Using entropy-TOPSIS method to evaluate urban rail transit system operation performance: The China case

Abstract Urban rail transit system operation performance evaluation results are important for government, transit operators and passengers. In this paper, we formulate an entropy-Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) Method to evaluate the urban rail transit system’s operation performance from the operator’s, passenger’s and government’s perspectives. Firstly, we establish the evaluation indicator system with 8 indicators and a total of 41 sub-indicators, the operational data of the 41 sub-indicators will be used as the input of the approach. Second, we formulate our new approach to obtain the performance evaluation: the Entropy Weight Method (EWM) will be used to calculate the weight of each sub-indicator; the product of the corresponding probability and weight will be used to formulate the performance from operator’s, passenger’s and government’s perspectives; the TOPSIS will be used to calculate the comprehensive evaluation values and rankings of performance for each month. Third, the Chengdu subway with 34 months initial data will be chosen as the case study to test our new approach, the related suggestions to the government, to the passengers as well as the operators and managers will also be given.

[1]  T. Oum,et al.  ECONOMIC EFFICIENCY OF RAILWAYS AND IMPLICATIONS FOR PUBLIC POLICY: A COMPARATIVE STUDY OF THE OECD COUNTRIES' RAILWAYS. IN: RAILWAYS , 2002 .

[2]  Arnold Reisman,et al.  Content analysis of data envelopment analysis literature and its comparison with that of other OR/MS fields , 2004, J. Oper. Res. Soc..

[3]  Dariusz Walczak,et al.  Project rankings for participatory budget based on the fuzzy TOPSIS method , 2017, Eur. J. Oper. Res..

[4]  Claude E. Shannon,et al.  A mathematical theory of communication , 1948, MOCO.

[5]  Semyon Slobounov,et al.  Application of a novel measure of EEG non-stationarity as ‘Shannon- entropy of the peak frequency shifting’ for detecting residual abnormalities in concussed individuals , 2011, Clinical Neurophysiology.

[6]  Abdolreza Ohadi,et al.  Application of wavelet energy and Shannon entropy for feature extraction in gearbox fault detection under varying speed conditions , 2014, Neurocomputing.

[7]  Slobodan P. Simonovic,et al.  An analytical procedure for multi-site, multi-season streamflow generation using maximum entropy bootstrapping , 2014, Environ. Model. Softw..

[8]  R. Cervero.  Cost and performance impacts of transit subsidy programs , 1984 .

[9]  Gordon J. Fielding,et al.  PERFORMANCE EVALUATION FOR BUS TRANSIT , 1985 .

[10]  Graziella Fornengo,et al.  The impact of ownership on the cost of bus service provision: an example from Italy , 2009 .

[11]  Bruce W. Lamar,et al.  MEASURING TRANSIT PERFORMANCE USING DATA ENVELOPMENT ANALYSIS , 1992 .

[12]  K. Kerstens.  Technical efficiency measurement and explanation of French urban transit companies , 1996 .

[13]  Ming-Miin Yu,et al.  Assessing the technical efficiency, service effectiveness, and technical effectiveness of the world's railways through NDEA analysis , 2008 .

[14]  Ting Kuo,et al.  A modified TOPSIS with a different ranking index , 2017, Eur. J. Oper. Res..

[15]  Hiroyuki Iseki,et al.  Measuring Cost Variability in Provision of Transit Service , 2010 .

[16]  David A. Hensher,et al.  Productive efficiency and ownership of urban bus services , 1987 .

[17]  Amer Shalaby.  Simulating Performance Impacts of Bus Lanes and Supporting Measures , 1999 .

[18]  Thomas R. Sexton,et al.  Operational performance of U.S. public rail transit and implications for public policy , 2014 .

[19]  M. Farrell.  The Measurement of Productive Efficiency , 1957 .

[20]  Sue McNeil,et al.  Comparing the Performance of Bus Routes after Adjusting for the Environment Using Data Envelopment Analysis , 2008 .

