Traffic flow (computer networking)
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Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting Open
Forecasting the traffic flows is a critical issue for researchers and practitioners in the field of transportation. However, it is very challenging since the traffic flows usually show high nonlinearities and complex patterns. Most existin…
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Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction Open
This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describi…
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A Survey on the Coordination of Connected and Automated Vehicles at Intersections and Merging at Highway On-Ramps Open
Connected and automated vehicles (CAVs) have the potential to improve safety by reducing and mitigating traffic accidents. They can also provide opportunities to reduce transportation energy consumption and emissions by improving traffic f…
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Traffic Flow Prediction via Spatial Temporal Graph Neural Network Open
Traffic flow analysis, prediction and management are keystones for building smart cities in the new era. With the help of deep neural networks and big traffic data, we can better understand the latent patterns hidden in the complex transpo…
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Automated and Cooperative Vehicle Merging at Highway On-Ramps Open
Recognition of necessities of connected and automated vehicles (CAVs) is gaining momentum. CAVs can improve both transportation network efficiency and safety through control algorithms that can harmonically use all existing information to …
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PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction Open
As a core technology of Intelligent Transportation System, traffic flow prediction has a wide range of applications. The fundamental challenge in traffic flow prediction is to effectively model the complex spatial-temporal dependencies in …
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Spatiotemporal Traffic Flow Prediction with KNN and LSTM Open
The traffic flow prediction is becoming increasingly crucial in Intelligent Transportation Systems. Accurate prediction result is the precondition of traffic guidance, management, and control. To improve the prediction accuracy, a spatiote…
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DeepSTN+: Context-Aware Spatial-Temporal Neural Network for Crowd Flow Prediction in Metropolis Open
Crowd flow prediction is of great importance in a wide range of applications from urban planning, traffic control to public safety. It aims to predict the inflow (the traffic of crowds entering a region in a given time interval) and outflo…
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Spatial-Temporal Transformer Networks for Traffic Flow Forecasting Open
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 dyna…
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Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework Open
Deep learning approaches have reached a celebrity status in artificial intelligence field, its success have mostly relied on Convolutional Networks (CNN) and Recurrent Networks. By exploiting fundamental spatial properties of images and vi…
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Trajectory data-based traffic flow studies: A revisit Open
In this paper, we review trajectory data-based traffic flow studies that have been conducted over the last 15 years. Our purpose is to provide a roadmap for readers who have an interest in the latest developments of traffic flow theory tha…
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Traffic Flow Prediction using Kalman Filtering Technique Open
Traffic flow prediction is an important research problem in many of the Intelligent Transportation Systems (ITS) applications. The use of Autoregressive Integrated Moving average (ARIMA) or seasonal ARIMA (SARIMA) for traffic flow predicti…
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Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction Open
Forecasting the flow of crowds is of great importance to traffic management and public safety, yet a very challenging task affected by many complex factors, such as inter-region traffic, events and weather. In this paper, we propose a deep…
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Deep Temporal Convolutional Networks for Short-Term Traffic Flow Forecasting Open
To reduce the increasingly congestion in cities, it is essential for intelligent transportation system (ITS) to accurately forecast the short-term traffic flow to identify the potential congestion sites. In recent years, the emerging deep …
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Automated vehicle-involved traffic flow studies: A survey of assumptions, models, speculations, and perspectives Open
Automated vehicles (AVs) are widely considered to play a crucial role in future transportation systems because of their speculated capabilities in improving road safety, saving energy consumption, reducing vehicle emissions, increasing roa…
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State-of-art review of traffic signal control methods: challenges and opportunities Open
Introduction Due to the menacing increase in the number of vehicles on a daily basis, abating road congestion is becoming a key challenge these years. To cope-up with the prevailing traffic scenarios and to meet the ever-increasing demand …
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Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network Open
Accurate forecasting of citywide traffic flow has been playing critical role in a variety of spatial-temporal mining applications, such as intelligent traffic control and public risk assessment. While previous work has made significant eff…
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Will Automated Vehicles Negatively Impact Traffic Flow? Open
With low-level vehicle automation already available, there is a necessity to estimate its effects on traffic flow, especially if these could be negative. A long gradual transition will occur from manual driving to automated driving, in whi…
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Short-Term Traffic Flow Prediction Method for Urban Road Sections Based on Space–Time Analysis and GRU Open
Accurate short-term traffic forecasts help people choose transportation and travel time. Through the query data, many models for traffic flow prediction have neglected the temporal and spatial correlation of traffic flow, so that the predi…
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A Review of the Self-Adaptive Traffic Signal Control System Based on Future Traffic Environment Open
The self-adaptive traffic signal control system serves as an effective measure for relieving urban traffic congestion. The system is capable of adjusting the signal timing parameters in real time according to the seasonal changes and short…
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The traffic signal control problem for intersections: a review Open
Background The intersection traffic signal control problem (ITSCP) has become even more important as traffic congestion has been more intractable. The ITSCP seeks an efficient schedule for traffic signal settings at intersections with the …
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Long-term traffic flow forecasting using a hybrid CNN-BiLSTM model Open
The increase of road traffic in large cities during the last years has produced that long and short-term traffic flow forecasting is a critical need for the authorities. The availability of good traffic flow prediction methods is a must to…
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An AutoEncoder and LSTM-Based Traffic Flow Prediction Method Open
Smart cities can effectively improve the quality of urban life. Intelligent Transportation System (ITS) is an important part of smart cities. The accurate and real-time prediction of traffic flow plays an important role in ITSs. To improve…
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Design and Implementation of a Smart Traffic Signal Control System for Smart City Applications Open
Infrastructure supporting vehicular network (V2X) capability is the key factor to the success of smart city because it enables many smart transportation services. In order to reduce the traffic congestion and improve the public transport e…
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Urban traffic flow prediction techniques: A review Open
In recent decades, the development of transport infrastructure has had a great development, although traffic problems continue to spread due to increase due to the increase in the population in urban areas that require the use of these mea…
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GSTNet: Global Spatial-Temporal Network for Traffic Flow Prediction Open
Predicting traffic flow on traffic networks is a very challenging task, due to the complicated and dynamic spatial-temporal dependencies between different nodes on the network. The traffic flow renders two types of temporal dependencies, i…
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Microscopic Pedestrian Flow Characteristics: Development of an Image Processing Data Collection and Simulation Model Open
Microscopic pedestrian studies consider detailed interaction of pedestrians to control their movement in pedestrian traffic flow. The tools to collect the microscopic data and to analyze microscopic pedestrian flow are still very much in i…
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Traffic flow prediction models – A review of deep learning techniques Open
Traffic 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 future. The study of traffic forecasting is useful …
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Traffic Flow Prediction with Rainfall Impact Using a Deep Learning Method Open
Accurate traffic flow prediction is increasingly essential for successful traffic modeling, operation, and management. Traditional data driven traffic flow prediction approaches have largely assumed restrictive (shallow) model architecture…
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Review of Data Fusion Methods for Real-Time and Multi-Sensor Traffic Flow Analysis Open
Recently, development in intelligent transportation systems (ITS) requires the input of various kinds of data in real-time and from multiple sources, which imposes additional research and application challenges. Ongoing studies on Data Fus…