Rapid growth in population along with urban-centric activities impose a massive demand on existing transportation systems, thus increasing traffic congestion and other mobility related challenges. To overcome such challenges, we need network-scale models to accurately predict real-time traffic demand and associated congestion. However, traditional network modeling approaches have shortcomings due to the complexity in traffic flow modeling, limited scope to incorporate real-time data available from emerging data sources and requiring excessive computation time to generate accurate estimation of traffic flows. Advancement in traffic sensing technologies with big data has created a new opportunity to overcome these challenges and implement deployable data-driven models to predict network-level traffic dynamics and congestion propagation in real time. However, existing data-driven approaches are limited in scope: they are developed for small-scale networks; they do not consider the fundamental concept of traffic flow propagation; and they are applied for short-term prediction ( < 1 hour). In this dissertation, we develop graph convolution based neural network architectures for network scale traffic modeling as a solution to overcome these limitations. First, we develop a Graph Convolutional Neural Network (GCNN) Model to solve the traffic assignment problem in a data-driven way; the validation results show that the model can learn the user equilibrium traffic flow well (mean error < 2%). Since the model can instantaneously determine the traffic flows of a large-scale network, this approach can overcome the challenges of deploying mathematical programming or simulation-based traffic assignment solutions for large-scale networks. Second, we scale this approach and develop a Graph Convolutional LSTM (GCN-LSTM) model for traffic movement volume prediction at intersection level. We rigorously tested the model over traffic movement volume data collected from Seminole County's automated signal performance measure (ATSPM) database which show that 90% of cases, absolute error of the predicted values is less than 20. Finally, we develop a Dynamic Graph Convolutional LSTM (DGCN-LSTM) model to predict evacuation traffic flow for interstate network of Florida. The implemented model can be applied to predict evacuation traffic over a longer forecasting horizon (6-hour) with higher accuracy (R^2score 0.95). Hence, it can assist transportation agencies to activate appropriate traffic management strategies to reduce delays for evacuating traffic.
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Doctor of Philosophy (Ph.D.)
College of Engineering and Computer Science
Civil, Environmental and Construction Engineering
Length of Campus-only Access
Doctoral Dissertation (Open Access)
Rahman, Rezaur, "Data Driven Methods for Large Scale Network Level Traffic Modeling" (2021). Electronic Theses and Dissertations, 2020-. 917.