Embedding a Neuro-Fuzzy Mode Choice Tool in Intelligent Agents
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[1] Yaser E. Hawas. Development and Calibration of Route Choice Utility Models: Neuro-Fuzzy Approach , 2004 .
[2] Michel Bierlaire,et al. A theoretical analysis of the cross-nested logit model , 2006, Ann. Oper. Res..
[3] Ch. Ravi Sekhar,et al. Development of neuro‐fuzzy‐based multimodal mode choice model for commuter in Delhi , 2018, IET Intelligent Transport Systems.
[4] Kay W. Axhausen,et al. Agent-Based Models in Transport Planning: Current State, Issues, and Expectations , 2020, ANT/EDI40.
[5] W. Greene,et al. Specification and estimation of the nested logit model: alternative normalisations , 2002 .
[6] Barbara Lenz,et al. New Mobility Concepts and Autonomous Driving: The Potential for Change , 2016 .
[7] Witold Pedrycz,et al. A survey of defuzzification strategies , 2001, Int. J. Intell. Syst..
[8] C. Manski. The structure of random utility models , 1977 .
[9] Chuen-Tsai Sun,et al. Neuro-fuzzy modeling and control , 1995, Proc. IEEE.
[10] Navneet Walia,et al. ANFIS: Adaptive Neuro-Fuzzy Inference System- A Survey , 2015 .
[11] Francesco Pinna,et al. Urban Policies and Mobility Trends in Italian Smart Cities , 2017 .
[12] Kalliopi Kravari,et al. A Survey of Agent Platforms , 2015, J. Artif. Soc. Soc. Simul..
[13] Diego López-de-Ipiña,et al. MASSHA: An agent-based approach for human activity simulation in intelligent environments , 2017, Pervasive Mob. Comput..
[14] Dragan Pamučar,et al. VEHICLE ROUTE SELECTION WITH AN ADAPTIVE NEURO FUZZY INFERENCE SYSTEM IN UNCERTAINTY CONDITIONS , 2018 .
[15] Jacob Marschak,et al. Stochastic models of choice behavior , 2007 .
[16] Dervis Karaboga,et al. Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey , 2018, Artificial Intelligence Review.
[17] Marc Gaudry. The inverse power transformation logit and dogit mode choice models , 1981 .
[18] Yinghua Lin,et al. Nonlinear system input structure identification: two stage fuzzy curves and surfaces , 1998, IEEE Trans. Syst. Man Cybern. Part A.
[19] Adel W. Sadek,et al. Toward More Effective Transportation Applications of Computational Intelligence Paradigms , 2003 .
[20] Pierre Lemarinier,et al. Agent Based Modelling and Simulation tools: A review of the state-of-art software , 2017, Comput. Sci. Rev..
[21] S.K. Singh,et al. Smart real-time traffic congestion estimation and clustering technique for urban vehicular roads , 2016, 2016 IEEE Region 10 Conference (TENCON).
[22] Dávid Földes,et al. Urban mobility scenarios until the 2030s , 2021 .
[23] Guang Ren,et al. Stability analysis and systematic design of Takagi-Sugeno fuzzy control systems , 2005, Fuzzy Sets Syst..
[24] Serge Guillaume,et al. Designing fuzzy inference systems from data: An interpretability-oriented review , 2001, IEEE Trans. Fuzzy Syst..
[25] Seiichi Kagaya,et al. Development of Transport Mode Choice Model by Using Adaptive Neuro-Fuzzy Inference System , 2006 .
[26] Giuseppe M. L. Sarnè,et al. Agents meet Traffic Simulation, Control and Management: A Review of Selected Recent Contributions , 2016, WOA.
[27] C. Bhat. Covariance heterogeneity in nested logit models: Econometric structure and application to intercity travel , 1997 .
[28] Joan L. Walker,et al. Generalized random utility model , 2002, Math. Soc. Sci..
[29] G. N. Pillai,et al. Recent advances in neuro-fuzzy system: A survey , 2018, Knowl. Based Syst..
[30] Constantinos Antoniou,et al. Factors affecting modal choice in urban mobility , 2012, European Transport Research Review.
[31] M. Postorino,et al. Evaluation of O/D Trip Matrices by Traffic Counts in Transit Systems , 2004 .
[32] Paulo Vitor de Campos Souza,et al. Fuzzy neural networks and neuro-fuzzy networks: A review the main techniques and applications used in the literature , 2020, Appl. Soft Comput..
[33] Robert LIN,et al. NOTE ON FUZZY SETS , 2014 .
[34] Johann Schrammel,et al. From mobility patterns to behavioural change: leveraging travel behaviour and personality profiles to nudge for sustainable transportation , 2018, Journal of Intelligent Information Systems.
[35] Kristen L Sanford Bernhardt,et al. Agent-Based Modeling in Transportation , 2007 .
