An analytical study on Directions and Profile Matching for Carpool System

Jyoti Ranjan Mohanty, Dr. Jitendra Sheetlani, Dr. Rasmi Ranjan Patra


These days, numbers of citizens are exploring different approaches to make  better effective use of existing resources. In country like India, there are nearly about one to two people for every car and the number of person in an average car is nearly five. As a result of this, it is clearly understandable that the car driving has great potential for competence in this regard, on the basis of sharing of personal vehicles. Plenty of time and energy is saved by Carpooling, from a public and environmental point of view, but most importantly it reduces air pollution. Carpooling can share the location of each car between people with similar path or route. Carpooling evolved as an economical and anxiety-free system for distribution. In this analytical study paper, presentation of information is made about an overview of computer-based platforms that improve sustainability. In particular, this discussion will help car users choose the basis of transportation solutions for their natural footprint based on their needs, preferences and location. The success rate of this automotive system is based on the relative routes that directly connect traditional points and destinations to the most involved passengers and increase the integer of association from single to double, requiring change in two successive passengers. This analytical study paper reviews from the artistic nature of the modern carpooling method. It identifies the most important challenges in adopting modern carpooling system and the anticipated results to the same identified problems.


Carpooling, Genetic algorithm, CLACSOON, AICS, HTTP, Mobile Client

Full Text:



. Agatz, N., Erera, A., Savelsbergh, M., & Wang, X. (2012).Optimization for dynamic ride-sharing: A review. European Journal of Operational Research, 223, 295–303.doi:10.1016/j.ejor.2012.05.028.

. Agatz, N., Erera, A. L., Savelsbergh, M. W. P., & Wang, X.(2011). Dynamic ride-sharing: A simulation study in metro Atlanta. Procedia-Social and Behavioral Sciences,17, 532–550. doi:10.1016/j.sbspro.2011.04.530.

. Bruglieri, M., Ciccarelli, D., Colorni, A., &Lu_e, A. (2011).PoliUniPool: A carpooling system for universities. Procedia-Social and Behavioral Sciences, 20, 558–567.doi:10.1016/j.sbspro.2011.08.062Calvo, R., Wolfler, F., de Luigi,

. Chou, S.-K., Jiau, M.-K., & Huang, S.-C. (2016). Stochastic set-based particle swarm optimization based on local exploration for solving the carpool service problem. IEEE Transactions on Cybernetics, 46, 1771–1783. doi:10.1109/TCYB.2016.2522471

. Guo, Y., Goncalves, G., & Hsu, T. (2011). A guided genetic algorithm for solving the long-term car-pooling problem. Paper presented at the computational intelligence in production and logistics systems (CIPLS), 2011. IEEE Workshop, Paris, France.

. Guo, Y., Goncalves, G., & Hsu, T. (2012). A clustering ant colony algorithm for the long-term car-pooling problem. International Journal of Swarm Intelligence Research(IJSIR), 3, 39–62. doi:10.4018/jsir.2012040103.

. Guo, Y., Goncalves, G., & Hsu, T. (2013a). RETRACTEDARTICLE: A multi-agent based self-adaptive genetic algorithm for the long-term car-pooling problem. Journal of Mathematical Modeling and Algorithms in Operations Research, 12(1)–45–66. doi:10.1007/s10852-012-9175-7

. Guo, Y., Goncalves, G., & Hsu, T. (2013b). A multi destination daily carpooling problem and an ant colony based resolution method. RAIRO – Operations Research,47, 399–428. doi:10.1051/ro/2013049

. Najmi, A., Rey, D., &Rashidi, T. H. (2017). Novel dynamic formulations for real-time ride-sharing systems. Transportation Research Part E: Logistics and Transportation Review, 108, 122–140.

. Pelzer, D., Xiao, J., Zehe, D., Lees, M. H., Knoll, A. C., &Aydt, H. (2015). A partition-based match making algorithm for dynamic ridesharing. IEEE Transactions on Intelligent Transportation Systems, 16, 2587–2598.

. Yan, S., & Chen, C.-Y. (2011). An optimization model and a solution algorithm for the many-to-many car pooling problem. Annals of Operations Research, 191, 37–71.doi:10.1007/s10479-011-0948-6

. 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, 47–61. doi:10.1109/TITS.2013.2272085



  • There are currently no refbacks.

Copyright (c) 2020 International Journal of Advanced Research in Computer Science