iTETRIS: An Integrated Wireless and Traffic Platform for Real-Time Road Traffic Management Solutions

Wireless vehicular cooperative systems have been identified as an attractive solution to improve road traffic management, thereby contributing to the European goal of safer, cleaner, and more efficient and sustainable traffic solutions. V2V-V2I communication technologies can improve traffic management through real-time exchange of data among vehicles and with road infrastructure. It is also of great importance to investigate the adequate combination of V2V and V2I technologies to ensure the continuous and costefficient operation of traffic management solutions based on wireless vehicular cooperative solutions. However, to adequately design and optimize these communication protocols and analyze the potential of wireless vehicular cooperative systems to improve road traffic management, adequate testbeds and field operational tests need to be conducted. Despite the potential of Field Operational Tests to get the first insights into the benefits and problems faced in the development of wireless vehicular cooperative systems, there is yet the need to evaluate in the long term and large dimension the true potential benefits of wireless vehicular cooperative systems to improve traffic efficiency. To this aim, iTETRIS is devoted to the development of advanced tools coupling traffic and wireless communication simulators

Constrained dynamic control of traffic junctions

Excessive traffic in our urban environments has detrimental effects on our health, economy and standard of living. To mitigate this problem, an adaptive traffic lights signalling scheme is developed and tested in this paper. This scheme is based on a state space representation of traffic dynamics, controlled via a dynamic programme. To minimise implementation costs, only one loop detector is assumed at each link. The comparative advantages of the proposed system over optimal fixed time control are highlighted through an example. Results will demonstrate the flexibility of the system when applied to different junctions. Monte Carlo runs of the developed scheme highlight the consistency and repeatability of these results.peer-reviewe

Traffic control designing using model predictive control in a high congestion traffic area

The paper investigates a designing method for urban traffic management system. A busy traffic area was chosen for test field in the 10^th district of Budapest. The control algorithm is based on model predictive control (MPC). The control aim is to relieve traffic congestion, reduce travel time and improve homogenous traffic flow. Theory and realization details of the control method are also presented. The MPC based control strategy was implemented into the test network´s management system. The applied environment contains microscopic traffic simulator, scientific mathematical software and some computational applications for the evaluation. The simulation results show that the system is able to ameliorate the network efficiency and reduce travel time. The designed MPC based traffic control strategy proves effectiveness by creating optimal flow in the network subjected to control input constraints

TRA-950: A DYNAMIC PROGRAMMING APPROACH FOR ARTERIAL SIGNAL OPTIMIZATION IN A CONNECTED VEHICLE ENVIRONMENT

Within the Connected Vehicle (CV) environment, vehicles are able to communicate with each other and with infrastructure via wireless communication technology. The collected data from CVs provide a much more complete picture of the arterial traffic states and can be utilized for signal control. Based on the real-time traffic information from CVs, this paper enhances an arterial traffic flow model for arterial signal optimization. Then a dynamic programming optimization model is created to solve the signal optimization application. A real-world arterial corridor is modeled in VISSIM to validate the algorithms. This approach is shown to generate good results and may be superior to well-tuned fixed-time control

Surrogate model for real time signal control: theories and applications

Traffic signal controls play a vital role in urban road traffic networks. Compared with fixed-time signal control, which is solely based on historical data, real time signal control is flexible and responsive to varying traffic conditions, and hence promises better performance and robustness in managing traffic congestion. Real time signal control can be divided into model-based and model-free approaches. The former requires a traffic model (analytical or simulation-based) in the generation, optimisation and evaluation of signal control plans, which means that its efficacy in real-world deployment depends on the validity and accuracy of the underlying traffic model. Model-free real time signal control, on the other hand, is constructed based on expert experience and empirical observations. Most of the existing model-free real time signal controls, however, focus on learning-based and rule-based approaches, and either lack interpretability or are non-optimised. This thesis proposes a surrogate-based real time signal control and optimisation framework, that can determine signal decisions in a centralised manner without the use of any traffic model. Surrogate models offer analytical and efficient approximations of complex models or black-box processes by fitting their input-output structures with appropriate mathematical tools. Current research on surrogate-based optimisation is limited to strategic and off-line optimisation, which only approximates the relationship between decisions and outputs under highly specific conditions based on certain traffic simulation models and is still to be attempted for real time optimisation. This thesis proposes a framework for surrogate-based real time signal control, by constructing a response surface that encompasses, (1) traffic states, (2) control parameters, and (3) network performance indicators at the same time. A series of comprehensive evaluations are conducted to assess the effectiveness, robustness and computational efficiency of the surrogate-based real time signal control. In the numerical test, the Kriging model is selected to approximate the traffic dynamics of the test network. The results show that this Kriging-based real time signal control can increase the total throughput by 5.3% and reduce the average delay by 8.1% compared with the fixed-time baseline signal plan. In addition, the optimisation time can be reduced by more than 99% if the simulation model is replaced by a Kriging model. The proposed signal controller is further investigated via multi-scenario analyses involving different levels of information availability, network saturation and traffic uncertainty, which shows the robustness and reliability of the controller. Moreover, the influence of the baseline signal on the Kriging-based signal control can be eliminated by a series of off-line updates. By virtue of the model-free nature and the adaptive learning capability of the surrogate model, the Kriging-based real time signal control can adapt to systematic network changes (such as seasonal variations in traffic demand). The adaptive Kriging-based real time signal control can update the response surface according to the feedback from the actual traffic environment. The test results show that the adaptive Kriging-based real time signal control maintains the signal control performance better in response to systematic network changes than either fixed-time signal control or non-adaptive Kriging-based signal control.Open Acces

