Estimation of Spatial Traffic Variables using Location Based Data under Heterogeneous Traffic Conditions

 

The traffic data can be classified as spatial data and location based data depending on the frame of measurement involved. Location based traffic data pertain to information at a point and spatial traffic data pertain to a longer section of the roadway. This makes spatial traffic data capable of quantifying the traffic condition and level of service (LOS) of the roadway section better than the location based data and hence are indispensable to any congestion mitigation program. However, the measurement of spatial traffic data is more difficult than location based data due to their greater spatial coverage. This study proposes a methodology to estimate three of the most important spatial traffic parameters, namely, density, space headway and space mean speed (SMS), from the location based parameters, namely, flow and time mean speed (TMS). The estimation scheme is built using a dynamic macroscopic traffic flow model formulated in the state space representation and are estimated using the Kalman filter. This framework also includes an empirical model built using field data using a parameter "α" which is used to estimate SMS from TMS. The calibration of "α" is formulated as a non-linear unconstrained optimization problem. The data required for the study include flow and TMS (location based parameters) for the implementation and density, space headway and SMS (spatial parameters) for the corroboration. The data were collected from Rajiv Gandhi Salai, Chennai, India, using point based (video graphic), area based (aerial photography) and stream based (probe vehicle) techniques. The corroboration of the proposed estimation scheme was carried out by comparing the estimates with field data and also by benchmarking the performance with other available standard methods. The results are promising and agree with the traffic behavior observed on the field.