scholarly journals Short-Term Traffic Flow Forecasting via Multi-Regime Modeling and Ensemble Learning

2020 ◽  
Vol 10 (1) ◽  
pp. 356 ◽  
Author(s):  
Zhenbo Lu ◽  
Jingxin Xia ◽  
Man Wang ◽  
Qinghui Nie ◽  
Jishun Ou
Keyword(s):  
Traffic Flow ◽  
Ensemble Learning ◽  
Traffic Management ◽  
Feasible Solution ◽  
Learning Strategy ◽  
Percentage Error ◽  
Forecasting Model ◽  
Short Term ◽  
Traffic Flow Forecasting ◽  
And Control

Short-term traffic flow forecasting is crucial for proactive traffic management and control. One key issue associated with the task is how to properly define and capture the temporal patterns of traffic flow. A feasible solution is to design a multi-regime strategy. In this paper, an effective approach to forecasting short-term traffic flow based on multi-regime modeling and ensemble learning is presented. First, to properly capture the different patterns of traffic flow dynamics, a regime identification model based on probabilistic modeling was developed. Each identified regime represents a specific traffic phase, and was used as the representative feature for the forecasting modeling. Second, a forecasting model built on an ensemble learning strategy was developed, which integrates the forecasts of multiple regression trees. The traffic flow data over 5-min intervals collected from four I-80 freeway segments, in California, USA, was used to evaluate the proposed approach. The experimental results show that the identified regimes are able to well explain the different traffic phases, and play an important role in forecasting. Furthermore, the developed forecasting model outperformed four typical models in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE) on three traffic flow measures.

scholarly journals Short-Term Traffic Flow Forecasting Model Based on GA-TCN

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Rongji Zhang ◽  
Feng Sun ◽  
Ziwen Song ◽  
Xiaolin Wang ◽  
Yingcui Du ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Convolutional Neural Network ◽  
Traffic Flow ◽  
Absolute Error ◽  
Forecasting Model ◽  
Average Absolute Error ◽  
Short Term ◽  
Traffic Flow Forecasting ◽  
Fitness Value

Traffic flow forecasting is the key to an intelligent transportation system (ITS). Currently, the short-term traffic flow forecasting methods based on deep learning need to be further improved in terms of accuracy and computational efficiency. Therefore, a short-term traffic flow forecasting model GA-TCN based on genetic algorithm (GA) optimized time convolutional neural network (TCN) is proposed in this paper. The prediction error was considered as the fitness value and the genetic algorithm was used to optimize the filters, kernel size, batch size, and dilations hyperparameters of the temporal convolutional neural network to determine the optimal fitness prediction model. Finally, the model was tested using the public dataset PEMS. The results showed that the average absolute error of the proposed GA-TCN decreased by 34.09%, 22.42%, and 26.33% compared with LSTM, GRU, and TCN in working days, while the average absolute error of the GA-TCN decreased by 24.42%, 2.33%, and 3.92% in weekend days, respectively. The results indicate that the model proposed in this paper has a better adaptability and higher prediction accuracy in short-term traffic flow forecasting compared with the existing models. The proposed model can provide important support for the formulation of a dynamic traffic control scheme.

Short-Term Traffic Flow Forecasting for Urban Roads Using Data-Driven Feature Selection Strategy and Bias-Corrected Random Forests

2017 ◽  
Vol 2645 (1) ◽  
pp. 157-167 ◽  
Author(s):  
Jishun Ou ◽  
Jingxin Xia ◽  
Yao-Jan Wu ◽  
Wenming Rao
Keyword(s):  
Feature Selection ◽  
Traffic Flow ◽  
Random Forests ◽  
Urban Traffic ◽  
Data Driven ◽  
Series Data ◽  
Selection Strategy ◽  
Percentage Error ◽  
Short Term ◽  
Traffic Flow Forecasting

Urban traffic flow forecasting is essential to proactive traffic control and management. Most existing forecasting methods depend on proper and reliable input features, for example, weather conditions and spatiotemporal lagged variables of traffic flow. However, the feature selection process is often done manually without comprehensive evaluation and leads to inaccurate results. For that challenge, this paper presents an approach combining the bias-corrected random forests algorithm with a data-driven feature selection strategy for short-term urban traffic flow forecasting. First, several input features were extracted from traffic flow time series data. Then the importance of these features was quantified with the permutation importance measure. Next, a data-driven feature selection strategy was introduced to identify the most important features. Finally, the forecasting model was built on the bias-corrected random forests algorithm and the selected features. The proposed approach was validated with data collected from three types of urban roads (expressway, major arterial, and minor arterial) in Kunshan City, China. The proposed approach was also compared with 10 existing approaches to verify its effectiveness. The results of the validation and comparison show that even without further model tuning, the proposed approach achieves the lowest average mean absolute error and root mean square error on six stations while it achieves the second-best average performance in mean absolute percentage error. Meanwhile, the training efficiency is improved compared with the original random forests method owing to the use of the feature selection strategy.

A hybrid short-term traffic flow forecasting model based on time series multifractal characteristics

2017 ◽  
Vol 48 (8) ◽  
pp. 2429-2440 ◽  
Author(s):  
Hong Zhang ◽  
Xiaoming Wang ◽  
Jie Cao ◽  
Minan Tang ◽  
Yirong Guo
Keyword(s):  
Time Series ◽  
Traffic Flow ◽  
Forecasting Model ◽  
Short Term ◽  
Model Based ◽  
Traffic Flow Forecasting

Short-term traffic flow forecasting model under missing data

2010 ◽  
Vol 30 (4) ◽  
pp. 1117-1120 ◽  
Author(s):  
Jian-rui XU ◽  
Xing-yi LI ◽  
Hua-ji SHI
Keyword(s):  
Missing Data ◽  
Traffic Flow ◽  
Forecasting Model ◽  
Short Term ◽  
Traffic Flow Forecasting

scholarly journals Short-Term Intersection Traffic Flow Forecasting

2020 ◽  
Vol 12 (19) ◽  
pp. 8158
Author(s):  
Wenrui Qu ◽  
Jinhong Li ◽  
Lu Yang ◽  
Delin Li ◽  
Shasha Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Traffic Management ◽  
Parametric Modeling ◽  
Neural Network Modeling ◽  
Short Term ◽  
Trip Planning ◽  
Elman Neural Network ◽  
Traffic Flow Forecasting ◽  
Short Period

The intersection is a bottleneck in an urban roadway network. As traffic demand increases, there is a growing congestion problem at urban intersections. Short-term traffic flow forecasting is crucial for advanced trip planning and traffic management. However, there are only a handful of existing models for forecasting intersection traffic flow. In addition, previous short-term traffic flow forecasting models usually were for predicting roadway conditions in a very short period, such as one minute or five minutes, which is often too late given that a driver may well be approaching the bottleneck already. Being able to accurately predict traffic congestions in about half-hour advance is very critical for advanced trip planning and traffic management. To fill this gap, this research develops a two-layer stacking model for intersection short-term traffic flow forecasting by integrating the K-nearest neighbor (KNN) and Elman Neural Network modeling methods. It was developed using the 24-h cycle by cycle traffic data collected at a signalized intersection in Jinan, China. The developed model is evaluated by applying it to the same intersection for forecasting the short-term traffic conditions in a different set of days. The prediction performance of this model was compared with four other models developed using some existing non-parametric modeling and machine learning methods, including clustering, backpropagation (BP) neural network, KNN, and Elman Neural Network. The results of this study indicate that the proposed model outperforms other existing models in terms of its prediction accuracy.

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