Transactions on Transport Sciences 2023, 14(2):54-67 | DOI: 10.5507/tots.2023.005
Returning to Public Transportation in Transitioning Out of COVID-19: Effects of Passenger Satisfaction on Frequency of Use of Rail Transport
- a. InfraTrans Consultants Co. Ltd., Ladprao, Bangkok 10230, Thailand
- b. Faculty of Engineering, Naresuan University, Phitsanulok 65000, Thailand
- c. Department of Civil Engineering, Chiang Mai University, Chiang Mai 50200, Thailand
As travel restrictions ease amid the COVID-19 pandemic, a revival of traffic congestion is observed caused by the increased use of private vehicles. To help alleviate this, return to public transport use is encouraged. Given the positive relationship between passenger satisfaction and their recurrent use of public transit, it is then recommended to improve the service quality offered by these systems. This paper illustrates the use of a combination of principal component analysis and ordinal logistic regression in determining how satisfaction with rail transport service quality affects passengers' usage frequency with Bangkok's mass rapid transit system as a case study. The results show that satisfaction with ticket sales, station facilities, station staff, public relations, and rolling stock significantly affect passengers' recurrent use of the rail transport system. It is also determined that among these indicators, the station facilities, rolling stock, and station staff are found to contribute greatly to the marginal effects on the probability of observing more frequent use compared to less frequent use. It is then recommended that these factors be prioritized and further improved to produce higher levels of passenger satisfaction, and consequently, increased ridership of the rail transport system. The findings of this study can also be beneficial for public agencies and rail transport operators with regard to policy and decision making amid and in the transition out of the COVID-19 pandemic.
Keywords: Passenger Satisfaction; Frequency of Use; COVID-19; Rail Transport; Ordinal Logistic Regression
Received: August 19, 2022; Revised: February 1, 2023; Accepted: March 1, 2023; Prepublished online: April 19, 2023; Published: September 5, 2023 Show citation
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