Public Transit Customer Satisfaction Dimensions Discovery from Online Reviews

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  • Published: 11 October 2016
  • Volume 2 , pages 146–152, ( 2016 )

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  • Yao Yu   ORCID: orcid.org/0000-0002-4933-1383 2 &
  • Wuling Liang 1  

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Online user-generated content provides a valuable source for identifying dimensions of services. This study proposes a framework for extracting the dimensions of consumer satisfaction of public transportation services using unsupervised latent Dirichlet allocation model. A pilot study was performed on 17,747 online user reviews collected from 1452 public transportation agencies (including streetcar, light rail, heavy rail, boat, and aerial tram) in the United States over 8 years. The proposed approach is able to identify a few dimensions that were not discussed in the previous literature. This research also provides an alternative method to collectively gather users’ feedback and efficiently pre-process textual data related to transit customer satisfaction.

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1 Introduction

Public transportation has been the subject of increasing interest in recent years, chiefly due to its potential to alleviate congestion, reduce emissions and protect the environment, provide critical support during emergencies and disasters, and enhance mobility in small urban and rural areas. To increase the ridership, public transport service needs to be more market-oriented, which can help maintain consumer loyalty and improve the long-term financial performance of public transit companies [ 1 ].

Satisfaction is considered as the main driver of consumer loyalty and behavior [ 2 ]. Coffel [ 3 ] found that the satisfaction level of public transit customers has a significant influence on whether they choose public transit as their primary commute method. Declining satisfaction levels among transit users lead to significant decrease in their customer loyalty regarding using transit again or recommending transit to a friend or relative. Lai and Chen [ 1 ] revealed the vital role of customer satisfactory in understanding the behavioral intention of public transit users. The authors found that passenger behavioral intentions significantly rely on passenger satisfaction.

Customer satisfaction also reflects the performance of a transit system regarding meeting customers’ needs [ 4 ]. Customer satisfaction measurement has been translated into service quality measures in the existing literature. For example, Eboli and Mazzulla [ 5 ] developed a customer satisfaction index to evaluate transit service quality. This index enables service quality monitoring, dissatisfaction identification, and future strategy definition. Nathanail [ 6 ] proposed a multi-criteria evaluation method to provide railway operator with a quality control toolbox. Results based on the multi-criteria evaluation provide transit planners and practitioners with valuable information for effective decision-making and marketing strategies.

For the reasons discussed above, researches on user satisfaction toward public transit service allow a better understanding of their behavior and provide directions for future planning and improvement strategies. However, most of the existing work in this area has relied on the use of “customer satisfaction surveys,” where participants express their point of view about services by filling out sample surveys. Two major concerns about the questionnaire-based studies are (1) the low response rate and (2) the potential lack of comprehensiveness due to the design of the questionnaire. To overcome these limitations, we propose a text mining framework utilizing online customer reviews to investigate customer satisfaction toward public transport services. In the following sections, previous studies on public transit customer satisfaction evaluation are reviewed and the latent Dirichlet allocation (LDA) topic model is discussed. Case study results and comparison with questionnaire surveys are presented at the end of the paper.

2 Related Work

As public transit becomes a more promising mode to serve all travel purposes, dedicated efforts were made to improve the existing service from various perspectives including accessibility, pricing, comfort, etc. The dimensions of customer satisfaction addressed by previous studies are summarized and discussed in the following sections.

Researchers have found that fare price has a great influence on the ridership of public transportation. For example, Cervero and Wachs [ 7 ] found that annual U.S. transit ridership declined by about 6 %, while average fares increased by 35 % between 1984 and 1987. According to the authors, customer dissatisfaction with fare price is the main reason for ridership decline. Goodwin [ 8 ] used fare elasticity index as an indicator to study customer satisfaction in public transportation. The author confirmed the significance of fare price in transit customer satisfaction. Coffel [ 3 ] also found that “service received for the fare paid” is one of the top satisfaction discriminators between “somewhat satisfied” and “very satisfied.” Wallin [ 9 ] identified “price level” as one of the nine service attributes that are believed to impact customer satisfaction. The author stated that price becomes an important factor when the offered service is considered to be of low quality. Perone and Volinski [ 10 ] found that while a fare-free policy is appropriate for smaller transit systems, it does not have the same effect for larger transit systems in major urban areas. Eboli and Mazzulla [ 5 ] used an index of customer satisfaction to evaluate transit service quality. The weight of ticket price was estimated to be 9.12 (scale from 1 to 10), which indicates that ticket price is crucial to customer satisfaction.

2.2 Wait and Travel Time

Wait and travel time are usually considered as critical measurements of transit customer satisfaction. For example, Cervero [ 11 ] found that transit riders are more sensitive to schedule reliability than almost any other service attributes. The author also found that riders are especially sensitive to out-of-vehicle travel time. Wall and McDonald [ 12 ] reported that in North West London, when the bus service frequency was changed from every 20 min to every 10 min, the estimated demand increased of around 20 %. More recently, Friman and Fellesson [ 13 ] found that there is a significant relationship between average public transportation speed and overall user satisfaction. Moreover, a survey report by Metropolitan Transportation Authority (MTA) in New York identified “how fast the public transit gets you where you want to go” as one of the highest satisfaction attributes concerned by the subway customers [ 14 ].

2.3 Cleanliness

Cleanliness is another popular topic in public transportation satisfaction surveys. Coffel [ 3 ] identified four top satisfaction discriminators between “somewhat satisfied” and “very satisfied.” These discriminators include “cleanliness of light rail vehicle interior,” “cleanliness of light rail vehicle exterior,” “cleanliness of heavy rail vehicle interior,” and “cleanliness of stations (waiting area).” Eboli and Mazzulla [ 5 ] studied cleanliness of both interior and exterior of transit vehicles and found that the weights of exterior and interior features are estimated to be 7.85 and 9.51, respectively (scale from 1 to 10). MTA [ 14 ] found that cleanliness had received a high attention from the subway user and the demand for improvement in cleanliness was overwhelming. The research concluded that cleanliness is one of the most important service attributes that transportation companies need to improve in the public transportation service.

2.4 Customer Service

Customer service is defined as the services provided by the employees of the public transportation agencies. It includes the behavior of the driver, conductor, in station customer service employees, etc. Wallin [ 9 ] developed a conceptual model to determine the relationships among customer preferences, customer satisfaction, and customer segments. The author reported that information service such as schedule timetables and corresponding lines have a significant impact on customer satisfaction. Coffel [ 3 ] identified “courtesy of bus drivers,” “courtesy and helpfulness of station staff (waiting area),” and “courtesy of the operator/conductor” as several staff behaviors that could attract more people to use public transit service.

2.5 Accessibility

The access to public transportation was another significant component of the overall transportation system. Coffel [ 3 ] identified “ease of making transfers from the station” as an important customer satisfaction dimension. Daganzo [ 15 ] investigated the structural effect of transit system on accessibility and proposed a combination of grid and hub-and-spoke network structure to improve the overall competitiveness of transit system over driving. Woldeamanuel and Cyganski [ 16 ] used a panel binomial probit model to analyze the parametric relationship between levels of traveler satisfaction and accessibility to public transport services. The results showed that travelers who tend to make frequent trips by public transportation demonstrate a higher probability of satisfaction with accessibility. According to MTA [ 14 ], “convenience of stops” is one of the most important dimensions among all public transit satisfaction measurements.

Turner [ 17 ] found that safety has great influence on commute experience of public transportation customers. Roberts et al. [ 18 ] reported that improving the security culture of public transportation will significantly improve the customer satisfaction as well as other aspects such as efficiency and employee morale. Safety is also among the nine service attributes identified by Wallin [ 9 ] that are believed to have impacts on customer satisfaction of public transportation. Coffel [ 3 ] identified the category of “safety from crime after getting off the bus” as an important factor to improve the customer satisfaction level from “somewhat satisfied” to “very satisfied.”

2.7 Crowdedness

Many researchers found that crowdedness has considerable influence on public transit customer satisfaction. For example, Lundberg [ 19 ] found that the crowdedness condition of public transit contributes more to travelers’ stress experience than trip duration. MTA [ 14 ] identified crowdedness as the most unsatisfied service attribute of New York City Transit. More recently, researchers found that user satisfaction is lower when individuals lack space in transit vehicles and the space between transit passengers is found as one of the main qualities desired by users [ 20 – 22 ].

2.8 Comfortability

The comfortability of public transportation is related to conditions such as seat condition, temperature in the vehicles, and smoothness of the ride. Coffel [ 3 ] identified “smoothness of ride” and “seating comfort” as top attributes that can enhance the customer satisfaction level from “somewhat satisfied” to “very satisfied.” It was also found that improving these attributes can increase loyalty and ridership among current and potential customers. MTA [ 14 ] also identified “comfort of temperature on vehicles” as an important service attribute.

2.9 Summary

The most common methods of data collection in the studies discussed above were interviews and surveys. However, these approaches are limited by the response rate and the variability and subjective nature of the response. Recently, a variety of new data sources and an expanding set of novel analysis methods open up new opportunities for studying transit user satisfaction. For example, Aranguren and Tonnelat [ 23 ] use transit users’ facial expression to study their willingness to cope with the crowdedness in the Paris Metro. In this paper, we propose studying public transit customer satisfaction by analyzing online reviews and comments, which contain words expressing user sentiment or opinions about public transit service. In this research, we downloaded user comments from public transit review website and applied an unsupervised topic model to identify sets of satisfaction dimensions. A total of 17,747 reviews and comments were collected, and the extracted dimensions were compared with the findings reported in the previous studies.

