A hierarchical customer satisfaction framework for evaluating rail transit systems of Istanbul

Aydın, Nezir; Çelik, Erkan; Gümüş, Alev Taşkın

doi:10.1016/j.tra.2015.03.029

Cited by 70 publications

(66 citation statements)

References 79 publications

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“…In line with dell 'Olio et al (2011) and Celik et al (2014), we found that waiting time is a highly-prioritized SQ factor on which the service provider should focus resources. Similarly, crowdedness in cars was also addressed as a highly important SQ factor in de and Aydin et al (2015). Givoni and Rietveld (2007) and Brons et al (2009) emphasized the importance of access to stations, whereas Bhat and Sardesai (2006) highlighted travel (in-vehicle) time as an important SQ factor.…”

Section: Resultsmentioning

confidence: 99%

Identifying Key Factors of Rail Transit Service Quality: An Empirical Analysis for Istanbul

Isikli¹,

Aydın²,

Çelik³

et al. 2017

JPT

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Section: Resultsmentioning

confidence: 99%

Identifying Key Factors of Rail Transit Service Quality: An Empirical Analysis for Istanbul

Isikli¹,

Aydın²,

Çelik³

et al. 2017

JPT

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“…In [108], the Fuzzy AHP was used to develop a risk assessment system for evaluating both qualitative and quantitative risk data and information associated with the safety management of railway systems. A hierarchical customer satisfaction framework was made by [109] where fuzzy AHP has been applied for calculation of weights of main criteria. After application of fault tree analysis to analyze historical general accidents in railway transportation in [110], AHP was employed to analyze relationships between the factors and general accidents.…”

Section: Analytic Hierarchy Process (Ahp)mentioning

confidence: 99%

Application of Multicriteria Decision-Making Methods in Railway Engineering: A Case Study of Train Control Information Systems (TCIS)

Djordjević¹,

Krmac²

2018

Modern Railway Engineering

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In order to improve its position in the transport market railway, as a complex system, it has to fulfill a number of objectives such as increased capacity and asset utilization, improved reliability and safety, higher customer service levels, better energy efficiency and fewer emissions, along with increased economic viability and profits. Some of these objectives call for the implementation of maximum values, while some of them require minimum values. Additionally, some can be expressed quantitatively, while some, for example, customer service, can be described qualitatively through a descriptive scale of points. The application of MCDM in railway engineering can play a significant role. Therefore, the major objective of this chapter is the review of the application of MCDM methods in railway engineering. As one of the means in achieving the objectives of railways and above all the utilization of capacity are Train Control Information Systems (TCIS). Based on that, the aim of this chapter is the evaluation of the efficiency of TCIS in the improvement of railway capacity utilization through defined technical-technological indicators. The non-radial Data Envelopment Analysis (DEA) model for the evaluation of TCIS efficiency in improvement of utilization of railway capacity using the selected indicators is proposed. The proposed non-radial DEA model for TCIS efficiency evaluation in using railway capacity could be applied to an overall network or for separate parts of railway lines.

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“…B 13 :Average speed (AS): M i is the mileage of valid taxi trip i; AT i is the off-taxi time; BT i is the on-taxi time.…”

Section: Productivity (B)mentioning

confidence: 99%

“…Due to the disunity between subjective opinions and the objective criteria, assessment of the overall performance usually composes of four steps: proposing the criteria framework, determining the weights, analyzing the data and finally provide suggestions [13]. GPS equipped in taxi can collect and transmit the position and time related data to control centers in taxi companies, which helps record trajectories, occupancy and fares [14].…”

Section: Introductionmentioning

confidence: 99%

A data-driven quantitative assessment model for taxi industry: the scope of business ecosystem’s health

Zhang

Zhong

Jiang

2017

Eur. Transp. Res. Rev.

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Introduction The taxi industry has boomed over the years, both in street-hail and dispatch market. However, few studies focus on comprehensive perspectives, which make decisions such as implementing regulative or incentive policies difficult. As an economic community working in the value-oriented process, the taxi industry requires a holistic performance evaluation to determine how to adapt strategies to survive and grow. Methods This paper proposed the concept BTaxi Industry Health degree^based on the theory of Business Ecosystem and the evaluation of its health. A four-layer criteria set is developed from aspects of robustness, productivity and sustainability, and weights are determined through Analytic Hierarchy Process. Results A synthetic evaluating model combining Fuzzy Comprehensive Evaluation and Artificial Neural Network is used to maintain the goal of multi-criteria decision-making. With the GPS and Taximeter dataset of taxicabs in the whole taxi industry in Wuxi, the model is applied to empirical studies. Conclusions This paper provides sensibility analysis on not only the company's order volatility, revenue growth and utilization of resources, but also influence on citizen's welfare, energy consumption and environmental pollution, which enables practical regulations and policies within taxi industry.

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A hierarchical customer satisfaction framework for evaluating rail transit systems of Istanbul

Cited by 70 publications

References 79 publications

Identifying Key Factors of Rail Transit Service Quality: An Empirical Analysis for Istanbul

Identifying Key Factors of Rail Transit Service Quality: An Empirical Analysis for Istanbul

Application of Multicriteria Decision-Making Methods in Railway Engineering: A Case Study of Train Control Information Systems (TCIS)

A data-driven quantitative assessment model for taxi industry: the scope of business ecosystem’s health

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