Short-Term Traffic Flow Forecasting of Intersection Based on Approximate Dynamic Programming

2013 ◽  
Vol 748 ◽  
pp. 779-784
Author(s):  
Xing Yi Li ◽  
Zhi Hong Zuo ◽  
Shuang Quan Zhou
Keyword(s):  
Dynamic Programming ◽  
Traffic Flow ◽  
Approximate Dynamic Programming ◽  
Error Function ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Traffic Flow Forecasting ◽  
Action Network ◽  
Control Matrix ◽  
Minimum Index

In this paper, we propose a model for short-term traffic flow forecasting based on approximate dynamic programming when considering the influence of related roads. First, construct the system performance index using the error function as the objective function, adjust the action network online according to the minimum index to generate the approximate optimal control vector and the control matrix, then we can get the traffic flow data at the specific road and time to forecast. To illustrate how this method works, we use intersection traffic flow of Hongli road and Shangbu road in Futian Shenzhen as experimental data, which can be demonstrated that this method can increase the forecasting accuracy and meet the real-time forecasting requirements.

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.

scholarly journals Long Short-Term Memory Networks for Traffic Flow Forecasting: Exploring Input Variables, Time Frames and Multi-Step Approaches

Informatica ◽  
2020 ◽  
pp. 1-27
Author(s):  
Bruno Fernandes ◽  
Fabio Silva ◽  
Hector Alaiz-Moreton ◽  
Paulo Novais ◽  
Jose Neves ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Traffic Flow Forecasting ◽  
Long Short Term Memory ◽  
Input Variables ◽  
Time Frames

scholarly journals GSA‐ELM: A hybrid learning model for short‐term traffic flow forecasting

2021 ◽  
Author(s):  
Zhihan Cui ◽  
Boyu Huang ◽  
Haowen Dou ◽  
Guanru Tan ◽  
Shiqiang Zheng ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Hybrid Learning ◽  
Learning Model ◽  
Short Term ◽  
Traffic Flow Forecasting

scholarly journals Short-Term Traffic Flow Forecasting Based on Data-Driven Model

Mathematics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 152 ◽  
Author(s):  
Su-qi Zhang ◽  
Kuo-Ping Lin
Keyword(s):  
Support Vector Machine ◽  
Traffic Flow ◽  
Extreme Learning Machine ◽  
Support Vector ◽  
Local Optimum ◽  
Short Term ◽  
Swarm Optimization ◽  
Traffic Flow Forecasting ◽  
Learning Machine ◽  
The One

Short-term traffic flow forecasting is the technical basis of the intelligent transportation system (ITS). Higher precision, short-term traffic flow forecasting plays an important role in alleviating road congestion and improving traffic management efficiency. In order to improve the accuracy of short-term traffic flow forecasting, an improved bird swarm optimizer (IBSA) is used to optimize the random parameters of the extreme learning machine (ELM). In addition, the improved bird swarm optimization extreme learning machine (IBSAELM) model is established to predict short-term traffic flow. The main researches in this paper are as follows: (1) The bird swarm optimizer (BSA) is prone to fall into the local optimum, so the distribution mechanism of the BSA optimizer is improved. The first five percent of the particles with better fitness values are selected as producers. The last ten percent of the particles with worse fitness values are selected as beggars. (2) The one-day and two-day traffic flows are predicted by the support vector machine (SVM), particle swarm optimization support vector machine (PSOSVM), bird swarm optimization extreme learning machine (BSAELM) and IBSAELM models, respectively. (3) The prediction results of the models are evaluated. For the one-day traffic flow sequence, the mean absolute percentage error (MAPE) values of the IBSAELM model are smaller than the SVM, PSOSVM and BSAELM models, respectively. The experimental analysis results show that the IBSAELM model proposed in this study can meet the actual engineering requirements.

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