Highway Traffic State Estimation With Mixed Connected and Conventional Vehicles

Abstract

Abstract-A macroscopic model-based approach for estimation of the traffic state, specifically of the (total) density and flow of vehicles, is developed for the case of "mixed" traffic, i.e., traffic comprising both ordinary and connected vehicles. The development relies on the following realistic assumptions: (i) The density and flow of connected vehicles are known at the (local or central) traffic monitoring and control unit on the basis of their regularly reported positions; and (ii) the average speed of conventional vehicles is roughly equal to the average speed of connected vehicles. Thus, complete traffic state estimation (for arbitrarily selected segments in the network) may be achieved by merely estimating the percentage of connected vehicles with respect to the total number of vehicles. A model is derived, which describes the dynamics of the percentage of connected vehicles, utilizing only well-known conservation law equations that describe the dynamics of the density of connected vehicles and of the total density of all vehicles. Based on this model, which is a linear time-varying system, an estimation algorithm for the percentage of connected vehicles is developed employing a Kalman filter. The estimation methodology is validated through simulations using a secondorder macroscopic traffic flow model as ground truth for the traffic state. The approach calls for a minimum of spot sensorbased total flow measurements according to a variety of possible location configurations.

Traffic State Estimation Using Connected Vehicles and Stationary Detectors

“…This requires that the connected vehicles have the same distribution of speeds as regular vehicles, similar to what has been done in the works of Astarita et al [14] and Bekiaris-Liberis et al [15]. Otherwise, the speed estimate would be biased towards the average speed of the connected vehicles.…”

“…Lately, when more data sources have become available, connected vehicles have been used as input to the filtering approaches in order to update the modeled traffic state. See, for example, Herrera and Bayen [10], Work et al [11], Yuan et al [12], Seo et al [13], Astarita et al [14], and Bekiaris-Liberis et al [15]. Other traffic state estimation methods making use of connected vehicle data without an underlying traffic model are presented by Herring et al [16], Herrera et al [17], Van Lint and Hoogendoorn [18], Qiu et al [19], Ma et al [20], Bhaskar et al [21], Zhang et al [22], Seo et al [23], and Montero et al [24].…”

“…For the combined methods, a weighted estimate based on both traditional detector measurements and connected vehicle measurements of, for example, speed, travel time, and/or location is used. Examples are the methods presented by Astarita et al [14], Qiu et al [19], Ma et al [20], Bhaskar et al [21], Zhang et al [22], and Bekiaris-Liberis et al [15]. The method by Qiu et al [19], which was later extended by Ma et al [20], detects the number of vehicles located within a segment.…”

“…Zhang et al [22] use probe vehicle data and detector stations in order to estimate the space mean speed, and not density, on a road stretch. Astarita et al [14] and Bekiaris-Liberis et al [15] develop macroscopic cell transmission type models for the dynamics of the percentage of connected vehicles along the considered road. It is assumed that the connected vehicles move with the same average speed as the nonconnected vehicles, and hence no modeling of the speed dynamics is needed.…”

“…In the work of Astarita et al [14], the density is estimated by the percentage of connected vehicles based on counts of connected vehicles moving from one segment to another in the network and inflows measured at the ramps and at the boundaries of the network. Similarly, Bekiaris-Liberis et al [15] use the penetration rate of connected vehicles, together with measurements of speed from the connected vehicles, the boundary flow, and the ramp flows measured through stationary detectors, to estimate the density. Also the combined methods do often require densely placed detectors to get good estimates.…”