城市出租车需求预测在降低出租车空车行驶率、缓解道路交通拥堵方面发挥着重要作用。然而,由于城市路网结构的复杂性,出租车流量的准确预测一直是一项挑战。为了更好地捕捉出租车数据的空间特征,准确预测未来的需求变化,我们提出了一种...城市出租车需求预测在降低出租车空车行驶率、缓解道路交通拥堵方面发挥着重要作用。然而,由于城市路网结构的复杂性,出租车流量的准确预测一直是一项挑战。为了更好地捕捉出租车数据的空间特征,准确预测未来的需求变化,我们提出了一种新颖的时空预测模型。该模型融合了Tucker分解和深度学习的优势,不仅能够捕获出租车需求数据之间的时空相关性,还考虑到了外部因素的潜在影响。最终,通过对五个真实世界的数据集进行出租车需求预测实验,我们验证了本文提出的模型在预测性能方面的有效性。Urban taxi demand forecasting plays an important role in reducing empty cab trips and easing road traffic congestion. However, accurate prediction of cab flows has been a challenge due to the complexity of urban road network structures. To better capture the spatial characteristics of cab data and accurately predict future demand changes, we propose a novel spatial-temporal prediction model. The model incorporates the strengths of Tucker decomposition and deep learning to not only capture the spatial-temporal correlation between cab demand data, but also take into account the potential impact of external factors. Ultimately, by conducting cab demand prediction experiments on five real-world datasets, we validate the effectiveness of the model proposed in this paper in terms of prediction performance.展开更多
How can we efficiently store and mine dynamically generated dense tensors for modeling the behavior of multidimensional dynamic data?Much of the multidimensional dynamic data in the real world is generated in the form...How can we efficiently store and mine dynamically generated dense tensors for modeling the behavior of multidimensional dynamic data?Much of the multidimensional dynamic data in the real world is generated in the form of time-growing tensors.For example,air quality tensor data consists of multiple sensory values gathered from wide locations for a long time.Such data,accumulated over time,is redundant and consumes a lot ofmemory in its raw form.We need a way to efficiently store dynamically generated tensor data that increase over time and to model their behavior on demand between arbitrary time blocks.To this end,we propose a Block IncrementalDense Tucker Decomposition(BID-Tucker)method for efficient storage and on-demand modeling ofmultidimensional spatiotemporal data.Assuming that tensors come in unit blocks where only the time domain changes,our proposed BID-Tucker first slices the blocks into matrices and decomposes them via singular value decomposition(SVD).The SVDs of the time×space sliced matrices are stored instead of the raw tensor blocks to save space.When modeling from data is required at particular time blocks,the SVDs of corresponding time blocks are retrieved and incremented to be used for Tucker decomposition.The factor matrices and core tensor of the decomposed results can then be used for further data analysis.We compared our proposed BID-Tucker with D-Tucker,which our method extends,and vanilla Tucker decomposition.We show that our BID-Tucker is faster than both D-Tucker and vanilla Tucker decomposition and uses less memory for storage with a comparable reconstruction error.We applied our proposed BID-Tucker to model the spatial and temporal trends of air quality data collected in South Korea from 2018 to 2022.We were able to model the spatial and temporal air quality trends.We were also able to verify unusual events,such as chronic ozone alerts and large fire events.展开更多
文摘城市出租车需求预测在降低出租车空车行驶率、缓解道路交通拥堵方面发挥着重要作用。然而,由于城市路网结构的复杂性,出租车流量的准确预测一直是一项挑战。为了更好地捕捉出租车数据的空间特征,准确预测未来的需求变化,我们提出了一种新颖的时空预测模型。该模型融合了Tucker分解和深度学习的优势,不仅能够捕获出租车需求数据之间的时空相关性,还考虑到了外部因素的潜在影响。最终,通过对五个真实世界的数据集进行出租车需求预测实验,我们验证了本文提出的模型在预测性能方面的有效性。Urban taxi demand forecasting plays an important role in reducing empty cab trips and easing road traffic congestion. However, accurate prediction of cab flows has been a challenge due to the complexity of urban road network structures. To better capture the spatial characteristics of cab data and accurately predict future demand changes, we propose a novel spatial-temporal prediction model. The model incorporates the strengths of Tucker decomposition and deep learning to not only capture the spatial-temporal correlation between cab demand data, but also take into account the potential impact of external factors. Ultimately, by conducting cab demand prediction experiments on five real-world datasets, we validate the effectiveness of the model proposed in this paper in terms of prediction performance.
基金supported by the Institute of Information&Communications Technology Planning&Evaluation (IITP)grant funded by the Korean government (MSIT) (No.2022-0-00369)by the NationalResearch Foundation of Korea Grant funded by the Korean government (2018R1A5A1060031,2022R1F1A1065664).
文摘How can we efficiently store and mine dynamically generated dense tensors for modeling the behavior of multidimensional dynamic data?Much of the multidimensional dynamic data in the real world is generated in the form of time-growing tensors.For example,air quality tensor data consists of multiple sensory values gathered from wide locations for a long time.Such data,accumulated over time,is redundant and consumes a lot ofmemory in its raw form.We need a way to efficiently store dynamically generated tensor data that increase over time and to model their behavior on demand between arbitrary time blocks.To this end,we propose a Block IncrementalDense Tucker Decomposition(BID-Tucker)method for efficient storage and on-demand modeling ofmultidimensional spatiotemporal data.Assuming that tensors come in unit blocks where only the time domain changes,our proposed BID-Tucker first slices the blocks into matrices and decomposes them via singular value decomposition(SVD).The SVDs of the time×space sliced matrices are stored instead of the raw tensor blocks to save space.When modeling from data is required at particular time blocks,the SVDs of corresponding time blocks are retrieved and incremented to be used for Tucker decomposition.The factor matrices and core tensor of the decomposed results can then be used for further data analysis.We compared our proposed BID-Tucker with D-Tucker,which our method extends,and vanilla Tucker decomposition.We show that our BID-Tucker is faster than both D-Tucker and vanilla Tucker decomposition and uses less memory for storage with a comparable reconstruction error.We applied our proposed BID-Tucker to model the spatial and temporal trends of air quality data collected in South Korea from 2018 to 2022.We were able to model the spatial and temporal air quality trends.We were also able to verify unusual events,such as chronic ozone alerts and large fire events.
