Analysis on Link Travel Time Estimation considering Time Headway Based on Urban Road RFID Data

Gu, Jian; Li, Miaohua; Yu, Linghua; Li, Shun; Long, Kejun

doi:10.1155/2021/8876626

Cited by 3 publications

(1 citation statement)

References 30 publications

(37 reference statements)

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“…Based on the relevant literature [43], the travel time of a road segment can be considered as the sum of the typical vehicle travel time and the travel time fluctuation of the road segment, as shown in Formula (4). Then, Formula (3) can be rewritten as Formula (5).…”

Section: Traffic Spatiotemporal Parameters With Queuing and Travel Timementioning

confidence: 99%

Urban Road Traffic Spatiotemporal State Estimation Based on Multivariate Phase Space–LSTM Prediction

Wang,

Zhang,

et al. 2023

Applied Sciences

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The road traffic state is usually analyzed from a temporal and macroscopic perspective; however, traffic flow parameters, such as density and spacing, can explain the evolution of traffic states from the microscopic perspective and the spatial distribution of vehicles in lanes. In this paper, we attempt to take both temporal and spatial characteristics into consideration simultaneously, and a parameter is defined as the traffic spatiotemporal state of urban road sections to represent the operational status of road traffic, using advanced prediction techniques to estimate its short-term trends. An estimation method is constructed for the traffic spatiotemporal state considering travel times, speeds, and queuing situations from temporal and spatial perspectives. Then, based on Takens’ theorem and the single variable phase space, the phase space of multiple traffic parameters is reconstructed and the chaotic characteristics are analyzed. Next, an LSTM prediction model is constructed based on the phase space reconstruction of multiple variables, and the traffic parameters are predicted by empirical analysis. The results show the proposed estimation method has a significantly improved accuracy. Finally, combined with RFID data, the traffic spatiotemporal state of the case section is calculated, which provides a theoretical basis and practical reference for road traffic state evaluations.

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Section: Traffic Spatiotemporal Parameters With Queuing and Travel Timementioning

confidence: 99%

Urban Road Traffic Spatiotemporal State Estimation Based on Multivariate Phase Space–LSTM Prediction

Wang,

Zhang,

et al. 2023

Applied Sciences

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Overview of Research Works on Applications of UHF RFID on Vehicles for Data Transmission

Abramian,

Larionov,

Fedotov

2024

Lecture Notes in Computer Science

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Research on Fault Diagnosis Model of Generative Adss Based on Improved Semisupervised Diagnosis Algorithm

Yi¹

2021

Mobile Information Systems

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With the advent of the era of big data and the rapid development of deep learning and other technologies, people can use complex neural network models to mine and extract key information in massive data with the support of powerful computing power. However, it also increases the complexity of heterogeneous network and greatly increases the difficulty of network maintenance and management. In order to solve the problem of network fault diagnosis, this paper first proposes an improved semisupervised inverse network fault diagnosis algorithm; the proposed algorithm effectively guarantees the convergence of generated against network model, makes full use of a large amount of trouble-free tag data, and obtains a good accuracy of fault diagnosis. Then, the diagnosis model is further optimized and the fault classification task is completed by the convolutional neural network, the discriminant function of the network is simplified, and the generation pair network is only responsible for generating fault samples. The simulation results also show that the fault diagnosis algorithm based on network generation and convolutional neural network achieves good fault diagnosis accuracy and saves the overhead of manually labeling a large number of data samples.

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Analysis on Link Travel Time Estimation considering Time Headway Based on Urban Road RFID Data

Cited by 3 publications

References 30 publications

Urban Road Traffic Spatiotemporal State Estimation Based on Multivariate Phase Space–LSTM Prediction

Urban Road Traffic Spatiotemporal State Estimation Based on Multivariate Phase Space–LSTM Prediction

Overview of Research Works on Applications of UHF RFID on Vehicles for Data Transmission

Research on Fault Diagnosis Model of Generative Adss Based on Improved Semisupervised Diagnosis Algorithm

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