Performance monitoring systems have experienced remarkable development in the past few decades. In today's world, an important issue for almost every industry is to find a way to appropriately evaluate the performance of the provided service. Having a reliable performance monitoring system is necessary, and researchers have developed assessment models and tools to deal with this concern. There are many approaches to the development of performance measurement and observation systems. The internet-of-things (IoT) creates a broad range of opportunities to monitor the systems by using the information from connected people and devices. The IoT is providing many new sources of data that need to be managed. One of the key issues that arises in any data management system is confidentiality and privacy.Significant progress has been made in development and deployment of performance monitoring systems in the signalized traffic environment. The current monitoring and data collection system relies mostly on infrastructure-based sensors, e.g. loop detectors, video surveillance, cell phone data, vehicle signatures, or radar. High installation and maintenance costs and a high rate of failure are the two major drawbacks of the existing system. Emerging technologies, i.e. connected vehicles (CV), will provide a new, high fidelity approach to be used for better performance monitoring and traffic control.This dissertation investigates the real-time performance observation system in a multi-modal connected vehicle environment. A trajectory awareness component receive and processes the connected vehicle data using the Basic Safety Message (BSM). A geo-fence section makes sure the infrastructure system (for example, roadside unit (RSU)) receives the BSM from only the connected vehicles on the roadway and within the communication range. The processed data can be used as an input to a real-time performance observer component.Three major classes of performance metrics, including mobility, signal, and CV-system measures, are investigated. Multi-modal dashboards that utilize radar diagrams are introduced to visualize large data sets in an easy to understand way. A mechanism to maintain the anonymity of vehicle information to ensure privacy was also developed. The proposed algorithm uses partial vehicle trajectories to estimate travel time average and variability on a link basis. It is shown that the model is not very sensitive to the market penetration rate of connected vehicles. This is a desirable feature especially because of the fact that the market penetration rate of connected vehicles will not be very high in near future. The system architecture for connected vehicle based performance observation applications was developed to be applicable for both a simulation environment and a real world traffic system. Both hardware-in-the-loop (HIL) and software-in-the-loop (SIL) simulation environments are developed and calibrated to mimic the real world. Comprehensive testing and assessment of the proposed models and algorithms are conducted in simulation as well as field test networks. A web application is also developed as part of a central system component to generate reports and visualizations of the data collection experiments.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/621582 |
| Date | January 2016 |
| Creators | Khoshmagham, Shayan, Khoshmagham, Shayan |
| Contributors | Head, K. Larry, Head, K. Larry, Valerdi, Ricardo, Liu, Jian, Valacich, Joseph |
| Publisher | The University of Arizona. |
| Source Sets | University of Arizona |
| Language | en_US |
| Detected Language | English |
| Type | text, Electronic Dissertation |
| Rights | Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author. |
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