Traffic prediction

Nov 29, 2022 · Internet traffic prediction has been considered a research topic and the basis for intelligent network management and planning, e.g., elastic network service provision and content delivery optimization. Various methods have been proposed in the literature for Internet traffic prediction, including statistical, machine learning and deep learning methods. However, most of the existing approaches ...

Ref. concluded that traffic prediction study is unpopular because there is a lack of computationally efficient methods and algorithms, including good quality data. Based on the implementations of previous studies, claimed that the performance of CNN for traffic prediction has been relatively unimpressive. Ref.As the development of cities, traffic congestion becomes an increasingly pressing issue, and traffic prediction is a classic method to relieve that issue. Traffic prediction is one specific application of spatio-temporal prediction learning, like taxi scheduling, weather prediction, and ship trajectory prediction. Against these problems, …Open access. Published: 04 September 2023. Road traffic can be predicted by machine learning equally effectively as by complex microscopic model. Andrzej Sroczyński & Andrzej Czyżewski....Meteorologists track and predict weather conditions using state-of-the-art computer analysis equipment that provides them with current information about atmospheric conditions, win...These models are required to predict the entire network traffic series {1, 3, 7, 14, 30} days, aligned with {96, 288, 672, 1344, 2880} prediction spans ahead in Table 1, and inbits is the target ...Traffic prediction is an important part of urban computing. Accurate traffic prediction assists the public in planning travel routes and relevant departments in traffic management, thus improving the efficiency of people’s travel. Existing approaches usually use graph neural networks or attention mechanisms to capture the spatial–temporal ...

Traffic prediction is the task of predicting future traffic measurements (e.g. volume, speed, etc.) in a road network (graph), using historical data (timeseries). Things are usually better defined through exclusions, so here are similar things that I do not include:Satellite communication is increasingly essential and widely used, especially with the rapid development of the Internet of Things (IoT) and networks beyond fifth-generation (B5G), providing ubiquitous coverage. However, the current reactive approaches to optimize resources have become inadequate due to the massive rise in IoT traffic with …Accurate traffic flow prediction is highly important for relieving road congestion. Due to the intricate spatial–temporal dependence of traffic flows, especially the hidden …Traffic prediction is a flourishing research field due to its importance in human mobility in the urban space. Despite this, existing studies only focus on short-term prediction of up to few hours in advance, with most being up to one hour only. Long-term traffic prediction can enable more comprehensive, informed, and proactive measures …Apr 23, 2019 ... Researchers of the Miguel Hernández University (UMH) of Elche have developed artificial intelligence solutions based on deep neural networks to ...Currently, the Google Maps traffic prediction system consists of the following components: (1) a route analyser that processes terabytes of traffic information to construct …Abstract: Traffic prediction plays an essential role in intelligent transportation system. Accurate traffic prediction can assist route planing, guide vehicle dispatching, and …

In this paper, we propose a Spatial-Temporal Large Language Model (ST-LLM) for traffic prediction. Specifically, ST-LLM redefines the timesteps at each location as tokens and incorporates a spatial- temporal embedding module to learn the spatial lo- cation and global temporal representations of to- kens. The traffic prediction quality shouldbe evaluated and focused on for the congested time periods of the day.Prediction errors of about 30% are reported for those heavily congestedsituations . The deviations of the “real” congested situation on theroad and the predicted situation have to be compared later on in thelaboratory to evaluate the ...More accurate traffic prediction can further improve the efficiency of intelligent transportation systems. However, the complex spatiotemporal correlation issues in transportation networks pose great challenges. In the past, people have carried out a great deal of research to solve this problem. Most studies are based on graph neural networks …As the development of cities, traffic congestion becomes an increasingly pressing issue, and traffic prediction is a classic method to relieve that issue. Traffic prediction is one specific application of spatio-temporal prediction learning, like taxi scheduling, weather prediction, and ship trajectory prediction. Against these problems, …Traffic prediction is an important component of the intelligent transportation system. Existing deep learning methods encode temporal information and spatial information separately or iteratively. However, the spatial and temporal information is highly correlated in a traffic network, so existing methods may not learn the complex spatial-temporal …Traffic prediction plays an important role in the intelligent transportation system (ITS), because it can increase people’s travel convenience. Despite the deep neural network …

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Open access. Published: 04 September 2023. Road traffic can be predicted by machine learning equally effectively as by complex microscopic model. Andrzej Sroczyński & Andrzej Czyżewski....More accurate traffic prediction can further improve the efficiency of intelligent transportation systems. However, the complex spatiotemporal correlation issues in transportation networks pose great challenges. In the past, people have carried out a great deal of research to solve this problem. Most studies are based on graph neural networks …Mar 29, 2018 ... The Maastricht Upper Area Control Centre (MUAC) recently introduced innovative machine-learning techniques to predict real-time flight ...A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation. Open access. Published: 23 January 2021. Volume 6 , pages 63–85, ( 2021 ) …

