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Urban metro transit systems are essential for socio-economic growth and to the achievement of sustainable urban development. To continuously raise the caliber of services, infrastructure performance must be monitored and evaluated on a regular basis. The effectiveness and efficiency of Delhi’s urban public transit system, i.e., Delhi Metro is investigated using Data Envelopment Analysis (DEA) and Super-DEA approaches. DEA is a non-parametric technique used in the estimation of production functions and has been used extensively to estimate measures of technical efficiency. Super-DEA is a linear optimization technique that calculates the relative efficacy of its decision-making units (DMUs) for a wide range of inputs and outputs. The Delhi Metro's "BLUE" line is studied in the present research considering various demographics factors. The relative rankings of the DMUs were assessed taking into account super-DEA after 630 valid responses to commuter-based questionnaires about demographic, travel time components and quality perception parameters were gathered. Each station along the BLUE line is treated as a DMU when analyzing efficiency. Results revealed efficiency, relative rankings and scores for which improvement strategies are suggested.
Słowa kluczowe
Rocznik
Tom
Strony
123--143
Opis fizyczny
Bibliogr. 31 poz.
Twórcy
autor
- Department of Building Engineering and Management, School of Planning and Architecture, New Delhi
autor
- Department of Electronics & Communication Engineering, Meera Bai Institute of Technology, Delhi Skill & Entrepreneurship University, New Delhi
autor
- Department of Civil Engineering, Faculty of Engineering and Technology, Jamia Millia Islamia, New Delhi
Bibliografia
- 1 Aboul-Atta T.A., S.B. Elmaraghy. 2022. „Factors affecting performance improvement of the metro system in cities”. Journal of Engineering and Applied Science 69(1): 27. DOI: https://doi.org/10.1186/s44147-022-00078-4.
- 2 Asmild M., J.C. Paradi, V. Aggarwall, C. Schaffnit. 2004. „Combining DEA Window Analysis with the Malmquist Index Approach in a Study of the Canadian Banking Industry”. Journal of Productivity Analysis 21(1): 67-89. DOI: https://doi.org/10.1023/B:PROD.0000012453.91326.ec.
- 3 Barnum D., S. McNeil, J. Hart. 2007. „Comparing the Efficiency of Public Transportation Subunits Using Data Envelopment Analysis”. Journal of Public Transportation 10(2): 1-16. DOI: https://doi.org/10.5038/2375-0901.10.2.1.
- 4 Bhargava A., P. Singh. 2021. ”Evaluating Metro Systems Using Data Envelopment Analysis: A Focus on Delhi Metro”. Transportation Research Procedia 55: 101-108.
- 5 Chen X., X. Liu, Z. Gong, J. Xie. 2021. „Three-stage super-efficiency DEA models based on the cooperative game and its application on the R&D green innovation of the Chinese high-tech industry”. Computers & Industrial Engineering 156: 107234. DOI: https://doi.org/10.1016/j.cie.2021.107234.
- 6 Doomernik J.E. 2015. „Performance and Efficiency of High-speed Rail Systems”. Transportation Research Procedia 8: 136-144. DOI: https://doi.org/10.1016/j.trpro.2015.06.049.
- 7 Eluru N., V. Chakour, A.M. El-Geneidy. 2012. “Travel mode choice and transit route choice behavior in Montreal: Insights from McGill University members commute patterns”. Public Transport 4(2): 129-149. DOI: https://doi.org/10.1007/s12469-012-0056-2.
- 8 Epstein M.K., J.C. Henderson. 1989. “Data Envelopment Analysis for Managerial Control and Diagnosis”. Decision Sciences 20(1): 90-119. DOI: https://doi.org/10.1111/j.1540-5915.1989.tb01399.x.
- 9 Goel R., G. Tiwari. 2016. ”Access-egress and other travel characteristics of metro users in Delhi and its satellite cities”. IATSS Research 39(2): 164-172. DOI: https://doi.org/10.1016/j.iatssr.2015.10.001.
- 10 Haghighi D., R. Babazadeh. 2020. „Efficiency Evaluation of Railway Freight Stations by Using DEA Approach”. Iranian Journal of Optimization 12(2): 175-185.
- 11 Jun Yang, Tang Yinghao, Ye Tan, Han Xiao, Gong Mengjie. 2022. „Optimization of Metro Trains Operation Plans Based on Passenger Flow Data Analysis”. Mathematical Problems in Engineering 2022: 1-11. DOI: https://doi.org/10.1155/2022/7494127.
- 12 Karlaftis M.G. 2003. „Investigating transit production and performance: A programming approach”. Transportation Research Part A: Policy and Practice 37(3): 225-240. DOI: https://doi.org/10.1016/S0965-8564(02)00013-7.
- 13 Kathuria A., M. Parida, Ch. Ravi Sekhar. 2017. „Route Performance Evaluation of a Closed Bus Rapid Transit System Using GPS Data”. Current Science 112(08): 1642. DOI: https://doi.org/10.18520/cs/v112/i08/1642-1652.