[36] Ricardo Faria,et al. Smart mobility: A survey , 2017, 2017 International Conference on Internet of Things for the Global Community (IoTGC).
[37] George J. Klir,et al. Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems - Selected Papers by Lotfi A Zadeh , 1996, Advances in Fuzzy Systems - Applications and Theory.
[38] Rj Allan,et al. Survey of Agent Based Modelling and Simulation Tools , 2009 .
[39] M. Trombetti,et al. Evaluating the impact of “Sustainable Urban Mobility Plans” on urban background air quality , 2019, Journal of environmental management.
[40] Michio Sugeno,et al. An introductory survey of fuzzy control , 1985, Inf. Sci..
[41] Giuseppe M. L. Sarnè,et al. An Agent-based Simulator for Urban Air Mobility Scenarios , 2020, WOA.
[42] Maria Nadia Postorino,et al. Fixed Point Approaches to the Estimation of O/D Matrices Using Traffic Counts on Congested Networks , 2001, Transp. Sci..
[43] Robert Babuska,et al. Neuro-fuzzy methods for nonlinear system identification , 2003, Annu. Rev. Control..
[44] L. Montero,et al. Transport Analytics approaches to the Dynamic Origin- Destination Estimation Problem , 2020 .
[45] Mario Versaci,et al. A Neuro-Fuzzy Approach to Simulate the User Mode Choice Behaviour in a Travel Decision Framework , 2008 .
[46] J. Pineda-Jaramillo. A review of Machine Learning (ML) algorithms used for modeling travel mode choice , 2019, DYNA.
[47] Mohammad Reza Nami,et al. Multi-Agent Systems: A Survey , 2010, PDPTA.
[48] Ali Ghaffari,et al. Design of an improved fuzzy logic based model for prediction of car following behavior , 2011, 2011 IEEE International Conference on Mechatronics.
[49] Navid Khademi,et al. Day-to-day travel time perception modeling using an adaptive-network-based fuzzy inference system (ANFIS) , 2016, EURO J. Transp. Logist..
[50] Jakub Zawieska,et al. Smart city as a tool for sustainable mobility and transport decarbonisation , 2018 .
[51] Dirk Neumann,et al. Intermodal Mobility , 2017, Bus. Inf. Syst. Eng..
[52] Giuseppe M. L. Sarné,et al. Reinventing Mobility Paradigms: Flying Car Scenarios and Challenges for Urban Mobility , 2020, Sustainability.
[53] Ali Ghaffari,et al. New fuzzy solution for determining anticipation and evaluation behavior during car-following maneuvers , 2018 .
[54] F. Koppelman,et al. Activity-Based Modeling of Travel Demand , 2003 .
[55] M. Postorino. A comparative analysis of different specifications of modal choice models in an urban area , 1993 .
[56] Michael Keane,et al. A Note on Identification in the Multinomial Probit Model , 1992 .
[57] José M. N. Vieira,et al. Neuro-Fuzzy Systems: A Survey , 2004 .
[58] Eric Bonabeau,et al. Agent-based modeling: Methods and techniques for simulating human systems , 2002, Proceedings of the National Academy of Sciences of the United States of America.
[59] M. Postorino,et al. Modelling User Mode Choices by an Ellipsoidal Fuzzy Approach , 2013 .
[60] Anders Karlström,et al. A link based network route choice model with unrestricted choice set , 2013 .
[61] E. Cascetta,et al. Dominance among alternatives in random utility models , 2009 .
[62] Catarina Ribeiro,et al. Life cycle assessment of a multi-material car component , 2007 .
[63] Michael Patriksson,et al. The Traffic Assignment Problem: Models and Methods , 2015 .
[64] László T. Kóczy,et al. An intelligent traffic congestion detection approach based on fuzzy inference system , 2021, 2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI).
[65] D. Hensher,et al. Stated Choice Methods: Analysis and Applications , 2000 .
[66] Bart Kosko,et al. Fuzzy Engineering , 1996 .
[67] Marcel G. Dagenais,et al. The dogit model , 1979 .
[68] F. Koppelman,et al. The generalized nested logit model , 2001 .
[69] D. Cox,et al. An Analysis of Transformations , 1964 .
[70] P. Alam. ‘G’ , 2021, Composites Engineering: An A–Z Guide.
[71] Giuseppe M. L. Sarnè,et al. A Study to Promote Car-Sharing by Adopting a Reputation System in a Multi-Agent Context , 2017, WOA.
[72] Stefania Bandini,et al. Agent Based Modeling and Simulation: An Informatics Perspective , 2009, J. Artif. Soc. Soc. Simul..
[73] F. Richard Yu,et al. A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges , 2019, IEEE Communications Surveys & Tutorials.
[74] M. N. Postorino,et al. The Analytic Hierarchy Process to Evaluate the Quality of Service in Transit Systems , 2006 .