Deep learning models for traffic flow prediction in autonomous vehicles: A review, solutions, and challenges
In the last few years, there has been an exponential increase in the usage of the
autonomous vehicles across the globe. It is due to an exponential increase in the popularity …
autonomous vehicles across the globe. It is due to an exponential increase in the popularity …
[HTML][HTML] Smarter and more connected: Future intelligent transportation system
Emerging technologies toward a connected vehicle-infrastructure-pedestrian environment
and big data have made it easier and cheaper to collect, store, analyze, use, and …
and big data have made it easier and cheaper to collect, store, analyze, use, and …
A novel wavelet-SVM short-time passenger flow prediction in Bei**g subway system
Y Sun, B Leng, W Guan - Neurocomputing, 2015 - Elsevier
In order to effectively manage the use of existing infrastructures and prevent the emergency
caused by the large gathered crowd, the short-term passenger flow forecasting technology …
caused by the large gathered crowd, the short-term passenger flow forecasting technology …
Prediction of hotel booking cancellations: Integration of machine learning and probability model based on interpretable feature interaction
Reliable hotel cancellation prediction can help establish appropriate operational strategies
for hotel management. In this sector, personal name records (PNR) data may be the most …
for hotel management. In this sector, personal name records (PNR) data may be the most …
Traffic sensor location problem: Three decades of research
M Owais - Expert Systems with Applications, 2022 - Elsevier
Traffic flow data is a decisive element in transportation planning and traffic management.
Over time, traffic sensors have been recognized as sources of such data. Despite their …
Over time, traffic sensors have been recognized as sources of such data. Despite their …
Sparsifying priors for Bayesian uncertainty quantification in model discovery
We propose a probabilistic model discovery method for identifying ordinary differential
equations governing the dynamics of observed multivariate data. Our method is based on …
equations governing the dynamics of observed multivariate data. Our method is based on …
Hybrid machine learning algorithm and statistical time series model for network-wide traffic forecast
We propose a novel approach for network-wide traffic state prediction where the statistical
time series model ARIMA is used to postprocess the residuals out of the fundamental …
time series model ARIMA is used to postprocess the residuals out of the fundamental …
Deep learning for integrated origin–destination estimation and traffic sensor location problems
M Owais - IEEE Transactions on Intelligent Transportation …, 2024 - ieeexplore.ieee.org
Traffic control and management applications require the full realization of traffic flow data.
Frequently, such data are acquired by traffic sensors with two issues: it is not practicable or …
Frequently, such data are acquired by traffic sensors with two issues: it is not practicable or …
A multinomial logit model-Bayesian network hybrid approach for driver injury severity analyses in rear-end crashes
Rear-end crash is one of the most common types of traffic crashes in the US A good
understanding of its characteristics and contributing factors is of practical importance …
understanding of its characteristics and contributing factors is of practical importance …
A Bayesian network approach for population synthesis
Agent-based micro-simulation models require a complete list of agents with detailed
demographic/socioeconomic information for the purpose of behavior modeling and …
demographic/socioeconomic information for the purpose of behavior modeling and …