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Artificial intelligence based commuter behaviour profiling framework using Internet of things for real-time decision-making

  • S.I. : Applying Artificial Intelligence to the Internet of Things
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Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Road traffic environments are highly dynamic and volatile with a multitude of roadside and external environmental factors contributing to its dynamicity. Apart from infrastructure-related means such as traffic lights, planned and unplanned road events and different road networks, a core component which contributes towards the traffic environment is the human factor which is heavily overlooked in the current studies. Due to diverse travel patterns of day-to-day activities, the commuter behaviour is directly depicted in traffic patterns providing an opportunity to further explore human behaviours using road traffic. Conducting such analysis would reveal different commuter behavioural patterns that can be used for optimization and timely management of operations. However, to conduct such real-time behaviour analysis, large volumes of high-frequency data are required with high granularity, as well as, a suitable technology to manage such data. Addressing these needs, we propose an environment-driven commuter behavioural model that can be used to elucidate diverse behaviours in road traffic environments. We conceptualized, designed and developed an artificial intelligence based commuter behaviour profiling framework to detect diverse commuter behavioural profiles, fluctuating and routine patterns among commuters using traffic flow profiling and travel trajectory analysis. We evaluated the framework using 190 million data points captured from the Bluetooth sensor network of the Melbourne arterial road network, in the state of Victoria in Australia. The results demonstrate that traffic flow profiling of the proposed framework can provide insights on recurrent commuter behaviours that are distinct to a selected area with a high granularity. Moreover, traffic trajectory analysis provides insights on non-recurrent behaviours such as accidents with regard to how such incidents impact the dynamics of the network and how the impact is propagated through the network. Besides road traffic management, the proposed framework will enable real-time decision-making when planning road infrastructure and support decision-making of government and business entities to optimize operations.

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Acknowledgements

This work was supported by a La Trobe University Postgraduate Research Scholarship.

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Authors and Affiliations

  1. Research Centre for in Data Analytics and Cognition, La Trobe University, Melbourne, Australia

    Tharindu Bandaragoda, Achini Adikari, Rashmika Nawaratne, Dinithi Nallaperuma, Thimal Kempitiya, Su Nguyen, Damminda Alahakoon & Daswin De Silva

  2. Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, Australia

    Naveen Chilamkurti

  3. The PNG University of Technology, Lae, Papua New Guinea

    Ashish Kr. Luhach

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  1. Tharindu Bandaragoda
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  2. Achini Adikari
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Correspondence to Naveen Chilamkurti.

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Bandaragoda, T., Adikari, A., Nawaratne, R. et al. Artificial intelligence based commuter behaviour profiling framework using Internet of things for real-time decision-making. Neural Comput & Applic 32, 16057–16071 (2020). https://doi.org/10.1007/s00521-020-04736-7

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