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Developing artificial neural networks to estimate real-time onboard bus ride comfort

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Abstract

The ride comfort of bus passengers is a critical factor that is recognised to attract greater ridership towards a sustainable public transport system. However, it is challenging to estimate bus passenger comfort onboard while travelling due to the complex non-linear interaction among various factors. A practicable method to collect real-time comfort ratings by passengers is also not readily available. This study developed an artificial neural network (ANN) model with three layers to precisely estimate real-time ride comfort of bus passengers. The inputs are vehicle-related parameters (speed, acceleration and jerk), passenger-related features (posture, location, facing, gender, age, weight and height), ride comfort index in ISO 2631-1997 (vibration dose value and maximum transient vibration value), and output is passenger rating (collected from a specialised mobile application). The ANN model provided a satisfactory performance and good correlation between inputs and output with an average MSE = 0.03 and R-value = 0.83, respectively. Sensitivity analysis was also conducted to quantify the relative contribution of each variable in the ANN model, revealing similar contributions among all influencing factors in the range of 4–6%. On average, passenger-related factors contribute slightly higher than vehicle-related factors to the ride comfort estimation based on the connection weight approach. The development of ANN model which can precisely estimate bus ride comfort is important as a considerable amount of machine learning and artificial intelligence are utilised to guide autonomous bus (AB). The present findings can help AB designers and engineers in improving AB technology to achieve a higher level of passengers’ onboard comfort.

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Acknowledgements

This work is financially supported by the Singapore National Research Foundation under its Campus for Research Excellence and Technological Enterprise (CREATE) programme.

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Authors and Affiliations

  1. Samwoh Innovation Centre Pte Ltd (SWIC), 51 Kranji Crescent, Singapore, 728661, Singapore

    Teron Nguyen

  2. Rapid Road Transport, TUMCREATE Ltd, 01 Create Way, #10-02 CREATE Tower, Singapore, 138602, Singapore

    Teron Nguyen & Y. D. Wong

  3. Centre for Infrastructure Systems, Nanyang Technological University (NTU), N1-01b-51, 50 Nanyang Avenue, Singapore, 639798, Singapore

    Y. D. Wong

  4. Faculty of Road and Bridge Engineering, University of Science and Technology – The University of Danang, 54 Nguyen Luong Bang Street, Lien Chieu District, Danang City, Vietnam

    Teron Nguyen & Duy Q. Nguyen-Phuoc

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  1. Teron Nguyen
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  2. Duy Q. Nguyen-Phuoc
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  3. Y. D. Wong
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Contributions

TN was the main initiator and author. His major contribution to the paper is as follows: study conception and design, data collection, analysis and interpretation of results, draft preparation. DQN provided discussion and solutions for ANN model. YDW has also drafted the manuscript and substantively revised it. All authors reviewed the results and approved the final version of the manuscript.

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Correspondence to Teron Nguyen.

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Nguyen, T., Nguyen-Phuoc, D.Q. & Wong, Y.D. Developing artificial neural networks to estimate real-time onboard bus ride comfort. Neural Comput & Applic 33, 5287–5299 (2021). https://doi.org/10.1007/s00521-020-05318-3

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