As a part of the global plan to achieve environmental sustainability, eco-driving defines a set of driving behaviors that benefit both the drivers, by reducing fuel or energy consumption, and the environment, by mitigating emissions. This paper refers to urban eco-driving concepts for electric vehicles with regenerative braking, aiming to mitigate energy consumption on trajectories from their current state to a fixed final state. Such trajectories are determined as solutions of an optimal control problem having the acceleration of the electric vehicle as control input, and the position and speed as state variables. A power-based energy model is used to calculate the energy consumption and the energy regeneration while braking, resulting in a non-smooth model with some discontinuities. In order to solve the problem, different approaches are considered, based on different approximations of the power-based model. Such approaches are then extended to address the stop point problem, considering a traffic signal placed on the way of the vehicle. A comparative analysis is reported and discussed in the paper for different initial state and signal timing scenarios. In particular, it is shown that the analytical solutions obtained using a quadratic approximation of the energy model are very similar to the numerical solutions but they can be obtained with less computational effort, making them applicable in real-world applications.

Implicit imitation reinforcement learning (IIRL) is a framework that aims to aid a trainee agent’s learning process via observing the state transitions of a mentor, but without access to the latter's action information. Standard IIRL assumes a shared Markov decision process (MDP) between the mentor and trainee, consequently implying an identical action space. This restriction imposes limitations on the applicability of implicit imitation frameworks in real-life scenarios where, possibly due to variations in physical characteristics, the mentor agent may possess distinct own actions, thereby creating a heterogeneous action setting. In this work, we extend the deep implicit imitation Q-networks (DIIQN) method -an online, model-free, deep RL algorithm for implicit imitation- to allow for heterogeneous action sets between mentor and trainee agents. Equipped with our heterogeneous actions DIIQN (HA-DIIQN) method, a trainee agent can harvest the benefits of IIRL even in heterogeneous action settings, achieving accelerated learning and outperforming non-optimal mentor agents.

The concept of Internal Boundary Control (IBC) has been recently incorporated into Lane-free automated vehicle movement with the aim of maximizing the utilization of cross-road infrastructure in both directions. Although IBC is generally successful in alleviating congestion, there are scenarios where it may not be able to fully dissolve it. Employing Ramp Metering (RM) can be an effective strategy for managing the flow of traffic from the on-ramp to the mainstream in this situation. This paper proposes an online scheme optimization approach for integrated IBC and RM in lane-free traffic. For controller design, a dynamic traffic flow model based on a first-order macroscopic model with the driver’s anticipation term is considered. The nonlinear and multivariable characteristics of this macroscopic highway model make it unlikely to be practical to create an online optimization mechanism for it, primarily due to the expected high computational expenses. So, a feedback linearization approach is employed to tackle the complexities of the macroscopic model. Following that, a linear Model Predictive Control (MPC) is designed based on the feedback linearized model to obtain an efficient and real-time solution. The suggested approach undergoes examination through a series of simulation tests to evaluate its performance. The simulation results verify the efficacy of the proposed approach in enabling real-time and efficient traffic management in future lane-free highway systems.

The need for additional capacity in motorway networks during periods of high demand is unavoidable if congestion is to be prevented. Increasing capacity by building new roads is often infeasible, leaving operation-based traffic control measures as the primary approach to exploit the existing infrastructure. In this paper, the novel concept of dynamic lane configuration is introduced, which opens a new avenue in motorway traffic control that harnesses the infrastructure for traffic improvement. The lateral capacity of the existing motorway infrastructure is under-utilized due to lanes that are much wider than the vehicle’s width. Dynamic lane configuration suggests that while current wide lanes ensure safety during high-speed driving, lower speed limits can be actively imposed during times of high traffic demand, allowing the lane width to be reduced, thanks to the reduced required lateral gap between vehicles at lower longitudinal speeds. By narrowing the lanes prior to congestion, it is possible to reclaim wasted space and add lanes to the road, leading to a dynamic capacity increase during the operation. This dynamic infrastructure layout with demand-responsive lane configuration during operation bridges the traffic management and infrastructure design. A model-based optimal control approach is developed to model the dynamic lane configuration and to define the times and locations of changing lane configuration. The proposed approach is tested in a simulation environment on two different motorway networks, each with a different configuration and demand profile. The promising results indicate the potential of the proposed approach in congestion mitigation and reducing travel time.

