Traffic Flow Prediction Model for Large-Scale Road Network Based on Cloud Computing

Yang, Zhaosheng; Mei, Duo; Yang, Qingjie; Zhou, Huxing; Li, Xiaowen

doi:10.1155/2014/926251

Cited by 17 publications

(7 citation statements)

References 14 publications

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“…However, an SVM is highly sensitive to the choices of the kernel function and parameters. Many researchers have attempted to optimize an SVM and apply it to traffic prediction to derive some improved SVM variants, such as chaos wavelet analysis SVMs [ 24 ], least squares SVMs [ 25 ], particle swarm optimization SVMs [ 26 ], and genetic algorithm SVMs [ 27 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks

Wang

et al. 2017

Sensors

512

232

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Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid representation method that can retain the fine-scale structure of a transportation network. Network-wide traffic speeds are converted into a series of static images and input into a novel deep architecture, namely, spatiotemporal recurrent convolutional networks (SRCNs), for traffic forecasting. The proposed SRCNs inherit the advantages of deep convolutional neural networks (DCNNs) and long short-term memory (LSTM) neural networks. The spatial dependencies of network-wide traffic can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An experiment on a Beijing transportation network with 278 links demonstrates that SRCNs outperform other deep learning-based algorithms in both short-term and long-term traffic prediction.

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Section: Literature Reviewmentioning

confidence: 99%

Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks

Wang

et al. 2017

Sensors

512

232

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“…Given that the fundamental prediction model was set up by taking the statistical theory and method as foundation, this section particularly reviews the selected statistical models and Interactive Multiple Model algorithm. Additional nonparametric models including Artificial Neural Network model and Support Vector Machine model have also been conducted but were not reviewed here because of the space limit [11,12].…”

Section: Summary Of Literature Reviewmentioning

confidence: 99%

Short-Term Bus Passenger Demand Prediction Based on Time Series Model and Interactive Multiple Model Approach

Xue

Sun

Chen

2015

Discrete Dynamics in Nature and Society

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Although bus passenger demand prediction has attracted increased attention during recent years, limited research has been conducted in the context of short-term passenger demand forecasting. This paper proposes an interactive multiple model (IMM) filter algorithm-based model to predict short-term passenger demand. After aggregated in 15 min interval, passenger demand data collected from a busy bus route over four months were used to generate time series. Considering that passenger demand exhibits various characteristics in different time scales, three time series were developed, named weekly, daily, and 15 min time series. After the correlation, periodicity, and stationarity analyses, time series models were constructed. Particularly, the heteroscedasticity of time series was explored to achieve better prediction performance. Finally, IMM filter algorithm was applied to combine individual forecasting models with dynamically predicted passenger demand for next interval. Different error indices were adopted for the analyses of individual and hybrid models. The performance comparison indicates that hybrid model forecasts are superior to individual ones in accuracy. Findings of this study are of theoretical and practical significance in bus scheduling.

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“…Many techniques have been applied or adapted from different disciplines, including Support vector machine (SVM) [8], k-nearest neighbors (K-NN) algorithm [9], neural network prediction [10]. Many researchers have attempted to change the choices of the kernel function and parameters for optimizing the SVMs [11], such as chaos wavelet analysis SVMs [12], genetic algorithm SVMs [13], novel wavelet-SVM [14] and single-step prediction SVMs [15]. The results of these methods have shown the superior performance of the non-parametric methods, compared to traditional parametric methods.…”

Section: Introductionmentioning

confidence: 99%

Deep Temporal Convolutional Networks for Short-Term Traffic Flow Forecasting

et al. 2019

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To reduce the increasingly congestion in cities, it is essential for intelligent transportation system (ITS) to accurately forecast the short-term traffic flow to identify the potential congestion sites. In recent years, the emerging deep learning method has been introduced to design traffic flow predictors, such as recurrent neural network (RNN) and long short-term memory (LSTM), which has demonstrated its promising results. In this paper, different from existing work, we study the temporal convolutional network (TCN) and propose a deep learning framework based on TCN model for short-term city-wide traffic forecast to accurately capture the temporal and spatial evolution of traffic flow. Moreover, we design the model with the Taguchi method to develop an optimized structure of the TCN model, which not only reduces the number of experiments, but also yields high accuracy of forecasting results. With the real-world traffic flow data collected from highways in Birmingham City of U.K., we compare our model with four deep learning based models including LSTM models, GRU models, SAE models, DeepTrend and CNN-LSTM models in terms of the mean absolute error (MAE) and mean relative error (MRE) regarding the actual flow data. The experimental results demonstrate that our framework achieves the state-of-art performance with superior accuracy in short-term traffic flow forecasting. INDEX TERMS Deep learning, temporal convolutional networks, short-term forecasting.

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Traffic Flow Prediction Model for Large-Scale Road Network Based on Cloud Computing

Cited by 17 publications

References 14 publications

Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks

Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks

Short-Term Bus Passenger Demand Prediction Based on Time Series Model and Interactive Multiple Model Approach

Deep Temporal Convolutional Networks for Short-Term Traffic Flow Forecasting

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