The majority of the fuzzy controllers for traffic signal control in the literature operate using raw data from single point detectors installed on the intersection's various approaches. The input variables to the fuzzy logic controllers are usually simple estimates of traffic measures such as flow, speed or occupancy, estimated from such single detector readings. A room for improvement is sought herein by developing a fuzzy logic model (FLM) that could be integrated with smarter "processing" tools to estimate several traffic measures from multiple detectors on each approach. The estimates obtained from this processing tool are integrated as input knowledge into the FLM. The devised FLM structure is presented. A mesoscopic simulation model is devised to test the effectiveness of the FLM. The premise of the presented FLM is that it accounts for the network congestion downstream the individual traffic signals. This makes the FLM applicable for network rather than isolated type of signal control. Furthermore, the FLM accounts for transit pre-emption control as warranted. Several simulation-based experiments are presented including the basic FLM for isolated signal control, the FLM control enabling downstream congestion effect, and the one enabling transit pre-emption. The results are presented and discussed in details.