (2011). Transit ridership forecasting at station level: an approach based on distance-decay weighted regression. Journal of Transport Geography, 19(6), pp 1081-1092. Simple Buffer Network Distance Network Distance + Distance Decay Buffer + Distance Decay
    • Esri UC2013 . Transit Oriented Development Planning Station Area / Catchment Area IMSEP: Using ArcGIS and Multiple Regression to Predict Transit Ridership Sung, H., Oh, J. (2011). Transit-oriented development in a high-density city: Identifying its association with transit ridership in Seoul, Korea. Cities, 28(1), 70-82. Roads and Trails Land Use
    • Esri UC2013 . Key Criteria for Transit Ridership Summary table of criteria from review of literature IMSEP: Using ArcGIS and Multiple Regression to Predict Transit Ridership INDEX Authors COUNT Parsons Brinckerhoff (1996) Kuby et al. (2004) Walters & Cervero (2003) Chu (2004) Gutierrez et al. (2011) Sung & Oh (2011) Cordozo et al. (2012) Dependent Variable 1 Daily station boarding x x x x 4 2 Average weekday boarding x 1 3 AM Peak period entrances/exits x 1 4 Monthly boarding x 1 Independent Variable 1 Population density x x 2 2 Employment density x x x 3 3 Terminal station (binary) x x 2 4 Park-and-ride (binary) x x 2 5 Feeder bus services (binary) x 1 6 Catchment size x 1 7 Distance to CBD x 1 8 Employment within walking distance x x 2 9 Population within walking distance x x x 3 10 Serving airport passengers (binary) x 1 11 International border (binary) x 1 12 # of park-and-ride spaces x x 2 13 # of bus connections x 1 14 Heating and cooling degree-days x 1
    • Esri UC2013 . Key Criteria for Transit Ridership Summary table of criteria from review of literature IMSEP: Using ArcGIS and Multiple Regression to Predict Transit Ridership INDEX Authors COUNT Parsons Brinckerhoff (1996) Kuby et al. (2004) Walters & Cervero (2003) Chu (2004) Gutierrez et al. (2011) Sung & Oh (2011) Cordozo et al. (2012) Dependent Variable 1 Daily station boarding x x x x 4 2 Average weekday boarding x 1 3 AM Peak period entrances/exits x 1 4 Monthly boarding x 1 Independent Variable 1 Population density x x 2 2 Employment density x x x 3 3 Terminal station (binary) x x 2 4 Park-and-ride (binary) x x 2 5 Feeder bus services (binary) x 1 6 Catchment size x 1 7 Distance to CBD x 1 8 Employment within walking distance x x 2 9 Population within walking distance x x x 3 10 Serving airport passengers (binary) x 1 11 International border (binary) x 1 12 # of park-and-ride spaces x x 2 13 # of bus connections x 1 14 Heating and cooling degree-days x 1 Independent Variable 15 Transfer station (binary) x 1 16 Normalized accessibility x 1 17 % of PMSA employment covered by system x 1 18 % of renters within walking distance x 1 19 Transit technology (light vs. heavy rail) x 1 20 Train frequency x 1 21 Feeder bus service levels (ordinal) x 1 22 Median household income in catchment area x 1 23 # of 0-car households in catchment area x x 2 24 Share of persons under 18 x 1 25 Share of person 18-64 x 1 26 Share of persons female x 1 27 Share of persons Hispanic x 1 28 Share of persons White x 1 29 Transit level of service (TLOS) x 1 30 Transit stops within 2-5 min walk time x x 2 31 Pedestrian factor x 1 32 Persons up/downstream without transfer x 1 33 Jobs up/downstream without transfer x 1 34 Including a trolley stop (binary) x 1 35 Number of other TLOS stops x 1 36 Nodal accessibility x 1 37 # of transit lines x x x 3 38 Foreign population x 1 39 # of workers x x 2 40 Employment in commercial sector x 1
