Adaptive traffic signal control using approximate dynamic programming

This paper presents a study on an adaptive traffic signal controller for real-time operation. The controller aims for three operational objectives: dynamic allocation of green time, automatic adjustment to control parameters, and fast revision of signal plans. The control algorithm is built on approximate dynamic programming (ADP). This approach substantially reduces computational burden by using an approximation to the value function of the dynamic programming and reinforcement learning to update the approximation. We investigate temporal-difference learning and perturbation learning as specific learning techniques for the ADP approach. We find in computer simulation that the ADP controllers achieve substantial reduction in vehicle delays in comparison with optimised fixed-time plans. Our results show that substantial benefits can be gained by increasing the frequency at which the signal plans are revised, which can be achieved conveniently using the ADP approach

A Review of Traffic Signal Control.

The aim of this paper is to provide a starting point for the future research within the SERC sponsored project "Gating and Traffic Control: The Application of State Space Control Theory". It will provide an introduction to State Space Control Theory, State Space applications in transportation in general, an in-depth review of congestion control (specifically traffic signal control in congested situations), a review of theoretical works, a review of existing systems and will conclude with recommendations for the research to be undertaken within this project

A rolling horizon optimization framework for the simultaneous energy supply and demand planning in microgrids

This work focuses on the development of optimization-based scheduling strategies for the coordination of microgrids. The main novelty of this work is the simultaneous management of energy production and energy demand within a reactive scheduling approach to deal with the presence of uncertainty associated to production and consumption. Delays in the nominal energy demands are allowed under associated penalty costs to tackle flexible and fluctuating demand profiles. In this study, the basic microgrid structure consists of renewable energy systems (photovoltaic panels, wind turbines) and energy storage units. Consequently, a Mixed Integer Linear Programming (MILP) formulation is presented and used within a rolling horizon scheme that periodically updates input data information

Decision-Based Forecast Evaluation of UK Interest Rate Predictability*

This paper illustrates the importance of density forecasting in portfolio decision making involving bonds of different maturities. The forecast performance of an atheoretic and a theory informed model of bond returns is evaluated. The decision making environment is fully described for an investor seeking to optimally allocate his portfolio between long and short Treasury Bills, over investment horizons of up to two years. Using weekly data over 1997 to 2007 we examine the impact of parameter uncertainty and predictability in returns on the investor's allocation. We describe how the forecasts are computed and used in this context. Both statistical and decision-based criteria are used to assess the out-of-sample forecasting performance of the models. Our results show sensitivity to the evaluation criterion used. In the context of investment decision making under an economic value criterion, we find some potential gain for the investor from assuming predictability.Density Forecasting; Interest rate Predictability; Parameter Uncertainty and Decision-Based Forecast Evaluation

A rolling-horizon quadratic-programming approach to the signal control problem in large-scale congested urban road networks

The paper investigates the efficiency of a recently developed signal control methodology, which offers a computationally feasible technique for real-time network-wide signal control in large-scale urban traffic networks and is applicable also under congested traffic conditions. In this methodology, the traffic flow process is modeled by use of the store-and-forward modeling paradigm, and the problem of network-wide signal control (including all constraints) is formulated as a quadratic-programming problem that aims at minimizing and balancing the link queues so as to minimize the risk of queue spillback. For the application of the proposed methodology in real time, the corresponding optimization algorithm is embedded in a rolling-horizon (model-predictive) control scheme. The control strategy’s efficiency and real-time feasibility is demonstrated and compared with the Linear-Quadratic approach taken by the signal control strategy TUC (Traffic-responsive Urban Control) as well as with optimized fixed-control settings via their simulation-based application to the road network of the city centre of Chania, Greece, under a number of different demand scenarios. The comparative evaluation is based on various criteria and tools including the recently proposed fundamental diagram for urban network traffic

The effect of foreknowledge of demand in case of a restricted capacity: the single-stage, singleproduct case with lost sales

Foreknowledge of demand is useful in the control of a production-inventory system. Knowingthe customer orders in advance makes it possible to anticipate properly. It is an importantcondition to produce and deliver the right quantity of the right product “just-in-time”. Itreduces the need of safety stock and spare capacity. But the question of the effectiveness offoreknowledge is not an easy one. Having foreknowledge of the customer orders does notremove the demand uncertainty completely. The effect of foreknowledge has to be consideredin a stochastic dynamic setting. The subject of this paper is the effect of foreknowledge incombination with a restricted production capacity. The lost-sales case is considered. The mainresult is that for high utilization rates and small forecast horizon, the inventory reduction dueto foreknowledge is equal to (1- pi).h, with h the forecast horizon