Arterial traffic signal optimization: a person-based approach

This paper presents a traffic responsive signal control system that optimizes signal settings based on minimization of person delay on arterials. The system's underlying mixed integer linear program minimizes person delay by explicitly accounting for the passenger occupancy of autos and transit vehicles. This way it can provide signal priority to transit vehicles in an efficient way even when they travel in conflicting directions. Furthermore, it recognizes the importance of schedule adherence for reliable transit operations and accounts for it by assigning an additional weighting factor on transit delays. This introduces another criterion for resolving the issue of assigning priority to conflicting transit routes. At the same time, the system maintains auto vehicle progression by introducing the appropriate delays for when interruptions of platoons occur. In addition to the fact that it utilizes readily available technologies to obtain the input for the optimization, the system's feasibility in real-world settings is enhanced by its low computation time. The proposed signal control system was tested on a segment of San Pablo Avenue arterial located in Berkeley, California. The findings have shown the system's capability to outperform static optimal signal settings and have demonstrated its success in reducing person delay for bus and in some cases even auto users

Traffic-responsive urban network control using multivariable regulators

The paper presents the philosophy, the aim, the development, the advantages, and the potential shortcomings of the TUC (Traffic-responsive Urban Control) strategy. Based on a store-and-forward modeling approach and using well-known methods of the Automatic Control Theory, the approach followed by TUC designs (off-line) and employs (on-line) a multivariable regulator for traffic-responsive co-ordinated network-wide signal control. Simulation investigations are used to demonstrate the efficiency of the proposed approach. Based on the presented investigations, summarising conclusions are drawn and future work is outlined

A hybrid strategy for real-time traffic signal control of urban road networks

The recently developed traffic signal control strategy known as traffic-responsive urban control (TUC) requires availability of a fixed signal plan that is sufficiently efficient under undersaturated traffic conditions. To drop this requirement, the well-known Webster procedure for fixed-signal control derivation at isolated junctions is appropriately employed for real-time operation based on measured flows. It is demonstrated via simulation experiments and field application that the following hold: 1) The developed real-time demand-based approach is a viable real-time signal control strategy for undersaturated traffic conditions. 2) It can indeed be used within TUC to drop the requirement for a prespecified fixed signal plan. 3) It may, under certain conditions, contribute to more efficient results, compared with the original TUC method

Control and optimization methods for traffic signal control in large-scale congested urban road networks

The problem of designing real-time traffic signal control strategies for large-scale congested urban road networks via suitable application of control and optimization methods is considered. Three alternative methodologies are proposed, all based on the store-and-forward modeling (SFM) paradigm. The first methodology results in a linear multivariable feedback regulator derived through the formulation of the problem as a linear-quadratic (LQ) optimal control problem. The second methodology leads to an open-loop constrained quadratic optimal control problem whose numerical solution is achieved via quadratic-programming (QP). Finally, the third methodology leads to an open-loop constrained nonlinear optimal control problem whose numerical solution is effectuated by use of a feasible-direction algorithm. A simulation-based investigation of the signal control problem for a large-scale urban network using these methodologies is presented. Results demonstrate the efficiency and real-time feasibility of the developed generic control methods

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

Store-and-forward based methods for the signal control problem in large-scale congested urban road networks

The problem of designing network-wide traffic signal control strategies for large-scale congested urban road networks is considered. One known and two novel methodologies, all based on the store-and-forward modeling paradigm, are presented and compared. The known methodology is a linear multivariable feedback regulator derived through the formulation of a linear-quadratic optimal control problem. An alternative, novel methodology consists of an open-loop constrained quadratic optimal control problem, whose numerical solution is achieved via quadratic programming. Yet a different formulation leads to an open-loop constrained nonlinear optimal control problem, whose numerical solution is achieved by use of a feasible-direction algorithm. A preliminary simulation-based investigation of the signal control problem for a large-scale urban road network using these methodologies demonstrates the comparative efficiency and real-time feasibility of the developed signal control methods

