Semantic N-Gram Feature Analysis and Machine Learning–Based Classification of Drivers’ Hazardous Actions at Signal-Controlled Intersections

Kwayu, Keneth Morgan; Kwigizile, Valerian; Zhang, Jiansong; Oh, Jun Seok

doi:10.1061/(asce)cp.1943-5487.0000895

Cited by 27 publications

(9 citation statements)

References 19 publications

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“…Many text mining applications, such as thematic analysis, content analysis, supervised modeling, unsupervised modeling, and NLP, can be used to extract insights from crash narrative textual data ( 11 ). For example, Kwayu et al ( 12 ) used crash narratives to conduct semantic analysis and classification of drivers’ risky behavior at signalized intersections. The proposed algorithm correctly identified “disregard traffic control” and “fail to yield” hazardous actions from crash narratives.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The proposed algorithm correctly identified “disregard traffic control” and “fail to yield” hazardous actions from crash narratives. Furthermore, the developed textual-based algorithm demonstrated promise in detecting potential errors made by police officers while coding hazardous actions in crash reports ( 12 ). Das et al ( 13 ) formed a framework for using machine learning models to classify crash types from unstructured textual content.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The XGBoost model was discovered to be the best classifier. Kwayu et al ( 12 ) also utilized crash narratives complemented by crash metadata to discern the prevalence and co-occurrence of themes that contribute to crash incidents by using text mining analytics and network topology ( 14 ).…”

Section: Literature Reviewmentioning

confidence: 99%

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Classifying Pedestrian Maneuver Types Using the Advanced Language Model

Das

Oliaee

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Pedestrians are the most vulnerable roadway user. While there is much emphasis on “green transportation,” a troubling fact emerges in the U.S.A.: pedestrian deaths are increasing significantly in comparison to motorist deaths, reaching nearly 6941 in 2020—the highest in over two decades. The Pedestrian and Bicycle Crash Analysis Tool was developed to determine motorists and non-motorists’ actions before a crash to accurately define the sequence of events and precipitating actions leading to traffic crashes between motor vehicles and pedestrians or bicyclists. Police report traffic crash data and crash narrative reports undoubtedly play a major role in decision-making for the safety engineers. Using crash data from three major cities in Texas (2018–2020), this study assessed the data quality of text narratives in police reports of pedestrian crashes. The objective of this study was to develop a framework for applying advanced language models to classify pedestrian maneuver types from unstructured textual content. The results show that although natural language processing models are promising as crash typing tools, narration inconsistency, data imbalance, and small sample sizes are holding back progress in this area. The framework demonstrated high accuracy for the binary classification task, but it was inconsistent for the more complex multiclass task. This framework provides the basis for applying advanced language models such as the bidirectional encoder representations from transformers model in identifying pedestrian maneuver types associated with pedestrian crashes.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Classifying Pedestrian Maneuver Types Using the Advanced Language Model

Das

Oliaee

et al. 2023

Transportation Research Record: Journal of the Transportation R

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“…The nature and type of intersection crashes vary by traffic control type. For example, failure to yield is the most common type of hazardous action at the stop-controlled intersections while failure to stop at a safe distance leading to rear-end crashes is the most frequent crash scenario at the signal-control intersections ( 4 ). Most of the intersection crash countermeasures are, therefore, designed to mitigate specific crash factors that are unique to the type of intersection traffic control ( 5 ).…”

Section: Literature Reviewmentioning

confidence: 99%

A Scalable Deep Learning Framework for Extracting Model Inventory of Roadway Element Intersection Control Types From Panoramic Images

Kwayu

Toth

Himmelein

2022

Transportation Research Record

Self Cite

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In the United States, the Model Inventory of Roadway Element (MIRE) provides a comprehensive list of data that are needed to support states’ data-driven safety programs. The intersection control is part of the MIRE Fundamental Data Elements (FDE) for which state Departments of Transportation are required to complete the collection by September 30, 2026. It is essential roadway data that have been used widely in traffic safety studies. This study proposes a scalable and automated deep learning framework for detecting and classifying stop and yield intersection controls using panoramic street view images. The Faster Region-based Convolutional Neural Networks (Faster R-CNN) model architecture was used to detect and classify stop and yield signs from the images. A transfer learning process was deployed using the Inception-ResNet-v2 generic feature extractor to accelerate the training and performance of the deep learning model with less data collection effort. The effectiveness and scalability of the proposed framework were tested on a sample of road intersections in the state of Michigan. The proposed deep learning model achieved a recall value of 97.7% and 98.2% for detecting and classifying stop and yield signs respectively. The evaluation of the model performance at a county level suggests that the model can be scaled to a statewide level without a substantial increase in the demand for computational resources. As demonstrated in this study, state DOTs can leverage the advancement of deep learning techniques and the availability of imagery data to expedite the process of collecting the MIRE data.

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“…There have been numerous ANN techniques developed to date, each of which may befit a specific application (e.g., self-organizing maps, recurrent neural networks, and feed-forward back-propagation neural networks). However, ANN is more commonly employed in predictive algorithms [54,56,57] and pattern recognition applications [23,36,55,58]. For the study presented herein, SOM was utilized using the Deep Learning Toolbox in MATLAB, where the Kohonen rule was adopted [55,59].…”

Section: Unsupervised Learning: Clusteringmentioning

confidence: 99%

Data-Driven Community Flood Resilience Prediction

Abdel-Mooty

El‐Dakhakhni

Coulibaly

2022

Water

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Climate change and the development of urban centers within flood-prone areas have significantly increased flood-related disasters worldwide. However, most flood risk categorization and prediction efforts have been focused on the hydrologic features of flood hazards, often not considering subsequent long-term losses and recovery trajectories (i.e., community’s flood resilience). In this study, a two-stage Machine Learning (ML)-based framework is developed to accurately categorize and predict communities’ flood resilience and their response to future flood hazards. This framework is a step towards developing comprehensive, proactive flood disaster management planning to further ensure functioning urban centers and mitigate the risk of future catastrophic flood events. In this framework, resilience indices are synthesized considering resilience goals (i.e., robustness and rapidity) using unsupervised ML, coupled with climate information, to develop a supervised ML prediction algorithm. To showcase the utility of the framework, it was applied on historical flood disaster records collected by the US National Weather Services. These disaster records were subsequently used to develop the resilience indices, which were then coupled with the associated historical climate data, resulting in high-accuracy predictions and, thus, utility in flood resilience management studies. To further demonstrate the utilization of the framework, a spatial analysis was developed to quantify communities’ flood resilience and vulnerability across the selected spatial domain. The framework presented in this study is employable in climate studies and patio-temporal vulnerability identification. Such a framework can also empower decision makers to develop effective data-driven climate resilience strategies.

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Semantic N-Gram Feature Analysis and Machine Learning–Based Classification of Drivers’ Hazardous Actions at Signal-Controlled Intersections

Cited by 27 publications

References 19 publications

Classifying Pedestrian Maneuver Types Using the Advanced Language Model

Classifying Pedestrian Maneuver Types Using the Advanced Language Model

A Scalable Deep Learning Framework for Extracting Model Inventory of Roadway Element Intersection Control Types From Panoramic Images

Data-Driven Community Flood Resilience Prediction

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