3 Methodology

In this research, the LDA topic model was employed to extract opinions from user reviews. Topic models are usually used to analyze and summarize topics from large volume of textual documents. A topic is defined as a group of words that tend to occur together frequently and a document is defined as the mixture of different topics [ 24 ]. The LDA model is a generative probabilistic approach to analyze the collections of discrete data [ 25 ]. In this research, customer satisfaction attributes are considered as topics to be obtained from the documents (review comments). The following gives a short overview of the mathematical basis of LDA.

Let M be the number of review comments, \(N_{m}\) be the number of words in the m th comments, V be the number of distinct words, and K be the number of topics. The number of topics, K , is a user-specified parameter that provides control over the level of details of the discovered topics. Also, let \(w_{m,n}\) be the n th word in the m th comment, \(z_{m,n}\) be the topic of topic of \(w_{m,n}\) , \({\varvec{\theta }}_{m}\) be the topic distribution for the m th document, \({\varvec{\phi }}_{k}\) be the word distribution for the k th topic, \({\varvec{\alpha }}\) be the prior distribution for topics in a review, and \({\varvec{{\varvec{\beta }}}}\) be the prior distribution for words in a topic. The words of the review comments are assumed to be generated in the following steps.

Step 1 The word distribution of the k th topic, \(\phi _{k}\) , is generated from a Dirichlet distribution with parameter \({\varvec{\beta }}\) .

where Dir represents the Dirichlet distribution.

Step 2 The topic distribution of the m th review comment, \({\varvec{\theta }}_{m}\) , is generated from a Dirichlet distribution with parameter \({\varvec{\alpha }}\) .

Step 3 The topic of the n th word in the m th review comment, \(z_{m,n}\) , is generated from the \({\varvec{\theta }}_{m}\) distribution as a discrete random variable.

Step 4 The n th word in the m th comment, \(w_{m,n}\) , is generated from the \({\varvec{\phi }}_{z_{m,n}}\) distribution as a discrete random variable.

Given the data generating process above, the joint probability of all the parameters is

\(p\left( w|\phi ,z\right) =\prod _{m=1}^{M}\prod _{n=1}^{N_{m}}p\left( w_{m,n}|\phi _{z_{m,n}}\right) \) ;

\(p\left( \phi |\beta \right) =\prod _{k=1}^{K}p\left( \phi _{k}|\beta \right) \) ;

\(p\left( z|\theta \right) =\prod _{m=1}^{M}\prod _{n=1}^{N_{m}}p\left( z_{m,n}|\theta _{m}\right) \) ; and

\(p\left( \theta |\alpha \right) =\prod _{m=1}^{M}p\left( \theta _{m}|\alpha \right) \) .

By integrating \(\theta \) and \(\phi \) out, we have

\(n_{m,k}\) represents the number of words in the m th document that are assigned to the \(k-\) th topic,

\(n_{k}^{v}\) represents the number of v word assigned to the \(k-\) th topic,

\(A=\sum _{k=1}^{K}\alpha _{k},\)

\(B=\sum _{v=1}^{V}\beta _{v},\)

and \(n_{k}\) represents the number of words assigned to the \(k-\) topic.

In order to use Gibbs sampling to implement the LDA model, we need the following conditional probability:

By plugging Eq. ( 6 ) into ( 7 ) and ignoring the terms that do not involve \(z_{m,n}\) , the conditional posterior of \(z_{m,n}\) becomes as follows:

\({\varvec{z}}^{\left( -m,n\right) } \,\) represents all the topic assignments other than \(z_{m,n}\) ;

\(n_{m,k}^{\left( -m,n\right) } \,\) represents the number of words (excluding the n th word) in the m th review comment that have been assigned to the k th topic.

\(n_{k}^{\left( -m,n\right) }\, \) represents the number of words (excluding the n th word in the m th review comment) assigned to the k th topic;

\(n_{k}^{v,\left( -m,n\right) } \,\) represents the number of the v th word assigned to the k th topic (excluding the n th word in the m th document).

In this paper, we used the Gibbs sampling algorithm to draw random samples from the derived condition posterior distribution [Eq. ( 8 )]. The idea behind Gibbs sampling is that we can obtain random samples from the joint posterior distribution by sequentially simulating individual parameters from the set of conditional distributions. Draws from this simulation algorithm will converge to the target posterior distribution.

4 Case Study

4.1 data description.

In this study, the LDA topic model was applied to 17,747 review comments extracted from 1452 different public transportation agencies on the website of www.yelp.com. These comments and reviews were posted between 2005 and 2013. Table 1 shows the descriptive statistics of the collected data. As shown in Table 1 , most of the comments were collected from rapid transit services and only 0.9 % was collected from semi-rapid transit. A majority of the review ratings (five being the best) were between 2 and 4 (79.2 %). More than a half (52.4 %) of the comments was posted between 2011 and 2013, only 9.7 % of the comments were posted before 2008. Figure 1 shows the word cloud of reviews from four different transit methods (rapid transit, semi-rapid transit, street transit, and others). A word cloud is a visualization of the words frequency in a given text with words of higher frequency displayed in larger size. The word cloud using the online review in Fig. 1 clearly shows four themes of transit methods.

The word cloud of reviews of different transit methods

The 10 agencies/facilities with the most reviews are listed in Table 2 . The Bay Area Rapid Transit (BART) received 755 reviews, which is the highest among the ten agencies/facilities. Moreover, seven of the 10 agencies/facilities are related to rapid transportation services, while two of the ten agencies/facilities are related to boat transportation service. Only one of them is related to street transportation service.

4.2 Topic Model Results

In this case study, the proposed unsupervised LDA topic model was used to summarize the top 10 customer satisfaction dimensions from the collected review comments. In the topic model, words with high associations were grouped together. The topic model results are presented in Table 3 . The dimensions most frequently identified include the following:

Waiting and travel time (the 1st topic of street transit, 1st and 2nd topics of semi-rapid transit, the 1st topic of rapid transit, the 7th and 9th topics of other public transits in Table 3 ).

Cleanliness of the vehicle (the 4th topic of street transit, the 9th topic of semi-rapid transit, the 10th topic of rapid transit, 2nd topic of other transit in Table 3 ). This is consistent with the existing studies, which have demonstrated the significance of the cleanliness in public transportation customer satisfaction.

Customer service (the 7th topic of street transit, the 4th topic of semi-rapid transit, the 3rd topic of rapid transit, the 5th topic of other transits in Table 3 ).

Transit price (the 9th topic of street transit, the 10th topic of semi-rapid transit, the 9th topic of rapid transit, the 8th topic of other transits in Table 3 ).

Accessibility (the 2nd topic of street transit, the 3rd topic of semi-rapid transit, the 4th topic of rapid transit, the 4th topic of other transits in Table 3 ).

Crowdedness (the 8th topic of street transit, the 2nd topic of rapid transit, the 8th topic of semi-rapid transit in Table 3 ).

Comfortability (the 3rd topic of street transit, the 6th topic of semi-rapid transit, the 6th topic of rapid transit, the 3rd and 6th topics of other public transits in Table 3 ).

Safety (the 10th topic of street transit, the 7th topic of semi-rapid transit, the 8th topic of rapid transit in Table 3 ).

Transfer service (the 6th topic in street transit, the 8th topic for semi-rapid transit, the 5th and 7th topics for rapid transit, the 10th topic for other public transit methods in Table 3 ).

Aesthetics (the 5th topic of street transit, the 1st topic for other public transits). This dimension was not found in the existing studies.

5 Conclusion

Transit customer satisfaction study helps understand customers’ behavior intentions and lays foundation for toolbox development to monitor service quality, evaluate system performance, identify customers' dissatisfaction, and develop improvement strategy. In this research, we applied a topic model to analyze the needs and expectations expressed by public transit customers. The proposed model identifies the most frequent customer satisfaction dimensions, which include waiting and travel time, cleanliness, customer service, price, accessibility, crowdedness, comfortability, safety, transfer service, and aesthetics. This research serves as a pilot study to test the feasibility and reliability of using online review comments to investigate transit users' satisfaction dimensions. With the research results confirming previous work on transit users’ perception of service quality, the proposed method prove to be a reliable way to study various dimensions of customer satisfaction toward transit system. Since online review comments can be obtained with low cost and labor, transit agencies can use this method to collectively gather feedback from its users, which contrasts the expensive and low responsive survey/interview approaches. Moreover, when combining the text mining method with the traditional survey/interview approach, the joint investigation will ensure the comprehensiveness of the results. The future directions of this research include testing other text mining models and integrating data collected from both online reviews and questionnaire-based surveys. These approaches have the potential to enhance model efficiency and effectiveness and will facilitate future model selection and modification to serve the emerging needs of transit customer satisfaction analysis.

