Formulating a New Express Minibus Service Design Problem as a Clustering Problem

References

• Audirac I (2008) Accessing transit as universal design. J. Planning Literature 23(1):4–16.Crossref, Google Scholar
• Bellini C, Dellepiane G, Quaglierini C (2003) The demand responsive transport services: Italian approach. Sucharov LJ, Brebbia CA, eds. Urban Transport IX: Urban Transport and the Environment in the 21st Century, Vol. 14 (WIT Press, Southampton, UK), 63–71.Google Scholar
• Bodin LD, Sexton TR (1986) The multi-vehicle subscriber dial-a-ride problem. TIMS Studies Management Sci. 26:73–86.Google Scholar
• Brake J, Nelson JD (2007) A case study of flexible solutions to transport demand in a deregulated environment. J. Transport Geograph. 15(4):262–273.Crossref, Google Scholar
• Brake J, Mulley C, Nelson JD, Wright S (2007) Key lessons learned from recent experience with flexible transport services. Transport Policy 14(6):458–466.Crossref, Google Scholar
• Chang CY, Cho YJ, Chen LF (2010) The development of demand responsive transport services in Taiwan. Proc. Conf. Web Based Business Management (Scientific Research Publishing, Dover, DE), 1263–1267.Google Scholar
• Chevrier R, Canalda P, Chatonnay P, Josselin D (2006) An oriented convergent mutation operator for solving a scalable convergent demand responsive transport problem. Proc. 2006 Internat. Conf. Service Systems Service Management, Vol. 2 (IEEE, Washington, DC), 959–964.Crossref, Google Scholar
• Cubillos C, Guidi-Polanco F, Demartini C (2004) Multi-agent infrastructure for distributed planning of demand-responsive passenger transportation service. IEEE Internat. Conf. Systems, Man, Cybernetics (IEEE, Piscataway, NJ), 2013–2017.Crossref, Google Scholar
• Deflorio FP (2011) Simulation of requests in demand responsive transport systems. Intelligent Transport Systems 5(3):159–167.Crossref, Google Scholar
• Diana M, Dessouky MM (2004) A new regret insertion heuristic for solving large-scale dial-a-ride problems with time windows. Transportation Res. Part B 38(6):539–557.Crossref, Google Scholar
• Eiró T (2010) Express minibus services in the Lisbon metropolitan area: An innovative concept and a feasibility analysis. Unpublished dissertation, Instituto Superior Técnico, University of Lisbon, Lisbon.Google Scholar
• Eiró T, Martínez LM, Viegas JM (2011) Configuration of innovative minibus service in the Lisbon, Portugal municipality. Transportation Res. Record: J. Transportation Res. Board 2217:127–135.Crossref, Google Scholar
• Eiró T, Viegas JM, Martínez LM (2011) A new optimisation procedure to design minibus services: An application for the Lisbon metropolitan area. Procedia Soc. Behav. Sci. 20:856–865.Crossref, Google Scholar
• European Commission (2007) Towards a new culture for urban mobility. Green paper, European Commission, Brussels. http://eur-lex.europa.eu/LexUriServ/site/en/com/2007/com2007_0551en01.pdf.Google Scholar
• Ferreira L, Charles P, Tether C (2007) Evaluating flexible transport solutions. Transportation Planning Tech. 30(2–3):249–269.Crossref, Google Scholar
• Gomes R, de Sousa JP, Dias TG (2012) Design and operation of demand responsive transportation systems. Proc. 15th Edition of the EURO Working Group on Transportation, Paris.Google Scholar
• Grootenboers F, de Weerdt M, Zargayouna M (2010) Impact of competition on quality of service in demand responsive transit. Dix J, Witteveen C, eds. Multiagent System Technologies, Lecture Notes Comput. Sci., Vol. 6251 (Springer, Berlin),113–124.Crossref, Google Scholar
• Jain AK, Murty MN, Flynn PJ (1999) Data clustering: A review. ACM Comput. Surveys 31(3):264–323.Crossref, Google Scholar
