Jan H. van Schuppen

Prediction of Traffic Flow at the Boundary of a Motorway Network

For online traffic control at traffic control centers, there is a need for predictions of the traffic flow during a short horizon, for example, 30 min ahead. For this effort, predictions are needed of the traffic inflow into the network at motorways on the network boundary and at on-ramps. This paper presents an adaptive prediction algorithm for the inflows into the network in regular traffic situations based on stochastic control theory. The prediction algorithm is based on an adaptive prediction algorithm of T. Bohlin. The algorithm is designed and tested on traffic flow data of the ring road of Amsterdam. The results show that the algorithm provides robust predictions of traffic demand with relatively small errors for the next 30 min in a large-scale real-time environment.

On-line distributed prediction of traffic flow in a large-scale road network

Abstract For on-line traffic control at traffic control centers there is a need for fast computations of predictions of traffic flow over a short prediction horizon, say 30xa0min, to evaluate the impact of different scenarios for the purpose of on-line scenario selection. A novel approach is presented to predict the traffic flow in a large-scale traffic network in an asynchronous, parallel, and distributed way at two or more subnetworks combined with a consistency check at the network level within a reasonable-small computation time.

A hierarchical model and implementation architecture for road traffic control

Control of traffic in a network of roads may involve a large number of individual control loops, with various scopes and time scales, some of them working locally, such as traffic signals at a crossing, some of them coordinating a number of local control loops within its scope. In order to organize such a set of control loops, this paper proposes a hierarchical network model which is primarily based on a modularity property of networks. One of the prime applications of this hierarchical model is to derive an implementation architecture for the development of operational control systems, including the necessary hardware and software infrastructure, in order to achieve highly flexible control systems and thereby support the research in road traffic network control. The primary goal of this paper is to list the research issues of this approach.

QHM: The quantitative hierarchical model for network-level traffic management

The QHM framework offers a scalable approach to network-level traffic management, in short: Network Management (NM), which is the coherent management of traffic in large road networks. The framework consists of a recursive decomposition of large networks and a recursive control strategy. The framework distributes the complexity of NM among different levels and, per level, among a number of subnetworks, such that each management instance has only a limited amount of complexity to cope with. In this way, NM becomes feasible and scalable, such that it can be applied to heterogeneous networks of arbitrary size.

Analysis of signaling in a finite stochastic system motivated by decentralized control

Analysis and control of large-scale systems such as interconnected power, biological, communication and traffic networks are limited by the extensive instrumentation required for data gathering, processing and transfer. In this paper, we study the signaling property for decentralized control of finite-state stochastic systems under the Observer-Controller Specialization. The problem is divided into three components: What to send? How to send? and How to control with the received signal? Solutions are provided to these three problems using concepts and results of control and system theory. An academic example illustrates the approach.

A hierarchical network model for road traffic control

Control of traffic in a network of roads may involve a large number of individual control loops, with various scopes and time scales, some of them working locally, such as traffic signals at a crossing, some of them coordinating a number of local control loops within its scope. In order to organize such a set of control loops, this paper proposes a hierarchical network model which is primarily based on a modularity property of networks. One of the prime applications of this hierarchical model is to derive an implementation architecture for the development of operational control systems, in order to achieve highly flexible control systems and thereby support the research in road traffic network control. The primary goal of this paper is to list the research issues in this approach.

Observability reduction algorithm for rational systems

An algorithm is provided to reduce an unobservable rational system to an observable rational system while preserving its input-output behavior. The core of the algorithm lies in finding generators for the observation algebra generated by the unobservable rational system. Respective steps of the algorithm refer to standard operations in algebraic geometry. Examples are provided.

Signaling of Information

Decentralized control problems with non-classical information structure relate, by definition, issues of information and control. If different controllers have different observations, a consensus estimate of the system state cannot be generated. To compensate for this information deficiency, any controller can generate an input signal that encodes part of its private information and which is subsequently observed by other controllers. The use of control actions to convey information through the system is known as signaling. Analysis and synthesis of signaling laws is an open and urgent problem of control theory.

Prediction of Traffic Flow in a Road Network

At traffic control centers there is a need to predict the traffic flow in road networks for a horizon of about 30 min or longer. At traffic control centers, there is a need to predict the traffic flow in road networks for a horizon of about 30 min or longer. The predictions are needed to detect troublesome traffic situations before they occur, primarily congestion, and to evaluate one or more control scenarios. The chapter summarizes the following: (1) an adaptive prediction algorithm for prediction of traffic flow at the boundary of the road network and (2) a method to compute coordinated–distributed prediction of traffic flow in a road network.

Computing optimal control laws for finite stochastic systems with non-classical information patterns

Computation of optimal control laws for systems with non-classical information patterns and its relation to signaling is still an open problem. The notion of information states redefines such optimal control problems as centralized problems on arbitrary function spaces. We propose a method to transform the resulting functional optimization problem into a multi-parametric mixed-integer program. While the underlying problem remains intractable for large-scale systems, our contribution allows to compute optimal decentralized control laws for limited size problems in a systematic fashion and reveals a signaling structure for decentralized systems. We illustrate the proposed technique by computing optimal control laws for a discrete-time finite-space formulation of the system used in the Witsenhausen counterexample. Numerical results show that the tuple of optimal control laws acts as a communication system (an n-bit quantizer followed by a ML-decoder).