This article presents a joint trajectory optimization algorithm for a number of connected and automated vehicles in a lane-free traffic environment with vehicle nudging. A double double-integrator model is utilized for the longitudinal and lateral movements of each vehicle. The objective function consists of several sub-objectives that reflect corresponding, partially competing driving aspects and concerns, including passenger comfort, low fuel consumption, vehicle advancing at desired speed, collision avoidance, suppressing of infeasible maneuvers. Fixed and state-dependent control input bounds account for various technical limitations as well as for road boundary respect. The solution of the formulated joint Optimal Control Problem (OCP) is computed by use of a very efficient Feasible Direction Algorithm, which exploits the structure of the state equations to map the OCP into a reduced Nonlinear Programming Problem. To demonstrate the efficiency of the proposed approach, challenging scenarios are examined on a lane-free straight motorway stretch. The results of the centralized (joint) OCP are compared with a previously investigated decentralized approach where OCPs are employed separately for individual vehicles.

Implicit imitation assumes that learning agents observe only the state transitions of an agent they use as a mentor, and try to recreate them based on their own abilities and knowledge of their environment. In this paper, we put forward a deep implicit imitation Q-network (DIIQN) model, which incorporates ideas from three well-known Deep Q-Network (DQN) variants. As such, we enable a novel implicit imitation method for online, model-free deep reinforcement learning. Our thorough experimentation in the complex environment of the emerging lane-free traffic paradigm, verifies the benefits of our approach. Specifically, we show that deep implicit imitation RL dramatically accelerates the learning process when compared to a “vanilla” DQN method; and, unlike explicit imitation reinforcement learning, it is able to outperform mentor performance without resorting to additional information, such as the mentor’s actions.

In this paper we put forward a novel extension of the classic Max-Sum algorithm to the framework of Continuous Distributed Constrained Optimization Problems (Continuous DCOPs), by utilizing a popular geometric algorithm, namely Quadtrees. In its standard form, Max-Sum can only solve Continuous DCOPs with an a priori discretization procedure. Existing Max-Sum extensions to continuous multiagent coordination domains require additional assumptions regarding the form of the factors, such as access to the gradient, or the ability to model them as continuous piecewise linear functions. Our proposed approach has no such requirements: we model the exchanged messages with Quadtrees, and, as such, the discretization procedure is dynamic and embedded in the internal Max-Sum operations (addition and marginal maximization). We apply Max-Sum with Quadtrees to lane-free autonomous driving. Our experimental evaluation showcases the effectiveness of our approach in this challenging coordination domain.

Aiming at operating effectively future traffic systems, we propose here a novel methodology for integrated lane-changing and ramp metering control that exploits the presence of connected vehicles. In particular, we assume that a percentage of vehicles can receive and implement specific control tasks (e.g., lane-changing commands), while ramp metering is available via an infrastructure-based system or enabled by connected vehicles. The proposed approach is designed to robustly maximise the throughput at motorway bottlenecks employing a feedback controller, formulated as a Linear Quadratic Integral regulator, which is based on a simplified linear time invariant traffic flow model. We also present an extremum seeking algorithm to compute the optimal set-points used in the feedback controller, employing only the measurement of a cost that is representative of the achieved traffic conditions. The method is evaluated via simulation experiments, performed on a first-order, multi-lane, macroscopic traffic flow model, also featuring the capacity drop phenomenon, which allows to demonstrate the effectiveness of the developed methodology and to highlight the improvement in terms of the generated congestion.

Vehicular traffic has evolved as a crucial means for the transport of persons and goods, and its importance for the economic and social life of modern society cannot be overemphasized. On the other hand, recurrent vehicular traffic congestion, which appears on a daily basis, particularly in metropolitan areas, around the globe, has been a (increasingly) serious, in fact threatening, problem that calls for drastic solutions. Traffic congestion causes excessive travel delays, substantial fuel consumption and environmental pollution, and reduced traffic safety. Conventional traffic management measures are valuable [1]-[3] but not sufficient to address the heavily congested traffic conditions, which must be addressed in a more comprehensive way that exploits gradually emerging and future ground-breaking capabilities of vehicles and the infrastructure.