Lai W-T, Chen C-F (2011) Behavioral intentions of public transit passengers: the roles of service quality, perceived value, satisfaction and involvement. Transp Policy 18(2):318–325

Article   Google Scholar  

Olsen SO (2007) Repurchase loyalty: the role of involvement and satisfaction. Psychol Mark 24(4):315–341

Coffel K (1995) Customer satisfaction index for the mass transit industry. TRANSIT-IDEA Program Project Final Report 10

Hill N, Brierley J, MacDougall R (2003) How to measure customer satisfaction. Gower Publishing, Ltd., Aldershot

Google Scholar  

Eboli L, Mazzulla G (2009) A new customer satisfaction index for evaluating transit service quality. J Public Transp 12(3):2

Nathanail E (2008) Measuring the quality of service for passengers on the hellenic railways. Transp Res Part A 42(1):48–66

Cervero R, Wachs M (1982) An answer to the transit crisis: the case for distance-based fares. J Contemp Stud 5(2):59–70

Goodwin PB (1992) A review of new demand elasticities with special reference to short and long run effects of price changes. J Transp Econ Policy 26:155–169

Andreassen TW (1995) (Dis) satisfaction with public services: the case of public transportation. J Serv Mark 9(5):30–41

Perone JS, Volinski JM (2003) Fare, free, or something in between? Free Fares

Cervero R (1990) Transit pricing research. Transportation 17(2):117–139

Wall G, McDonald M (2007) Improving bus service quality and information in winchester. Transp Policy 14(2):165–179

Friman M, Fellesson M (2009) Service supply and customer satisfaction in public transportation: the quality paradox. J Public Transp 12(4):4

Metropolitan Transportation Authority (MTA) (2014) Customer satisfaction survey subway report

Daganzo CF (2010) Structure of competitive transit networks. Transp Res Part B 44(4):434–446

Woldeamanuel MG, Cyganski R (2011) Factors affecting travellers’s satisfaction with accessibility to public transportation. In Proceedings European transport conference

Turner BA (1994) Causes of disaster: sloppy management. Br J Manag 5(3):215–219

Roberts H, Retting R, Webb T, Colleary A, Turner B, Wang X, Toussaint R, Simpson G, White C (2015) Improving safety culture in public transportation

Lundberg ULF (1976) Urban commuting: crowdedness and catecholamine excretion. J Hum Stress 2(3):26–32

Litman T (2008) Valuing transit service quality improvements. J Public Transp 11(2):3

Cantwell M, Caulfield B, Mahony MO (2009) Examining the factors that impact public transport commuting satisfaction. J Public Transp 12(2):1

Olio LD, Ibeas I, Cecin P (2011) The quality of service desired by public transport users. Transp Policy 18(1):217–227

Aranguren M, Tonnelat S (2014) Emotional transactions in the paris subway: combining naturalistic videotaping, objective facial coding and sequential analysis in the study of nonverbal emotional behavior. J Nonverbal Behav 38(4):495–521

Charles E (2010) Text mining and topic models. Lecture notes

Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3:993–1022

MATH   Google Scholar  

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Gao, L., Yu, Y. & Liang, W. Public Transit Customer Satisfaction Dimensions Discovery from Online Reviews. Urban Rail Transit 2 , 146–152 (2016). https://doi.org/10.1007/s40864-016-0042-0

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Received : 10 March 2016

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Accepted : 06 September 2016

Published : 11 October 2016

Issue Date : December 2016

DOI : https://doi.org/10.1007/s40864-016-0042-0

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Measuring the customer satisfaction of public transportation in Tehran during the COVID-19 pandemic using MCDM techniques

Amir shabani.

a Department of Industrial Engineering, Faculty of Engineering, University of Qom, Qom, Iran

Alireza Shabani

b Department of Industrial Engineering, Faculty of Engineering, College of Farabi, University of Tehran, Iran

Bahareh Ahmadinejad

c Department of Management, Islamic Azad University (IAU), Qazvin Branch, Qazvin, Iran

Ali Salmasnia

The expeditiously spreading of coronavirus disease 2019 (COVID-19) has affected every facet of human lives, including transportation. Due to some characteristics of COVID-19, like high infectivity, people prefer to use their private cars more than before. On the one hand, this circumstance caused public transportation to face an unprecedented decrease in demand and, consequently, revenue. On the other hand, it could intensify traffic congestion during rush hours. This study provides a computational framework to assess public transportation's customer satisfaction in Tehran during the COVID-19 pandemic. To this end, a combined multi-criteria decision-making (MCDM) approach based on the best-worst method (BWM) and fuzzy technique for order performance by similarity to ideal solution (fuzzy TOPSIS) is introduced, which benefits from all the advantages of BWM and fuzzy TOPSIS procedure and consequently provides consistent and reliable outcomes. Outcomes of the implemented model provide precious insight for improving service quality during and after the pandemic; for example, it reveals the performance of each transport mode about each criterion which can help policymakers and transit agencies to allocate resources more intelligently. Final results indicate that during the pandemic, taxis had a better performance compared to other transportation modes.

1. Introduction

The emergence of coronavirus disease 2019 (COVID-19) and its different variants have deeply affected human life. Some of these impacts resulted from governmental countermeasures against pandemic and others from the people themselves to reduce the risk of contamination. Public transportation is one of the sectors that has experienced the harshest changes during this period ( Aparicio et al., 2021 , Jenelius and Cebecauer, 2020 , Tirachini and Cats, 2020 ). High infectivity of COVID-19 and some of the characteristics of public transportation like limited space and high passenger density, especially in rush hours, have increased the risk of contamination in public transportation and declined demand for it.

Iran is one of the highly-affected countries by COVID-19, and its capital Tehran has been a COVID-19 hot spot since December 2019 ( Arab-Mazar et al., 2020 ). Tehran with more than 9.4 million in the city ( The municipality of Tehran, 2019 ) and 15 million in its metropolitan area is the most populous city of Iran ( Statistical Center of Iran (2019) ). Over the past five decades, Tehran's population has grown, partly due to the surging birthrate and increasing migration rate, but also because of being the largest economic center in the country. Despite population growth, Tehran's infrastructure and especially transportation system has not developed adequately, making Tehran one of the most congested and polluted cities in the region. Tehran's principal public transport modes are taxis, vans, metro, which is an underground electric railway system, buses, and bus rapid transits (BRT). It should be stated that taxis are usually shared between four passengers, and they are a part of public transportation in Iran. According to statistics, 19.3 million trips are made daily in Tehran, and approximately half of them are made by public transportation systems ( The municipality of Tehran, 2019 ).

The traffic congestion in Tehran has been turned into a formidable challenge for policymakers and the municipality. This situation has deteriorated after COVID-19 outbreaks as people prefer to use their private cars more to minimize the risk of contamination in public transportation. On one hand, this contemporary phenomenon caused an unexpected decrease in demand and consequently revenue of the public transportation sector, and on the other hand, it could exacerbate the traffic congestion in rush hours. Moreover, there is a risk that this behavior becomes a habit, and this irreplaceable sector is perceived as unhealthy, even after the pandemic.

The first step to overcome the mentioned complex situation is performance measurement of public transportation because it is essential for monitoring, comparing performance over time, and continuously improving the transportation system. Efficiency, effectiveness, productivity, and service quality are fundamental parts to measure the performance of transportation systems. However, efficiency, effectiveness, and productivity are the performance measures from the transit agency viewpoint ( Eboli and Mazzulla, 2011 ), while many academicians consider the customer's viewpoint as the most significant element for assessing transportation systems performance; for instance, Berry et al. (1990) mentioned that “customers are the sole judge of service quality.”. Service quality is a comparison between customer expectations of service and his/her perceptions ( Eboli and Mazzulla, 2011 ). Measuring customer satisfaction could help policymakers optimally allocate resources and improve service quality in public transportation. Therefore, this study provides a framework to evaluate the customer satisfaction of public transportation systems in Tehran throughout the COVID-19 pandemic. For this aim, a hybrid multi-criteria decision-making method integrating BWM and fuzzy TOPSIS is introduced. This study's first intention is to identify and prioritize the most prominent criteria of the evaluation process. Literature review, Delphi method, and panel discussion were used to identify the criteria. Further, the BWM method was applied to determine the priority of each criterion. The second intention of the present research is to evaluate and prioritize public transportation systems in Tehran during the COVID-19 pandemic. For this purpose, 392 online questionnaires were filled by current passengers and potential users of the public transportation system. A combination of fuzzy concept and the TOPSIS method was utilized to deal with the uncertainty of respondents' thoughts.

Main advantages of this study can be summarized as follows: (i) this is the first research work that addresses the problem of measuring customer satisfaction of public transportation during the COVID-19 pandemic. (ii) customer satisfaction assessment of public transportation systems during the pandemic may provide precious insight for improving the service quality during and after the pandemic and helps policymakers and transit agencies restore public transportation systems' ability to fulfill their social role. (iii) former studies in the field of measuring the customer satisfaction level in Tehran like Ebrahimi and Bridgelall (2020) and Nassereddine and Eskandari (2017) considered narrow criteria for the assessment, while in our study, the decision team has taken a closer look at the topic for selecting and finalizing criteria based on a comprehensive literature review, Delphi method, and hours of panel discussion with the decision team (iv) some of the criteria can affect contagion, so after evaluation, it can be seen which transport mode meets which criteria better. (v) there is some research that used BWM and fuzzy TOPSIS concurrently, but to the extent of our knowledge, there is no research to assess public transportation systems using a model integrating BWM and fuzzy TOPSIS.

