Developing artificial neural networks to estimate real-time onboard bus ride comfort
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ORIGINAL ARTICLE
Developing artificial neural networks to estimate real-time onboard bus ride comfort Teron Nguyen1,2,4
•
Duy Q. Nguyen-Phuoc4
•
Y. D. Wong2,3
Received: 31 March 2020 / Accepted: 2 September 2020 Springer-Verlag London Ltd., part of Springer Nature 2020
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. Keywords Ride comfort Bus passenger Artificial neural network Autonomous bus Machine learning
& Teron Nguyen [email protected]; [email protected]; [email protected] Duy Q. Nguyen-Phuoc [email protected] Y. D. Wong [email protected] 1
Samwoh Innovation Centre Pte Ltd (SWIC), 51 Kranji Crescent, Singapore 728661, Singapore
2
Rapid Road Transport, TUMCREATE Ltd, 01 Create Way, #10-02 CREATE Tower, Singapore 138602, Singapore
3
Centre for Infrastructure Systems, Nanyang Technological University (NTU), N1-01b-51, 50 Nanyang Avenue, Singapore 639798, Singapore
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
1 Introduction Bus plays a vital role in public transport; for example, in Singapore, almost half of the daily 7.9 million ridership is carried by buses [1]. Improving passenger ride comfort will
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