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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 system are revealed through service quality analysis. This information
serve as valuable inputs in constantly updating the quality of public transportation
services. A...
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... In a more detailed examination, an exploration into the feasibility of predicting the perceived quality of public transport services, as perceived by users, has been undertaken. This investigation relies on artificial intelligence (AI) models trained with data derived from a questionnaire survey gauging 655 users' perceptions of urban bus services in Dhaka, the capital and largest city of Bangladesh [44]. Out of twenty-two selected service quality features, the most pivotal characteristics were systematically ranked based on their influence on user decision-making procedures regarding public transport utilization. ...
The analysis and modeling of parameters influencing parents’ decisions regarding school travel mode choice have perennially been a subject of interest. Concurrently, the evolution of artificial intelligence (AI) can effectively contribute to generating reliable predictions across various topics. This paper begins with a comprehensive literature review on classical models for predicting school travel mode choice, as well as the diverse applications of AI methods, with a particular focus on transportation. Building upon a published questionnaire survey in the city of Thessaloniki (Greece) and the conducted analysis and exploration of factors shaping the parental framework for school travel mode choice, this study takes a step further: the authors evaluate and propose a machine learning (ML) classification model, utilizing the pre-recorded parental perceptions, beliefs, and attitudes as inputs to predict the choice between motorized or non-motorized school travel. The impact of potential changes in the input values of the ML classification model is also assessed. Therefore, the enhancement of the sense of safety and security in the school route, the adoption of a more active lifestyle by parents, the widening of acceptance of public transportation, etc., are simulated and the impact on the parental choice ratio between non-motorized and motorized school commuting is quantified.
... This is a key factor that motivated the present study. Moreover, depending on various characteristics, such as the location, economy, and demographics, the elements influencing the SQ may differ significantly [12]. In addition, the European Committee for Standardization 2002 identified one standard of SQ for different nations and times, which may not be an appropriate or applicable method [13]. ...
... Moreover, Thøgersen concluded that a specific group of PT customers should be considered, such as research on frequent users, as this will provide a good understanding of travel mode choices [22]. For example, passengers who prefer to use PT and irregular users have different levels of knowledge about or experience with the provided PT service, making their opinions valuable [12,23]. ...
... Further studies have integrated the PLS-SEM and the necessary condition analysis (NCA) [1], whereas others have relied on the PLS-SEM/SEM and Bayesian network (BN) to investigate the PT service quality and satisfaction with PT [58,59]. Table 1 shows that recent research used the PLS-SEM and BLR [51] with varied statistical methods, such as OLM/MLM, rather than Artificial Neural Network (ANN) approaches, to build better prediction models [12]. In addition, ordinal regression models are widely used to avoid the pitfalls of using ANOVA-type models on ordered categorical data [60]. ...
Transport policymakers need to have an in-depth understanding of public transport (PT) customers in order to effectively manage transport systems and maintain the attractiveness of these systems to potential users. This research aims to compare the perceptions and satisfaction levels of two groups of PT users (habitual and occasional) among university staff and students regarding the quality of PT through a new integrated approach. A sample of 500 participants from Budapest, Hungary was used. Two stages of analysis were conducted: a descriptive analysis was conducted in the first stage, and Student's t-tests of two independent samples were applied to identify the varying perceptions and overall satisfaction. Second, a new integrated ordered probit model (OPM) and an importance-performance analysis (IPA) were used to envisage how best to prioritize actions for transport enhancement. The results show that in the circle of commuters, the habitual PT users were more satisfied with the existing PT service than the occasional PT users. According to the findings of the IPA, for habitual users, the attribute "information provided" has a high priority for improvement, whereas the cost for both user types was found to be significant for all models, contributing to overall satisfaction. This factor was included in the possible overkill quadrant, suggesting that there might be more cost resources than needed. The new model, along with the case study results, may help policymakers and transport operators to make better decisions regarding the identification of service priority areas.