The remainder of this paper is organized as follows: Section 2 provides a review of the related literature. A new methodology based on the BWM and fuzzy TOPSIS methods is explained in Section 3 . Section 4 presents an application of the introduced methodology. Ultimately, conclusions are presented in the Section 5 .

2. Literature review

This section is dedicated to a literature review of the application of Multi-Criteria Decision-Making (MCDM) procedures in transportation, and the use of the mentioned methods in the assessment of customer satisfaction in public transportation.

MCDM is a methodological approach for structuring, analyzing, and finding the solution to complicated decision problems ( Kahraman, 2008 ). MCDM procedures are powerful tools that can be used in various fields. Behzad et al. (2020) utilized MCDM to evaluate waste management performance in the Nordic countries, Mousavi-Nasab and Sotoudeh-Anvari (2017) for solving the material selection problem, and Ahmadinejad et al. (2021) to prioritize the factors that inhibit COVID-19 transmission.

In the transportation area, every decision should be based on numerous criteria associated with environmental, economic, and socio-political features ( Camargo Pérez et al., 2014 ). Thus, MCDM has turned into one of the vital procedures employed by academicians and practitioners for evaluating and decision making in the transportation section ( Awasthi et al., 2011b , Camargo Pérez et al., 2014 , Tsamboulas et al., 1999 ). Awasthi et al. (2018) applied three different MCDM techniques to evaluate the sustainability of urban mobility projects. In the first step of their proposed methodology, the criteria for evaluation were selected by experts. In the next step, the criteria were evaluated using linguistic scales, and in the final step, three sustainable mobility projects in the city of Luxembourg were assessed. The results of this study yielded that the implementation of a new tramway has priority over other projects. Kabak et al. (2018) used a hybrid MCDM approach, including analytical hierarchy process (AHP) and multi-objective optimization by ratio analysis (MOORA), to assess the status of bike-share stations. They determined the criteria using a literature review and expert opinion. Further, the authors applied AHP to obtain criteria weights and MOORA to assess the alternatives of bike-sharing stations. Recently Sobhani et al. (2020) evaluated the competitiveness and sustainability of the unconventional modes of transport (UMT) in Dhaka, Bangladesh. They used a novel framework which integrated AHP, and TOPSIS method.

There is a considerable amount of research studies that have worked on the problem of assessing customer satisfaction of public transportation systems. In the following, a short review will be presented of papers that deal with the problem of evaluating customer satisfaction in the public transportation system and the application of MCDM methods in the mentioned problem.

Eboli and Mazzulla (2011) offered a methodological framework to assess service quality for transportation systems. The proposed framework is considered both customer's and transit agencies' point of view. Celik et al. (2013) presented a mathematical framework for evaluating customer satisfaction in the public transportation section in Istanbul. They used a combination of statistical analysis, survey study, and MCDM techniques in their research. Their suggested MCDM method integrated the gray relational analysis (GRA), TOPSIS, and type-2 fuzzy sets. Aydin et al. (2015) provided a framework to assess the rail transit systems of Istanbul. The used framework combined statistical analysis, fuzzy AHP method, and Choquet integral to evaluate rail transit lines' performance. Mardani et al. (2016) reviewed a number of papers about the application of MCDM methods in transportation systems. They concluded that AHP, as a singular technique, and hybrid MCDM, as an integrated approach, are the most prevalent techniques. Efthymiou and Antoniou (2017) explored the impact of the financial crisis in Greece on bus customer's satisfaction and demand. They compared the importance of factors and users' satisfaction level between 2008 and 2013 and 2013–2017. The comparison of results showed some changes in the preferences of the respondents. The importance of some factors like on-time performance and waiting time were decreased, while the importance of the information provision criterion was increased. Also, the usage of public transportation during these years has increased. Coppola and Silvestri (2020) introduced a methodology to evaluate railway stations passengers' perceived security and safety. The findings of this study showed that security issue is more threatening than safety from train travelers' viewpoint. Also, criminal acts like thieving and frauding have the most significant impact on travelers' perception of safety and security. Kiani Mavi et al. (2018) offered a computational framework to improve BRT performance. For this aim, they defined and then simulated four different scenarios as possible alternatives. Then, they utilized a hybrid MCDM method integrating the step-wise weight assessment ratio analysis (SWARA) and complex proportional assessment of alternatives (COPRAS) to evaluate and rank scenarios.

Nassereddine and Eskandari (2017) presented a hybrid approach integrating the Delphi method, group-AHP, and preference ranking organization method for enrichment of evaluations (PROMETHEE) to evaluate five major public transport modes in Tehran. They evaluated public transportation systems with respect to six criteria (cost of travel, waiting time, duration of travel, accessibility, suitability, and safety). The outcomes of this research indicated that metro is ranked first, followed by taxi and BRT. However, their proposed method neglected the vagueness and uncertainty involved in the responses of decision-makers. Ebrahimi and Bridgelall (2020) applied a hybrid MCDM method to assess public transportation modes in Tehran. The authors considered reliability, frequency, cost of travel, safety, accessibility, comfort, and information provision as the essential criteria. The final results demonstrated that the metro rated first, followed by ride-hailing, BRT, bus, and taxi. They used the fuzzy AHP method to evaluate criteria and transport modes that need more pairwise comparisons and provide less consistent and reliable results in comparisons with the Best-Worst method ( Mi et al., 2019 ). Also, they ignored passengers' points of view which increases the error margin of their findings.

However, all the aforementioned studies were performed in a normal situation without considering a global pandemic in evaluation process. To this end, this paper provides a computational framework to evaluate the customer satisfaction of public transportation in Tehran during the COVID-19 pandemic to understand passengers' needs and improve the service quality. Therefore, a novel MCDM approach based on the BWM and fuzzy TOPSIS procedure is introduced.

3. Methodology

The proposed integrated method has three steps. Firstly, using a comprehensive literature review, Delphi method, and panel discussion, the criteria will be selected and finalized. Then, each criterion's weight will be computed by applying BWM method, and finally, fuzzy TOPSIS will be employed for computing the rank of public transportation systems. The basic definition of BWM, fuzzy set theory, and fuzzy TOPSIS procedures are presented in the following sections.

3.1. Delphi

Delphi method was originally introduced through a series of experiments by the scientists of the RAND Corporation in the 1950 s. Delphi method seeks to achieve consensus and stable results on a topic by a structured group of experts. In this method, practitioners separately respond to a questionnaire based on their opinion and knowledge. After each round, an anonymous summary of answers is shared with the participants, and they can revise their responses considering others' opinions. This process will continue until the convergence of responses and achievement of consensus ( Dalkey and Helmer, 1963 ).

3.2. Best worst method

Best Worst Method (BWM) is one of the strongest and latest MCDM techniques that is extensively used by many academics and practitioners to solve their decision-making problems. The aforementioned method is based on pairwise comparisons between the most desirable criterion to the other criteria and all the other criteria to the least desirable criterion ( Rezaei et al., 2016 ). There are diverse MCDM procedures like the analytic hierarchy process (AHP), simple multi attribute rating technique (SMART), measuring attractiveness by a categorical-based evaluation technique (MACBETH), etc. in the purpose of weighting criteria, but BWM has some salient advantages such as ( Mi et al., 2019 , Rezaei et al., 2016 ):

  • 1) The BWM needs fewer times of pairwise comparisons. For instance, in AHP, the decision-maker has to compare each criterion with all the other criteria, and it means n ( n - 1 ) / 2 pairwise comparisons, while in the best worst method, the decision-maker only needs to do 2 n - 3 times of pairwise comparisons.
  • 2) It provides more consistent and reliable outcomes due to its structure and elimination of redundant pairwise comparisons.
  • 3) In this method, the decision-maker only uses integer values for pairwise comparisons, which is more understandable than fractions.

Procedural steps of the BWM method are summarized as below ( Rezaei, 2016 , Rezaei, 2015 ):

Step 1: Specify a set of decision criteria. The set of criteria can be evaluated as K 1 , K 2 , K 3 , . . . , K n .

Step 2: Identify the best (most significant) and the worst (least significant) criterion from the set of decision criteria.

Step 3: Compare the best criterion K B to all the other criteria by applying numbers in the range of 1 to 9 and then establish the Best to Other (BO) vector.

Where a Bj denotes the preference degree of the best criterion K B over criterion j.

Step 4: Compare all the other criteria to the worst criterion by applying numbers in the range of 1 to 9 and then establish the Others to Worst (OW) vector.

Where a 1 W denotes the preference degree of the criterion j over the worst criterion K W .

Step 5: Determine the optimal weights of criteria ( w 1 ∗ , w 2 ∗ , . . . , w n ∗ ) by minimizing the maximum absolute differences w B - a Bj w j and w j - a jW w W for all j . This can be formulated as follows:

Model (1) can be transferred to a linear model which gives better results, the model is illustrated below:

By solving model (2) optimal weights ( w 1 ∗ , w 2 ∗ , . . . , w n ∗ ) and the optimal value of ξ L ∗ are obtained. ξ L ∗ is defined as the consistency ratio of the comparison system. The closeness of value ξ L ∗ to zero means a minimal inconsistency of the comparison system.

