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

arXiv: https://arxiv.org/abs/2312.04609 · HTML: https://mlsanity.com/paper/2312.04609/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 76 references that are in the map.
- First posted on arXiv 7 Dec 2023.

## Builds on (most cited first, 16 of 16)

- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [Graph WaveNet for Deep Spatial-Temporal Graph Modeling](https://mlsanity.com/paper/1906.00121/) (2019)
- [Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks](https://mlsanity.com/paper/2005.11650/) (2020)
- [Temporal Convolutional Networks for Action Segmentation and Detection](https://mlsanity.com/paper/1611.05267/) (2016)
- [Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction](https://mlsanity.com/paper/1610.00081/) (2016)
- [Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting](https://mlsanity.com/paper/2012.09641/) (2020)
- [Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting](https://mlsanity.com/paper/2106.12931/) (2021)
- [Spatial-Temporal Transformer Networks for Traffic Flow Forecasting](https://mlsanity.com/paper/2001.02908/) (2020)
- [Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction](https://mlsanity.com/paper/1802.08714/) (2018)
- [PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction](https://mlsanity.com/paper/2301.07945/) (2023)
- [Graph Neural Controlled Differential Equations for Traffic Forecasting](https://mlsanity.com/paper/2112.03558/) (2021)
- [Conditional Time Series Forecasting with Convolutional Neural Networks](https://mlsanity.com/paper/1703.04691/) (2017)
- [Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting](https://mlsanity.com/paper/1911.12093/) (2019)
- [Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks](https://mlsanity.com/paper/1705.02699/) (2017)
- [Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework](https://mlsanity.com/paper/1612.01022/) (2016)

## 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.