Estimating Traffic Stream Density Using Connected Vehicle Data

Abstract

The macroscopic traffic stream density is crucial in advanced traffic management systems. However, measuring the traffic density in the field is difficult since it is categorized as a spatial measurement. In this report, several estimation approaches are developed to estimate the traffic stream density on signalized links using connected vehicle (CV) data. First, the report introduces a novel variable estimation interval that allows for higher estimation precision, as the updating time interval always contains a fixed number of CVs. After that, the report develops a linear Kalman filter (KF), a linear adaptive KF (AKF), and a nonlinear particle filter (PF) to estimate the traffic stream density using CV data only. The proposed approaches are evaluated using empirical and simulated data, the former of which were collected along a signalized link in downtown Blacksburg, Virginia. Results indicate that density estimates produced by the linear KF approach are the most accurate. A sensitivity of the estimation approaches to various factors, including the level of market penetration (LMP) rate of CVs, the initial conditions, the number of particles in the PF approach, traffic demand levels, traffic signal control methods, and vehicle length, is presented. Providing accurate LMP estimates should improve the estimation accuracy of the vehicle counts. Therefore, in this research, a machine-learning model is developed to provide real-time estimates of the LMP values. Then, the developed filtering model is combined with the developed machine learning model (AKFNN) to improve the vehicle count estimation accuracy. Results show that the accuracy of the density estimate increases as the LMP increases. The KF is the least sensitive to the initial traffic density estimate, while the PF is the most sensitive to the initial traffic density estimate. The results also demonstrate that the proposed estimation approaches work better at higher demand levels given that more CVs exist for the same LMP scenario. For traffic signal control methods, the results demonstrate a higher estimation accuracy for fixed traffic signal timings at low traffic demand levels, while the estimation accuracy is better when the adaptive phase split optimizer is activated for high traffic demand levels. The report also investigates the sensitivity of the KF estimation approach to vehicle length, demonstrating that the presence of longer vehicles (e.g., trucks) in the traffic link reduces the estimation accuracy. In conclusion, the use of the linear KF approach is highly recommended in the application of traffic density estimation due to its simplicity and applicability in the field.

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Impacts and Outcomes

Traffic stream density is critical information for traffic management systems. This research develops a machine-learning model to provide real-time estimates of the level of market penetration and improve the accuracy of vehicle counts.

Universities and Sponsoring Organizations Involved

Virginia Tech

U.S. Department of Transportation Office of the Secretary-Research

Principal Investigators

Mohammad A. Aljamal

Hossam M. Abdelghaffar

Hesham A. Rakha

Funding Sources and Amounts

USDOT: $100,000; match $55,000

Completion Date

April 2021

Keywords

Real-time estimation; probe vehicle; traffic density; neural network; connected vehicles; level of market penetration rate