A common need in all of these methods is the use of traffic predictions for supporting planning and operation of the traffic lights and traffic management schemes. This paper focuses on comparing the forecasting effectiveness of three machine learning models, namely Random Forests, Support Vector Regression, and Multilayer …Once notoriously inefficient, the Department of Motor Vehicles has stepped into the twenty-first century and now happily accepts online payments for moving traffic violations. Par...It requires network traffic prediction, which is the basis for network control. Therefore, under limited network resources, the establishment of network traffic prediction model to predict the network in real time in order to make controls or adjustments for the network in time will greatly improve network performance and network service quality.To overcome this shortcoming, we apply Windows-Based Tensor Completion to short-term traffic prediction. Windows-Based Tensor Completion is a kind of dynamic tensor completion, which efficiency computes a compact summary for real-time high-order and high-dimensional data and reveals the hidden correlations (Sun et al., 2008).In recent years, cellular communication systems have continued to develop in the direction of intelligence. The demand for cellular networks is increasing as they meet the public’s pursuit of a better life. Accurate prediction of cellular network traffic can help operators avoid wasting resources and improve management efficiency. Traditional …Apr 23, 2019 ... Researchers of the Miguel Hernández University (UMH) of Elche have developed artificial intelligence solutions based on deep neural networks to ...Accurate traffic prediction can assist route planing, guide vehicle dispatching, and mitigate traffic congestion. This problem is challenging due to the complicated and dynamic spatio-temporal …Cellular traffic prediction is crucial for intelligent network operations, such as load-aware resource management and proactive network optimization. In this paper, to explicitly characterize the temporal dependence and spatial relationship of nonstationary real-world cellular traffic, we propose a novel prediction method. First, we decompose traffic …Real-time closed-circuit cameras along with traffic information feed from connected vehicle data, loop detectors, signal timing, etc. are able to report live traffic data due to edge processing with low latency cloud services (Arun et al., 2021b). This opens a new era of estimation and prediction of conflict measures in real-time.Mar 29, 2018 ... The Maastricht Upper Area Control Centre (MUAC) recently introduced innovative machine-learning techniques to predict real-time flight ...

Traffic forecasting has emerged as a core component of intelligent transportation systems. However, timely accurate traffic forecasting, especially long-term forecasting, still remains an open challenge due to the highly nonlinear and dynamic spatial-temporal dependencies of traffic flows. In this paper, we propose a novel paradigm of …

Traffic prediction is an important part of urban computing. Accurate traffic prediction assists the public in planning travel routes and relevant departments in traffic management, thus improving the efficiency of people’s travel. Existing approaches usually use graph neural networks or attention mechanisms to capture the spatial–temporal ...Timely and accurate large-scale traffic prediction has gained increasing importance for traffic management. However, it is a challenging task due to the high nonlinearity of traffic flow and complex network topology. This study aims to develop a large-scale traffic flow prediction model exploring the interaction of multiple traffic parameters …Dec 4, 2021 · Ref. concluded that traffic prediction study is unpopular because there is a lack of computationally efficient methods and algorithms, including good quality data. Based on the implementations of previous studies, claimed that the performance of CNN for traffic prediction has been relatively unimpressive. Ref. Meteorologists track and predict weather conditions using state-of-the-art computer analysis equipment that provides them with current information about atmospheric conditions, win...Dec 19, 2023 · The main challenge of current traffic prediction tasks is to integrate the information of external factors into the prediction model. The summary of traffic flow prediction methods based on considering external factors is shown in Table 1. Several methods exist in existing studies to deal with external factors, one approach is to concatenate ... Feb 10, 2021 · Traffic prediction plays an essential role in intelligent transportation system. Accurate traffic prediction can assist route planing, guide vehicle dispatching, and mitigate traffic congestion. This problem is challenging due to the complicated and dynamic spatio-temporal dependencies between different regions in the road network. Recently, a significant amount of research efforts have been ... Baltimore bridge collapse: Marine traffic site shows moment of cargo ship crash. The container ship Dali, hit the 1.6-mile long bridge in Baltimore at around 1:30am local time.Q-Traffic Introduced by Liao et al. in Deep Sequence Learning with Auxiliary Information for Traffic Prediction Q-Traffic is a large-scale traffic prediction dataset, which consists of three sub-datasets: query sub-dataset, traffic speed …The intelligent transportation system (ITS) was born to cope with increasingly complex traffic conditions. Traffic prediction is an essential part of ITS, which can help to prevent traffic congestion and reduce traffic accidents. Traffic prediction has two major challenges: temporal dependencies and spatial dependencies. Traditional statistical methods and …

