Abstract
Carpooling is a mobility concept that appears to be the answer when it comes to challenges in urban mobility derived by population growth. In carpooling, the same amount of people move with fewer vehicles leading to reduced traffic congestion and consequently to less CO2 emissions, fuel consumption and drivers frustration. However, there has always been scepticism around carpooling due to the inherent mistrust between drivers and passengers. In recent years, some reputation systems have been proposed to reduce the impact of mistrust on carpooling applications. Among them, the work of Salamanis et al. (Salamanis, 2018), in which a reputation assessment mechanism based on clustering users travel preferences, was introduced. In this paper, we provide an extended version of the previous mechanism and we thoroughly evaluate its robustness in relation with different types of malicious attacks and clustering algorithms. In addition, we compare our mechanism with a benchmarking reputation system that utilizes the simple arithmetic mean to calculate reputation values based on users ratings. The evaluation results indicate that the extended reputation assessment mechanism exhibits more robust behavior compared to the benchmarking system in all types of attacks when using the hierarchical clustering algorithm.
Original document
The different versions of the original document can be found in:
- https://doi.org/10.5220/0007796602320241 under the license cc-by-nc-nd
Document information
Published on 01/01/2019
Volume 2019, 2019
DOI: 10.5220/0007796602320241
Licence: Other
Share this document
Keywords
claim authorship
Are you one of the authors of this document?