Models for Supporting Mobility as a Service (MaaS) Design
Smart Cities
https://doi.org/10.3390/SMARTCITIES5010013Abstract
Mobility as a Service (MaaS) is the new approach in transportation systems that allows users to use different transport services as a single option, by using digital platforms and with integrated design. In MaaS many actors can be identified: MaaS operators, MaaS companies, MaaS users, citizens, system manager/planner. In order to be able to design the system in an integrated way, it is necessary to identify comprehensive methodologies that make it possible to reach sustainability targets in a context where the decisions to be taken are shared between several operators and affect users and citizens. In this paper, the methods to be adopted for the design of an integrated transport service system have been studied. The main aim of this paper concerns the specification of transport system models for estimating the effects of decision-makers’ actions on MaaS. The consolidated design methodologies of transport networks have been extended in the context of the MaaS. The paper reports a m...
References (68)
- United Nations. Mobilizing Sustainable Transport for Development. Analysis and Policy Recommendations from the United Nations Secretary-General's High-Level Advisory Group on Sustainable Transport. Available online: https: //sustainabledevelopment.un.org/index.php?page=view&type=400&nr=2375&menu=35 (accessed on 9 February 2022).
- United Nations. Sustainable Development Goals (SDGs). Available online: http://www.un.org/sustainabledevelopment/ sustainable-development-goals/ (accessed on 9 February 2022).
- Banister, D. The sustainable mobility paradigm. Transp. Policy 2008, 15, 73-80. [CrossRef]
- Shibayama, T.; Emberger, G. New mobility services: Taxonomy, innovation and the role of ICTs. Transp. Policy 2020, 98, 79-90.
- Pantelidis, T.P.; Chow, J.Y.; Rasulkhani, S. A many-to-many assignment game and stable outcome algorithm to evaluate collaborative mobility-as-a-service platforms. Transp. Res. Part B Methodol. 2020, 140, 79-100. [CrossRef]
- Kamargianni, M.; Li, W.; Matyas, M. A comprehensive review of "mobility as a service" systems. In Proceedings of the 95th Transportation Research Board Annual Meeting, Washington, DC, USA, 10-14 January 2016.
- Chilà, G.; Musolino, G.; Polimeni, A.; Rindone, C.; Russo, F.; Vitetta, A. Transport models and intelligent transportation system to support urban evacuation planning process. IET Intell. Transp. Syst. 2016, 10, 279-286.
- Nuzzolo, A.; Lam, W.H.K. (Eds.) Modelling Intelligent Multi-Modal Transit Systems, 1st ed.; CRC Press: Boca Raton, FL, USA, 2016.
- Nuzzolo, A.; Comi, A. Advanced public transport and intelligent transport systems: New modelling challenges. Transportmetrica A Transp. Sci. 2016, 12, 674-699. [CrossRef]
- Kamargianni, M.; Yfantis, L.; Muscat, J.; Azevedo, C.; Ben-Akiva, M. Incorporating the Mobility as a Service Concept Into Transport Modelling and Simulation Frameworks; MaaSLab Working Paper Series Paper No. 18-05; MaaSLab: London, UK, 2018.
- ERTICO. Mobility as a Service (MaaS) and Sustainable Urban Mobility Planning. ITS Europe (Editor). Available online: https: //www.eltis.org/sites/default/files/mobility_as_a_service_maas_and_sustainable_urban_mobility_planning.pdf (accessed on 9 February 2022).
- Hietanen, S. "Mobility as a service"-The new transport model? Eurotransport 2014, 12, 2-4.
- Jittrapirom, P.; Caiati, V.; Feneri, A.M.; Ebrahimigharehbaghi, S.; Alonso-González, M.J.; Narayan, J. Mobility as a service: A critical review of definitions, assessments of schemes, and key challenges. Urban Plan. 2017, 2, 13-25. [CrossRef]
- Holmberg, P.-E.; Collado, M.; Sarasini, S.; Williander, M. Mobility as a Service-MaaS. Describing the Framework (Final Report MaaS Framework);
- Viktoria Swedish ICT: Gothenburg, Sweden, 2016.
- Atkins. Journeys of the Future. Introducing Mobility as a Service. Available online: https://exploreconsulting.careers/uploads/ Atkins-Journeys-of-the-future_300315.pdf (accessed on 9 February 2022).
- Atasoy, B.; Ikeda, T.; Song, X.; Ben-Akiva, M.E. The concept and impact analysis of a flexible mobility on demand system. Transp. Res. Part C Emerg. Technol. 2015, 56, 373-392. [CrossRef]
- CIVITAS. Mobility-as-a-Service: A New Transport Model. Available online: https://civitas.eu/sites/default/files/civitas_ insight_18_mobility-as-a-service_a_new_transport_model.pdf (accessed on 9 February 2022).
