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 …
Spatiotemporal traffic forecasting: review and proposed directions
A Ermagun, D Levinson - Transport Reviews, 2018 - Taylor & Francis
This paper systematically reviews studies that forecast short-term traffic conditions using
spatial dependence between links. We extract and synthesise 130 research papers …
spatial dependence between links. We extract and synthesise 130 research papers …
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 …
Long short-term memory neural network for traffic speed prediction using remote microwave sensor data
Neural networks have been extensively applied to short-term traffic prediction in the past
years. This study proposes a novel architecture of neural networks, Long Short-Term Neural …
years. This study proposes a novel architecture of neural networks, Long Short-Term Neural …
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 …
Dynamic graph convolution network for traffic forecasting based on latent network of Laplace matrix estimation
Traffic forecasting is a challenging problem in the transportation research field as the
complexity and non-stationary changing of the traffic data, thus the key to the issue is how to …
complexity and non-stationary changing of the traffic data, thus the key to the issue is how to …
An improved fuzzy neural network for traffic speed prediction considering periodic characteristic
This paper proposes a new method in construction fuzzy neural network to forecast travel
speed for multi-step ahead based on 2-min travel speed data collected from three remote …
speed for multi-step ahead based on 2-min travel speed data collected from three remote …
Short-term traffic flow rate forecasting based on identifying similar traffic patterns
FG Habtemichael, M Cetin - Transportation research Part C: emerging …, 2016 - Elsevier
The ability to timely and accurately forecast the evolution of traffic is very important in traffic
management and control applications. This paper proposes a non-parametric and data …
management and control applications. This paper proposes a non-parametric and data …
Traffic flow prediction based on combination of support vector machine and data denoising schemes
Traffic flow prediction with high accuracy is definitely considered as one of most important
parts in the Intelligent Transportation Systems. As interfering by some external factors, the …
parts in the Intelligent Transportation Systems. As interfering by some external factors, the …
[PDF][PDF] Short-term traffic and travel time prediction models
JWC Van Lint, C Van Hinsbergen - … Intelligence Applications to …, 2012 - onlinepubs.trb.org
Delft University of Technology oad traffic is the visible result of the complex interplay
between traffic demand (the amount and mix of vehicles arriving at a particular place and …
between traffic demand (the amount and mix of vehicles arriving at a particular place and …