Adaptive performance optimization for large-scale traffic control systems

In this paper, we study the problem of optimizing (fine-tuning) the design parameters of large-scale traffic control systems that are composed of distinct and mutually interacting modules. This problem usually requires a considerable amount of human effort and time to devote to the successful deployment and operation of traffic control systems due to the lack of an automated well-established systematic approach. We investigate the adaptive fine-tuning algorithm for determining the set of design parameters of two distinct mutually interacting modules of the traffic-responsive urban control (TUC) strategy, i.e., split and cycle, for the large-scale urban road network of the city of Chania, Greece. Simulation results are presented, demonstrating that the network performance in terms of the daily mean speed, which is attained by the proposed adaptive optimization methodology, is significantly better than the original TUC system in the case in which the aforementioned design parameters are manually fine-tuned to virtual perfection by the system operators

Perimeter and boundary flow control for heterogeneous transportation networks

In this paper, we macroscopically describe the traffic dynamics in heterogeneous transportation networks by utilizing the Macroscopic Fundamental Diagram (MFD) for urban networks a widely observed relation between network-wide mean flow and density of vehicles. A generic mathematical model for multi-reservoir networks with well-defined MFDs for each reservoir is presented first. Then, an optimal control methodology is employed for the design of perimeter and boundary flow control strategies that aim at distributing the accumulation in each reservoir as homogeneously as possible, and maintaining the rate of vehicles that are allowed to enter each reservoir around a desired point, while the system's throughput is maximized. Perimeter control occurs at the periphery of the network while boundary control occurs at the inter-transfers between neighborhood reservoirs. Based on this control methodology, control actions may be computed in real-time through a linear multivariable integral feedback regulator (LQI). To this end, the heterogeneous network of Downtown San Francisco is partitioned into three homogeneous reservoirs that exhibit well-defined MFDs. These MFDs are then used to design and compare the proposed LQI regulator with a pre-timed signal control plan and a bang-bang controller. Finally, the impact of the control actions to the network is demonstrated via simulation by the use of the corresponding MFDs and other performance measures

Feedback perimeter control for multi-region and heterogeneous congested cities

It was recently observed from empirical data that by aggregating the highly scattered plots of flow versus density from individual loop detectors for city regions with homogeneous spatial distribution of congestion, the scatter almost diminishes and a well-defined Macroscopic Fundamental Diagram (MFD) exists between space-mean flow and density. These results can be of great importance to unveil simple and robust perimeter control policies in such a way that maximizes the network capacity and outflow. Single region perimeter control might not be optimal if there is a significant number of destinations outside the region of analysis or if the city is heterogeneously loaded. This paper integrates an MFD modeling to perimeter control optimization for large-scale cities with multiple centers of congestion, if these cities can be partitioned in a small number of homogeneous regions. Perimeter control actions may be computed in real-time through a linear multivariable feedback regulator or a linear multivariable integral feedback regulator. The impact of the perimeter control actions to a three-region urban network is demonstrated via micro-simulation. A key advantage of this approach is that it does not require high computational effort and future demand data if the state of each region can be observed

Feedback perimeter control for multi-region large-scale congested networks

It was recently observed from empirical traffic data that by aggregating the highly scattered plots of flow versus density from individual loop detectors for city regions with homogeneous spatial distribution of congestion, the scatter significantly decreases and a well-defined Macroscopic Fundamental Diagram (MFD) exists between space-mean flow and density. This result can be of great importance to unveil simple perimeter control policies in such a way that maximizes the network outflow (trip endings). Single-region perimeter control might be sub-optimal if there is a significant number of destinations outside the region of analysis or if the network is heterogeneously loaded. This paper integrates an MFD modeling to perimeter and boundary control optimization for large-scale networks with multiple centers of congestion, if these networks can be partitioned into a small number of homogeneous regions. Perimeter control actions may be computed in real-time through a linear multivariable feedback regulator. The impact of the perimeter control actions to a three-region real urban network is demonstrated via micro- simulation. A key advantage of the proposed approach is that it does not require high computational effort and future demand data if the current state of each region can be observed