Dynamic Division about Traffic Control Sub-area Based on Back Propagation Neural Network

Li, Chungui; Xie, Ying; Zhang, Honglei; Yan, Xiaohui

doi:10.1109/ihmsc.2010.104

Cited by 12 publications

(7 citation statements)

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“…Some scholars have studied the traffic signal control area division method based on a variety of indicators and parameters. For example, Li et al [14] put forward a dynamic traffic control partition method based on BP (Backpropagation) neural network on the basis of comprehensively considering the three factors of traffic flow, intersection spacing, and cycle. Tian [15] put forward a dynamic subarea division method based on the improved Newman community rapid division by comprehensively considering the distance between adjacent intersections in road network, traffic flow, travel time, discrete characteristics of traffic flow, signal cycle, and traffic flow density of sections.…”

Section: Existing Workmentioning

confidence: 99%

Subdivision of Urban Traffic Area Based on the Combination of Static Zoning and Dynamic Zoning

Luo

Zhang

2021

Discrete Dynamics in Nature and Society

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In this paper, the traffic area of subzone division in urban road network is studied and a subzone division method based on the combination of static partition and dynamic partition is proposed. The static partition is carried out for the road network when the traffic flow is in a noncongested state, so as to provide the decision-making basis for the traffic green wave signal control strategy. At the same time, aiming at the road network when the traffic flow is congested, the dynamic partition is carried out on the basis of static partition to provide the decision-making basis for the traffic maximum flow signal control strategy. In view of the fact that it is difficult to determine the clustering center point during the initial division, this method proposes to determine the clustering center point according to the value of nodes of betweenness centrality. In order to solve the problem that it is difficult to collect traffic data, a method for estimating traffic flow density is proposed. In order to solve the problem of normalization of different probability distribution among various parameters, Mahalanobis distance is used as the fusion index of subzone division. Model verification shows that the method is feasible and effective.

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Section: Existing Workmentioning

confidence: 99%

Subdivision of Urban Traffic Area Based on the Combination of Static Zoning and Dynamic Zoning

Luo

Zhang

2021

Discrete Dynamics in Nature and Society

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“…Therefore, establishing a learning model to learn the hidden characteristics of data and taking effective measures to deal with the uncertainty in the data is particularly important in traffic flow prediction. So far, a great deal of research has been done in this field and many models and methods have been adopted, such as: Kalman state space filtering models [8], autoregressive integrated moving average [9], support vector machine model [10], neural network [11], fuzzy logic approach [12], fuzzy-neural systems [13], back propagation neural network model(BPNN) [14], K-nearest neighbor (KNN) model [15], Bayesian network model [16], portfolio models [17], and some deep learning models [18]- [20]. In these prediction methods, the deep learning model has become the focus of many experts and scholars due to the effectiveness of its data feature extraction and the outstanding processing ability of big traffic data [21].…”

Section: Related Workmentioning

confidence: 99%

A Novel Fuzzy-Based Convolutional Neural Network Method to Traffic Flow Prediction With Uncertain Traffic Accident Information

et al. 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 is particularly concerned. In this paper, a novel fuzzy-based convolutional neural network (F-CNN) method is proposed to predict the traffic flow more accurately, in which a fuzzy approach has been applied to represent the traffic accident features when introducing uncertain traffic accidents information into the CNN at the first time. 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 inflow and outflow types. Second, uncertain traffic accident information is generated from the real traffic flow data by utilizing a fuzzy inference mechanism. Finally, the F-CNN model is realized to train the internal information of the trend sequence, the uncertain traffic accident information, and the external information. Moreover, pre-training and fine-tuning strategies are efficiently developed to learn the parameters of the F-CNN. At last, the real Beijing taxicab trajectory and the meteorology datasets are employed to show that the proposed method has superior performance compared with the state-of-the-art approaches.INDEX TERMS Traffic flow prediction, traffic accident information, fuzzy inference system (FIS), convolutional neural network (CNN), fuzzy-based convolutional neural network (F-CNN).

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“…Furthermore, some researchers put forward the dimension-reduced processing and genetic algorithms to optimize subarea division. Li et al [30] proposed a method to divide traffic control subarea dynamically on the basis of Back Propagation (BP) neural network. This method divides traffic subarea by considering traffic flow, distance of intersections, and cycle.…”

Section: Traffic Subarea Divisionmentioning

confidence: 99%

“…Recently, several parallel -Means algorithms [14][15][16][17][18][19][20][21][22] have been proposed to meet the rapidly growing demands of clustering big data sets. Meanwhile, some methods [23][24][25][26][27][28][29][30][31] have been presented for traffic subarea division. All the previous approaches have almost achieved desirable properties but also have some limitations, especially the capacity of data processing that has not been improved substantially, and thus might have difficulty in dividing traffic subarea with a large number of GPS trajectories of taxicabs.…”

Section: Introductionmentioning

confidence: 99%

An Efficient MapReduce-Based Parallel Clustering Algorithm for Distributed Traffic Subarea Division

Xia

Wang

et al. 2015

Discrete Dynamics in Nature and Society

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Traffic subarea division is vital for traffic system management and traffic network analysis in intelligent transportation systems (ITSs). Since existing methods may not be suitable for big traffic data processing, this paper presents a MapReduce-based Parallel Three-PhaseK-Means (Par3PKM) algorithm for solving traffic subarea division problem on a widely adopted Hadoop distributed computing platform. Specifically, we first modify the distance metric and initialization strategy ofK-Means and then employ a MapReduce paradigm to redesign the optimizedK-Means algorithm for parallel clustering of large-scale taxi trajectories. Moreover, we propose a boundary identifying method to connect the borders of clustering results for each cluster. Finally, we divide traffic subarea of Beijing based on real-world trajectory data sets generated by 12,000 taxis in a period of one month using the proposed approach. Experimental evaluation results indicate that when compared withK-Means, Par2PK-Means, and ParCLARA, Par3PKM achieves higher efficiency, more accuracy, and better scalability and can effectively divide traffic subarea with big taxi trajectory data.

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Dynamic Division about Traffic Control Sub-area Based on Back Propagation Neural Network

Cited by 12 publications

References 0 publications

Subdivision of Urban Traffic Area Based on the Combination of Static Zoning and Dynamic Zoning

Subdivision of Urban Traffic Area Based on the Combination of Static Zoning and Dynamic Zoning

A Novel Fuzzy-Based Convolutional Neural Network Method to Traffic Flow Prediction With Uncertain Traffic Accident Information

An Efficient MapReduce-Based Parallel Clustering Algorithm for Distributed Traffic Subarea Division

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