• Jin X, Wang DH (2008) An intelligent model for urban demand-responsive transport system control. 2008 Internat. Sympos. Intelligent Inform. Tech. Appl. Workshops (IEEE Computer Society, Washington, DC), 151–154.Crossref, Google Scholar
• Jin X, Abdulrab H, Itmi M (2008a) A multi-agent based model for urban demand-responsive passenger transport services. IEEE Internat. Joint Conf. Neural Networks (IEEE, Piscataway, NJ), 3668–3675.Crossref, Google Scholar
• Jin X, Abdulrab H, Itmi M (2008b) A multi-agent based model for urban demand-responsive transport system intelligent control. IEEE Intelligent Vehicles Sympos. (IEEE, Piscataway, NJ), 350–355.Crossref, Google Scholar
• Jin X, Itmi M, Abdulrab H (2008) An intelligent based model for urban demand-responsive passenger transportation. Elleithy K, ed. Innovations and Advanced Techniques in Systems, Computing Sciences and Software Engineering (Springer, Dordrecht, The Netherlands), 520–525.Crossref, Google Scholar
• Lastrucci C, Lastrucci L, Casati F (1999) PowerDriverDTSS: The advanced demand responsive transport service. Proc. IEEE Internat. Conf. Multimedia Comput. Systems (IEEE, Piscataway, NJ), 1013–1014.Crossref, Google Scholar
• Logan P (2007) Best practice demand-responsive transport (DRT) policy. Road Transport Res. 16(2):50–59.Google Scholar
• Mageean J, Nelson JD (2003) The evaluation of demand responsive transport services in Europe. J. Transport Geograph. 11(4):255–270.Crossref, Google Scholar
• Martínez LM, Correia G, Viegas JM (2012) An agent-based model to assess the impacts of introducing a shared-taxi system in Lisbon (Portugal). Vasirani M, Klügl F, Camponogara E, Hattori H, eds. Proc. 7th Internat. Workshop Agents Traffic Transportation, Valencia, Spain, 133–142.Google Scholar
• Parragh SN, Doerner KF, Hartl RF (2008) A survey on pickup and delivery problems. J. für Betriebswirtschaft 58(1):21–51.Crossref, Google Scholar
• Pujari AK, Rajesh K, Reddy DS (2001) Clustering techniques in data mining—A survey. IETE J. Res. 47(1–2):19–28.Crossref, Google Scholar
• Quadrifoglio L, Dessouky MM, Palmer K (2007) An insertion heuristic for scheduling mobility allowance shuttle transit (MAST) services. J. Scheduling 10(1):25–40.Crossref, Google Scholar
• Rodriguez J, Murtha T (2009) Travel demand management: Strategy paper. Report, Chicago Metropolitan Agency for Planning, Chicago.Google Scholar
• Schofer JL (2003) Resource requirements for demand-responsive transportation services. TCRP Report 98, Transportation Research Board, Washington, DC.Google Scholar
• Sexton TR, Bodin LD (1985a) Optimizing single vehicle many-to-many operations with desired delivery times: 1. Scheduling. Transportation Sci. 19(4):378–410.Link, Google Scholar
• Sexton TR, Bodin LD (1985b) Optimizing single vehicle many-to-many operations with desired delivery times: 2. Routing. Transportation Sci. 19(4):411–435.Link, Google Scholar
• Uchimura K, Takahashi H, Saitoh T (2002) Demand responsive services in hierarchical public transportation system. IEEE Trans. Vehicular Tech. 51(4):760–766.Crossref, Google Scholar
• Viegas JM, Martinez LM (2010) Generating the universe of urban trips from a mobility survey sample with minimum recourse to behavioural assumptions. Viegas J, Macário R, eds. Proc. 12th World Conf. Transport Res., Lisbon, Portugal.Google Scholar
• Xu J, Yin W, Huang Z (2009) A study of multi-agent based metropolitan demand responsive transport systems. Adv. Intelligent Soft Comput. 56:711–720.Crossref, Google Scholar
• Zografos KG, Androutsopoulos KN, Sihvola T (2008) A methodological approach for developing and assessing business models for flexible transport systems. Transportation 35(6):777–795.Crossref, Google Scholar