Abstract
Traffic state estimation is a crucial element in traffic management systems and in providing traffic information to road users. In this article, we evaluate traffic sensing data-based estimation error characteristics in macroscopic traffic state estimation. We consider two types of sensing data, that is, loop-detector data and probe speed data. These data are used to estimate the mean speed in a discrete space-time mesh. We assume that there are no errors in the sensing data. This allows us to study the errors resulting from the differences in characteristics between the sensing data and desired estimate together with the incomplete description of the relation between the two. The aim of the study is to evaluate the dependency of this estimation error on the traffic conditions and sensing data characteristics. For this purpose, we use microscopic traffic simulation, where we compare the estimates with the ground truth using Edie’s definitions. The study exposes a relation between the error distribution characteristics and traffic conditions. Furthermore, we find that it is important to account for the correlation between individual probe data-based estimation errors. Knowledge related to these estimation errors contributes to making better use of the available sensing data in traffic state estimation.
Highlights
Traffic state estimation is an important element in traffic management applications and traffic information services
Throughout the remainder of this paper, we will refer to the free flow and congested space-time areas as homogeneous traffic conditions
We focus on two combinations of a desired estimation output: type of traffic sensing data and estimation approach
Summary
Traffic state estimation is an important element in traffic management applications and traffic information services. The traffic state can be described on different levels [1]. The microscopic traffic state describes the traffic on an individual vehicle level, using the time and space headways and individual vehicle speed. The macroscopic traffic state describes the traffic flow conditions using the mean speed, density, and flow. Different types of information can be used. In addition to sensing data, information related to the traffic dynamics is captured in the form of traffic flow models, for example, the LWR model [6, 7]. Traffic flow models are based on physical laws and historical data. These information types, sensing data and traffic flow models, allow us to estimate the traffic state. The focus is put on these estimation errors
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