3.3. Fuzzy set theory

For the first time, Zadeh (1996) proposed fuzzy set theory to represent vagueness, uncertainty, and imprecision ( Awasthi et al., 2011a , Li et al., 2020 ). Due to the mentioned characteristics, the fuzzy set theory used extensively in decision-making problems. The use of fuzzy set theory allows decision-makers to verbalize their preferences in linguistic terms ( Chen and Chen, 2007 ). In fuzzy sets theory, all the numbers have a varying degree of membership between zero and unity rather than the traditional binary association. To be clear, fuzzy set numbers choose a value in the range of 0 to 1; the nearer the value to unity, the higher the grade of membership ( Zadeh et al., 1996 ). There are various types of fuzzy numbers, including trapezoidal, Gaussian, and triangular. Triangular fuzzy numbers are extensively utilized to represent fuzzy numbers due to its simplicity of membership function and easier algebraic operation ( Pedrycz, 1994 ).

A triangular fuzzy number G ∼ can be expressed by a triplet r , s , t . Where r , s , t are real numbers and r ⩽ s ⩽ t , which r and t stand for lower and upper value of G ∼ and s gives the most probable value of it. The membership function μ G ∼ x of G ∼ is described as ( Awasthi et al., 2011a , Chang, 1996 ):

For two triangular fuzzy numbers G ∼ = ( r 1 , s 1 , t 1 ) and H ∼ = ( r 2 , s 2 , t 2 ) basic arithmetic operations are described as follows ( Chang, 1996 , Sirisawat and Kiatcharoenpol, 2018 ):

And the distance between G ∼ and H ∼ using the vertex method is calculated as:

3.4. Fuzzy TOPSIS method

TOPSIS is an acronym that stands for Technique for Order Performance by Similarity to Ideal Solution. It is a multi-criteria decision making technique that was proposed by Hwang and Yoon in 1981. As the name implies, TOPSIS evaluates alternatives to rank them based on their distance from the positive ideal solution (PIS) and negative ideal solution (NIS) ( Hwang and Yoon, 1981 ). The mentioned procedure has been successfully applied in diverse areas like supply chain management, manufacturing systems, business and marketing, tourism industry, healthcare management, environment management, urban management, etc. ( Behzadian et al., 2012 ).

The TOPSIS is characterized by some noticeable specifications such as: ease of use and low mathematical complexity, comprehensibility, rationality, good computational efficiency, and the ability to perform with any number of positive or negative criteria ( Roszkowska, 2011 , Samaie et al., 2020 ). Although this method is interesting, it fails to take the uncertainty of human thoughts into account. In the old version of the TOPSIS method decision-makers express their opinion by crisp values, and it cannot consider decision-makers' ambiguity and uncertainty. The fuzzy type of TOPSIS was introduced to rectify the mentioned problem. In this procedure, the decision matrix elements individually or with the weight of criteria are expressed with fuzzy numbers. The fuzzy TOPSIS method based on triangular fuzzy numbers and for T decision makers D t = ( t = 1 , 2 , . . . , T ) and a decision making problem with m criteria C j ( j = 1 , 2 , . . . , m ) and n alternatives A i = ( i = 1 , 2 , . . . , n ) consist of the following steps ( Awasthi et al., 2011a , Chen, 2000 ):

Step 1: The weights of criteria and ratings of alternatives with respect to each criterion can be calculated as:

Where the rating and the importance weight of the T th decision-maker are expressed by x ∼ ij T and w ∼ j T .

Step 2: Construct the fuzzy decision matrix as follows:

Where x ∼ ij are linguistic variables and can be described by triangular fuzzy numbers.

Step 3: Calculate the normalized fuzzy decision matrix R ∼ through the following equations:

Step 4: In this step, we should compute the weighted normalized decision matrix V ∼ by multiplying the weights ( w ∼ j ) of evaluation criteria with the normalized fuzzy decision matrix r ∼ ij .

Step 5: Compute the fuzzy positive ideal solution (FPIS) and fuzzy negative ideal solution (FNIS) as follows:

Step 6: Distances of alternatives from (FPIS) and (FNIS) are obtained using the following equations:

Step 7: Calculate the closeness coefficient C C i of each alternative through the following equation:

Step 8: Compute the ranks of alternatives according to the closeness coefficient in decreasing sequence. The preferred alternative is closest to the (FPIS) and farthest from the (FNIS).

4. Case analysis and application using the proposed methodology

In this section, we apply our proposed model for the evaluation of public transportation systems of Tehran in the age of COVID-19. Our model consists of three steps.

  • 1. Selection and finalization of evaluation criteria
  • 2. Criteria and sub-criteria's weights computation using the BWM
  • 3. Ranking of transportation systems using fuzzy TOPSIS

The steps of the proposed methodology are presented in detail in Fig. 1 .

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The framework of the introduced BWM-fuzzy TOPSIS methodology.

4.1. Selection and finalization of criteria

For the purpose of selection and finalization of evaluation criteria, we formed a decision team consists of 10 experts with academic and engineering background in the field of transportation and at least 15 years of experience. Thereafter, based on a comprehensive review of literature, discussion with experts using the Delphi method, and panel discussion with the decision team, the criteria and sub-criteria to evaluating transportation systems during the COVID-19 pandemic were identified. Initially, after a comprehensive review of related literature, primitive criteria were extracted and presented to the decision team for finalization. After a series of discussions using the Delphi method and then a panel discussion with our decision team total of 21 criteria were selected. Afterward, the selected criteria were clustered into six main criteria for the purpose of evaluation. Table 1 indicates the finalized main criteria and sub-criteria.

Criteria for evaluation of public transportation systems.

4.2. Criteria and sub-criteria's weights computation using BWM

After identifying and finalizing the criteria, the weights of them were calculated using the BWM. The decision team was asked to determine the most significant and the least significant criteria among the main criteria and then sub-criteria. The selected best and worst main criteria for each member of the decision team are illustrated in Table 2 .

Best and worst main criteria identified by decision team.

After identifying the worst and the best criteria, all the experts who are the members of the decision team were asked to compare their chosen best criterion to all the other criteria and the other criteria to their chosen worst criterion on a scale of 1 to 9 for the main criteria as well as sub-criteria. A score of 1 means equal importance, and 9 means extremely more preferred. Accordingly, best to others (BO) and others to worst (OW) vectors were established. The results of the comparison matrix for the main criteria by all members of the decision team are presented in Table 3 and Table 4 . Next, using model (2) and the linear programming, the weight of main criteria, and subsequently sub-criteria were calculated for all members of the decision team, and then the final weight of each criterion was computed by the arithmetic mean. The final weight of each criterion is given in Table 5 . The consistency ratio of all comparison matrix were computed; all of them were fairly close to zero that shows an acceptable level of consistency in pairwise comparisons.

BO vectors for each member of decision team.

C = Comfort, A = Accessibility, T = Time, P = Payment, SS = Safety and security, E = Environmental impact.

OW vectors for each member of decision team.

Final weights.

4.3. Survey

Most scientific researches are carried out based on a sample because studying the whole population is a time-consuming process and not economically efficient ( Verma and Verma, 2020 ). Determining the sample size is one of the most critical steps of research because an inadequate sample size could significantly affect the quality of research. In this study, the sample size is determined using Cochran's formula with 5 percent of the margin of error and 95% confidence interval (William G. Cochran, 1977 ). So, for a population of more than one million, the sample size should be 385. For this reason, a total of 439 online questionnaires were filled by current passengers and potential users of public transportation. However, 47 incomplete questionnaires have been omitted from the public transportation evaluation process. Table 7 gives the demographic information of the participators.

Demographic information of the respondents.

4.4. Ranking of transportation systems using fuzzy TOPSIS

In the final, fuzzy TOPSIS is employed for ranking public transportation systems. First, an online questionnaire, which is designed based on TOPSIS method, is used to evaluate public transportation systems. Respondents were asked to assess the alternatives with linguistic terms mentioned in Table 6 . The linguistic terms were then converted into fuzzy numbers, and aggregate fuzzy weights of alternatives were computed. The aggregate fuzzy decision matrix for the alternatives is shown in Table A1 . Afterward, the fuzzy normalized decision matrix was computed using Eqs. (13–15) and is presented in Table A2 . Next, the weighted normalized fuzzy decision matrix was constructed using Eq. (16) and it can be seen from Table A3 . Consequently, the FPIS and FNIS were determined using Eqs. (17) , (18) . Then, the distance of each alternative from the FPIS and FNIS was calculated using Eqs. (19) , (20) . Finally, the closeness coefficient C C i values were obtained by using Eq. (21) . By comparing the coefficients of closeness, ranking of all transportation systems was obtained, and the outcomes are illustrated in Table 8 .

Linguistic terms for alternative ratings.

Final ranking of alternatives.

Aggregate fuzzy decision matrix.

Normalized fuzzy decision matrix.

Weighted normalized fuzzy decision matrix.