A freeway stretch with even one pair of unmeasured on/off-ramps is not fully observable in traffic states. Flow observability is essential for freeway traffic modeling, surveillance, and control. It is a longstanding and tricky issue to estimate flows for unmeasured ramp pairs. This problem seems to be hardly tractable in conventional approaches, and this paper intends to handle it based on machine learning. The work was partially inspired by transfer learning. Consider that no measurements are available for a target ramp pair, and the knowledge about ramp flow estimation may be drawn from other (measured) ramp pairs, provided that measured and unmeasured ramp pairs would share similarities in some key traffic flow patterns. Two simple machine learning algorithms, random forest (RF) and gradient boosting machine (GBM), were employed to this end. RF and GBM were driven by real measurement data to establish models that relate ramp flows to adjacent mainstream traffic conditions. The models were then applied for our task. The estimation performance was evaluated using real measurement data from the Shanghai Urban Expressway and the Intercity Highway in California, with satisfactory results obtained.

Eco-driving, a timely and well-known subject, aims at reducing fuel consumption by appropriately maneuvering a vehicle with a human or automated driver. In this work, the eco-driving problem is cast in an optimal control framework. State equations reflect the simple vehicle kinematics for position and speed, with the acceleration acting as a control input. Initial and final states (position and speed) are fixed. For the fuel consumption estimation, a number of alternatives are employed. To start with, a realistic, but nonlinear and non-smooth formula from the literature is considered. Simple smoothing procedures are then applied to enable the application of powerful numerical algorithms for the efficient solution of the resulting nonlinear optimal control problem. Furthermore, simpler quadratic approximations of the nonlinear formula are also considered, which enable analytical problem solutions. A comprehensive comparison on the basis of various driving scenarios demonstrates that the often utilized, but sometimes strongly questioned, square-of-acceleration term delivers excellent approximations for fuel minimizing trajectories in the present setting. A GLOSA (Green Light Optimal Speed Advisory) approach, based on the analytical solution of an optimal control problem is also presented.

We develop a predictor-based adaptive cruise control design with integral action (based on a nominal constant time-headway policy) for the compensation of large actuator and sensor delays in vehicular systems utilizing measurements of the relative spacing as well as of the speed and the short-term history of the desired acceleration of the ego vehicle. By employing an input-output approach, we show that the predictor-based adaptive cruise control law with integral action guarantees all of the four typical performance specifications of adaptive cruise control designs, namely, 1) stability, 2) zero steady-state spacing error, 3) string stability, and 4) non-negative impulse response, despite the large input delay. The effectiveness of the developed control design is shown in simulation considering various performance metrics.

This paper proposes an extended Kalman filter for quasi-dynamic estimation/updating of o-d flows from traffic counts. The quasi-dynamic assumption-that is considering constant o-d shares across a reference period, whilst total flows leaving each origin may vary for each sub-period within the reference period-has been proven already realistic and effective in off-line o-d flows estimation using generalized least squares estimators. The specification of the state variables and of the corresponding transition and measurement equations of a quasi-dynamic extended Kalman filter are illustrated, and a closed-form linearization is presented under the assumption of an uncongested network and error-free assignment matrix. Results show satisfactory performance and parsimonious computational burden on real-size networks.

The efficiency of freeway systems is often hindered by recurrent and non-recurrent congestion. The occurrence of these events is due not only to the high number of vehicles trying to use a shared infrastructure, but also by phenomena that temporarily deteriorate the capacity of the infrastructure itself. Among them, road accidents are considered as one of the primary causes of non-recurrent congestion. At the same time, several studies also identify traffic breakdowns as events leading to vehicle crashes. A specific goal of this research is to develop a global safety index that quantifies the expected number of crashes as a function of the current traffic state in the freeway system. Moreover, on the basis of this new index and the performance indicator generally adopted to evaluate the traffic delay, a coordinated ramp metering scheme is proposed jointly considering the reduction of travel times for the drivers and the improvement of safety in the freeway system. The control strategy is sought by defining a nonlinear optimal control problem with constrained control variables, solved by applying a specific gradient-based algorithm. The simulation analysis investigates the multi-objective nature of the problem assessing whether and to what extent the two components of the cost criterion are conflicting objectives.

