Journal ArticleDOI
A probabilistic estimation of traffic congestion using Bayesian network
Tanzina Afrin,Nita Yodo +1 more
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TLDR
The case study results showed that the proposed BN models could quantify the probable congestion level in terms of a probability for each state in a variable, at the presence of different combinations of prior variables’ state.About:
This article is published in Measurement.The article was published on 2021-04-01. It has received 25 citations till now. The article focuses on the topics: Probabilistic logic & Bayesian network.read more
Citations
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A Novel K-Means Clustering Algorithm with a Noise Algorithm for Capturing Urban Hotspots
TL;DR: In this paper, a novel K-means clustering algorithm based on a noise algorithm is developed to capture urban hotspots in which the noise algorithm was employed to randomly enhance the attribution of data points and output results of clustering by adding noise judgment in order to automatically obtain the number of clusters for the given data and initialize the center cluster.
Journal ArticleDOI
Advances of UAVs toward Future Transportation: The State-of-the-Art, Challenges, and Opportunities
TL;DR: In this article, the authors present a survey on the recent advances of UAVs and their roles in current and future transportation systems and highlight the challenges and opportunities of integrating UAV towards future intelligent and resilient transportation systems.
Journal ArticleDOI
A Long Short-Term Memory-based correlated traffic data prediction framework
Tanzina Afrin,Nita Yodo +1 more
TL;DR: In this paper, a Long Short-Term Memory (LSTM)-based correlated traffic data prediction framework was proposed for two different real-time traffic datasets, which can help control traffic congestion and ensure a more robust traffic management system in the future.
Journal ArticleDOI
A Long Short-Term Memory-based correlated traffic data prediction framework
TL;DR: In this paper , a Long Short-Term Memory (LSTM)-based correlated traffic data prediction framework was proposed for two different real-time traffic datasets, which can help control traffic congestion and ensure a more robust traffic management system in the future.
Journal ArticleDOI
Predicting Traffic Flow Propagation Based on Congestion at Neighbouring Roads Using Hidden Markov Model
TL;DR: In this article, the authors investigated the relationship of roads in a neighboring area based on the similarity of traffic condition and identified roads with high relationship with other neighbouring roads by extracting spatial and temporal features using traffic state clustering.
References
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Journal ArticleDOI
A Traffic Congestion Assessment Method for Urban Road Networks Based on Speed Performance Index
TL;DR: Based on analyses the proposed congestion indexes can well assess the traffic congestion conditions of urban road networks, more importantly, such an assessment study provides traffic control and management agencies an accurate and clear understanding of operation status of traffic networks.
Journal ArticleDOI
Cooperative parallel particle filters for online model selection and applications to urban mobility
TL;DR: In this paper, a sequential Monte Carlo scheme for the dual purpose of Bayesian inference and model selection is proposed for the joint problem of online tracking and detection of the current modality.
Journal ArticleDOI
Reinforcement Learning-Based Variable Speed Limit Control Strategy to Reduce Traffic Congestion at Freeway Recurrent Bottlenecks
TL;DR: The primary objective of this paper was to incorporate the reinforcement learning technique in variable speed limit (VSL) control strategies to reduce system travel time at freeway bottlenecks and showed that the proposed Q-learning (QL)-based VSL strategy outperformed the feedback-based V SL strategy.
Journal ArticleDOI
PCNN: Deep Convolutional Networks for Short-Term Traffic Congestion Prediction
Meng Chen,Xiaohui Yu,Yang Liu +2 more
TL;DR: A novel method named PCNN is proposed, which is based on a deep convolutional neural network, modeling periodic traffic data for short-term traffic congestion prediction, and experimental results on a real-world urban traffic data set confirm that folding time series data into a 2-D matrix is effective and PCNN outperforms the baselines significantly for the task of short- term congestion prediction.
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