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Traffic Flow Prediction Model for Large-Scale Road Network Based on Cloud Computing
2014
Mathematical Problems in Engineering
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On the basis of the traffic flow data of Haizhu District in Guangzhou City, the proposed model was verified and compared with the serial GA-SVM model and parallel GA-SVM model based on MPI (message passing ...
In cloud computing environment, firstly, SVM parameters are optimized by the parallel genetic algorithm, and then this optimized parallel SVM model is used to predict traffic flow. ...
With 4 * 144 = 576 groups of data from Monday to Thursday as the training samples, the traffic flow data on Friday is predicted. ...
doi:10.1155/2014/926251
fatcat:mhcab47swfebnpxtipk6vqzqti
MF-TCPV: A Machine Learning and Fuzzy Comprehensive Evaluation based Framework for Traffic Congestion Prediction and Visualization
2020
IEEE Access
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After careful consideration, we process traffic flow data with a 5minute interval. The method of predicting traffic speed is the same as the method of predicting traffic flow. ...
Finally, the new feature set is input into RVM for prediction. In past research, traffic flow data was mostly processed as time series data with intervals of 10-minutes, 5-minutes, or 2minutes. ...
Construct a traffic congestion cause-effect diagram and transform a datadriven model into a cause-effect driven model. ...
doi:10.1109/access.2020.3043582
fatcat:3n2nih7pqfb53cviwur4lmoage
Learning spatiotemporal features from incomplete data for traffic flow prediction using hybrid deep neural networks
[article]
2022
arXiv
pre-print
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Urban traffic flow prediction using data-driven models can play an important role in route planning and preventing congestion on highways. ...
This study focuses on hybrid deep neural networks to predict traffic flow in the California Freeway Performance Measurement System (PeMS) with missing values. ...
As can be seen, the hybrid model LSTM2-SP-CNN3 with a series-parallel connection outperforms the other models in predicting future traffic flows with smaller errors in all cases. ...
arXiv:2204.10222v1
fatcat:sl4unyio45anbffnltgpgakd2q
Traffic Flow Prediction Using SPGAPSO-CKRVM Model
2020
Revue d'intelligence artificielle : Revue des Sciences et Technologies de l'Information
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Traffic flow prediction is popular research of ITS. Traffic flow prediction models based on machine learning have recently been widely applied. ...
Finally, the proposed model is verified with the real data of Whitemud Drive in Canada. ...
So, we deal with traffic flow data into 5 min time span. ...
doi:10.18280/ria.340303
fatcat:ggcbvdqjjfafbii4i2q7p4brqq
FLOWPROPHET: Generic and Accurate Traffic Prediction for Data-Parallel Cluster Computing
2015
2015 IEEE 35th International Conference on Distributed Computing Systems
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In this paper, we design and implement FLOWPROPHET, a general framework to predict traffic flows for DCFs. ...
Data-parallel computing frameworks (DCF) such as MapReduce, Spark, and Dryad etc. have tremendous applications in big data and cloud computing, and throw tons of flows into data center networks. ...
We also show that simple network optimizations with aheadof-time flow predictions can provide substantial improvement in application performance. ...
doi:10.1109/icdcs.2015.43
dblp:conf/icdcs/WangCCLZGQLG15
fatcat:p6zoctv6o5gydpk6hdi6k36wqm
Scalable Traffic Predictive Analysis using GPU in Big Data
[article]
2021
arXiv
pre-print
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The major contribution of this paper is to improve the performance of machine learning in distributed parallel computing systems with GPU to predict the traffic congestion. ...
The paper adopts parallel computing systems for predictive analysis in both CPU and GPU leveraging Spark Big Data platform. ...
Jams attributes
Prediction with Machine Learning
Machine Learning Flow We aim to predict the traffic jam with classification model. ...
arXiv:2106.15151v1
fatcat:jn57j2zcqnajjhmrmt62n3frbm
TrafPS: A Visual Analysis System Interpreting Traffic Prediction in Shapley
[article]
2022
arXiv
pre-print
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To tackle this problem, we adapt Shapley value and present a visualization analysis system , which can provide experts with the interpretation of traffic flow prediction. ...
In recent years, deep learning approaches have been proved good performance in traffic flow prediction, many complex models have been proposed to make traffic flow prediction more accurate. ...
Parallel Coordinates Plot (Fig. 2(c 4 )) draws each grid's traffic flow change in future 60 minutes with the same color in Square Chart (Fig. 2(c 3 )). ...
arXiv:2203.06213v1
fatcat:iq2boay6lvc67ipzhz7j2suid4
A Noise-Immune Boosting Framework for Short-Term Traffic Flow Forecasting
2021
Complexity
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The noisy and unstable traffic flow data also prevent the models from effectively learning the underlying patterns for forecasting future traffic flow. ...
of the traffic flow. ...
