A web service based advanced traveller information system for itinerary planning in an uncertain multimodal network

References

• Avila-Torres P, Caballero R, Litvinchev I, Lopez-Irarragorri F, Vasant P. 2018. The urban transport planning with uncertainty in demand and travel time: a comparison of two defuzzification methods. J Ambient Intell Human Comput. 9(3):843–856.
   Web of Science ®Google Scholar
• Ben-Akiva ME, Lerman SR, Lerman SR. 1985. Discrete choice analysis: Theory and application to travel demand. Cambridge, MA: MIT press .
   Google Scholar
• Bhanumurthy V, Shankar GJ, Rao KRM, Nagamani P. 2015. Defining a framework for integration of geospatial technologies for emergency management. Geocarto Int. 30(9):963–983.
   Web of Science ®Google Scholar
• Bon AT, Utami SF. 2015. An analytical hierarchy process and fuzzy inference system tsukamoto for production planning: A review and conceptual research. Int J Business Econ Develop (IJBED). 3(1):101–111.
   Google Scholar
• Braga M, Santos MY, Moreira A. 2014. Integrating public transportation data: creation and editing of gtfs data. In: New perspectives in information systems and technologies, vol. 2. Cham: Springer; p. 53–62.
   Google Scholar
• Brito J, Moreno JA, Verdegay JL. 2012. Transport route planning models based on fuzzy approach. Iran J Fuzzy Syst. 9(1):141–158.
   Web of Science ®Google Scholar
• Campbell PA, Junger JR, Havlicek JP, Stevenson AR, Barnes RD. 2011. Pathfinder: Oklahoma's advanced traveler information system ed. Intelligent Transportation Systems (ITSC), 2011 14th International IEEE Conference on IEEE, 402–407.
   Google Scholar
• Cao J, Cao X, Zhang C, Huang X. 2016. The gaps in satisfaction with transit services among Brt, metro, and bus riders: evidence from Guangzhou. J Transp Land Use. 9(3):97–109.
   Web of Science ®Google Scholar
• Chang M-S, Chen H-K. 2000. A fuzzy user-optimal route choice problem using a link-based fuzzy variational inequality formulation. Fuzzy Sets Syst. 114(2):339–345.
   Web of Science ®Google Scholar
• Chen Y-W, Tzeng G-H. 2001. Using fuzzy integral for evaluating subjectively perceived travel costs in a traffic assignment model. Eur J Oper Res. 130(3):653–664.
   Web of Science ®Google Scholar
• Chiu DK, Lee OK, Leung H-F, Au EW, Wong MC. 2005. A multi-modal agent based mobile route advisory system for public transport networked. 38th Hawaii International Conference on System Sciences (HICSS-38 2005), CD-ROM/Abstracts Proceedings, 3-6 January 2005, Big Island, HI, USA.
   Google Scholar
• Daganzo CF, Sheffi Y. 1977. On stochastic models of traffic assignment. Transp Sci. 11(3):253–274.
   Google Scholar
• De Borger B, Fosgerau M. 2008. The trade-off between money and travel time: a test of the theory of reference-dependent preferences. J Urban Econ. 64(1):101–115.
   Web of Science ®Google Scholar
• Dell’olio L, Ibeas A, Cecin P. 2011. The quality of service desired by public transport users. Transport Policy. 18(1):217–227.
   Web of Science ®Google Scholar
• Dell’orco M, Teodorovic D. 2009. Data fusion for updating information in modelling drivers’ choice behaviour. Transportmetrica. 5(2):107–123.
   Web of Science ®Google Scholar
• Di Pace R, Marinelli M, Bifulco GN, Dell M. 2011. Modeling risk perception in atis context through fuzzy logic. Procedia Soc Behav Sci. 20:916–926.
   Google Scholar
• Díaz L, Granell C, Gould M. 2008. Case study: geospatial processing services for web based hydrological applications. Boston (MA): Springer.
   Google Scholar
• Dotoli M, Zgaya H, Russo C, Hammadi S. 2017. A multi-agent advanced traveler information system for optimal trip planning in a co-modal framework. IEEE Trans Intell Transport Syst. 18(9):2397–2412.
   Web of Science ®Google Scholar
• Dotspatial. 2019. Geographic information system library written for.Net [online]. [accessed 2019]. https://github.com/DotSpatial/DotSpatial.
