MODIFYING SIGNAL RETIMING PROCEDURES AND POLICIES: A CASE OF HIGH-FIDELITY MODELING WITH MEDIUM-RESOLUTION DATA

Signal retiming, or signal optimization process, has not changed much over the last few decades. Traditional procedures rely on low-resolution data and a low-fidelity modeling approach. Such developed signal timing plans always require a fine-tuning process for deployed signal plans in field, thus questioning the very benefits of signal optimization. New trends suggest the use of high-resolution data, which are not easily available. At the same time, many improvements could be made if the traditional signal retiming process was modified to include the use of medium-resolution data and high-fidelity modeling. This study covers such an approach, where a traditional retiming procedure is modified to utilize large medium-resolution data sets, high-fidelity simulation models, and powerful stochastic optimization to develop robust signal timing plans. The study covers a 28-intersection urban corridor in Southeastern Florida. Medium-resolution data are used to identify peak-hour, Day-Of-Year (DOY) representative volumes for major seasons. Both low-fidelity and high-fidelity models are developed and calibrated with high precision to match the field signal operations. Then, by using traditional and stochastic optimization tools, signal timing plans are developed and tested in microsimulation. The findings reveal shortcomings of the traditional approach. Signal timing plans developed from medium-resolution data and high-fidelity modeling approach reduce average delay by 5%-26%. Travel times on the corridor are usually reduced by up to 10.5%, and the final solution does not transfer delay on the other neighboring streets (illustrated through latent delay), which is also decreased by 10%-49% when compared with the traditional results. In general, the novel approach has shown a great potential. The next step should be field testing and validation.