AIMC Topic: Transportation

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Tracing Road Network Bottleneck by Data Driven Approach.

PloS one
Urban road congestions change both temporally and spatially. They are essentially caused by network bottlenecks. Therefore, understanding bottleneck dynamics is critical in the goal of reasonably allocating transportation resources. In general, a typ...

Course Control of Underactuated Ship Based on Nonlinear Robust Neural Network Backstepping Method.

Computational intelligence and neuroscience
The problem of course control for underactuated surface ship is addressed in this paper. Firstly, neural networks are adopted to determine the parameters of the unknown part of ideal virtual backstepping control, even the weight values of neural netw...

Evaluating clustering methods within the Artificial Ecosystem Algorithm and their application to bike redistribution in London.

Bio Systems
This paper proposes and evaluates a solution to the truck redistribution problem prominent in London's Santander Cycle scheme. Due to the complexity of this NP-hard combinatorial optimisation problem, no efficient optimisation techniques are known to...

Travel Time Estimation Using Freeway Point Detector Data Based on Evolving Fuzzy Neural Inference System.

PloS one
Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system...

Large-scale transportation network congestion evolution prediction using deep learning theory.

PloS one
Understanding how congestion at one location can cause ripples throughout large-scale transportation network is vital for transportation researchers and practitioners to pinpoint traffic bottlenecks for congestion mitigation. Traditional studies rely...

The intelligence of dual simplex method to solve linear fractional fuzzy transportation problem.

Computational intelligence and neuroscience
An approach is presented to solve a fuzzy transportation problem with linear fractional fuzzy objective function. In this proposed approach the fractional fuzzy transportation problem is decomposed into two linear fuzzy transportation problems. The o...

Machine learning insights on the effectiveness of non-pharmaceutical interventions against COVID-19 in Nigeria.

International health
BACKGROUND: The lack of effective pharmacological measures during the early phase of the COVID-19 pandemic prompted the implementation of non-pharmaceutical interventions (NPIs) as initial mitigation strategies. The impact of these NPIs on COVID-19 i...

Bilinear Spatiotemporal Fusion Network: An efficient approach for traffic flow prediction.

Neural networks : the official journal of the International Neural Network Society
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
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
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...