Application of seasonal SVR with chaotic immune algorithm in traffic flow forecasting

Hong, Wei‐Chiang

doi:10.1007/s00521-010-0456-7

Cited by 95 publications

(43 citation statements)

References 49 publications

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“…Williams and Hoel () took the seasonal trend of traffic flow into account and proposed a seasonal ARIMA prediction method. Hong () presented a seasonal SVR model for traffic flow forecasting. There are also a few studies that took weather factors into account in the prediction of traffic flows.…”

Section: Related Workmentioning

confidence: 99%

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A reliable traffic prediction approach for bike‐sharing system by exploiting rich information with temporal link prediction strategy

Zhou

Zhu

et al. 2019

Transactions in GIS

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Bike‐sharing systems have been widely used in major cities across the world. As bike borrowing and return at different stations in different periods are not balanced, the bikes in a bike‐sharing system need to be redistributed frequently to rebalance the system. Therefore, traffic flow forecasting of the bike‐sharing system is an important issue, as this is conducive to achieving rebalancing of the bike system. In this article, we present a new traffic flow prediction approach based on the temporal links in dynamic traffic flow networks. A station clustering algorithm is first introduced to cluster stations into groups. A temporal link prediction method based on the dynamic traffic flow network method (STW+M) is then proposed to predict the traffic flow between stations. In our method, the non‐negative tensor decomposition and time‐series analysis model capture the rich information (temporal variabilities, spatial characteristics, and weather information) of the across‐clusters transition. Then, a temporal link prediction strategy is used to forecast potential links and weights in the traffic flow network by investigating both the network structure and the results of tensor computations. In order to assess the methods proposed in this article, we have used the data of bike‐sharing systems in New York and Washington, DC to conduct bike traffic prediction and the experimental results have shown that our method produces the lowest root mean square error (RMSE) and mean square error (MSE). Compared to four prediction methods from the literature, our RMSE and MSE of the two datasets have been lowered by an average of 2.55 (Washington, DC) and 2.41 (New York) and 3.35 (Washington, DC) and 2.96 (New York), respectively. The results show that the proposed approach improves predictions of traffic flow.

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Section: Related Workmentioning

confidence: 99%

“…Williams and Hoel (2003) took the seasonal trend of traffic flow into account and proposed a seasonal ARIMA prediction method. Hong (2012) presented a seasonal SVR model for traffic flow forecasting.…”

Section: Rel Ated Workmentioning

confidence: 99%

A reliable traffic prediction approach for bike‐sharing system by exploiting rich information with temporal link prediction strategy

Zhou

Zhu

et al. 2019

Transactions in GIS

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“…However, one limitation of ARIMA is its natural tendency to concentrate on the mean values of the past series data. Therefore, it remains challenging to capture a rapidly changing process [5]. Support Vector Regression (SVR) has been successfully applied for time series prediction, but it also has disadvantages like the lack of structured means to determine some key parameters of the model [5].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning with Long Short-Term Memory for Time Series Prediction

et al. 2019

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Time series prediction can be generalized as a process that extracts useful information from historical records and then determines future values. Learning longrange dependencies that are embedded in time series is often an obstacle for most algorithms, whereas Long Short-Term Memory (LSTM) solutions, as a specific kind of scheme in deep learning, promise to effectively overcome the problem. In this article, we first give a brief introduction to the structure and forward propagation mechanism of the LSTM model. Then, aiming at reducing the considerable computing cost of LSTM, we put forward the Random Connectivity LSTM (RCLSTM) model and test it by predicting traffic and user mobility in telecommunication networks. Compared to LSTM, RCLSTM is formed via stochastic connectivity between neurons, which achieves a significant breakthrough in the architecture formation of neural networks. In this way, the RCLSTM model exhibits a certain level of sparsity, which leads to an appealing decrease in the computational complexity and makes the RCLSTM model become more applicable in latency-stringent application scenarios. In the field of telecommunication networks, the prediction of traffic series and mobility traces could directly benefit from this improvement as we further demonstrate that the prediction accuracy of RCLSTM is comparable to that of the conventional LSTM no matter how we change the number of training samples or the length of input sequences.

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“…SVM is mainly used to resolve classification and regression problems. Nonlinear regression SVM has been used to forecast traffic flow and obtained good results [6].…”

Section: Introductionmentioning

confidence: 99%

Traffic Flow Forecasting Based on Combination of Multidimensional Scaling and SVM

Sun

Fox

2013

Int. J. ITS Res.

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Abstract:Traffic flow forecasting is a popular research topic of Intelligent Transportation Systems (ITS). With the development of information technology, lots of history electronic traffic flow data are collected. How to take full use of the history traffic flow data to improve the traffic flow forecasting precision is an important issue. More history data are considered, more computation cost should be taken. In traffic flow forecasting, many traffic parameters can be chosen to forecast traffic flow. Traffic flow forecasting is a real-time problem, how to improve the computation speed is a very important problem. Feature extraction is an efficient means to improve computation speed. Some feature extraction methods have been proposed, such as PCA, SOM network, and Multidimensional Scaling (MDS) and so on. But PCA can only measure the linear correlation between variables. The computation cost of SOM network is very expensive. In this paper, MDS is used to decrease the dimension of traffic parameters, interpolation MDS is used to increase computation speed. It is combined with nonlinear regression Support Vector Machines (SVM) to forecast traffic flow. The efficiency of the method is illustrated through analyzing the traffic data of Jinan urban transportation.

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Application of seasonal SVR with chaotic immune algorithm in traffic flow forecasting

Cited by 95 publications

References 49 publications

A reliable traffic prediction approach for bike‐sharing system by exploiting rich information with temporal link prediction strategy

A reliable traffic prediction approach for bike‐sharing system by exploiting rich information with temporal link prediction strategy

Deep Learning with Long Short-Term Memory for Time Series Prediction

Traffic Flow Forecasting Based on Combination of Multidimensional Scaling and SVM

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