- 14 Krygsman S., M. Dijst. 2001. „Multimodal Trips in the Netherlands: Microlevel Individual Attributes and Residential Context”. Transportation Research Record: Journal of the Transportation Research Board 1753(1): 11-19. DOI: https://doi.org/10.3141/1753-02.
- 15 Krygsman S., M. Dijst, T. Arentze. 2004. “Multimodal public transport: An analysis of travel time elements and the interconnectivity ratio”. Transport Policy 11(3): 265-275. DOI: https://doi.org/10.1016/j.tranpol.2003.12.001.
- 16 Kumar A., R. Sharma. 2022. „Efficiency Assessment of Urban Transit Systems Using DEA: Application to Delhi Metro”. Journal of Public Transportation 25(1): 45-62.
- 17 Lobo A., A. Couto. 2016. „Technical Efficiency of European Metro Systems: The Effects of Operational Management and Socioeconomic Environment”. Networks and Spatial Economics 16(3): 723-742. DOI: https://doi.org/10.1007/s11067-015-9295-5.
- 18 Manchanda A., D. Choudhury, B. Kumar. 2024. „Spatial Parameters for Multimodal Integration at Metro Stations a Conducive Case Study of Metro Stations in Delhi”. ShodhKosh: Journal of Visual and Performing Arts 5: 90-105. DOI: https://doi.org/10.29121/shodhkosh.v5.i ICoMABE.2024.2161.
- 19 Mishra S., T.F. Welch, M.K. Jha. 2012. „Performance indicators for public transit connectivity in multi-modal transportation networks”. Transportation Research Part A: Policy and Practice 46(7): 1066-1085. DOI: https://doi.org/10.1016/j.tra.2012.04.006.
- 20 Obiora A. Nnene, Johan W. Joubert, Mark H.P. Zuidgeest. 2023. „A simulation-based optimization approach for designing transit networks”. Public Transport 15(2): 377-409. DOI: https://doi: 10.1007/s12469-022-00312-5.
- 21 Saxena P., R.R. Saxena. 2010. “Measuring efficiencies in Indian public road transit: A data envelopment analysis approach”. Opsearch 47(3): 195-204. DOI: https://doi.org/10.1007/s12597-011-0034-5.
- 22 Sheth C., K. Triantis, D. Teodorović. 2007. „Performance evaluation of bus routes: A provider and passenger perspective. Transportation Research Part E: Logistics and Transportation Review 43(4): 453-478. DOI: https://doi.org/10.1016/j.tre.2005.09.010.
- 23 Staat M., M. Hammerschmidt. 2005. „Product performance evaluation: A super-efficiency model”. International Journal of Business Performance Management 7(3): 304. DOI: https://doi.org/10.1504/IJBPM.2005.006722.
- 24 Sun L., J. Rong, F. Ren, L. Yao. 2007. „Evaluation of Passenger Transfer Efficiency of an Urban Public Transportation Terminal”. 2007 IEEE Intelligent Transportation Systems Conference: 436-441. DOI: https://doi.org/10.1109/ITSC.2007.4357762.
- 25 Suriyamart V., J. Liangrokapart. 2020. „Determining efficiency of metro operations”. 10th International Conference on Operations and Supply Chain Management: 1-17.
- 26 Swami M., M. Parida. 2015. „Comparative Appraisal of Metro Stations in Delhi Using Data Envelopment Analysis in a Multimodal Context”. Journal of Public Transportation 18(3): 29-51. DOI: https://doi.org/10.5038/2375-0901.18.3.3.
- 27 Taboada G.L., L. Han. 2020. “Exploratory Data Analysis and Data Envelopment Analysis of Urban Rail Transit”. Electronics 9(8): 1270. DOI: https://doi.org/10.3390/electronics9081270.
- 28 TCRP Report 165 “Transit capacity and quality of service manual” (Third). 2013. Transportation Research Board. Available at: https://nap.nationalacademies.org/read/24766/chapter/1#3
- 29 Wei Z., P. Zhao, S. Ai. 2012. „Efficiency Evaluation of Beijing Intelligent Traffic Management System Based on super-DEA”. Journal of Transportation Systems Engineering and Information Technology 12(3): 19-23. DOI: https://doi.org/10.1016/S1570-6672(11)60200-6.
- 30 Yu M.M. 2008. „Assessing the technical efficiency, service effectiveness, and technical effectiveness of the world’s railways through NDEA analysis”. Transportation Research Part A: Policy and Practice 42(10): 1283-1294. DOI: https://doi.org/10.1016/j.tra.2008.03.014.
- 31 Yu M.M., E.T.J. Lin. 2008. „Efficiency and effectiveness in railway performance using a multi-activity network DEA model”. Omega 36(6): 1005-1017. DOI: https://doi.org/10.1016/j.omega.2007.06.003.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-6be453fe-a16f-4402-bdce-52dfc830fef0
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