   Google Scholar
• Eboli L, Mazzulla G. 2011. A methodology for evaluating transit service quality based on subjective and objective measures from the passenger’s point of view. Transport Policy. 18(1):172–181.
   Web of Science ®Google Scholar
• Feki MF, Kamoun MA, Hammadi S. 2008. An agent oriented information system for itineraries search using web services compositioned. Systems, Man and Cybernetics, 2008. SMC 2008. IEEE International Conference on IEEE. p. 1935–1941.
   Google Scholar
• Fernandez CG, Khadraoui D, Ayed H, Habbas Z, Alba E. 2009. Distributed approach for solving time-dependent problems in multimodal transport networks. Adv Operat Res. 2009:15.
   Google Scholar
• Gao Y. 2011. Shortest path problem with uncertain arc lengths. Comput Math Appl. 62(6):2591–2600.
   Web of Science ®Google Scholar
• Ghavami SM, Taleai M. 2017. Towards a conceptual multi-agent-based framework to simulate the spatial group decision-making process. J Geogr Syst. 19(2):109–132.
   Web of Science ®Google Scholar
• Henn V. 2005. What is the meaning of fuzzy costs in fuzzy traffic assignment models? Transport Res Part C: Emerg Technol. 13(2):107–119.
   Web of Science ®Google Scholar
• Ho W, Ma X. 2017. The state-of-the-art integrations and applications of the analytic hierarchy process. Eur J Oper Res. 267:399–414.
   Web of Science ®Google Scholar
• Hofer B, Mäs S, Brauner J, Bernard L. 2017. Towards a knowledge base to support geoprocessing workflow development. Int J Geogr Inform Sci. 31(4):694–716.
   Web of Science ®Google Scholar
• Hu M, Wang Y, Shi Q. 2002. Developing Beijing traveler information systems frameworked. Intelligent Transportation Systems, 2002. Proceedings. The IEEE 5th International Conference on IEEE, p. 381–386.
   Google Scholar
• Kem O, Balbo F, Zimmermann A. 2017. Traveler-oriented advanced traveler information system based on dynamic discovery of resources: potentials and challenges. Transport Res Proc. 22:635–644.
   Google Scholar
• Kumar P, Singh V, Reddy D. 2005. Advanced traveler information system for Hyderabad city. IEEE Trans Intell Transport Syst. 6(1):26–37.
   Web of Science ®Google Scholar
• Liu H, Ban X, Ran B, Mirchandani P. 2003. Formulation and solution algorithm for fuzzy dynamic traffic assignment model. Transport Res Rec. 1854(1):114–123.
   Google Scholar
• Liu K, Xu Y. 2019. Route choice behavior: Understanding the impact of asymmetric preference on travelers’ decision making. Symmetry. 11(1):66.
   Google Scholar
• Mahmoud MS. 2018. Basics of fuzzy control. In Fuzzy control, estimation and diagnosis. Switzerland: Springer; p. 15–44.
   Google Scholar
• Mamdani EH, Assilian S. 1974. Application of fuzzy algorithms for control of simple dynamic plant. IEEE Proc Inst Electr Eng UK. 121:1585–1588.
   Web of Science ®Google Scholar
• Martins E, Pascoal M. 2000. A new implementation of yen's ranking loopless paths algorithm. Coimbra, Portugal: Springer-Verlag.
   Google Scholar
• Miralinaghi M, Lou Y, Hsu YT, Shabanpour R, Shafahi Y. 2016. Multiclass fuzzy user equilibrium with endogenous membership functions and risk‐taking behaviors. J Adv Transp. 50(8):1716–1734.
   Web of Science ®Google Scholar
• Moraitis P, Petraki E, Spanoudakis NI. 2005. An agent-based system for infomobility serviced. EUMAS, p. 224–235.
   Google Scholar
• Moreno A, Perallos A, López-De-Ipiña D, Onieva E, Salaberria I, Masegosa AD. 2015. A novel software architecture for the provision of context-aware semantic transport information. Sensors. 15(6):12299–12322.
   PubMed Web of Science ®Google Scholar
• Nugroho A. 2017. Mobile expert system using fuzzy tsukamoto for diagnosing cattle disease. Proc Comput Sci. 116:27–36.
