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  • 1
    Online Resource
    Online Resource
    Deep Reinforcement Learning-based Trajectory Pricing on Ride-hailing Platforms
    Association for Computing Machinery (ACM) ; 2022
    In:  ACM Transactions on Intelligent Systems and Technology Vol. 13, No. 3 ( 2022-06-30), p. 1-19
    Details
    In: ACM Transactions on Intelligent Systems and Technology, Association for Computing Machinery (ACM), Vol. 13, No. 3 ( 2022-06-30), p. 1-19
    Abstract: Dynamic pricing plays an important role in solving the problems such as traffic load reduction, congestion control, and revenue improvement. Efficient dynamic pricing strategies can increase capacity utilization, total revenue of service providers, and the satisfaction of both passengers and drivers. Many proposed dynamic pricing technologies focus on short-term optimization and face poor scalability in modeling long-term goals for the limitations of solution optimality and prohibitive computation. In this article, a deep reinforcement learning framework is proposed to tackle the dynamic pricing problem for ride-hailing platforms. A soft actor-critic (SAC) algorithm is adopted in the reinforcement learning framework. First, the dynamic pricing problem is translated into a Markov Decision Process (MDP) and is set up in continuous action spaces, which is no need for the discretization of action space. Then, a new reward function is obtained by the order response rate and the KL-divergence between supply distribution and demand distribution. Experiments and case studies demonstrate that the proposed method outperforms the baselines in terms of order response rate and total revenue.
    Type of Medium: Online Resource
    ISSN: 2157-6904 , 2157-6912
    URL: Article
    DOI: 10.1145/3474841
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2022
    detail.hit.zdb_id: 2584437-4
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  • 2
    Online Resource
    Online Resource
    Driving Route Recommendation With Profit Maximization in Ride Sharing
    Oxford University Press (OUP) ; 2020
    In:  The Computer Journal Vol. 63, No. 11 ( 2020-11-19), p. 1607-1623
    Details
    In: The Computer Journal, Oxford University Press (OUP), Vol. 63, No. 11 ( 2020-11-19), p. 1607-1623
    Abstract: Due to the positive impact of ride sharing on urban traffic and environment, it has attracted a lot of research attention recently. However, most existing researches focused on the profit maximization or the itinerary minimization of drivers, only rare work has covered on adjustable price function and matching algorithm for the batch requests. In this paper, we propose a request matching algorithm and an adjustable price function that benefits drivers as well as passengers. Our request-matching algorithm consists of an exact search algorithm and a group search algorithm. The exact search algorithm consists of three steps. The first step is to prune some invalid groups according to the total number of passengers and the capacity of vehicles. The second step is to filter out all candidate groups according to the compatibility of requests in same group. The third step is to obtain the most profitable group by the adjustable price function, and recommend the most profitable group to drivers. In order to enhance the efficiency of the exact search algorithm, we further design an improved group search algorithm based on the idea of original simulated annealing. Extensive experimental results show that our method can improve the income of drivers, and reduce the expense of passengers. Meanwhile, ride sharing can also keep the utilization rate of seats 80%, driving distance is reduced by 30%.
    Type of Medium: Online Resource
    ISSN: 0010-4620 , 1460-2067
    URL: Article
    DOI: 10.1093/comjnl/bxz075
    RVK:
    SQ 1100
    RVK:
    SA 3740
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2020
    detail.hit.zdb_id: 1477172-X
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  • 3
    Online Resource
    Online Resource
    Multi-View Spatial–Temporal Graph Neural Network for Traffic Prediction
    Oxford University Press (OUP) ; 2022
    In:  The Computer Journal ( 2022-07-05)
    Details
    In: The Computer Journal, Oxford University Press (OUP), ( 2022-07-05)
    Abstract: Spatial–temporal graph neural network has drawn more and more attention in recent years and is widely used to various real-world applications. However, learning the spatial–temporal graph neural network structure presents unique challenges including: (i) the dynamic spatial correlation; (ii) the dynamic temporal correlation. Even the existing methods take into account the spatial correlation, they still learn the static road network structure information, which cannot reflect the dynamic of road relations. Some of the works has focused on modeling the long-term time series, but the improvements have been limited tightly. To overcome these challenges, we proposed a novel approach called Multi-View Spatial–Temporal Graph Neural Network. Differ from the existing research, we designed a multi-view temporal transformer module to extract dynamic temporal correlation and enhance the expression of medium and long-term temporal features. We propose a multi-view spatial structure and a corresponding multi-view graph convolutional module, which are capable of simultaneously combining the features of static road network structure and dynamic changes. Compared with 11 baselines, our proposed model has achieved significant improvement in the accuracy of prediction.
