Driver Location Intelligence at Scale using Apache Spark, Delta Lake, and MLflow on Databricks

1. WIFI SSID:Spark+AISummit | Password: UnifiedDataAnalytics

2. Sergio Ballesteros, TomTom Kia Eisinga, TomTom Driver Location Intelligence at Scale using Apache Spark, Delta Lake and MLflow on Databricks #UnifiedDataAnalytics #SparkAISummit

3. Ourvision A safe, connected, autonomous world that is free of congestion and emissions.

4. 4 Bigdatadrivesour business,but dataprivacyalways comesfirst

5. Data • Anonymous location (GPS) traces 5

6. 742.000.000kmevery day 18.000 x 6

8. Data • Anonymous location (GPS) Traces • Community inputs • User events • Journalistic data • Car sensor data 8

9. Dataflow 9 ~150 trillion data points ~80 billion data points per day

10. Dataflow 10

11. Dataflow 11

12. In dash systems are outperformed by smartphones The embedded systemis expected to be up-to-date, with no user interaction. And the most visible component of it is a map. Usecase1:IQMapsanalytics 12

13. Driversdonotupdatetheirmaps Today’s solutions provide manual updates, oftenwith a necessity to drive to the dealer. This is way too complex and inefficient. 13

14. 14

15. OEMsrequire dataefficient solutions While drivers expect up-to-date system, the carmakers are usually concerned about the data cost required for the map management. 15

16. 98% OF TRIPS ARE DRIVEN WITHIN150KM RADIUS99.8% OF TRIPS ARE DRIVEN WITHIN1000KM RADIUS 16

17. Whenradiusis0km • User drives within 2 regions every week day • Radius of 0 km. • Download and install justhome regions • Cellular data usage kept to a minimum 17

18. Whenradiusis150km • User drives within 2 update regions every week day • Radius of 150 km. • Home region: 6 update regions. • Cellular data usage increased 18

19. IQMapsdemowithMLflow 19

20. 20

21. Realresultsusing0.5Mtrips 21 “This insight has led me to the conclusion that a default radius of 150km is unnecessary, and a small radius of ~10km would already satisfy mostdrivers while keeping cellular data usage low for OEMs.” - Rolf Dorland, PM at TomTom

22. Goingonholidays • User goes for his holiday (less frequent updated region) • Once user starts driving, updates for all update regions the route goes through are downloaded and installed. 22

23. 23 Destinationprediction

24. 24

25. Opportunity 25 Past: Rule-based solution Delta Lake pipelines Present: Machine Learning

26. Data 26 Original trace data from 1 source 227K device serials Filtering out invalid trips 143K device serials Users with at least 50 trips 3.6K device serials Devices feasible for modelling 2.5K device serials

27. Features For each trip, we have the following information: • Where did the trip start? • At what speed were you driving when the trip started? • What was the time of day (morning/afternoon/evening) when the trip started? • Was it rush hour when the trip started? • What day of the week was it? • Was it a weekend day? • What was the season? • Which driver profile do you belong to? Historical information: • Which destination did you go to your last trip? And the one before that? And the one before that? • If it is a, let's say Monday, where did you go to the last Monday you made a trip? (do this for every weekday) To predict: To which destination are you going? What do we use in the end? 27

28. Labels • We are given the latitude and longitude of a destination of a trip. • In order to find out which latitude and longitudes belong to the same destination, we apply a clustering algorithm called DBSCAN. • DBSCAN clusters together destinations that are within 500 meters from each other. We should have at least 5 trips to a destination in order to call it a cluster. How do we define where you are going? 28

29. 29

30. Train,validationandtestsplit Trip ID Date Destination Trip 1 January1 Cluster1 Trip 2 January22 Cluster2 Trip 3 February3 Cluster1 Trip 4 February15 Cluster2 Trip 5 March 2 Cluster1 Trip 6 March 14 Cluster1 Trip 7 March 27 Cluster2 Trip 8 April 4 Cluster1 Trip 9 April 16 Cluster2 Trip 10 May 8 Cluster1 Train & validation dataset Test dataset TIME-SERIES CROSS-VALIDATION Iterativeevaluation of the trips to avoid overfitting Trip ID Date Destination Trip 1 January1 Cluster1 Trip 2 January22 Cluster2 Trip 3 February3 ? Trip ID Date Destination Trip 1 January1 Cluster1 Trip 2 January22 Cluster2 Trip 3 February3 Cluster1 Trip 4 February15 ? Data for 1 driver: Trip ID Date Destination Trip 1 January1 Cluster1 Trip 2 January22 Cluster2 Trip 3 February3 Cluster1 Trip 4 February15 Cluster1 … … … Trip 10 May 8 ? 30

31. Rapidexperimentation 31

32. 32

33. Majoritybaseline Distribution of precision on the test set with a majority baseline classifier 33

34. Results Distribution of precision on the test set with a tuned classifier 34

35. AcceleratingtheFutureofMobility By embracing Apache Spark, Databricks and the Azure cloud 3535

36. DON’T FORGET TO RATE AND REVIEW THE SESSIONS SEARCH SPARK + AI SUMMIT

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