    • Esri UC2013 . Key Criteria for Transit Ridership Summary table of criteria from review of literature IMSEP: Using ArcGIS and Multiple Regression to Predict Transit Ridership INDEX Authors COUNT Parsons Brinckerhoff (1996) Kuby et al. (2004) Walters & Cervero (2003) Chu (2004) Gutierrez et al. (2011) Sung & Oh (2011) Cordozo et al. (2012) Dependent Variable 1 Daily station boarding x x x x 4 2 Average weekday boarding x 1 3 AM Peak period entrances/exits x 1 4 Monthly boarding x 1 Independent Variable 1 Population density x x 2 2 Employment density x x x 3 3 Terminal station (binary) x x 2 4 Park-and-ride (binary) x x 2 5 Feeder bus services (binary) x 1 6 Catchment size x 1 7 Distance to CBD x 1 8 Employment within walking distance x x 2 9 Population within walking distance x x x 3 10 Serving airport passengers (binary) x 1 11 International border (binary) x 1 12 # of park-and-ride spaces x x 2 13 # of bus connections x 1 14 Heating and cooling degree-days x 1 Independent Variable 41 Employment in education sector x 1 42 Land use mix x x x 3 43 Urban bus lines x x 2 44 Suburban bus lines x x 2 45 Average headway x 1 46 # of short bus routes (<20km) x 1 47 Distance between stations x 1 48 # of existing stations x 1 49 Residential density x 1 50 Commercial density x 1 51 Business density x 1 52 Commercial/business land use mix x 1 53 Subway accessibility x 1 54 Rail accessibility x 1 55 Total road length x 1 56 Average road width x 1 57 % of drive way x 1 58 Four-way intersection density x 1 59 Dead end roads x 1 60 Average building group area x 1 61 Average building area x 1 62 Street density x 1
    • Esri UC2013 . A Python add-in for ArcGIS IMSEP: Integrated Mode Share Estimation Platform Station Area
    • Esri UC2013 . What is IMSEP? IMSEP: Using ArcGIS and Multiple Regression to Predict Transit Ridership
    • Esri UC2013 . Integrated Mode Share Estimation Platform • Interactive GUI created by leveraging wxPython • Interrogates current Map and File Geodatabase • Fits a regression model with the existing conditions • Enables users to modify existing conditions and see a change (∆) in transit ridership IMSEP: Using ArcGIS and Multiple Regression to Predict Transit Ridership
    • Esri UC2013 . Integrated Mode Share Estimation Platform Regression Model • Sung, H., Oh, J. (2011). Transit-oriented development in a high-density city: Identifying its association with transit ridership in Seoul, Korea. Cities, 28(1), 70-82. Average headway; # of short bus routes (<20km); distance between stations; # of station exits; # of bus stops; residential density; total road length; business density; commercial/business land use mix; average building group area; average building area; commercial density; subway accessibility; rail accessibility; average road width; % of homes with drive way; four-way intersection density; dead end roads IMSEP: Using ArcGIS and Multiple Regression to Predict Transit Ridership
    • Esri UC2013 . Integrated Mode Share Estimation Platform IMSEP: Using ArcGIS and Multiple Regression to Predict Transit Ridership
    • Esri UC2013 . Integrated Mode Share Estimation Platform IMSEP: Using ArcGIS and Multiple Regression to Predict Transit Ridership
    • Esri UC2013 . Integrated Mode Share Estimation Platform IMSEP: Using ArcGIS and Multiple Regression to Predict Transit Ridership
    • Esri UC2013 . Integrated Mode Share Estimation Platform IMSEP: Using ArcGIS and Multiple Regression to Predict Transit Ridership
    • Esri UC2013 . Integrated Mode Share Estimation Platform IMSEP: Using ArcGIS and Multiple Regression to Predict Transit Ridership