    Type of Medium: Online Resource
    ISSN: 0010-4620 , 1460-2067
    URL: Article
    DOI: 10.1093/comjnl/bxac086
    RVK:
    SQ 1100
    RVK:
    SA 3740
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 1477172-X
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  • 4
    Online Resource
    Online Resource
    Robust spatial temporal imputation based on spatio-temporal generative adversarial nets
    Elsevier BV ; 2023
    In:  Knowledge-Based Systems Vol. 279 ( 2023-11), p. 110919-
    Details
    In: Knowledge-Based Systems, Elsevier BV, Vol. 279 ( 2023-11), p. 110919-
    Type of Medium: Online Resource
    ISSN: 0950-7051
    URL: Article
    DOI: 10.1016/j.knosys.2023.110919
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2023
    detail.hit.zdb_id: 2017495-0
    SSG: 24,1
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  • 5
    Online Resource
    Online Resource
    Monte carlo tree search for dynamic bike repositioning in bike-sharing systems
    Springer Science and Business Media LLC ; 2022
    In:  Applied Intelligence Vol. 52, No. 4 ( 2022-03), p. 4610-4625
    Details
    In: Applied Intelligence, Springer Science and Business Media LLC, Vol. 52, No. 4 ( 2022-03), p. 4610-4625
    Type of Medium: Online Resource
    ISSN: 0924-669X , 1573-7497
    URL: Article
    DOI: 10.1007/s10489-021-02586-x
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 1479519-X
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  • 6
    Online Resource
    Online Resource
    Establishment of a Zebrafish Xenograft Model for in Vivo Investigation of Nasopharyngeal Carcinoma
    SAGE Publications ; 2022
    In:  Cell Transplantation Vol. 31 ( 2022-01), p. 096368972211160-
    Details
    In: Cell Transplantation, SAGE Publications, Vol. 31 ( 2022-01), p. 096368972211160-
    Abstract: Nasopharyngeal carcinoma (NPC) is a unique malignant tumor of the head and neck. Despite higher survival rates by the combination of radiotherapy and chemotherapy, the recurrence or metastasis of NPC still occurs at about 10%. Therefore, there is urgent demand to develop more effective in vivo models for preclinical trials to investigate the mechanisms of NPC development and progression and to explore better treatment approaches. In this study, we transplanted human NPC CNE1 cells into zebrafish embryos to establish a xenograft model of NPC, where the proliferation and invasion behaviors of NPC cells were investigated in vivo. Combining in vitro and in vivo analyses, we found that activating transcription factor 7 (ATF7) was involved in the occurrence and development of NPC regulated by peptidyl-prolyl cis- trans isomerase NIMA-interacting 1 (Pin1). The zebrafish NPC xenograft model established here thereby provides an in vivo tool for exploring the occurrence and development of NPC, which may help to identify new tumor markers and develop new therapeutic strategies for the treatment of NPC.
