Flow-based Adaptive Split Signal Control

Over the last 35 years many adaptive traffic signal control systems have been developed presenting alternative strategies to improve traffic signal operations. However, less than 1% of all traffic signals in the United States are controlled by adaptive systems today. The extensive infrastructure necessary including reliable communication and complex calibration leads to a time consuming and costly process. In addition, the most recent National Traffic Signal Report Card indicated an overall grade of D for the nation’s traffic signal control and operations. Recent economic adversity adds to the already difficult task of proactively managing aged signal timing plans. Therefore, in an attempt to escape the status quo, a flow based adaptive split signal control model is presented, having the principal objective of updating the split table based solely on real-time traffic conditions and without disrupting coordination. Considering the available typical traffic signal control infrastructure in cities today, a non centralized system is proposed, directed to the improvement of National Electrical Manufacturers Association (NEMA) based systems that are compliant with the National Transportation Communications for Intelligent Transportation System Protocol (NTCIP) standards. The approach encompasses the User Datagram Protocol (UDP) for system communication allowing an external agent to gather flow information directly from a traffic signal controller detector status and use it to better allocation of phase splits. The flow based adaptive split signal control was not able to consistently yield significant lower average vehicle delay than a full actuated signal controller when evaluated on an intersection operating a coordinated timing plan. However, the research proposes the ability of an external agent to seamless control a traffic signal controller using real-time data, suggesting the encouraging results of this research can be improved upon

Computational intelligence-based traffic signal timing optimization

 Traffic congestion has explicit effects on productivity and efficiency, as well as side effects on environmental sustainability and health. Controlling traffic flows at intersections is recognized as a beneficial technique, to decrease daily travel times. This thesis applies computational intelligence to optimize traffic signals\u27 timing and reduce urban traffic

Applying Machine Learning Techniques to Improve Safety and Mobility of Urban Transportation Systems Using Infrastructure- and Vehicle-Based Sensors

The importance of sensing technologies in the field of transportation is ever increasing. Rapid improvements of cloud computing, Internet of Vehicles (IoV), and intelligent transport system (ITS) enables fast acquisition of sensor data with immediate processing. Machine learning algorithms provide a way to classify or predict outcomes in a selective and timely fashion. High accuracy and increased volatility are the main features of various learning algorithms. In this dissertation, we aim to use infrastructure- and vehicle-based sensors to improve safety and mobility of urban transportation systems. Smartphone sensors were used in the first study to estimate vehicle trajectory using lane change classification. It addresses the research gap in trajectory estimation since all previous studies focused on estimating trajectories at roadway segments only. Being a mobile application-based system, it can readily be used as on-board unit emulators in vehicles that have little or no connectivity. Secondly, smartphone sensors were also used to identify several transportation modes. While this has been studied extensively in the last decade, our method integrates a data augmentation method to overcome the class imbalance problem. Results show that using a balanced dataset improves the classification accuracy of transportation modes. Thirdly, infrastructure-based sensors like the loop detectors and video detectors were used to predict traffic signal states. This system can aid in resolving the complex signal retiming steps that is conventionally used to improve the performance of an intersection. The methodology was transferred to a different intersection where excellent results were achieved. Fourthly, magnetic vehicle detection system (MVDS) was used to generate traffic patterns in crash and non-crash events. Variational Autoencoder was used for the first time in this study as a data generation tool. The results related to sensitivity and specificity were improved by up to 8% as compared to other state-of-the-art data augmentation methods

Sistema de control de tráfico para la coexistencia entre vehículos autónomos y manuales mediante comunicaciones inalámbricas

Premio Extraordinario de Doctorado 2012Los avances en el campo de los sistemas inteligentes de transporte (ITS, del inglés Intelligent Transportation Systems) en los últimos años han propiciado la aparición de sistemas que ayudan de manera significativa a los conductores facilitando su labor, relegándoles de tareas tediosas. No es demasiado utópico pensar en un futuro en vehículos completamente automatizados circulando por las carreteras. Sin embargo, se precisa de un sistema de transición desde los vehículos que actualmente circulan por las carreteras hasta los vehículos completamente automatizados y, por ende, la coexistencia entre ellos. En el presente trabajo de tesis doctoral se presenta el diseño, desarrollo e implementación de un sistema global para el control del tráfico con vehículos guiados por conductores humanos o automáticos basado en comunicaciones inalámbricas con un doble objetivo: en primer lugar, disminuir de manera significativa la congestión actual del tráfico, fundamentalmente en entornos urbanos, y en segundo lugar, presentar un sistema seguro que permita pensar en una reducción del número de accidentes en las carreteras o, al menos, mitigar las consecuencias. Para lograr los objetivos propuestos se utilizarán diversas fuentes de información ya sean ubicadas en los vehículos -sistemas de navegación global por satélite (GNSS, del inglés Global Navigation Satellite System), sistemas inerciales (IMU, del inglés Inertial Measurement Unit) o cámaras- o en la infraestructura -unidades de control, sensores para detectar situaciones del tráfico. La arquitectura presentada busca la escalabilidad para permitir de manera sencilla la inclusión de nuevos dispositivos que permitan mejorar las prestaciones. Para validar la solución propuesta, se presentan diferentes experimentos llevados a cabo con vehículos comerciales, algunos de ellos modificados para permitir el control automático de los mismos en la pista de pruebas del IAI-CSIC. Dichos experimentos incluyen situaciones habituales del tráfico como pueden ser la conducción en atascos, la gestión de preferencias en intersecciones sin señalización, la evasión de un peatón que se cruce en la carretera o la llegada a una curva peligrosa no señalizada. El sistema propuesto soluciona estas situaciones reales de tráfico de forma eficiente y segura. Como principales aportaciones se destacan el sistema de control local del tráfico al que se le dota de inteligencia para optimizar las comunicaciones inalámbricas, las mejoras conseguidas sobre la arquitectura de control de los vehículos y la presentación de sistemas para el control de situaciones de tráfico en entornos desestructurados