Pick-Up Point Recommendation Using Users’ Historical Ride-Hailing Orders

Zhang, Lingyu; He, Zhiyuan; Wang, Xiao; Zhang, Ying; Ji, Liang; Wu, Guobin; Z, Yu; Zhang, Penghui; Ji, Minghao; Xu, Ping; Wang, Yunhai

doi:10.1007/978-3-031-19214-2_33

Cited by 3 publications

(2 citation statements)

References 20 publications

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“…There have been several studies interested in discussing the applications for the clustering analysis method in transportation systems. Zhang et al [19], used DBSCAN to cluster PUDO data from a ride-hailing service in China. A pick-up points recommendation model (PPRM) is introduced, utilizing DBSCAN to cluster historical orders.…”

Section: Shen Et Almentioning

confidence: 99%

Hotspot Identification Through Pick-Up and Drop-Off Analysis of Ride-Hailing Transport Service

Saputra,

Sihabudin

2023

IJACSA

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It is important to extract hotspots in urban traffic networks to improve driver route efficiency. This research aims to identify hotspot pick-up and drop-off (PUDO) areas in ridehailing transportation services using a clustering approach. However, there are challenges in applying clustering algorithms to trajectory data in the coordinates of the Global Positioning System (GPS). So this research proposes modifications to the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm by considering the radius from the center of the cluster to determine the presence of amenities around the cluster. We used a dataset containing 55,988 trip trajectories of Grab drivers over a two-week period in Jakarta. A preliminary statistical analysis was carried out to understand the distribution of trips. Next, we identify the PUDO point of each trip for use in the clustering analysis. The research explores the various parameters and settings of the clustering method and their impact on the results. The study found that the results obtained from the clustering method are sensitive to parameter selection, including epsilon radius and minimum number of points needed to form a cluster. The optimal cluster with the best parameters (eps: 0.25, minpts: 100) in the pick-up (PU) location analysis produced 17 clusters with the silhouette coefficient of 0.752, while in the drop-off (DO) location there are 18 clusters with a silhouette coefficient of 0.694. Overall, the research highlights the potential of the clustering analysis method for ride-hailing transportation.

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Section: Shen Et Almentioning

confidence: 99%

Hotspot Identification Through Pick-Up and Drop-Off Analysis of Ride-Hailing Transport Service

Saputra,

Sihabudin

2023

IJACSA

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“…In recent years, with the rapid development of artificial intelligence technologies, numerous centralized deep learning models [2][3][4] have been proposed for recommending passenger pickup areas for ride-hailing services on urban roads, achieving promising results. These models collect and upload GPS data of drivers' passenger pickups, along with data on drivers' driving habits, to centralized servers for storage and analysis to uncover dependencies within the data [5].…”

Section: Introductionmentioning

confidence: 99%

Ride-Hailing Pick-Up Area Recommendation in a Vehicle-Cloud Collaborative Environment: A Feature-Aware Personalized Clustering Federated Learning Approach

Zhou,

Liao,

Zhao

et al. 2024

Preprint

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Ride-hailing pick-up area recommendations based on GPS data can significantly enhance driver efficiency, improve passenger experience, and optimize urban traffic management. However, direct access to sensitive driver and passenger data, lack of data sharing between ride-hailing platforms, and other barriers severely undermine the reliability of current models and increase the difficulty of training and building these models. Additionally, the centralized mining of driver pick-up data can lead to spatial imbalances in ride-hailing platform order supplies, resulting in the recommendation of the same pick-up areas to different drivers in cases with similar spatiotemporal characteristics. This scenario not only exacerbates competition in certain areas but also impacts the overall service efficiency of the platform.To address these issues, this paper proposes a feature-aware personalized clustering federated learning model based on vehicle-cloud collaboration. This model achieves data sharing while protecting privacy, providing personalized pick-up area recommendations for drivers, and incorporating a feature-aware mechanism to prevent uneven order distribution. Specifically, the study introduces a clustering analysis algorithm based on Dynamic Time Warping (DTW) to accurately align and measure the similarity of drivers' order time series, classifying driver order-taking behavior patterns for more comprehensive behavior pattern analysis and improving model accuracy and reliability. Furthermore, we propose a deep matrix factorization algorithm, AFFM, that integrates driver features and geographical information. This algorithm deeply learns the interaction relationships between drivers and geographical features, modulates the attention weights of driver features, and constructs personalized models tailored to different driver pick-up characteristics, thus enhancing the precision and effectiveness of recommendations.Additionally, we present a federated learning algorithm, CFL\_AFFM, based on vehicle-cloud collaboration. This algorithm leverages cloud computing resources to compensate for the limited computational resources of vehicles, allowing the AFFM algorithm to effectively utilize DTW clustering results. By sharing global models and clustering parameters through the cloud platform, broader collaborative effects are achieved, enhancing the model's generalization ability and stability while protecting driver and passenger privacy.Finally, we design a reachability matrix that incorporates the driver's current location and features. Based on the predictions of the CFL\_AFFM algorithm, reliable pick-up areas are recommended, ensuring personalized and efficient ride-hailing services.

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Collaborative Federated Learning in Mobile Vehicle Clouds for Online Ride-Hailing Passenger Zones Recommendation

Liao,

Zhou,

Liang

et al. 2024

IEEE Internet Things J.

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Pick-Up Point Recommendation Using Users’ Historical Ride-Hailing Orders

Cited by 3 publications

References 20 publications

Hotspot Identification Through Pick-Up and Drop-Off Analysis of Ride-Hailing Transport Service

Hotspot Identification Through Pick-Up and Drop-Off Analysis of Ride-Hailing Transport Service

Ride-Hailing Pick-Up Area Recommendation in a Vehicle-Cloud Collaborative Environment: A Feature-Aware Personalized Clustering Federated Learning Approach

Collaborative Federated Learning in Mobile Vehicle Clouds for Online Ride-Hailing Passenger Zones Recommendation

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