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

Abstract

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.

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