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Future Dispatching

Future Dispatching. Bill Cumpston and Jason Lawrie. REQUIREMENTS . TAXIS As many bookings as possible Wait times not major consideration Driver not normally relevant. HIRE CARS, WATS Control numbers of bookings Wait times critical for hire cars, important for WATs. Driver often relevant.

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Future Dispatching

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  1. Future Dispatching Bill Cumpston and Jason Lawrie

  2. REQUIREMENTS • TAXIS • As many bookings as possible • Wait times not major consideration • Driver not normally relevant • HIRE CARS, WATS • Control numbers of bookings • Wait times critical for hire cars, important for WATs. • Driver often relevant

  3. OPTIMISE Minimize Distance

  4. OPTIMISE Current Rules

  5. OPTIMISE Time

  6. Dispatch is Complicated • Various algorithms – zone, distance, cover • Fleets have different requirements • Explanations are complicated • Not yet perfect

  7. Our Current Algorithm Inputs • Pickup Addresses • Destination Addresses • Requested Pickup Time • ASAP or Pre-Booking • Requested Vehicle Attributes (WAT, etc.)

  8. Our Current Algorithm Inputs • Preferred vehicle or driver • Current passenger wait time • Relative priority of attributes (must do maxi) • Driver plotting • Vehicle vacant time • Blacklist preferences

  9. It will not be getting simpler! • Driving time from current position • Time criticality (e.g. meeting a train) • Current driver earnings per hour • Driver rewards earned • Driver penalties incurred

  10. And that’s not all …. • Driver end of shift time and location • Distribution (E.g. “trip” run fairness) • Pre-allocation (E.g. private jobs) • Distribution to sub-networks or “friends” • Changes for peak or normal periods

  11. Many things are Trade-offs • Customer experience vs. Cost Reduction? • Driver fairness vs. rewards and penalties?

  12. Weighting Based Algorithm • High level categories provide a guide • This is combined with an “input” weighting • Add reward or penalty scores • Car or job with highest score wins

  13. Who Likes Zones and Layering? • Can we eliminate zones? • Perhaps have some “special” regions (ranks) • All distance calculations based on actual directions (including current time of day)

  14. What will drivers see? • “Their Score” • Will increase over time until they get a job • Rewards or penalties will affect their scores • In a localised region (e.g. rank) the highest score in a similar vehicle will get job first

  15. But why did .….. get job …… ? • Click “Explain” on any offer • Get scores for every vehicle for that offer • Can explain to drivers if needed • Can be used to tweak weighting

  16. THANK YOU! Questions?

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