Network signal control is an effective way to mitigate traffic congestion. However, most network signal control methods ignore the risk of queue spillback. Although local and decentralized control methods have the potential to address spillback, their performance at the system/network level is not guaranteed, making it challenging to achieve the global optimum. This study proposes a novel hierarchical model predictive control (MPC) approach that utilizes the link transmission model (LTM) with queue transmission at both road segment and turn levels to optimize traffic signals for road networks. At the network level, the controller employs a state-of-the-art LTM framework that can describe segment-level flow dynamics and uses quadratic programming to determine the effective fractions of green time for network throughput maximization. The network decisions of green time fractions are sent to the local layer as a reference. The local layer builds on a refined LTM with turn-level queue transmission and formulates a nonlinear programming problem to track the reference to ensure that the optimal network decision is realized at the individual intersections while eliminating potential spillback. Simulation experiments on both an arterial and a grid network are conducted to verify the performance of the proposed approach. The results reveal that the proposed MPC leads to substantial improvements in terms of throughput and queue length, especially in oversaturated conditions, and is robust against demand prediction errors.

This work presents a novel and practical real-time switching control policy for the dynamic hard shoulder running lane control on motorways. Using the hard shoulder as a running lane in peak traffic periods increases the motorway capacity at bottlenecks, increasing throughput and avoiding the onset of congestion. The proposed policy enables a smooth switching on of the hard shoulder lane at peak periods. It is based on properties of the fundamental diagram of motorway traffic with easy and unambiguous interpretation and implementation by traffic operators. Simulation analysis on a 5 km-long motorway stretch with three lanes and one hard shoulder lane using the AIMSUN microscopic simulator is demonstrated. The results have showed that the proposed switching control improves the motorway throughput compared to rival control schemes.

Evidence in the literature shows that automated and human driving modes demonstrate different driving characteristics, i.e., headway policy, spacing policy, reaction time, comfortable acceleration, and others. These differences alter observed traffic dynamics and have an impact on energy consumption. This paper assesses the energy footprint of commercially implemented adaptive cruise control (ACC) systems and human drivers in car-following formation via different models using empirical observations on very similar driving cycles and/or routes. Most importantly, it initiates a critical discussion of the findings under the behavioral properties of each mode. Findings show that: ACC systems propagate an increasing energy consumption upstream, while human drivers do not; they succeed in maintaining a constant time-headway policy, operating very reliably; they develop a strong bond with their leader compared to their human counterparts; the two modes (humans and ACCs) are operating in different phase-space areas with room for improvement. Overall, findings show that ACC systems must be optimized to achieve a trade-off between functional requirements and eco-driving instructions.

A real-time traffic management policy that integrates traffic signal control and multi-commodity routing of connected vehicles in networks with multiple destinations is developed. The proposed policy is based on a multi-commodity formulation of the store-and-forward model and assumes all vehicles are able to exchange information with the infrastructure. Vehicles share information about their current location and final destination. Based on this information, the strategy determines both optimized signal timings at every intersection and vehicle-specific routing information at every link of the network. The control actions, i.e., signal times and routing information, are updated at every cycle and delivered by a finite horizon optimal control problem cast into a rolling horizon framework. The underlying optimization problem is convex, and thus the method is suitable for real-time operation in large networks. The method is validated via a micro-simulation study in networks with up to twenty intersections and, in all simulations, outperforms a real-time traffic-responsive signal control strategy that is based on a single-commodity store-and-forward model. The scalable computation effort for increasing network sizes and prediction horizon confirms the computational efficiency of the method.

Bus bunching is an instability problem where buses operating on high-frequency public transport lines arrive at stops in bunches. This work unveils that bus-following models can be used to design bus-to-bus cooperative control strategies and mitigate bunching. The use of bus-following models avoids the explicit modelling of bus-stops, which would render the resulting problem discrete, with events occurring at arbitrary time intervals. In a follow-the-leader two-bus system, bus-to-bus communication allows the driver of the following bus to observe (from a remote distance) the position and speed of the leading bus operating in the same transport line. The information transmitted from the leader is then used to control the speed of the follower to eliminate bunching. A platoon of buses operating in the same transit line can be then controlled as leader-follower dyads. In this context, we propose practical control laws to regulate speeds, which would lead to bunching cure. A combined state estimation and remote control scheme is developed to capture the effect of disturbances and randomness in passenger arrivals. To investigate the performance of the developed schemes the 9-km 1-California line in San Francisco with about 50 arbitrary spaced bus stops is used. Simulations with empirical passenger data are carried out. Results show bunching avoidance and improvements in terms of schedule reliability of bus services and delays. The proposed control is robust, scalable in terms of transit network size, and thus easy to deploy by transit agencies to improve communication and guidance to drivers, and reduce costs.

The expansion of road infrastructure, in spite of increasing congestion levels, faces severe restrictions from all sorts: economical, environmental, social, or technical. An efficient and, usually, less expensive alternative to improve mobility and the use of available infrastructure is the adoption of traffic management. A particular case of interest occurs when inbound and outbound traffic on a given facility is unbalanced throughout the day. This scenario may benefit of a lane management strategy called tidal flow (or reversible) lane control, in which case the direction of one or more contraflow buffer lanes is reversed according to the needs of each direction. This paper proposes a simple and practical real-time strategy for efficient motorway tidal flow lane control. A state-feedback switching policy based on the triangular fundamental diagram, that requires only aggregated measurements of density, is adopted. A theoretical analysis based on the kinematic wave theory shows that the strategy provides a Pareto-optimal solution. Microsimulations using empirical data from the A38(M) Aston Expressway in Birmingham, UK, are used to demonstrate the operation of the proposed strategy. The robustness of the switching policy to parameter variations is demonstrated by parametric sensitivity analysis. Simulation results confirm an increase of motorway throughput and a smooth operation for the simulated 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.

A major barrier preventing the wide employment of mobile networks of robots in tasks such as exploration, mapping, surveillance, and environmental monitoring is the lack of efficient and scalable multi-robot passive and active sensing (estimation) methodologies. The main reason for this is the absence of theoretical and practical tools that can provide computationally tractable methodologies which can deal efficiently with the highly nonlinear and uncertain nature of multi-robot dynamics when employed in the aforementioned tasks. In this paper, a new approach is proposed and analyzed for developing efficient and scalable methodologies for a general class of multi-robot passive and active sensing applications. The proposed approach employs an estimation scheme that switches among linear elements and, as a result, its computational requirements are about the same as those of a linear estimator. The parameters of the switching estimator are calculated off-line using a convex optimization algorithm which is based on optimization and approximation using Sum-of-Squares (SoS) polynomials. As shown by rigorous arguments, the estimation accuracy of the proposed scheme is equal to the optimal estimation accuracy plus a term that is inversely proportional to the number of estimator's switching elements (or, equivalently, to the memory storage capacity of the robots' equipment). The proposed approach can handle various types of constraints such as communication and computational constraints as well as obstacle avoidance and maximum speed constraints and can treat both problems of passive and active sensing in a unified manner. The efficiency of the approach is demonstrated on a 3D active target tracking application employing flying robots.