A fast matrix autoregression algorithm based on Tucker decomposition for online prediction of nonlinear real-time taxi-hailing demand without pre-training

Zhihao Xu, Zhiqiang Lv, Benjia Chu and Jianbo Li

Chaos, Solitons & Fractals, 2024, vol. 189, issue P1

Abstract: Online prediction of real-time taxi-hailing demand generally provides better real-time decision support for passengers and taxi drivers compared with offline prediction. Current studies focused on using deep spatial-temporal models to predict complex nonlinear taxi-hailing demand. However, whether these models can be used for online prediction of real-time taxi-hailing demand through online training or offline pre-training is hardly discussed. Generally, deep models are not lightweight enough for online training, and pre-training these models requires some time and computational resources. Therefore, a lightweight Fast Matrix Autoregression algorithm based on Tucker Decomposition (FMAR-TD) is proposed for online real-time training and prediction of nonlinear taxi-hailing demand without pre-training. The experimental results show that FMAR-TD achieves millisecond-level online prediction of real-time taxi-hailing demand. Compared with baselines, the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of FMAR-TD marginally increase by 2.51 % and 2.56 %, while the computation time (sum of training time and prediction time) significantly reduces by 86.16 %. Open-source link: https://github.com/qdu318/FMAR-TD.

Keywords: Online prediction; Real-time taxi-hailing demand; Without pre-training (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960077924012128
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:189:y:2024:i:p1:s0960077924012128

DOI: 10.1016/j.chaos.2024.115660

Access Statistics for this article

Chaos, Solitons & Fractals is currently edited by Stefano Boccaletti and Stelios Bekiros

More articles in Chaos, Solitons & Fractals from Elsevier
Bibliographic data for series maintained by Thayer, Thomas R. ().