Modeling Traffic Information using Bayesian Networks

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Dutch freeways suffer from severe congestion during rush hours or incidents. Research shows that 64% of congested traffic during rush hour consists of commuter traffic [30]. A traffic congestion increases travel time, resulting in a delay for travelers. Reliable travel time predictions are essential for Dynamic Routing, in which travelers can be rerouted to avoid congestions. Travel times can be calculated from vehicle speed [41] in case of free flowing traffic. In case of congestion, we will make an estimation error regarding the travel time. Therefore, an accurate speed prediction model is necessary. In this thesis, the predictability of the average vehicle speed by Bayesian Networks is investigated. A case study is conducted where several Bayesian Network models we propose are evaluated for a well known traffic bottleneck in the Netherlands. We show that Bayesian Networks are capable of predicting the start or end of a congestion at the bottleneck reasonably accurate for a prediction horizon until 30 minutes. Further, we propose a prediction model based on historical data, which is able to predict the average vehicle speed at the bottleneck location for longer prediction horizons. In the end, we propose a hybrid model which combines our Bayesian Network and our prediction model based on historical data. This hybrid model is able to predict a traffic congestion with an accuracy of 85% for a prediction horizon of 2.5 hours. The results of our case study show that modeling traffic using Bayesian Networks is promising. Our models can form the input for a travel time prediction model for Dynamic Routing.