Traffic flow prediction with parallel data

Author: tmia

August undefined, 2024

Splet25. nov. 2024 · Abstract: The goal of the investigation is to develop and test a system capable of providing short-term (less than an hour) traffic flow predictions in an urban … SpletWe use Monte Carlo simulations to evaluate our methodology. Our simulations demonstrate the accuracy of the proposed approach. The traffic flow prediction errors vary from an …

Real-Time Traffic Flow Prediction Using Big Data Analytics

Splet29. mar. 2024 · Traffic prediction is the task of predicting future traffic measurements (e.g. volume, speed, etc.) in a road network (graph), using historical data (timeseries). SpletTraffic flow prediction heavily depends on historical and real-time traffic data collected from various sensor sources, including inductive loops, radars, cameras, mobile Global Positioning System, crowdsourcing, social media, and so forth. steve timmons

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SpletData-parallel computing frameworks (DCF) such as MapReduce, Spark, and Dryad etc. Have tremendous applications in big data and cloud computing, and throw tons o … Splet01. nov. 2024 · The evolving of parallel system paradigm for traffic prediction and the algorithm to incrementally train traffic data generation models and traffic prediction … SpletIn this paper, we propose a Differential Time-variant (DT) Traffic Flow Prediction method, which can remarkably improve the accuracy and reduce the variance of traffic flow forecast based on deep learning models. To extract the temporal trend of the traffic flow at different locations, we apply data difference to preprocess the raw traffic data. steve timms commerce city

Spark Cloud-Based Parallel Computing for Traffic Network Flow ...

Parallel computing method of deep belief networks and its …

Splet15. jun. 2024 · Traffic Prediction Papers Reviews [TITS 2015] Traffic Flow Prediction With Big Data: A Deep Learning Approach [KDD 2024] Deep Learning for Spatio-Temporal Data Mining: A Survey [Information Fusion 2024] Urban flow prediction from spatiotemporal data using machine learning: A survey [Arxiv 2024] Deep Learning on Traffic Prediction: … Splet24. feb. 2024 · Traffic flow in a road network is mutually interactive and interdependent with each other. It is challenging to describe the dynamics of traffic network flow by Traffic … steve tiner facebookSplet01. mar. 2024 · Experimental results show that the parallel data have similar distributions to the original data, and the prediction models trained by the mixed data perform better than those trained only using the original data. ... such as the electricity load forecasting, and the traffic flow prediction. Previous article in issue; Next article in issue ... steve timothy accident

"SpletThe evolving of parallel system paradigm for traffic prediction and the algorithm to incrementally train traffic data generation models and traffic prediction models are … " - Traffic flow prediction with parallel data

Traffic flow prediction with parallel data

A multiple spatio‐temporal features fusion approach for …

SpletTraffic flow is defined as the number of vehicles passing through a spatial unit, such as a road segment or traffic sensor point, in a given time period. An accurate traffic flow … Splet12. avg. 2014 · Wang proposed a parallel traffic flow prediction method based on SVM, and the experimental results showed that the result of parallel SVM method is better than …

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SpletTraffic flow prediction is an essential part of the intelligent transport system. This is the accurate estimation of traffic flow in a given region at a particular interval of time in the … Splet29. mar. 2024 · Traffic prediction is the task of predicting future traffic measurements (e.g. volume, speed, etc.) in a road network (graph), using historical data (timeseries). …

Splet20. mar. 2024 · Traffic flow prediction is primarily concerned with traffic data on roadways, which has both temporal and spatial correlations. Aiming at the spatiotemporal characteristics, this paper studies two aspects and designs a traffic flow prediction model with a deep neural network. Splet01. nov. 2024 · Trafﬁc prediction is an elemental function of Intelligent Transportation Systems, and accurate and timely prediction is of great signiﬁcance to both trafﬁc …

Splet26. feb. 2024 · The parallel-connected structure of convolutional neural network and long short-term memory reflects much powerful performance in traffic flow prediction. To apply the parallel spatiotemporal deep learning network in large dataset prediction, a dataset of Shanghai inner ring elevated road is used to predict 591 sensors in 6 months. Splet25. maj 2024 · Based on different methods, traffic flow prediction analyzes and generalizes the traffic characteristics of both common and special areas (e.g., schools and hospitals) …

Splet21. apr. 2024 · Urban traffic flow prediction using data-driven models can play an important role in route planning and preventing congestion on highways. These methods utilize …

Splet01. jan. 2024 · ABSTRACT Air quality forecasting is crucial to reducing air pollution in China, which has detrimental effects on human health. Atmospheric chemical-transport models can provide air pollutant forecasts with high temporal and spatial resolution and are widely used for routine air quality predictions (e.g., 1–3 days in advance). However, the model’s … steve tinberg obituarySpletThe random forest algorithm creates multiple decision trees and merges their data to obtain accurate predictions. It’s quite fast and can produce effective results given sufficient … steve tims cylinder headsSplet23. avg. 2024 · Traffic flow prediction is a combination of time series prediction and Big Data analysis. There are many approaches to time series prediction problem based on deep learning, machine learning algorithms, etc. For example, using spatial temporal graph neural network [ 1 ], which can comprehensively capture spatial and temporal patterns and ... steve timoney smsSplet01. jan. 2024 · 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 … steve timperman hilton headSplet09. sep. 2014 · In this paper, a novel deep-learning-based traffic flow prediction method is proposed, which considers the spatial and temporal correlations inherently. A stacked … steve tingle athens gaSplet14. apr. 2024 · 2.1 Traffic Prediction. Traffic prediction is a classical spatial-temporal prediction problem that has been extensively studied in the past decades [22, 23].Compared with statistical methods VAR [] and ARIMA [], deep learning methods Recurrent Neural Networks (RNNs) [], Long-Short-Term-Memory networks (LSTM) [] break away from the … steve tingley-hockSplet05. mar. 2024 · This study applies gated recurrent neural network to predict urban traffic flow considering weather conditions. Running results show that, under the review of weather influences, their method... steve tims heads