Observing Human Activity Through Sensing

Gautama, Sidharta; Atzmueller, Martin; Kostakos, Vassilis; Gillis, Dominique; Hosio, Simo

doi:10.1007/978-3-319-25658-0_3

Cited by 3 publications

(4 citation statements)

References 34 publications

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“…This suggests congestion problems on the route, where traffic police should intervene to facilitate the circulation of the public transport. This way, the sensed data assist partners to optimize safety and comfort to the visitors of the festivity [30]. The given example, illustrates the potential of using Bluetooth scanning for deriving origin and destination locations within the city or travel times.…”

Section: Figurementioning

confidence: 97%

“…Foremski et al [85] showed that smartphones can be used for crowd sensing with the decrease in battery lifetime by approximately 20%, which they found to be acceptable by users. Figure 6 shows Routecoach smartphone application [86,87] that was developed at Ghent University [30] for collection of mobility data for the province of Flemish-Brabant in the frame of the Interreg IVb NWE project NISTO. The aim of NISTO (New Integrated Smart Transport Options) was to develop an evaluation and planning toolkit for mobility projects which is applicable transnationally and can be adopted by planners.…”

Section: "Passive" Trackingmentioning

confidence: 99%

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Sensing Human Activity for Smart Cities’ Mobility Management

Šemanjski¹,

Gautama²

2016

Smart Cities Technologies

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Knowledge about human mobility patterns is the key element towards efficient mobility management. Traditionally, these data are collected by paper/phone household surveys or travel diaries and serve as input for transportation planning models. In this chapter, we report on current state-of-the-art techniques for sensing human activity and report on their applicability for smart city mobility management purposes. We particularly focus on the use of location-enabled devices and their potential towards replacing traditional data collection approaches. Furthermore, to illustrate applicability of smartphones as ubiquitous sensing devices we report on the use of Routecoach application that was used for mobility data collection in the city of Leuven, Belgium. We provide insights into lessons learned, ways in which collected data were used by different stakeholders, and identify existing gaps and future research needs in this field.

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Section: Figurementioning

confidence: 97%

Section: "Passive" Trackingmentioning

confidence: 99%

Sensing Human Activity for Smart Cities’ Mobility Management

Šemanjski¹,

Gautama²

2016

Smart Cities Technologies

Self Cite

View full text Add to dashboard Cite

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“…Because one of the characteristic features of segment-based transportation segmentation is that the mode detection results is time-delay. Thus segment-based transportation segmentation of GPS data is usually used for the research that is related to transportation-science (e.g., travel diary analysis) [ 78 ] or human-geography (e.g., human moving pattern mining [ 79 ]). For these applications, segment-based classification for MMT is usually taken as the data preprocessing to provide GPS data with specific transportation mode labels.…”

Section: Gps Data Classification Based On the Transportation Modementioning

confidence: 99%

“…Meanwhile, an enhanced transition point identification method [ 83 ] was proposed on the basis of the work in Ref. [ 79 ] to identify the transportation mode of segments by using Random Forest-based detection model. Unlike the study conducted by Ref.…”

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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A Review of the Internet of Floods: Near Real-Time Detection of a Flood Event and Its Impact

Ackere

Verbeurgt

Sloover

et al. 2019

Water

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Worldwide, flood events frequently have a dramatic impact on urban societies. Time is key during a flood event in order to evacuate vulnerable people at risk, minimize the socio-economic, ecologic and cultural impact of the event and restore a society from this hazard as quickly as possible. Therefore, detecting a flood in near real-time and assessing the risks relating to these flood events on the fly is of great importance. Therefore, there is a need to search for the optimal way to collect data in order to detect floods in real time. Internet of Things (IoT) is the ideal method to bring together data of sensing equipment or identifying tools with networking and processing capabilities, allow them to communicate with one another and with other devices and services over the Internet to accomplish the detection of floods in near real-time. The main objective of this paper is to report on the current state of research on the IoT in the domain of flood detection. Current trends in IoT are identified, and academic literature is examined. The integration of IoT would greatly enhance disaster management and, therefore, will be of greater importance into the future.

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Observing Human Activity Through Sensing

Cited by 3 publications

References 34 publications

Sensing Human Activity for Smart Cities’ Mobility Management

Sensing Human Activity for Smart Cities’ Mobility Management

A Review of GPS Trajectories Classification Based on Transportation Mode

A Review of the Internet of Floods: Near Real-Time Detection of a Flood Event and Its Impact

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