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.

A Control-Theoretic Approach for Scalable and Robust Traffic Density Estimation Using Convex Optimization

“…If a highway segment i is connected to both on-and off-ramp, then i ∈ E I ∩ E O with N IO = |E I ∩ E O | denotes the number of highway segments connected to both on-and off-ramps. Moreover, we require that the upstream flow on the first highway segment f in , upstream flow on each onrampf i for i ∈Ê, and downstream flow on each off-rampf i for i ∈Ě are all known, which in a real situation, can be obtained from conventional traffic detectors [22]. The exit ratio for all off-ramps are also assumed to be known and fixed.…”

“…In general, methods for traffic state estimation can be categorized into model-driven and data-driven. In model-driven traffic state estimation, statistical state estimators such as Particle Filter [19]- [21], Kalman Filter [22], [23], Extended Kalman Filter (EKF) [24]- [27], Unscented Kalman Filter (UKF) [28], [29], and Ensemble Kalman Filter (EnKF) [11], [20], [30], [31] are among of the most extensively used methods-see [32, Tables 1 and 2] for a list of state estimators used in the recent literature. To mention a few, traffic density estimation has been studied based on a switching-mode scheme of cell transmission model (CTM) [33].…”

Density and flow reconstruction in urban traffic networks using heterogeneous data sources

“…In more recent works [13], [14], authors proposed to combine both FCD and MLD for flow and density estimation. The study in [1] considers sources of connected vehicles and not connected ones for the density reconstruction. A different approach [4] formulated the problem for lagrangian and loop data integration into a model from variational calculus perspective.…”

Comparing traffic state estimators for mixed human and automated traffic flows

“…Compared to human operated tra c without connectivity, filtering based approaches for tra c containing connected or automated vehicles is still relatively unexplored. The most closely related approaches are works of Bekiaris-Liberis et al [40,41] and Roncoli et al [42], which design a tra c state estimator under two key assumptions on mixed VACS and human operated vehicle flows. First, a scalar tra c flow model is used in the tra c evolution equation in which the velocity field along the roadway is treated as a known timevarying parameter, and is assumed to be provided by the connected vehicles in the tra c stream.…”