Development and evaluation of cooperative intersection management algorithm under connected vehicles environment

Recent technological advancements in the automotive and transportation industry established a firm foundation for development and implementation of various automated and connected vehicle (C/AV) solutions around the globe. Wireless communication technologies such as the dedicated short-range communication (DSRC) protocol are enabling instantaneous information exchange between vehicles and infrastructure. Such information exchange produces tremendous benefits with the possibility to automate conventional traffic streams and enhance existing signal control strategies. While many promising studies in the area of signal control under connected vehicle (CV) environment have been introduced, they mainly offer solutions designed to operate a single isolated intersection or they require high technology penetration rates to operate in a safe and efficient manner. Applications designed to operate on a signalized corridor with imperfect market penetration rates of connected vehicle technology represent a bridge between conventional traffic control paradigm and fully automated corridors of the future. Assuming utilization of the connected vehicle environment and vehicle to infrastructure (V2I) technology, all vehicular and signal-related parameters are known and can be shared with the control agent to control automated vehicles while improving the mobility of the signalized corridor. This dissertation research introduces an intersection management strategy for a corridor with automated vehicles utilizing vehicular trajectory-driven optimization method. The Trajectory-driven Optimization for Automated Driving (TOAD) provides an optimal trajectory for automated vehicles while maintaining safe and uninterrupted movement of general traffic, consisting of regular unequipped vehicles. Signal status parameters such as cycle length and splits are continuously captured. At the same time, vehicles share their position information with the control agent. Both inputs are then used by the control algorithm to provide optimal trajectories for automated vehicles, resulting in the reduction of vehicle delay along the signalized corridor with fixed-time signal control. To determine the most efficient trajectory for automated vehicles, an evolutionary-based optimization is utilized. Influence of the prevailing traffic conditions is incorporated into a control algorithm using conventional data collection methods such as loop detectors, Bluetooth or Wi-Fi sensors to collect vehicle counts, travel time on corridor segments, and spot speed. Moreover, a short-term, artificial intelligence prediction model is developed to achieve reasonable deployment of data collection devices and provide accurate vehicle delay predictions producing realistic and highly-efficient longitudinal vehicle trajectories. The concept evaluation through microsimulation reveals significant mobility improvements compared to contemporary corridor management approach. The results for selected test-bed locations on signalized arterials in New Jersey reveals up to 19.5 % reduction in overall corridor travel time depending on different market penetration and lane configuration scenario. It is also discovered that operational scenarios with a possibility of utilizing reserved lanes for movement of automated vehicles further increases the effectiveness of the proposed algorithm. In addition, the proposed control algorithm is feasible under imperfect C/AV market penetrations showing mobility improvements even with low market penetration rates

Assessment of a signalized cross intersection optimized by GPS data through V2I connection

[ES] El objetivo principal de este trabajo es analizar la influencia de los errores de datos de posicionamiento de vehículos conectados en la transmisión de la información a los controladores de una intersección en cruz semaforizada. Teniendo en cuenta que la llegada del 5G permite la transmisión de datos masivos de una manera eficiente, esta investigación se centra en la comparación de la precisión de los datos de geolocalización de dispositivos que podrían hallarse en vehículos conectados, como son: (i) dispositivos GPS de alta precisión (errores de posicionamiento despreciables); (ii) dispositivos GPS convencionales (errores de posicionamiento mayores que un metro); y (iii) dispositivos GPS incorporados en teléfonos móviles (errores de posicionamiento total mayores). Para ello, se han llevado a cabo tres simulaciones de una hora en el software de microsimulación VISSIM para los siguientes escenarios de tráfico propuestos: (1) bajo nivel de demanda; (2) aproximación a la congestión de la intersección; y (3) intersección sobresaturada. Para evaluar y comparar la influencia de la precisión de los dispositivos GPS bajo los diferentes escenarios se han empleado dos controladores, que son la longitud de cola de vehículos en la intersección y el número de vehículos que acceden a la intersección por cada ramal.[EN] The main objective of this work is to analyze the influence of the positioning data errors of connected vehicles in the transmission of information to the controllers of a signalized cross intersection. Taking into account that the arrival of 5G allows the transmission of massive data in an efficient way, this research focuses on comparing the accuracy of the geolocation data of devices that could be found in connected vehicles such as (i) High Accurate GPS devices (negligible positioning errors); (ii) Standard GPS devices (positioning errors greater than one meter); and GPS incorporated in mobile phones (major total positioning errors). For this, three one-hour simulations have been carried out in the VISSIM microsimulation software for the following proposed traffic scenarios: (1) low demand level; (2) approach to congestion of the intersection; and (3) over-saturated intersection. To evaluate and compare the influence of the accuracy of GPS devices in different scenarios, two controllers have been used, which are the queue length of vehicles at the intersection and the number of vehicles that access the intersection at each approach.Pino Verona, HD. (2020). Análisis de una intersección en cruz semaforizada optimizada a partir de datos de GPS mediante conexión V2I. http://hdl.handle.net/10251/142944TFG

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

Mobile Oriented Future Internet (MOFI)

This Special Issue consists of seven papers that discuss how to enhance mobility management and its associated performance in the mobile-oriented future Internet (MOFI) environment. The first two papers deal with the architectural design and experimentation of mobility management schemes, in which new schemes are proposed and real-world testbed experimentations are performed. The subsequent three papers focus on the use of software-defined networks (SDN) for effective service provisioning in the MOFI environment, together with real-world practices and testbed experimentations. The remaining two papers discuss the network engineering issues in newly emerging mobile networks, such as flying ad-hoc networks (FANET) and connected vehicular networks

Traffic Control Strategy Formulation and Optimization Enabled by Homogenous Connected and Autonomous Vehicle Systems.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017