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Tytuł artykułu

Diagnostic evaluation of urban metro transit system post-covid-19

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Public transportation usage in Delhi has declined, with the Delhi Metro having a significant share. However, due to fare hikes and COVID-19 restrictions, the DM's share has been decreasing further. To improve ridership, a study is being conducted to evaluate the DM's performance and identify areas for improvement in passenger convenience and comfort. The Magenta line is investigated through an on-board survey to collect primary data. The survey covers commuter perceptions of safety & security, financial & economic factors, infrastructure & comfort and functional & operational features. The Relative Importance Index approach is used to analyse the data and evaluate DM performance. An ANN model is also presented to determine the factors influencing the choice to travel on the DM, with the “metro fare per trip” factor being a key consideration. Based on the analysis results, recommendations are made to improve the DM's performance. The study found that safety and security had the highest RII, followed by efficiency and viability, functional and operational features, infrastructure and comfort, and financial and economic factors. The subway fare had the lowest RII. The ANN model is adapted to understand the reasons behind low metro ridership.
Rocznik
Tom
Strony
77--91
Opis fizyczny
Bibliogr. 33 poz.
Twórcy
  • Department of Building Engineering and Management, School of Planning and Architecture, New Delhi, India
  • Department of Electronics & Communication Engineering, MeeraBai Institute of Technology, Delhi Skill & Entrepreneurship University, New Delhi, India
Bibliografia
  • 1. Bhandari K., M. Advani, P. Parida, S. Gangopadhyay. 2014. „Consideration of access and egress trips in carbon footprint estimation of public transport trips: case study of Delhi”. Journal of Cleaner Production 85: 234-240. DOI: 10.1016/j.jclepro.2014.05.013.
  • 2. Stopher P.R., B.D. Spear, P.O. Sucher. 1974. „Towards the Development of Measures of Convenience for Travel Modes”. Transportation Research Record 527: 16-32.
  • 3. Buran B., M. Erçek. 2022. „Public transportation business model evaluation with Spherical and Intuitionistic Fuzzy AHP and sensitivity analysis”. Expert Systems With Applications 204: 1-8. DOI: 10.1016/j.eswa.2022.117519.
  • 4. Delhi Metro Magenta Line Route Map. Available at: https://www.delhimetrotimes.in/delhi/maps/delhi-metro-magenta-line.html.
  • 5. Eboli L., G. Mazzulla. 2012. „Performance indicators for an objective measure of public transport service quality”. European Transport \ Trasporti Europei 51: 1-4.
  • 6. Delhi Metro fare hike chart: DMRC raises ticket prices, take a look at full list of revised fare. Financial Express. 2017. Available at: https://www.financialexpress.com/india-news/delhi-metro-fare-hike-chart-dmrc-raises-ticket-prices-take-a-look-at-full-list-of-revised-fare/888315/.
  • 7. Gallo M., G. De Luca, L. D’Acierno, M. Botte. 2019. “Artificial Neural Networks for Forecasting Passenger Flows on Metro Lines”. Sensors 19(15): 3424. DOI: 10.3390/s19153424.
  • 8. Gandhiok J. 2022. „Ridership yet to hit pre-pandemic levels: Metro data. Hindustan Times”. Available at: https://www.hindustantimes.com/cities/delhi-news/ridership-yet-to-hit-pre-pandemic-levels-metro-data-101650843276426.html.
  • 9. Ghosh T., T. Kanitkar, R. Srikanth. 2022. „Way Forward to Provide Affordable, Accessible, and Sustainable Transport for Megacities”. Social Science Research Network: 1-30. DOI: 10.2139/ssrn.4211070.
  • 10. Hounsell M. 2023. „Empirical analysis supporting research to develop Collaborative Multi-Modal Customer-Centric Key Performance Indicators for Transport”. DOI: 10.13140/RG.2.2.17438.51520/1.
  • 11. Kanuri C., K. Venkat, S. Maiti, P. Mulukutla. 2019. „Leveraging innovation for last-mile connectivity to mass transit”. Transportation Research Procedia 41: 655-669. DOI: 10.1016/j.trpro.2019.09.114
  • 12. Keijer M.J.N., P. Rietveld. 2004. „How do people get to the railway station; a spatial analysis of the first and the last part of multimodal trips”. Journal of Transport Planning and Technology 11(3): 265-275.
  • 13. Khursheed S., F.A. Kidwai. 2021. “Post COVID Performance Evaluation of Delhi Metro”. In: Proceedings of the 2nd International Conference on Futuristic and Sustainable Aspects in Engineering and Technology (FSAET-21). Mathura, India. December 24-26, 2021. DOI: 10.1063/5.0154102.
  • 14. Khursheed S., F.A. Kidwai. 2022. “Post COVID-19 Access Egress Attributes for Urban Metro Transit Users in Delhi”. Journal of Applied Engineering Sciences 12(25): 53-60. DOI: 10.2478/jaes-2022-0009.
