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

Zhaosheng Yang, Duo Mei, Qingfang Yang, Huxing Zhou, Xiaowen Li
2014 Mathematical Problems in Engineering  
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

Leixiao Li, Hao Lin, Jianxiong Wan, Zhiqiang Ma, Hui Wang
2020 IEEE Access  
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]

Mehdi Mehdipour Ghazi, Amin Ramezani, Mehdi Siahi, Mostafa Mehdipour Ghazi
2022 arXiv   pre-print
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

Hao Lin, Leixiao Li, Hui Wang, Yongsheng Wang, Zhiqiang Ma
2020 Revue d'intelligence artificielle : Revue des Sciences et Technologies de l'Information  
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

Hao Wang, Li Chen, Kai Chen, Ziyang Li, Yiming Zhang, Haibing Guan, Zhengwei Qi, Dongsheng Li, Yanhui Geng
2015 2015 IEEE 35th International Conference on Distributed Computing Systems  
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]

Dalyapraz Dauletbak, Junghoon Heo, Sooyoung Kim, Yeon Pyo Kim, Jongwook Woo
2021 arXiv   pre-print
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]

Yifan Jiang, Zezheng Feng, Hongjun Wang, Zipei Fan, Xuan Song
2022 arXiv   pre-print
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

Shiqiang Zheng, Shuangyi Zhang, Youyi Song, Zhizhe Lin, Dazhi Jiang, Teng Zhou, chuan lin
2021 Complexity  
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

A.T. Chronopoulos, C.M. Johnston
1998 IEEE Transactions on Vehicular Technology  
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]

Gunavathie, UmaMaheswariS
2020 Advances in Parallel Computing  
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

Jiaqi Wang, Yingying Ma, Xianling Yang, Teng Li, Haoxi Wei, Feng-Jang Hwang
2021 Journal of Advanced Transportation  
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

Asma Belhadi, Youcef Djenouri, Djamel Djenouri, Jerry Chun-Wei Lin
2020 Applied intelligence (Boston)  
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

Chuanxiang Ren, Chunxu Chai, Changchang Yin, Haowei Ji, Xuezhen Cheng, Ge Gao, Heng Zhang, Erfan Hassannayebi
2021 Journal of Advanced Transportation  
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

Ziwen Song, Feng Sun, Rongji Zhang, Yingcui Du, Chenchen Li, Ling Huang
2021 Journal of Advanced Transportation  
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

Jiyao An, Li Fu, Meng Hu, Weihong Chen, Jiawei Zhan
2019 IEEE Access  
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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