Set-Valued Transformer Network for High-Emission Mobile Source Identification

TL;DR

This paper introduces a Set-Valued Transformer Network (SVTN) that improves high-emission vehicle detection accuracy by effectively learning from imbalanced, nonlinear, and high-dimensional emission data, reducing missed detections.

Contribution

The paper proposes a novel SVTN model combining transformer-based similarity measurement and set-valued classification for high-emission vehicle identification, addressing data imbalance and nonlinear challenges.

Findings

Achieved a 9.5% reduction in missed detection rate.

Validated effectiveness on Hefei city diesel vehicle data.

Outperformed baseline transformer models in detection accuracy.

Abstract

Identifying high-emission vehicles is a crucial step in regulating urban pollution levels and formulating traffic emission reduction strategies. However, in practical monitoring data, the proportion of high-emission state data is significantly lower compared to normal emission states. This characteristic long-tailed distribution severely impedes the extraction of discriminative features for emission state identification during data mining. Furthermore, the highly nonlinear nature of vehicle emission states and the lack of relevant prior knowledge also pose significant challenges to the construction of identification models.To address the aforementioned issues, we propose a Set-Valued Transformer Network (SVTN) to achieve comprehensive learning of discriminative features from high-emission samples, thereby enhancing detection accuracy. Specifically, this model first employs the transformer to measure the temporal similarity of micro-trip condition variations, thus constructing a mapping rule that projects the original high-dimensional emission data into a low-dimensional feature space. Next, a set-valued identification algorithm is used to probabilistically model the relationship between the generated feature vectors and their labels, providing an accurate metric criterion for the classification algorithm. To validate the effectiveness of our proposed approach, we conducted extensive experiments on the diesel vehicle monitoring data of Hefei city in 2020. The results demonstrate that our method achieves a 9.5\% reduction in the missed detection rate for high-emission vehicles compared to the transformer-based baseline, highlighting its superior capability in accurately identifying high-emission mobile pollution sources.