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.

1. Geospatial Analytics for Logistics
March 8, 2019
Mohamed Batran
Geo Team | GDSD Dept.
Rakuten, Inc.

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Our Services – serving users worldwide through businesses based in 30 countries/regions
https://global.rakuten.com/corp/about/ access date: Feb. 18th 2019

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https://global.rakuten.com/corp/about/ access date: Feb. 18th 2019

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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

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Parcel
Overload

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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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

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Tracking
&
Map Matching

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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. Background map with
vector road network
Hidden Markov Model
Map Matching
GPS Trace
Clustering analysis
Map Matching

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Map Matching
Technical Implementing

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Map Matching
Noisy Location Matched Location Noisy Location
Matched Route
@openstreetmap`
@openstreetmap
@openstreetmap`
@openstreetmap`

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Map Matching Demo
https://rak.box.com/s/xr9iti0ujt0vf1wden7sarjtuq9bpwmk

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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

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Estimated Time of
Delivery [ ETD ]
Can we reduce missed deliveries by notifying our customer with an
accurate predicting delivery time ?

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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

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Estimated time of Arrival [ETA] DEMO
https://rak.box.com/s/zvfh70gteifq26j5w784ol9w7ra7edwq
Note !! All delivery destinations are displaced with random noise