... They found that there is direct link between public transport equity and public transport quality. Islam et al. (2016) proposed a bus service quality prediction model using ANN, found that public opinion and service frequency are most affecting factors for bus service quality. ...
India has been witnessing rapid growth in travel with the occurrence of online cab service especially in urban sector. The high demand for transport service and lack of good quality of public transport has given the opportunity to many cab aggregators to gain customers. People go for cab because it offers desirable comfort, flexibility and privacy. It also helps to avoid problem like parking issues. Hence, it’s important to assess the aspects that the customer/user is looking for the most while hailing an online cab service. To study the relationship of service quality attributes with overall satisfaction of the cab users, a web-based survey was conducted in Tiruchirappalli and Pune city. This paper proposes the use of Artificial Neural Network (ANN) to analyze perceived service quality of online cab users and to find out the attributes of importance a customer consider while hailing an online cab service. Model developed show that the attributes like safety, waiting time, driver behavior, comfort, cleanliness and trust on driver are some of the variables that have relatively higher influence on overall satisfaction.
... Therefore, the SQ dimensions identification and improvements of public transportation systems, as well as the result of SQ assessments, may encourage more people to use them. The local governments must analyze and monitor SQ and develop strategies to improve public transportation quality while also implementing pandemic regulations (Islam et al. 2016). ...
... The driver's route expertise was a major factor (they emphasize that this is to be assumed as it dictates the duration of the trip or how much the trip costs, and the client placed a high value on all factors connected to safe driving. In addition, the relevance of these factors is supported by Islam et al. (2016). They used entropic weighting to determine each quality factor's relative weight, as well as the well-known fuzzy TOPSIS analysis (Awasthi et al. 2011) to identify each explanatory factor's satisfaction index. ...
... Huang et al. (2020) reported that responsiveness is an important measure of quality in train transportation. Islam et al. (2016) argued that the tangibility items were correlated with service performance, it is strongly associated with customer loyalty. Wan et al. (2016) explained the methods in which reliability elements of public buses would help to improve service performance. ...
In order to encourage the use of public transportation, it is necessary to make it more appealing to commuters by conducting frequent Service Quality (SQ) evaluations and modifications. Understandingpassengers’expectations of public transportation are important, and evaluation of the SQisan essential tool for assessing the overall performance of the public transportation system. The purpose of present study was to examine the expectations and perceptions of core passengers regarding SQ in public bus transportation. By surveying 598 passengers in rural public transportation in India, the study results are illustrated and further discussed to guide possible bus SQ improvements in rural areas. In addition, the impact of these expectations and perceptions on satisfaction levels of rural public bus transportation services by applying the Interval-Valued Pythagorean Fuzzy (IVPF). The outcomes of the survey were indicated significant disparities among expectations and perceptions of passengers, as well as widespread dissatisfaction with the delivery of bus services in rural areas as a whole. The dependability and adaptiveness of the bus service have been critical in describing the overall quality of bus services in rural areas, and best practices from around the world were used to develop a set of recommendations for transportation operators and local officials.
... Also, traffic jams, accidents, and weather situations cause additional delays. Taking into account all of the above factors, it is tough to create a version that can accurately account for various space-time elements and predicted appearance times as suggested by Islam et al. [2]. ...
Due to fast-growing urbanization, the traffic management system
becomes a crucial problem owing to the rapid growth in the number of vehicles. The research proposes an Intelligent public transportation system where information regarding all the buses connecting in a city will be gathered, processed and accurate bus arrival time prediction will be presented to the user. Various linear
and time-varying parameters such as distance, waiting time at stops, red signal duration at a traffic signal, traffic density, turning density, rush hours, weather conditions, number of passengers on the bus, type of day, road type, average vehicle speed limit, current vehicle speed affecting traffic are used for the analysis.
