Space–time correlation analysis of traffic flow on road network

Su, Fei; Dong, Hui; Jia, Limin; Zhao, Tian; Sun, Xuan

doi:10.1142/s0217979217500278

Cited by 14 publications

(14 citation statements)

References 16 publications

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“…After receiving the packet from the application layer protocol stack and adding the target information, when the network device activates the dedicated wireless host between the network base station and the user map via the user plane, the network device is arranged in the MAC layer after transmission. The network device also receives the application layer packet from the physical layer of the protocol stack [30][31].…”

Section: Construction Of the Theoretical Model Of Road Network Traffic State Information Accident Prediction Perceptionmentioning

confidence: 99%

VR Technology and the Theoretical Model and Method of Road Network Traffic State Information Accident Prediction and Perception under the Environment of Internet of Vehicles

et al. 2024

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With the rapid increase in the number of private cars and buses in the country, the living standard has gradually improved, and vehicles as a means of transportation commonly used by people have caused more traffic accidents. The present study mainly discusses the theoretical models and methods of VR technology and road network traffic status information accident prediction perception on the Internet of Vehicles (IoV) environment. Use of VR technology to simulate the experimental environment. The main techniques of VR real-time rendering are the visualization and roaming of large-area spatial data and the simulation of things that currently exist in computer vision simulation methods. Use VR technology to simulate ordinary highways, and use VR technology to simulate six sets of virtual cars. Use the real-time positioning function and data transmission function of the IoV technology to systematically collect driving data in the VR environment and advance collisions in advance to perceive and control the speed of the cars. Experimental data shows that when the time level is 2, the brake response sensitivity in the data is 88.9%, and the brake sensitivity value is relatively high. When the distance level is 1, the car safety factor value is 97.5%, and the data value is the highest. The research results show that when the time level is 2, the brake sensitivity value is relatively high, indicating that the VR technology and the traffic network information environment accident prediction effect on the Internet of Vehicles environment are better. When the distance level is 1, the performance of road network traffic status information accident perception based on VR technology and IoV environment is the best. Through the real-time sharing of the image recognition performance of VR technology and the geographic location of the Internet of Vehicles technology, the real-time prediction and perception of road network traffic status information accidents can minimize the chances of accidents.

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Section: Construction Of the Theoretical Model Of Road Network Traffic State Information Accident Prediction Perceptionmentioning

confidence: 99%

VR Technology and the Theoretical Model and Method of Road Network Traffic State Information Accident Prediction and Perception under the Environment of Internet of Vehicles

et al. 2024

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“…The change characteristics of the traffic information considering both the time and space dimensions are analysed. To date, many scholars have extended several indexes to measure the STC, such as the simple spatial–temporal autocorrelation index (ST-ACF) and cross-correlation function (CCF) [ 32 , 33 , 34 ]. The common calculation methods of STC include: ST-ACF

where

is ST-ACF.…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach to Calculate the Spatial–Temporal Correlation for Traffic Flow Based on the Structure of Urban Road Networks and Traffic Dynamic Theory

Yang

2021

Sensors

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Determining the spatial–temporal correlation (STC) between roads can help clarify the operation characteristics of road traffic. Moreover, this correlation affects the utilization quality of traffic data in related research fields. Therefore, it is of significance to provide more reasonable correlation information for other research, such as in traffic speed prediction. Most of the traditional correlation calculation methods for traffic are based on only statistical theory. These methods are simple, but their ability to explain the actual phenomenon is limited due to the lack of consideration of the actual traffic operation characteristics. Therefore, to provide more reasonable correlation information between roads, this paper analysed the influence mechanism of urban traffic based on the traffic dynamic model, and two parameters, traffic complete influence time and traffic correlation strength, were proposed to bring physical meaning to the calculation of STC. Then, an improved calculation model of the STC between different roads considering the adjacency between roads was proposed in this paper. Finally, this paper verified this method against two common traditional methods through different experiments. The verification results show that the calculation method proposed in this paper has better interpretability for the STC between different roads and can better reveal the internal traffic operation characteristics of the road network.

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“…When considering the traffic flow, it should be a directed graph [16], [17]. The adjacency matrix can be expressed as follows [18]: When there is a direct traffic flow that impacts the relationship between edge i and j, it can be viewed that they are first-order neighbors. Therefore, the 1st-order adjacency matrix W 1 is composed of all firstorder relationships between all edges in a network, and the 2nd-order adjacency matrix W 2 is the 1st-order relationships of matrix W 1 for the road network.…”

Section: A Section Influence Degreementioning

confidence: 99%

“…Because of the relationship and direction of influence between traffic flows, there is some differences in the adjacency matrix from other common directed graphs [18]:…”

Section: A Section Influence Degreementioning

confidence: 99%

Disequilibrium Measurement and Analysis of a Road Traffic Network Based on Section Influence Degree and Flow Betweenness

Qin

Zou

et al. 2020

IEEE Access

Self Cite

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Traffic congestion usually occurs in certain areas or sections, which could be regarded as the reflection of road network disequilibrium caused by the traffic flow distribution, network structure, etc. It is found that unbalance analysis of the road network and the unbalanced points study could provide great support for the road network structure optimization, traffic capacity adjustment, and the efficiency improvement of road network resources. Furthermore, it is helpful to reduce the traffic congestion significantly. In order to design a method to analyze the disequilibrium of road networks to find out the unbalanced or bottleneck points, the road network structure, traffic flow distribution and the location of sections are considered in the analysis of road network disequilibrium, and the disequilibrium is measured using Gini coefficient and Theil index from road network attributes of influence and capability in this paper. First, section influence degree and section flow betweenness are employed to separately represent the differences in the influence relationship and capability distribution of a section from others in the network. Second, these two indicators are taken respectively as the input, and the road network equilibrium is analyzed multi-dimensionally using Gini coefficient and Theil index. The level of the disequilibrium is analyzed from the view of the overall network. Meanwhile, the contributions of a certain section to the disequilibrium of the whole network is also analyzed from the perspective of the section. Finally, a case study based on Beijing's regional road network is conducted, and the result shows the similar performance compared with the actual road network traffic situation, which verifies the feasibility and validity of the method in multi-dimension analysis of road network disequilibrium. INDEX TERMS Road traffic flow, traffic network equilibrium analysis, section influence degree, section flow betweenness, Gini coefficient.

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Space–time correlation analysis of traffic flow on road network

Cited by 14 publications

References 16 publications

VR Technology and the Theoretical Model and Method of Road Network Traffic State Information Accident Prediction and Perception under the Environment of Internet of Vehicles

VR Technology and the Theoretical Model and Method of Road Network Traffic State Information Accident Prediction and Perception under the Environment of Internet of Vehicles

A Novel Approach to Calculate the Spatial–Temporal Correlation for Traffic Flow Based on the Structure of Urban Road Networks and Traffic Dynamic Theory

Disequilibrium Measurement and Analysis of a Road Traffic Network Based on Section Influence Degree and Flow Betweenness

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