Travel mode detection based on GPS track data and Bayesian networks

Xiao, Guangnian; Juan, Zhicai; Zhang, Chunqin

doi:10.1016/j.compenvurbsys.2015.05.005

Cited by 133 publications

(93 citation statements)

References 30 publications

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“…Geo-tagged social media messages and GPS tracking data (from mobile phones and vehicles) have been used to understand human movement behavior and spatiotemporal patterns [7][8][9][10]. For example, studying social media "check-in" patterns can provide a better explanation of urban dynamics, as well as a deeper understanding of land use pattern changes [11].…”

Section: Introductionmentioning

confidence: 99%

Mapping Dynamic Urban Land Use Patterns with Crowdsourced Geo-Tagged Social Media (Sina-Weibo) and Commercial Points of Interest Collections in Beijing, China

Wang

Tsou

et al. 2016

Sustainability

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Abstract:In fast-growing cities, especially large cities in developing countries, land use types are changing rapidly, and different types of land use are mixed together. It is difficult to assess the land use types in these fast-growing cities in a timely and accurate way. To address this problem, this paper presents a multi-source data mining approach to study dynamic urban land use patterns. Spatiotemporal social media data reveal human activity patterns in different areas, social media text data reflects the topics discussed in different areas, and Points of Interest (POI) reflect the distribution of urban facilities in different regions. Human activity patterns, topics of discussion on social media, and the distribution of urban facilities in different regions were combined and analyzed to infer urban land use patterns. We collected 9.5 million geo-tagged Chinese social media (Sina-Weibo) messages from January 2014 to July 2014 in the urban core areas of Beijing and compared them with 385,792 commercial Points of Interest (POI) from Datatang (a Chinese digital data content provider). To estimate urban land use types and patterns in Beijing, a regular grid of 400 m × 400 m was created to divide the urban core areas into 18,492 cells. By analyzing the temporal frequency trends of social media messages within each cell using K-means clustering algorithm, we identified seven types of land use clusters in Beijing: residential areas, university dormitories, commercial areas, work areas, transportation hubs, and two types of mixed land use areas. Text mining, word clouds, and the distribution analysis of POI were used to verify the estimated land use types successfully. This study can help urban planners create up-to-date land use patterns in an economic way and help us better understand dynamic human activity patterns in a city.

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Section: Introductionmentioning

confidence: 99%

Mapping Dynamic Urban Land Use Patterns with Crowdsourced Geo-Tagged Social Media (Sina-Weibo) and Commercial Points of Interest Collections in Beijing, China

Wang

Tsou

et al. 2016

Sustainability

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“…Thus, the raw data must undergo a cleaning process to filter out incomplete and erroneous data records before the GPS tracking data can be used for any study. 1,2,26,29,30 Based on the actual condition of the GPS tracking data collection and raw data analysis, combined with the practical requirements of the travel mode detection, this article cleaned the raw data by deleting the following: (1) GPS tracking points of the surveyed subjects not in a traveling state; (2) GPS tracking points missing spatio-temporal positioning information; (3) GPS tracking points with no or incomplete PR survey record information; (4) GPS tracking points that were not located in the area of Shanghai, China; (5) GPS tracking points with an instantaneous speed that is not consistent with common sense; (6) GPS tracking points in low sampling rate regions; and (7) GPS tracking points corresponding to a traveling period shorter than 240 s.…”

Section: Data Cleaningmentioning

confidence: 99%

“…A total of 318 volunteers participated in the PR survey. 29 As an important supplement to the GPS tracking data automatically collected via smartphone software, the PR survey data, including starting/ending time, starting/ending point, travel mode, and travel purpose, were manually verified based on the GPS tracking data. Since in this study, the PR survey records is used as the ground truth in model training, the manual curation is able to correct misreporting and false-reporting by the surveyed subjects.…”

Section: Data Collection and Formatmentioning

confidence: 99%

Travel mode detection method based on big smartphone global positioning system tracking data

Zhou

Jia

Gao

et al. 2017

Advances in Mechanical Engineering

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This article proposes a machine learning-based travel mode detection method using urban residents' travel routes as the data source, collected via smartphone global positioning system modules. A data-driven machine learning strategy was chosen in the model construction. This study performed data cleaning and mining on over 4400 pieces of urban resident travel records containing several millions of global positioning system tracking points. Series of characteristic values of speed, travel distance, and direction are calculated, which reflect the travel mode of smartphone holders. In travel mode identification, first, the transition regions of travel segments of different travel modes are effectively distinguished; then, continuous tracking points for single-mode travel are connected into single-mode travel segments. The travel mode of the surveyed subjects is identified based on the calculated features of average speed, average acceleration, and average change of direction within each single-mode segment. The random forest method is chosen as the basis model to classify travel mode. Three-quarters of the travel records were used to construct the random forest classifier, and the detection accuracy of the established model for the remaining ¼ of the travel record reached 94.4%. The proposed method uses massive smartphone global positioning system tracking points as the basis; the detection results are consistent with manually collected prompted recall survey records.

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“…Removing the burden and fatigue from the survey respondents and allowing researchers to collect detailed travel data are other important advantages of GPS-based data collection methods [23,24]. In view of the very low level of burden and fatigue on respondents, the surveys' length can be extended from the traditional single day to multi-day travel information collection, which provides a chance to test the dynamics of multi-day travel patterns [1,25]. Although the time and positional characteristics of travel can be recorded accurately by GPS devices, important attributes such as travel mode, trip purpose, and start and end of trip cannot be extracted from the data collected by GPS devices.…”

Section: Introductionmentioning

confidence: 99%

“…In practice, travel surveys are widely used to collect crucial infrastructure data for traffic demand analysis in transportation system planning [1,2].…”

Section: Introductionmentioning

confidence: 99%

Travel Mode Detection Based on GPS Raw Data Collected by Smartphones: A Systematic Review of the Existing Methodologies

Yang

Jing

2016

Information

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Abstract:Over the past couple of decades, global positioning system (GPS) technology has been utilized to collect large-scale data from travel surveys. As the precise spatiotemporal characteristics of travel could be provided by GPS devices, the issues of traditional travel survey, such as misreporting and non-response, could be addressed. Considering the defects of dedicated GPS devices (e.g., the need for a large sum of money to buy devices, forgetfulness in the taking of devices to collect data, limiting of the sample size because of the number of devices, etc.), and the fact that the smartphone is becoming one of the necessities of life, there is an opportunity for smartphones to replace dedicated GPS devices. Although several general reviews of GPS travel survey and GPS data-processing methods have been written, a systematic review of smartphone-based GPS data collection and travel mode detection has not be made.. The studies were collected from six databases. The purpose of this review is to critically evaluate the current literature on the existing methodologies of travel mode detection based on GPS raw data collected by smartphones. Meanwhile, according to a systematic comparison of the different methods of data preprocessing for travel mode detection, this paper details the strengths and weaknesses of the existing methods. Furthermore, it is a very important step towards developing methodologies and applications for GPS raw data collected by smartphones.

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Travel mode detection based on GPS track data and Bayesian networks

Cited by 133 publications

References 30 publications

Mapping Dynamic Urban Land Use Patterns with Crowdsourced Geo-Tagged Social Media (Sina-Weibo) and Commercial Points of Interest Collections in Beijing, China

Mapping Dynamic Urban Land Use Patterns with Crowdsourced Geo-Tagged Social Media (Sina-Weibo) and Commercial Points of Interest Collections in Beijing, China

Travel mode detection method based on big smartphone global positioning system tracking data

Travel Mode Detection Based on GPS Raw Data Collected by Smartphones: A Systematic Review of the Existing Methodologies

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