Queuing Theory Guided Intelligent Traffic Scheduling through Video Analysis using Dirichlet Process Mixture Model

  title={Queuing Theory Guided Intelligent Traffic Scheduling through Video Analysis using Dirichlet Process Mixture Model},
  author={Santhosh Kelathodi Kumaran and D. P. Dogra and P. Roy},
Abstract Intelligent traffic signaling is an important part of city road traffic management systems. In many countries, it is done through supervised/semi-supervised ways. With the advances in computer vision and machine learning, it is now possible to develop expert systems guided intelligent traffic signaling systems that are unsupervised in nature. In order to schedule traffic signals, it is essential to learn the traffic characterization parameters such as the number of vehicles, their… Expand
8 Citations
Signal Adaptive Cooperative Control of Two Adjacent Traffic Intersections Using a Two-Stage Algorithm
  • Y. Zou, Renhuai Liu, Ya Li, Yingshuang Ma, Guoxin Wang
  • Computer Science
  • 2021
Modeling of mixed data for Poisson prediction
  • 1


Neural Networks for Real-Time Traffic Signal Control
  • 303
  • PDF
Detection of traffic congestion and incidents from GPS trace analysis
  • 83
Unsupervised Tracking With the Doubly Stochastic Dirichlet Process Mixture Model
  • Xing Sun, N. Yung, E. Lam
  • Mathematics, Computer Science
  • IEEE Transactions on Intelligent Transportation Systems
  • 2016
  • 11
Citywide Estimation of Traffic Dynamics via Sparse GPS Traces
  • 33
  • PDF
Traffic light control in non-stationary environments based on multi agent Q-learning
  • M. Abdoos, N. Mozayani, A. Bazzan
  • Engineering, Computer Science
  • 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC)
  • 2011
  • 139
  • PDF
Trajectory Analysis and Semantic Region Modeling Using Nonparametric Hierarchical Bayesian Models
  • 163
  • PDF
Temporal Analysis of Motif Mixtures Using Dirichlet Processes
  • 22
  • Highly Influential
  • PDF
Temporal Unknown Incremental Clustering Model for Analysis of Traffic Surveillance Videos
  • 11
  • PDF