AIMC Topic: Transportation
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Bilinear Spatiotemporal Fusion Network: An efficient approach for traffic flow prediction.
Neural networks : the official journal of the International Neural Network Society
Jul 1, 2025
Accurate traffic flow forecasting is critical for intelligent transportation systems, yet increasing model complexity in spatiotemporal graph neural networks does not always yield proportional gains. In this paper, we present a Bilinear Spatiotempora...
Intelligent traffic congestion forecasting using BiLSTM and adaptive secretary bird optimizer for sustainable urban transportation.
Scientific reports
May 26, 2025
Traffic congestion forecasting is one of the major elements of the Intelligent Transportation Systems (ITS). Traffic congestion in urban road networks significantly influences sustainability by increasing air pollution levels. Efficient congestion ma...
Harnessing optimization with deep learning approach on intelligent transportation system for anomaly detection in pedestrian walkways.
Scientific reports
May 19, 2025
Anomaly Detection (AD) in pedestrian walkways is significant in urban safety and security methods. It is generally employed for perceiving unusual or abnormal situations, behaviours, or actions in regions devoted to pedestrian traffic, like pedestria...
Empirical study of daily link traffic volume forecasting based on a deep neural network.
PloS one
Jan 1, 2025
Forecasting the daily link traffic volume is critical in transportation demand analysis in feasibility studies for planning transportation facilities. The high purchase and maintenance cost of commercial transport planning software poses a challenge ...
T-RippleGNN: Predicting traffic flow through ripple propagation with attentive graph neural networks.
PloS one
Jan 1, 2025
Recently, accurate traffic flow prediction has become a significant part of intelligent transportation systems, which can not only satisfy citizens' travel need and life satisfaction, but also benefit urban traffic management and control. However, tr...
From COVID-19 to future electrification: Assessing traffic impacts on air quality by a machine-learning model.
Proceedings of the National Academy of Sciences of the United States of America
Jun 29, 2021
The large fluctuations in traffic during the COVID-19 pandemic provide an unparalleled opportunity to assess vehicle emission control efficacy. Here we develop a random-forest regression model, based on the large volume of real-time observational dat...
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