Forecast based traffic signal coordination using congestion modelling and real-time data

This dissertation focusses on the implementation of a Real-Time Simulation-Based Signal Coordination module for arterial traffic, as proof of concept for the potential of integrating a new generation of advanced heuristic optimisation tools into Real-Time Traffic Management Systems. The endeavour represents an attempt to address a number of shortcomings observed in most currently marketed on-line signal setting solutions and provide better adaptive signal timings. It is unprecedented in its use of a Genetic Algorithm coupled with Continuous Dynamic Traffic Assignment as solution evaluation method, only made possible by the recently presented parallelisation strategies for the underlying algorithms. Within a fully functional traffic modelling and management framework, the optimiser is developed independently, leaving ample space for future adaptations and extensions, while relying on the best available technology to provide it fast and realistic solution evaluation based on reliable real-time supply and demand data. The optimiser can in fact operate on high quality network models that are well calibrated and always up-to-date with real-world road conditions; rely on robust, multi-source network wide traffic data, rather than being attached to single detectors; manage area coordination using an external simulation engine, rather than a na¨ıve flow propagation model that overlooks crucial traffic dynamics; and even incorporate real-time traffic forecast to account for transient phenomena in the near future to act as a feedback controller. Results clearly confirm the efficacy of the proposed method, by which it is possible to obtain relevant and consistent corridor performance improvements with respect to widely known arterial bandwidth maximisation techniques under a range of different traffic conditions. The computational efforts involved are already manageable for realistic real-world applications, and future extensions of the presented approach to more complex problems seem within reach thanks to the load distribution strategies already envisioned and prepared for in the context of this work

Veränderungen an dem Traffic-Responsive Urban Control Verfahren

The current work has its focus on further improvements envisioned for an existing traffic control system called Traffic-responsive Urban Control (TUC). Originally conceived for corridor networks, TUC only offers the possibility to maintain synchronized traffic lights that give right-of-way for the vehicles traveling through the main routes or, more specifically, the routes that do not intersect. This synchronization is achieved through the adjustment of the Offset parameter, and it is known to avoid unnecessary stops at the successive traffic controlled intersections, reducing traffic delays and increasing the drivers' comfort. The present investigation proposes an extension to TUC's original formulation, enabling it to handle more complex networks (meshed networks), where the secondary intersecting routes may also profit from traffic lights synchronization. Moreover, TUC's original method, employed during the necessary changes in Offsets, is also improved. The new method takes into consideration the impacts that the change in Offsets may incur to the operation of the network. TUC's main input information, during its operation, is the description of current traffic queue lengths. Complementing the mentioned modifications to TUC, a new method for the estimation/prediction of traffic queues is presented. The proposed Queue Estimator/Predictor uses a macroscopic traffic model to capture the traffic dynamics of the network, and uses this information for improving the traffic queue estimations calculated in a previous step. Finally, the evaluation of the current developments is presented. The evaluation is carried out through the simulation of a real network during a whole day operation. The new developments are not only compared to TUC's original formulation, but also against a recently developed Adaptive Traffic Control System (ATCS) prototype. The results show that the developments proposed in the current work were indeed beneficial to TUC's operation, even though the improvements were not quite as high as expected.Das Verkehrs-Steuersystem Traffic-responsive Urban Control (TUC) wurde ursprünglich konzipiert für Hauptverkehrsadern, und bietet nur die Möglichkeit, die Schaltzeit der Lichtsignalanlagen zu synchronisieren, die die Durchfahrt der Hauptvekehrsströme vorberechtigen. Nebenströmen , die die Hauptroute überschneiden, können durch TUC in der Regel nicht synchronisiert werden. Diese Synchronisierung wird durch die Einstellung der Versatzzeit erreicht, und sie vermeidet die unnötige Stopps an aufeinanderfolgenden signalgesteuerten Knotenpunkten. Dadurch werden Verzögerungen im Verkehrsablauf reduziert und der Komfort der Fahrer erhöht. Die vorliegende Arbeit schlägt eine Erweiterung der ursprünglichen Formulierung TUCs vor, wodurch komplexe Netzwerke behandelt werden können, und auch die sekundären Nebenströmen von der Synchronisierung profitieren. Darüber hinaus ist die ursprüngliche Methode TUC, die für die notwendigen Veränderungen in Versatzzeiten verwendet wird, auch verbessert. Die neue Methode berücksichtigt die Auswirkungen auf den Betrieb des Verkehrsnetzes, die die Änderung der Versatzzeiten ergeben. Eine der wichtigsten Eingangsdaten TUCs ist die Beschreibung der aktuellen Rückstaulängen. Ergänzend zu den oben genannten Änderungen zu TUC, eine neue Methode für die Schätzung/Prognose von Rückstaus wird vorgestellt. Die vorgeschlagene Rückstauschätzer-prädiktor verwendet ein makroskopisches Verkehrsmodell, um die Verkehrsdynamik des Netzes zu erfassen. Diese Dynamik wird dann benutzt, um die Schätzung der Rückstaulängen, die in einem vorherigen Schritt berechnet wurden, zu verbessern. Schliesslich ist die Beurteilung der aktuellen Entwicklung dargestellt. Die Auswertung erfolgt durch die Simulation eines realen Netz während eines ganzen Tages. Die neuen Entwicklungen sind nicht nur mit der ursprünglichen Formulierung TUCs verglichen, sondern auch gegen eine andere kürzlich entwickelte Adaptive Traffic Control System (ATCS) Prototyp. Die Ergebnisse zeigen, dass die vorgeschlagene Entwicklungen in Vorteil für TUC waren, obwohl die Verbesserungen nicht ganz so hoch wie erwartet waren