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As the development of cities, traffic congestion becomes an increasingly pressing issue, and traffic prediction is a classic method to relieve that issue. Traffic prediction is one specific application of spatio-temporal prediction learning, like taxi scheduling, weather prediction, and ship trajectory prediction. Against these problems, …Road link speed is often employed as an essential measure of traffic state in the operation of an urban traffic network. Not only real-time traffic demand but also signal timings and other local planning factors are major influential factors. This paper proposes a short-term traffic speed prediction approach, called PL-WGAN, for urban road …Apr 18, 2020 · Traffic prediction plays an essential role in intelligent transportation system. Accurate traffic prediction can assist route planing, guide vehicle dispatching, and mitigate traffic congestion. This problem is challenging due to the complicated and dynamic spatio-temporal dependencies between different regions in the road network. Recently, a significant amount of research efforts have been ... Traffic predicting model in SDN for good QoS. In provisioning QoS for real-time traffic, the proposed QoS provision in SDN improves users` QoE to get appropriate QoS requirements on demand 25.To ...8.4.2 Traffic flow prediction with Big Data. Accurate and timely traffic flow information is currently strongly needed for individual travelers, business sectors, and government agencies. It has the potential to help road users make better travel decisions, alleviate traffic congestion, reduce carbon emissions, and improve traffic operation ...Abstract: Traffic speed prediction based on real-world traffic data is a classical problem in intelligent transportation systems (ITS). Most existing traffic speed prediction …Robust prediction of citywide traffic flows at different time periods plays a crucial role in intelligent transportation systems. While previous work has made great efforts to model spatio-temporal correlations, existing methods still suffer from two key limitations: i) Most models collectively predict all regions' flows without accounting for spatial …Robust prediction of citywide traffic flows at different time periods plays a crucial role in intelligent transportation systems. While previous work has made great efforts to model spatio-temporal correlations, existing methods still suffer from two key limitations: i) Most models collectively predict all regions' flows without accounting for spatial …Baltimore bridge collapse: Marine traffic site shows moment of cargo ship crash. The container ship Dali, hit the 1.6-mile long bridge in Baltimore at around 1:30am local time.Enhancing the accuracy of traffic prediction relies on building a graph that effectively captures the intricate spatiotemporal correlations in traffic data. It is a widely observed phenomenon that different urban traffic activities exhibit an asymmetric mutual influence. However, existing methods for graph construction largely overlook this …Jan 24, 2020 · Sr. Product Manager Traffic and Travel Information. Jan 24, 2020 · 8 min read. Traffic prediction is the task of forecasting real-time traffic information based on floating car data and historical traffic data, such as traffic flow, average traffic speed and traffic incidents. Have you ever sat in traffic wondering how much time you could have ... ….

Abstract: Traffic prediction facilitates various applications in the fields of smart vehicles and vehicular communications, and the key of successfully and accurately forecasting urban traffic state is to model the complex spatiotemporal correlations within urban traffic networks. However, even though great efforts have been devoted to modeling the …A common need in all of these methods is the use of traffic predictions for supporting planning and operation of the traffic lights and traffic management schemes. This paper focuses on comparing the forecasting effectiveness of three machine learning models, namely Random Forests, Support Vector Regression, and Multilayer …The LSTM-based traffic prediction algorithm, TrafficPredict, proposed by Ma et al. (2019), contains instance and category layers. Fang et al. (2020) proposed a two-stage motion prediction framework, Trajectory Proposal Network (TPNet), which generated candidate sets and then made the final predictions under physical constraints. The …Traffic prediction is essential for the progression of Intelligent Transportation Systems (ITS) and the vision of smart cities. While Spatial-Temporal Graph Neural Networks (STGNNs) have shown promise in this domain by leveraging Graph Neural Networks (GNNs) integrated with either RNNs or Transformers, they present challenges …Traffic prediction is an important component of the intelligent transportation system. Existing deep learning methods encode temporal information and spatial information separately or iteratively. However, the spatial and temporal information is highly correlated in a traffic network, so existing methods may not learn the complex spatial-temporal …Traffic prediction is an important component in Intelligent Transportation Systems(ITSs) for enabling advanced transportation management and services to address worsening traffic congestion problems. The methodology for traffic prediction has evolved significantly over the past decades from simple statistical models to recent complex ...Short-term traffic flow prediction is an effective means for intelligent transportation system (ITS) to mitigate traffic congestion. However, traffic flow data with temporal features and periodic characteristics are vulnerable to weather effects, making short-term traffic flow prediction a challenging issue. However, the existing models …The traffic within the satellite coverage region varies greatly with the satellite movement. Traffic prediction in the satellite constellation networks is beneficial and necessary. The satellite coverage traffic model is formulated and the traffic prediction model is proposed with two variables: the geographic longitude of ascending node and the time from … Traffic prediction, [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1]