Real time estimation of traffic state is essential for an efficient traffic management system. In the recent years, probe vehicles have been increasingly used for traffic monitoring. This study proposes a methodology to estimate traffic conditions on urban arterials using Dedicated short-range communications (DSRC)-based mobile sensors. Probe vehicles equipped with WiFi sensors and GPS are used for state estimation. Corridor-level estimation of traffic state variables is carried out using the virtual vehicle trajectories obtained from spatiotemporal data recorded by integrated WiFi sensors. Modified Edie’s generalized definitions for flow, density, and speed are used to estimate the traffic state variables. To evaluate the method’s performance, a field experiment is done on a bidirectional highway under actual traffic conditions. The experiment results indicate that the estimated speed and travel time values using the proposed method match closely with the empirical data. Pearson Correlation coefficient for the speed estimated using Method 1 and Method 2 was found to be 0.8752 and 0.9269 respectively.