A systematic approach to evaluate risks and failures of public transport systems with a real case study for bus rapid system in Istanbul

Abstract Maintenance planning and risk evaluation of public transport systems (PTS) is completely critical and important for the natural structure and ecological balance of the city and its’ environment. It is noteworthy to take the correct actions and decisions for the process to make a significant contribution towards the development and ecological sustainability of the city. We also know that failures occurring in PTS leads to personal and social damages such as hazards for passenger life and financial loss that occurs as a result of damage to vehicles due to these failures. Therefore, it is essential to analyze failures of PTS and to improve maintenance planning for obtain a reliable transport system. For this aim, a systematic approach based on maintenance decision support system has been suggested to eliminate the risks arising from failures for Bus Rapid Transit (BRT) system in this paper. The suggested system aims to minimize the harmful effects of these risks with respect to individuals, society and environment perspectives. The proposed integrated systematic approach consists of fuzzy rule based system (FRBS), fuzzy multi criteria decision making (MCDM), stochastic MCDM, mathematical modelling, information theory and heuristic approaches. The mathematical modeling has been applied to evaluate membership functions (MFs) of FRBS and a heuristic approach named particle swarm optimization (PSO) has been also used to solve the problem. Additionally, MCDM methods are also used to prioritize the failures and to determine weights of them. The suggested systematic approach has been applied on a real case study for BRT system in Istanbul and some actions that includes more details and information about risks and failures have been suggested to obtain a more effective maintenance plan. The validity analysis also confirmed the obtained results of the suggested systematic approach.

[1]  Sergio A. Useche,et al.  Stress-related psychosocial factors at work, fatigue, and risky driving behavior in bus rapid transport (BRT) drivers. , 2017, Accident; analysis and prevention.

[2]  Sergio A. Useche,et al.  Work stress and health problems of professional drivers: a hazardous formula for their safety outcomes , 2018, PeerJ.

[3]  Hadi Dowlatabadi,et al.  Minimizing the health and climate impacts of emissions from heavy-duty public transportation bus fleets through operational optimization. , 2013, Environmental science & technology.

[4]  Ali Karaşan,et al.  Location selection of electric vehicles charging stations by using a fuzzy MCDM method: a case study in Turkey , 2018, Neural Computing and Applications.

[5]  Murat Ergun,et al.  Istanbul Metrobus: first intercontinental bus rapid transit , 2012 .

[6]  I. Kaya,et al.  A fuzzy-based risk assessment model for evaluations of hazards with a real-case study , 2020, Human and Ecological Risk Assessment: An International Journal.

[7]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[8]  Bohdan S. Butkiewicz,et al.  A Method for Automatic Membership Function Estimation Based on Fuzzy Measures , 2007, IFSA.

[9]  Florian Brauner,et al.  A methodology to compare risk management (RM) systems for the application and validation of specific threats in public transportation , 2014 .

[10]  Chung-Hsing Yeh,et al.  Fuzzy multicriteria analysis for performance evaluation of bus companies , 2000, Eur. J. Oper. Res..

[11]  Hideki Katagiri,et al.  A Subjective and Objective Constructing Approach for Reasonable Membership Function Based on Mathematical Programming , 2016, 2016 Joint 8th International Conference on Soft Computing and Intelligent Systems (SCIS) and 17th International Symposium on Advanced Intelligent Systems (ISIS).

[12]  Gabriel Alves,et al.  A performability model for the BRT system , 2018, 2018 Annual IEEE International Systems Conference (SysCon).

[13]  Subeh Chowdhury,et al.  Modelling public-transport users’ behaviour at connection point , 2013 .

[14]  Takashi Hasuike,et al.  Construction of an appropriate membership function based on size of fuzzy set and mathematical programming , 2016, IMECS 2016.

[15]  Cristina Esteban,et al.  Perception of the Impact of Certain Health Conditions on Driving Performance , 2016 .

[16]  Sergio A. Useche,et al.  Distraction of cyclists: how does it influence their risky behaviors and traffic crashes? , 2018, PeerJ.

[17]  Raghu Krishnapuram.  Generation of membership functions via possibilistic clustering , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[18]  Sergio A. Useche,et al.  Working Conditions, Job Strain, and Traffic Safety among Three Groups of Public Transport Drivers , 2018, Safety and health at work.

[19]  İhsan Kaya,et al.  Prioritization of renewable energy alternatives by using an integrated fuzzy MCDM model: A real case application for Turkey , 2017 .

[20]  H. Trussell,et al.  Constructing membership functions using statistical data , 1986 .

[21]  Hideki Katagiri,et al.  Constructive method for appropriate membership function integrating fuzzy entropy with smoothing function into interval estimation , 2014, 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International Symposium on Advanced Intelligent Systems (ISIS).

[22]  R. Macário,et al.  A review of general practice in contracting public transport services and transfer to BRT systems , 2016 .

[23]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[24]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Hum. Comput. Stud..

[25]  M. Bilgili,et al.  Medical waste management in Turkey: A case study of Istanbul. , 2009, Waste management.

