Traffic prediction

This work proposes a novel uncertainty quantification framework for long-term traffic flow prediction (TFP) based on a sequential deep learning model. Quantifying the uncertainty of TFP is crucial for intelligent transportation system (ITS) to make robust traffic congestion analysis and efficient traffic management due to the inherent uncertain and …

Traffic prediction, a critical component for intelligent transportation systems, endeavors to foresee future traffic at specific locations using historical data. Although existing …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 …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....An ostrich that escaped from a zoo in the South Korean town of Seongnam has been captured, local authorities said, after it spent an hour dodging cars in heavy traffic, …Nov 19, 2022 · To solve the high order nonlinear model of traffic congestion, this paper proposes the model linearization iterative updating method and develops a traffic prediction and decision system. The ... Suspect refused to get out of car during traffic stop, police say. According to police, Diller and his partner conducted the traffic stop at 1919 Mott. Ave., around 5:48 p.m. …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 …In traffic accident prediction tasks, deep learning models typically provide better prediction results than traditional prediction models. This is due to the fact that deep learning …

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 techniques can often be applied across various timescales or time-independently, so criteria are needed to classify techniques into short-term or long-term categories. For the purpose of this paper, ‘short-term’ refers to the prediction and application of techniques in the timeframe of minutes, hours, and days. ...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 flow prediction is a crucial measure in Intelligent Transportation System. It helps in efficiently handling the future vehicular load on the roads that will assist in managing traffic, reducing congestions and accident rates. Therefore, this study has been conducted on Jawaharlal Nehru University (JNU) located in New Delhi, India that covers … survey aims to provide a comprehensive overview of traffic prediction methodologies. Specifically, we focus on the recent advances and emerging research opportunities in Artificial Intelligence (AI)-based traffic prediction methods, due to their recent suc-cess and potential in traffic prediction, with an emphasis on multivariate traffic time Astrology is an ancient practice that has fascinated and guided individuals for centuries. By using the position of celestial bodies at the time of your birth, astrology can offer ...Evacuation traffic prediction is one of the most critical elements for deploying pro-active traffic management strategies. However, evacuation traffic patterns differ from non-evacuation traffic condition such as the presence of higher traffic volume and unexpected shifts in evacuation trends. Thus, it is more challenging to learn such ...With the accelerated popularization of 5G applications, accurate cellular traffic prediction is becoming increasingly important for efficient network management. Currently, the latest algorithms for cellular traffic prediction generally neglect extraction of the shallow features of cellular traffic and the prediction accuracy is hence limited. …

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 is an essential and challenging task for traffic management and commercial purposes, such as estimating arrival time for delivery services. Machine learning methods for traffic prediction usually treat traffic conditions as time-series due to obvious temporal patterns. Recently, spatial relationships among roads in a road network have …An accurate prediction of the four-dimensional (4D) trajectory of aircraft serves as a fundamental technique to improve the predictability of air traffic for the TBO 10 to achieve downstream tasks ...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.Cellphone video obtained by CBS New York shows the chaos after the encounter, with members of the the NYPD rushing to Diller's side, quickly getting him into a vehicle and …Pytorch implementation for the paper: TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents (AAAI), Oral, 2019 The repo has been forked initially from Anirudh Vemula 's repository for his paper Social Attention: Modeling Attention in Human Crowds (ICRA 2018).

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Kiwis will be hitting the road in droves over the summer holidays this year, and Waka Kotahi NZ Transport Agency has updated our on-line Holiday Journeys traffic prediction tool to help people plan ahead and minimise delays. The tool shows predicted traffic flow across popular journeys over the Christmas and New Year’s holiday based …3.1 System Partitioning. In traditional studies, some researchers treat traffic prediction as a kind of time series problems. But in advanced system, locations (such as roads, stations, intersections, etc.) are usually well connected into a typical traffic network and have nonnegligible relationships with each other.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 …It is possible to predict whether an element will form a cation or anion by determining how many protons an element has. If an element has more protons than electrons, it is a cati...By The Associated Press March 26, 2024 5:51 am. NEW YORK — A New York City police officer was shot and killed Monday during a traffic stop, the city's mayor said. “We …

