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  • Open Access


    Fusing Spatio-Temporal Contexts into DeepFM for Taxi Pick-Up Area Recommendation

    Yizhi Liu1,3, Rutian Qing1,3, Yijiang Zhao1,3,*, Xuesong Wang1,3, Zhuhua Liao1,3, Qinghua Li1,2, Buqing Cao1,3

    Computer Systems Science and Engineering, Vol.45, No.3, pp. 2505-2519, 2023, DOI:10.32604/csse.2023.021615

    Abstract Short-term GPS data based taxi pick-up area recommendation can improve the efficiency and reduce the overheads. But how to alleviate sparsity and further enhance accuracy is still challenging. Addressing at these issues, we propose to fuse spatio-temporal contexts into deep factorization machine (STC_DeepFM) offline for pick-up area recommendation, and within the area to recommend pick-up points online using factorization machine (FM). Firstly, we divide the urban area into several grids with equal size. Spatio-temporal contexts are destilled from pick-up points or points-of-interest (POIs) belonged to the preceding grids. Secondly, the contexts are integrated into deep factorization machine (DeepFM) to mine… More >

  • Open Access


    Extracting Campus’ Road Network from Walking GPS Trajectories

    Yizhi Liu, Rutian Qing, Jianxun Liu*, Zhuhua Liao, Yijiang Zhao, Hong Ouyang

    Journal of Cyber Security, Vol.2, No.3, pp. 131-140, 2020, DOI:10.32604/jcs.2020.010625

    Abstract Road network extraction is vital to both vehicle navigation and road planning. Existing approaches focus on mining urban trunk roads from GPS trajectories of floating cars. However, path extraction, which plays an important role in earthquake relief and village tour, is always ignored. Addressing this issue, we propose a novel approach of extracting campus’ road network from walking GPS trajectories. It consists of data preprocessing and road centerline generation. The patrolling GPS trajectories, collected at Hunan University of Science and Technology, were used as the experimental data. The experimental evaluation results show that our approach is able to effectively and… More >

  • Open Access


    An Entropy-Based Model for Recommendation of Taxis’ Cruising Route

    Yizhi Liu1, 2, Xuesong Wang1, 2, Jianxun Liu1, 2, *, Zhuhua Liao1, 2, Yijiang Zhao1, 2, Jianjun Wang1, 2

    Journal on Artificial Intelligence, Vol.2, No.3, pp. 137-148, 2020, DOI:10.32604/jai.2020.010620

    Abstract Cruising route recommendation based on trajectory mining can improve taxidrivers' income and reduce energy consumption. However, existing methods mostly recommend pick-up points for taxis only. Moreover, their performance is not good enough since there lacks a good evaluation model for the pick-up points. Therefore, we propose an entropy-based model for recommendation of taxis' cruising route. Firstly, we select more positional attributes from historical pick-up points in order to obtain accurate spatial-temporal features. Secondly, the information entropy of spatial-temporal features is integrated in the evaluation model. Then it is applied for getting the next pick-up points and further recommending a series… More >

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