Modeling

The velocity based cell transmission model (CTM-v) is a discrete time dynamical model that mimics the evolution of the traffic velocity field on highways. In this paper the CTM-v model is used together with an ensemble Kalman filter (EnKF) for the purpose of velocity sensor data assimilation. We present a calibration framework for the CTM-v and EnKF. The framework consists of two separate phases. The first phase is the calibration of the parameters of the fundamental diagram and the second phase is the calibration of demand and filter parameters. Results from the calibrated model are...

The development of sustainable transportation infrastructure for people and goods, using new technology and business models, can prove beneficial or detrimental for mobility, depending on its design and use. The focus of this paper is on the increasing impact new mobility services have on traffic patterns and transportation efficiency in general. Over the last decade, the rise of the mobile internet and the usage of mobile devices have enabled ubiquitous traffic information. With the increased adoption of specific smartphone applications, the number of users of routing applications has...

This article demonstrates the use of deep neural networks (NN) and deep reinforcement learning (deep-RL) for modeling and control of longitudinal heavy duty truck dynamics. Instead of explicit use of analytical model derived information or parameters about the truck, the deep NN model is fitted to data using a brief set of historical data collected from an arbitrary driving cycle. The deep model is used in this article to design a cruise controller for the truck using model-free deep-RL. The deep model and the control loop performances are demonstrated both using state-of-the-art...

In a mixed-autonomy traffic scenario, where human drivers and autonomous vehicles share the streets, self-driving cars need to be able to predict in a robust manner the behaviour of human-driven vehicles, in order to guarantee a safe and smooth driving experience. Although traffic theory provides several models of human drivers, these models are often parameterized by few parameters which can limit their performance in modeling complex behaviors. The lack of sufficient model capacity and the behavioral shifts in human driving reduces the usefulness of these methods in real-life...