  • 15. Khursheed S., F.A. Kidwai. 2022. “Evaluation of Users Approbation Indicators of Delhi Metro”. In: Proceedings of the 8th International Conference on Transportation System Engineering & Management. NIT-Calicut and selected for publication in book series “Recent Advances in Transportation Systems Engineering and Management”. P. 349-365. DOI: 10.1007/978-981-19-2273-2_24.
  • 16. Khursheed S., F. Ahmad Kidwai. 2022. “Post-COVID-19 Performance Evaluation of Urban Metro Transit System in Delhi and Influence on Access Mode”, Case Studies on Transport Policy 10(3): 1862-1871. DOI: 10.1016/j.cstp.2022.07.015.
  • 17. Kurniawati Y.R., J. Sumabrata, G.N. Christin. 2023. „Analysis Of The Variables Affecting Krl Commuter Line Passengers' Choices on The Feeder Modes To And From Bogor Station”. International Journal Of Engineering Advanced Research 5(2): 1-10.
  • 18. Lake M., L. Ferreira. 2002. Towards A Methodology To Evaluate Public Transport Projects. Available at: https://www.researchgate.net/publication/27464625_Towards_A_Methodology_To_Eval.uate_Public_Transport_Projects/link/5417527f0cf2218008bee35e/download
  • 19. Liu S., X. Zhu. 2004. „An Integrated GIS Approach to Accessibility Analysis”. Transactions in GIS 8: 45-62. DOI: 10.1111/j.1467-9671.2004.00167.x.
  • 20. Loutzenheiser D.R. 1997. „Pedestrian access to transit: model of walk trips and their design and urban form determinants around Bay Area rapid transit stations”. Transportation Research Record 1604: 40-49.
  • 21. Mathur A. 2023. „Yet to fully recover from the pandemic blow, Delhi Metro is in red for the second year”. The Times of India. Available at: https://timesofindia.indiatimes.com/city/delhi/yet-to-fully-recover-from-pandemic-blow-metro-in-red-for-secondyear/articleshow/97502612.cms.
  • 22. Mittal T. 2023. „New Delhi metro must keep up as capital becomes world’s largest city by 2028”. ThePrint. Available at: https://theprint.in/india/new-delhi-metro-must-keep-up-as-capital-becomes-worlds-largest-city-by-2028/1681173/.
  • 23. Nandal M., N. Mor, H. Sood. 2020. „An Overview of Use of Artificial Neural Network in Sustainable Transport System”. Advances in Intelligent Systems and Computing 1227: 83-91. DOI: 10.1007/978-981-15-6876-3_7.
  • 24. Naser I.H., A.M. Mahdi, Y.H. Jasim. 2020. “Performance of Artificial Neural Networks (ANN) At Transportation Planning Model”. IOP Conference Series: Materials Science and Engineering 928: 022-032. DOI: 10.1088/1757-899x/928/2/022032.
  • 25. Nurhadi L., S. Borén, H. Ny. 2014. „A Sensitivity Analysis of Total Cost of Ownership for Electric Public Bus Transport Systems in Swedish Medium-Sized Cities”. Transportation Research Procedia 3: 818-827. DOI: 10.1016/j.trpro.2014.10.058
  • 26. O’Sullivan S., J. Morrall. 1996. „Walking distances to and from light-rail transit stations”. Transportation Research Record 1538: 19-26. DOI: 10.1177/0361198196153800103.
  • 27. Paulley N., R.J. Balcombe, R. Mackett, H. Titheridge, J. Preston, M. Wardman, J. Shires, P. White. 2006. „The demand for public transport: The effects of fares, quality of service, income and car ownership”. Transport Policy 13(4): 295-306. DOI: 10.1016/j.tranpol.2005.12.004.
  • 28. Qu L., Y. Chen. 2008. „A Hybrid MCDM Method for Route Selection of Multimodal Transportation Network. Lecture Notes in Computer Science: 374-383. DOI: 10.1007/978-3-540-87732-5_42.
  • 29. Sanjana Agnihotri. 2018. „Delhi Metro daily ridership 32 per cent less than projected in 2018: CSE”. India Today. Available at: https://www.indiatoday.in/india/story/delhi-metro-daily-ridership-32-per-cent-less-than-projected-in-2018-cse-1332172-2018-09-05.
  • 30. Saraswat I., K. Girish. 2020. „The Delhi Metro: A rail of issues”. Available at: https://www.researchgate.net/publication/346646804_The_Delhi_Metro_A_rail_of_issues.
  • 31. Tiwari G. 2013. „Metro Rail and the City: Derailing Public Transport”. Economic and Political Weekly 48(48): 65-76.
  • 32. Widyaningsih N., W.H. Mohtar, N.I. Yussof, M.D. Putri. 2022. „Impact Of Large-Scale Social Restrictions On Transportation Modes”. International Journal on Technical and Physical Problems of Engineering 14(51): 216-221.
  • 33. Khursheed S., F.A. Kidwai. 2023. “Level of Service based Performance Model of BLUE Line of Delhi Metro post-COVID-19”. In: Proceedings of the 15th International Conference of Eastern Asia Society for Transportation Studies (EASTS). Vol.14. 2023, September 4-7, Selangor, Malaysia.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d8ee1393-fc44-4169-aad0-a7962cd180c5
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