The development and deployment of simple, yet efficient, coordinated and integrated control tools for motorway traffic control remains a challenge. A generic integrated feedback-based motorway traffic flow control concept is proposed in this paper. It is based on the combination and suitable extension of control algorithms and tools proposed or deployed in other studies, such as ramp metering or variable speed limit (VSL)-enabled cascade-feedback mainstream traffic flow control, and allows for consideration of multiple bottlenecks. The new controller enables coordination of ramp metering actions at a series of on-ramps, as well as integration with VSL control actions, toward a common control goal, which is bottleneck throughput maximization. While doing this, the approach considers a pre-specified (desired) balancing of the incurred delays upstream of the employed actuators, via a suitably designed knapsack problem. Despite the multitude of the offered configurations, options, and possibilities, the generic control algorithm remains simple, efficient, and suitable for field implementation. The control algorithm is demonstrated and evaluated using a validated macroscopic traffic flow model for a number of scenarios.

We present a macroscopic model-based approach for the estimation of the total density and flow of vehicles, for the case of “mixed” traffic, i.e., traffic comprising both ordinary and connected vehicles, utilizing only average speed measurements reported by connected vehicles and a minimum number (sufficient to guarantee observability) of spot-sensor-based total flow measurements. The approach is based on the realistic and validated assumption that the average speed of conventional vehicles is roughly equal to the average speed of connected vehicles, and consequently, it can be obtained at the (local or central) traffic monitoring and control unit from connected vehicles' reports. Thus, complete traffic state estimation (for arbitrarily selected segments in the network) may be achieved by estimating the total density of vehicles. Recasting the dynamics of the total density of vehicles, which are described by the well-known conservation law equation, as a linear parameter-varying system, we employ a Kalman filter for the estimation of the total density. We demonstrate the fact that the developed approach allows for a variety of different measurement configurations. We also present an alternative estimation methodology in which traffic state estimation is achieved by estimating the percentage of connected vehicles with respect to the total number of vehicles. The alternative development relies on the alternative requirement that the density and flow of connected vehicles are known to the traffic monitoring and control unit on the basis of their regularly reported positions. We validate the performance of the developed estimation schemes through simulations using a well-known second-order traffic flow model as ground truth for the traffic state.

This paper proposes a macroscopic model and two control algorithms for the dynamic operation of reversible lanes on freeways. The proposed model is an extension of the second-order traffic flow model METANET. The reversible lanes are modeled like variable lane drops (taking into account that the cars in the closed/opened lanes need a certain time to leave/enter the corresponding segments). Based on this model, two kinds of dynamic controllers have been developed. The first one is an easy-to-implement logic-based controller that takes into account the congestion lengths generated by the reversible lane bottleneck and uses this information for the dynamic operation of the lanes. The second one is a discrete model predictive control that minimizes the total time spent of the modeled network within some constraints for the maximum values of the generated bottleneck queues. The discrete optimization is carried out via evaluation of the cost function for all the leaves in a reduced search tree. The proposed model and control algorithms are simulated and tested using loop detector data collected over a section of the SE-30 freeway in Seville, Spain. The modeled network includes the Centenario Bridge, which is a bottleneck with a reversible lane that creates recurrent congestion during the morning rush-hour period. The results show that the proposed model is able to reproduce traffic congestion due to the reversible lanes and that all the proposed controllers (which can be computed in a short time) substantially reduce this congestion.

Mainstream traffic flow control (MTFC) enabled via variable speed limits (VSLs) has been investigated in previous studies, utilizing various control strategies. In this paper, an extended feedback control strategy is proposed for MTFC enabled via VSLs, considering multiple-bottleneck locations. Feedback-based results are compared with optimal control results for the evaluation of the controller using a validated macroscopic model. The performance of the feedback controller is shown to approach the optimal control results, despite the fact that many practical and safety restrictions are additionally considered by the feedback controller.

A new gating strategy for concentric cities based on the notion of the macroscopic or network fundamental diagram and the feedback-based gating concept is introduced and successfully tested. Different regions of large-scale urban networks may experience congestion at different levels and times during the peak period. In this paper, the zone, including the initial core of congestion, is considered as the first region, which has to be protected from congestion via gating; eventually, as the congestion continues to expand, the border of an extended network part becomes the second perimeter for gating control. Remarkable extensions while distributing the ordered controller flow to the gated traffic signals in case of low demand or occurrence of spillback are also considered. A greater part of the San Francisco urban network is used as test-bed within a microscopic simulation environment. Significant improvements in terms of network-wide mean speed and average delay per kilometer are obtained compared to the single perimeter gating and non-gating simulation scenarios.

