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Route Planning of Road Trips via Multi-attribute Group Decision-making and Role-based Collaboration

Hua Ma et al.

International Journal of Information Technology and Decision Making2026https://doi.org/10.1142/s0219622026500550article
ABDC C
Weight
0.50

What the paper says

Recently, road trips have become increasingly popular. However, route planning for road trips has remained an open field of research. Existing approaches ignore the negative effects of conflicting preferences among members of tourist group, making the plan difficult to satisfy all members. This paper proposes a novel approach to route planning of road trips, namely RPG-RBC. RPG-RBC innovatively utilizes the non-compensatory group decision-making algorithm ELECTRE-III to model the preferences of tourist group. It first calculates the individual preferences of each tourist for point-of-interests and then aggregates them to obtain the overall preferences. Essentially the road trip route planning can be conceived as an RBC problem, and the environments-classes, agents, roles, groups, and objects (ECARGO) model is applied to formalize this problem. The relevant constraints are identified and the CPLEX optimization package is used to solve the problem. Finally, the effectiveness of RPG-RBC is verified through a case study and simulation experiments.

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https://doi.org/https://doi.org/10.1142/s0219622026500550

Or copy a formatted citation

@article{hua2026,
  title        = {{Route Planning of Road Trips via Multi-attribute Group Decision-making and Role-based Collaboration}},
  author       = {Hua Ma et al.},
  journal      = {International Journal of Information Technology and Decision Making},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1142/s0219622026500550},
}

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Route Planning of Road Trips via Multi-attribute Group Decision-making and Role-based Collaboration

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.