Tamás Tettamanti

Model predictive control in urban traffic network management

The paper investigates a model predictive control (MPC) strategy specialized in urban traffic management in order to relieve traffic congestion, reduce travel time and improve homogenous traffic flow. Over the theory the realization of the control method is also presented. To validate the effectiveness of the controller a busy traffic network was chosen for test field. The MPC strategy was implemented into the test network control system (hardware in loop simulation). The applied environment is a microscopic traffic simulator with mathematical software and proper computational applications for the evaluation. The simulation results prove the effectiveness of the designed MPC based traffic control strategy. The system is able to improve the network efficiency and reduce travel time, creating optimal flow in the network subjected to control input constraints.

Robust Control for Urban Road Traffic Networks

The aim of the presented research is to elaborate a traffic-responsive optimal signal split algorithm taking uncertainty into account. The traffic control objective is to minimize the weighted link queue lengths within an urban network area. The control problem is formulated in a centralized rolling-horizon fashion in which unknown but bounded demand and queue uncertainty influences the prediction. An efficient constrained minimax optimization is suggested to obtain the green time combination, which minimizes the objective function when worst case uncertainty appears. As an illustrative example, a simulation study is carried out to demonstrate the effectiveness and computational feasibility of the robust predictive approach. By using real-world traffic data and microscopic traffic simulator, the proposed robust signal split algorithm is analyzed and compared with well-tuned fixed-time signal timing and to nominal predictive solutions under different traffic conditions.

Uncertainty modeling and robust control in urban traffic

The paper investigates the problem of uncertainty modeling and constrained robust control of urban traffic. Linear polytopic approach is used by state-space representations to describe the uncertain network system. In order to handle model mismatches, robust and infinite horizon model predictive control (MPC) method is proposed. The control strategy is an efficient method to reduce travel time and improve homogeneous traffic flow under changing model conditions. Centralized numerical solution has been carried out as a solution of Linear Matrix Inequalities (LMI) by using semidefinite programming (SDP). Closed-loop control results were tested in simulation environment taking alternative model uncertainty levels into account.

Traffic speed prediction method for urban networks — an ANN approach

The paper proposes a traffic speed prediction algorithm for urban road traffic networks. The motivation of the prediction is to provide short time forecast in order to support ITS (Intelligent Transport System) functionalities, such as traveler information systems, route guidance (navigation) systems, as well as adaptive traffic control systems. A potential and efficient solution to this problem is the application of a soft computing method. Namely, an artificial neural network (ANN) is used for the forecast by involving the measured speed patterns. The ANN is trained by using data produced by Vissim (a microscopic road traffic simulator) simulations. The proposed algorithm is developed and analyzed on a real-word test network (part of downtown in Budapest).

Macroscopic modeling and control of emission in urban road traffic networks

AbstractThis work suggests a framework for modeling the traffic emissions in urban road traffic networks that are described by the Network Fundamental Diagram (NFD) concept. Traffic emission is formalized in finite spatiotemporal windows as a function of aggregated traffic variables, i.e. Total Travel Distances (TTDs) in the network and network average speed. The framework is extended for the size of an urban network during a signal cycle – the size of a window in which the network aggregated parameters are modeled in the NFD concept. Simulations have been carried out for model accuracy analysis, using the microscopic Versit + Micro model as reference. By applying the macroscopic emission model function and the traffic modeling relationships, the control objective for pollution reduction has also been formalized. Basically, multi-criteria control design has been introduced for two criteria: maximization of the TTD and minimization of traffic emissions within the network.

ROAD TRAFFIC MEASUREMENT AND RELATED DATA FUSION METHODOLOGY FOR TRAFFIC ESTIMATION

Abstract The knowledge of road traffic parameters is of crucial importance to ensure state-of-the-art traffic services either in public or private transport. In our days, a plethora of road traffic data are continuously collected producing historical and real-time traffic information as well. The available information, however, arrive from inhomogeneous sensor systems. Therefore, a data fusion methodology is proposed based on Switching Kalman Filter. The concept enables efficient travel time estimation for urban road traffic network. On the other hand, the method may contribute to a better macroscopic traffic modelling.

Cost and risk sensitive decision making and control for highway overtaking maneuver

Independently controlled systems may become coupled giving rise to risks; communication, control and optimization tasks have to be solved. Solutions should take into account potential inaccuracies due to disturbances and that information distribution can be inexact, incomplete, or late. In this paper, a highway overtaking scenario with two units is considered. In principle, either of the units can be composed of multiple vehicles. We performed simulations using the validated vehicle simulator CarSim®. The corresponding vehicle models calculated the necessary brake and throttle inputs to match the reference signals. We considered the information observed by the two simulated vehicles together to deduce conditions when overtaking can be successful. Minimization of the cost can be achieved by selecting from the set of feasible solutions. Cost and risk estimations enable one to cast the problem as a Markov Decision Process, which scales well to more complex scenarios.

Extensions of the Activity Chain Optimization Method

ABSTRACT For the optimization of daily activity chains a novel method has been elaborated, where flexible demand points were introduced. Some activities are not necessarily fixed temporally and spatially, therefore they can be realized in different times or locations. By using flexible demand points, the method is capable of finding new combinations of activity chains and choosing the optimal set of activities. The optimization algorithm solves the TSP-TW (Traveling Salesman Problem – Time Window) problem with many flexible demand points, which resulted in high complexity and long processing times. Therefore, two extensions were developed to speed up the processes. A POI (Point Of Interest) search algorithm enabled to search demand points in advance and store them in an offline database. Furthermore GA (genetic algorithm) was applied and customized to realize lower optimization times. During the implementation, three different transportation modes were defined: car, public transport, and combined (public transport with car-sharing opportunity). The simulations were performed on arbitrarily chosen test networks using Matlab. Promising test results were obtained for all transportation modes with total travel time reduction of 10–15 percent. The application of the extended optimization method produced shorter activity chains and decreased total travel time for the users.

Urban perimeter control for emission reduction and traffic performance improvement

This work suggests a NMPC controller for the urban perimeter control problem. The system model is based on the mathematical formalization of the problem, proposed in [1] expanded by describing the queue dynamics at the perimeter gates. For the extended model a multicriteria control problem is formalized: the conventional control objective of traffic network control, i.e. the improvement of TTD performance is accompanied by another control goal: the reduction of traffic emission. For the emission modeling, an average-speed based model framework is used, utilizing aggregated network parameters such as the total travel distance and the total time spent in the network. The NMPC controller is compared to a PID controller in a case study. The simulations show acceptable performance of the NMPC controller.