4.5. Results

The weights of the main criteria and sub-criteria were computed via the BWM and are illustrated in Table 5 . A total of 21 sub-criteria were finalized and then grouped into six categories by the decision team. The order of priority of main criteria was given as C > A > SS > P > T > E. Further, the final weights of each sub-criteria were obtained by multiplying the preference weights of sub-criteria with the weight of the respective category. Among the sub-criteria, travel cost (P1) was considered as the criterion with the highest priority with a weight of 0.109. Security (SS2) was ranked second among the sub-criteria. Passenger density (C3) was the third sub-criteria that is given the most importance. Safety (SS1) was the fourth most important sub-criteria, and travel time (T1) was the next sub-criteria that is given importance.

After ranking the criteria and sub-criteria, Tehran’s public transportation systems were ranked using the fuzzy TOPSIS procedure. For this purpose, 392 respondents filled an online questionnaire. The respondents were asked to rate public transportation systems with linguistic terms. In this evaluation, taxi was ranked first and followed by metro, BRT, bus, and van respectively. The outcomes demonstrate that taxi had a better performance during the coronavirus pandemic. Compared with similar studies conducted by Ebrahimi and Bridgelall (2020) and Nassereddine and Eskandari (2017) , metro was ranked first in both studies, while in this study, taxi ranked highest in passengers viewpoint. It seems that the mentioned difference is under the effect of some criteria such as hygiene and passenger density which have been gotten more attention of riders in the pandemic era.

It should be mentioned that four of the selected criteria (passenger density, hygiene, ventilation, and mode of payment) could directly influence the risk of COVID-19 contagion ( Shen et al., 2020 , Tirachini and Cats, 2020 ). Based on the reported data in Table A1 , Table A2 , Table A3 , by considering each of the mentioned criteria separately, it becomes clear that taxi in passenger density and hygiene, BRT in ventilation, and metro in the mode of payment had a better performance. Also, taking into account all the stated criteria together again taxi had more desirable performance than other transportation modes. These results imply that some of the taxi features like its low passenger density and some of the prevention and control measures adopted by taxis like using separators, equipping with hand sanitizer, and mask enforcement were successful.

4.6. Sensitivity analysis

Sensitivity analysis is an invaluable technique to test the robustness of a mathematical model’s outcomes in the case of the existence of uncertainty ( Gupta and Barua, 2017 , Prakash and Barua, 2015 ). Sensitivity analysis is described as the study of how uncertainty in the inputs of a mathematical model can cause uncertainty in the outputs ( Saltelli et al., 2004 ). To examine the robustness of the results of the BWM-fuzzy TOPSIS model a sensitivity analysis is carried out. For this purpose, the weight of the most important criteria (Comfort) is varied in the range of 0.1 to 0.9 and consequently, the weight of all the other main criteria is changed. The details are presented in Table 9 .

Weights of all main criteria after varying weight of Comfort.

Further, the weights of sub-criteria were computed using the weights of the main criteria for different experiments, and results are presented in Fig. 2 . Then, using the weights of sub-criteria, the final weights of alternatives were calculated. The transportation systems were ranked using fuzzy TOPSIS for each experiment, and the outcomes are presented in Table 10 . According to sensitivity analysis's results, it can be deduced that varying the weight of the main criterion with the highest priority will not conclude to meaningful changes in the ranking of alternatives. In all the nine experiments, taxi remained in the first rank and van in the last rank. Also, Bus acquired the fourth rank in all the nine runs. There is a minor change in the two last runs where the ranks of metro and BRT are swapped. This shift does not considerably affect the results of the BWM-fuzzy TOPSIS model. Thus, the results are robust, and the model is validated.

Ranking of alternatives when the weight of the main criterion increasing via sensitivity analysis.

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Ranking of sub-criteria when the weight of the main criterion (Comfort) increasing via sensitivity analysis.

5. Conclusion and discussion

Public transportation plays a vital role in contemporary human life. This kind of transportation provides an economical, environmentally friendly, and safe way to transit within and between cities. Lately, the public transportation sector faces a severe crisis due to COVID-19 outbreaks. During this period, people prefer to use their private cars to minimize the risk of contamination; consequently, it caused an unexpected decline in demand and revenue for the public transportation sector. Moreover, there is a risk that the public transportation sector is perceived as unhealthy and unsafe, even after the pandemic.

Changing the mentioned circumstance and restoring the ability of public transportation requires a better understanding of passengers' needs and augmenting service quality. This research provides a framework to assess public transportation's customer satisfaction in Tehran during the COVID-19 pandemic. To do this, we utilized panel discussion and literature review to identify primitive criteria, then with the use of the Delphi method, and panel discussion evaluation criteria were finalized. The output of this step was a total of 21 criteria that were grouped into six main criteria. It should be mentioned that four criteria, namely passenger density, hygiene, ventilation, and mode of payment, directly influence the risk of COVID-19 contagion. In the next step, the BWM procedure was applied to determine the priority of each criterion. This step's outcomes imply that travel cost is the most significant criterion in the evaluation process, which means if the government considers some sort of discount or economic encouragement for those who use public transportation regularly, people will encourage to use it more. Also, security has the second-highest priority in respondents' point of view, which shows that if responsible departments are fond of attracting private car users, they should allocate more resources to improve public transportation security. The third most important criterion is passenger density which shows the necessity of rescheduling in public transportation to avoid overcrowding, especially during the pandemic.

In the final step, the fuzzy TOPSIS technique was adopted to rank public transportation systems in Tehran. This step's results showed that taxi is the best transport mode from passengers' point of view and it follows by metro, BRT, bus, and van. Compared with similar studies conducted by Ebrahimi and Bridgelall, 2020 , Nassereddine and Eskandari, 2017 , metro was ranked first in both studies, while in this study, taxi ranked highest in passengers viewpoint. Additionally, about the stated criteria that could directly influence the risk of COVID-19 contagion results demonstrated that taxis had more desirable performance than other transportation modes. With a closer look at the coronavirus-related criteria, it is obvious that they had a massive impact on the decision-making process (the weight of them together is 0.217), so any action to improve transportation systems in the mentioned criteria could be effective in attracting people to public transportation.

This research will inform responsible departments and policymakers about the weaknesses of each mode of public transportation; furthermore, they could use the results of this research to improve the public transportation service quality during and after the pandemic. The mathematical model could be used to evaluate public transportation of other megacities, and also it could be used several times to determine the improvement of each criterion and transportation mode.

In future researches, public transportation systems of other cities can be studied to demonstrate a significant difference in public transportation demand before and after taking countermeasures against COVID-19 contagion. Ride-hailing services as a new transportation mode can be taken into account to measure customer satisfaction as well. Finally, as a future direction, optimization methods can be used to optimize public transportation scheduling according to the pandemic situation.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

