Traffic state estimation on highway: A comprehensive survey
Traffic state estimation (TSE) refers to the process of the inference of traffic state variables
(ie, flow, density, speed and other equivalent variables) on road segments using partially …
(ie, flow, density, speed and other equivalent variables) on road segments using partially …
Traffic density estimation in vehicular ad hoc networks: A review
Abstract Nowadays, vehicular Ad hoc Networks (VANETs) are gaining enormous research
interest. Even though the leading reason for developing VANETs is traffic safety, many …
interest. Even though the leading reason for developing VANETs is traffic safety, many …
Optimized graph convolution recurrent neural network for traffic prediction
Traffic prediction is a core problem in the intelligent transportation system and has broad
applications in the transportation management and planning, and the main challenge of this …
applications in the transportation management and planning, and the main challenge of this …
Short-term traffic forecasting: Where we are and where we're going
EI Vlahogianni, MG Karlaftis, JC Golias - Transportation Research Part C …, 2014 - Elsevier
Since the early 1980s, short-term traffic forecasting has been an integral part of most
Intelligent Transportation Systems (ITS) research and applications; most effort has gone into …
Intelligent Transportation Systems (ITS) research and applications; most effort has gone into …
Daily traffic flow forecasting through a contextual convolutional recurrent neural network modeling inter-and intra-day traffic patterns
Traffic flow forecasting is an important problem for the successful deployment of intelligent
transportation systems, which has been studied for more than two decades. In recent years …
transportation systems, which has been studied for more than two decades. In recent years …
Travel time estimation for urban road networks using low frequency probe vehicle data
E Jenelius, HN Koutsopoulos - Transportation Research Part B …, 2013 - Elsevier
The paper presents a statistical model for urban road network travel time estimation using
vehicle trajectories obtained from low frequency GPS probes as observations, where the …
vehicle trajectories obtained from low frequency GPS probes as observations, where the …
Daily long-term traffic flow forecasting based on a deep neural network
Daily traffic flow forecasting is critical in advanced traffic management and can improve the
efficiency of fixed-time signal control. This paper presents a traffic prediction method for one …
efficiency of fixed-time signal control. This paper presents a traffic prediction method for one …
Dual dynamic spatial-temporal graph convolution network for traffic prediction
Recently, Graph Convolution Network (GCN) and Temporal Convolution Network (TCN) are
introduced into traffic prediction and achieve state-of-the-art performance due to their good …
introduced into traffic prediction and achieve state-of-the-art performance due to their good …
An automatic traffic density estimation using Single Shot Detection (SSD) and MobileNet-SSD
Traffic density estimation is a very important component of an automated traffic monitoring
system. Traffic density estimation can be used in a number of traffic applications–from …
system. Traffic density estimation can be used in a number of traffic applications–from …
Real-time joint traffic state and model parameter estimation on freeways with fixed sensors and connected vehicles: State-of-the-art overview, methods, and case …
Y Wang, M Zhao, X Yu, Y Hu, P Zheng, W Hua… - … Research Part C …, 2022 - Elsevier
This paper addresses real-time joint traffic state and model parameter estimation on
freeways using data from fixed sensors and connected vehicles. It investigates how the …
freeways using data from fixed sensors and connected vehicles. It investigates how the …