Recommender systems for multimodal transportation systems in smart cities
| dc.contributor.author | Madadi Mounia | |
| dc.date.accessioned | 2025-01-21T20:41:53Z | |
| dc.date.available | 2025-01-21T20:41:53Z | |
| dc.date.issued | 2025-01-21 | |
| dc.description.abstract | Transportation recommendation systems have been trending for quite some time due to their continu ous potential for improvement. Among the most interesting advancements are multimodal transportation recommendation systems, which provide suggestions for traveling from one location to another using a combination of available transportation modes. In this thesis, we present a multimodal transportation recommendation system that recommends trajectories to users based on their personal preferences. Our system consists of two main phases. The first phase involves trajectory generation, where we search for optimal trajectory combinations between the starting point and the destination using Particle Swarm Op timization, followed by post-processing on the trajectories. Once the trajectories are generated, we rank them using the RankNet model trained on previously selected user trajectories, employing a content-based approach.After testing our system, we observed that the generated trajectories were quite feasible and the recommendations were highly accurate. | |
| dc.identifier.uri | https://dspace.estin.dz/handle/123456789/25 | |
| dc.language.iso | en | |
| dc.publisher | Tassadit | |
| dc.title | Recommender systems for multimodal transportation systems in smart cities | |
| dc.type | Thesis |
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