Institutional Repository
Technical University of Crete
Technical University of Crete
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| URI: | http://purl.tuc.gr/dl/dias/7F4A8554-D7BE-45F4-A6E9-571990C74C12 | ||
| Year | 2015 | ||
| Type of Item | Conference Full Paper | ||
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| Bibliographic Citation | N. Bekiaris-Liberis, C. Roncoli and M. Papageorgiou, "Highway traffic state estimation with mixed connected and conventional vehicles using speed measurements," in 18th International IEEE Conference on Intelligent Transportation Systems, 2015, pp. 2806-2811. doi: 10.1109/ITSC.2015.451 https://doi.org/10.1109/ITSC.2015.451 | ||
| Appears in Collections |
We present a macroscopic model-based approach for estimation of the total density and flow of vehicles, for the case of “mixed” traffic, i.e., traffic comprising both ordinary and connected vehicles, utilizing only average speed measurements reported by connected vehicles and a minimum number (sufficient to guarantee observability) of spot sensor-based total flow measurements. The approach is based on the realistic assumption that the average speed of conventional vehicles is roughly equal to the average speed of connected vehicles, and consequently, it can be obtained at the (local or central) traffic monitoring and control unit from connected vehicles’ reports.Thus, complete traffic state estimation (for arbitrarily selected segments in the network) may be achieved by estimating the total density of vehicles. Recasting the dynamics of the total density of vehicles, which are described by the well-known conservation law equation, as a linear time-varying system, we employ a Kalman filter for the estimation of the total density. We demonstrate the fact that the developed approach allows a variety of different measurement configurations, by also considering the case in which additional mainstream total flow measurements are employed to replace a corresponding number of total flow measurements at on-ramps or off-ramps. We validate the performance of the developed estimation scheme through simulations using a well-known second-order traffic flow model as ground truth for the traffic state.
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