Mainstream traffic flow control (MTFC) with variable speed limits (VSLs) is a freeway traffic control method that aims to maximize throughput by regulating the mainstream flow upstream from a bottleneck. Previous studies in a macroscopic simulator have shown optimal and feedback MTFC potential to improve traffic conditions. In this paper, local feedback MTFC is applied in microscopic simulation for an on-ramp merge bottleneck. Traffic behavior reveals important aspects that had not been previously captured in macroscopic simulation. Mainly, the more realistic VSL application at specific points instead of along an entire freeway section produces a slower traffic response to speed limit changes. In addition, the nonlinear capacity flow/speed limit relation observed in the microscopic model is more pronounced than what was observed at the macroscopic level. After appropriate modifications in the control law, significant improvements in traffic conditions are obtained.

The well-known feedback ramp metering algorithm ALINEA can be applied for local ramp metering or used as a key component in a coordinated ramp metering system. ALINEA uses real-time occupancy measurements from the ramp-flow merging area that may be at most few hundred meters downstream of the metered on-ramp nose. In many practical cases, however, bottlenecks with smaller capacity than the merging area may exist further downstream for various reasons, which suggests using measurements from those further downstream bottlenecks rather than from the merging area. This paper addresses the local ramp metering problem in such a downstream bottleneck case. Theoretical analysis indicates that ALINEA may lead to a poorly damped closed-loop behavior in this case, but PI-ALINEA, which is a suitable proportional-integral (PI) extension of ALINEA, can lead to satisfactory control performance. The stability of the closed-loop ramp metering system with PI-ALINEA is rigorously proved by Lyapunov stability arguments. The root locus method is also employed to analyze the linearized closed-loop system performance of ALINEA and PI-ALINEA with and without a downstream bottleneck to provide insights on both controllers' performance. Simulation studies are conducted using a macroscopic traffic flow model to demonstrate that the ramp metering performance of ALINEA indeed deteriorates in the distant downstream bottleneck case, whereas a significant improvement is obtained using PI-ALINEA. Moreover, with its control parameters appropriately tuned, PI-ALINEA is found to be universally applicable to a range of distances between the on-ramp and downstream bottlenecks. This indicates that little fine-tuning would be necessary in field applications.

Recent research has proposed mainstream traffic flow control (MTFC), enabled via variable speed limits (VSLs), as a novel motorway traffic management tool and has demonstrated its efficiency based on sophisticated optimal control methods that may face difficulties in practical field implementations. A simple local MTFC feedback controller is designed in this paper, taking into account a number of practical requirements and restrictions. The MTFC controller relies only on readily available real-time measurements (no online model usage and no demand predictions are needed) and is therefore robust and suitable for field implementations. The controller is evaluated in simulation and compared with optimal control results. Despite its simplicity, the new controller's performance is shown to approach the optimal control results while considering several practical and safety restrictions for a number of investigated scenarios.

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.

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.

Metered dual-branch on-ramps may feature strongly different (relative) queues or waiting times on each of their branches. Different methods are developed to balance the queues or relative queues or waiting times on both branches. The methods are evaluated and compared based on extensive microscopic simulations. As a by-product, a waiting-time estimation and control method for metered ramps is also developed. The developed methods are implemented in the operational ramp-metering system of the Monash Freeway (Melbourne, Australia). The developed concepts are also applicable to other kinds of traffic control problems involving merging traffic streams.

This paper reports on some large-scale field-testing results of a real-time freeway network traffic surveillance tool that has recently been developed to enable a number of real-time traffic surveillance tasks. This paper first introduces the related network traffic flow model and the approaches employed to traffic state estimation, traffic state prediction, and incident alarm. The field testing of the tool for these surveillance tasks in the A3 freeway of 100 km between Naples and Salerno in southern Italy is then reported in some detail. The results obtained are quite satisfactory and promising for further future implementations of the tool.

