Traffic prediction

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 …

Traffic prediction is a vital part of intelligent transportation systems. The ability of traffic risk prediction is of great significance to prevent traffic accidents and reduce the damages in a proactive way. Because of the complexity, uncertainty and dynamics of spatiotemporal dependence of traffic flow, accurate traffic state prediction becomes a …May 13, 2023 · 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 to improve the prediction performance. To achieve ... Mar 29, 2018 ... The Maastricht Upper Area Control Centre (MUAC) recently introduced innovative machine-learning techniques to predict real-time flight ... Traffic prediction involves estimating the future behavior of traffic in a particular area. This information is useful for a variety of purposes, including reducing congestion, optimizing transportation systems, and improving road safety. In the past, traffic prediction has been based on traditional methods such as rule-based models and time ... Currently, the Google Maps traffic prediction system consists of the following components: (1) a route analyser that processes terabytes of traffic information to construct …Traffic prediction with different methods (black: original, blue: prediction) and anomaly detection based on traffic prediction (actual: NA, detected: red) for a specific client - …Jan 1, 2022 · This prediction will be helpful for the people who are in need to check the immediate traffic state. The traffic data is predicated on a basis of 1 h time gap. Live statistics of the traffic is ...

The advances in wireless communication techniques, mobile cloud computing, automotive and intelligent terminal technology are driving the evolution of vehicle ad hoc networks into the Internet of Vehicles (IoV) paradigm. This leads to a change in the vehicle routing problem from a calculation based on static data towards real-time traffic …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 …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 ...Jan 27, 2021 · Traffic forecasting is important for the success of intelligent transportation systems. Deep learning models, including convolution neural networks and recurrent neural networks, have been extensively applied in traffic forecasting problems to model spatial and temporal dependencies. In recent years, to model the graph structures in transportation systems as well as contextual information ... 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 …In maritime traffic prediction, it is necessary to have ship movement data with the attributes such as position, velocity and course. In addition, there are other traffic-related factors such as ship length, ship type, ship destination, Pilot Onboard (POB) and Caution Area Estimated Time of Arrival (CAETA). Ship movement data, ship length and ...paper targets at traffic prediction using LoRa, also known as Long Range Wide Area Network Technology. LoRa is a technology connected to LPWAN (Low Power Wide Area Networks), which is a wirelessOutcomes can be predicted mathematically using statistics or probability. To determine the probability of an event occurring, take the number of the desired outcome, and divide it ...

Modeling complex spatiotemporal dependencies in correlated traffic series is essential for traffic prediction. While recent works have shown improved prediction performance by using neural networks to extract spatiotemporal correlations, their effectiveness depends on the quality of the graph structures used to represent the spatial …AccuWeather.com has become a household name when it comes to weather forecasting. With its accurate and reliable predictions, the website has gained the trust of millions of users ... 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 machine learning methods focus on ... 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 ...

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Satellite networks are characterized by rapid topology changes, quick updates in the coverage of subsatellite points, and large variations in service traffic access in different regions, but they are also likely to cause congestion and blockage in the network. In order to solve this problem, a network traffic prediction method based on long short-term memory (LSTM) and generative adversarial ... 1. Introduction. Existing traffic prediction methods are often of limited use to early morning commuters. According to American Community Survey (2011–2015) by U.S. Census Bureau (2015), 13% of the population nationwide were reported to leave home for work before 6am to avoid the worst commute times, and 4.4% were even out the door …Traffic prediction, as a core component of intelligent transportation systems (ITS), has been investigated thoroughly in the literature. Nevertheless, timely accurate traffic prediction still remains an open challenge due to the nonlinearities and complex patterns of traffic flows. In addition, most of the existing traffic prediction methods focus on grid …Aug 16, 2023 · Traffic prediction analyses large amounts of data from traffic sensors and is an important aspect of managing traffic flow. “Accurate traffic prediction empowers road users to make informed decisions and contributes to the alleviation of traffic congestion,” explained Peisheng Qian and Ziyuan Zhao, research engineers at A*STAR’s Institute ...

