Understanding Traffic Congestion Through Multiscale Analysis: Sensor Deployment, Car-Following and Universal Scaling

Traffic congestion is a persistent and multifaceted problem in urban transportation systems. Its characterization requires understanding not only driver behavior and infrastructure but also the limitations of the data and models used to represent traffic dynamics. This thesis approaches the problem through a multiscale framework, addressing how congestion is measured, modeled, and interpreted from both empirical and theoretical perspectives. Macroscopic representations of traffic states, such as the macroscopic fundamental diagram (MFD), are widely used to capture the relationship between average flow and density across urban networks. While loop detector data is the most commonly used source for estimating the MFD, its accuracy can be affected by how detectors are positioned along the network. Analytical derivations, empirical analyses, and simulation experiments show that the spatial distribution of detectors introduces systematic bias in the observed MFD. These findings point to structural limitations in how traffic states are aggregated and motivate practical guidance on sensor deployment and interpretation of aggregated traffic data. At the microscopic level, accurate representation of car-following behavior is essential for reproducing traffic phenomena such as capacity drop and oscillations. The existing stochastic car-following models provide a flexible framework for capturing traffic instabilities such as stop-and-go waves and capacity drop, but its calibration is challenging due to its stochastic formulation and high-dimensional parameter space. This study evaluates numerical and heuristic optimization methods under a maximum likelihood estimation framework to improve parameter robustness. Results show that while traditional methods are sensitive to initial conditions and prone to local optima, global search strategies such as genetic algorithms enhance reliability in the presence of noisy and limited data. Despite improvements in measurement and modeling, traffic congestion often emerges in unexpected ways, especially in dense networks operating near capacity. To better understand this concepts from statistical physics are applied to investigate whether congestion exhibits underlying critical behavior. High-resolution trajectory data are used to reinterpret traffic density as a growing surface and congestion as a percolating fractal structure, where stop-and-go waves form percolation clusters. This formulation enables the estimation of dynamic and roughness exponents. The results show strong empirical evidence that traffic congestion exhibits criticality and follows scaling behavior consistent with the Kardar-Parisi-Zhang universality class. Recognizing this criticality opens new possibilities for phase-aware traffic control strategies that incorporate fluctuation statistics to detect and mitigate near-critical traffic states. By integrating measurement, modeling, and theory, this thesis provides a comprehensive view of traffic congestion, from how it is empirically observed, to how individual driving behaviors can be inferred and calibrated under uncertainty, and to how its large-scale dynamics can be understood through universal scaling relationships. The results support more reliable data interpretation, robust calibration methods for stochastic models, and a new theoretical framing of traffic as a critical system with universal features.

Date

2025-07-29

Resource Type

Text

Resource Subtype

Dissertation

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