Ablation Study. e observed traffic flow data quality is crucial for traffic flow prediction accuracy, and thus data quality control is essential to smooth the noisy traffic flow data. ...
doi:10.1155/2021/5582974
fatcat:sqh5ppof4vb5hauubaxtcpauyq
A real-time traffic simulation system
1998
IEEE Transactions on Vehicular Technology
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Tests with real traffic data collected from the freeway network in the metropolitan area of Minneapolis, MN, were used to validate the accuracy and computational rate of the parallel simulation system. ...
We designed a traffic-flow simulation code and mapped it onto a parallel computer architecture. This traffic simulation system is capable of simulating freeway traffic flow in real time. ...
Such a system would be fed with real-time traffic input data, and it would predict traffic conditions in real time. ...
doi:10.1109/25.661057
fatcat:5sifvq4yzzc2zjfkejqzqcpwb4
A Survey on Traffic Prediction and Classification in SDN
[chapter]
2020
Advances in Parallel Computing
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This intelligence facilitates traffic prediction and classification that can assist activities like traffic analysis, dynamic updating of flow rules, intelligence routing, flow scheduling and security. ...
In this paper, we discussed the existing traffic prediction and traffic classification methods in the SDN. ...
Abdullah Baz [12] framed a flow prediction algorithm using a bayesian machine learning (BML) to reduce the overhead of communicating with controller in handling traffic. ...
doi:10.3233/apc200168
fatcat:lfnbeuqlujfylpxxjput6bxxm4
Short-Term Traffic Prediction considering Spatial-Temporal Characteristics of Freeway Flow
2021
Journal of Advanced Transportation
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This paper presents a short-term traffic prediction method, which takes the historical data of upstream points and prediction point itself and their spatial-temporal characteristics into consideration. ...
Third, some meaningful experiments are carried out using real data with different upstream points, time steps, and prediction model structures. ...
With the rapid development of ITS and improvement of data quality, more nonparametric prediction methods are used in the prediction of traffic flow. ...
doi:10.1155/2021/5815280
fatcat:gpwj4b4g6rbchm7r5db74sfvja
A recurrent neural network for urban long-term traffic flow forecasting
2020
Applied intelligence (Boston)
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This paper investigates the use of recurrent neural network to predict urban long-term traffic flows. ...
They also confirm the scalability of GRNN-LF compared to the most competitive GPU-based software tools when dealing with big traffic flow such as Beijing urban data. ...
In addition, RNN-LF could predict long-term traffic flows from real case of Odense traffic flow data. To deal with big traffic flow data in real time, HPC-based version of RNN-LF has been developed. ...
doi:10.1007/s10489-020-01716-1
fatcat:juifwbh5r5fovgyt3nud4gwwam
Short-Term Traffic Flow Prediction: A Method of Combined Deep Learnings
2021
Journal of Advanced Transportation
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Finally, the training set and test set for the CDLP model are established through the processing of traffic flow data collected from the field. ...
Short-term traffic flow prediction can provide a basis for traffic management and support for travelers to make decisions. ...
predict traffic flow with high accuracy. ...
doi:10.1155/2021/9928073
fatcat:3cx4xr5danhmfbjs7r65w7hyhm
Prediction of Road Network Traffic State Using the NARX Neural Network
2021
Journal of Advanced Transportation
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Meanwhile, the prediction performance of NARX, LSTM, and GRU is experimentally compared with traffic prediction, and it is found that NARX NN can predict long-term flow and the prediction performance is ...
Finally, according to the traffic state classification interval, the traffic state of the road network for the next day is obtained from the current MFD, the predicted traffic flow, and the corresponding ...
Figure 7 : 7 Figure 7: Traffic flow raw data from four monitoring stations.
Figure 8 : 8 Figure 8: architectures of NARX networks. (a) Parallel architecture. (b) Series-parallel architecture. ...
doi:10.1155/2021/2564211
fatcat:fh5ld6k6cravldqxznsf5d3rou
A novel fuzzy-based convolutional neural network method to traffic flow prediction with uncertain traffic accident information
2019
IEEE Access
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As a key part of the method of improving traffic capacity, traffic flow prediction is becoming a research hot-spot of traffic science and intelligent technology, in which the accuracy of traffic flow prediction ...
First, for the sake of extracting the spatial-temporal characteristics of the traffic flow data, this paper divides the whole area into small blocks of 32 × 32 and constructs three trend sequences with ...
potential knowledge hidden in traffic big data to predict traffic flow? ...
doi:10.1109/access.2019.2896913
fatcat:de5g3mcaujcmpfs4u5ypfm4fse
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