Short-Term Traffic Flow Prediction Based on Flocking Theory and RBF Neural Network

Zhu, Jian; Cao, Jinxin; Fan, Yan

doi:10.1061/9780784413036.129

Cited by 4 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The literature review shows that although the parametric approach is relatively simple, the models following this approach typically can only capture linear relationships, therefore their performance deteriorates dramatically under non-linear conditions ( 21, 22 ). The non-parametric approach, on the other hand, is well suited to dealing with complex non-linear data, thus is widely used for traffic forecasting ( 23–25 ). However, the approach is prone to poor generalization and performance on previous unseen data during the test phase, especially the artificial neural network (ANN) method.…”

mentioning

confidence: 99%

Short-Term Travel-Time Prediction using Support Vector Machine and Nearest Neighbor Method

Meng

Toan

Wong

et al. 2022

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

This paper presents an investigation into the performance of support vector machine (SVM) in short-term travel-time prediction in comparison with baseline methods, including the historical mean, current time based, and time varying coefficient predictors. To demonstrate the SVM performance, 1-month time-series speed data on a section of Pan-Island Expressway in Singapore were used to estimate the travel time for training and testing the SVM model. The results show that the SVM method significantly outperforms the baseline methods in both normal and recurring congestion over a wide range of prediction intervals. In studying SVM prediction behavior under incident situations, the results show that all the predictors are not responsive enough using 15-minute aggregated field data, but the SVM predicted outcome follows the test data profile closely for 2-minute aggregated simulated data. Finally, to improve the prediction performance, an empirical k-nearest neighbor method is introduced to retrieve patterns closest to the test vector for SVM training. The results show that k-Nearest Neighbor is an attractive tool for SVM travel-time prediction. In retrieving the most similar patterns for SVM training, k-nearest neighbor allows dramatic reduction of training size to accelerate the training task while maintaining prediction accuracy.

show abstract

mentioning

confidence: 99%

Short-Term Travel-Time Prediction using Support Vector Machine and Nearest Neighbor Method

Meng

Toan

Wong

et al. 2022

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

show abstract

“…The second category, the Machine Learning approach, is able to deal with complex non-linear data and is thus widely used in traffic flow forecasting. The Artiﬁcial Neural Network (ANN) method ( 26 – 28 ) is naturally a methodological candidate for forecasting with multiple inputs and outputs. The ANN model, with its parallel structure and learning capability, is suitable for solving complex problems like prediction of traffic parameters ( 29 ).…”

mentioning

confidence: 99%

Support Vector Machine for Short-Term Traffic Flow Prediction and Improvement of Its Model Training using Nearest Neighbor Approach

Toan

Truong

2020

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

Short-term prediction of traffic flow is essential for the deployment of intelligent transportation systems. In this paper we present an efficient method for short-term traffic flow prediction using a Support Vector Machine (SVM) in comparison with baseline methods, including the historical average, the Current Time Based, and the Double Exponential Smoothing predictors. To demonstrate the efficiency and accuracy of the SVM method, we used one-month time-series traffic flow data on a segment of the Pan Island Expressway in Singapore for training and testing the model. The results show that the SVM method significantly outperforms the baseline methods for most prediction intervals, and under various traffic conditions, for the rolling horizon of 30 min. In investigating the effect of the input-data dimension on prediction accuracy, we found that the rolling horizon has a clear effect on the SVM’s prediction accuracy: for the rolling horizon of 30–60 min, the longer the rolling horizon, the more accurate the SVM prediction is. To look for a solution for improvement of the SVM’s training performance, we investigate the application of k-Nearest Neighbor method for SVM training using both actual data and simulated incident data. The results show that the k- Nearest Neighbor method facilitates a substantial reduction of SVM training size to accelerate the training without compromising predictive performance.

show abstract

Survey of neural network‐based models for short‐term traffic state prediction

Loan

Taherifar

2018

WIREs Data Min & Knowl

View full text Add to dashboard Cite

Traffic state prediction is a key component in intelligent transport systems (ITS) and has attracted much attention over the last few decades. Advances in computational power and availability of a large amount of data have paved the way to employ advanced neural network (NN) models for ITS, including deep architectures. There have been various NN‐based approaches proposed for short‐term traffic state prediction that are surveyed in this article, where the existing NN models are classified and their application to this area is reviewed. An in‐depth discussion is provided to demonstrate how different types of NNs have been used for different aspects of short‐term traffic state prediction. Finally, possible further research directions are suggested for additional applications of NN models, especially using deep architectures, to address the dynamic nature in complex transportation networks. This article is categorized under: Technologies > Prediction Technologies > Machine Learning Application Areas > Science and Technology

show abstract

Short-Term Traffic Flow Prediction Based on Flocking Theory and RBF Neural Network

Cited by 4 publications

References 3 publications

Short-Term Travel-Time Prediction using Support Vector Machine and Nearest Neighbor Method

Short-Term Travel-Time Prediction using Support Vector Machine and Nearest Neighbor Method

Support Vector Machine for Short-Term Traffic Flow Prediction and Improvement of Its Model Training using Nearest Neighbor Approach

Survey of neural network‐based models for short‐term traffic state prediction

Contact Info

Product

Resources

About