Abstract
Several researches in the scientific, industrial and commercial fields are supporting the reduction of traditional combustion cars’ use. The main purpose is to increase the quality of life into the metropolitan cities through the reduction of CO2 emissions and global warming. Accordingly, one of the most successful models is the carpooling system. Currently, people are investigating the sustainability and durability of carpooling business model from both economic and organizational point of view. The present research aims to develop a Multicriteria Decision Support System (MDSS) in order to offer a carpooling system’s platform based on different criteria. The MDSS is developed from driver’s point of view and settled on two levels of optimization. Firstly, a genetic algorithm is proposed to solve an orienteering problem that optimizes the total revenue of driver based on the car’s capability and the time schedule. Secondly, the best optimization solutions are compared with multicriteria analysis respect to other criteria not included in the first optimization. The outcome of MDSS is a schedule for drivers, which gives maximum satisfaction in terms of profitability, punctuality and comfort of the travel.
Similar content being viewed by others
References
Askin, R. G., Baffo, I., & Xia, M. (2013). Research Multi-commodity warehouse location and distribution planning with inventory consideration. International Journal of Production. doi:10.1080/00207543.2013.787171.
Awasthi, A., & Chauhan, S. S. (2011). Using AHP and Dempster–Shafer theory for evaluating sustainable transport solutions. Environmental Modelling and Software, 26(6), 787–796.
Awasthi, A., & Omrani, H. (2009). A hybrid approach based on AHP and belief theory for evaluating sustainable transportation solutions. International Journal of Global Environmental Issues, 9(3), 212–226.
Azam, M., Othman, J., Begum, R. A., Abdullah, S. M. S., & Nor, N. G. M. (2016). Energy consumption and emission projection for the road transport sector in Malaysia: An application of the LEAP model. Environment, Development and Sustainability, 18(4), 1027–1047.
Baldacci, R., Maniezzo, V., & Mingozzi, A. (2004). An exact method for the car pooling problem based on Lagrangean column generation. Operations Research, 52(2004), 422–439.
Baptista, P., Melo, S., & Rolim, C. (2014). Energy, environmental and mobility impacts of car sharing systems. Empirical results from Lisbon, Portugal. Procedia Social and Behavioral Sciences, 111, 28–37.
Bellemans, T., De Schutter, B., & De Moor, B. (2003). Anticipative model predictive control for ramp metering in freeway networks. In Proceedings of the 2003 american control conference, Denver, Colorado (pp. 4077–4082).
Bellemans, T., Bothe, S., Cho, S., Giannotti, F., Janssens, D., Knapen, L., et al. (2012). An agent-based model to evaluate carpooling at large manufacturing plants. Procedia Computer Science, 10, 1221–1227.
Berlingerio, M., Ghaddar, B., Guidotti, R., Pascale, A., & Sassi, A. (2017). The GRAAL of carpooling: GReen And sociAL optimization from crowd-sourced data. Transportation Research Part C Emerging Technologies, 80, 20–36.
Berrittella, M., Certa, A., Enea, M., & Zito, P. (2007). An analytic hierarchy process for the evaluation of transport policies to reduce climate change impacts. Fondazione Enrico Mattei. http://www.feem.it/Feem/Pub/Publications/WPapers/default.htm.
Boukhater, C. M., Dakroub, O., Lahoud, F., Awad, M., & Artail, H. (2014). An intelligent and fair GA carpooling scheduler as a social solution for greener transportation. In Proceedings of the mediterranean electrotechnical conference—MELECON 6820528 (pp. 182–186).
Boyacı, B., Zografos, K. G., & Geroliminis, N. (2015). An optimization framework for the development of efficient one-way car sharing systems. European Journal of Operational Research, 240(3), 718–733.
Bruglieri, M., Ciccarelli, D., Colorni, A., & Lué, A. (2017). Poliunipool: a carpooling system for universities. Procedia Social and Behavioral Sciences, 2011(20), 558–567.
Calvo, R. W., De Luigi, F., Haastrup, P., & Maniezzo, V. (2004). A distributed geographic information system for the daily car pooling problem. Computers & Operations Research, 31(13), 2263–2278.