- Smith, G.; Hensher, D.A. Towards a framework for mobility-as-a-service policies. Transp. Policy 2020, 89, 54-65. [CrossRef]
- Nemtanu, F.; Schlingensiepen, J.; Buretea, D.; Iordache, V. Mobility as a service in smart cities. In Responsible Entrepreneurship- Vision, Development and Ethics, Proceedings of the 9th International Conference for Entrepreneurship, Innovation and Regional Development, Bucharest, Romania, 23-24 June 2016; Zbuchea, A., Nikolaidis, D., Eds.; Comunicare.ro: Bucharest, Romania, 2016; pp. 425-435.
- Giesecke, R.; Surakka, T.; Hakonen, M. Conceptualising Mobility as a service. A user centric view on key issues of mobility services. In Proceedings of the 11th International Conference on Ecological Vehicles and Renewable Energies (EVER), Monte Carlo, Monaco, 6-8 April 2016.
- König, D.; Eckhardt, J.; Aapaoja, A.; Sochor, J.; Karlsson, M. Business and Operator Models for Mobility as a Service (MaaS) (Deliverable 3 to the MAASiFiE Project); Centre for Effective Dispute Resolution: Brussels, Belgium, 2016.
- Wang, X.; Yan, X.; Zhao, X.; Cao, Z. Identifying latent shared mobility preference segments in low-income communities: Ride-hailing, fixed-route bus, and mobility-on-demand transit. Travel Behav. Soc. 2021, 26, 134-142. [CrossRef]
- Song, Y.; Li, D.; Cao, Q.; Yang, M.; Ren, G. The whole day path planning problem incorporating mode chains modeling in the era of mobility as a service. Transp. Res. Part C Emerg. Technol. 2021, 132, 103360. [CrossRef]
- Ortuzar, J.; Willumsen, L.G. Modelling Transport, 3rd ed.; John and Wiley and Sons: Hoboken, NJ, USA, 2001.
- Cascetta, E. Transportation Systems Analysis. Models and Applications; Springer: New York, NY, USA, 2009.
- Azevedo, C.L.; Seshadri, R.; Gao, S.; Atasoy, B.; Akkinepally, A.P.; Christofa, E.; Ben-Akiva, M. Tripod: Sustainable travel incentives with prediction, optimization, and personalization. In Proceedings of the Transportation Research Board 97th Annual Meeting, Washington, DC, USA, 7-11 January 2018.
- Ho, C.Q.; Mulley, C.; Hensher, D.A. Public preferences for mobility as a service: Insights from stated preference surveys. Transp. Res. Part A 2020, 131, 70-90. [CrossRef]
- Billheimer, J.W.; Gray, P. Network design with fixed and variable cost elements. Transp. Sci. 1973, 7, 49-74. [CrossRef]
- Chen, M.; Alfa, A.S. A Network design algorithm using a stochastic incremental traffic assignment approach. Transp. Sci. 1991, 25, 215-224. [CrossRef]
- Zhou, Y.; Cao, C.; Feng, Z. Optimization of multimodal discrete network design problems based on super networks. Appl. Sci. 2021, 11, 10143. [CrossRef]
- Cantarella, G.E.; Pavone, G.; Vitetta, A. Heuristics for urban road network design: Lane layout and signal settings. Eur. J. Oper. Res. 2006, 175, 1682-1695. [CrossRef]
- Cantarella, G.E.; Vitetta, A. The multi-criteria road network design problem in an urban area. Transportation 2006, 33, 357-588.
- Webster, F.V. Traffic Signal Settings; Road Research Technical Paper No. 39; Road Research Laboratory: London, UK, 1958.
- Allsop, R.E. SIGSET: A computer program for calculating traffic capacity of signal controlled road junctions. Traffic Eng. Control 1971, 12, 58-60.
- Allsop, R.E. Some possibilities for using traffic control to influence trip destinations and route choice. In Proceedings of the 6th International Symposium on Transportation and Traffic Theory, Amsterdam, The Netherlands, 26-28 August 1974; pp. 345-374.
- Cantarella, G.E.; Improta, G.; Sforza, A. Road network signal setting: Equilibrium conditions. In Concise Encyclopaedia of Traffic and Transportation Systems; Papageorgiou, M., Ed.; Pergamon Press: Oxford, UK, 1991; pp. 366-371.
- Little, J.D.C. The synchronisation of traffic signals by mixed-integer-linear-programming. Oper. Res. 1966, 14, 568-594. [CrossRef]
- Robertson, D.I. TRANSYT method for area traffic control. Traffic Eng. Control 1969, 10, 276-281.
- Gartner, N.H. Area traffic control and network equilibrium. In Traffic Equilibrium Methods, Lecture Notes in Economics and Mathematical Systems; Florian, M., Ed.; Springer: Berlin/Heidelberg, Germany, 1976; Volume 118, pp. 274-297.
- Smith, M.J. The existence, uniqueness and stability of traffic equilibria. Transp. Res. B 1979, 13, 295-304. [CrossRef]
- Ceylan, H.; Bell, M.G.H. Traffic signal timing optimization based on genetic algorithm approach, including driver's routing. Transp. Res. B 2004, 38, 329-342. [CrossRef]
- Russo, F.; Vitetta, A. A topological method to choose optimal solutions after solving the multi-criteria urban road network design problem. Transportation 2006, 33, 347-370. [CrossRef]
- Russo, F. Transit frequencies design for enhancing the efficiency of public urban transportation systems: An optimization model and an algorithm. In Logistics Management and Environmental Aspects. Intelligent Transportation and Telemetric Systems. Marketing, Vehicle Finance and Leasing; International Symposium on Automotive Technology and Automation: Dusseldorf, Germany, 1998.
