[HTML][HTML] Short-term prediction of outbound truck traffic from the exchange of information in logistics hubs: A case study for the port of Rotterdam
Short-term traffic prediction is an important component of traffic management systems.
Around logistics hubs such as seaports, truck flows can have a major impact on the …
Around logistics hubs such as seaports, truck flows can have a major impact on the …
Truck traffic speed prediction under non-recurrent congestion: Based on optimized deep learning algorithms and GPS data
J Zhao, Y Gao, Z Yang, J Li, Y Feng, Z Qin, Z Bai - IEEE Access, 2019 - ieeexplore.ieee.org
Due to the restriction of traffic management measure in large cities, large heavy-haul trucks
can only travel on the circuits and expressways around the city, which often causes …
can only travel on the circuits and expressways around the city, which often causes …
An improved k-nearest neighbours method for traffic time series imputation
Intelligent transportation systems (ITS) are becoming more and more effective, benefiting
from big data. Despite this, missing data is a problem that prevents many prediction …
from big data. Despite this, missing data is a problem that prevents many prediction …
Short-term passenger flow prediction in urban public transport: Kalman filtering combined k-nearest neighbor approach
Short-term prediction of passengers' flow is one of the essential elements of the operation
and real time control for public transit. Although fine prediction methodologies have been …
and real time control for public transit. Although fine prediction methodologies have been …
RSAB-ConvGRU: A hybrid deep-learning method for traffic flow prediction
Accurate and real-time traffic flow prediction is crucial in intelligent transportation systems
(ITS), and the traditional shallow prediction methods are challenging to capture the …
(ITS), and the traditional shallow prediction methods are challenging to capture the …
[HTML][HTML] Travel speed prediction based on learning methods for home delivery
The travel time to proceed from one location to another in a network is an important
consideration in many urban transportation settings ranging from the planning of delivery …
consideration in many urban transportation settings ranging from the planning of delivery …
Robust Truck Transit Time Prediction through GPS Data and Regression Algorithms in Mixed Traffic Scenarios
A Ghazikhani, S Davoodipoor, AM Fathollahi-Fard… - Mathematics, 2024 - mdpi.com
To enhance safety and efficiency in mixed traffic scenarios, it is crucial to predict freight truck
traffic flow accurately. Issues arise due to the interactions between freight trucks and …
traffic flow accurately. Issues arise due to the interactions between freight trucks and …
Deep-learning architectures to forecast bus ridership at the stop and stop-to-stop levels for dense and crowded bus networks
J Baek and, K Sohn - Applied Artificial Intelligence, 2016 - Taylor & Francis
The conventional transit assignment models that depend on either probabilistic or
deterministic theory have failed to accurately estimate rider demand for dense and crowded …
deterministic theory have failed to accurately estimate rider demand for dense and crowded …
Short-term traffic flow prediction using a self-adaptive two-dimensional forecasting method
M Ma, S Liang, H Guo, J Yang - Advances in Mechanical …, 2017 - journals.sagepub.com
Short-term traffic volume forecasting is widely recognized as an important element of
intelligent transportation systems, because the accuracy of predictive methods determines …
intelligent transportation systems, because the accuracy of predictive methods determines …
Traffic flow estimation using graph neural network with Aggregation of traffic features
The increasing vehicle volume every year affects the prediction of the traffic system. The
purpose of predicting traffic flow is to estimate the lost data caused by sensor malfunctions …
purpose of predicting traffic flow is to estimate the lost data caused by sensor malfunctions …