  • Ahmadinejad, B., Shabani, A., Jalali, A., 2021. Implementation of Clean Hospital Strategy and Prioritizing Covid-19 Prevention Factors Using Best-Worst Method. https://doi.org/10.1080/00185868.2021.1997129 1–11. https://doi.org/10.1080/00185868.2021.1997129. [ PubMed ]
  • Aparicio, J.T., Arsenio, E., Henriques, R., 2021. Understanding the Impacts of the COVID-19 Pandemic on Public Transportation Travel Patterns in the City of Lisbon. Sustain. 2021, Vol. 13, Page 8342 13, 8342. https://doi.org/10.3390/SU13158342.
  • Arab-Mazar Z., Sah R., Rabaan A.A., Dhama K., Rodriguez-Morales A.J. Mapping the incidence of the COVID-19 hotspot in Iran – Implications for Travellers. Travel Med. Infect. Dis. 2020; 34 doi: 10.1016/J.TMAID.2020.101630. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Awasthi A., Chauhan S.S., Omrani H. Application of fuzzy TOPSIS in evaluating sustainable transportation systems. Expert Syst. Appl. 2011; 38 :12270–12280. doi: 10.1016/j.eswa.2011.04.005. [ CrossRef ] [ Google Scholar ]
  • Awasthi A., Chauhan S.S., Omrani H., Panahi A. A hybrid approach based on SERVQUAL and fuzzy TOPSIS for evaluating transportation service quality. Comput. Ind. Eng. 2011; 61 :637–646. doi: 10.1016/j.cie.2011.04.019. [ CrossRef ] [ Google Scholar ]
  • Awasthi A., Omrani H., Gerber P. Investigating ideal-solution based multicriteria decision making techniques for sustainability evaluation of urban mobility projects. Transp. Res. Part A Policy Pract. 2018; 116 :247–259. doi: 10.1016/j.tra.2018.06.007. [ CrossRef ] [ Google Scholar ]
  • Aydin N., Celik E., Gumus A.T. A hierarchical customer satisfaction framework for evaluating rail transit systems of Istanbul. Transp. Res. Part A Policy Pract. 2015; 77 :61–81. doi: 10.1016/j.tra.2015.03.029. [ CrossRef ] [ Google Scholar ]
  • Behzad M., Hashemkhani Zolfani S., Pamucar D., Behzad M. A comparative assessment of solid waste management performance in the Nordic countries based on BWM-EDAS. J. Clean. Prod. 2020; 266 doi: 10.1016/j.jclepro.2020.122008. [ CrossRef ] [ Google Scholar ]
  • Behzadian M., Khanmohammadi Otaghsara S., Yazdani M., Ignatius J. A state-of the-art survey of TOPSIS applications. Expert Syst. Appl. 2012; 39 :13051–13069. doi: 10.1016/j.eswa.2012.05.056. [ CrossRef ] [ Google Scholar ]
  • Berry, L.L., Zeithaml, V.A., Parasuraman, A.C.-S.Q., 1990. Five Imperatives for Improving Service Quality. Sloan Manage. Rev. 31, 29-38 ST-Five Imperatives for Improving Service.
  • Camargo Pérez J., Carrillo M.H., Montoya-Torres J.R. Multi-criteria approaches for urban passenger transport systems: a literature review. Ann. Oper. Res. 2014; 226 :69–87. doi: 10.1007/s10479-014-1681-8. [ CrossRef ] [ Google Scholar ]
  • Celik E., Bilisik O.N., Erdogan M., Gumus A.T., Baracli H. An integrated novel interval type-2 fuzzy MCDM method to improve customer satisfaction in public transportation for Istanbul. Transp. Res. Part E Logist. Transp. Rev. 2013; 58 :28–51. doi: 10.1016/j.tre.2013.06.006. [ CrossRef ] [ Google Scholar ]
  • Chang D.Y. Applications of the extent analysis method on fuzzy AHP. Eur. J. Oper. Res. 1996; 95 :649–655. doi: 10.1016/0377-2217(95)00300-2. [ CrossRef ] [ Google Scholar ]
  • Chen C.T. Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets Syst. 2000; 114 :1–9. doi: 10.1016/S0165-0114(97)00377-1. [ CrossRef ] [ Google Scholar ]
  • Chen S.J., Chen S.M. Fuzzy risk analysis based on the ranking of generalized trapezoidal fuzzy numbers. Appl. Intell. 2007; 26 :1–11. doi: 10.1007/s10489-006-0003-5. [ CrossRef ] [ Google Scholar ]
  • Coppola P., Silvestri F. Assessing travelers’ safety and security perception in railway stations. Case Stud. Transp. Policy. 2020; 8 :1127–1136. doi: 10.1016/J.CSTP.2020.05.006. [ CrossRef ] [ Google Scholar ]
  • Dalkey N., Helmer O. An Experimental application of the DELPHI method to the use of experts. An Experimental Application of the DELPHI Method to the Use of Experts. 1963; 9 (3):458–467. [ Google Scholar ]
  • Eboli L., Mazzulla G. A methodology for evaluating transit service quality based on subjective and objective measures from the passenger’s point of view. Transp. Policy. 2011; 18 :172–181. doi: 10.1016/j.tranpol.2010.07.007. [ CrossRef ] [ Google Scholar ]
  • Ebrahimi S., Bridgelall R. A fuzzy Delphi analytic hierarchy model to rank factors influencing public transit mode choice: A case study. Res. Transp. Bus. Manag. 2021; 39 :100496. [ Google Scholar ]
  • Efthymiou D., Antoniou C. Understanding the effects of economic crisis on public transport users’ satisfaction and demand. Transp. Policy. 2017; 53 :89–97. doi: 10.1016/j.tranpol.2016.09.007. [ CrossRef ] [ Google Scholar ]
  • Gupta H., Barua M.K. Supplier selection among SMEs on the basis of their green innovation ability using BWM and fuzzy TOPSIS. J. Clean. Prod. 2017; 152 :242–258. doi: 10.1016/j.jclepro.2017.03.125. [ CrossRef ] [ Google Scholar ]
  • Hwang, C.-L., Yoon, K., 1981. Methods for Multiple Attribute Decision Making. pp. 58–191. https://doi.org/10.1007/978-3-642-48318-9_3.
  • Jenelius E., Cebecauer M. Impacts of COVID-19 on public transport ridership in Sweden: Analysis of ticket validations, sales and passenger counts. Transp. Res. Interdiscip. Perspect. 2020; 8 doi: 10.1016/J.TRIP.2020.100242. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Kabak M., Erbaş M., Çetinkaya C., Özceylan E. A GIS-based MCDM approach for the evaluation of bike-share stations. J. Clean. Prod. 2018; 201 :49–60. doi: 10.1016/j.jclepro.2018.08.033. [ CrossRef ] [ Google Scholar ]
  • Kahraman, C., 2008. Fuzzy Multi-Criteria Decision Making: Theory and Applications with Recent Developments.
  • Kiani Mavi R., Zarbakhshnia N., Khazraei A. Bus rapid transit (BRT): A simulation and multi criteria decision making (MCDM) approach. Transp. Policy. 2018; 72 :187–197. doi: 10.1016/j.tranpol.2018.03.010. [ CrossRef ] [ Google Scholar ]
  • Li M., Wang H., Wang D., Shao Z., He S. Risk assessment of gas explosion in coal mines based on fuzzy AHP and bayesian network. Process Saf. Environ. Prot. 2020; 135 :207–218. doi: 10.1016/j.psep.2020.01.003. [ CrossRef ] [ Google Scholar ]
  • Mardani A., Zavadskas E.K., Khalifah Z., Jusoh A., Nor K.MD. Multiple criteria decision-making techniques in transportation systems: a systematic review of the state of the art literature. Transport. 2016; 31 (3):359–385. [ Google Scholar ]
  • Mi X., Tang M., Liao H., Shen W., Lev B. The state-of-the-art survey on integrations and applications of the best worst method in decision making: Why, what, what for and what’s next? Omega (United Kingdom) 2019; 87 :205–225. doi: 10.1016/j.omega.2019.01.009. [ CrossRef ] [ Google Scholar ]
  • Mousavi-Nasab S.H., Sotoudeh-Anvari A. A comprehensive MCDM-based approach using TOPSIS, COPRAS and DEA as an auxiliary tool for material selection problems. Mater. Des. 2017; 121 :237–253. doi: 10.1016/j.matdes.2017.02.041. [ CrossRef ] [ Google Scholar ]
  • Nassereddine M., Eskandari H. An integrated MCDM approach to evaluate public transportation systems in Tehran. Transp. Res. Part A Policy Pract. 2017; 106 :427–439. doi: 10.1016/j.tra.2017.10.013. [ CrossRef ] [ Google Scholar ]
  • Pedrycz W. Why triangular membership functions? Fuzzy Sets Syst. 1994; 64 :21–30. doi: 10.1016/0165-0114(94)90003-5. [ CrossRef ] [ Google Scholar ]
  • Prakash C., Barua M.K. Integration of AHP-TOPSIS method for prioritizing the solutions of reverse logistics adoption to overcome its barriers under fuzzy environment. J. Manuf. Syst. 2015; 37 :599–615. doi: 10.1016/j.jmsy.2015.03.001. [ CrossRef ] [ Google Scholar ]
  • Rezaei J. Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega (United Kingdom) 2016; 64 :126–130. doi: 10.1016/j.omega.2015.12.001. [ CrossRef ] [ Google Scholar ]
  • Rezaei J. Best-worst multi-criteria decision-making method. Best-worst multi-criteria decision-making method. Omega (United Kingdom) 2015; 53 :49–57. [ Google Scholar ]
  • Rezaei J., Nispeling T., Sarkis J., Tavasszy L. A supplier selection life cycle approach integrating traditional and environmental criteria using the best worst method. J. Clean. Prod. 2016; 135 :577–588. doi: 10.1016/j.jclepro.2016.06.125. [ CrossRef ] [ Google Scholar ]
  • Roszkowska E. Multi-criteria decision making models by applying the topsis method to crisp and interval data. Mult. Criteria Decis. Mak. 2011; 6 :200–230. [ Google Scholar ]
  • Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M., 2004. Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models, in: Sensitivity Analysis in Practice. John Wiley & Sons, Ltd, Chichester, UK, pp. 151–192. https://doi.org/10.1002/0470870958.ch6.
  • Samaie F., Meyar-Naimi H., Javadi S., Feshki-Farahani H. Comparison of sustainability models in development of electric vehicles in Tehran using fuzzy TOPSIS method. Sustain. Cities Soc. 2020; 53 doi: 10.1016/j.scs.2019.101912. [ CrossRef ] [ Google Scholar ]
  • Shen J., Duan H., Zhang B., Wang J., Ji J.S., Wang J., Pan L., Wang X., Zhao K., Ying B.O., Tang S., Zhang J., Liang C., Sun H., Lv Y., Li Y., Li T., Li L.I., Liu H., Zhang L., Wang L., Shi X. Prevention and control of COVID-19 in public transportation: Experience from China. Environ. Pollut. 2020; 266 :115291. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Sirisawat P., Kiatcharoenpol T. Fuzzy AHP-TOPSIS approaches to prioritizing solutions for reverse logistics barriers. Comput. Ind. Eng. 2018; 117 :303–318. doi: 10.1016/j.cie.2018.01.015. [ CrossRef ] [ Google Scholar ]
  • Sobhani M.G., Imtiyaz M.N., Azam M.S., Hossain M. A framework for analyzing the competitiveness of unconventional modes of transportation in developing cities. Transp. Res. Part A Policy Pract. 2020; 137 :504–518. doi: 10.1016/j.tra.2019.02.001. [ CrossRef ] [ Google Scholar ]
  • Statistical Center of Iran, 2019. Iran Statistical Yearbook. Statistical Center of Iran.
  • The municipality of Tehran, 2019. Tehran Statisticak Yearbook. The municipality of Tehran.
  • Tirachini A., Cats O. COVID-19 and Public Transportation: Current Assessment, Prospects, and Research Needs. J. Public Transp. 2020; 22 :1. doi: 10.5038/2375-0901.22.1.1. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Tsamboulas D., Yiotis G.S., Panou K.D. Use of multicriteria methods for assessment of transport projects. J. Transp. Eng. 1999; 125 :407–414. doi: 10.1061/(ASCE)0733-947X(1999)125:5(407). [ CrossRef ] [ Google Scholar ]
  • Verma, J.P., Verma, P., 2020. Determining Sample Size and Power in Research Studies. Determ. Sample Size Power Res. Stud. https://doi.org/10.1007/978-981-15-5204-5.
  • Cochran W.G. Third Edition. Ther. Drug Monit. Toxicol. by Liq; Chromatogr: 1977. Sampling Techniques. [ Google Scholar ]
  • Zadeh, L.A., 1996. FUZZY SETS. pp. 394–432. https://doi.org/10.1142/9789814261302_0021.
  • Zadeh L.A., Klir G.J., Yuan B. Fuzzy sets, fuzzy logic, and fuzzy systems, advances in fuzzy systems — applications and theory. World Scientific. 1996 doi: 10.1142/2895. [ CrossRef ] [ Google Scholar ]

