This paper proposed the model and algorithms for traffic data monitoring and signal timing optimization based on continuum traffic model and wireless sensor networks. Given the scenario that sensor nodes are sparsely installed along the segment between signalized intersections, an analytical model is built based on continuum traffic equations, and an adaptive interpolation method is proposed to estimate traffic parameters with scattered sensor data. Based on the principle of traffic congestion formation, a congestion factor is introduced which can be used to evaluate the real-time status of traffic congestion along the segment, and to predict the subcritical state of traffic jams. The result is expected to support the signal timing optimization of traffic light control for the purpose to avoid traffic jams before its formation. We simulated the traffic monitoring based on Mobile Century dataset, and analyzed the performance of signal control on VISSIM platform when congestion factor is introduced into the phase optimization model. The simulation result shows that this method can improve the spatial-temporal resolution of traffic data monitoring, and it’s helpful to alleviate urban traffic congestion that remarkably decreases the average delays and maximum queue length.