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Short-term prediction of construction waste transport activities using AI-Truck

arXiv 2312.04609 ↗First posted 7 Dec 2023cs.LG cs.AIcited by 0

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Abstract (arXiv)

Construction waste hauling trucks (or `slag trucks') are among the most commonly seen heavy-duty diesel vehicles in urban streets, which not only produce significant carbon, NOx and PM2.5 emissions but are also a major source of on-road and on-site fugitive dust. Slag trucks are subject to a series of spatial and temporal access restrictions by local traffic and environmental policies. This paper addresses the practical problem of predicting levels of slag truck activity at a city scale during heavy pollution episodes, such that environmental law enforcement units can take timely and proactive measures against localized truck aggregation. A deep ensemble learning framework (coined AI-Truck) is designed, which employs a soft vote integrator that utilizes Bi-LSTM, TCN, STGCN, and PDFormer as base classifiers. AI-Truck employs a combination of downsampling and weighted loss is employed to address sample imbalance, and utilizes truck trajectories to extract more accurate and effective geographic features. The framework was deployed for truck activity prediction at a resolution of 1km×1km×0.5h, in a 255 km2 area in Chengdu, China. As a classifier, AI-Truck achieves a macro F1 of 0.747 in predicting levels of slag truck activity for 0.5-h prediction time length, and enables personnel to spot high-activity locations 1.5 hrs ahead with over 80% accuracy.

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References · 16

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4 more
  1. 2017Conditional Time Series Forecasting with Convolutional Neural Networkscited by 121
  2. 2019Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecastingcited by 69
  3. 2017Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networkscited by 35
  4. 2016Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning frameworkcited by 23

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