WiFi Sensing System for Monitoring Public Transportation Ridership: A Case Study

Ryu, Seunghan; Park, Byungkyu Brian; El-Tawab, Samy

doi:10.1007/s12205-020-0316-7

Cited by 30 publications

(15 citation statements)

References 35 publications

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“…Most of them aim to facilitate the process of data collection and communication between different resources. For example, [Ryu et al, 2020] present a WiFi sensing system to estimate passengers' waiting time at bus stops using their smartphone MAC address and [Frez et al, 2019] present a method to support the decision making of transport networks based on crowdsourced data. Using the Semantic Web, Houda et al in [Houda et al, 2010] have developed a public transport ontology that considers several concepts related to travel planning.…”

Section: Public Transportmentioning

confidence: 99%

An Integration of Health Monitoring System in Public Transport Using the Semantic Web of Things

Hadjadj¹,

Halimi²

2021

jucs

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The integration of the Internet of Things (IoT) technology and artificial intelligence has become essential in many aspects of daily life since the expansion of the communications and information field. Healthcare is one area that urgently needs to benefit from these technologies to keep up with the dramatic evolution of communications for contemporary human life. IoT, through wearable devices, provides real-time data related to the measurement of a person’s vital signs of health. However, for this data to become more relevant and valuable, it needs to be linked to other domains. Public transport is a domain related to the daily activity of people who take advantage of the IoT to provide exemplary transport services whose quality of service can greatly affect people’s health. The integration of these two domains offers many benefits, especially when providing services adapted to passengers’ health status, making them safer and healthier. This paper proposes an approach based on an IoT architecture using Semantic Web technologies; it aims to integrate health monitoring in public transport, provide passengers with quality transport services, and ensure continuous health monitoring. The use of Semantic Web technologies overcomes the lack of interoperability due to the heterogeneity of data collected by different devices and generated by two different domains. An experimental study was conducted, and the proposed approach’s results were compared with those obtained by the evaluation of a physician. The results show that the approach is effective and should allow passengers to benefit from appropriate transport services that better match their health status.

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Section: Public Transportmentioning

confidence: 99%

An Integration of Health Monitoring System in Public Transport Using the Semantic Web of Things

Hadjadj¹,

Halimi²

2021

jucs

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“…Second, an approach to incorporate machine learning models into the system to enable a deeper understanding of the collected data and foresight [15]. Third, an approach to efficiently collect daily ridership data -unlike some previous work in this topic [16] -through the use of only a mobile application without the use of cumbersome hardware modules.…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Machine Learning Based Public Transport Bus-Passenger Information System

Skhosana¹,

Ezugwu²

2021

Preprint

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The era of Big Data and the Internet of Things is upon us, and it is time for developing countries to take advantage of and pragmatically apply these ideas to solve real-world problems. Many problems faced daily by the public transportation sector can be resolved or mitigated through the collection of appropriate data and application of predictive analytics. In this body of work, we are primarily focused on problems affecting public transport buses. These include the unavailability of real-time information to commuters about the current status of a given bus or travel route; and the inability of bus operators to efficiently assign available buses to routes for a given day based on expected demand for a particular route. A cloud-based system was developed to address the aforementioned. This system is composed of two subsystems, namely a mobile application for commuters to provide the current location and availability of a given bus and other related information, which can also be used by drivers so that the bus can be tracked in real-time and collect ridership information throughout the day, and a web application that serves as a dashboard for bus operators to gain insights from the collected ridership data. These were integrated with a machine learning model trained on collected ridership data to predict the daily ridership for a given route. Our novel system provides a holistic solution to problems in the public transport sector, as it is highly scalable, cost-efficient and takes full advantage of the currently available technologies in comparison with other previous work in this topic.

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“…In the field of wireless sensor networks, the Bluetooth gateway uses a star structure to network multiple sensor nodes carrying Bluetooth devices [1], which can minimize the power of the data transmission process. When the gateway and the server are far away, Wi-Fi and other long-distance communication protocols are used to realize the transmission between the cached data in the gateway and the server [2,3].…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of Intelligent Gateway System for Monitoring Livestock and Poultry Feeding Environment Based on Bluetooth Low Energy

Sun

Zheng

et al. 2021

Information

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This article proposes a gateway system design method applied to the monitoring of a breeding environment based on Bluetooth low energy consumption. Through the queue mechanism and buffer management method, the key information that has the greatest impact on the monitoring effect is sent first, and the feedback mechanism is used to adjust the sampling period of the node in conjunction with the abnormal degree of the monitoring parameter. Experimental results show that the system constructed in this article significantly shortens the transmission delay of key information, with an average packet loss rate of 1.09%. Compared with the traditional method, this method is more timely and effective in handling abnormal data. It effectively solves the short distance and low efficiency of Bluetooth “point-to-multipoint” communication in the case of high data concurrency, and improves the reliability and stability of communication. In turn, the losses caused by environmental mutations are reduced.

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WiFi Sensing System for Monitoring Public Transportation Ridership: A Case Study

Cited by 30 publications

References 35 publications

An Integration of Health Monitoring System in Public Transport Using the Semantic Web of Things

An Integration of Health Monitoring System in Public Transport Using the Semantic Web of Things

A Real-Time Machine Learning Based Public Transport Bus-Passenger Information System

Design and Implementation of Intelligent Gateway System for Monitoring Livestock and Poultry Feeding Environment Based on Bluetooth Low Energy

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