Applying Clustered KNN Algorithm for Short-Term Travel Speed Prediction and Reduced Speed Detection on Urban Arterial Road Work Zones

Park, Hyun Su; Park, Yong Woo; Kwon, Oh Hoon; Park, Shin Hyoung

doi:10.1155/2022/1107048

Cited by 2 publications

(1 citation statement)

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“…There's been an increased level of interest worldwide over the last few years in investigating road traffic collisions, particularly in analyzing and modeling collision data to improve understanding and assessment of the causes and impacts of these collisions [3]. In this study, we perform accident analysis, by applying a two-layer ensemble method using logistic regression as a Disclaimer/Publisher's Note: The statements, opinions, and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s).…”

Section: Introductionmentioning

confidence: 99%

Predicting Road Traffic Collisions Using a Two-Layer Ensemble Machine Learning Algorithm.

Oyoo,

Wekesa,

Ogada

2023

Preprint

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Road collisions are among the world’s critical issues causing many casualties, deaths, and economic losses. To deal with this scenario, precise analysis is required. In this paper, we use Machine Learning (ML) techniques to assess and predict road traffic collisions. An ensemble machine learning model with two layers was developed to analyze simulation data from the driving simulator. The first (base) layer integrates supervised learning techniques namely, k- Nearest Neighbors (k-NN) AdaBoost, Naive Bayes (NB), and Decision Trees (DT). The second layer predicts road collisions by combining the base layer outputs by employing the stacking ensemble method using logistic regression as a meta-classifier. The data for this study were imbalanced; therefore, we performed a SMOTE resampling strategy to handle the dataset imbalance. To simplify the model, the PSO algorithm was used to select the most important features in our dataset. The two-layer ensemble model proposed had the best outcomes with an accuracy of 88%, F1 score of 83%, and an AUC of 86% as compared with k-NN, DT, NB, and AdaBoost.

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Section: Introductionmentioning

confidence: 99%

Predicting Road Traffic Collisions Using a Two-Layer Ensemble Machine Learning Algorithm.

Oyoo,

Wekesa,

Ogada

2023

Preprint

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Traffic flow prediction: A 3D adaptive multi‐module joint modeling approach integrating spatial‐temporal patterns to capture global features

Ul Abideen,

Sun,

Sun

2024

Journal of Forecasting

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The challenges in citywide traffic flow are intricate, encompassing various factors like temporal and spatial dependencies, holidays, and weather. Despite the complexity, there are still research gaps in effectively incorporating these spatio‐temporal relations through deep learning. Addressing these gaps is crucial for tackling issues such as traffic congestion, public safety, and efficient traffic management within cities. This paper underscores notable research gaps, including the development of models capable of handling both local and global traffic flow patterns, integrating multi‐modal data sources, and effectively managing spatio‐temporal dependencies. In this paper, we proposed a novel model named 3D spatial–temporal‐based adaptive modeling graph convolutional network (3D(STAMGCN)) that addresses for traffic flow data in better periodicity modeling. In contrast to earlier studies, 3D(STAMGCN) approaches the task of traffic flow prediction as a periodic residual learning problem. This is achieved by capturing the input variation between historical time segments and the anticipated output for future time segments. Forecasting traffic flow, as opposed to a direct approach, is significantly simpler when focusing on learning more stationary deviations. This, in turn, aids in the training of the model. Nevertheless, the networks enable residual generation at each time interval through learned variations between future conditions and their corresponding weekly observations. Consequently, this significantly contributes to achieving more accurate forecasts for multiple steps ahead. We executed extensive experiments on two real‐world datasets and compared the performance of our model to state‐of‐the‐art (SOTA) techniques.

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Applying Clustered KNN Algorithm for Short-Term Travel Speed Prediction and Reduced Speed Detection on Urban Arterial Road Work Zones

Cited by 2 publications

References 43 publications

Predicting Road Traffic Collisions Using a Two-Layer Ensemble Machine Learning Algorithm.

Predicting Road Traffic Collisions Using a Two-Layer Ensemble Machine Learning Algorithm.

Traffic flow prediction: A 3D adaptive multi‐module joint modeling approach integrating spatial‐temporal patterns to capture global features

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