Towards Multi-Modal Advance Journey Planner in a Co-Modal Framework

International Journal of Computer Trends and Technology (IJCTT)          
© 2018 by IJCTT Journal
Volume-61 Number-2
Year of Publication : 2018
Authors : Dr.G.Anandharaj, K.Navaneethakrishnan


MLA Style: Dr.G.Anandharaj, K.Navaneethakrishnan "Towards Multi-Modal Advance Journey Planner in a Co-Modal Framework" International Journal of Computer Trends and Technology 61.2 (2018):87-92.

APA Style: Dr.G.Anandharaj, K.Navaneethakrishnan (2018). Towards Multi-Modal Advance Journey Planner in a Co-Modal Framework International Journal of Computer Trends and Technology, 61(2),87-92

Traveler information systems play a significant role in most travelers’ daily trips. These systems assist travelers in choosing the best routes to reach their destinations and possibly select suitable departure times and modes for their trips. we present an advanced traveler information system (ATIS) for public and private transportation, including vehicle sharing and pooling services. The ATIS uses an agent based architecture and multi-objective optimization to answer trip planning requests from multiple users in a co-modal setting, considering vehicle preferences and conflicting criteria. At each set of user’s requests, the transportation network is represented by a co-modal graph that allows decomposing the trip planning problem into smaller tasks: the shortest routes between the network nodes are determined and then combined to obtain possible itineraries. Using multi-objective optimization, the set of user vehicle-route combinations is determined according to the user’s preferences, and all possible route agents’ coalitions are ranked. The ATIS is tested for the real case study of the Lille metropolitan area (Nord Pas de Calais, France).

[1] M.A.Abdel-Aty and M. F. Abdalla, “Examination of multiple mode/route-choice paradigms under ATIS,” IEEE Trans. Intell. Transp.Syst., vol. 7, no. 3, pp. 332–348, Sep. 2006.
[2] J.L.Adler, G. Satapathy, V. Manikonda, B. Bowles, and V. J. Blue,“A multi-agent approach to cooperative traffic management and routeguidance,” Transp. Res. B, Methodol., vol. 39, no. 4, pp. 297–318,2005.
[3] T.A.Arentze, “Adaptive personalized travel information systems: A Bayesian method to learn users‟ personal preferences in multimodal transport networks,” IEEE Trans. Intell. Transp. Syst., vol. 14, no. 4,pp. 1957–1966, Dec. 2013.
[4] H.Ayed, C. Galvez-Fernandez, Z. Habbas, and D. Khadraoui, “Solving time-dependent multimodal transport problems using a transfer graph model,” Comput. Ind. Eng., vol. 61, no. 2, pp. 391–401, 2011.
[5] F.Bellifemine, G. Caire, T. Trucco, and G. Rimassa. JADE Programmer‟s Guide, accessed on Oct. 14, 2015. [Online]. Available:
[6] N.Borole, D. Rout, N. Goel, P. Vedagiri, and T. V. Mathew, “Multimodal public transit trip planner with real-time transit data,” Procedias-Soc. Behavioral Sci., vol. 104, pp. 775–784, Dec. 2013.
[7] B.Chen and H. H. Cheng, “A review of the applications of agent technology in traffic and transportation systems,” IEEE Trans. Intell. Transp. Syst., vol. 11, no. 2, pp. 485–497, Jun. 2010.
[8] C.Chen, D. Zhang, B. Guo, X. Ma, G. Pan, and Z. Wu, “TripPlanner: Personalized trip planning leveraging heterogeneous crowdsourced digital footprints,” IEEE Trans. Intell. Transp. Syst., vol. 16, no. 3, pp. 1259–1273, Jun. 2015.
[9] G.Chen, S. Wu, J. Zhou, and A. K. H. Tung, “Automatic itinerary planning for traveling services,” IEEE Trans. Knowl. Data Eng., vol. 26, no. 3, pp. 514–527, Mar. 2014.
[10] D.K.W.Chiu, O. K. F. Lee, H.-F. Leung, E. W. K. Au, and M. C. W. Wong, “A multi-modal agent based mobile route advisory system for public transport network,” in Proc. 38th Annu. Hawaii Int. Conf. Syst. Sci., Jan. 2005, p. 92b.
[11] M.Dotoli, N. Epicoco, M. Falagario, and G. Cavone, “A timed Petri nets model for performance evaluation of intermodal freight transport terminals,” IEEE Trans. Autom. Sci. Eng., vol. 13, no. 2, pp. 842–857, Apr. 2016.
[12] M.Dotoli, S. Hammadi, K. Jeribi, C. Russo, and H. Zgaya, “A multi-agent decision support system for optimization of co-modal transportation route planning services,” in Proc. IEEE CDC, Florence, Italy, Dec. 2013, pp. 911–916.
[13] H.M.Foo, H. W. Leong, Y. Lao, and H. C. Lau, “A multi-criteria, multi-modal passenger route advisory system,” in Proc. IES-CTR, 1999, pp. 1–16.
[14] T.Genin and S. Aknine, “Coalition formation strategies for selfinterested agents in task oriented domains,” in Proc. IEEE/WIC/ACM Int. Conf. Web Intell. Intell. Agent Technol., Aug. 2010, pp. 205–212.
[15] Google Developers. Google Maps, accessed on Oct. 14, 2015. [Online]. Available:
[16] G.Götzenbrucker and M. Köhl, “Advanced traveller information systems for intelligent future mobility: The case of „Anachb‟ in Vienna,” IET Intell. Transp. Syst., vol. 6, no. 4, pp. 494–501, Dec. 2012.
[17] M.Houda, M. Khemaja, K. Oliveira, and M. Abed, “A public transportation ontology to support user travel planning,” in Proc. IEEE 4th Int. Conf. Res. Challenges Inf. Sci., Nice, France, May 2010, pp. 127–136.
[18] E.Hyytiã, A. Penttinen, and R. Sulonen, “Non-myopic vehicle and route selection in dynamic DARP with travel time and workload objectives,” Comput. Oper. Res., vol. 39, no. 12, pp. 3021–3030, 2012.
[19] J.Jariyasunant, D. B. Work, B. Kerkez, R. Sengupta, S. Glaser, and A. Bayen, “Mobile transit trip planning with real time data,” in Transp. Res. Board Annu. Meeting, Washington, DC, USA, 2010, pp. 1–17.
[20] K.Jeribi, H. Mejiri, H. Zgaya, and S. Hammadi, “Vehicle sharing services optimization based on multi-agent approach,” in Proc. 18th IFAC World Congr., Jan. 2011, pp. 13040–13045.

Advanced traveller information system, trip planning, public transport, private transport, co-modal transport, multi-agent systems, directed graphs, optimization