Real-Time Estimation of Traffic Stream Density using Connected Vehicle Data

The macroscopic measure of traffic stream density is crucial in advanced traffic management systems. However, measuring the traffic stream density in the field is difficult since it is a spatial measurement. In this dissertation, several estimation approaches are developed to estimate the traffic stream density on signalized approaches using connected vehicle (CV) data. First, the dissertation 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 dissertation develops model-driven approaches, such as 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 model-driven approaches are evaluated using empirical and simulated data, the former of which were collected along a signalized approach in downtown Blacksburg, VA. 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) 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. 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 dissertation 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. Data-driven approaches are also developed to estimate the traffic stream density, such as an artificial neural network (ANN), a k-nearest neighbor (k-NN), and a random forest (RF). The data-driven approaches also utilize solely CV data. Results demonstrate that the ANN approach outperforms the k-NN and RF approaches. Lastly, the dissertation compares the performance of the model-driven and the data-driven approaches, showing that the ANN approach produces the most accurate estimates. However, taking into consideration the computational time needed to train the ANN approach, the large amount of data needed, and the uncertainty in the performance when new traffic behaviors are observed (e.g., incidents), 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.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Civil Engineering

Department dc:contributor.department

Civil and Environmental Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2020

Author dc:creator

• Aljamal, Mohammad Abdulraheem

Chair dc:contributor.committeechair

• Rakha, Hesham A.

Committee members dc:contributor.committeemember

• Heaslip, Kevin Patrick
• Hotle, Susan
• Du, Jianhe

• Real-Time Estimation
• Connected Vehicles
• Traffic Density
• Machine learning
• Kalman Filter
• Particle Filter
• Artificial Neural Network
• Random Forest
• k-Nearest Neighbors
• Level of Market Penetration Rate

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:27543

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/100149