Adaptive traffic signals have the potential to alleviate congestion in heterogeneous and lane less traffic (mixed traffic) when the scope of road infrastructure enhancement is limited. Considering the dearth of available studies, the specific problem considered in this research was the development of a traffic signal adaptive to variations in mixed traffic conditions in order to reduce congestion. A model that characterizes traffic density under heterogeneous and lane less traffic was formulated with area occupancy as measurement variable. Based on this, adaptive Kalman filter density estimation schemes were developed, whose corroboration was performed using data generated from the traffic simulation software VISSIM. A model-based control scheme to maintain optimal density for a typical mid-block section was developed next. Since the implementation of a basic model-based state feedback control algorithm is inadequate for a traffic network with multiple intersections and constraints, a model predictive control (MPC) scheme was designed. The control scheme integrated with the estimator was implemented in a VISSIM-MATLAB simulation environment considering practical limitations. Performance evaluation showed mean absolute percentage error (MAPE) of less than 6 % in tracking the desired traffic density indicating good performance of the developed MPC scheme.