Roberto Horowitz received a B.S. degree with highest honors in 1978 and a Ph.D. degree in 1983 in mechanical engineering from the University of California at Berkeley. In 1982 he joined the Department of Mechanical Engineering at the University of California at Berkeley, where he is currently a Professor. Dr. Horowitz teaches and conducts research in the areas of adaptive, learning, nonlinear and optimal control. His current research interests include: Micro-mechatronics, control of computer disk file systems, robotics, mechatronics of smart exercise machines and paper handling devices, and Intelligent Vehicle and Highway Systems (IVHS).
Vehicular traffic congestion remains one of the major world-wide sources of productivity and efficiency loss, wasteful energy consumption, and avoidable air pollution. For example it is estimated that in 2007, congestion caused urban Americans to travel an additional 4.2 billion hours and to purchase an extra 2.9 billion gallons of fuel. In this talk I will describe a set of modeling and simulation Tools for Operational Planning (TOPL) developed to provide quick and quantitative assessments of the benefits that Transportation Management Center (TMC) control policies can provide on freeway corridors, in order to decrease congestion. A freeway corridor typically comprises a 40 kilometer freeway segment on a highly populated urban area, together with its adjoining major urban streets or arterials. The movement of vehicles in a corridor is regulated by programmable field control elements including arterial intersection signals, ramp-metering signals, and message signs that announce emergency conditions, set speed limits and tolls, and provide driver information. Traffic data is primarily collected through inductive loop detectors buried roughly every kilometer along the freeways' payment, as well as detectors located in some of the major corridor arterials. TOPL contains a self-calibrated Cell Transmission Model (CTM) traffic macroscopic simulator. This simulator relies on a well-accepted theoretical model of traffic flow; it is parsimonious and does not require parameters that cannot be estimated from traffic data; and has been tested for reliability on several freeways. Moreover, it is fast, running several hundred times faster than real time, which can be used with real-time measurements and statistically predicted short term future traffic demands to keep track of the current freeway traffic state, as well as make short-term predictions. I will also discuss the qualitative behavior of a single freeway based on the CTM, and will focus on several results regarding the structure and stability of the set of equilibrium states in single freeway, including the fact that the freeway decomposes into disjoint contiguous segments demarcated by bottleneck links, with each segment having qualitatively the same behavior. These properties will be further explored in the formulation of traffic responsive and coordinated ramp-metering policies, including a coordinated policy that minimizes travel time, model calibration and missing on-ramp imputation techniques, and congestion and state estimation techniques.