Abstract Manager

There is a transformational change underway in incorporating big data like Automated Traffic Signal Performance Measures (ATSPMs) in the traffic signal optimization process.

In the traditional traffic signal optimization process, there is data collection, review of the existing traffic signal timings, modeling and simulation, optimizing signal timing parameters, implementation, and field calibration. 

This presentation will go over the limitations of the traditional signal optimization process, which includes a limited data set (usually 13-hours or peak-hour traffic counts only) and a significant amount of labor (up to 70% is attributed to data collection, modeling/simulation and observation trips) to evaluate the effectiveness of the existing traffic signal timings. A new process of signal optimization using data sources like ATSPMs will be discussed. We will focus on how the new process can create efficiencies, use larger data sets and, overall, create better traffic signal optimization plans at a lower or equal cost when compared to the cost of the traditional signal optimization process. Finally, this presentation will go into ways to meet expectations with all stakeholders by going through the G-COST (goals, context, objectives, strategies and tactics) methodology, which can be incorporated into the new process of signal optimization because of the robust data set and efficiencies created (reallocating budget in the signal optimization process). When applying the G-COST methodology in the traffic signal optimization process, the traffic community will get to know their traffic signal operations better, learn how to group these signals and analyze the data to meet expectations of the public. 

One or more case studies (depending on the status of the projects at the time of the conference) will be included in the presentation to show real world results and impact.