IMAGES

  1. (PDF) Customer Satisfaction of Public Transport: An Empirical Study in

    literature review on customer satisfaction in public transport

  2. (PDF) Determinants of Customer Satisfaction on Service Quality: A Study

    literature review on customer satisfaction in public transport

  3. CUSTOMER SATISFACTION LEVELS IN PUNE MUNICIPAL TRANSPORT

    literature review on customer satisfaction in public transport

  4. Questions for the satisfaction level with public transport service

    literature review on customer satisfaction in public transport

  5. (PDF) Assessment of Passenger Satisfaction with Public Bus Transport

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    literature review on customer satisfaction in public transport

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COMMENTS

  1. What influences satisfaction and loyalty in public transport? A review

    This paper analyses relevant literature regarding the causes of satisfaction and loyalty in public transport. We find that the service factors most associated with satisfaction are on-board cleanliness and comfort, courteous and helpful behaviour from operators, safety, as well as punctuality and frequency of service.

  2. (PDF) Customer Satisfaction of Public Transport: An ...

    Customer Satisfaction of Public Transport: An Empirical Study in Klang Valley Malaysia. International Journal of Engineering and Technology Authors: Kavitha Haldorai Florida State University...

  3. (PDF) Quality of Service in Public Transport Based on Customer

    Quality of Service in Public Transport Based on Customer Satisfaction Surveys: A Review and Assessment of Methodological Approaches Transportation Science Authors: Juan De Oña University...

  4. Assessing travel satisfaction in public transport: A configurational

    Volume 93, April 2021, 102732 Assessing travel satisfaction in public transport: A configurational approach Alexandre Sukhov a , Katrin Lättman a , Lars E. Olsson a , Margareta Friman a , Satoshi Fujii b Add to Mendeley https://doi.org/10.1016/j.trd.2021.102732 Get rights and content Under a Creative Commons license open access • • • Keywords fsQCA

  5. Preferences in regional public transport: a literature review

    The purpose of this article is to analyse quality attributes of regional public transport and their influence on modal choice, demand, and customer satisfaction through a literature review.

  6. (PDF) Service quality and customer satisfaction in public transport

    Samuel Adams Ghana Institute of Management and Public Administration Abstract and Figures This study presents an assessment of the relationship between service quality and customer satisfaction...

  7. Quality of Service in Public Transport Based on Customer Satisfaction

    Keywords : service quality; public transport; customer satisfaction surveys; derived importance; stated importance History : Received: June 2013; revisions received: December 2013, March 2014; accepted: April 2014. ... literature review, survey of operators, focus groups, pilot users survey, and statistical tests to identify whether an ...

  8. PDF Satisfaction Review

    A critical review of the literature. Transport Reviews, 38(1), 52‐72. ... Customer satisfaction in public transport has been studied since the mid-1960s (Transportation Research Board, 1999, 2002), and since the 1990s, the application of marketing techniques has

  9. Service Supply and Customer Satisfaction in Public Transportation: The

    Arguably, place km/inhabitant corresponds to satisfaction with the number of seats in public transport. There are significant differences (p<.005) in how satisfied the citizens of the six cities are regarding the possibility of having a seat.The citizens of Helsinki and Vienna are the most satisfied, whereas the citizens of Barcelona are the least satisfied (Table 1).

  10. PDF Preferences in regional public transport: a literature review

    Previous literature reviews have pointed out quality at-tributes commonly found to be important in local public transport. For customer satisfaction, four quality attributes are of particular importance: frequency, travel time, safety, and punctuality [14]. In addition, costs, staff behaviour, on-board cleanliness and comfort are also commonly dis-

  11. Evaluation of passenger satisfaction of urban multi-mode public transport

    Passenger satisfaction of public transport service refers to a psychological state of satisfaction or disappointment after comparing the expectations of passengers about the services provided by the public transport system with their overall feelings after receiving the services [ 1 ], which can be expressed by the average score of the questionn...

  12. Quality of Service in Public Transport Based on Customer Satisfaction

    The growth of literature in the field of quality of service in the public transport (PT) sector shows increasing concern for a better understanding of the factors affecting service quality (SQ) in PT organizations and companies.

  13. Quality of Service in Public Transport Based on Customer Satisfaction

    J. Public Transportation 10(3):21-34. Google Scholar Cross Ref; Eboli L, Mazzulla G (2008) A stated preference experiment for measuring service quality in public transport. Transportation Planning Tech. 31(5):509-523. Google Scholar Cross Ref; Eboli L, Mazzulla G (2009) A new customer satisfaction index for evaluating transit service quality. J.

  14. Importance-Performance Analysis in Public Transportation

    11. de Oña J., de Oña R. Quality of Service in Public Transport Based on Customer Satisfaction Surveys: A Review and Assessment of Quality of Service in Public Transport Based on Customer Satisfaction Surveys: A Review and Assessment of Methodological Approaches. Transportation Science, Vol. 49, No. 3, 2015, pp. 605-622.

  15. PDF Public Transit Customer Satisfaction Dimensions Discovery ...

    A pilot study was performed on 17,747 online user reviews collected from 1452 public transportation agencies (including streetcar, light rail, heavy rail, boat, and aerial tram) in the United States over 8 years. The proposed approach is able to identify a few dimensions that were not discussed in the previous literature.

  16. PDF What influences satisfaction and loyalty in public transport? A review

    What influences satisfaction and loyalty in public transport? A review of the literature Dea van Lierop a, Madhav G. Badamia,b and Ahmed M. El-Geneidy a aMcGill School of Urban Planning, Montréal ...

  17. Rail-based Public Transport Service Quality and User Satisfaction

    (PDF) Rail-based Public Transport Service Quality and User Satisfaction - A Literature Review PDF | While rail-based public transport is clearly a more advanced and preferable alternative to...

  18. Public Transit Customer Satisfaction Dimensions Discovery from Online

    Online user-generated content provides a valuable source for identifying dimensions of services. This study proposes a framework for extracting the dimensions of consumer satisfaction of public transportation services using unsupervised latent Dirichlet allocation model. A pilot study was performed on 17,747 online user reviews collected from 1452 public transportation agencies (including ...

  19. The Impact of Service Quality and Customer Satisfaction on Reuse

    Section "Literature review and hypotheses development" reviews the related studies and develops the hypotheses. ... Agarwal R. (2008). Public transportation and customer satisfaction: The case of Indian Railways. ... de Oña R. (2015). Quality of service in public transport based on customer satisfaction surveys: A review and assessment of ...

  20. What influences satisfaction and loyalty in public transport? A review

    ABSTRACT Public transport ridership retention is a challenge for many cities. To develop comprehensive strategies aimed at retaining riders, it is necessary to understand the aspects of public transport that influence users to become loyal to the system. This paper analyses relevant literature regarding the causes of satisfaction and loyalty in public transport. We find that the service ...

  21. Service Supply and Customer Satisfaction in Public Transportation: The

    Service Supply and Customer Satisfaction in Public Transportation: The Quality Paradox Margareta Friman and Markus Fellesson Karlstad University, Sweden Abstract Satisfaction measures obtained from citizens are frequently used in performance- based contracts due to their presumed link with company performance.

  22. Measuring the customer satisfaction of public transportation in Tehran

    High infectivity of COVID-19 and some of the characteristics of public transportation like limited space and high passenger density, especially in rush hours, have increased the risk of contamination in public transportation and declined demand for it.

  23. PDF Passengers Perception and Satisfaction Towards Public Transport: Study

    LITERATURE REVIEW: 2.1 Customer Satisfaction towards Services Quality of Public Transportation By Thian Wan Jun: He investigates consumer satisfaction with regard to public transportation service quality in his article. As indices of customer satisfaction, SERVQUAL was used on five dimensions.