Bus service quality prediction and attribute ranking: a neural network approach

Md Rokibul Islam (), Md Hadiuzzaman (), Rajib Banik (), Md Mehedi Hasnat (), Sarder Rafee Musabbir () and Sanjana Hossain ()
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Md Rokibul Islam: Bangladesh University of Engineering and Technology (BUET)
Md Hadiuzzaman: Bangladesh University of Engineering and Technology (BUET)
Rajib Banik: European University of Bangladesh (EUB)
Md Mehedi Hasnat: Ahsanullah University of Science and Technology (AUST)
Sarder Rafee Musabbir: Bangladesh University of Engineering and Technology (BUET)
Sanjana Hossain: Bangladesh University of Engineering and Technology (BUET)

Public Transport, 2016, vol. 8, issue 2, No 7, 295-313

Abstract: Abstract Evaluation of service quality (SQ) based on user preferences has become a primary concern for the transportation authorities. The most significant attributes of public transportation systems are revealed through service quality analysis. This information serves as valuable input in constantly updating the quality of public transportation services. An appropriate tool is therefore mandatory in this regard. This paper represents a comparative study on the bus SQ prediction capabilities of three effective Artificial Neural Network (ANN) approaches, namely: Generalized Regression Neural Network (GRNN), Probabilistic Neural Network (PNN) and Pattern Recognition Neural Network (PRNN). To calibrate the parameters of the developed ANN models, data on users’ perception toward bus services of Dhaka city, Bangladesh, have been used. Taking the public opinion as baseline, GRNN and PNN have proven to be better prediction models since both have achieved higher accuracy compared to PRNN. Among 22 selected SQ attributes, the most significant attributes have been ranked according to their influence on the users’ decision making process. According to the GRNN and PNN models, punctuality and reliability, service frequency, seat availability and commuting experience are found to be the most significant attributes, which support the user-stated preferences.

Keywords: Public transport; Service quality; Attributes; Artificial neural network (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s12469-016-0124-0

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