2015/04/26 by Nikolaos Bekiaris‐Liberis, Bekiaris-Liberis, Nikolaos, Claudio Roncoli +3 · 1 citation
Engineering · #Traffic Prediction and Management Techniques #Traffic control and management #Vehicle emissions and performance
paper · pdf · doi:10.48550/arxiv.1504.06879
A macroscopic model-based approach for estimation of the traffic state,\nspecifically of the (total) density and flow of vehicles, is developed for the\ncase of "mixed" traffic, i.e., traffic comprising both ordinary and connected\nvehicles. The development relies on the following realistic assumptions: (i)\nThe density and flow of connected vehicles are known at the (local or central)\ntraffic monitoring and control unit on the basis of their regularly reported\npositions, and (ii) the average speed of conventional vehicles is roughly equal\nto the average speed of connected vehicles. Thus, complete traffic state\nestimation (for arbitrarily selected segments in the network) may be achieved\nby merely estimating the percentage of connected vehicles with respect to the\ntotal number of vehicles. A model is derived, which describes the dynamics of\nthe percentage of connected vehicles, utilizing only well-known conservation\nlaw equations that describe the dynamics of the density of connected vehicles\nand of the total density of all vehicles. Based on this model, which is a\nlinear time-varying system, an estimation algorithm for the percentage of\nconnected vehicles is developed employing a Kalman filter. The estimation\nmethodology is validated through simulations using a second-order macroscopic\ntraffic flow model as ground truth for the traffic state. The approach calls\nfor a minimum of spot sensor-based total flow measurements according to a\nvariety of possible location configurations.\n