[26]  Sergio A. Useche,et al.  Relationships of working conditions, health problems and vehicle accidents in bus rapid transit (BRT) drivers , 2018, American journal of industrial medicine.

[27]  Environmental and Health Risk Associated with Air pollution Emitted by Public Transportation, and a New Methodology for Reducing the Risk , 2011 .

[28]  Hideki Katagiri,et al.  An Interactive Algorithm to Construct an Appropriate Nonlinear Membership Function Using Information Theory and Statistical Method , 2015, Complex Adaptive Systems.

[29]  Shengwei Zhu,et al.  Experimental and numerical investigation of micro-environmental conditions in public transportation buses , 2010, Building and Environment.

[30]  H. Zimmermann,et al.  Fuzzy Set Theory and Its Applications , 1993 .

[31]  P Esper,et al.  Making public transport safer , 2005 .

[32]  Takayuki Yoshizumi.  A Mathematical Programming-Based Approach to Determining Objective Functions from Qualitative and Subjective Comparisons , 2015, AAAI.

[33]  Gökhan Polat.  A rule based expert system generation framework using an open source business rule engine , 2006 .

[34]  Jonathan M. Bunker.  High volume bus stop upstream average waiting time for working capacity and quality of service , 2018, Public Transp..

[35]  Xinping Yan,et al.  Safety of Public Transportation Occupational Drivers: Risk Perception, Attitudes, and Driving Behavior , 2010 .

[36]  Hideki Katagiri,et al.  An objective formulation of membership function based on fuzzy entropy and pairwise comparison , 2017, J. Intell. Fuzzy Syst..

[37]  Subeh Chowdhury,et al.  A fuzzy logic-genetic algorithm approach to modelling public transport users’ risk-taking behaviour , 2018 .

[39]  Melike Erdogan,et al.  Analysis and control of variability by using fuzzy individual control charts , 2017, Appl. Soft Comput..

[40]  Reza Kiani Mavi,et al.  Bus rapid transit (BRT): A simulation and multi criteria decision making (MCDM) approach , 2018, Transport Policy.

[41]  İhsan Kaya,et al.  Prioritizing failures by using hybrid multi criteria decision making methodology with a real case application , 2019, Sustainable Cities and Society.

[42]  Wen-Hui Lin,et al.  A face-recognition approach based on secret sharing for user authentication in public-transportation security , 2018, 2018 IEEE International Conference on Applied System Invention (ICASI).

[43]  Maria Rita Ciceri,et al.  How does a collision warning system shape driver's brake response time? The influence of expectancy and automation complacency on real-life emergency braking. , 2015, Accident; analysis and prevention.

[44]  Mert Kompil,et al.  A genetic-fuzzy system modeling of trip distribution , 2010 .

[45]  Reda Alhajj,et al.  A clustering algorithm with genetically optimized membership functions for fuzzy association rules mining , 2003, The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03..

[46]  Swarup Medasani,et al.  An overview of membership function generation techniques for pattern recognition , 1998, Int. J. Approx. Reason..

[47]  Rong-Chang Jou,et al.  Using HLM to investigate the relationship between traffic accident risk of private vehicles and public transportation , 2019, Transportation Research Part A: Policy and Practice.

[48]  W. Veeneman,et al.  Organisation and performance of public transport: A systematic cross-case comparison of metropolitan areas in Europe, Australia, and Canada , 2019, Transportation Research Part A: Policy and Practice.

[49]  Cengiz Kahraman,et al.  A TWO-PHASED FUZZY MULTICRITERIA SELECTION AMONG PUBLIC TRANSPORTATION INVESTMENTS FOR POLICY-MAKING AND RISK GOVERNANCE , 2012 .

[50]  J. Stanley,et al.  Risk and reward in public transport contracting , 2008 .

[51]  M. Yasin Özsağlam,et al.  Optimizasyon Problemlerinin Çözümü için Parçaçık Sürü Optimizasyonu Algoritması , 2008 .

[52]  K Hemanta Baruah,et al.  Construction of the membership function of normal fuzzy numbers , 2013 .

[53]  Melike Erdogan,et al.  Selection of the best outsourcing firm for WEEE under hesitant fuzzy environment , 2018, J. Intell. Fuzzy Syst..

[54]  George J. Klir,et al.  Fuzzy sets and fuzzy logic - theory and applications , 1995 .

[55]  Hideki Katagiri,et al.  Constructing membership function based on fuzzy shannon entropy and human's interval estimation , 2012, 2012 IEEE International Conference on Fuzzy Systems.

[56]  Fakhri Karray,et al.  Fuzzy entropy: a brief survey , 2001, 10th IEEE International Conference on Fuzzy Systems. (Cat. No.01CH37297).

[57]  Rehan Sadiq,et al.  Prioritizing monitoring locations in a water distribution network: a fuzzy risk approach , 2009 .

[58]  Hideki Katagiri,et al.  A Constructing Algorithm for Appropriate Piecewise Linear Membership Function based on Statistics and Information Theory , 2015, KES.