    Type of Medium: Online Resource
    ISSN: 0963-6897 , 1555-3892
    URL: Article
    DOI: 10.1177/09636897221116085
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2022
    detail.hit.zdb_id: 2020466-8
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  • 7
    Online Resource
    Online Resource
    Multi-dimensional spatial–temporal graph convolution for urban sensors imputation and enhancement
    Elsevier BV ; 2023
    In:  Knowledge-Based Systems Vol. 278 ( 2023-10), p. 110856-
    Details
    In: Knowledge-Based Systems, Elsevier BV, Vol. 278 ( 2023-10), p. 110856-
    Type of Medium: Online Resource
    ISSN: 0950-7051
    URL: Article
    DOI: 10.1016/j.knosys.2023.110856
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2023
    detail.hit.zdb_id: 2017495-0
    SSG: 24,1
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  • 8
    Online Resource
    Online Resource
    Long-term sequence dependency capture for spatiotemporal graph modeling
    Elsevier BV ; 2023
    In:  Knowledge-Based Systems Vol. 278 ( 2023-10), p. 110818-
    Details
    In: Knowledge-Based Systems, Elsevier BV, Vol. 278 ( 2023-10), p. 110818-
    Type of Medium: Online Resource
    ISSN: 0950-7051
    URL: Article
    DOI: 10.1016/j.knosys.2023.110818
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2023
    detail.hit.zdb_id: 2017495-0
    SSG: 24,1
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  • 9
    Online Resource
    Online Resource
    Central Station-Based Demand Prediction for Determining Target Inventory in a Bike-Sharing System
    Oxford University Press (OUP) ; 2022
    In:  The Computer Journal Vol. 65, No. 3 ( 2022-03-14), p. 573-588
    Details
    In: The Computer Journal, Oxford University Press (OUP), Vol. 65, No. 3 ( 2022-03-14), p. 573-588
    Abstract: Predicting the bike demand can help rebalance the bikes and improve the service quality of a bike-sharing system. A lot of works focus on predicting the bike demand for all the stations, which is unnecessary as the travel cost of rebalance operations increases sharply as the number of stations increases. In this paper, we propose a framework for predicting the hourly bike demand based on the central stations we define. Firstly, we propose Two-Stage Station Clustering Algorithm to assign central stations and common stations into each cluster. Secondly, we propose a hierarchical prediction model to predict the hourly bike demand for every cluster and each central station progressively. Thirdly, we use a well-studied queuing model to determine the target initial inventory for each central station. The most innovative contribution of this paper is proposing the concept of central station, the use of a novel algorithm to cluster the central stations and present a hierarchical model, containing the Time and Weather Similarity Weighted K-Nearest Neighbor Algorithm and a linear model to predict the bike demand for central stations. The experimental results on the New York citi bike system demonstrate that our proposed method is more accurate than other methods in solving existing problems.
    Type of Medium: Online Resource
    ISSN: 0010-4620 , 1460-2067
    URL: Article
    DOI: 10.1093/comjnl/bxaa086
    RVK:
    SQ 1100
    RVK:
    SA 3740
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 1477172-X
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  • 10
    Online Resource
    Online Resource
    Detecting Urban Anomalies Using Factor Analysis and One Class Support Vector Machine
    Oxford University Press (OUP) ; 2023
    In:  The Computer Journal Vol. 66, No. 2 ( 2023-02-19), p. 373-383
    Details
    In: The Computer Journal, Oxford University Press (OUP), Vol. 66, No. 2 ( 2023-02-19), p. 373-383
    Abstract: The detection of anomalies in spatiotemporal traffic data is not only critical for intelligent transportation systems and public safety but also very challenging. Anomalies in traffic data often exhibit complex forms in two aspects, (i) spatiotemporal complexity (i.e. we need to associate individual locations and time intervals formulating a panoramic view of an anomaly) and (ii) multi-source complexity (i.e. we need an algorithm that can model the anomaly degree of the multiple data sources of different densities, distributions and scales). To tackle these challenges, we proposed a three-step method that uses factor analysis to extract features, then uses the goodness-of-fit test to obtain the anomaly score of a single data point and then uses one class support vector machine to synthesize the anomaly score. Finally, we conduct extensive experiments on real-world trip data include taxi and bike data. And these extensive experiments demonstrate the effectiveness of our proposed approach.
    Type of Medium: Online Resource
    ISSN: 0010-4620 , 1460-2067
    URL: Article
    DOI: 10.1093/comjnl/bxab166
    RVK:
    SQ 1100
    RVK:
    SA 3740
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 1477172-X
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