Comparing and Combining Existing and Emerging Data Collection and Modeling Strategies in Support of Signal Control Optimization and Management
Mohammed Hadi, Florida International University
Virginia Sisiopiku, University of Alabama at Birmingham
For decades, traffic signal management agencies have used signal timing optimization tools combined with fine-tuning of signal timing based on field observations in their updates of time-of-day signal timing plans. These traditional signal optimization methods and tools use very limited amount of data and depend on default values in the signal timing optimization/simulation tools to estimate network performance under different signal optimization strategies. In recent years, new data collection technologies are emerging including high resolution controller data, more advanced detection technologies such as video image detection that are based on vehicle tracking and possible integration with microwave detectors, automatic-vehicle based identification technologies, third party crowdsourcing data, connected vehicles, and connected automated vehicles data. The objective of the proposed study is to propose methods and algorithms to combine data collected from existing and emerging sources with enhanced models and optimization algorithms to optimize and manage signal operations. The results from applying the developed methods and algorithms will be compared with traditional signal timing and optimization methods currently used by transportation agencies.
Model – Developed a hybrid machine learning and fuzzy logic model for signal timing selection under non-recurrent conditions. The methods can be used by signal agencies to better select signal timing, particularly special signal timing plans that can be activated in real time during non-recurrent events such as incidents and weather events. The methods are based on data, tools, and optimization algorithms.