Control Of Urban Networks Research Articles

N-MP: A network-state-based Max Pressure algorithm incorporating regional perimeter control

The Max Pressure (MP) framework has been shown to be an effective real-time decentralized traffic signal control algorithm. However, despite its superior performance and desirable features – such as the maximum stability property – it may still suffer from deterioration in network mobility due to the rise of congestion within specific regions of an urban traffic network. To address this drawback and further improve the performance of MP control in urban networks, this paper proposes a novel MP algorithm that incorporates regional traffic states into the MP framework. The proposed model – called N-MP – simultaneously integrates perimeter metering control at the boundary of regions of a network to be protected with traditional local intersection control. The proposed model is the first to incorporate perimeter metering control fully within a decentralized signal control environment and inherits the maximum stability property. In addition, it does not require extra traffic state measurements compared to the original MP algorithms, beyond a measure of congestion within the protected region of the network. Microscopic traffic simulation results demonstrate that the proposed model can outperform two baseline perimeter control models – Bang–Bang control and feedback gating – under various traffic conditions. More interestingly, this superiority is maintained in both fully and partially connected environments.

H∞ robust perimeter flow control in urban networks with partial information feedback

Perimeter control is an effective city-scale solution to tackle congestion problems in urban networks. To accommodate the unpredictable dynamics of congestion propagation, it is essential to incorporate real-time robustness against travel demand fluctuations into a pragmatic perimeter control strategy. This paper proposes robust perimeter control algorithms based on partial information feedback from the network. The network dynamics are modeled using the concept of the Macroscopic Fundamental Diagram (MFD), where a heterogeneously congested network is assumed to be partitioned into two homogeneously congested regions, and an outer region that acts as demand origin and destination. The desired operating condition of the network is obtained by solving an optimization program. Observer-based H∞ proportional (P) and proportional-integral (PI) controllers are designed based on Lyapunov theory, to robustly regulate the accumulation of each region and consequently to maximize the network outflow. The controller design algorithms further accommodate operational constraints by guarantying: (i) the boundedness of the perimeter control signals and (ii) a bounded offset between the perimeter control signals. Control parameters are designed off-line by solving a set of linear matrix inequalities (LMI), which can be solved efficiently. Comprehensive numerical studies conducted on the nonlinear model of the network highlight the effectiveness of the proposed robust control algorithms in improving the congestion in the presence of time-varying disturbance in travel demand.

A hierarchical urban network control with integration of demand balance and traffic signal coordination**This work is supported in part by the National Science Foundation of China (Grant No. 61433002, 61521063, 61374110, 61473288), the Beijing Natural Science Foundation (Grant No. 4142055), NSFC International Cooperation Project (Grant No. 71361130012).

Abstract: This paper concerns the integration of demand balance control and traffic flow coordination control in a hierarchical framework for complex urban traffic networks. At the first level, a complex traffic network is first partitioned into several subnetworks. The traffic dynamics in each subnetwork is modeled by utilizing the concept of Macroscopic Fundamental Diagram (MFD), which relates the network-wide space-mean flow and the number of vehicles at the aggregated level. This model aims at designing a demand balance model predictive controller (MPC), which can improve the internal flow inside the subnetwork by regulating the input flows from outside. At the second level, based on a more detailed traffic flow model, the signal timings of all intersections for each subnetwork are determined by a flow coordination MPC controller, which aims at distributing the number of vehicles in each subnetwork as homogeneously as possible. The effectiveness of the proposed approach is investigated via simulation under different scenarios on a hypothetical urban traffic network, and the performance is compared with other control strategies.

Congestion Control in Urban Networks via Feedback Gating

Traffic signal control for urban road networks has been an area of intensive research efforts for several decades, and various algorithms and tools have been developed and implemented to increase the network traffic flow efficiency. Despite the continuous advances in the field of traffic control under saturated conditions, novel and promising developments of simple concepts in this area remains a significant objective, because some proposed approaches that are based on various metacheuristic optimization algorithms can hardly be used in a real–time environment. To address this problem, the recently developed notion of network fundamental diagram for urban networks is exploited to improve mobility in saturated traffic conditions via application of gating measures, based on an appropriate feedback control structure. As a case study, the proposed methodology is applied to the urban network of Chania, Greece, using microscopic simulation. The results show that the total delay in the network decreases significantly and the mean speed increases accordingly.

Cellular automata model of self-organizing traffic control in urban networks

Cellular automata model of self-organizing traffic control in urban networksA model of city traffic based on Nagel-Schreckenberg cellular automaton (CA) model is presented. Traffic control is realized at intersections with two conflicting streams each (at any time at most one stream can have "green light" assigned to it). For simple and regular lattice-like networks which are considered, it is easy to find optimal switching periods giving maximum possible flow rates. These optimal strategies are compared with a self-controlling approach proposed by [1], which has not been implemented in a CA model until now. Previous work proved that generally this method gives superior results when compared to classical methods. In this paper we show that for deterministic scenario such control leads to self-organization, and that the solution always quickly converges to the optimal solution which is known in this case. Moreover, we consider also non-deterministic case, in the sense that possibility of turning with given probability is allowed. It is shown that the self-controlling strategy always gives better results than any solution based on fixed cycles with green waves.

Descent direction algorithm with multicommodity flow problem for signal optimization and traffic assignment jointly

Network managers wish to optimize control parameters such as signal setting which are very related to the traffic assignment models. On the other hand traffic assignment patterns as an important instrument for predicting the amount of flow on network links are dependent to control decisions. According to the significance of this concept, some important papers about this mutually relation are reviewed in this paper. Then we implement a nonlinear algorithm on a minimal cost multicommodity flow (MCMF) problem to optimize some control policies subject to optimal flows. Although we take signal times into account, but this approach has a far more reaching application in urban network control and design. We employ a hybrid intelligent algorithm integrating decent direction algorithm and an interior point algorithm in a mutually consistent scheme for obtaining optimal signals and equilibrium flows. An example is given to illustrate the effectiveness of our scheme.

Application and Evaluation of the Signal Traffic Control Strategy TUC in Chania

The article presents results of a field implementation and evaluation of the traffic-responsive signal control strategy Traffic-responsive Urban Control (TUC) in two junctions of the urban network of Chania, Greece. Based on a store-and-forward modelling of the urban network traffic and using the Linear-Quadratic regulator theory, the design of TUC leads to a multivariable regulator for traffic-responsive coordinated network-wide signal control that is particularly suitable for saturated traffic conditions. The presented results, although limited in space (two junctions) and time of evaluation, are promising for the particular site and for the control of urban networks in general as they indicate a superior performance of TUC compared to an established signal control strategy.

A Hierarchical Strategy for Traffic Control in Urban Networks

The paper presents an application of hierarchical multi level system theory in network traffic control problem solving. Street network is represented by a directed graph. Nodes represent signalized intersections, and arcs represent street sections between adjacent signal ized intersections. Traffic process is described by delay-difference-of-offset functions. Loop constraints on offsets are incorporated in the model. The problem is to minimize loses (delay) in the network. Two-level hierarchical system structure is used in solving the problem. For subproblems coordination interaction balance principle is applied. It is shown that tasks performed by first and second level subsystems are quite simple, and suitable for real time control.