Spiking neural networks

S Ghosh-Dastidar, H Adeli - International journal of neural systems, 2009 - World Scientific
Most current Artificial Neural Network (ANN) models are based on highly simplified brain
dynamics. They have been used as powerful computational tools to solve complex pattern …

Artificial neural networks applications in construction and building engineering (1991–2021): science mapping and visualization

M Marzouk, A Elhakeem, K Adel - Applied Soft Computing, 2024 - Elsevier
Artificial neural network (ANN) has acquired noticeable interest from the research
community to handle complex problems in Construction and Building engineering (CB). This …

Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems

A Boukerche, Y Tao, P Sun - Computer networks, 2020 - Elsevier
In recent years, the Intelligent transportations system (ITS) has received considerable
attention, due to higher demands for road safety and efficiency in highly interconnected road …

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 …

Statistical methods versus neural networks in transportation research: Differences, similarities and some insights

MG Karlaftis, EI Vlahogianni - Transportation Research Part C: Emerging …, 2011 - Elsevier
In the field of transportation, data analysis is probably the most important and widely used
research tool available. In the data analysis universe, there are two 'schools of thought'; the …

Deep and embedded learning approach for traffic flow prediction in urban informatics

Z Zheng, Y Yang, J Liu, HN Dai… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Traffic flow prediction has received extensive attention recently, since it is a key step to
prevent and mitigate traffic congestion in urban areas. However, most previous studies on …

A probabilistic neural network for earthquake magnitude prediction

H Adeli, A Panakkat - Neural networks, 2009 - Elsevier
A probabilistic neural network (PNN) is presented for predicting the magnitude of the largest
earthquake in a pre-defined future time period in a seismic region using eight …

Dynamic near-term traffic flow prediction: system-oriented approach based on past experiences

H Chang, Y Lee, B Yoon, S Baek - IET intelligent transport systems, 2012 - IET
Short-term prediction is one of the essential elements of intelligent transportation systems
(ITS). Although fine prediction methodologies have been reported, most prediction methods …

Traffic volume forecasting based on radial basis function neural network with the consideration of traffic flows at the adjacent intersections

JZ Zhu, JX Cao, Y Zhu - Transportation Research Part C: Emerging …, 2014 - Elsevier
The forecasting of short-term traffic flow is one of the key issues in the field of dynamic traffic
control and management. Because of the uncertainty and nonlinearity, short-term traffic flow …

Towards data-driven car-following models

V Papathanasopoulou, C Antoniou - Transportation Research Part C …, 2015 - Elsevier
Car following models have been studied with many diverse approaches for decades.
Nowadays, technological advances have significantly improved our traffic data collection …