Graph-Based Approach for Personalized Travel Recommendations

IF 1.1 Q3 TRANSPORTATION SCIENCE & TECHNOLOGY Transport and Telecommunication Journal Pub Date : 2023-11-01 DOI:10.2478/ttj-2023-0033
Francesco Maria Turno, Irina Jackiva
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Abstract

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.
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基于图的个性化旅行推荐方法
摘要 在不断发展的城市交通系统领域,将技术与以用户为中心的战略相结合至关重要。这项研究基于 "移动即服务"(Mobility-as-a-Service)这一基本概念,这是一种以用户为中心的智能移动管理分配系统,旨在优先考虑人的需求而非单纯的技术基础设施。本研究深入探讨了移动传感技术所提供的大量数据,强调了其对人类移动模式所提供的前所未有的理解,从而促进了个性化路线推荐。文献将研究领域分为三个相互关联的类别:兴趣点(POI)推荐、旅行规划和轨迹建模。本研究大踏步地引入了对胡人移动数据的全面理解,并提出了一个新颖的框架,旨在为旅行规划用户提供个性化推荐。该创新框架采用基于苏塞克斯-华为运动数据集的图方法,利用贝尔曼-福特算法的改编。这种修改考虑了感知疲劳、前往特定地点的频率以及与 POIs 的接近程度等因素,并根据用户过去的经验和偏好推荐路径。
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来源期刊
Transport and Telecommunication Journal
Transport and Telecommunication Journal TRANSPORTATION SCIENCE & TECHNOLOGY-
CiteScore
3.00
自引率
0.00%
发文量
21
审稿时长
35 weeks
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