Final report on the evaluation of RRM/CRRM algorithms

Deliverable public del projecte EVERESTThis deliverable provides a definition and a complete evaluation of the RRM/CRRM algorithms selected in D11 and D15, and evolved and refined on an iterative process. The evaluation will be carried out by means of simulations using the simulators provided at D07, and D14.Preprin

A Real-Time Offset Transitioning Algorithm for Coordinating Traffic Signals

This report introduces an adaptive real-time offset transitioning algorithm that can be viewed as an integrated optimization approach designed to work with traditional coordinated-actuated systems. The Purdue Real-Time Offset Transitioning Algorithm for Coordinating Traffic Signals (PRO-TRACTS) adds to the controllers the ability to adaptively change their offsets in response to changes in traffic pattern, providing an intermediate solution between traditional coordinated-actuated control systems and adaptive control systems. To facilitate implementation, a new National Transportation Communication for ITS Protocol (NTCIP) object for capturing detector actuation at the controller’s level is defined in this report. The unique cycle-based tabulation of volume and occupancy profiles at upstream detectors is used by a newly defined metric to examine the existence of shockwaves generated due to a poor offset downstream. The procedure is modeled after the analysis of variance testing. This procedure is performed on cycle-by-cycle basis to evaluate the offset performance and adjust it accordingly. Simulations of two case studies revealed 0-16% savings in total travel time and up to 44% saving in total number of stops for the coordinated movement when applied to systems with poor offsets. The algorithm is best suited for arterials with primarily through traffic. Heavy movements from the side streets onto the arterial make it difficult for the algorithm to determine which movement should be favored. PRO-TRACTS mitigates problems such as early-return-to-green, waiting queues, and improperly designed offsets using current setups of traffic signals/detectors in the US. The algorithm capitalizes on the existing knowledge and familiarity of traffic engineers and personnel with the current actuated control system to provide a cost-effective solution to improving signal coordination. Future research is needed to improve the stability of the algorithm with highly dispersed platoons and oscillatory traffic patterns caused by situations such as controllers skipping phases due to light traffic volume. It is also recommended that the algorithm should be extended to improve two-way signal progression instead of one-way progression

Smart Traffic Operation: from Human-Driven Cars to Mixed Vehicle Autonomy

The goal of my research is to enhance urban mobility by developing reliable and efficient traffic control and management strategies. As cities grow everywhere, and urban roadways become overburdened, the need for the development of such strategies becomes more evident. With the prevalence of smart sensing devices, such as smart phones and smart intersections, cities are becoming smart. Moreover, with the emergence of new and inevitable technologies, such as autonomous and connected vehicles, electric vehicles, and mobility on demand systems, smart cities are rapidly evolving. As we experience the arrival of such technologies, there is an opportunity to reclaim urban mobility. However, a blind utilization of these technologies may deflect us from reaching this goal. In this dissertation, we study the efficient operation of smart cities via management strategies that can guarantee overall societal benefits both in the cities of today and future.We focus on two natural instances of this agenda. In the first part, we tackle some of the existing challenges in the smart operation of traffic networks which are solely shared by human-driven cars. If all vehicles are human-driven, there is room for improving the efficiency of traffic networks by appropriate coordination and control of traffic signal lights. For these networks, we develop signal control algorithms that are capable of minimizing the number of stop-and-go movements, encoding fairness among vehicular arrivals, and are robust to the knowledge of system parameters. In the second part, we analyze fundamentals of traffic networks with mixed vehicle autonomy, where both human-driven and autonomous cars coexist on roadways. We study the mobility implications of selfish autonomy, i.e. autonomous cars that are not concerned about their overall impact and simply attempt to optimize their own travel benefits. Having shown the negative consequences that the increased deployment of selfish autonomy may have, we develop a pricing mechanism which can guarantee the overall societal-scale efficiency of traffic networks with mixed vehicle autonomy. Finally, we show how autonomy can act altruistically, i.e. by taking into account the decision making process of humans, autonomous cars can potentially plan for their actions in the favor of the overall good

Intelligent Transportation Related Complex Systems and Sensors

Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems. It includes twenty-four papers, which cover scientific concepts, frameworks, architectures and various other ideas on analytics, trends and applications of transportation-related data

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Benelux meeting on systems and control, 23rd, March 17-19, 2004, Helvoirt, The Netherlands

Book of abstract

Optical Switching for Scalable Data Centre Networks

This thesis explores the use of wavelength tuneable transmitters and control systems within the context of scalable, optically switched data centre networks. Modern data centres require innovative networking solutions to meet their growing power, bandwidth, and scalability requirements. Wavelength routed optical burst switching (WROBS) can meet these demands by applying agile wavelength tuneable transmitters at the edge of a passive network fabric. Through experimental investigation of an example WROBS network, the transmitter is shown to determine system performance, and must support ultra-fast switching as well as power efficient transmission. This thesis describes an intelligent optical transmitter capable of wideband sub-nanosecond wavelength switching and low-loss modulation. A regression optimiser is introduced that applies frequency-domain feedback to automatically enable fast tuneable laser reconfiguration. Through simulation and experiment, the optimised laser is shown to support 122×50 GHz channels, switching in less than 10 ns. The laser is deployed as a component within a new wavelength tuneable source (WTS) composed of two time-interleaved tuneable lasers and two semiconductor optical amplifiers. Switching over 6.05 THz is demonstrated, with stable switch times of 547 ps, a record result. The WTS scales well in terms of chip-space and bandwidth, constituting the first demonstration of scalable, sub-nanosecond optical switching. The power efficiency of the intelligent optical transmitter is further improved by introduction of a novel low-loss split-carrier modulator. The design is evaluated using 112 Gb/s/λ intensity modulated, direct-detection signals and a single-ended photodiode receiver. The split-carrier transmitter is shown to achieve hard decision forward error correction ready performance after 2 km of transmission using a laser output power of just 0 dBm; a 5.2 dB improvement over the conventional transmitter. The results achieved in the course of this research allow for ultra-fast, wideband, intelligent optical transmitters that can be applied in the design of all-optical data centres for power efficient, scalable networking

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