Estimation of Freeway Traffic Density with Loop Detector and Probe Vehicle Data

Abstract

Density, speed, and flow are the three critical parameters for traffic analysis. High-performance traffic management and control require the estimation–prediction of space mean speed and density for large spatial and temporal coverage. Speed, including spot mean speed and space mean speed, and flow estimation are relatively easy to measure and estimate, while less attention has been devoted to measuring and estimating density. Because IntelliDrive (previously known as vehicle infrastructure integration) is a promising technology for providing a new type of real-time traffic data, and loop detector systems have already been widely deployed, this paper proposes a method to estimate freeway traffic density with both loop detector data and IntelliDrive-based probe vehicle data. The proposed method has been validated with Berkeley Highway Laboratory loop detector data combined with field-collected probe vehicle data in the first validation study and next-generation simulation video trajectory data in the second validation test. The algorithm can be used offline and in real time.

Traffic State Estimation Using Connected Vehicles and Stationary Detectors

“…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.…”

“…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. The density estimate is calculated by counting the number of vehicles that have passed the detector upstream of the segment at the times when a connected vehicle enters and exits a segment.…”

“…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].…”

“…The density estimate is calculated by counting the number of vehicles that have passed the detector upstream of the segment at the times when a connected vehicle enters and exits a segment. According to Qiu et al [19], the accuracy of the density estimates is better than when only stationary detector data is used to estimate density. 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.…”

“…Also the combined methods do often require densely placed detectors to get good estimates. The methods are often based on retrospective measurements, such as travel time at an earlier point in time (see, e.g., Qiu et al [19] and Ma et al [20]). As a result, the density estimate might not reflect the current situation.…”