The number of vehicles that are included in a metered motorway ramp or an urban signalized link at any time is valuable information for real-time control. A recently developed Kalman-filter-based real-time estimator for the vehicle count within signalized links, using three detector cross stations, is simplified in this paper to allow for reliable estimates on the basis of one single time-occupancy measurement that is typically available in urban signalized links. The simplified vehicle-count estimator is tested and compared with the three-detector estimation scheme via microscopic simulation for a variety of scenarios and traffic conditions. Several related issues are addressed: the effect of utilizing more measurements, as well as the impact of the update period, signal cycle, vehicle length, and link length. The simulation investigations indicate less-accurate but still reasonable and robust estimation performance of the simplified estimator with low calibration effort needed, which facilitates easy applicability of the method.

In a recent series of articles with largely identical contents and results, some claims are raised about the pertinence and performance of the well-known and widely field-applied local ramp metering algorithm ALINEA and of some extended versions thereof. The expressed claims are based on simulation results with a self-made microscopic simulator. This paper shows that the produced simulation results and derived conclusions are based on an insufficient understanding of the feedback character of the ALINEA algorithm, which led to an inappropriate application of the method. More specifically, the mainstream measurement that feeds ALINEA was misplaced so that any occurring congestion could not be monitored; this renders ALINEA blind to the traffic conditions under control and negates the very notion of feedback.

A new traffic-responsive ramp-metering strategy is presented that coordinates local ramp-metering actions, thus enabling the linked control of the inflow from two (or more) consecutive on-ramps to the freeway mainstream. The proposed linked ramp-metering scheme is simple and utterly reactive, i.e., based on readily available real-time measurements without any need for real-time model calculations or external disturbance prediction. The well-known feedback strategy, known as Asservissement LINeaire d'Entree Autoroutiere (ALINEA), is used at a local level. Simulation results are presented for a hypothetical freeway axis with two successive on-ramps. Some pitfalls and misapplications of the local ramp metering are also illustrated via appropriately designed simulation scenarios. The proposed linked strategy is demonstrated to outperform the uncoordinated local ramp metering and, thus, to increase the achievable control benefit over the no-control case. In fact, the new strategy is shown to reach the efficiency of sophisticated proactive optimal control schemes.

Traffic congestion in urban road and freeway networks leads to a strong degradation of the network infrastructure and accordingly reduced throughput, which can be countered via suitable control measures and strategies. After illustrating the main reasons for infrastructure deterioration due to traffic congestion, a comprehensive overview of proposed and implemented control strategies is provided for three areas: urban road networks, freeway networks, and route guidance. Selected application results, obtained from either simulation studies or field implementations, are briefly outlined to illustrate the impact of various control actions and strategies. The paper concludes with a brief discussion of future needs in this important technical area.

This paper employs previously developed modeling, validation, and stimulation tools to address, for the first time, the realistic macroscopic simulation of a real large-scale motorway network. More specifically, the macroscopic simulator METANET, involving a second-order traffic flow model as well as network-relevant extensions, is utilized. A rigorous quantitative validation procedure is applied to individual network links, and subsequently a heuristic qualitative validation procedure is employed at a network level. The large-scale motorway network around Amsterdam, The Netherlands, is considered in this investigation. The main goal of the paper is to describe the application approach and procedures and to demonstrate the accuracy and usefulness of macroscopic modeling tools for large-scale motorway networks.

Recurrent and nonrecurrent congestion on freeways may be alleviated if today's "spontaneous" infrastructure utilization is replaced by an orderly, controllable operation via comprehensive application of ramp metering and freeway-to-freeway control, combined with powerful optimal control techniques. This paper first explains why ramp metering can lead to a dramatic amelioration of traffic conditions on freeways. An overview of ramp metering algorithms is provided next, ranging from early fixed-time approaches to traffic-responsive regulators and to modern sophisticated nonlinear optimal control schemes. Finally, a large-scale example demonstrates the high potential of advanced ramp metering approaches.

It is shown that the fourth level of a four-level hierarchical optimization algorithm proposed by Fawzy is superfluous.

A hierarchical optimization method based on the interaction prediction principle is applied to the freeway traffic control problem. The solution method is implemented on a multimicrocomputer system. Both analytical and experimental results concerning certain properties of the decentralized computation structure such as computation time, storage space, and communication requirements are presented.