Time Series Traffic Speed Prediction Using k-Nearest Neighbour Based on Similar Traffic Data

Priambodo, Bagus; Jumaryadi, Yuwan

doi:10.1051/matecconf/201821803021

Cited by 11 publications

(4 citation statements)

References 15 publications

(22 reference statements)

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“…Initially, the prediction of traffic speed on roads was considered to be the problem of a time-series sequence that could be handled using autoregressive moving-average techniques [11]. There are several other approaches, such as gradient boosting [12], k-nearest neighbour [13], and SVR (support vector regression) [14], which are referred to as statistical learning techniques. Statistical learning techniques are more advantageous over time-series techniques because of their capability in aggregating relevant information, which ultimately leads to improvement in prediction performance.…”

Section: Prediction Of City Road-traffic Speedmentioning

confidence: 99%

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A Graph Neural Network (GNN)-Based Approach for Real-Time Estimation of Traffic Speed in Sustainable Smart Cities

Sharma

Nikashina

et al. 2023

Sustainability

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Planning effective routes and monitoring vehicle traffic are essential for creating sustainable smart cities. Accurate speed prediction is a key component of these efforts, as it aids in alleviating traffic congestion. While their physical proximity is important, the interconnection of these road segments is what significantly contributes to the increase of traffic congestion. This interconnectedness poses a significant challenge to increasing prediction accuracy. To address this, we propose a novel approach based on Deep Graph Neural Networks (DGNNs), which represent the connectedness of road sections as a graph using Graph Neural Networks (GNNs). In this study, we implement the proposed approach, called STGGAN, for real-time traffic-speed estimation using two different actual traffic datasets: PeMSD4 and PeMSD8. The experimental results validate the prediction accuracy values of 96.67% and 98.75% for the PeMSD4 and PeMSD8 datasets, respectively. The computation of mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) also shows a progressive decline in these error values with increasing iteration count, demonstrating the success of the suggested technique. To confirm the feasibility, reliability, and applicability of the suggested STGGAN technique, we also perform a comparison analysis, including several statistical, analytical, and machine-learning- and deep-learning-based approaches. Our work contributes significantly to the field of traffic-speed estimation by considering the structure and characteristics of road networks through the implementation of DGNNs. The proposed technique trains a neural network to accurately predict traffic flow using data from the entire road network. Additionally, we extend DGNNs by incorporating Gated Graph Attention Network (GGAN) blocks, enabling the modification of the input and output to sequential graphs. The prediction accuracy of the proposed model based on DGNNs is thoroughly evaluated through extensive tests on real-world datasets, providing a comprehensive comparison with existing state-of-the-art models for traffic-flow forecasting.

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Section: Prediction Of City Road-traffic Speedmentioning

confidence: 99%

“…Figure10. Comparative analysis for the proposed STGGAN model with state-of-the-art approaches[12][13][14][25][26][27] in terms of prediction accuracy and execution time.…”

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A Graph Neural Network (GNN)-Based Approach for Real-Time Estimation of Traffic Speed in Sustainable Smart Cities

Sharma

Nikashina

et al. 2023

Sustainability

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“…Data trafik di yang diperoleh dari sensor [1] [2] [3] [4] [5]sering diterapkan menggunakan arima atau jaringan saraf tiruan. Peningkatan kinerja prediksi dapat ditingkat dengan penambahan klasifikasi hari [3], [6]- [9], volume lalu lintas dan zona [10], data cuaca [11], [12], serta data kecepatan dari berbagai kendaraan, hari, dan kepadatan lalu lintas [13]. Prediksi lain menggunakan data lintasan GPS, data cuaca, hari khusus , berdasarkan kesamaan temporal spasial.…”

Section: Pendahuluanunclassified

Prediksi Perambatan Arus Lalu Lintas Berdasarkan Korelasi Tertinggi Antar Jalan

Priambodo

Jumaryadi²,

Salamah³

2022

Jur. Ris. Kom.

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Over the past few years, many algorithms for traffic flow predictions have been proposed to predict traffic flow. Time series models and neural network models have been widely implemented to predict traffic flow and traffic congestion based on traffic data, vehicle speed, weather, accidents, and special days. However, most of previous studies are used to predict traffic flow, not applied to predict the propagation of traffic flow. Traffic flow propagation is an interesting study. Using this finding the driver can avoid neighboring roads affected by road congestion. We propose the correlation method to find the relationship between the road. To evaluate the relationship between the road, we display the correlation results in the map. The visualization show that the correlation of traffic when congestion occurs shows better in showing the relationship between the road than the correlation at all times

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Poly-linear regression with augmented long short term memory neural network: Predicting time series data

Ahmed

Chakrabortty

Essam

et al. 2022

Information Sciences

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Time Series Traffic Speed Prediction Using k-Nearest Neighbour Based on Similar Traffic Data

Cited by 11 publications

References 15 publications

A Graph Neural Network (GNN)-Based Approach for Real-Time Estimation of Traffic Speed in Sustainable Smart Cities

A Graph Neural Network (GNN)-Based Approach for Real-Time Estimation of Traffic Speed in Sustainable Smart Cities

Prediksi Perambatan Arus Lalu Lintas Berdasarkan Korelasi Tertinggi Antar Jalan

Poly-linear regression with augmented long short term memory neural network: Predicting time series data

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