Real-Time Transportation Mode Detection via Tracking Global Positioning System Mobile Devices

Byon, Young-Ji; Abdulhai, Baher; Shalaby, Amer

doi:10.1080/15472450903287781

Cited by 60 publications

(33 citation statements)

References 6 publications

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“…Most publications concentrate on the classification and omit the segmentation problem (Byon et al, 2009;Dodge et al, 2009;Reddy et al, 2008). On average, the published methods classify between four and five transportation modes and use around four indicators.…”

Section: Related Workmentioning

confidence: 99%

Transportation mode-based segmentation and classification of movement trajectories

Biljecki

Ledoux

Oosterom

2013

International Journal of Geographical Information Science

133

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The knowledge of the transportation mode used by humans (e.g. bicycle, on foot, car, and train) is critical for travel behaviour research, transport planning and traffic management. Nowadays, new technologies such as the GPS have replaced traditional survey methods (paper diaries, telephone) since they are more accurate and problems such as underreporting are avoided. However, although the movement data collected (timestamped positions in digital form) have generally high accuracy, they do not contain the transportation mode. We present in this paper a new method for segmenting movement data into single-mode segments and to classify them according to the transportation mode used. Our fully automatic method differs from previous attempts for five reasons: (1) it relies on fuzzy concepts found in expert systems, i.e. membership functions and certainty factors; (2) it uses OpenStreetMap data to help the segmentation and classification process; (3) we can distinguish between 10 transportation modes (incl. between tram, bus, and car) and we propose a hierarchy; (4) it handles data with signal shortages and noise, and other real-life situations; (5) in our implementation, there is a separation between the reasoning and the knowledge, so that users can easily modify the parameters used and add new transportation modes. We have implemented the method and tested it with a 17-million point dataset collected in the Netherlands and elsewhere in Europe. The accuracy of the classification with the developed prototype, determined with the comparison of the classified results with the reference data derived from manual classification, is 91.6 percent.

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Section: Related Workmentioning

confidence: 99%

Transportation mode-based segmentation and classification of movement trajectories

Biljecki

Ledoux

Oosterom

2013

International Journal of Geographical Information Science

133

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“…Other data sets, such as mobile positioning data obtained via GPS devices and mobile phones, might meet this detail level. Recently, several researchers have explored the possibilities of these data sets, for example Ahas et al (2008), Song et al (2010) and Byon et al (2009). In Belgium, as in many other countries, mobile positioning has already been used successfully to track vehicles and estimate traffic jams.…”

Section: Discussionmentioning

confidence: 99%

Coastal flood risks and seasonal tourism: analysing the effects of tourism dynamics on casualty calculations

et al. 2011

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Since coastal tourism is one of the fastest growing sectors of tourism industry, coastal areas have become increasingly vulnerable in case of flooding. While in recent years a number of different methods have been put forward to map coastal flood risks, the implications of tourism dynamics for the assessment of human casualties has remained largely overlooked in these models. This chapter examines to what extent the ignorance of (residential) coastal tourism may bias the calculations of human casualties. To this end, a case study has been conducted on the Belgian coast. Both the dynamic nature of coastal tourism and the behaviour of residential tourists in storm surge scenarios are considered. The results of this study show that including tourism dynamics in flood risk management is justified and appropriate, depending on the tourist attractiveness of the flood-prone area and its temporal fluctuations.

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“…The method adopts a post-processing of data and is useful for ITS applications with longer time horizons. Gonzalez et al [18] and Byon et al [19] developed real-time mode identification methods with a conventional GPS receiver with ANNs. Their methods used both directly-measured and indirectly-processed data.…”

Section: Transportation Mode Detectionmentioning

confidence: 99%

Real-Time Transportation Mode Identification Using Artificial Neural Networks Enhanced with Mode Availability Layers: A Case Study in Dubai

Byon

Cho

et al. 2017

Applied Sciences

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Traditionally, departments of transportation (DOTs) have dispatched probe vehicles with dedicated vehicles and drivers for monitoring traffic conditions. Emerging assisted GPS (AGPS) and accelerometer-equipped smartphones offer new sources of raw data that arise from voluntarily-traveling smartphone users provided that their modes of transportation can correctly be identified. By introducing additional raster map layers that indicate the availability of each mode, it is possible to enhance the accuracy of mode detection results. Even in its simplest form, an artificial neural network (ANN) excels at pattern recognition with a relatively short processing timeframe once it is properly trained, which is suitable for real-time mode identification purposes. Dubai is one of the major cities in the Middle East and offers unique environments, such as a high density of extremely high-rise buildings that may introduce multi-path errors with GPS signals. This paper develops real-time mode identification ANNs enhanced with proposed mode availability geographic information system (GIS) layers, firstly for a universal mode detection and, secondly for an auto mode detection for the particular intelligent transportation system (ITS) application of traffic monitoring, and compares the results with existing approaches. It is found that ANN-based real-time mode identification, enhanced by mode availability GIS layers, significantly outperforms the existing methods.

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Real-Time Transportation Mode Detection via Tracking Global Positioning System Mobile Devices

Cited by 60 publications

References 6 publications

Transportation mode-based segmentation and classification of movement trajectories

Transportation mode-based segmentation and classification of movement trajectories

Coastal flood risks and seasonal tourism: analysing the effects of tourism dynamics on casualty calculations

Real-Time Transportation Mode Identification Using Artificial Neural Networks Enhanced with Mode Availability Layers: A Case Study in Dubai

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