1. Real-Time Trip Information Service for a Large Taxi Fleet
Rajesh Krishna Balan, Nguyen Xuan Khoa, and Lingxiao Jiang
MobiSys 2011

2. Introduction
Real-time trip information system that provides passengers with the expected fare and trip duration of the taxi ride they are planning to take.
15000 taxi, 21 month, 250 million data in Singapore
Large scale implementation and evaluations

3. Motivation Unscrupulous driver who take longer routes
Passenger can estimate trip time and fares by themselves.
Failed solution : Google Maps
Latency
Trip fare
Not accurate
35% time error

4. Taxi Network Taxi are cheap Taxi are common and found everywhere
Most pickups are street pickups
Used for all activities

5. Taxi locations in one day

6. challenge Large amount data Real time query requirement
Various time-related factors
How much data is sufficient?
How to filter the data?

7. Service requirements Accuracy Real time capability
Fares
Real time capability
Low computational requirements
Easy to deploy operationally

8. Method design
Partition
Time
location
Prediction
Hash table
KNN

9. Time partition Hour Days of week(DoW) Hourly DoW Peak period
24*7=168Hr
Peak period
Week day 7am~10am, 5pm~8pm +35%
Week day 6am-7am, 10am~5pm non-peak
Weekend 6am~0am non-peak
night 0am~6am +50%

10. location partition Static zone Dynamic zone 25km x 50km
50x50m~500x500m
to divide zones
Dynamic zone
Adjust zone size for each trip

11. Prediction Input : start time, start GPS, end GPS Static Dynamic
Similar historical data and average ( fare, duration, distance
Index and hash table
Dynamic
KNN
Data set (start time, S_long, S_latt, E_long, E_latt)

12. Evaluation Set1: 20 subsets for training
2010/8
2010/7+8
…..
2009/1~2010/8
Set2 : 1 subset for testing(query)
2010/9

13. Evaluation LOC: start and end location PEAK: peak hour
DoW: days of week
HR: 24 hour
DoW x HR: 168hr

14. Fare and duration in Static zone
Fare error : 0.87$~2.53$
Duration error: 2min ~4min

15. Hit rate in static zone
Hit rate: % of test trips having a non-empty entry in prediction table
Hit rate in static zone is 17%~58%

16. Fare and duration in dynamic
Fare error : 1.05$~1.25$
Duration error: <3min
K=25 is the optimal choice

17. PEAK predictor w/ various K
Save the fare 15 cents at most
Save the time 15 sec at mosy

18. Radius of dynamic zone
Mean: 375m
Std.dev. :741m

19. Speed and memory Static is efficient than dynamic
Dynamic costs lots of memory space
static zones dynamic zones

20. Accuracy analysis Still not very accurate using three basic features
Why?
Indirect routing
Traffic conditions

21. Accuracy analysis PEAK predictor with 200m zones
Same start time, start point ,end point
Distance error
6km max
Duration error
1000 sec max

22. Filter design Filter 1: Filter 2:
Trip distance > 2 straight distance of Start and End
Filter 2:
Average speed <20 km/h or >100km/h

23. Apply filter result
Save fare 25 cents
Save time 30 sec

24. Traffic conditions Rainfall is severe Save fare 10 cents
Save time 60 sec

25. Future work Different zone size for various location
Zone size determined by radius of dynamic

26. Conclusion reducing the data size through aggregation
and smart filtering is essential.
real world data needs to be cleaned before use
deploying a research prototype into a real production environment requires far more work than we naively expected

27. contribution
Detailed description of the steps to build such real time taxi system
Method of identifying real-time patterns, applicable for other transportation network
Principled approach to balance the tradeoffs between accuracy, real time performance
KNN method to produce accurate predictor
Insight into challenge from prototype to operational environment