How Long to Wait?: Predicting Bus Arrival Time with Mobile Phone based Participatory Sensing Pengfei Zhou, Yuanqing Zheng, Mo Li -twohsien 2012.9.3.

How Long to Wait?: Predicting Bus Arrival Time with Mobile Phone

based Participatory Sensing

Pengfei Zhou, Yuanqing Zheng, Mo Li

-twohsien 2012.9.3

Outline

• Introduction• System design• Evaluation• Limitations• Conclusion

Introduction

• Why travelers do not like to travel by bus?– Excessively long waiting time

• Existing methods to predict arrival time– Timetable ( operating hours, time intervals, etc.)– Special location tracking devices on buses

Who will pay

for this?

$$$$$$$$$$$$

Objective

• Crowd-participated approach– Sharing users– Querying users

– Backend server

• Energy friendly– Microphone, accelerometer

Mobile Phone

Main idea

• Map the bus routes to a space featured by sequences of nearby cellular towers

Challenges

• Bus Detection• Bus Classification• Information Assembling

System Design

Pre-processing Celltower Data

300 meters apart

Top-3 strongest cell towers

Example

Bus Detection• Audio detection : short beep audio response

Peak at 1 kHz and 3kHz

Bus Detection

• Sliding window, size: 32 samples• Empirical threshold: three standard deviation

Bus Detection

• Accelerometer detection– Bus v.s. Rapid train

Bus Detection• Threshold– Small: trains will be misdetected as buses– Big: miss detection of actual buses

Bus Classification

• Cell tower sequence matching– Smith-Waterman algorithm

• If ui = Cw S∈ j , ui and Sj are matching with each other, and mismatching otherwise

Bus Classification

• w: rank of signal strenthpenalty cost for mismatches : -0.5

Overlapped route

• Survey 50 bus route

Range of cell tower:300-900 meters

threshold of celltower sequence length : 7

Cell tower Sequence Concatenation

Arrival Time Prediction

EVALUATION

Experimental Methodology

• Mobile phones– Samsung Galaxy S2 i9100– HTC Desire

• Experiment environment– 4 campus shuttle bus routes– 2 SBS transit bus route 179 and 241

Bus Detection Performance

Bus vs. MRT Train

False detection: Driving along straight routes late during night time

Bus Classification Performance

Arrival Time Prediction

Arrival Time Prediction

System Overhead

• Battery lifetime

Limitation and On-going Work

• Alternative reference points• Number of passengers• First few bus stops• Overlapped routes

Conclusion

• Present a crowd-participated bus arrival time prediction system using commodity mobile phones.

• Evaluate the system through a prototype system deployed on the Android platform with two types of mobile phones.