Modeling and Control of Macroscopic Freeway Traffic Systems with Integrated Service Stations

Macroscopic traffic modeling and control are fundamental to the effective operation of modern freeway networks, with direct implications for congestion mitigation, travel time reliability, and energy efficiency. Traditionally, Service Stations (STs) have been treated as passive roadside elements. However, their increasingly dynamic interaction with traffic, due to changing driver behavior and growing network complexity, demands a shift in perspective. This thesis proposes a comprehensive, infrastructure-aware modeling and control framework that explicitly incorporates the dynamics of STs into freeway traffic management. Chapter 2 introduces METANET with service station (METANET-s), a novel second-order macroscopic traffic model that extends the classical METANET model to account for ST dynamics via a dedicated Service Area Facility modeled by a Store-and-forward (saf) link. The model enforces physical capacity constraints at STs and simulates critical behaviors such as queue formation, dwell times, and re-entry effects. Chapter 3 presents the Infrastructure-Dependent ramp-metering Control (IDC) strategy designed for METANET-s. This two-tier control scheme combines ALINEA ramp-metering at the ST exit with route guidance mechanisms that manage vehicle entry into the ST during peak congestion. The IDC scheme enables the active use of STs as traffic regulators, improving flow distribution and reducing congestion-induced delays along freeway segments. Building on this foundation, Chapter 4 introduces the Service Station-Enhanced Mainstream Traffic Flow Control (ST-MTFC) architecture, which integrates Variable Speed Limit (VSL) and IDC scheme including ramp-metering, and route guidance within a unified feedback control framework. By treating STs as dynamic storage actuators, ST-MTFC enhances classical Mainstream Traffic Feedback Control (MTFC) strategies, improves bottleneck throughput, protects ramp accessibility, and mitigates upstream queuing. The effectiveness of this integrated approach is validated through microsimulation in AIMSUN Next using a real-world freeway segment. Chapter 5 proposes a refined segment-aware speed-density relationship, Road Segment Proximity to Service Stations (RSP2S), that incorporates ST-related perturbations into the speed estimation function. The formulation embeds anticipatory terms for upstream slowdown and correction terms for downstream merging effects, making it responsive to local dynamics induced by ST operations. The RSP2S model is calibrated and validated against AIMSUN data, showing improved behavioral fidelity and empirical accuracy over classical formulations. Overall, this thesis advances a new generation of infrastructure-aware traffic flow models and control strategies by repositioning service stations as active elements within macroscopic frameworks. The proposed methods offer increased flexibility, improved congestion management, and a deeper integration of roadside infrastructure into traffic control design.