Identifying Transportation Modes from Raw GPS Data

Zhu, Qiuhui; Zhu, Min; Li, Mingzhao; Fu, Min; Huang, Zhibiao; Gan, Qihong; Zhou, Zhenghao

doi:10.1007/978-981-10-2053-7_35

Cited by 26 publications

(26 citation statements)

References 15 publications

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“…In particular: minSpeed was set to 8.2 km/h to reduce the number of wrongly detected other-stages. This is higher than the 6.48 km/h specified by Zhu et al (2016), but this was appropriate since in this research it was possible to correct falsely identified walks later using the mode detection algorithm. The stageMinDuration was set to 50 seconds because it is unlikely that a users other-stage would last less than 50 seconds.…”

Section: Trip Segmentationmentioning

confidence: 88%

“…Since people usually walk or stop between two different transport modes Zheng et al (2010) used a threshold of speed and of acceleration to divide the points into walks and non-walks, then they merged segments of points of the same type according to rules depending on the segment length. Zhu et al (2016) labeled points as walk or non-walk based on speed and acceleration threshold values, then adjusted the labels based on nearby points: if at least M (a value dependent on the number of points) of the previous and posterior points have a different label, then the point's label is changed. Zhang et al (2011) used heading change to identify stops.…”

Section: Trip Segmentationmentioning

confidence: 99%

“…Therefore, a key tool for increasing the efficiency of smartphone-based travel diary data collection and analysis is automatic mode detection. Several studies have used automatic mode detection from GPS data including Schuessler and Axhausen (2009), Stopher et al (2005), Zhu et al (2016), Zheng et al (2010), Zhang et al (2011). These studies devide the problem of mode detection into the following four tasks (with small variations):…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Developing a passive GPS tracking system to study long-term travel behavior

Marra

Becker

Corman

2019

Transportation Research Part C: Emerging Technologies

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This paper describes development and testing of a passive GPS tracking smartphone application and corresponding data analysis methodology designed to increase the quality of travel behavior information collected in long-term travel surveys. The new approach is intended to replace the pencil-and-paper travel diaries and prompted recall methods that require more user involvement due to requirements for manual data entry and/or high battery usage. Reducing the burden placed on users enables researchers to collect data over longer periods, thus improving the quality of travel behavior research. To reduce battery use the smartphone-based application collects GPS data less frequently than other methods. Therefore, new algorithms were developed to identify trips and activities, transport mode, and even the specific vehicle used by the traveler. An important finding was the significant advantage of using users past data to improve mode detection results. The system was tested successfully in Zürich and Basel (Switzerland).

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Section: Trip Segmentationmentioning

confidence: 88%

Section: Trip Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Developing a passive GPS tracking system to study long-term travel behavior

Marra

Becker

Corman

2019

Transportation Research Part C: Emerging Technologies

View full text Add to dashboard Cite

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“…Unlike the study conducted by Ref. [ 84 ], authors in Ref. [ 83 ] first merge segments whose distance meets the threshold merely into its following segment instead of preceding and following segments to identify more transition points.…”

Section: Gps Data Classification Based On the Transportation Modementioning

confidence: 99%

A Review of GPS Trajectories Classification Based on Transportation Mode

Yang

Stewart

Tang

et al. 2018

Sensors

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GPS trajectories generated by moving objects provide researchers with an excellent resource for revealing patterns of human activities. Relevant research based on GPS trajectories includes the fields of location-based services, transportation science, and urban studies among others. Research relating to how to obtain GPS data (e.g., GPS data acquisition, GPS data processing) is receiving significant attention because of the availability of GPS data collecting platforms. One such problem is the GPS data classification based on transportation mode. The challenge of classifying trajectories by transportation mode has approached detecting different modes of movement through the application of several strategies. From a GPS data acquisition point of view, this paper macroscopically classifies the transportation mode of GPS data into single-mode and mixed-mode. That means GPS trajectories collected based on one type of transportation mode are regarded as single-mode data; otherwise it is considered as mixed-mode data. The one big difference of classification strategy between single-mode and mixed-mode GPS data is whether we need to recognize the transition points or activity episodes first. Based on this, we systematically review existing classification methods for single-mode and mixed-mode GPS data and introduce the contributions of these methods as well as discuss their unresolved issues to provide directions for future studies in this field. Based on this review and the transportation application at hand, researchers can select the most appropriate method and endeavor to improve them.

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“…On the other hand, some studies did not use GIS data because these might not be available for many study areas. However, some of the studies were able to achieve moderate to high predictive accuracy (Xiao, Wang, Fu, & Wu, ; Zhu et al, ).…”

Section: Past Studies On Pa and Transport‐mode Classificationmentioning

confidence: 99%

Automatic physical activity and in‐vehicle status classification based on GPS and accelerometer data: A hierarchical classification approach using machine learning techniques

Lee

Kwan

2018

Transactions in GIS

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Due to the advancement of tracking technology, a large quantity of movement data has been collected and analyzed in various research domains. In human mobility and physical activity (PA) research, GPS trajectories and the capabilities of geographic information systems (GIS) facilitate a better understanding of the associations between PA and various environmental factors taking individuals' daily travels into account. PA research, however, needs to widen its focus from the intensity of PA to types of PA, which may provide useful clues for understanding specific health behaviors in particular geographic contexts. This study proposes and develops an algorithm to automatically classify PA types and in-vehicle status using GPS and accelerometer data. Walking, standing, jogging, biking and sedentary/in-vehicle statuses are identified through hierarchical classification processes based on machine learning and geospatial techniques. The proposed algorithm achieved high predictive accuracy on real-world GPS and accelerometer data. It can greatly reduce participants' and researchers' burdens by automatically identifying PA types and in-vehicle status for human mobility research, which is also known as travel mode imputation in transportation research. | 1523 LEE and KWAN

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Identifying Transportation Modes from Raw GPS Data

Cited by 26 publications

References 15 publications

Developing a passive GPS tracking system to study long-term travel behavior

Developing a passive GPS tracking system to study long-term travel behavior

A Review of GPS Trajectories Classification Based on Transportation Mode

Automatic physical activity and in‐vehicle status classification based on GPS and accelerometer data: A hierarchical classification approach using machine learning techniques

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