[21]  Ziyou Gao,et al.  A case study on the coordination of last trains for the Beijing subway network , 2015 .

[22]  Ziyou Gao,et al.  Estimating the influence of common disruptions on urban rail transit networks , 2016 .

[23]  Charles F. Hockett,et al.  A mathematical theory of communication , 1948, MOCO.

[24]  Kristiaan Kerstens,et al.  Public transit performance: What does one learn from frontier studies? , 2002 .

[25]  Morteza Yazdani,et al.  A state-of the-art survey of TOPSIS applications , 2012, Expert Syst. Appl..

[26]  Anne Yvrande-Billon,et al.  Ownership, Contractual Practices and Technical Efficiency: The Case of Urban Public Transport in France , 2007 .

[27]  A. Charnes,et al.  Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis , 1984 .

[28]  Yan Leng,et al.  Integrated weight-based multi-criteria evaluation on transfer in large transport terminals: A case study of the Beijing South Railway Station , 2014 .

[29]  Ming-Miin Yu,et al.  Efficiency and effectiveness in railway performance using a multi-activity network DEA model , 2008 .

[30]  Daniel J. Graham,et al.  Productivity and efficiency in urban railways: Parametric and non-parametric estimates , 2008 .

[31]  K. Kerstens,et al.  The Performance of Bus Transit Operators , 2000 .

[32]  Kevin Cullinane,et al.  The impact of governance development models on urban rail efficiency , 2008 .

[33]  Isabel María García Sánchez.  Technical and Scale Efficiency in Spanish Urban Transport: Estimating with Data Envelopment Analysis , 2009, Adv. Oper. Res..

[34]  Alexi Delgado,et al.  Environmental conflict analysis using an integrated grey clustering and entropy-weight method: A case study of a mining project in Peru , 2016, Environ. Model. Softw..

[35]  Jonathan Cowie,et al.  Organisation form, scale effects and efficiency in the British bus industry , 1999 .

[36]  Carlo Scarpa,et al.  Do competition and ownership matter? Evidence from local public transport in Europe , 2010 .

[37]  J. M. Pastor,et al.  Productivity, efficiency and technical change in the European railways: A non-parametric approach , 1999 .

[38]  Qiting Zuo,et al.  Description and Application of a Mathematical Method for the Analysis of Harmony , 2015, TheScientificWorldJournal.

[39]  Kaoru Tone,et al.  Decomposing technical efficiency and scale elasticity in two-stage network DEA , 2014, Eur. J. Oper. Res..

[40]  Wang Lei,et al.  Corrected Entropy Based Operation Performance Evaluation about Urban Rail Transportation Non-networks System , 2016 .

[41]  He Yue.  Study on index weighting method based on improved group-G2 , 2010 .

[42]  Zuo Qi-ting.  Mathematical Description Method and Its Application of Harmony Theory , 2009 .

[43]  Dimitrios A Tsamboulas,et al.  Assessing Performance under Regulatory Evolution: A European Transit System Perspective , 2006 .

[44]  Guangnian Xiao,et al.  Performance evaluation of public transit systems using a combined evaluation method , 2016 .

[45]  Lawrence M. Seiford,et al.  Stability regions for maintaining efficiency in data envelopment analysis , 1998, Eur. J. Oper. Res..

[46]  Matthew G. Karlaftis,et al.  A DEA approach for evaluating the efficiency and effectiveness of urban transit systems , 2004, Eur. J. Oper. Res..

[47]  Matthew G. Karlaftis,et al.  Subsidy and public transit performance: A factor analytic approach , 1997 .

[48]  James Odeck,et al.  Congestion, ownership, region of operation, and scale: Their impact on bus operator performance in Norway , 2006 .

[49]  Snehamay Khasnabis,et al.  Comparative Study of Two Techniques of Transit Performance Assessment: AHP and GAT , 2002 .

[50]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[51]  James Odeck,et al.  The effect of mergers on efficiency and productivity of public transport services , 2008 .