Methods in intelligent transportation systems exploiting vehicle connectivity, autonomy and roadway data

Intelligent transportation systems involve a variety of information and control systems methodologies, from cooperative systems which aim at traffic flow optimization by means of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, to information fusion from multiple traffic sensing modalities. This thesis aims to address three problems in intelligent transportation systems, one in optimal control of connected automated vehicles, one in discrete-event and hybrid traffic simulation model, and one in sensing and classifying roadway obstacles in smart cities. The first set of problems addressed relates to optimally controlling connected automated vehicles (CAVs) crossing an urban intersection without any explicit traffic signaling. A decentralized optimal control framework is established whereby, under proper coordination among CAVs, each CAV can jointly minimize its energy consumption and travel time subject to hard safety constraints. A closed-form analytical solution is derived while taking speed, control, and safety constraints into consideration. The analytical solution of each such problem, when it exists, yields the optimal CAV acceleration/deceleration. The framework is capable of accommodating for turns and ensures the absence of collisions. In the meantime, a measurement of passenger comfort is taken into account while the vehicles make turns. In addition to the first-in-first-out (FIFO) ordering structure, the concept of dynamic resequencing is introduced which aims at further increasing the traffic throughput. This thesis also studies the impact of CAVs and shows the benefit that can be achieved by incorporating CAVs to conventional traffic. To validate the effectiveness of the proposed solution, a discrete-event and hybrid simulation framework based on SimEvents is proposed, which facilitates safety and performance evaluation of an intelligent transportation system. The traffic simulation model enables traffic study at the microscopic level, including new control algorithms for CAVs under different traffic scenarios, the event-driven aspects of transportation systems, and the effects of communication delays. The framework spans multiple toolboxes including MATLAB, Simulink, and SimEvents. In another direction, an unsupervised anomaly detection system is developed based on data collected through the Street Bump smartphone application. The system, which is built based on signal processing techniques and the concept of information entropy, is capable of generating a prioritized list of roadway obstacles, such that the higher-ranked entries are most likely to be actionable bumps (e.g., potholes) requiring immediate attention, while those lower-ranked are most likely to be nonactionable bumps(e.g., flat castings, cobblestone streets, speed bumps) for which no immediate action is needed. This system enables the City to efficiently prioritize repairs. Results on an actual data set provided by the City of Boston illustrate the feasibility and effectiveness of the system in practice

Event-Triggered Action-Delayed Reinforcement Learning Control of a Mixed Autonomy Signalised Urban Intersection

We propose an event-triggered framework for deciding the traffic light at each lane in a mixed autonomy scenario. We deploy the decision after a suitable delay, and events are triggered based on the satisfaction of a predefined set of conditions. We design the trigger conditions and the delay to increase the vehicles’ throughput. This way, we achieve full exploitation of autonomous vehicles (AVs) potential. The ultimate goal is to obtain vehicle-flows led by AVs at the head. We formulate the decision process of the traffic intersection controller as a deterministic delayed Markov decision process, i.e., the action implementation and evaluation are delayed. We propose a Reinforcement Learning based model-free algorithm to obtain the optimal policy. We show - by simulations - that our algorithm converges, and significantly reduces the average wait-time and the queues length as the fraction of the AVs increases. Our algorithm outperforms our previous work [1] by a quite significant amount

Modelo sistêmico para análise de cenário de consumo de energia residencial

This paper presents the methodology and development of a systemic model, built using the systems dynamic methodology, for the analysis of energy consumption scenarios by air conditioning in the residential sector. The model allows to analyze the total energy consumption of homes and disaggregated by equipment applying efficiency measures. For the modeling of the consumption for cooling and heating, different equipment was taken into account considering for each one: the quantity, nominal powers, labeling levels according to current norms and the average hours of use. Likewise, the possibility of analyzing different energy efficiency measures was included in the model. The developed model allows analyzing the temporal evolution of the disaggregated and total energy demand of the building equipment, allowing to compare, through sensitivity analysis, different possible scenarios from the adoption of various technological and energy efficiency measures.Este trabajo presenta el desarrollo de un modelo sistémico, construido utilizando la metodología de dinámica de sistemas, para el análisis de escenarios de consumo energético por climatización en el sector residencial. El modelo permite analizar el consumo de energía total de las viviendas y desagregado por equipamiento, aplicando medidas de eficiencia. Para la modelización del consumo para refrigeración y calefacción se tuvieron en cuenta diferentes equipos considerando para cada uno la cantidad, potencias nominales, niveles de etiquetado acordes con normas vigentes y las horas de uso promedio. Asimismo, se incluyó en el modelo la posibilidad de analizar diferentes medidas de eficiencia energética. El modelo desarrollado permite analizar la evolución temporal de la demanda energética desagregada y total del equipamiento edilicio y posibilita comparar, mediante análisis de sensibilidad, distintos escenarios, a partir de la adopción de diversas medidas tecnológicas y de eficiencia energética.Este artigo apresenta o desenvolvimento de um modelo sistêmico, construído a partir da metodologia de dinâmica de sistemas, para a análise de cenários de consumo de energia por resfriamento e aquecimento no setor residencial. O modelo permite analisar o consumo total de energia das residências e discriminado por equipamentos, aplicando medidas de eficiência. Para a modelagem de consumo para refrigeração e aquecimento, foram considerados diferentes equipamentos, considerando para cada um: a quantidade, potências nominais, níveis de rotulagem de acordo com a regulamentação em vigor e as horas médias de uso. Da mesma forma, foi incluída no modelo a possibilidade de analisar diferentes medidas de eficiência energética. O modelo desenvolvido permite analisar a evolução temporal da procura energética desagregada e total dos equipamentos construtivos, permitindo a comparação, através de análise de sensibilidade, de diferentes cenários possíveis com base na adoção de várias medidas tecnológicas e de eficiência energética