Distributed control of urban traffic networks using hybrid models

Urban traffic control poses a challenging problem in terms of coordinating the different traffic lights that can be used in order to influence the traffic flow. Model based control requires hybrid systems models consisting of interacting fluid flow Petri net models for controlled and uncontrolled intersections, and cell transmission models for links connecting the intersections. This paper proposes a simulation based distributed model predictive control algorithm for solving this problem

A platoon based model for urban traffic networks: identification, modeling and distributed control

Urban traffic control poses a challenging problem in terms of coordinating the different traffic lights that can be used in order to influence the traffic flow. The goal of this approach is to identify and to develop hybrid system models of controlled and uncontrolled intersections and links in urban traffic networks based on formation of platoons. The other purpose is to develop a feedback control algorithm that optimizes the signal timing plan based on the strategy of platoons formation estimated via the vehicle re-identification technology

Particle filter state estimator for large urban networks

This paper applies a particle filter (PF) state estimator to urban traffic networks. The traffic network consists of signalized intersections, the roads that link these intersections, and sensors that detect the passage time of vehicles. The traffic state X(t) specifies at each time time t the state of the traffic lights, the queue sizes at the intersections, and the location and size of all the platoons of vehicles inside the system. The basic entity of our model is a platoon of vehicles that travel close together at approximately the same speed. This leads to a discrete event simulation model that is much faster than microscopic models representing individual vehicles. Hence it is possible to execute many random simulation runs in parallel. A particle filter (PF) assigns weights to each of these simulation runs, according to how well they explain the observed sensor signals. The PF thus generates estimates at each time t of the location of the platoons, and more importantly the queue size at each intersection. These estimates can be used for controlling the optimal switching times of the traffic light

Particle filter for platoon based models of urban traffic

This paper proposes a particle filter (PF) state estimator, using a platoon based model for urban traffic networks. The urban traffic network model consists of signalized intersections (representing queues of vehicles competing for service) connected to each other through links with predefined receiving capacities and stochastic delays. Sensors detect the passage of vehicles at the sensor locations. The algorithm is flexible and robust and can be used in real-time applications such as on-line control of switching times of traffic lights

Platoon based model for urban traffic control

The paper proposes a new platoon based (PB) model of traffic behavior in an urban traffic network. By grouping the vehicles that travel closely together at approximately the same speed into a platoon the model provides an abstract representation of the dynamical evolution of the traffic state. This leads to efficient discrete event simulation tools, much faster than the microscopic models representing individual vehicles, while explicitly representing the heterogeneity characterizing urban traffic, something that is not possible with a macroscopic model. The validity of the PB model is justified by careful analysis of real measurements. Its fast simulations enable the application of model based estimation (using particle filtering) and (model predictive) feedback control tools for urban traffi

CSP for coordination of distributed systems

A distributed system consists of many interacting components, each controlled by a local control agent. Agents can communicate in order to solve a common goal. The aim of this paper is to analyse the coordination control of simple interacting components, interconnected in a network, that form distributed system. A good example of such systems are the urban traffic networks, with local agents setting the switching time of traffic lights

Distributed particle filter for urban traffic networks using a platoon based model

Raw measurement data are too noisy to directly obtain queue and traffic flow estimates usable for feedback control of urban traffic. In this paper, we propose a recursive filter to estimate traffic state by combining the real-time measurements with a reduced model of expected traffic behavior. The latter is based on platoons rather than individual vehicles in order to achieve faster implementations. This new model is used as a predictor for real-time traffic estimation using the particle filtering framework. As it becomes infeasible to let a truly large traffic network be managed by one central computer, with which all the local units would have to communicate, we also propose a distributed version of the particle filter (PF) where the local estimators exchange information on flows at their common boundaries. We assess the quality of our platoon-based PFs, both centralized and distributed, by comparing their queue-size estimates with the true queue sizes in simulated data

Distributed collision avoidance for autonomous vehicles: world automata representation

The automatic control of interacting autonomous vehicles (AVs) is one of the problems that engineers are currently trying to solve. The present paper deals with the design of local control laws governing the movement and collision avoidance of such groups of AVs, enforcing the safeness of the operations as well as task completions. This problem is inspired by the automation of a container terminal where each AV executes tasks assigned by a supervisor. A task involves moving an AV from an assigned origin to an assigned destination by a given deadline. The constraints imposed by the bounded workspace (a long and narrow quay in the container terminal example), the deadlines assigned to each task, and the uncertainty in the detection and communication make the problem difficult to solve in a centralized way. Therefore a distributed control approach is preferred with a local control agent in each AV adjusting its trajectory, so that its task is completed without collisions. By applying a fixed set of priority rules the computational complexity for each agent is reduced compared to the centralized case. Whenever an AV detects a possible conflict, i.e. the estimated position of another AV within the detection range, it must adjust its own speed and trajectory in order to avoid a future collision, reducing the number of cases where a supervisor has to intervene in order to resolve conflicts that degenerate in dead-locks. The modelling and validation of the system is performed by using the world automata theory