IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i22p14767-d967662.html
   My bibliography  Save this article

Optimal Aggregate Size of Traffic Sequence Data Based on Fuzzy Entropy and Mutual Information

Author

Listed:
  • Junzhuo Li

    (School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China)

  • Wenyong Li

    (School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China)

  • Guan Lian

    (Key Laboratory of Intelligent Traffic System, Guilin University of Electronic Technology, Guilin 541004, China)

Abstract

Before traffic forecasting, it is usually necessary to aggregate the information by a certain length of time. An aggregation size that is too short will make the data unstable and cause the forecast result to be too biased. On the other hand, if the aggregation size is too large, the data information will be lost, resulting in the forecast results tending towards an average or slow response. With the development of intelligent transportation systems, especially the development of urban traffic control systems, high requirements are placed on the real-time accuracy of traffic forecasting. Therefore, it is an essential topic of traffic forecasting research to determine aggregation sizes. In this paper, the mutual information between the forecast input information and the forecast result and the sequence complexity of the forecast result measured by approximate entropy, sample entropy, and fuzzy entropy are considered; then, the optimal data aggregation size is given. To verify the proposed method, the validated data obtained from the simulation is aggregated and calculated with different aggregation sizes, then used for forecasting. By comparing the prediction performance of different aggregate sizes, the optimal aggregate size was found to reduce MSE by 14–30%. The results show that the method proposed in this paper is helpful for selecting the optimal data aggregation size in forecasting and can improve the performance of prediction.

Suggested Citation

  • Junzhuo Li & Wenyong Li & Guan Lian, 2022. "Optimal Aggregate Size of Traffic Sequence Data Based on Fuzzy Entropy and Mutual Information," Sustainability, MDPI, vol. 14(22), pages 1-17, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:14767-:d:967662
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/22/14767/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/22/14767/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Vladislav Krivda & Jan Petru & David Macha & Jakub Novak, 2021. "Use of Microsimulation Traffic Models as Means for Ensuring Public Transport Sustainability and Accessibility," Sustainability, MDPI, vol. 13(5), pages 1-38, March.
    2. Saba Inam & Azhar Mahmood & Shaheen Khatoon & Majed Alshamari & Nazia Nawaz, 2022. "Multisource Data Integration and Comparative Analysis of Machine Learning Models for On-Street Parking Prediction," Sustainability, MDPI, vol. 14(12), pages 1-21, June.
    3. Fernandez, Rodrigo & Valenzuela, Eduardo & Casanello, Federico & Jorquera, Carola, 2006. "Evolution of the TRANSYT model in a developing country," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(5), pages 386-398, June.
    4. Ma, Tao & Zhou, Zhou & Antoniou, Constantinos, 2018. "Dynamic factor model for network traffic state forecast," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 281-317.
    5. Fahad Alrukaibi & Rushdi Alsaleh & Tarek Sayed, 2019. "Applying Machine Learning and Statistical Approaches for Travel Time Estimation in Partial Network Coverage," Sustainability, MDPI, vol. 11(14), pages 1-18, July.
    6. Min Li & Mengshan Li & Bilong Liu & Jiang Liu & Zhen Liu & Dijia Luo, 2022. "Spatio-Temporal Traffic Flow Prediction Based on Coordinated Attention," Sustainability, MDPI, vol. 14(12), pages 1-17, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xianing Wang & Zhan Zhang & Ying Wang & Jun Yang & Linjun Lu, 2022. "A Study on Safety Evaluation of Pedestrian Flows Based on Partial Impact Dynamics by Real-Time Data in Subway Stations," Sustainability, MDPI, vol. 14(16), pages 1-19, August.
    2. Ivana Štimac Grandić & Paulo Šćulac & Davor Grandić & Iva Vodopija, 2024. "The Accessible Design of Pedestrian Bridges," Sustainability, MDPI, vol. 16(3), pages 1-17, January.
    3. Tao Wang & Sixuan Li & Wenyong Li & Quan Yuan & Jun Chen & Xiang Tang, 2023. "A Short-Term Parking Demand Prediction Framework Integrating Overall and Internal Information," Sustainability, MDPI, vol. 15(9), pages 1-25, April.
    4. Kayvan Aghabayk & Alireza Soltani & Nirajan Shiwakoti, 2022. "Investigating Pedestrians’ Exit Choice with Incident Location Awareness in an Emergency in a Multi-Level Shopping Complex," Sustainability, MDPI, vol. 14(19), pages 1-21, September.
    5. Tianhe Lan & Xiaojing Zhang & Dayi Qu & Yufeng Yang & Yicheng Chen, 2023. "Short-Term Traffic Flow Prediction Based on the Optimization Study of Initial Weights of the Attention Mechanism," Sustainability, MDPI, vol. 15(2), pages 1-16, January.
    6. Chen, Xinqiang & Chen, Huixing & Yang, Yongsheng & Wu, Huafeng & Zhang, Wenhui & Zhao, Jiansen & Xiong, Yong, 2021. "Traffic flow prediction by an ensemble framework with data denoising and deep learning model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    7. Zhiguo Shao & Li Zhang & Chuanfeng Han & Lingpeng Meng, 2022. "Measurement and Prediction of Urban Land Traffic Accessibility and Economic Contact Based on GIS: A Case Study of Land Transportation in Shandong Province, China," IJERPH, MDPI, vol. 19(22), pages 1-15, November.
    8. Mohsen Momenitabar & Jeremy Mattson, 2021. "A Multi-Objective Meta-Heuristic Approach to Improve the Bus Transit Network: A Case Study of Fargo-Moorhead Area," Sustainability, MDPI, vol. 13(19), pages 1-25, September.
    9. Chen, Yi-Ting & Sun, Edward W. & Chang, Ming-Feng & Lin, Yi-Bing, 2021. "Pragmatic real-time logistics management with traffic IoT infrastructure: Big data predictive analytics of freight travel time for Logistics 4.0," International Journal of Production Economics, Elsevier, vol. 238(C).
    10. Binglei Xie & Yu Sun & Xiaolong Huang & Le Yu & Gangyan Xu, 2020. "Travel Characteristics Analysis and Passenger Flow Prediction of Intercity Shuttles in the Pearl River Delta on Holidays," Sustainability, MDPI, vol. 12(18), pages 1-23, September.
    11. Maksymilian Mądziel, 2023. "Vehicle Emission Models and Traffic Simulators: A Review," Energies, MDPI, vol. 16(9), pages 1-31, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:14767-:d:967662. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.