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

Real-Time Trip Information Service for a Large Taxi Fleet

Rajesh Krishna Balan, Nguyen Xuan Khoa, and Lingxiao Jiang

MobiSys 2011

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

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

Taxi Network

• Taxi are cheap• Taxi are common and found everywhere• Most pickups are street pickups• Used for all activities

Taxi locations in one day

challenge

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

Service requirements

• Accuracy– Time– Fares

• Real time capability• Low computational requirements• Easy to deploy operationally

Method design

• Partition – Time– location

• Prediction – Hash table– KNN

Time partition

• Hour• Days of week(DoW)• Hourly DoW– 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%

location partition

• Static zone– 25km x 50km– 50x50m~500x500m to divide zones

• Dynamic zone– Adjust zone size for each trip

Prediction

• Input : start time, start GPS, end GPS• Static – 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)

Evaluation

• Set1: 20 subsets for training– 2010/8– 2010/7+8…..– 2009/1~2010/8

• Set2 : 1 subset for testing(query)– 2010/9

Evaluation

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

Fare and duration in Static zone

• Fare error : 0.87$~2.53$• Duration error: 2min ~4min

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%

Fare and duration in dynamic

• Fare error : 1.05$~1.25$• Duration error: <3min• K=25 is the optimal choice

PEAK predictor w/ various K

• Save the fare 15 cents at most• Save the time 15 sec at mosy

Radius of dynamic zone

• Mean: 375m• Std.dev. :741m

Speed and memory

• Static is efficient than dynamic• Dynamic costs lots of memory space static zones dynamic zones

Accuracy analysis

• Still not very accurate using three basic features

• Why?– Indirect routing– Traffic conditions

Accuracy analysis

• PEAK predictor with 200m zones• Same start time, start point ,end point• Distance error– 6km max

• Duration error– 1000 sec max

Filter design

• Filter 1:– Trip distance > 2 straight distance of Start and End

• Filter 2:– Average speed <20 km/h or >100km/h

Apply filter result

• Save fare 25 cents • Save time 30 sec

Traffic conditions

• Rainfall is severe• Save fare 10 cents • Save time 60 sec

Future work

• Different zone size for various location• Zone size determined by radius of dynamic

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

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