Doctor of Philosophy (PhD)
Civil and Environmental Engineering
The emerging connected vehicle (CV) technology plays a promising role in providing more operable and safer transportation environments. Yet, many questions remain unanswered as to how various user and system characteristics of CV-enabled networks can shape the successful implementation of the technology to maximize the return on investment. This research attempts to capture the effect of multiple factors such as traffic density, market penetration, and transmission range on the communication stability and overall network performance by developing a new CONnectivity ROBustness (CONROB) model. The model was tested with data collected from microscopic simulation of a 195 sq-mile traffic network and showed a potential to capture the effect of such factors on the communication stability in CV environments. The information exchanged among CVs can also be used to estimate traffic conditions in real time by invoking the probe vehicle feature of CV technology. Since factors affecting the connectivity robustness also have an impact on the performance of traffic condition estimation models, a direct relationship between connectivity robustness and traffic condition estimation performance was established. Simulation results show that the CONROB model can be used as a tool to predict the accuracy of the estimated traffic conditions (e.g. travel times), as well as the reliability of such estimates, given specific system characteristics. The optimal deployment of road-side units (RSUs) is another important factor that affects the communication stability and the traffic conditions estimates and reliability. Thus, an optimization approach was developed to identify the optimal RSUs locations with the objective function of maximizing the connectivity robustness. Simulation results for the developed approach show that CONROB model can help identify the optimal RSUs locations. This shows the importance of CONROB model as a planning tool for CV environments. For the individual user performance characteristics, a preliminary driving simulator test bed for CV technology was developed and tested on thirty licensed drivers. Forward collision warning messages were delivered to drivers when predefined time-to-collision values take place. The findings show improved reaction times of drivers when receiving the warning messages which lend credence to the safety benefits of the CV technology.
Document Availability at the Time of Submission
Secure the entire work for patent and/or proprietary purposes for a period of one year. Student has submitted appropriate documentation which states: During this period the copyright owner also agrees not to exercise her/his ownership rights, including public use in works, without prior authorization from LSU. At the end of the one year period, either we or LSU may request an automatic extension for one additional year. At the end of the one year secure period (or its extension, if such is requested), the work will be released for access worldwide.
Osman, Osama Abdulmonaem Thabet, "Connected Vehicle Technology: User and System Performance Characteristics" (2015). LSU Doctoral Dissertations. 1064.