Naji Hasan A H, Xue Qingji, Li Tianfeng
School of Digital Media, Nanyang Institute of Technology, Chang Jiang Road No. 80, Nanyang 473004, China.
Sensors (Basel). 2024 Oct 17;24(20):6683. doi: 10.3390/s24206683.
With the ubiquitous deployment of mobile and sensor technologies in modes of transportation, taxis have become a significant component of public transportation. However, vacant taxis represent an important waste of transportation resources. Forecasting taxi demand within a short time achieves a supply-demand balance and reduces oil emissions. Although earlier studies have forwarded highly developed machine learning- and deep learning-based models to forecast taxicab demands, these models often face significant computational expenses and cannot effectively utilize large-scale trajectory sensor data. To address these challenges, in this paper, we propose a hybrid deep learning-based model for taxi demand prediction. In particular, the Variational Mode Decomposition (VMD) algorithm is integrated along with a Bidirectional Long Short-Term Memory (BiLSTM) model to perform the prediction process. The VMD algorithm is applied to decompose time series-aware traffic features into multiple sub-modes of different frequencies. After that, the BiLSTM method is utilized to predict time series data fed with the relevant demand features. To overcome the limitation of high computational expenses, the designed model is performed on the Spark distributed platform. The performance of the proposed model is tested using a real-world dataset, and it surpasses existing state-of-the-art predictive models in terms of accuracy, efficiency, and distributed performance. These findings provide insights for enhancing the efficiency of passenger search and increasing the profit of taxicabs.
随着移动和传感器技术在交通方式中的广泛应用，出租车已成为公共交通的重要组成部分。然而，空驶出租车是交通资源的重要浪费。在短时间内预测出租车需求可实现供需平衡并减少尾气排放。尽管早期研究提出了基于机器学习和深度学习的高度发达模型来预测出租车需求，但这些模型往往面临巨大的计算成本，且无法有效利用大规模轨迹传感器数据。为应对这些挑战，本文提出了一种基于混合深度学习的出租车需求预测模型。具体而言，将变分模态分解（VMD）算法与双向长短期记忆（BiLSTM）模型相结合来执行预测过程。VMD算法用于将时间序列感知交通特征分解为不同频率的多个子模态。之后，利用BiLSTM方法预测输入相关需求特征的时间序列数据。为克服高计算成本的限制，所设计的模型在Spark分布式平台上运行。使用真实世界数据集对所提模型的性能进行测试，结果表明该模型在准确性、效率和分布式性能方面均优于现有最先进的预测模型。这些发现为提高乘客搜索效率和增加出租车利润提供了思路。
