Extracting Campus’ Road Network from Walking GPS Trajectories

Liu, Yizhi; Qing, Rutian; Liu, Jianxun; Liao, Zhuhua; Zhao, Yulin; Ouyang, Hong

doi:10.32604/jcs.2020.010625

Cited by 3 publications

(1 citation statement)

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“…Liu et al mined candidate pick-up points based on GPS data by spatio-temporal analysis, and created personalized hot spots by integrating a probabilistic optimization model with project-based collaborative filtering method [33]. To maximize the possibility of getting passengers at shorter travel distances, they also applied road network to provide a range of hotspots for taxi drivers [45]. Zou et al put forward a dynamic probability model for travel routes recommendation with high probability of carrying passengers to no-load taxis [46].…”

Section: Spatio-temperal Contextsmentioning

confidence: 99%

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

Liu¹,

Qing²,

Zhao³

et al. 2023

Computer Systems Science and Engineering

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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 high-order interaction relationships from grids. And a novel algorithm named STC_DeepFM is presented for offline pick-up area recommendation. Thirdly, we devise the architecture of offline-to-online (O2O) recommendation respectively based on DeepFM and FM model in order to tradeoff the accuracy and efficiency. Some experiments are designed on the DiDi dataset to evaluate step by step the performance of spatio-temporal contexts, different recommendation models, and the O2O architecture. The results show that the proposed STC_DeepFM algorithm exceeds several state-of-the-art methods, and the O2O architecture achieves excellent real-time performance.

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Section: Spatio-temperal Contextsmentioning

confidence: 99%