The proposed model exploits the feasibility and applicability of ELM in the travel time forecasting area. Multiple ELMs (MELM) for explicitly training dynamic, road and trajectory information are used in the proposed approach. A large-scale dataset (historical data) obtained from Kerala State Road Transport Corporation is
used for training. Simulations are carried out by using MATLAB R2021a. The experiments revealed that the efficiency of MELM is independent of the time of day and day of the week. It can manage huge volumes of data with less human intervention at greater learning speeds. It is found MELM yields prediction with
accuracy in the range of 96.7% to 99.08%. The MAE value is between 0.28 to 1.74 minutes with the proposed approach. The study revealed that there could be regularity in bus usage and daily bus rides are predictable with a better degree of accuracy. The research has proved that MELM is superior for arrival time predictions in terms of accuracy and error, compared with other approaches.
... However, questionnaire survey-based ANN applications is a method of a rather limited research, as only a few studies have been found to follow such a methodological approach. The possibility of forecasting the perceived quality of public transport services provided by users has been investigated, based on ANN models trained using data collected from questionnaire survey, regarding the users' perceptions of Dhaka (capital and largest city of Bangladesh) urban bus services [18]. Among twenty-two selected service quality features, the most important features were ranked according to their impact on the user decision-making process for the use of public transport. ...
Human behavior pattern recognition exploited using Artificial Neural Networks (ANN) is at the core of Artificial Intelligence (AI) classification applications. School trip transport forecasting mode selection based upon ANN forecasting ability provided an easy-to-use scientific toolkit for sustainable urban policies designing and implementation. The paper capitalized acknowledged forecasting ability of ANN to recognize and classify behavior patterns leading to parental decisions of school mode choice. Main stages of this research included the conduction of an extended questionnaire survey in 512 parents of school students in Thessaloniki (northern Greece) and the investigation whether ANN forecasting model could classify school mode choice. Research provided promising results (forecasting ability ranging from 76% to 93%) on school mode parental selection forecasting models based on ANN classifier, providing a solid proof of concept for further investigation.
... Statistical results have shown that frequency is the most useful feature in SQ. Islam et al. [26] proposed various ANN approaches to define which feature of the SQ criteria is more important than others. They presented a comparative study using Generalized Regression Neural Network (GRNN), Probabilistic Neural Network (PNN), and Pattern Recognition Neural Network (PRNN). ...
Service quality is one of the main issues that today's world. Firms operating in the transportation sector are also trying to improve the quality of the service they provide to their passengers. It is crucial to determine the passengers' service quality perceptions and priorities to evaluate and improve the service in this context. In this study, Kocaeli's tram service users' service quality perceptions have been evaluated by applying a survey consisting of 20 questions and user satisfaction levels from different service dimensions. Later, an artificial neural network model was developed using the users' demographic data and their responses to the survey questions to mimic their service quality satisfaction. The artificial neural network model developed has been examined to understand the importance that tram users give to service quality. Using the developed the “change of score” method, how the changes to be made in the tram system will affect the quality of service and how the opinions of different user groups will be affected can be examined in detail. The artificial neural network model's prediction capability was compared with the multiple linear regression model and found superior. According to the developed Change of Score Method, the most frequent user attaches the highest importance to the service dimensions of the convenience to pay for the tram, getting his/her destination on time, and reducing environmental pollution.
... Researchers have used different techniques to measure service quality of the transit system using user's perception, as summarized by Güner [15]. Some of the statistical techniques are structural equation modelling [7], factor analysis [6,17,22,27], discrete choice logit models [8,11,12,16,29,31], combination of factor analysis and structural equation model [10,36], multi-criteria decision-making techniques like the Analytic Hierarchy Process (AHP) [15,20], Artificial Neural Networks (ANN) [14,19], Law of Successive Interval Scaling [5], etc. ...