Chan, N. D., & Shaheen, S. A. (2012). Ridesharing in north america: Past, present, and future. Transport Reviews, 32(1), 93–112.
Chen, N., Xu, Z., & Xia, M. (2015). The ELECTRE I multi-criteria decision-making method based on hesitant fuzzy sets. International Journal of Information Technology and Decision Making, 3, 621–657.
Cho, S., Knapen, L., Bellemans, T., Janssens, D., & Wets, G. (2012). A conceptual design of an agent-based interaction model for the carpooling application. Procedia Computer Science, 10, 801–807.
Clune, A., Smith, M., & Xiang, Y. (1999). A theoretical basis for implementation of a quantitative decision support system–using bilevel optimisation. In 14th International symposium on transportation and traffic theory.
De Falco, I., Scafuri, U., & Tarantino, E. (2015). A multiobjective evolutionary algorithm for personalized tours in street networks. In A. Mora & G. Squillero (Eds.), Applications of evolutionary computation. EvoApplications 2015. Lecture Notes in Computer Science (Vol. 9028). Cham: Springer.
De Felice, F. (2012). Research and applications of AHP/ANP and MCDA for decision making in manufacturing. International Journal of Production Research, 50(17), 4735–4737.
De Felice, F., & Petrillo, A. (2013). Absolute measurement with analytic hierarchy process: A case study for Italian racecourse. International Journal of Applied Decision Sciences, 6(3), 209–227. doi:10.1504/IJADS.2013.054931.
De Felice, F., & Petrillo, A. (2014). Proposal of a structured methodology for the measure of intangible criteria and for decision making. International Journal of Simulation and Process Modelling, 9(3), 157–166.
Delhomme, P., & Gheorghiu, A. (2016). Comparing French carpoolers and non-carpoolers: Which factors contribute the most to carpooling? Transportation Research Part D, 42(2016), 1–15.
Di Martino, S., Galiero, R., Giorio, C., Ferrucci, F., & Sarro, F. (2011). A matching-algorithm based on the cloud and positioning systems to improve carpooling in DMS (pp. 90–95). Knowledge Systems Institute: Skokie.
Dimitrakopoulos, G., Demestichas, P., & Koutra, V. (2012). Intelligent management functionality for improving transportation efficiency by means of the car pooling concept. IEEE Transactions on Intelligent Transportation Systems, 13(2), 424–436.
Ebrahimnejad, S., Hashemi, H., Mousavi, S. M., & Vahdani, B. (2015). A new interval-valued intuitionistic fuzzy model to group decision making for the selection of outsourcing providers. Economic Computation and Economic Cybernetics Studies and Research, 49(2), 269–290.
Faria, R., Moura, P., Delgado, J., & de Almeida, A. T. (2012). A sustainability assessment of electric vehicles as a personal mobility system. Energy Conversion and Management, 61 (C), 19–30.
Figueira, J., Mousseau, V., & Roy, B. (2005). ELECTRE methods. In J. Figueira, S. Greco & M. Ehrogott (Eds.), Multiple criteria decision analysis: State of the art surveys. Volume 78 of the series International Series in Operations Research & Management Science (pp. 133–153). New York: Springer.
Filcek, G., & Gąsior, D. (2014). Common route planning for carpoolers—Model and exact algorithm. Advances in Intelligent Systems and Computing, 240, 543–551.
Filcek, G., & Żak, J. (2017). The multiple criteria optimization problem of joint matching carpoolers and common route planning: Modeling and the concept of solution procedure. Advances in Intelligent Systems and Computing, 523, 225–236.
Galland, S., Knapen, L., Gaud, N., Janssens, D., Lamotte, O., Koukam, A., et al. (2014). Multi-agent simulation of individual mobility behavior in carpooling. Transportation Research Part C Emerging Technologies, 45, 83–98.
Goldberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning. Boston, MA: Addison Wesley.
Govindan, K., & Brandt, Jepsen M. (2016). ELECTRE: A comprehensive literature review on methodologies and applications. European Journal of Operational Research, 250(1), 1–29.
