A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Urban sensing: Using smartphones for transportation mode classification
https://orcid.org/0000-0003-1623-9166
https://orcid.org/0000-0002-7531-5299
Highlights A transport mode classification method for crowdsourcing urban sensing is proposed. Individual travels are classified into travel modes using sensors in a smartphone. Our approach yields high accuracy despite the low sampling interval for efficiency. The sampling window is dynamic which can cover an entire vehicle travel period. Our prototype provides 82% overall accuracy performed in Zurich, Switzerland.
Abstract We present a prototype mobile phone application that implements a novel transportation mode detection algorithm. The application is designed to run in the background, and continuously collects data from built-in acceleration and network location sensors. The collected data is analyzed automatically and partitioned into activity segments. A key finding of our work is that walking activity can be robustly detected in the data stream, which, in turn, acts as a separator for partitioning the data stream into other activity segments. Each vehicle activity segment is then sub-classified according to the vehicle type. Our approach yields high accuracy despite the low sampling interval and does not require GPS data. As a result, device power consumption is effectively minimized. This is a very crucial point for large-scale real-world deployment. As part of an experiment, the application has been used by 495 samples, and our prototype provides 82% accuracy in transportation mode classification for an experiment performed in Zurich, Switzerland. Incorporating location type information with this activity classification technology has the potential to impact many phenomena driven by human mobility and to enhance awareness of behavior, urban planning, and agent-based modeling.
Urban sensing: Using smartphones for transportation mode classification
https://orcid.org/0000-0003-1623-9166
https://orcid.org/0000-0002-7531-5299
Highlights A transport mode classification method for crowdsourcing urban sensing is proposed. Individual travels are classified into travel modes using sensors in a smartphone. Our approach yields high accuracy despite the low sampling interval for efficiency. The sampling window is dynamic which can cover an entire vehicle travel period. Our prototype provides 82% overall accuracy performed in Zurich, Switzerland.
Abstract We present a prototype mobile phone application that implements a novel transportation mode detection algorithm. The application is designed to run in the background, and continuously collects data from built-in acceleration and network location sensors. The collected data is analyzed automatically and partitioned into activity segments. A key finding of our work is that walking activity can be robustly detected in the data stream, which, in turn, acts as a separator for partitioning the data stream into other activity segments. Each vehicle activity segment is then sub-classified according to the vehicle type. Our approach yields high accuracy despite the low sampling interval and does not require GPS data. As a result, device power consumption is effectively minimized. This is a very crucial point for large-scale real-world deployment. As part of an experiment, the application has been used by 495 samples, and our prototype provides 82% accuracy in transportation mode classification for an experiment performed in Zurich, Switzerland. Incorporating location type information with this activity classification technology has the potential to impact many phenomena driven by human mobility and to enhance awareness of behavior, urban planning, and agent-based modeling.
Urban sensing: Using smartphones for transportation mode classification
https://orcid.org/0000-0003-1623-9166
https://orcid.org/0000-0002-7531-5299
Highlights A transport mode classification method for crowdsourcing urban sensing is proposed. Individual travels are classified into travel modes using sensors in a smartphone. Our approach yields high accuracy despite the low sampling interval for efficiency. The sampling window is dynamic which can cover an entire vehicle travel period. Our prototype provides 82% overall accuracy performed in Zurich, Switzerland.
Abstract We present a prototype mobile phone application that implements a novel transportation mode detection algorithm. The application is designed to run in the background, and continuously collects data from built-in acceleration and network location sensors. The collected data is analyzed automatically and partitioned into activity segments. A key finding of our work is that walking activity can be robustly detected in the data stream, which, in turn, acts as a separator for partitioning the data stream into other activity segments. Each vehicle activity segment is then sub-classified according to the vehicle type. Our approach yields high accuracy despite the low sampling interval and does not require GPS data. As a result, device power consumption is effectively minimized. This is a very crucial point for large-scale real-world deployment. As part of an experiment, the application has been used by 495 samples, and our prototype provides 82% accuracy in transportation mode classification for an experiment performed in Zurich, Switzerland. Incorporating location type information with this activity classification technology has the potential to impact many phenomena driven by human mobility and to enhance awareness of behavior, urban planning, and agent-based modeling.
Urban sensing: Using smartphones for transportation mode classification
Shin, Dongyoun (author) / Aliaga, Daniel (author) / Tunçer, Bige (author) / Arisona, Stefan Müller (author) / Kim, Sungah (author) / Zünd, Dani (author) / Schmitt, Gerhard (author)
Computers, Environments and Urban Systems ; 53 ; 76-86
2014-01-01
11 pages
Article (Journal)
Electronic Resource
English
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