    • Esri UC2013 . Integrated Mode Share Estimation Platform IMSEP: Using ArcGIS and Multiple Regression to Predict Transit Ridership
    • Esri UC2013 . Integrated Mode Share Estimation Platform Database Schema • Data is stored based on its relationship to the station catchment area. • A one-to-many relationship exists between catchment area and criteria data. IMSEP: Using ArcGIS and Multiple Regression to Predict Transit Ridership
    • Esri UC2013 . Integrated Mode Share Estimation Platform Initial Setup Wizard IMSEP: Using ArcGIS and Multiple Regression to Predict Transit Ridership
    • Esri UC2013 . Integrated Mode Share Estimation Platform Initial Setup Wizard IMSEP: Using ArcGIS and Multiple Regression to Predict Transit Ridership
    • Esri UC2013 . Integrated Mode Share Estimation Platform Initial Setup Wizard IMSEP: Using ArcGIS and Multiple Regression to Predict Transit Ridership
    • Esri UC2013 . Integrated Mode Share Estimation Platform Initial Setup Wizard IMSEP: Using ArcGIS and Multiple Regression to Predict Transit Ridership
    • Esri UC2013 . Integrated Mode Share Estimation Platform Initial Setup Wizard IMSEP: Using ArcGIS and Multiple Regression to Predict Transit Ridership
    • Esri UC2013 . Integrated Mode Share Estimation Platform Initial Setup Wizard IMSEP: Using ArcGIS and Multiple Regression to Predict Transit Ridership
    • Esri UC2013 . Integrated Mode Share Estimation Platform Initial Setup Wizard IMSEP: Using ArcGIS and Multiple Regression to Predict Transit Ridership
    • Esri UC2013 . Integrated Mode Share Estimation Platform Initial Setup Wizard IMSEP: Using ArcGIS and Multiple Regression to Predict Transit Ridership
    • Esri UC2013 . Integrated Mode Share Estimation Platform Kennedy Subway Station (TTC): Base Scenario ‒ Model ∆ = 0
    • Esri UC2013 . Integrated Mode Share Estimation Platform Kennedy Subway Station (TTC): Base Scenario ‒ Model ∆ = 0 Residential GFA: 113,802 m2
    • Esri UC2013 . Integrated Mode Share Estimation Platform Kennedy Subway Station (TTC): Base Scenario ‒ Model ∆ = 0 Residential GFA: 113,802 m2 Bus Stops: 17
    • Esri UC2013 . Integrated Mode Share Estimation Platform Residential GFA: 113,802 m2 Bus Stops: 17 ~800 Condo Units + 2 Bus Stops ‒ Model ∆ = +2610 Daily Ridership
    • Esri UC2013 . Integrated Mode Share Estimation Platform Bus Stops: 17 Residential GFA: 213,802 m2 (∆ +100,000 m2) ~800 Condo Units + 2 Bus Stops ‒ Model ∆ = +2610 Daily Ridership
    • Esri UC2013 . Integrated Mode Share Estimation Platform Bus Stops: 19 (∆ +2) Residential GFA: 213,802 m2 (∆ +100,000 m2) ~800 Condo Units + 2 Bus Stops ‒ Model ∆ = +2610 Daily Ridership
    • Esri UC2013 . The IMSEP can expedite transportation mode share analyses. It adds value to projects by allowing us to run more scenarios in shorter amounts of time. IMSEP: Using ArcGIS and Multiple Regression to Predict Transit Ridership
    • Esri UC2013 . Future Work • Utilize 3D visualization and modeling with CityEngine • Validate IMSEP predictions against the real world • Automate data capture and digitization • Implement and explore a variety of regression models IMSEP: Using ArcGIS and Multiple Regression to Predict Transit Ridership
    • Esri UC2013 . Thank you. Michael Markieta GIS Consultant, Arup & MSA Candidate, Ryerson University, Toronto, Canada www.arup.com | michael.markieta@arup.com IMSEP: Using ArcGIS and Multiple Regression to Predict Transit Ridership Special acknowledgements to Dr. Claus Rinner and the Natural Sciences and Engineering Research Council of Canada (NSERC) for partially supporting this work.