Factors Driving Public Bike Share Demand: The Case of Bike Share Toronto Wafic El-Assi Undergraduate Research Fellow Supervisor: Prof. Khandker Nurul Habib

Presentation Outline  Background and Motivation  Data Analysis  Models Developed  Results  Potential Tools

 Four Generations: 1.“White Bikes” implemented in Amsterdam in “Coin Deposit System” introduced in the mid 1990s to reduce theft incidents 3.“IT based Systems” added transaction kiosks to solve the problem of anonymous users 4.“Demand-Responsive Multimodal systems” improved on docking stations, bicycle redistribution, and integration with other transport modes History of Public Bike Share Systems

Worldwide Bike Sharing Programs  Over 300 bike sharing programs (Shaheen et al.,2010)

Canadian Bike Sharing Programs  Bike Share Toronto -Launched in 2011  80 stations  1000 Bicycles Subscribers in 2012  BIXI Montreal -Launched in 2009  300 Stations  3000 Bicycles Subscribers in Expanded network 2013  450 Stations  5120 Bicycles

Canada’s Biking Mode Share  The proportion of commuters via bicycle in Toronto is 1.2%  This represents a 30% increase over previous census years  Commuting via bicycle most common in Downtown area

Average Distance Travelled per Commuter Trip Mode  The average trip distance in the downtown core is 2.25 km

Who Uses Bike Share Toronto? User AttributesResults Age Group % Bike Ownership64% Male67% University Degree79% Full Time Worker76% Income > $80000/yr52%

How is Toronto Using Bike Share? Home-Work Commute 2 To 5 Times Per Week40% Utilitarian Trip Purposes 1 To 4 Times Per Month39% Leisure Trip Purposes 1 To 4 Times Per Month36%

Factors Affecting Bike Share Ridership: User Perspective Location of Stations Very/Extremely Important92% Number of Stations Very/Extremely Important86% Availability of Bikes/Docks Very/Extremely Important85%

Bike Share as a Multimodal Transportation Service

Available Resources  Bike Share Data  Individuals’ detailed trip information:  day of trip  start time  end time  trip duration  Station Location coordinates

Available Resources  Historical Weather Data from Environment Canada: Relative Humidity Precipitation Snow on Ground Hourly Temperature Hourly Wind Speed Visibility

Available Resources  Bike Network Level of Service (LOS) Attributes  Generated by Google Maps API and analyzed in GIS  Number of intersections  Percentage of bike route that has a bike infrastructure  Type of bike infrastructure  Surrounding transit stations  Distance between stations

Available Resources  2011 Transportation Tomorrow Survey (TTS) Data Detailed trip Information by all travel modes within the study area Zonal employment and population  2013 Bike Share Customer Feedback Survey

Year Round Trip Distribution by Time Period Year Round

Trip Distribution by Time Period Year Round - Registered Year Round - Casual

Trip Distribution by Time Period August February

Trip Distribution by Time Period August - Registered August - Casual

Trip Distribution by Time Period February - Registered February - Casual

Weekend vs Weekday Trip Distribution

Trip Attraction at Bike Share Stations

Trip Generation at Bike Share Stations

Most/Least Active Routes

Regression Models  We developed three models:  Trip Attraction Model  Trip Generation Model  Station to Station Origin-Destination Model

VariablesDefinitionUnit Aggregation Level Socio- Demographic MaleMale to female ratio%Zonal EmpdenAverage employment densityPers/kM 2 Zonal popdenAverage population density Pers/kM 2 Zonal Weather TempPerceived Temperature oCoCDaily SnowAmount of snow on groundcmDaily HumRelative humidity%Daily PrecipAmount of precipitationmmDaily Variables Included in Attraction/Generation Model

VariablesDefinitionUnit Aggregation Level Built Environment n_stationsNumber of stations in 200m bufferNAStation DocksNumber of docks per stationNAStation University = 1 if the zone has university campus; = 0 otherwise NAZonal TransitNumber of Subway/Commuter Rail Stations NAZonal Variables Included in Attraction/Generation Model

VariablesDefinitionUnit Correlation Socio- Demographic MaleMale to female ratio% EmpdenAverage employment densityPers/kM 2 popdenAverage population density Pers/kM 2 Weather TempPerceived Temperature oCoC SnowAmount of snow on groundcm HumRelative humidity% PrecipAmount of precipitationmm Attraction-Generation Model - Results

VariablesDefinitionUnitCorrelation Built Environment n_stationsNumber of stations in 200m bufferNA DocksNumber of docks per station > 18NA University = 1 if the zone has university campus; = 0 otherwise NA TransitNumber of Subway/Commuter Rail Stations NA Attraction-Generation Model - Results

VariablesDefinitionUnit Aggregation Level Built Environment n_stationsNumber of stations in 200m bufferNAStation distance_travelled Distance traveled between an OD pair kmBike Path intersections Number of intersections with major roads between an OD pair NABike Path bike_infra Percent of bike infrastructure compared to total bike path %Bike Path DocksNumber of docks per stationNAStation University = 1 if the zone has university campus; = 0 otherwise NAZonal TransitNumber of Subway/Commuter Rail Stations NAZonal Variables Included in Origin- Destination Model

VariablesDefinitionUnitCorrelation Built Environment n_stationsNumber of stations in 200m bufferNA distance_travelled Distance traveled between an OD pair km intersections Number of intersections with major roads between an OD pair NA bike_infra Percent of bike infrastructure > 50% % Docks Number of docks per station = 1 if number of Docks > 18 NA University = 1 if the zone has university campus; = 0 otherwise NA TransitNumber of Subway/Commuter Rail Stations NA Origin-Destination Model Results

Why is This Important?  Develop a policy tool capable of forecasting bike share demand at the station level  Utilize models and ArcMap GIS to establish future station locations  Develop policy recommendations for bike lane infrastructure expansion in Toronto

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