In the digital age, music has become more accessible than ever before. With just a few clicks, you can stream your favorite songs or even download them for offline listening. In th...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 …Apr 29, 2020 · This leads to the construction of three separate data sets corresponding to the US-101 highway, 4 pm I-80 highway, and 5 pm I-80 highway. Supplementary Figures 1 and 2 demonstrate the resulting ... Accurate traffic prediction is crucial to the construction of intelligent transportation systems. This task remains challenging because of the complicated and dynamic spatiotemporal dependency in traffic networks. While various graph-based spatiotemporal networks have been proposed for traffic prediction, most of them rely …Traffic prediction has drawn increasing attention due to its essential role in smart city applications. To achieve precise predictions, a large number of approaches have been proposed to model spatial dependencies and temporal dynamics. Despite their superior performance, most existing studies focus datasets that are usually in large geographic …4 days ago · Traffic prediction has long been a focal and pivotal area in research, witnessing both significant strides from city-level to road-level predictions in recent years. With the advancement of Vehicle-to-Everything (V2X) technologies, autonomous driving, and large-scale models in the traffic domain, lane-level traffic prediction has emerged as an indispensable direction. However, further progress ... 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).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 the fast-paced world of professional football, making accurate predictions can be a challenging task. With so many variables at play, it’s no wonder that both fans and bettors o...Groundhog Day is a widely celebrated holiday in North America, particularly in the United States and Canada. Held annually on February 2nd, it has become a tradition to gather arou...

As a type of neural network which directly operates on a graph structure, GNNs have the ability to capture complex relationships between ob-jects and make inferences based on data described by graphs. GNNs have been proven e ective in various node-level, edge-level, and graph-level prediction tasks (Jiang, 2022).

As a type of neural network which directly operates on a graph structure, GNNs have the ability to capture complex relationships between ob-jects and make inferences based on data described by graphs. GNNs have been proven e ective in various node-level, edge-level, and graph-level prediction tasks (Jiang, 2022).Network traffic prediction can guarantee high-quality communication, so it is widely used in many satellite applications. Satellite traffic has complex characteristics such as self-similarity and long correlation. Different from the terrestrial network, the available resources of the satellite network are more limited, and the topological ...Feb 17, 2022 ... A Survey of Traffic Prediction Based on Deep Neural Network: Data, Methods and Challenges --- Authors: Cao, Pengfei; Dai, Fei (Southwest ...Traffic prediction task can be formulated as a multivariate time series forecasting problem with auxiliary prior knowledge. Generally, the prior knowledge is the pre-defined adjacency matrix denoted as a weighted directed graph \( \mathcal {G}=(\mathcal {V},\mathcal {E},A) \).In today’s data-driven world, businesses are constantly seeking ways to gain a competitive edge. One powerful tool that has emerged in recent years is predictive analytics programs...Nov 4, 2019 ... A team of Berkeley Lab computer scientists is working with the California Department of Transportation and UC Berkeley to use high ...Outcomes · it provides good prediction accuracy for a large number of counting stations, · its usage is based on a tailored selection of past learning horizon .....Wireless traffic prediction is essential for cellular networks to realize intelligent network operations, such as load-aware resource management and predictive control. Existing prediction approaches usually adopt centralized training architectures and require the transferring of huge amounts of traffic data, which may raise delay and …

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With the emerging concepts of smart cities and intelligent transportation systems, accurate traffic sensing and prediction have become critically important to support urban management and traffic control. In recent years, the rapid uptake of the Internet of Vehicles and the rising pervasiveness of mobile services have produced unprecedented …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 ...According to the National Snow & Ice Data Center, blizzard prediction relies on modeling weather systems, as well as predicting temperatures. The heavy snowfall that blizzards crea...Are you seeking daily guidance and predictions to navigate through life’s ups and downs? Look no further than Eugenia Last, a renowned astrologer known for her accurate and insight...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 …4 days ago · Traffic prediction has long been a focal and pivotal area in research, witnessing both significant strides from city-level to road-level predictions in recent years. With the advancement of Vehicle-to-Everything (V2X) technologies, autonomous driving, and large-scale models in the traffic domain, lane-level traffic prediction has emerged as an indispensable direction. However, further progress ... Traffic prediction, a critical component for intelligent transportation systems, endeavors to foresee future traffic at specific locations using historical data. Although existing traffic prediction models often emphasize developing complex neural network structures, their accuracy has not seen improvements accordingly. Recently, Large …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 ...May 22, 2022 ... How to forecast traffic on a road, traffic forecasting methods, road crash analysis. justification of a project of road widening, ...Traffic prediction task can be formulated as a multivariate time series forecasting problem with auxiliary prior knowledge. Generally, the prior knowledge is the pre-defined adjacency matrix denoted as a weighted directed graph \( \mathcal {G}=(\mathcal {V},\mathcal {E},A) \). ….

