The objective of this study is to investigate the properties of network-level traffic flow relationships in a freeway network with the use of commonly available loop detector data. The impact of the spatial and temporal distribution of congestion in a selected network on the shape and properties of the flow–density relation is investigated, with emphasis on the formation and characterization of hysteresis patterns. Accordingly, a path-dependent characterization of hysteresis patterns in freeway networks is introduced and illustrated conceptually as well as through empirical observations. Comparison of the spatial and temporal distribution of congestion throughout a selected subnetwork on different days suggests a relationship between the size of the hysteresis loop and the inhomogeneity of the traffic distribution. The maximum network average flow is not a constant value but varies across different days. In addition, for the same value of average network occupancy, the variation of occupancy is higher during the recovery period compared with the loading period. The observed large variation in network occupancy during recovery implies the formation of fragmented queues and traffic instability. A chaotic pattern is also to be expected in the networkwide flow–occupancy plane when the spatial distribution of link densities is inhomogeneous and the average network occupancy remains consistently high and roughly unchanged for successive time intervals. Overall, the study results provide a deeper understanding of the properties of networkwide relations on freeway networks.
The spread of traffic jams in urban networks has long been viewed as a complex spatio-temporal phenomenon that often requires computationally intensive microscopic models for analysis purposes. In this study, we present a framework to describe the dynamics of congestion propagation and dissipation of traffic in cities using a simple contagion process, inspired by those used to model infectious disease spread in a population. We introduce two novel macroscopic characteristics of network traffic, namely congestion propagation rate and congestion dissipation rate . We describe the dynamics of congestion propagation and dissipation using these new parameters, , and , embedded within a system of ordinary differential equations, analogous to the well-known Susceptible-Infected-Recovered (SIR) model. The proposed contagion-based dynamics are verified through an empirical multi-city analysis, and can be used to monitor, predict and control the fraction of congested links in the network over time.Unlike individual link traffic shockwaves in a two-dimensional time-space diagram, which are categorized as forward or backward moving, network traffic jams evolve in multi directions over space. Therefore, we propose that a network's propagation and recovery can be characterized by two average rates, namely the congestion propagation rate and a congestion recovery rate , which together reflect the number of congested links in the network over time. These two macroscopic characteristics are critical in modeling congestion propagation and dissipation as a simple contagion process [21].Despite the complex human behavior-driven nature of traffic, we demonstrate that urban network traffic congestion follows a surprisingly similar spreading pattern as in other systems, including the spread of infectious disease in a population or diffusion of ideas in a social network, and can be described using a similar parsimonious theoretical network framework. Specifically, we model the spread of congestion in urban networks by adapting a classical epidemic model to include a propagation and recovery mechanism dependent on time-varying travel demand and consistent with fundamentals of network traffic flow theory. We illustrate the model to be a robust and predictive analytical model, and validate the framework using empirical and simulation-based numerical experiments.
The objective of this study was to characterize hysteresis and capacity drop phenomena in freeway networks with the use of commonly available loop detector data from three networks: Chicago, Illinois; Portland, Oregon; and Irvine, California. For exploration of the effects of variations in network topology and size on the network fundamental diagram, a comparison was made by using the observed flow–occupancy diagrams of the selected freeway networks. The results provide further confirmation that findings from the literature for a limited number of networks are also valid for freeway networks not previously studied. Freeway networks have been found more likely to exhibit an inconsistent hysteretic pattern in shape and size that depends on the spatial distribution of congestion over the network. On the basis of empirical observations, hysteresis loops were characterized by their shape and size. Two shapes of hysteresis loops, H1 and H2, were identified and characterized. The size of each hysteresis loop was believed to be characterized by its width, height, and the area covered by the hysteresis loop. The authors postulated that the capacity drop phenomenon existed in freeway networks in a manner similar to that in individual freeway sections. Two types of capacity drop were identified. Type 1 was associated with the inability of the freeway network to sustain its throughput at its peak value for a relatively long time, and therefore, capacity dropped while demand was still high and the network was loading. Type 2 was associated with the instability of network traffic when the network underwent reloading (e.g., afternoon peak period) after an incomplete recovery from the initial loading (e.g., morning peak period). In some cases, this reloading resulted in a lower capacity in the afternoon than in the morning. Empirical results showed that the observed phenomena were reproducible on different days and for different networks.
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