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Dubuque Smarter Travel


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During the 2017 National Regional Transportation Conference, Chandra Ravada shared East Central Intergovernmental Association's work to use smartphone technology to better understand volunteers' movements within Dubuque, Iowa and the surrounding region. This information will improve regional planning and offer opportunities for transit planning.

Published in: Government & Nonprofit
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Dubuque Smarter Travel

  1. 1. Dubuque Smarter Travel Smarter Travel NADO Conference 0/201
  2. 2. Smart Travel City of Dubuque Transit in 1980’s 2
  3. 3. Smart Travel City of Dubuque Transit in 2010 5.0 Miles 3
  4. 4. Impact of Route changes on Jule Transit Smart Travel Increase in Length of the trip & not designing to action areas Decrease in Ridership Bigger head ways Less Reliability Increase in operating costs Less Fare Box Less Frequency Negative Perception Few funds to improve system Reduction in Federal Funds 4
  5. 5. Process to Improve Jule Transit Smart Travel Plan Optimize Transit Routes Optimize Stop Placement Contrast Supply vs Demand Optimize Operations Measure unmet demand Suggest new bus routes What to do Time of Day Activity Based New Service area & Demand How to do Census Data Traditional Surveys Online surveys Data gathering using technology X X Implement Design new routes Redesign services by time of day and activity Create new marketing plan 5
  6. 6. Smarter Travel Project Description 6 • Project Goal • Develop, test, and validate an integrated platform to leverage data captured from mobile devices complemented with travel diary surveys to generate information about travel patterns of citizens in the City of Dubuque, Iowa. • Data Generated • O/D Matrices • Corridor Speed • Meaningful Locations • Travel Modalities • Trip Purpose, etc. • Project Outcome • Primary - Public Transit Route Optimization • Secondary – Adjust Signal Timing, Reduce Accidents, Resource Planning, etc. Metropolitan Agency Emergency Management Small Cities Department of Transportation Regional Planning Law Enforcement City Engineering City Planning
  7. 7. Smarter Travel Proposed Analytics/Optimization Process 7 Trip mode estimation Duration of Stay Estimation Trip Segmentation Trip Purpose Estimation Meaningful Location Classification O/D from Smart phone Points of Interest O/D Airsage Data Smartphone Data Cell phone data O/D Travel Survey Compare With Travel Diary info Household Travel Survey DMATS Four step model Screen line test Clean Sheet route Optimization Optimal Routes Recruitment • Household Income • Household size • Number of Workers • Location Travel Diary Data Travel Diary Smart Phone Apps Sampling Size Phase 1 Phase 2 Phase 2 Phase 3 Phase 4 Phase 5
  8. 8. Smarter Travel Mobile Application 8 Infrastructure • Private IBM cloud • Secure and anonymized transmission of samples • Integration with other datasets Supported Platforms • iOS 7.1.1+ • Android 4.3+ User Experience • Periodic uploads • Battery-optimized sampling • Accuracy enhance sampling • Client notifications
  9. 9. Smarter Travel Trip Segmentation Analysis 9 • Display daily trajectories. • Display stops and trips. Clicking on each stop or trip will display its properties, such as starting/stopping time, duration, land use, trip purpose and trip mode. • Ability to pin custom locations on the map.
  10. 10. Smarter Travel Trip Purpose Classification and O/D Matrix 10 • 3 categories of POIs (schools, shopping/restaurants, other) • Classify work and home locations based on duration of stay and time of day • Trip purpose: home-based work, non-home-based work, home-based school, non-home- based school, home-based shopping, non-home-based shopping, home-based other, and non-home-based other. These categories will be used to partition the O/D matrix • The O/D matrix is aggregated between all the users and for different time intervals
  11. 11. Smarter Travel Trip Purpose Classification and O/D from Travel Diary 11
  12. 12. Smarter Travel Validation of Smartphone and Travel diary data 12 The Smarter phone data and Travel Diary data are compared at different levels. Level 1: Data collection The Smartphone data and Travel Diary data are compared to check accuracy of • Location • Missing trips • Mode choice Level 2: Trip purpose The Smart phone data is compared to Travel Diary data to check purpose of the trip Level 3: Origin/Destination matrix The origin/Destination matrix from both sources are compared to each other once the survey sample is extrapolated to MPO Smartphone peak O/D Travel Diary peak O/D
  13. 13. Smarter Travel Meaningful Location 13 • Time options: days of week, all weekends, all weekdays and all days of week. • View data in time periods. • Overlay location clusters.
  14. 14. Smarter Travel Corridor Speed and Travel Time 14 • Corridor speed or travel time • Time options: Time of Day • Direction of Travel.
  15. 15. Smarter Travel Bus Route Optimization approach 15 • Input data: • Street intersections and street links • Travel time of various travel modes on each link • Maximum number of buses and bus capacities. • O/D matrix • Additional constraints/requirements • Generate a set of candidate routes • Can include constraints such as hubs, limited change from current routes, etc. • Choose an optimal set of routes minimizing average travel time by formulating objective function and optimization problem as an mixed integer program (MIP). • Solve MIP using 2 types of algorithms: CPLEX and Volume algorithm • Routes are adjusted based on feedback and expert guidance from Jule Generate candidate routes Select optimal set of routes
  16. 16. Smarter Travel Optimized Bus routes 16 Bus routes based on peak period O/D
  17. 17. Smarter Travel Contact info Contacts 17 Chandra Ravada Director of Transportation Department East Central Intergovernmental Association ph.: 563-556-4166 e-mail: Web Sources