The urban population in India is growing at a very fast pace and so the demand for transport. Also, with economic upliftment, there is a trend in mode shift of users to private vehicles, which is causing detrimental effects in urban areas. The bus transit system is considered in this study as it is widely used public transport in India. However, due to poor serviceability, users are shifting to private vehicles for travel. Therefore, service quality needs to be improved. SERVQUAL method is used in this study to determine service quality based on the user’s perceptions and expectations. The West Bengal Transport Corporation buses in Kolkata have been considered as a case study. Questionnaire survey has been carried out to collect user’s perception and expectation on the factors considered. The suitability of collected data has been checked by statistical methods like Paired Sample t-test and Cronbach’s Alpha value. The LOS of the service is calculated by the percentage deviation of user perceptions from their expectations and a suitable scale is adopted to categorize the service quality. The importance of factors towards overall satisfaction is determined with the help of regression analysis and effort has been made to incorporate user expectations as well. It has been found through analysis that the bus service provides LOS 2 category service. Reliability and comfort play a vital role in overall satisfaction of users. Based on the results of data analysis, recommendations have been proposed to improve the service quality of bus transit service.KeywordsService qualityUser’s perceptionUser’s expectationSERVQUALBus transitLevel-of-service
... on bus service quality have focused on regular cities, hardly any have investigated industrial cities in 5 developing countries, which have been portrayed in Table 1 Although the exact cut-off percentage of secondary sector share has never been mentioned in 10 previous literature, studies agree that industrial cities have a noticeably large secondary sector (32). 11 Thus, this study considered cities as industrial based on the secondary sector. If no reliable sector-wise 12 GDP share can be found, reports from reliable sources were used to understand the nature of economic 13 activities of that city. ...
Bus service quality has been assessed using different approaches especially for major cities around the world. However, no work has been done previously to assess bus service quality in industrial cities in developing countries or the opinions of economically marginalized groups prevalent in such areas. This study aims to fill this gap by examining how people’s perception in industrial cities are different from those of other cities. To achieve this objective, the paper investigates two hypotheses and two models to assess the inter-relationships among service attributes, demographic and travel characteristics, and Overall Passenger Satisfaction (OPS). Structural Equation Modeling (SEM) was used on opinions of 700 bus passengers, drawing upon seventeen service attributes, five demographic characteristics and five travel characteristics. Nine service attributes, two demographic factors, and three travel characteristics have significantly influenced OPS in this study, out of which six unique factors are only applicable to low-income groups in the study area, namely, Lighting Facility, Age, Education, Journey Frequency, Journey Reason and Journey Distance. The results suggest that policy makers should focus on people regularly commuting long distances using busess. Besides, weekday travel, staff behaviour, seating arrangement and ride smoothness should be given priority, as these will heavily impact passenger satisfaction according to this study. Subsequent attributes can then be prioritized as per the attributes ranked and according to budget considerations of the authority.
... It is important to evaluate the quality of service (QoS) of BRT station platform operation. Measures of QoS relate to passengers' perception regarding the availability, and comfort and convenience of the transit service offered (Islam et al. 2016;Kittelson and Assoc 2013). Insufficient space on a platform can cause passenger inconvenience and discomfort. ...
It is important to evaluate the quality of service (QoS) of bus rapid transit (BRT) station platform operation. Passenger-specific area (PSA) is used as a QoS measure which is determined by considering passenger activities separately. As passengers perform various activities on the same platform space, there is a need to evaluate BRT platform QoS by considering the activities collectively. When evaluating transit station platforms, many researchers calculated PSA for the whole platform area, while very few researchers highlighted the importance of evaluating the platform as small, partitioned areas. By considering these findings and gaps in the literature, this study evaluates QoS of the platform on a cell by cell basis using PSA. We use time–space analysis and passenger-minutes of each activity to develop a methodology to determine PSA, by considering stationary passengers, circulating passengers, and passengers overall. To evaluate platform QoS, we define threshold service levels using passenger-minutes of activities and Fruin’s QoS criteria. For the case study BRT station, we find that PSA varies significantly between platform cells. It is evident from the results that it is important to identify highly congested areas in the platform and apply measures to improve platform QoS.
