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Real-time tracking for logistics

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IoT is a promising field enabling a complete transformation of existing businesses. This project is a logistics related effort to enable real-time visibility of drivers during the last mile delivery process. The objective can be achieved through location tracking on a background map data. In addition, the project aims to build multiple applications such as calculating estimated arrival time of packages using machine learning techniques.

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Real-time tracking for logistics

  1. 1. Geospatial Analytics for Logistics March 8, 2019 Mohamed Batran Geo Team | GDSD Dept. Rakuten, Inc.
  2. 2. 2 Our Services – serving users worldwide through businesses based in 30 countries/regions https://global.rakuten.com/corp/about/ access date: Feb. 18th 2019
  3. 3. 3 https://global.rakuten.com/corp/about/ access date: Feb. 18th 2019
  4. 4. 4 30Countries & Regions 1.2BillionGlobal Members 70+ Services 12.9Trillion JPY Global Gloss Transaction Value Rakuten Ecosystem https://global.rakuten.com/corp/about/ access date: Feb. 18th 2019
  5. 5. 5 Parcel Overload
  6. 6. 6Source: https://www.nippon.com/en/currents/d00321/parcel-overload-japan%E2%80%99s-delivery-crisis-and-how-to-tackle-it.html Access date: Feb. 18, 2019 Source: https://www.japantimes.co.jp/news/2017/04/16/national/home-delivery-firms-strain-e-commerce-load/#.XGtJMc8zajR Access date: Feb. 18, 2019 q Constantly rising demand with booming E-commerce q Companies demanding same-day delivery qEmployee shortages qMissed deliveries “20 percent of all parcels have to be redelivered each year ” “2015 transport ministry report”
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  9. 9. 9 Shopper place order • Order Processing • Warehousing • Production and fulfillment TrackingMap Matching ETD Routing Vehicle Routing Problem Optimization Last mile logistics is the least efficient stage of the supply chain and comprises up to 28% of the total delivery cost. Ranieri, Luigi, et al. "A review of last mile logistics innovations in an externalities cost reduction vision." Sustainability 10.3 (2018): 782. High Level Logistics Cycle
  10. 10. 10 Tracking & Map Matching
  11. 11. 11 Map Matching • Consistently Knowing driver location on the correct road segment Why ? Applications • Mapping historical route and total journey distance • Estimate Time of Arrival [ ETA ] @openstreetmap
  12. 12. Background map with vector road network Hidden Markov Model Map Matching GPS Trace Clustering analysis Map Matching
  13. 13. 13 Map Matching Technical Implementing
  14. 14. 14 Map Matching Noisy Location Matched Location Noisy Location Matched Route @openstreetmap` @openstreetmap @openstreetmap` @openstreetmap`
  15. 15. 15 Map Matching Demo https://rak.box.com/s/xr9iti0ujt0vf1wden7sarjtuq9bpwmk
  16. 16. 16 Map Matching Edge cases (1) parallel roads (2) Two level topography road and bridge (3) sharp edge with a point in the wrong road entry @openstreetmap @openstreetmap @openstreetmap
  17. 17. 17 Estimated Time of Delivery [ ETD ] Can we reduce missed deliveries by notifying our customer with an accurate predicting delivery time ?
  18. 18. 18 Estimated Time of Delivery [ ETD ] Traffic Forecasting Delivery duration prediction Alternativeroute Drivingduration Dynamic Routing If order is cancelled Dynamic Routing For Redelivery with time constraint
  19. 19. 19 Estimated time of Arrival [ETA] DEMO https://rak.box.com/s/zvfh70gteifq26j5w784ol9w7ra7edwq Note !! All delivery destinations are displaced with random noise

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