Holland, J. H. (1975). Adaptation in natural and artificial systems. Ann Arbor, MI: University of Michigan Press.
Kelly, K. L. (2007). Casual carpooling-enhanced. Journal of Public Transportation, 10(4), 6.
Li, W., Li, Y., Fan, J., & Deng, H. (2017). Siting of carsharing stations based on spatial multi-criteria evaluation: A case study of Shanghai EVCARD. Sustainability (Switzerland), 9(1), 152.
Luè, A., Colorni, A., & Nocerino, R. (2014). Cognitive mapping and multi-criteria analysis for decision aiding: An application to the design of an electric vehicle sharing service. In Proceedings of transport research arena 14–17 April 2014, Paris.
Mallus, M., Colistra, G., Atzori, L., Murroni, M., & Pilloni, V. (2017) A persuasive real-time carpooling service in a smart city: A case-study to measure the advantages in urban area. In Proceedings of the 2017 20th conference on innovations in clouds, internet and networks, ICIN 2017 7899428 (pp. 300–307).
Maniezzo, V., Carbonaro, A., & Hildmann, H. (2004) An ANTS heuristic for the long—term car pooling problem. In G. C. Onwubolu & B. V. Babu (Eds.), New optimization techniques in engineering. Volume 141 of the series Studies in Fuzziness and Soft Computing (pp. 411–430). Berlin: Springer.
Manzini, R., & Pareschi, A. (2012). A decision-support system for the car pooling problem. Journal of Transportation Technologies, 2012(2), 85–101.
Nosal, K., & Solecka, K. (2014). Application of AHP method for multi-criteria evaluation of variants of the integration of urban public transport. Transportation Research Procedia, 3, 269–278.
Pimentel, F. L. (2016). Improving carpool flexibility without compromising trust or guaranteed rides. European Transport Trasporti Europei, 62, 1–30.
Ramanathan, R. (2001). A note on the use of the analytic hierarchy process for environmental impact assessment. Journal of Environmental Management, 63(1), 27–35.
Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15, 234–281.
Saaty, T. L. (1990). Multicriteria decision making: The analytic hierarchy process. Pittsburgh, PA: RWS Publications.
Sueyoshi, T., Shang, J., & Chiang, W.-C. (2009). A decision support framework for internal audit prioritization in a rental car company: A combined use between DEA and AHP. European Journal of Operational Research, 199(2009), 219–231.
The state of European Car sharing. (2010). The state of European Car sharing. Available at: http://www.motiva.fi/files/4138/WP2_Final_Report.pdf.
Uesugi, K., Mukai, N., & Watanabe, T. (2007). Optimization of vehicle assignment for car sharing system. In B. Apolloni, R. J. Howlett & L. Jain (Eds.), Knowledge-based intelligent information and engineering systems. KES 2007. Lecture Notes in Computer Science (Vol. 4693). Berlin: Springer.
Vansteenwegen, P., Souffriaua, W., & Van Oudheusdena, D. (2011). The orienteering problem: A survey. European Journal of Operational Research, 209(1), 1–10.
Yan, S., Chen, C. Y., & Chang, S. C. (2014). A car pooling model and solution method with stochastic vehicle travel times. IEEE Transactions on Intelligent Transportation Systems, 15(1), 47–61.
Yang, Z.-J., Wang, Z., Wang, Y., Min, M.-H., & Li, Z.-S. (2016). Two-stage estimation of distribution algorithm to solve multi-vehicle carpooling problem. Journal of Transportation Systems Engineering and Information Technology, 16(2), 164–169.
Zhang, F., Yang, Z. J., Wang, Y., & Kuang, F. (2016). An augmented estimation of distribution algorithm for multi-carpooling problem with time window. In IEEE vehicular technology conference 2016 July.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Petrillo, A., Carotenuto, P., Baffo, I. et al. A web-based multiple criteria decision support system for evaluation analysis of carpooling. Environ Dev Sustain 20, 2321–2341 (2018). https://doi.org/10.1007/s10668-017-9991-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10668-017-9991-z