Predicting experienced travel time with neural networks: a PARAMICS simulation study
The 7th International IEEE Conference on Intelligent Transportation Systems 2004, , , 2004
Abstract: The implementation of intelligent transportation systems (ITS) in recent years has resulted in the development of systems capable of monitoring roadway conditions and disseminating traffic information to travelers in a network. However, the development of algorithms and methodologies specialized in handling large amounts of data for the purpose of real-time control has lagged behind the sensing and communication technological developments in ITS. In this study, data generated by a PARAMICS model of a real-world freeway section are used to develop an artificial neural network (ANN) capable of predicting experienced travel time between two points on the transportation network. Computational experiments demonstrate that the studied ANNs were able to reasonably predict the experienced travel time. Generally, the study shows that the length of the time lag did not have a statistically significant effect on ANN performance, that speed appears to be the most influential input variable, and no statistically significant difference in ANN performance was observed when data from the left lane loop detector was substituted for data from the right lane loop detector.
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).