Taxis' Short-Term Out-of-Service Behaviors Detection Using Big Trace Data

LIU Huihui; KAN Zihan; SUN Fei; DUAN Qian; TANG Luliang; WU Huayi

doi:10.13203/j.whugis20150569

Volume 41 Issue 9

Sep. 2016

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Geomatics and Information Science of Wuhan University > 2016 > 41(9): 1192-1198. > DOI: 10.13203/j.whugis20150569

LIU Huihui, KAN Zihan, SUN Fei, DUAN Qian, TANG Luliang, WU Huayi. Taxis' Short-Term Out-of-Service Behaviors Detection Using Big Trace Data[J]. Geomatics and Information Science of Wuhan University, 2016, 41(9): 1192-1198. DOI: 10.13203/j.whugis20150569

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Taxis' Short-Term Out-of-Service Behaviors Detection Using Big Trace Data

1.
State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2.
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China

Funds:

The National Natural Science Foundation of China 41571430

The National Natural Science Foundation of China 41271442

The National Natural Science Foundation of China 40801155

More Information

Author Bio:
LIU Huihui, PhD candidate, specializes in space-time GIS, space-time data processing and space-time trajectory data analysis. E-mail: hhliu@sgg.whu.edu.cn
Corresponding author:
KAN Zihan, PhD candidate. E-mail: kzh@whu.edu.cn
Received Date: February 23, 2016
Published Date: September 04, 2016

Graphical Abstract

Abstract

Abstract

Existing studies of big data taxi GPS tracesdo not consider the characteristics and demands of out-of-service taxi driver activities, such as refueling, dining, and shifting activities. This paper studies the these short-term out-of-service behaviors, extracts short-term out-of-service behaviors from taxi trace data, and analyzes the spatio temporal distribution of these events with kernel density estimation (KDE) for linear features. We also analyze the spatial correlation between short-term taxi out-of-service behaviors and locations of gas stations, using Ripley's K function. Our experimental results show that this approach effectively uncovers short-term taxi driver out-of-service demands and exposed the ineffective allocation of urban public resources, by analyzing spatio temporal distribution of short-term out-of-service taxi activities. Our results couldsupport decision-making concerning adjustment and optimization of public resources.
- space-time trace,
- big data,
- short-term out-of-service behavior,
- space-time distribution,
- floating car data

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References (20)

References

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