A Bayesian Linear Regression Approach to Predict Traffic Congestion

RegTraffic: A Regression based Traffic Simulator for Spatiotemporal Traffic Modeling, Simulation and Visualization

“…It's worth noting that the dependent road link ŷ is an outbound while all the independent road links x s 1 , x s 2 , ..., x s n inbound to the intersection. Our proposed traffic modeling approach described in [18] indicates that the dependent spatial feature must be an outbound road link and the independent spatial features must be inbound road links. The model incorporates a set of temporal features that can be extracted from both independent and dependent spatial features through exploratory data analysis.…”

“…2) Posterior Probability Distribution: We use a novel Bayesian linear regression approach for spatiotemporal traffic modeling of a road link proposed in [18]. Bayesian linear regression formulates a posterior probability distribution of the model parameters rather than just finding a single point estimate.…”

“…The posterior distribution of the model parameters is proportional to the multiplication of the likelihood of the data and the prior probability of the parameters. A detailed description of the model is described in [18].…”

Statistical estimation of traffic volume for the Minneapolis-St Paul Metropolitan area

“…There are several related works that address the topic in question, In [4] is proposed a Bayesian inference model for traffic prediction, capable of incorporating spatial and temporal components. Furthermore, according to the authors, the proposed solution works well with missing data points, taking advantage of previous information.…”