A novel Spatial-Temporal Dynamic Network (STDN) framework is proposed, which proposes a flow gating mechanism to learn the dynamic similarity between locations via traffic flow and extends the framework from region-based traffic prediction to traffic prediction for road intersections by using graph convolutional structure. Spatial …Traffic prediction with different methods (black: original, blue: prediction) and anomaly detection based on traffic prediction (actual: NA, detected: red) for a specific client - …The traffic flow prediction is fast becoming a key instrument in the transportation system, which has achieved impressive performance for traffic management. The graph neural network plays a critical role in the development of the traffic network management. However, it is worthwhile mentioning that the complexity of road networks …See full list on altexsoft.com Abstract: Accurate and real-time prediction of network traffic can not only help system operators allocate resources rationally according to their actual business needs but also help them assess the performance of a network and analyze its health status. In recent years, neural networks have been proved suitable to predict time series data, represented by …Traffic Prediction is a task that involves forecasting traffic conditions, such as the volume of vehicles and travel time, in a specific area or along a particular road. This task is important for optimizing transportation systems …Given the flow prediction task as example (the traffic prediction task is exactly the same as the flow prediction task): cd flow-prediction/. The settings of the models are in the folder src/model_setting, saved as yaml format.Three models are provided: seq2seq, gat-seq2seq, and st-metanet.Other baselines refers to DCRNN and ST-ResNet, respectively. ...Traffic prediction task can be formulated as a multivariate time series forecasting problem with auxiliary prior knowledge. Generally, the prior knowledge is the pre-defined adjacency matrix denoted as a weighted directed graph \( \mathcal {G}=(\mathcal {V},\mathcal {E},A) \). Traffic prediction, Apr 3, 2020 · Long-term traffic prediction is highly challenging due to the complexity of traffic systems and the constantly changing nature of many impacting factors. In this paper, we focus on the spatio-temporal factors, and propose a graph multi-attention network (GMAN) to predict traffic conditions for time steps ahead at different locations on a road network graph. , Short-term traffic prediction provides tools for improved road management by allowing the reduction of delays, incidents and other unexpected events. Different real-time approaches provide traffic managers with varying but valuable information. This paper reviews the literature regarding model-driven and data-driven approaches focusing on short-term …, The recent popularity of graph convolutional networks (GCNs) has opened up new possibilities for real-time traffic prediction and many GCN-based models have been proposed to capture the spatial correlation on the urban road network. However, the graph-based approaches fail to capture the intricate dependencies of consecutive road …, 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 ..., Abstract: With the explosive growth of communication traffic and the arrival of 5G technologies, wireless big data has become an enabler for operators to manage and improve their wireless communication systems. Although many mobile traffic prediction methods have been proposed in the past few years, few prediction methods combine …, Dec 1, 2022 · A primary problem in traffic forecasting is accurately predicting the outcome of non-recurrent traffic events, which account for about 50% of all traffic congestion according to the Federal Highway Administration (FHWA) (FHWA, 2021). Thus, traffic prediction during non-recurrent events is a critical research area that needs more attention. , Open access. Published: 19 November 2022. Research on highway traffic flow prediction model and decision-making method. Yuyu Zhu, QingE Wu & Na Xiao. Scientific Reports 12, Article …, In network function virtualization enabled networks with dynamic traffic, virtual network function (VNF) migration has been considered as an effective way to improve quality of service as well as resource utilization. However, due to time-varying network traffic, designing a fast and accurate VNF migration algorithm is still a great challenge. To …, Heathrow and Gatwick air traffic control are eschewing traditional pen and paper in favor of digital aviation technology. The busiest airspace in the world is entering the 21st cen..., Traffic flow prediction is an important part of intelligent traffic management system. Because there are many irregular data structures in road traffic, in order to improve the accuracy of traffic flow prediction, this paper proposes a combined traffic flow prediction model based on deep learning graph convolution neural network (GCN), long …, Abstract: Traffic prediction plays an essential role in intelligent transportation system. Accurate traffic prediction can assist route planing, guide vehicle dispatching, and …, 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., Mobile traffic prediction enables the efficient utilization