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...In traffic accident prediction tasks, deep learning models typically provide better prediction results than traditional prediction models. This is due to the fact that deep learning …Traffic prediction plays a crucial role in alleviating traffic congestion which represents a critical problem globally, resulting in negative consequences such as lost hours of …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 ...Astrology is an ancient practice that has fascinated and guided individuals for centuries. By using the position of celestial bodies at the time of your birth, astrology can offer ...The traffic flow prediction task is essential to the urban intelligent transportation system. Due to the complex correlation of traffic flow data, insufficient use of spatiotemporal features will often lead to significant deviations in prediction results. This paper proposes an adaptive traffic flow prediction model AD-GNN based on …Sep 3, 2020 · To accurately predict future traffic, Google Maps uses machine learning to combine live traffic conditions with historical traffic patterns for roads worldwide. This process is complex for a number of reasons. Based on this, we further propose a time-series similarity-based graph attention network, TSGAN, for the spatial-temporal cellular traffic prediction. The simulation results show that our proposed TSGAN outperforms three classic prediction models based on GNNs or GRU on a real-world cellular network dataset in short-term, …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.Jan 23, 2021 · A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation. Open access. Published: 23 January 2021. Volume 6 , pages 63–85, ( 2021 ) Cite this article. Download PDF. You have full access to this open access article. Data Science and Engineering Aims and scope. Haitao Yuan & Guoliang Li. 27k Accesses. 134 Citations.

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...

Network traffic is nonlinear and nonsmooth, so it is difficult to accurately predict long-term traffic. To improve the accuracy of network traffic prediction, this paper proposes a WP-depth Gaussian network traffic prediction model using the wavelet denoising method and deep Gaussian process. Firstly, the traffic sequences containing noisy signals are …Abstract: Traffic prediction plays an essential role in intelligent transportation system. Accurate traffic prediction can assist route planing, guide vehicle dispatching, and …2.2 Traffic Prediction Traffic prediction aims to predict future traffic features based on historical traffic data, which is crucial for intelligent transportation systems [Ye et al., 2021; Shao et al., 2022; Miao et al., 2023]. Traditionally, the traffic prediction model is based on statistics, such as ARIMA and Kalman filter[Ku-Mar 13, 2023 · Traffic Prediction with Transfer Learning: A Mutual Information-based Approach. Yunjie Huang, Xiaozhuang Song, Yuanshao Zhu, Shiyao Zhang, James J.Q. Yu. In modern traffic management, one of the most essential yet challenging tasks is accurately and timely predicting traffic. It has been well investigated and examined that deep learning-based ... Network traffic prediction plays a significant role in network management. Previous network traffic prediction methods mainly focus on the temporal relationship between network traffic, and used time series models to predict network traffic, ignoring the spatial information contained in traffic data. Therefore, the prediction accuracy is limited, …Network traffic prediction plays a significant role in network management. Previous network traffic prediction methods mainly focus on the temporal relationship between network traffic, and used time series models to predict network traffic, ignoring the spatial information contained in traffic data. Therefore, the prediction accuracy is limited, …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 ...

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Jun 21, 2022 · Traffic prediction is a modeling technique for creating traffic projections using a mix of historical and real-time data points on traffic volumes, travel patterns, and weather conditions. Modern traffic prediction systems like those employed by Google Maps or TomTom can precisely estimate traffic congestion in a matter of seconds — and ... Meteorologists track and predict weather conditions using state-of-the-art computer analysis equipment that provides them with current information about atmospheric conditions, win...To overcome the problem of traffic congestion, the traffic prediction using machine learning which contains regression model and libraries like pandas, os, numpy, matplotlib.pyplot are used to predict the traffic. This has to be implemented so that the traffic congestion is controlled and can be accessed easily.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 23, 2021 · A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation. Open access. Published: 23 January 2021. Volume 6 , pages 63–85, ( 2021 ) Cite this article. Download PDF. You have full access to this open access article. Data Science and Engineering Aims and scope. Haitao Yuan & Guoliang Li. 27k Accesses. 134 Citations. 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 …A Novel Traffic Prediction System based on Floating Car Data and Machine Learning. NISS '19: Proceedings of the 2nd International Conference on Networking, Information Systems & Security . Intelligent Transportation Systems have become a necessity with the increasing number of cars running, especially in the urban roads. This …A Novel Traffic Prediction System based on Floating Car Data and Machine Learning. NISS '19: Proceedings of the 2nd International Conference on Networking, Information Systems & Security . Intelligent Transportation Systems have become a necessity with the increasing number of cars running, especially in the urban roads. This …The goal of network traffic prediction is to forecast the future traffic status based on historical observations. Precise and real-time network traffic prediction plays an important role in IP network management and operation tasks, such as traffic engineering, network planning and anomaly detection [].For example, the traffic engineering task … ….

Wireless traffic prediction can effectively reduce the uncertainty in network demand and supply, and thus is a key enabler of smart management in next-generation wireless networks. To the best of our knowledge, this paper is the first to establish a wireless traffic prediction model by applying the Gaussian Process (GP) method based on real 4G …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...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 ...Astrology is an ancient practice that has fascinated and guided individuals for centuries. By using the position of celestial bodies at the time of your birth, astrology can offer ...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 …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 … 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. 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 …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 … 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]