Travel route recommendation with a trajectory learning model
Abstract This study addresses a critical issue in location-based services: travel route recommendation. It leverages historical trajectory data to predict the actual route on a road network from a starting point to a destination, given a specific departure time. However, capturing the latent pattern...
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| Format: | Article |
| Language: | English |
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Springer
2024-11-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-024-01611-z |
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| _version_ | 1832571151900999680 |
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| author | Xiangping Wu Zheng Zhang Wangjun Wan |
| author_facet | Xiangping Wu Zheng Zhang Wangjun Wan |
| author_sort | Xiangping Wu |
| collection | DOAJ |
| description | Abstract This study addresses a critical issue in location-based services: travel route recommendation. It leverages historical trajectory data to predict the actual route on a road network from a starting point to a destination, given a specific departure time. However, capturing the latent patterns in complex trajectory data for accurate route planning presents a significant challenge. Existing route recommendation methods commonly face two major problems: first, inadequate integration of multi-source data, which fails to fully consider the potential factors affecting route choice; and second, limited capability to capture road network characteristics, which restricts the effective application of node features and negatively impacts recommendation accuracy. To address these issues, this research introduces a Trajectory Learning Model for Route Recommendation (TLMR) based on deep learning techniques. TLMR enhances the understanding of user route choice behavior in complex environments by integrating multi-source data. Moreover, by incorporating road network features, TLMR more effectively captures and utilizes the structural and dynamic information of the road network. Specifically, TLMR first employs a Position-aware Graph Neural Network to learn features of intersections from the road network, incorporating context features like weather and traffic conditions. Then, it integrates this information through neural networks to predict the next intersection. Finally, a beam search algorithm is applied to generate and recommend multiple candidate routes. Extensive experiments on four large real-world datasets demonstrate that TLMR outperforms existing methods in four key performance metrics. These results prove the effectiveness and superiority of TLMR in route recommendation. |
| format | Article |
| id | doaj-art-28b3a0921e754212adc6a4a70564b209 |
| institution | Kabale University |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-28b3a0921e754212adc6a4a70564b2092025-02-02T12:49:34ZengSpringerComplex & Intelligent Systems2199-45362198-60532024-11-0111111310.1007/s40747-024-01611-zTravel route recommendation with a trajectory learning modelXiangping Wu0Zheng Zhang1Wangjun Wan2College of Information Engineering, China Jiliang UniversityCollege of Information Engineering, China Jiliang UniversityTechnology Center of Hangzhou Customs DistrictAbstract This study addresses a critical issue in location-based services: travel route recommendation. It leverages historical trajectory data to predict the actual route on a road network from a starting point to a destination, given a specific departure time. However, capturing the latent patterns in complex trajectory data for accurate route planning presents a significant challenge. Existing route recommendation methods commonly face two major problems: first, inadequate integration of multi-source data, which fails to fully consider the potential factors affecting route choice; and second, limited capability to capture road network characteristics, which restricts the effective application of node features and negatively impacts recommendation accuracy. To address these issues, this research introduces a Trajectory Learning Model for Route Recommendation (TLMR) based on deep learning techniques. TLMR enhances the understanding of user route choice behavior in complex environments by integrating multi-source data. Moreover, by incorporating road network features, TLMR more effectively captures and utilizes the structural and dynamic information of the road network. Specifically, TLMR first employs a Position-aware Graph Neural Network to learn features of intersections from the road network, incorporating context features like weather and traffic conditions. Then, it integrates this information through neural networks to predict the next intersection. Finally, a beam search algorithm is applied to generate and recommend multiple candidate routes. Extensive experiments on four large real-world datasets demonstrate that TLMR outperforms existing methods in four key performance metrics. These results prove the effectiveness and superiority of TLMR in route recommendation.https://doi.org/10.1007/s40747-024-01611-zRoute recommendationTrajectory data miningGraph neural networkDeep learning |
| spellingShingle | Xiangping Wu Zheng Zhang Wangjun Wan Travel route recommendation with a trajectory learning model Complex & Intelligent Systems Route recommendation Trajectory data mining Graph neural network Deep learning |
| title | Travel route recommendation with a trajectory learning model |
| title_full | Travel route recommendation with a trajectory learning model |
| title_fullStr | Travel route recommendation with a trajectory learning model |
| title_full_unstemmed | Travel route recommendation with a trajectory learning model |
| title_short | Travel route recommendation with a trajectory learning model |
| title_sort | travel route recommendation with a trajectory learning model |
| topic | Route recommendation Trajectory data mining Graph neural network Deep learning |
| url | https://doi.org/10.1007/s40747-024-01611-z |
| work_keys_str_mv | AT xiangpingwu travelrouterecommendationwithatrajectorylearningmodel AT zhengzhang travelrouterecommendationwithatrajectorylearningmodel AT wangjunwan travelrouterecommendationwithatrajectorylearningmodel |