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Incidents whose frequencies are unknown cause a large part of the unreliability of road networks. Especially risk-averse road users avoid the possibility of large travel times under a disruption. This paper proposes a traffic assignment which accounts for risk-averseness. Scenarios for all possible disruptions are simulated. The risk-averseness of a user is generalized in a two-parameter route choice...
Short-term forecasting of travel time is one of the central topics in current ITS research and practice. The most widely applied travel time forecasting approach is the neural network. Usually many candidate neural networks are trained and the network performing best on an independent validation dataset is selected. However, the training data then needs to be divided in two, leading to less well trained...
Modeling realistic driving behavior at signalized intersections is crucial for many applications, for instance to determine the traffic signal performance, to assess the effect of different control strategies, or to estimate traffic emissions. In these applications, often microscopic models are used to simulate the trajectory of each vehicle. Despite the possibility to model vehicles with great detail...
This paper discusses the importance of spillback modeling in assessing the impacts of dynamic traffic management. Specifically, the paper compares the influence of route information in a simulation with spillback and without spillback modeling. The considered case entails an evacuation of a city (circa 250,000 cars). The results indicate that in the non-spillback simulation, the network performance...
The calculation of delays at an intersection is an old problem that has been studied and solved by many researchers. This paper reanalyzes the problem by using a Markov chain model for the probability distribution of queue length. From the dynamics of the expectation value of the queue length, the authors derive a formula for the delay in fixed time traffic control. The effect of the overflow queue...
In this paper we introduce the use of microscopic measurement data to calibrate driving behavior in microscopic simulation models. Based on data gathered from remote sensing and equipped vehicle data, a dynamic decision network has been designed to determine drivers' action to certain situations. Since this kind of pattern matching leads to a deterministic driving behavior, which cannot be assumed...
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