… learning model based on LSTM, called Random Connectivity LSTM (RCLSTM). Compared
… to predict traffic and validate that the RCLSTM with even 35% neural connectivity still shows …

… graphical models, have been used for traffic prediction before and it is a research topic of …
[4] introduce spatio-temporal random fields and apply these models to similar traffic data as …

… -tasks: traffic prediction and access control configuration. In detail, the traffic prediction relies
… neural networks that can accurately capture traffic statistics over consecutive frames, while …

… random geometric graphs to analyze VANET connectivity. Their results show that exponential
random … [15] studied how VANET connectivity in a highway traffic scenario changes when …

… traffic distribution with more details. In Section IV, we apply this modification to compute bounds
for the probability of connectivity. … connectivity of one-dimensional MANETs with random …

… , Thailand, where he leads a project on vehicular traffic prediction. His current research
interests include vehicular ad hoc networks, intelligent transport systems, and machine learning. …

… , logistic regression, random forests, and neural networks, for short-term traffic congestion …
built in this study with the assumption of full connectivity. The comparison is based on the three …

… Usual applications are connectivity mining in social networks, gene regularity networks and
viral marketing… Fortunately, the random walk assumption made above is not realistic for most …

… properties like connectivity of nodes are important for predicting traffic flow. The change of
… In the test data set in the 5 days, we randomly pick 60 minutes to predict the next 10 minutes …

… corresponding historical traffic data X, and the optional underlying road connectivity. GALEN
… traffic dynamics are more diverse, and random traffic factors exhibit stronger influence than …