of network resources and enhances user experience. In this paper, we propose a state transition graph-based spatial–temporal attention network (STG-STAN) for cell-level mobile traffic prediction, which is designed to exploit the underlying spatial–temporal dynamic …, The stability and efficiency of neural network for short term prediction of traffic volume with mixed Indian traffic flow conditions on 4-lane undivided highways were studied by Kumar et al. . Kumar et al. [ 17 ] considered ANN model for traffic flow forecasting and used traffic volume, speed, traffic density, time and day of week as …, If the issue persists, it's likely a problem on our side. Unexpected token < in JSON at position 4. SyntaxError: Unexpected token < in JSON at position 4. Refresh. Hourly traffic data on four different junctions., Traffic prediction has been an active research topic in the domain of spatial-temporal data mining. Accurate real-time traffic prediction is essential to improve the safety, stability, and versatility of smart city systems, i.e., traffic control and optimal routing. The complex and highly dynamic spatial-temporal dependencies make effective …, 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 …, Sep 9, 2019 ... The autoregressive integrated moving average (ARIMA) model is a suitable model to predict traffic in short time periods. However, it requires a ..., Traffic prediction constitutes a pivotal facet within the purview of Intelligent Transportation Systems (ITS), and the attainment of highly precise predictions holds profound significance for efficacious traffic management. The precision of prevailing deep learning-driven traffic prediction models typically sees an upward trend with a rise in …, The traffic flow prediction is fast becoming a key instrument in the transportation system, which has achieved impressive performance for traffic management. The graph neural network plays a critical role in the development of the traffic network management. However, it is worthwhile mentioning that the complexity of road networks …, The Spatio-Temporal Traffic Prediction (STTP) problem is a classical problem with plenty of prior research efforts that benefit from traditional statistical learning and recent deep learning approaches. While STTP can refer to many real-world problems, most existing studies focus on quite specific applications, such as the prediction of taxi …, The methods proposed by [2, 29] are a typical kind of approaches for eliminating the daily-periodic trend for traffic prediction . Article occupies the fourth place with 149 citations. This article focuses on the application of DL models for traffic flow prediction and receives 149 citations in less than five years., Sep 3, 2020 · Predicting traffic with advanced machine learning techniques, and a little bit of history. To predict what traffic will look like in the near future, Google Maps analyzes historical traffic patterns for roads over time. , In recent years, automation has revolutionized various industries, including manufacturing. With advancements in technology and the adoption of artificial intelligence (AI) and rob..., Timely and accurate traffic speed prediction has gained increasing importance for urban traffic management and helping one to make advisable travel decision. However, the existing approaches have difficulty extracting features of large-scale traffic data. This study proposed a hybrid deep learning method named AB-ConvLSTM for large …, , Dec 13, 2021 · Short-term Traffic prediction plays an important role in the success of Intelligent Transport Systems (ITS) particularly for travel information systems, adaptive traffic management systems, public ... , Smart cities emerge as highly sophisticated bionetworks, providing smart services and ground-breaking solutions. This paper relates classification with Smart City projects, particularly focusing on traffic prediction. A systematic literature review identifies the main topics and methods used, emphasizing on various Smart Cities components, …, 1. Introduction. With the acceleration of urbanization, traffic congestion has become a global problem. In response to this problem, many cities have begun to adopt intelligent transportation systems to optimize urban traffic flow and improve traffic efficiency [1].Intelligent transportation systems must accurately predict urban traffic flow to adjust …, Meteorologists track and predict weather conditions using state-of-the-art computer analysis equipment that provides them with current information about atmospheric conditions, win..., Timely and accurate traffic speed prediction has gained increasing importance for urban traffic management and helping one to make advisable travel decision. However, the existing approaches have difficulty extracting features of large-scale traffic data. This study proposed a hybrid deep learning method named AB-ConvLSTM for large …, See full list on altexsoft.com , Abstract: With the explosive growth of communication traffic and the arrival of 5G technologies, wireless big data has become an enabler for operators to manage and improve their wireless communication systems. Although many mobile traffic prediction methods have been proposed in the past few years, few prediction methods combine …