Robust Traffic Speed Inference With Ensemble Learning

doi:10.1109/TITS.2022.3160869

Robust Traffic Speed Inference With Ensemble Learning

2022-10-01

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DOI：10.1109/TITS.2022.3160869

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Traffic speed inference enables many applications that are essential for everyday life. Most traffic-prediction approaches assume that a constant number of sensors are deployed on the roads, whether they are either stationary loop detectors or vehicles equipped with Global Positioning System (GPS) tracking devices. The static nature of those fixtures limits their ability to adapt to scenarios that are more dynamic. Rather than relying on several fixed sensors to detect changes and infer traffic, we use crowdsourcing to judiciously select individuals and then make predictions. Our solution consists of three core components: dynamic seed selection, regional cluster building and ensemble traffic prediction. In the first phase, we employ Efficient Transition Probability (ETP) to evaluate candidate seed sets. Road clusters are then formed using hierarchical clustering that is tweaked by a dynamic programming technique. This method assesses the eccentricity of every cluster to bond every road within each cluster more closely. Subsequently, we develop an ensemble-learning strategy in conjunction with Lasso regression to forecast traffic. The strength of our ensemble approach is its ability to manage absent seeds, a condition that has never been investigated, to our knowledge. Substantial experimental evaluation indicates that our claim of dynamic updates is valid and effective. Our solution outperforms the state-of-the-art techniques by a wide margin, in terms of prediction accuracy.

Keywords:

Vehicle dynamics

Roads

Tensors

Sensors

Predictive models

Soft sensors

Global Positioning System

Intelligent transportation systems

smart transportation

computational and artificial intelligence

machine learning

clustering algorithms

prediction methods

Robust Traffic Speed Inference With Ensemble Learning

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