Data Driven Transportation Office Ramses Madou Stanford Parking & Transportation Services CHESC 2014

Topics Pressures Driving Metrics Tools Data Sources Data Driven Transportation Office

Pressures for Measuring Regulatory General Use Permit Communications Show value of programs to management and public Program effectiveness Managing programs to get the best out of them

Tools Dashboards (Pentaho) R, Python & Excel

5 Data Sources Sales data Parking pay stations Transit system Travel Survey License plate Recognition (future) Student registrar Human resources Biannual traffic counts

Data Driven Transportation Office Mode Choice Model Bike Program Transit TDM Parkin g Sales Integrating the entire departments activities into a single model and metrics system.

Core Model Mode Choice Model Model Broad Expandable Multiple time frames Metrics Annual commuter survey Cost per trip offset Use Basis for justification of programs Basis for metrics of programs

Data Driven Parking Mode Choice Model Parking Model outputs Parking needs Metrics Occupancy Model inputs Calibration of parking needs Calibration of drive alone, carpool mode

Data Driven Parking Mode Choice Model Parking Model outputs Quantities, demand Metrics Permits and transit pass sales Model inputs Calibration of parking type needs Calibration of transit mode Sales

Data Driven TDM Mode Choice Model Parking Model outputs Alt commute projections Target submarkets Metrics Campaign participation Cost per trip reduced Model inputs Mode changes TDMSales

Data Driven Bike Program Mode Choice Model TDM Model outputs Expected bike users Metrics Bike mode split Daily bike counts (future) Model inputs Bike usage Bike Progra m Parkin g Sales

Data Driven Transit Mode Choice Model Bike Program Model outputs Expected transit riders Metrics Ridership Model inputs Calibration of transit ridership Local and from connecting modes Transit TDM Parkin g Sales