Emerging technologies for smart cities' transportation: geo-information, data analytics and machine learning approaches
With the recent increase in urban drift, which has led to an unprecedented surge in urban
population, the smart city (SC) transportation industry faces a myriad of challenges …
population, the smart city (SC) transportation industry faces a myriad of challenges …
Survey of neural network‐based models for short‐term traffic state prediction
LNN Do, N Taherifar, HL Vu - Wiley Interdisciplinary Reviews …, 2019 - Wiley Online Library
Traffic state prediction is a key component in intelligent transport systems (ITS) and has
attracted much attention over the last few decades. Advances in computational power and …
attracted much attention over the last few decades. Advances in computational power and …
Smarter traffic prediction using big data, in-memory computing, deep learning and GPUs
Road transportation is the backbone of modern economies, albeit it annually costs 1.25
million deaths and trillions of dollars to the global economy, and damages public health and …
million deaths and trillions of dollars to the global economy, and damages public health and …
Model evaluation for forecasting traffic accident severity in rainy seasons using machine learning algorithms: Seoul city study
J Lee, T Yoon, S Kwon, J Lee - Applied Sciences, 2019 - mdpi.com
There have been numerous studies on traffic accidents and their severity, particularly in
relation to weather conditions and road geometry. In these studies, traditional statistical …
relation to weather conditions and road geometry. In these studies, traditional statistical …
A multi-pattern deep fusion model for short-term bus passenger flow forecasting
Short-term passenger flow forecasting is one of the crucial components in transportation
systems with data support for transportation planning and management. For forecasting bus …
systems with data support for transportation planning and management. For forecasting bus …
City-wide traffic flow forecasting using a deep convolutional neural network
S Sun, H Wu, L **ang - Sensors, 2020 - mdpi.com
City-wide traffic flow forecasting is a significant function of the Intelligent Transport System
(ITS), which plays an important role in city traffic management and public travel safety …
(ITS), which plays an important role in city traffic management and public travel safety …
Softadapt: Techniques for adaptive loss weighting of neural networks with multi-part loss functions
Adaptive loss function formulation is an active area of research and has gained a great deal
of popularity in recent years, following the success of deep learning. However, existing …
of popularity in recent years, following the success of deep learning. However, existing …
Traffic matrix prediction and estimation based on deep learning in large-scale IP backbone networks
Network traffic analysis has been one of the most crucial techniques for preserving a large-
scale IP backbone network. Despite its importance, large-scale network traffic monitoring …
scale IP backbone network. Despite its importance, large-scale network traffic monitoring …
TrafficWave: Generative deep learning architecture for vehicular traffic flow prediction
Vehicular traffic flow prediction for a specific day of the week in a specific time span is
valuable information. Local police can use this information to preventively control the traffic …
valuable information. Local police can use this information to preventively control the traffic …
DeepSense: A novel learning mechanism for traffic prediction with taxi GPS traces
X Niu, Y Zhu, X Zhang - 2014 IEEE global communications …, 2014 - ieeexplore.ieee.org
The urban road traffic flow condition prediction is a fundamental issue in the intelligent
transportation management system. While extracting the high-dimensional, nonlinear and …
transportation management system. While extracting the high-dimensional, nonlinear and …