<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In the evolving domain of urban mobility systems, the integration of technology with user-centric strategies is pivotal. This research stands on the foundational concept of Mobility-as-a-Service, a user-centric intelligent mobility management distribution system that seeks to prioritize human needs over mere technological infrastructure. The study delves deep into the wealth of data available through mobile sensing technologies, highlighting the unprecedented understanding it offers into human mobility patterns, thus facilitating personalized route recommendations.</p>
<p>The literature categorizes the study area into three interlinked categories: point-of-interest (POI) recommendation, travel planning, and trajectory modelling. In a significant stride, this research introduces a comprehensive understanding of hu-man mobility data and proposes a novel framework designed to tender personalized recommendations to travel planner users. The innovative framework employs a graph-based approach rooted in the Sussex-Huawei Locomotion dataset, leveraging an adaptation of Bellman-Ford’s algorithm. This modification considers factors such as perceived fatigue, frequency of trips to specific locations, and proximity to POIs, promising a path routed in past user experiences and preferences.</p>
</abstract>

In the evolving domain of urban mobility systems, the integration of technology with user-centric strategies is pivotal. This research stands on the foundational concept of Mobility-as-a-Service, a user-centric intelligent mobility management distribution system that seeks to prioritize human needs over mere technological infrastructure. The study delves deep into the wealth of data available through mobile sensing technologies, highlighting the unprecedented understanding it offers into human mobility patterns, thus facilitating personalized route recommendations.
The literature categorizes the study area into three interlinked categories: point-of-interest (POI) recommendation, travel planning, and trajectory modelling. In a significant stride, this research introduces a comprehensive understanding of hu-man mobility data and proposes a novel framework designed to tender personalized recommendations to travel planner users. The innovative framework employs a graph-based approach rooted in the Sussex-Huawei Locomotion dataset, leveraging an adaptation of Bellman-Ford’s algorithm. This modification considers factors such as perceived fatigue, frequency of trips to specific locations, and proximity to POIs, promising a path routed in past user experiences and preferences.

Graph-Based Approach for Personalized Travel Recommendations

Transport and Telecommunication Journal

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Graph-Based Approach for Personalized Travel Recommendations"}

In the evolving domain of urban mobility systems, the integration of technology with user-centric strategies is pivotal. This research stands on the...

Graph-Based Approach for Personalized Travel Recommendations

Online veröffentlicht: 17. Nov. 2023

Seitenbereich: 423 - 433

DOI: https://doi.org/10.2478/ttj-2023-0033

Schlüsselwörter
Mobility-as-a-Service, Human Mobility Patterns, Route Recommendation, Bellman-Ford’s algorithm, Shortest Path

© 2023 Francesco Maria Turno et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Graph-Based Approach for Personalized Travel Recommendations

Online veröffentlicht: 17. Nov. 2023

Seitenbereich: 423 - 433

DOI: https://doi.org/10.2478/ttj-2023-0033

SchlüsselwörterMobility-as-a-Service, Human Mobility Patterns, Route Recommendation, Bellman-Ford’s algorithm, Shortest Path

© 2023 Francesco Maria Turno et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Mobility-as-a-Service, Human Mobility Patterns, Route Recommendation, Bellman-Ford’s algorithm, Shortest Path