Service quality estimation and improvement plan of bus Service: A perception and expectation based analysis

Deb, Saikat; Ahmed, Mokaddes Ali; Das, Debasish

doi:10.1016/j.cstp.2022.07.008

Cited by 17 publications

(6 citation statements)

References 44 publications

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“…Therefore, improving the key service indicators that affect rural residents' satisfaction will increase their overall satisfaction with bus services. However, similar conclusions have not been reached in previous studies of urban buses [23,37]. This implies that there are differences in the demand for bus services between rural and urban residents.…”

Section: Discussionsupporting

confidence: 55%

“…Zheng et al [35] found gender differences in both user satisfaction and service-quality improvement priorities. In addition, some scholars evaluated the service quality according to the gap between users' perceptions of the current and expected service quality [36,37].…”

Section: Literature Review 21 the Quality Of Public Transportation Se...mentioning

confidence: 99%

“…In terms of research methodology, early scholars directly asked customers to rank the importance of service attributes through satisfaction surveys [38,39]. In recent years, many methods have been proposed to assess service quality, such as regression analysis [40], path analysis [37], principal component analysis [28], structural equation model [11,19], decision tree model [41], artificial neural networks (ANNs) [25] and the fuzzy evaluation method [42]. Among them, SEM is the most widely used method [23].…”

Section: Literature Review 21 the Quality Of Public Transportation Se...mentioning

confidence: 99%

See 2 more Smart Citations

Investigating the Impacts of Urban–Rural Bus Service Quality on Rural Residents’ Travel Choices Using an SEM–MNL Integration Model

Cui

Zhu

et al. 2023

Sustainability

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The high-quality development of urban–rural buses is an important way to narrow the gap between urban and rural areas. However, studies on the quality of bus services and its impact on bus travel-mode choice have mostly focused on urban buses, with less attention paid to urban–rural buses. This paper aims to explore how urban–rural bus service quality affects rural residents’ travel-mode-choice behavior based on survey data from rural residents in Henan Province, China. A structural equation model (SEM) is applied to examine the critical factors affecting rural residents’ satisfaction with urban–rural bus service quality and the heterogeneity of satisfaction among rural residents with different attributes. Furthermore, an integrated approach of SEM and the multinomial logit (MNL) model is proposed to identify the key factors that affect rural residents’ bus travel-mode choice. The results indicate that reliability and convenience are the key factors affecting satisfaction with urban–rural bus service quality. There are significant differences in rural residents’ satisfaction by gender, age, income, education level, occupation, and travel-mode attributes. Urban–rural bus service quality has a significant positive impact on rural residents’ bus travel-choice behavior, and its degree of influence is greater than all explicit variables except private car ownership. The findings of this study can help operators and governments formulate policies to improve the service quality of urban–rural buses and ultimately promote the use of buses.

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Section: Discussionsupporting

confidence: 55%

Section: Literature Review 21 the Quality Of Public Transportation Se...mentioning

confidence: 99%

Section: Literature Review 21 the Quality Of Public Transportation Se...mentioning

confidence: 99%

See 1 more Smart Citation

Investigating the Impacts of Urban–Rural Bus Service Quality on Rural Residents’ Travel Choices Using an SEM–MNL Integration Model

Cui

Zhu

et al. 2023

Sustainability

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“…Sheng et al [14] proposed a leader-follower game model to design quality incentive scheme to balance the cost and quality of public bus service in a large bus network. Deb et al [15] explored the quality of the public bus service by a questionnaire survey in the city scale. Te aforementioned studies do not consider the spatial heterogeneity in bus service, whereas the zone-based analysis can do so.…”

Section: Literature Reviewmentioning

confidence: 99%

A Bus-Service-Based Zone Division Approach for the Spatial Analysis of Public Bus: A Case Study in South China

Fu,

Xie,

Cai

et al. 2024

Journal of Advanced Transportation

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Public bus is one of the major green transport modes in most cities, and a proper analysis of public bus can enhance its sustainable development. Hence, the analysis of public bus is always a hot research topic. Generally, to consider the heterogeneity in the studied area and reduce the computation difficulties, the study area is divided into multiple zones and then various analytical methods are applied. However, an improper zone division could lower the quality of the analysis results and then probably mislead the improvement strategy design. This study examines the influence of the two commonly used zone division methods (the grid-based method and the natural-boundary-based method) on the spatial analysis of public bus service using the analytical example. The result shows that the impact of zone division exists. Therefore, this study aims to identify the possible approach to reduce the negative impact and suggests a new zone division method, the bus-service-based (BS-based) method, for the spatial analysis of public bus service. The BS-based method is compared with the commonly used zone division methods using the real-world data, and the comparison results demonstrate that the BS-based method can be more efficient to support the spatial analysis of bus service.

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“…Deb et al [46] validated artificial neural networks (ANN) for modeling public transportation service quality. Focused on Granada's metropolitan bus service, the study analyzed data from a survey conducted with 858 passengers.…”

Section: Literature Reviewmentioning

confidence: 99%

Assessing Impact Factors That Affect School Mobility Utilizing a Machine Learning Approach

Kolidakis,

Kotoula,

Botzoris

et al. 2024

Sustainability

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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.

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Service quality estimation and improvement plan of bus Service: A perception and expectation based analysis

Cited by 17 publications

References 44 publications

Investigating the Impacts of Urban–Rural Bus Service Quality on Rural Residents’ Travel Choices Using an SEM–MNL Integration Model

Investigating the Impacts of Urban–Rural Bus Service Quality on Rural Residents’ Travel Choices Using an SEM–MNL Integration Model

A Bus-Service-Based Zone Division Approach for the Spatial Analysis of Public Bus: A Case Study in South China

Assessing Impact Factors That Affect School Mobility Utilizing a Machine Learning Approach

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