An Automated Testing Tool for Traffic Signal Controllers
The State of Idaho ITS strategic plan identified several traffic signal improvement projects as short term high-priority projects. Updating traffic controllers is a major component of these signal improvement projects. ITD District traffic engineers and technicians are faced with the challenge of testing these new controllers to ensure that they comply with the state requirements prior to deployment. Currently the test is being done manually using manual “suitcase testers.” These tests can be tedious and time consuming and come with many limitations such as the inability to document the results. The purpose of this research project is to develop and test an automated testing tool for traffic signal controller functionality. Automation can be used to replace or supplement manual testing. Benefits to traffic engineers include increased test quality, reduced testing time, repeatable and consistent test procedures, reduced testing costs, and improved testing quality and productivity. Work performed on this project will allow ITD traffic section to implement a unified testing procedure for all districts.
The traditional “suitcase tester” and emulator require the user to input commands manually. The information related to any specific test cannot be automatically recorded for future playback. The operator is required to manually record the commands and input the sequence of instructions each time the test is to be repeated. This process is vulnerable to human error in either recording the test or in repeating the sequence.
The automated testing tool will utilize the University of Idaho National Institute for Advanced Transportation Technology’s Controller Interface Device (CID). In a current ITD research project, all ITD six districts have acquired a CID that can be utilized as part of the testing tool. ITD District traffic engineers are familiar with the CID operation and capabilities, which will make it easier to integrate it within the automated testing procedure. Our plan is to generate computer models that are “trained” to recognize traffic controllers that meet performance expectation using fuzzy logic system identification techniques.