Using GPS-enabled Cell Phones to Improve Multimodal Planning and Facilitate Travel Behavior Change

Transcription

1 Using GPS-enabled Cell Phones to Improve Multimodal Planning and Facilitate Travel Behavior Change Center for Urban Transportation Research (CUTR) Computer Science & Engineering (CSE)

2 Overview Background Challenges and Opportunities Components and System Design Using the Data: TRAC-IT Findings from Phase I of TRAC-IT Next Steps

3 Challenges Paper activity diary problems Many data omissions/errors Requires extensive manual data entry Misses advanced trends and patterns Covers a limited time period (usually only 1 to 7 days) Discrete travel patterns not recognized by human analysis Vehicle-based GPS Misses trips made by transit and nonmotorized modes Need method to improve the quantity and quality of travel behavior data

4 Opportunities Proliferation of personal computing devices Over ¼ of the world s population (1.5 billion people) own cell phones (Oct. 2004) Portable Global Positioning Systems (GPS) E911 mandate for locating cell phones Devices can interact with the user (text, sounds, video, touch) New online GIS mapping tools Google Yahoo Microsoft

5 Enabling Portable Technology Personal Digital Assistants (PDAs) Small computer-like devices (i.e. Pocket PCs or Palm Pilots ) Touch screen operated with stylus (pen-like device) Cell phones Smart phones are now capable of many computer functions & text messaging Playing and recording sounds, video Ex. Verizon s V-Cast service

6 Portable Technology (Cont.) Global Positioning System (GPS) An electronic device that uses satellite signals to determine its current physical location Accurate up to 3-5 meters Now small enough to manufacture as a chip inside another device (i.e. cell phone) Source:

7 Quality and Quantity of Transportation Data Collected Using These Technologies Path which roads are used Travel time Travel distance Travel speed Travel purpose Intermediate stops (e.g., day care) Frequency of travel to a specific location Frequency of non-motorized travel Behavior of others in household

8 Using the Data: TRAC-IT Influencing Travel Behavior Transportation Demand Management (TDM) programs try and reduce VMT and VT Research has proven that advice tailored to observed travel patterns can reduce VMT and VT Group that received customized suggestions reduced their travel by an average of one trip per day and five miles per day more than the group that did not receive the suggestions (Tampa) 10 percent reduction in VKT and a slightly higher percentage reduction in VT and total hours spent in the car (Adelaide) Opportunities to reduce VMT and VT are missed because TDM agencies lack information about travel patterns Current paper data collection methods are cumbersome TRAC-IT: Records a person s travel behavior (An electronic trip diary ) Expert system analyzes data and provides feedback to user on past behavior

9 System Components Devices Software PDA w/ Cell Phone Service Separate GPS device Or PDA User Interface & Data storage/transfer GPS-enabled Cell Phone

10 Smart Diary Wizard - PDA A) B) C) (On Log Off ) Startup (On Startup) (On Enter ) (On Begin New Trip ) User logs in Main Screen User picks starting location (On Startup if User has previously logged in) (On Sync ) B.1) D) E) (On Start Trip ) (On End Trip ) Patent Pending (On Change Modes ) (On Done ) B) D.1) Screen shown during Trip (On QuickStop ) D.2) User selects ending location, purpose, mode of transportation, ( and driver status and occupancy if relevant)

11 Collected Data -Walk - Bicycle -Bus -Car

12 Using the Data: Expert System Gives personalized suggestions on how a participant might save time/money based on their real travel behavior Utilizes rules and user s personal description of places (i.e. Home, Work, or Publix ) instead of addresses to generate feedback. Distance Distance User Feedback Combined Trips Trip Chaining Alternate Loc. New Trip Data Mode Date Patent Pending Database Tables Coordinates Data From PDA Expert System Rules Database Tables

13 Feedback rules suggest: Riding a bicycle or walking for close locations Use of Transit (and gives nearby stops) Eliminating trips through technology (Internet or phone) Trip chaining (linking trips) Combining trips within the household Combining trips with members outside the household Combining trips across several days

14 Online GIS tools show trips and feedback to user Google Maps Plugin Free No GIS databases or servers to maintain Plugs into any webpage Tested technology runs on many browsers Web page interface currently in development for Phase 2 of project

15 Sample Web Interface

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17 Finding #1: Increased the Quantity of Data Collected Collected the location of the traveler, travel path, and speed (GPS) Recorded travel for all major modes (auto, transit, bike, and walk) to provide a richer and deeper view of travel behavior Recorded GPS information as well as collect data on travel purpose and occupancy information (PDA) Reduced the amount of non-essential data via algorithm (PDA)

18 Finding #2: Improved Data Quality Minimized the need for user inputs through the adoption of GUI wizards & GPS Change of mode, frequently visited locations and QuickStops Reduced respondent fatigue via Smart Diary components Allowing for longer periods of data collection Populating the fields based on previous patterns by performing complicated real-time data processing and analysis. Eliminated the need for data entry and associated errors Data synched directly into the database

19 Finding #3. Analyze travel behavior at the individual and household levels Uploaded the data directly to the server Provided suggestions via expert system for more efficient transportation options based on the household travel behavior

20 Next Steps Link with data from traffic sensors on highways for real-time feedback Give user incentive to participate in survey Use participants past travel-behavior to anticipate path and deliver relevant info Further evaluation of real impact of feedback on travel behavior (control vs. experimental groups)

21 Contact Information Sean J. Barbeau Research Associate Center for Urban Transportation Research University of South Florida 4202 E. Fowler Avenue, CUT100 Tampa, FL (813) (813) (fax)