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Real-time, Personalized Path Prediction and Automobile Accident Detection System

A project by Southern University and University of South Florida aiming to predict travel paths and detect accidents in real-time, reducing traffic congestion caused by accidents. Software developed for GPS-enabled devices. Methodology involves recording travel behavior using GPS for two weeks, creating polylines and buffers to detect accidents along the route. Dynamic and static testing conducted to ensure accuracy. Future plans to provide alternate routes to clients. Acknowledgements to collaborators and mentors. Contact Sean Barbeau for more information.

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Real-time, Personalized Path Prediction and Automobile Accident Detection System

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  1. Real-time, Personalized Path Prediction and Automobile Accident Detection System Tiffany Burrell Southern University and A & M College Baton Rouge, LA Center for Urban Transportation Research University of South Florida

  2. TRACIT Project • TRACIT’s objective is to develop a more effective way to collect data about people’s travel behavior in order to help the traffic engineers and civil engineers determine what would be the most efficient road plan for a specific area. • Software has been developed to put on GPS-enabled mobile devices to collect information about a persons travel behavior. • Currently the project is at the point of improving the automation of the software package.

  3. Global Positioning System GPS System • 27 Satellites • Orbit the Earth twice a day • Equipped with an Atomic clock GPS Receiver 3-D Trilateration • GPS Receiver locates 4 satellites above it • Then it calculates the distance to GPS satellites by timing a signal's journey from satellite to receiver

  4. My Research Assignment • THE PROJECT • Develop code that will predict a client's path at runtime without user input. • Warn the client of up coming traffic that they may encounter. • THE PURPOSE • To reduce traffic congestion caused by accidents • Save time • Reduce the number of irrelevant traffic warnings received from a traffic detection subscription • Reduce the number of times people say,” Had I known there was going to be traffic, I would have gone the other way.”

  5. Requirements • In order to predict someone’s travel path, their travel behavior should be recorded for at least two weeks using GPS technology.

  6. Collection of Coordinates

  7. Create a Polyline There is a polyline created for each TripID

  8. Place a Buffer Around the Polyline The Buffer is like a fence around a house. It is there to detect when someone is within your property.

  9. Methodology • Every five points the method is invoked • Query all polygons of the client that the short path lies within and that occurred within a hour of the current time • Detect all accidents that lie within those polygons • Send the client a text message that describes the description of the location of the accident

  10. Methodology

  11. Methodology Continued

  12. Methodology Continued

  13. Static Testing • Purpose • Make sure the buffers were large enough • Detect active automobile accidents • Make sure the correct polygons were queried • Detect other logical errors in the code • Test the time and user id factor of the algorithm • Material • Java Thread • 20 Accidents • 2015 Trips • 15 short paths • Results • Passed

  14. Dynamic Test • Purpose • Determine if the user would receive the warning in ample time • Test spatial reference • Material • GPS enabled cellular phone • TRACIT Software • Java thread • Results • Due to a problem between the ArcObject and Java interface the code could not detect users travel path at runtime. Parts of the application will have to be redone using ArcServer.

  15. Conclusion It is our hope that once people receive the accident warning, they would choose an alternate route in order to decrease traffic congestion within the vicinity of the accident.

  16. Future Endeavor When an accident is detected along a client’s most frequently pursued route, TRACIT will provide them with an alternate route.

  17. Acknowledgements • University of South Florida • Dr. Miguel Labrador • Mentor: Sean Barbeau • Narin Persad-Maharaj • Alfredo Perez • Center for Urban Transportation Research • National Science Foundation • Science & Engineering Alliance (SEA) • Timbuktu Academy/ LS- LAMP • Ronald E. McNair Program

  18. Questions?For more information about TRACIT please contact Sean Barbeau at barbeau@cutr.usf.edu

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