AbstractReliable, real-time prediction of delay and density is challenging as direct measurement of these variables is difficult. Though studies yielding reasonably accurate predictions of delay and density are reported in the literature, a comprehensive methodology to simultaneously predict both delay and density is lacking. Hence, a recursive technique that uses minimal real-time data for dynamic simultaneous prediction of midblock density and intersection delay is proposed. This study uses conservation equation-based recursive prediction of the number of vehicles inside the midblock section (density), which in turn is used to predict delay using shockwave theory. The Kalman Filter is a one-step-ahead density prediction method that can yield reliable density predictions even under the presence of errors in detector data. The one-step-ahead delay predictions obtained had a Mean Absolute Percentile Error (MAPE) of 10.4%, whereas the one-step-ahead density predictions obtained had a MAPE of 9.96%. Due to its robustness, this method can be used to arrive at one-step-ahead predictions of parameters like delay and queue length for any traffic scenario for which shockwave diagrams can be produced.