Estimating Spatial Traffic States with Location-Based Data under Heterogeneous Conditions

Abstract

Traffic data can be classified into spatial data and location-based data depending on the frame of measurement involved. Location-based traffic data pertain to information at a certain point, and spatial traffic data pertain to a longer section of the roadway. This larger coverage makes spatial traffic data better indicators of the traffic condition and the level of service of the roadway section. Therefore, spatial traffic data are preferred in any congestion mitigation program. Because the greater spatial coverage makes measurement of spatial traffic data more difficult than that of location-based data, the spatial data are usually estimated from other easily measurable location-based parameters. This paper proposes a methodology to estimate two of the most important spatial traffic parameters—density and space mean speed (SMS)—from the location-based parameters—flow and time mean speed (TMS)—under Indian traffic conditions. The estimation scheme was based on the Kalman filter and incorporated a dynamic macroscopic traffic flow model formulated in the state–space representation. This framework also included an empirical model calibrated with field data and a parameter α, which was used to relate SMS to TMS. The parameter estimation was formulated as an optimization problem, and the estimation scheme was corroborated with a comparison of density estimates with field data. Because the collection of SMS from the field was difficult, travel time collected from probe vehicle data was used as a surrogate measure to corroborate the SMS estimates. The results were promising and agreed well with the traffic behavior observed in the field.