- Guihaireab, V.; Hao, J.-K. Transit network design and scheduling: A global review. Transp. Res. Part A Policy Pract. 2008, 42, 1251-1273. [CrossRef]
- Bourbonnais, P.-L.; Morency, C.; Trépanier, M.; Martel-Poliquin, É. Transit network design using a genetic algorithm with integrated road network and disaggregated O-D demand data. Transportation 2021, 48, 95-130. [CrossRef]
- Huang, D.; Liu, Z.; Fu, X.; Blythe, P.T. Multimodal transit network design in a hub-and-spoke network framework. Transp. A Transp. Sci. 2018, 14, 706-735. [CrossRef]
- Laporte, G. What you should know about the vehicle routing problem. Nav. Res. Logist. 2007, 54, 811-819. [CrossRef]
- Laporte, G. Fifty years of vehicle routing. Transp. Sci. 2009, 43, 408-416. [CrossRef]
- Gendreau, M.; Potvin, J.Y.; Bräysy, O.; Hasle, G.; Løkketamgen, A. Metaheuristics for the vehicle routing problem and its extensions: A categorized bibliography. In The Vehicle Routing Problem: Latest Advances and New Challenges (143-169);
- Golden, B., Raghavan, S., Wasil, E., Eds.; Springer: Berlin/Heidelberg, Germany, 2008.
- Fisher, M.L. Optimal solution of vehicle routing problems using minimum K-trees. Oper. Res. 1994, 42, 626-642. [CrossRef]
- Kallehauge, B.; Larsen, J.; Madsen, O.B.G. Lagrangian duality applied to the vehicle routing problem with time windows. Comput. Oper. Res. 2006, 33, 1464-1487. [CrossRef]
- Chabrier, A. Vehicle routing problem with elementary shortest path based column generation. Comput. Oper. Res. 2006, 33, 2972-2990. [CrossRef]
- Choi, E.; Tchab, D.W. A column generation approach to the heterogeneous fleet vehicle routing problem. Comput. Oper. Res. 2007, 34, 2080-2095. [CrossRef]
- Azi, N.; Gendreau, M.; Potvin, J.-Y. An exact algorithm for a single-vehicle routing problem with time windows and multiple routes. Eur. J. Oper. Res. 2007, 178, 755-766. [CrossRef]
- Baker, B.M.; Ayechew, M.A. A genetic algorithm for the vehicle routing problem. Comput. Oper. Res. 2003, 30, 787-800. [CrossRef]
- Berger, J.; Barkaoui, M. A parallel hybrid genetic algorithm for the vehicle routing problem with time windows. Comput. Oper. Res. 2004, 31, 2037-2053. [CrossRef]
- Gribkovskaia, I.; Laporte, G.; Shyshou, A. The single vehicle routing problem with deliveries and selective pickups. Comput. Oper. Res. 2008, 35, 2908-2924. [CrossRef]
- Brandão, J. A deterministic tabu search algorithm for the fleet size and mix vehicle routing problem. Eur. J. Oper. Res. 2009, 195, 716-728. [CrossRef]
- Osman, I.H. Metastrategy simulated annealing and tabu search algorithms for the vehicle routing problem. Ann. Oper. Res. 1993, 41, 421-451. [CrossRef]
- Tavakkoli-Moghaddam, R.; Safaei, N.; Gholipour, Y. A hybrid simulated annealing for capacitated vehicle routing problems with the independent route length. Appl. Math. Comput. 2006, 176, 445-454. [CrossRef]
- Lin, C.; Choy, K.; Ho, G.; Chung, S.; Lam, H. Survey of green vehicle routing problem: Past and future trends. Expert Syst. Appl. 2013, 41, 1118-1138. [CrossRef]
- Keskin, M.; Çatay, B. Partial recharge strategies for the electric vehicle routing problem with time windows. Transp. Res. Part C Emerg. Technol. 2016, 65, 111-127. [CrossRef]
- Hiermann, G.; Puchinger, J.; Ropke, S.; Hartl, R.F. The electric fleet size and mix vehicle routing problem with time windows and recharging stations. Eur. J. Oper. Res. 2016, 252, 995-1018. [CrossRef]
- Lin, J.; Zhou, W.; Wolfson, O. Electric vehicle routing problem. Transp. Res. Procedia 2016, 12, 508-521. [CrossRef]
- Musolino, G.; Rindone, C.; Polimeni, A.; Vitetta, A. Planning urban distribution center location with variable restocking demand scenarios: General methodology and testing in a medium-size town. Transp. Policy 2018, 80, 157-166. [CrossRef]
- Cascetta, E.; Cantarella, G.E. A day-to-day and within-day dynamic stochastic assignment model. Transportation 1991, 25, 277-291.
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