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Pregled bibliografske jedinice broj: 1232324

Predicting public transport arrival time and congestion based on BLE beacon crowdsourced data


Zorić, Bruno; Dudjak, Mario; Bajer, Dražen
Predicting public transport arrival time and congestion based on BLE beacon crowdsourced data // 2022 IEEE Zooming Innovation in Consumer Technologies Conference (ZINC)
Novi Sad, 2022. str. 81-86 doi:10.1109/ZINC55034.2022.9840632 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


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Naslov
Predicting public transport arrival time and congestion based on BLE beacon crowdsourced data

Autori
Zorić, Bruno ; Dudjak, Mario ; Bajer, Dražen

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
2022 IEEE Zooming Innovation in Consumer Technologies Conference (ZINC) / - Novi Sad, 2022, 81-86

ISBN
978-1-6654-8374-2

Skup
Zooming Innovation in Consumer Technologies Conference (ZINC 2022)

Mjesto i datum
Novi Sad, Srbija, 25.05.2022. - 26.05.2022

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
arrival time prediction ; Bluetooth low energy beacons ; crowdsourced data ; machine learning ; public transport

Sažetak
Public transport networks play an important role in minimising congestion and improving environmental sustainability of developed cities. However, they face a number of challenges in achieving these goals, especially during the ongoing pandemic. In order to overcome these challenges, at least to some extent, public transport must be made accessible and attractive to potential passengers. To this end, a design for augmenting the transit network is proposed in this paper. The utilisation of Bluetooth low energy beacons as one of the key components makes it cost-effective and easily applicable in such an environment. Additionally, it incorporates a simple mobile application used to enable crowdsourced data acquisition on which machine learning-based models can be built to predict information relevant to consumers, like arrival time and congestion estimates. A prototype of the proposed system design, albeit of limited functionality, was deployed and evaluated on a tram route in the city of Osijek, Croatia. Promising results were obtained in terms of congestion and arrival time prediction, but some challenges remain to be addressed, like motivating users to participate in the crowdsourced data collection.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo



POVEZANOST RADA


Ustanove:
Fakultet elektrotehnike, računarstva i informacijskih tehnologija Osijek

Profili:

Avatar Url Dražen Bajer (autor)

Avatar Url Mario Dudjak (autor)

Avatar Url Bruno Zorić (autor)

Poveznice na cjeloviti tekst rada:

doi ieeexplore.ieee.org

Citiraj ovu publikaciju:

Zorić, Bruno; Dudjak, Mario; Bajer, Dražen
Predicting public transport arrival time and congestion based on BLE beacon crowdsourced data // 2022 IEEE Zooming Innovation in Consumer Technologies Conference (ZINC)
Novi Sad, 2022. str. 81-86 doi:10.1109/ZINC55034.2022.9840632 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Zorić, B., Dudjak, M. & Bajer, D. (2022) Predicting public transport arrival time and congestion based on BLE beacon crowdsourced data. U: 2022 IEEE Zooming Innovation in Consumer Technologies Conference (ZINC) doi:10.1109/ZINC55034.2022.9840632.
@article{article, author = {Zori\'{c}, Bruno and Dudjak, Mario and Bajer, Dra\v{z}en}, year = {2022}, pages = {81-86}, DOI = {10.1109/ZINC55034.2022.9840632}, keywords = {arrival time prediction, Bluetooth low energy beacons, crowdsourced data, machine learning, public transport}, doi = {10.1109/ZINC55034.2022.9840632}, isbn = {978-1-6654-8374-2}, title = {Predicting public transport arrival time and congestion based on BLE beacon crowdsourced data}, keyword = {arrival time prediction, Bluetooth low energy beacons, crowdsourced data, machine learning, public transport}, publisherplace = {Novi Sad, Srbija} }
@article{article, author = {Zori\'{c}, Bruno and Dudjak, Mario and Bajer, Dra\v{z}en}, year = {2022}, pages = {81-86}, DOI = {10.1109/ZINC55034.2022.9840632}, keywords = {arrival time prediction, Bluetooth low energy beacons, crowdsourced data, machine learning, public transport}, doi = {10.1109/ZINC55034.2022.9840632}, isbn = {978-1-6654-8374-2}, title = {Predicting public transport arrival time and congestion based on BLE beacon crowdsourced data}, keyword = {arrival time prediction, Bluetooth low energy beacons, crowdsourced data, machine learning, public transport}, publisherplace = {Novi Sad, Srbija} }

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