   Google Scholar
• OGC-WPS. 2018. Web processing service [online]. [accessed 2018]. http://www.opengeospatial.org/standards/wps.
   Google Scholar
• OGC. 2018. Open geospatial consortium (ogc) [online]. [accessed 2018]. http://www.opengeospatial.org/about.
   Google Scholar
• Onlymoov. 2018., Only moov [online]. [accessed 2018].
   Google Scholar
• Opentripplanner. 2018. Opentripplanner; multimodal trip planning [online]. [accessed 2018]. http://www.opentripplanner.org/.
   Google Scholar
• Pagani A, Bruschi F, Rana V, Restelli M. 2017. User context estimation for public travel assistance and intelligent service schedulinged. Intelligent Transportation Systems (ITSC), 2017. IEEE 20th International Conference on IEEE, p. 1–8.
   Google Scholar
• Peng Z-R, Huang R. 2000. Design and development of interactive trip planning for web-based transit information systems. Transp Res Part C Emerg Technol. 8(1–6):409–425.
   Web of Science ®Google Scholar
• Petit-Rozé C, Grislin-Le Strugeon E. 2006. Mapis, a multi-agent system for information personalization. Inf Softw Technol. 48(2):107–120.
   Web of Science ®Google Scholar
• Robenek T, Azadeh SS, Maknoon Y, Bierlaire M. 2017. Hybrid cyclicity: combining the benefits of cyclic and non-cyclic timetables. Transp Res Part C Emerg Technol. 75:228–253.
   Web of Science ®Google Scholar
• Rometorio. 2018. Rome torio [online]. [accessed 2018]. http://www.rome2rio.com/.
   Google Scholar
• Rosatti G, Zorzi N, Zugliani D, Piffer S, Rizzi A. 2018. A web service ecosystem for high-quality, cost-effective debris-flow hazard assessment. Environmental Modelling & Software. 100:33–47.
   Web of Science ®Google Scholar
• Ross T. 2004. Fuzzy logic with engineering. University of New Mexico, USA: Wiley Ltd.
   Google Scholar
• Saaty T. 1980. The analytic hierarchy process New York, NY: McGraw-Hill.
   Google Scholar
• Sawant S, Durbha SS, Jagarlapudi A. 2017. Interoperable agro-meteorological observation and analysis platform for precision agriculture: a case study in citrus crop water requirement estimation. Comput Electron Agri. 138:175–187.
   Web of Science ®Google Scholar
• Schiel U, Baptista CDS, Maia ACDJ, De Andrade FG. 2007. Sei-tur: A system based on composed web-service discovery to support the creation of trip planned. Digital Society, 2007. ICDS'07. First International Conference on the IEEE, p. 28–28.
   Google Scholar
• Sci. 2018. Statistical center of Iran [online]. [accessed 2018]. https://www.amar.org.ir/english/.
   Google Scholar
• Shen X, Feng S, Li Z, Hu B. 2016. Analysis of bus passenger comfort perception based on passenger load factor and in-vehicle time. Springerplus. 5(1):62. https://doi.org/10.1186/s40064-016-1694-7] [https://doi.org/26839755].
   PubMedGoogle Scholar
• Singh H, Karnatak HC, Garg R. 2019. An automated and optimized approach for online spatial biodiversity model: a case study of OGC web processing service. Geocarto Int. 34(2):194–214.
   Web of Science ®Google Scholar
• Sipahi S, Timor M. 2010. The analytic hierarchy process and analytic network process: an overview of applications. Manage Decis. 48(5):775–808.
   Web of Science ®Google Scholar
• Stasch C, Pross B, Gräler B, Malewski C, Förster C, Jirka S. 2018. Coupling sensor observation services and web processing services for online geoprocessing in water dam monitoring. Int J Digit Earth. 11(1):64–78.
   Web of Science ®Google Scholar
• Stollberg B, Zipf A. 2008. Geoprocessing services for spatial decision support in the domain of housing market analyses. Proceedings AGILE.
   Google Scholar
• Subartini B, Damayanti P, Supian S, Budiono R, Kamarudin M, Ghazali A, Gasim M. 2018. Fuzzy inference system of tsukamoto method in decision making on determination of insurance premium amount for due damages of flood natural disaster. J Fund Appl Sci. 10(1S):79–94.
   Google Scholar
• Sugeno M, Kang GT. 1988. Structure identification of fuzzy model. Fuzzy Sets Syst. 28(1):15–33.
