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App. Res. of Comp. Accepted Paper
Vol. 41, 2024 No. 12

Dynamic pick-up point recommendation based on multi-modal deep forest and iterative Kuhn-Munkres algorithm

Guo Yuhan
Zhu Rushi
School of science / School of big data science, Zhejiang University of Science & Technology, Hangzhou Zhejiang 310023, China
DOI: 10.19734/j.issn.1001-3695.2024.04.0123

Abstract

To address the bottleneck issues of global optimality and computational efficiency in existing dynamic pick-up point allocation models for large-scale scenarios, a model was developed based on four key influencing factors: passenger walking distance, passenger walking time, pick-up point road conditions, and the cost to the passenger's destination. A multi-modal deep forest-based dynamic pick-up point prediction algorithm and an iterative Kuhn-Munkres pick-up point allocation algorithm were proposed. The prediction algorithm integrated a multi-modal decision tree structure with deep learning techniques to enhance prediction accuracy. The allocation algorithm utilized a multi-scenario adaptive mechanism to automatically adjust edge weights and select the optimal edges for augmentation, aiming to achieve the optimal allocation for all passengers and pick-up points. Experimental results demonstrated that the proposed prediction algorithm reduced the mean absolute error by 2.705, the mean squared error by 5.915, increased the coefficient of determination by 0.214, and improved the explained variance by 0.195 compared to other mainstream prediction models. Under conditions where passenger quantity advantage, the allocation algorithm improved average scheduling effectiveness by 2.04% compared to other schemes tested in the experiments. These findings indicate that the prediction and allocation algorithms are highly practical, with the allocation algorithm showing significant advantages in handling large-scale instances.

Foundation Support

国家自然科学基金面上项目(12271484)
浙江省自然科学基金重点项目(LZ22F020007)
浙江科技大学青年科学基金项目(2023QN022)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.04.0123
Publish at: Application Research of Computers Accepted Paper, Vol. 41, 2024 No. 12

Publish History

[2024-09-02] Accepted Paper

Cite This Article

郭羽含, 朱茹施. 基于多模深度森林和迭代Kuhn-Munkres的动态上车点推荐算法 [J]. 计算机应用研究, 2024, 41 (12). (2024-09-11). https://doi.org/10.19734/j.issn.1001-3695.2024.04.0123. (Guo Yuhan, Zhu Rushi. Dynamic pick-up point recommendation based on multi-modal deep forest and iterative Kuhn-Munkres algorithm [J]. Application Research of Computers, 2024, 41 (12). (2024-09-11). https://doi.org/10.19734/j.issn.1001-3695.2024.04.0123. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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