Research on heavy truck operation characteristics recognition method based on machine learning

Jiahao Zhan; Shengwen Yang; Xueyin Wang; Jun Li; Xingyun Shi

doi:10.1117/12.2673560

22 May 2023 Research on heavy truck operation characteristics recognition method based on machine learning

Jiahao Zhan, Shengwen Yang, Xueyin Wang, Jun Li, Xingyun Shi

Proceedings Volume 12640, International Conference on Internet of Things and Machine Learning (IoTML 2022); 126401R (2023) https://doi.org/10.1117/12.2673560
Event: International Conference on Internet of Things and Machine Learning (IoTML 2022), 2022, Harbin, China

Abstract

Regional cargo transportation optimization is the key to the overall efficiency improvement of logistics. Heavy trucks are especially important in connecting regional long-distance transportation and heavy cargo transportation. Relying on transport flow data, we take 6-axle trucks as an example, and build a K-means clustering model to label freight vehicle groups based on the analysis of truck trip intensity and travel time differences, and subsequently design the Random Forest-Recursive Feature Elimination (RF-RFE) algorithm to rank the importance of freight features, and use the filtered feature indicators to verify the travel differences of different groups.The results show that (1) heavy-duty trucks have a higher proportion of nighttime trips, staggered features with other models, and assume more medium and long-distance transport functions; (2) from the K-means++ clustering results, six-axle truck transport can be divided into three types: heavy-duty long-distance transport type, heavy-duty short-distance transport type and light-duty short-distance transport type. (3) RF-RFE model feature ranking in vehicle weighing and travel distance importance ranking the top two, ranking correct rate higher than up to 91%, indicating that loading and travel distance can effectively distinguish heavy-duty truck operation characteristics.

Citation Download Citation

Jiahao Zhan, Shengwen Yang, Xueyin Wang, Jun Li, and Xingyun Shi "Research on heavy truck operation characteristics recognition method based on machine learning", Proc. SPIE 12640, International Conference on Internet of Things and Machine Learning (IoTML 2022), 126401R (22 May 2023); https://doi.org/10.1117/12.2673560

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