   Web of Science ®Google Scholar
• Taneja S, Suri B, Gupta S, Narwal H, Jain A, Kathuria A. 2018. A fuzzy logic based approach for data classification. In Data engineering and intelligent computing. Singapore: Springer; p. 605–616. [
   Google Scholar
• Teodorović D, Lučić P. 2005. Schedule synchronization in public transit using the fuzzy ant system. Transport Plan Technol. 28(1):47–76.
   Web of Science ®Google Scholar
• Trafiken. 2018. [online]. [accessed 2018]. https://trafiken.nu/stockholm/.
   Google Scholar
• Tran VX, Tsuji H. 2009. Using semantic technologies for dynamic and flexible trip planning. Digital Ecosystems and Technologies, 2009. DEST'09. 3rd IEEE International Conference on IEEE, p. 481–486.
   Google Scholar
• Transportforlondon. 2018. [online]. [accessed 2018] https://tfl.gov.uk/.
   Google Scholar
• Tsukamoto Y. 1979. An approach to fuzzy reasoning method. Adv Fuzzy Set Theory Appl. 137:149.
   Google Scholar
• Turksen I, Tian Y. 1993. Combination of rules or their consequences in fuzzy expert systems. Fuzzy Sets Syst. 58(1):3–40.
   Web of Science ®Google Scholar
• Wahyuni I, Mahmudy WF, Iriany A. 2016. Rainfall prediction in Tengger region Indonesia using tsukamoto fuzzy inference system. Information Technology, Information Systems and Electrical Engineering (ICITISEE), International Conference on IEEE, p. 130–135.
   Google Scholar
• Wardrop JG. 1952. Some theoretical aspects of road traffic researched. Inst Civil Engineers Proc London/UK/.
   Google Scholar
• Wets G, Vanthienen J, Timmermans H. 1998. Modelling decision tables from data. In Research and development in knowledge discovery and data mining. Berlin: Springer; p. 412–413.
   Google Scholar
• Wu C-H, Su D-C, Chang J, Wei C-C, Ho J-M, Lin K-J, Lee D. 2003. An advanced traveler information system with emerging network technologies. Proc. 6th Asia-Pacific Conf. Intelligent Transportation Systems Forum Citeseer, p. 230–231.
   Google Scholar
• Wu L, Ma W, Yang Y, Wang K. 2019. A competitive analysis approach for route choice with uncertain travel times and blocked nodes. J Amb Intell Human Comput. 10(1):345–355.
   Web of Science ®Google Scholar
• Yoon G, Kim K, Lee K. 2017. Linkage of OGC WPS 2.0 to the e-government standard framework in Korea: an implementation case for geo-spatial image processing. IJGI. 6(1):25.
   Google Scholar
• Zadeh LA. 1976. A fuzzy-algorithmic approach to the definition of complex or imprecise concepts. In Systems theory in the social sciences. Birkhäuser, Basel: Springer; p. 202–282.
   Google Scholar
• Zapater JJS, Escrivá DML, García FRS, Durá JJM. 2015. Semantic web service discovery system for road traffic information services. Expert Syst Appl. 42(8):3833–3842.
   Web of Science ®Google Scholar
• Zhang K, Zhou K, Zhang F. 2014. Evaluating bus transit performance of Chinese cities: developing an overall bus comfort model. Transport Res Part A: Policy Pract. 69:105–112.
   Web of Science ®Google Scholar
• Zhang Y, Liu P, Yang L, Gao Y. 2015. A bi-objective model for uncertain multi-modal shortest path problems. J Uncertain Anal Appl. 3(1):8.
   Google Scholar
• Zhou J, Yang F, Wang K. 2014. An inverse shortest path problem on an uncertain graph. J Netw. 9(9):2353.
   Google Scholar
• Zhou J, Yi X, Wang K, Liu J. 2016. Uncertain distribution-minimum spanning tree problem. Int J Unc Fuzz Knowl Based Syst. 24(04):537–560.
   Google Scholar
• Ziering E. 2007. Transxml: Xml schemas for exchange of transportation data: Washington (DC): Transportation Research Board.
   Google Scholar
• Zimmermann H. 1992. Fuzzy set theory and its applications. 2nd, revised ed. Netherlands: Springer.
   Google Scholar