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 Who will pay for this? $$$$$$$$$$$$ Why travelers do not like to travel by bus? Excessively long waiting time Existing methods to predict arrival time Timetable ( operating hours, time intervals, etc.) Special location tracking devices on buses Who will pay for this? $$$$$$$$$$$$

Objective Crowd-participated approach Energy friendly 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 Top-3 strongest cell towers 300 meters apart

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 ∈ Sj , ui and Sj are matching with each other, and mismatching otherwise

Bus Classification 𝑓 𝑠 𝑤 = 0.5 𝑤−1 w: rank of signal strenth penalty 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.