A data-centric weak supervised learning for highway traffic incident detection

Journal Article · Fri Aug 19 00:00:00 EDT 2022 · Accident Analysis and Prevention

DOI:https://doi.org/10.1016/j.aap.2022.106779· OSTI ID:1960722

Sun, Yixuan ^[1]; Mallick, Tanwi ^[2]; Balaprakash, Prasanna ^[2]; Macfarlane, Jane ^[3]

---

1. Purdue Univ., West Lafayette, IN (United States); Argonne National Lab. (ANL), Argonne, IL (United States)
2. Argonne National Lab. (ANL), Argonne, IL (United States)
3. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

Using the data from loop detector sensors for near-real-time detection of traffic incidents on highways is crucial to averting major traffic congestion. While recent supervised machine learning methods offer solutions to incident detection by leveraging human-labeled incident data, the false alarm rate is often too high to be used in practice. Specifically, the inconsistency in the human labeling of the incidents significantly affects the performance of supervised learning models. To that end, we focus on a data-centric approach to improve the accuracy and reduce the false alarm rate of traffic incident detection on highways. We develop a weak supervised learning workflow to generate high-quality training labels for the incident data without the ground truth labels, and we use those generated labels in the supervised learning setup for final detection. This approach comprises three stages. First, we introduce a data preprocessing and curation pipeline that processes traffic sensor data to generate high-quality training data through leveraging labeling functions, which can be domain knowledge-related or simple heuristic rules. Second, we evaluate the training data generated by weak supervision using three supervised learning models-random forest, k-nearest neighbors, and a support vector machine ensemble-and long short-term memory classifiers. The results show that the accuracy of all of the models improves significantly after using the training data generated by weak supervision. Third, we develop an online real-time incident detection approach that leverages the model ensemble and the uncertainty quantification while detecting incidents. Finally, we show that our proposed weak supervised learning workflow achieves a high incident detection rate (0.90) and low false alarm rate (0.08).

• Research Organization: Argonne National Laboratory (ANL), Argonne, IL (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Office of Sustainable Transportation. Vehicle Technologies Office (VTO); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities Division
• Grant/Contract Number: AC02-06CH11357
• OSTI ID: 1960722
• Alternate ID(s): OSTI ID: 1960570
• Journal Information: Accident Analysis and Prevention, Journal Name: Accident Analysis and Prevention Vol. 176; ISSN 0001-4575
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
Data-centric machine learning
recurrent neural network
traffic incident detection
weak supervision