Arterial traffic signal systems, predominantly in the United States, deploy multiple signal timing plans to account for daily variability of traffic demand. Those types of traffic flow deviations should be anticipated when timing plans are designed and, therefore, serviced satisfactorily. When traffic flow patterns are no longer predictable, a predetermined time-of-day (TOD) plan may no longer be the optimal one. This research aimed to examine signal timing optimality by applying a method similar to the selection of a traffic responsive plan to recognize automatically the best timing plan suited to current traffic conditions. The proposed method attempted to determine whether the optimality of signal timing settings could have been effectively estimated when systematic detector counts of the major approach were available. The study used 4 months of data from field microwave detectors coupled with data of turning-movement counts obtained over several days. The findings show that TOD signal timing plans mainly depended on adequate data collection that best describes a specific set of traffic conditions. Thus, the designed plan was as optimal as the related traffic information was reliable, whereas a problem arose in the case of limited-availability and low-quality data. New technologies are capable of collecting and storing massive amounts of data. Even if the granularity of collected data is low, the data can be used to improve traffic performance (i.e., reduce corridor delay). This realization could be of particular importance to traffic agencies that have installed, or plan to install, new field devices.