Short-term traffic speed prediction under different data collection time intervals using a SARIMA-SDGM hybrid prediction model

Song, Zhanguo; Ye, Guo; Wu, Yao; Ma, Jing

doi:10.1371/journal.pone.0218626

Cited by 33 publications

(22 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In past research, traffic flow data was mostly processed as time series data with intervals of 10-minutes, 5-minutes, or 2minutes. For the time series prediction problem, the shorter the time interval, the more practical application value it has and the nonlinearity of the data has [40], [41]. Meanwhile, for traffic flow data, short intervals will increase the sparsity of the data.…”

Section: Input Matrix Lstm Layermentioning

confidence: 99%

MF-TCPV: A Machine Learning and Fuzzy Comprehensive Evaluation-Based Framework for Traffic Congestion Prediction and Visualization

Lin

Wan

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Section: Input Matrix Lstm Layermentioning

confidence: 99%

MF-TCPV: A Machine Learning and Fuzzy Comprehensive Evaluation-Based Framework for Traffic Congestion Prediction and Visualization

Lin

Wan

et al. 2020

IEEE Access

View full text Add to dashboard Cite

“…In short-term traffic forecasting, estimating traffic conditions usually focuses on speed and flow predictions. Speed prediction, in particular, is directly related to proactive traffic control system development [6,7]. However, estimating vehicle speed is problematic because it is affected by driver behaviour, road environment and the spatial and temporal complexities of traffic conditions.…”

Section: Literature Reviewmentioning

confidence: 99%

Short-Term Traffic Forecasting: An LSTM Network for Spatial-Temporal Speed Prediction

Abduljabbar

Dia

Tsai

et al. 2021

Future Transportation

View full text Add to dashboard Cite

Traffic forecasting remains an active area of research in the transport and data science fields. Decision-makers rely on traffic forecasting models for both policy-making and operational management of transport facilities. The wealth of spatial and temporal real-time data increasingly available from traffic sensors on roads provides a valuable source of information for policymakers. This paper adopts the Long Short-Term Memory (LSTM) recurrent neural network to predict speed by considering both the spatial and temporal characteristics of real-time sensor data. A total of 288,653 real-life traffic measurements were collected from detector stations on the Eastern Freeway in Melbourne/Australia. A comparative performance analysis among different models such as the Recurrent Neural Network (RNN) that has an internal memory that is able to remember its inputs and Deep Learning Backpropagation (DLBP) neural network approaches are also reported. The LSTM results showed average accuracies in the outbound direction ranging between 88 and 99 percent over prediction horizons between 5 and 60 min, and average accuracies between 96 and 98 percent in the inbound direction. The models also showed resilience in accuracies as the prediction horizons increased spatially for distances up to 15 km, providing a remarkable performance compared to other models tested. These results demonstrate the superior performance of LSTM models in capturing the spatial and temporal traffic dynamics, providing decision-makers with robust models to plan and manage transport facilities more effectively.

show abstract

“…Although all these time series methods are widely used in traffic prediction, the majority of the research found in the literature focuses on applying these methods on large time windows [46]. A study by Song et al on short-term traffic speed prediction provides a comparison between four prediction methods with different data collected in a varying time window ranging from 1 min up to 30 mins [47]. The study proposes a seasonal discrete grey model (SDGM) and compares the prediction accuracy with the seasonal autoregressive integrated moving average (SARIMA) model, artificial neural network (ANN) model, and support vector regression (SVR) model.…”

Section: Autoregressive (Ar) Modelmentioning

confidence: 99%

Spatiotemporal Traffic Prediction Using Hierarchical Bayesian Modeling

2021

View full text Add to dashboard Cite

Hierarchical Bayesian models (HBM) are powerful tools that can be used for spatiotemporal analysis. The hierarchy feature associated with Bayesian modeling enhances the accuracy and precision of spatiotemporal predictions. This paper leverages the hierarchy of the Bayesian approach using the three models; the Gaussian process (GP), autoregressive (AR), and Gaussian predictive processes (GPP) to predict long-term traffic status in urban settings. These models are applied on two different datasets with missing observation. In terms of modeling sparse datasets, the GPP model outperforms the other models. However, the GPP model is not applicable for modeling data with spatial points close to each other. The AR model outperforms the GP models in terms of temporal forecasting. The GP model is used with different covariance matrices: exponential, Gaussian, spherical, and Matérn to capture the spatial correlation. The exponential covariance yields the best precision in spatial analysis with the Gaussian process, while the Gaussian covariance outperforms the others in temporal forecasting.

show abstract

Short-term traffic speed prediction under different data collection time intervals using a SARIMA-SDGM hybrid prediction model

Cited by 33 publications

References 47 publications

MF-TCPV: A Machine Learning and Fuzzy Comprehensive Evaluation-Based Framework for Traffic Congestion Prediction and Visualization

MF-TCPV: A Machine Learning and Fuzzy Comprehensive Evaluation-Based Framework for Traffic Congestion Prediction and Visualization

Short-Term Traffic Forecasting: An LSTM Network for Spatial-Temporal Speed Prediction

Spatiotemporal Traffic Prediction Using Hierarchical Bayesian Modeling

Contact Info

Product

Resources

About