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DOiT Dynamic Optimization in Transportation

DOiT Dynamic Optimization in Transportation. Ragnhild Wahl, SINTEF (Per J. Lillestøl SINTEF). Background. Many transporters have a high degree of dynamics that requires an on-line/operative VRP-solver: Transportation orders (occurrence, properties) Fleet status (break-down, etc.)

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DOiT Dynamic Optimization in Transportation

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  1. DOiT Dynamic Optimization in Transportation Ragnhild Wahl, SINTEF (Per J. Lillestøl SINTEF)

  2. Background • Many transporters have a high degree of dynamics that requires an on-line/operative VRP-solver: • Transportation orders (occurrence, properties) • Fleet status (break-down, etc.) • Travel Time All these areas contain dynamic and uncertain information • Commercial VRP-solvers are (usually): • Static (planning completed before operation) • Deterministic This is sub-optimal

  3. Background • Dynamic traffic information is not available to planners In sum, these factors reduces the possibility for efficient use of resources through advanced transportation planning.

  4. Goals • Phase 1: Establish a “Normal Travel Time” for the road network in Oslo • Phase 2: Develop an on-line information service for dynamic travel times in Oslo • Enhance commercial VRP-solver that handles dynamics and uncertainty in the given areas • Enhance end user transportation planners in goods- and public transportation

  5. The DOiT project plan

  6. FCD – Floating Car Data Floating Car Data is a term that is used to describe continuous data collection from a moving vehicle. FCD is a general term for several different traffic data. However, we often mean travel time when we speak about FCD. Sensors and techniques to collect FCD varies. The most common technology for collecting FCD is GPS.

  7. FCD – Other projects We have searched for similar projects, and we found several different test projects. The most interesting projects for us were: • German Aerospace Center (In Germany) • OPTIS ( In Sweden) • To discuss experiences and solutions we have paid a visit to both countries.

  8. 3 methods for floating car data (1) • Positions are collected at a fixed frequency, and a continuous position log is being stored for each participating vehicle • This method provides information about speed, direction and route choice • After collection, the position log must be decomposed into actual sections of the roads • It is necessary to identify the vehicles in order to gain a continuous log for each one • Due to this, it is important that the system protects the driver's privacy

  9. 3 methods for floating car data (2) 2. Positions are collected at a fixed frequency, but only single point data is being collected and stored • The method provides information about speed, but direction data has poor quality at low speed • Point data must be converted to section data • Route choice cannot be measured and must be estimated • Participating vehicles are not identified, thus the system is anonymous and protection of privacy is not an issue

  10. 3 methods for floating car data (3) 3. Time and vehicle identification are being collected at predefined positions, and a continuous log is being stored for each vehicle • This method provides information about speed, direction and route choice, same as for method 1 • By using predefined positions it is not necessary to decompose the log afterwards in this method • Protection of privacy will be similar as for method 1

  11. Traffic Data Collection • There are 1900 Taxies in Oslo with GPS • Up to 55 000 trips per day • Closed communication radio network which can transfer position data • Capacity of network must be tested in “real life”

  12. Architecture data flow Raw data from taxi Data input Formatted data (map matching) Identifying links Calculating Travel times Travel time - Data base Traffic data Spider server

  13. Oslo Taxi AS SINTEF Connect DOiT-server DOiT-klient LogIn Telegrammer Data communication LAN

  14. Log data • Taxi ID • Longitude • Latitude • Direction • Speed • Time • Status

  15. Project status • Data communication system is developed and tested on simulated data • System for extracting data from the GPS and the taxi’s internal system is under development but delayed • Thus, the total travel time system is delayed • Plan is too implement solution next spring

  16. Organisation

  17. Best practice in this project • Cooperation between Sweden and Norway in this project. • Exchange of experience • Sharing algorithms (data maping) • Sharing developed application for collecting data from the taxi system • Use of dynamic data in VRP-solving

  18. Thank you for your attention! Contact: ragnhild.wahl@sintef.no

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