Traffic Flow Forecasting for Road Tunnel Using PSO-GPR Algorithm with Combined Kernel Function

Guo, Jiaqi; Chen, Fan; Xu, Chong

doi:10.1155/2017/2090783

Cited by 6 publications

(6 citation statements)

References 18 publications

(17 reference statements)

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“…The values of network blocking values are specified in brackets, calculated in the analysis of real transport systems using numerical simulation. The blocking value is calculated according to the following equation: one minus the percolation threshold calculated using Equation (5) or Equation (6). A comparison of the data presented in Tables 3 and 4 (which consider inaccuracies in reporting of traffic density and numerical simulation) enables us to draw two conclusions:…”

Section: Discussionmentioning

confidence: 99%

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The Influence of Transport Link Density on Conductivity If Junctions and/or Links Are Blocked

Aleshkin¹

2021

Mathematics

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This paper examines some approaches to modeling and managing traffic flows in modern megapolises and proposes using the methods and approaches of the percolation theory. The author sets the task of determining the properties of the transport network (percolation threshold) when designing such networks, based on the calculation of network parameters (average number of connections per crossroads, road network density). Particular attention is paid to the planarity and nonplanarity of the road transport network. Algorithms for building a planar random network (for modeling purposes) and calculating the percolation thresholds in the resulting network model are proposed. The article analyzes the resulting percolation thresholds for road networks with different relationship densities per crossroad and analyzes the effect of network density on the percolation threshold for these structures. This dependence is specified mathematically, which allows predicting the qualitative characteristics of road network structures (percolation thresholds) in their design. The conclusion shows how the change in the planar characteristics of the road network (with adding interchanges to it) can improve its quality characteristics, i.e., its overall capacity.

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Section: Discussionmentioning

confidence: 99%

“…In [5], the authors used a Gaussian regression model (GPR), optimized using particle swarm algorithm (PSO), to predict undefined, nonlinear, and complex traffic in a road tunnel.…”

Section: Introductionmentioning

confidence: 99%

The Influence of Transport Link Density on Conductivity If Junctions and/or Links Are Blocked

Aleshkin¹

2021

Mathematics

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“…It is the combined kernel function [26] composed of the Gaussian kernel function and the polynomial kernel function, where n � 1, . .…”

Section: Fuzzy Combined Kernel Rvm Based Coal Spontaneous Combustion ...mentioning

confidence: 99%

Coal Spontaneous Combustion Temperature Prediction Based on Fuzzy Combined Kernel Relevance Vector Machine

Wang¹,

Liu

2022

Mathematical Problems in Engineering

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This paper proposes a fuzzy combined kernel relevance vector machine method for the coal spontaneous combustion temperature prediction to avoid the shortcomings of traditional machine learning algorithms, such as the large prediction error, the weak generalization ability of the single kernel function, and the inability to deal with abnormal values. First, build a platform to simulate the coal spontaneous combustion scene and obtain the data of different index gas concentrations and the coal spontaneous combustion temperature through experiments. Second, the fuzzy algorithm is used to give the training samples different membership degrees with attention, so as to reduce the influence of outliers on the model. Third, the combined kernel function weighted by the polynomial kernel function and Gaussian kernel function is used to construct the input sample matrix to map the data from the low-dimensional space to a high-dimensional space, so as to obtain a better training model. Finally, the fuzzy combined kernel relevance vector machine model is constructed and compared with methods based on the radial basis function neural network, least squares support vector machine, Gaussian kernel relevance vector machine, and combined kernel relevance vector machine to verify the effectiveness of prediction for coal spontaneous combustion temperature. The results show that the fuzzy combined kernel relevance vector machine not only has the characteristics of strong generalization ability and weakening the influence of abnormal data, but also with a more sparse model, which is suitable for the prediction of coal spontaneous combustion temperature.

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“…Laña et al [29] use random forest regressors on traffic flow time series. Lv et al in [30] predict traffic flow using a deep learning approach and Guo et al in [31] use statistical hybrid Gaussian process regression. Altogether, the various research methods show rather strong prediction capabilities.…”

Section: New Road Urban Tunnel Traffic Controlmentioning

confidence: 99%

Interdisciplinary Urban Tunnel Control within Smart Cities

2021

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Nowadays, urban road tunnels are considered to be independent entities within a city. Their interactions with the rest of the city and vice versa are usually not considered and, if they are, are only considered in a limited way (for example, through the nearest traffic controller). Typically, only the traffic parameters and not the environmental impacts are considered. This paper has two major objectives. First, we provide a systemic view on a road urban tunnel. The major focus is on the interfaces between the tunnel and the rest of the city and the way they will be managed. We are providing a tool to take into consideration a sustainable development of a tunnel (i.e., not only traffic flow parameters such as average speed, but also environmental and societal characteristics). This model expresses the actual traffic situation in a monetary form (i.e., cost of congestions). The second objective is to provide a new road urban tunnel control approach that follows the original methodology and systemic view described in the paper. If the tunnel is controlled autonomously, which corresponds to the current state-of-the-art in many cities, the algorithm decides to close it based on only local parameters. However, the proposed new algorithm takes into consideration not only the traffic situation in the tunnel (expressed by the parameter traffic density), but also the actual traffic situation within the city (expressed by its level of service (LOS)). This allows more environmentally, socially and economically sustainable oriented road urban tunnel management. The described algorithm is demonstrated on a specific example of the tunnel complex Blanka in Prague.

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Traffic Flow Forecasting for Road Tunnel Using PSO-GPR Algorithm with Combined Kernel Function

Cited by 6 publications

References 18 publications

The Influence of Transport Link Density on Conductivity If Junctions and/or Links Are Blocked

The Influence of Transport Link Density on Conductivity If Junctions and/or Links Are Blocked

Coal Spontaneous Combustion Temperature Prediction Based on Fuzzy Combined Kernel Relevance Vector Machine

Interdisciplinary Urban Tunnel Control within Smart Cities

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