Learning traffic as images: A deep convolutional neural network for large-scale transportation network speed prediction
This paper proposes a convolutional neural network (CNN)-based method that learns traffic
as images and predicts large-scale, network-wide traffic speed with a high accuracy …
as images and predicts large-scale, network-wide traffic speed with a high accuracy …
Day-ahead traffic flow forecasting based on a deep belief network optimized by the multi-objective particle swarm algorithm
Traffic flow forecasting is a necessary part in the intelligent transportation systems in
supporting dynamic and proactive traffic control and making traffic management plan …
supporting dynamic and proactive traffic control and making traffic management plan …
Missing value imputation for traffic-related time series data based on a multi-view learning method
In reality, readings of sensors on highways are usually missing at various unexpected
moments due to some sensor or communication errors. These missing values do not only …
moments due to some sensor or communication errors. These missing values do not only …
Real-time accident detection: Coping with imbalanced data
Detecting accidents is of great importance since they often impose significant delay and
inconvenience to road users. This study compares the performance of two popular machine …
inconvenience to road users. This study compares the performance of two popular machine …
A hybrid method for traffic flow forecasting using multimodal deep learning
Traffic flow forecasting has been regarded as a key problem of intelligent transport systems.
In this work, we propose a hybrid multimodal deep learning method for short-term traffic flow …
In this work, we propose a hybrid multimodal deep learning method for short-term traffic flow …
Estimation of missing values in heterogeneous traffic data: Application of multimodal deep learning model
With the development of sensing technology, a large amount of heterogeneous traffic data
can be collected. However, the raw data often contain corrupted or missing values, which …
can be collected. However, the raw data often contain corrupted or missing values, which …
Real-time traffic incident detection based on a hybrid deep learning model
Small sample sizes and imbalanced datasets have been two difficulties in previous traffic
incident detection-related studies. Moreover, real-time characteristics of incident detection …
incident detection-related studies. Moreover, real-time characteristics of incident detection …
A proactive decision support system for predicting traffic crash events: A critical analysis of imbalanced class distribution
ZE Abou Elassad, H Mousannif… - Knowledge-Based …, 2020 - Elsevier
Real-time crash prediction plays a key role in enhancing traffic safety as well as mitigating
disruptions to road users. The further improvements of predictability require the systemic …
disruptions to road users. The further improvements of predictability require the systemic …
Class-imbalanced crash prediction based on real-time traffic and weather data: A driving simulator study
Z Elamrani Abou Elassad, H Mousannif… - Traffic injury …, 2020 - Taylor & Francis
Objective: Crash occurrence prediction has been of major importance in proactively
improving traffic safety and reducing potential inconveniences to road users. Conventional …
improving traffic safety and reducing potential inconveniences to road users. Conventional …
Spatio-Temporal vehicle traffic flow prediction using multivariate CNN and LSTM model
S Narmadha, V Vijayakumar - Materials today: proceedings, 2023 - Elsevier
Traffic congestion is a major problem in developing and developed countries vehicle traffic
management systems. Traffic control system works based on the idea of removing …
management systems. Traffic control system works based on the idea of removing …