Development of service quality model computing ridership of metro rail system using fuzzy system
Gradual increase in the pace of urbanization has brought an increase in demand for rapid transit systems throughout the nation. An increase in traffic congestion on roads has led to the recent advent of the Mass rapid transit system thereby accelerating the commuter's interest to prefer them ov...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-03-01
|
| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123023000737 |
| _version_ | 1828032468668645376 |
|---|---|
| author | Priyanka Prabhakaran S. Anandakumar E.B. Priyanka S. Thangavel |
| author_facet | Priyanka Prabhakaran S. Anandakumar E.B. Priyanka S. Thangavel |
| author_sort | Priyanka Prabhakaran |
| collection | DOAJ |
| description | Gradual increase in the pace of urbanization has brought an increase in demand for rapid transit systems throughout the nation. An increase in traffic congestion on roads has led to the recent advent of the Mass rapid transit system thereby accelerating the commuter's interest to prefer them over the conventional railway system. Over the past few years, rapid transit systems like metros have been trying to maintain a steady count of ridership by attracting new commuters while retaining the existing commuters. Thus, it has become routine for organizations to identify the service parameters that serve as the essence of commuter satisfaction or dissatisfaction by implementing additional necessary changes based on commuter perceptions. The study is focused on the service quality measures adopted by the Chennai metro rail operations which have led them to maintain a steady pace of passengers amidst the pandemic season. The resultant statistical analysis and fuzzy evaluation method are confined to about 74.6% indicating decent fitness of the sample size. The significance value of coach conditioning amenities is 0.002 and the ambiance facility is 0.003, further indicating the need to maintain their service pace. Hospitality facility that accounts for a significance value of about 0.000 needs the most improvement to influence the overall service quality satisfaction of commuters towards improving their ridership statistics. |
| first_indexed | 2024-04-10T15:05:54Z |
| format | Article |
| id | doaj.art-f8745c635ef342cca49720eb59a86351 |
| institution | Directory Open Access Journal |
| issn | 2590-1230 |
| language | English |
| last_indexed | 2024-04-10T15:05:54Z |
| publishDate | 2023-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj.art-f8745c635ef342cca49720eb59a863512023-02-15T04:28:40ZengElsevierResults in Engineering2590-12302023-03-0117100946Development of service quality model computing ridership of metro rail system using fuzzy systemPriyanka Prabhakaran0S. Anandakumar1E.B. Priyanka2S. Thangavel3Department of Civil Engineering, Kongu Engineering College, Thoppupalayam Post, Perundurai, Erode, 638060, Tamilnadu, India; Corresponding author.Department of Civil Engineering, Kongu Engineering College, Perundurai, Erode, 638060, TamilnaduIndiaAssistant Professor/Department of Mechatronics, Kongu Engineering College, Perundurai, Erode, 638060, Tamilnadu, IndiaAssistant Professor/Department of Mechatronics, Kongu Engineering College, Perundurai, Erode, 638060, Tamilnadu, IndiaGradual increase in the pace of urbanization has brought an increase in demand for rapid transit systems throughout the nation. An increase in traffic congestion on roads has led to the recent advent of the Mass rapid transit system thereby accelerating the commuter's interest to prefer them over the conventional railway system. Over the past few years, rapid transit systems like metros have been trying to maintain a steady count of ridership by attracting new commuters while retaining the existing commuters. Thus, it has become routine for organizations to identify the service parameters that serve as the essence of commuter satisfaction or dissatisfaction by implementing additional necessary changes based on commuter perceptions. The study is focused on the service quality measures adopted by the Chennai metro rail operations which have led them to maintain a steady pace of passengers amidst the pandemic season. The resultant statistical analysis and fuzzy evaluation method are confined to about 74.6% indicating decent fitness of the sample size. The significance value of coach conditioning amenities is 0.002 and the ambiance facility is 0.003, further indicating the need to maintain their service pace. Hospitality facility that accounts for a significance value of about 0.000 needs the most improvement to influence the overall service quality satisfaction of commuters towards improving their ridership statistics.http://www.sciencedirect.com/science/article/pii/S2590123023000737Mass rapid transit systemService attributesCommuter retentionRidershipStatistical analysisFuzzy evaluation |
| spellingShingle | Priyanka Prabhakaran S. Anandakumar E.B. Priyanka S. Thangavel Development of service quality model computing ridership of metro rail system using fuzzy system Results in Engineering Mass rapid transit system Service attributes Commuter retention Ridership Statistical analysis Fuzzy evaluation |
| title | Development of service quality model computing ridership of metro rail system using fuzzy system |
| title_full | Development of service quality model computing ridership of metro rail system using fuzzy system |
| title_fullStr | Development of service quality model computing ridership of metro rail system using fuzzy system |
| title_full_unstemmed | Development of service quality model computing ridership of metro rail system using fuzzy system |
| title_short | Development of service quality model computing ridership of metro rail system using fuzzy system |
| title_sort | development of service quality model computing ridership of metro rail system using fuzzy system |
| topic | Mass rapid transit system Service attributes Commuter retention Ridership Statistical analysis Fuzzy evaluation |
| url | http://www.sciencedirect.com/science/article/pii/S2590123023000737 |
| work_keys_str_mv | AT priyankaprabhakaran developmentofservicequalitymodelcomputingridershipofmetrorailsystemusingfuzzysystem AT sanandakumar developmentofservicequalitymodelcomputingridershipofmetrorailsystemusingfuzzysystem AT ebpriyanka developmentofservicequalitymodelcomputingridershipofmetrorailsystemusingfuzzysystem AT sthangavel developmentofservicequalitymodelcomputingridershipofmetrorailsystemusingfuzzysystem |