Identification and Analysis of Queue Spillovers in City Street Networks

Geroliminis, Nikolas; Skabardonis, Alexander · 2011 · OpenAlex-citations

DOI: 10.1109/tits.2011.2141991

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Summary

This paper addresses the identification and analysis of queue spillovers in city street networks with signalized intersections, a phenomenon where growing queues at downstream signals block vehicle departures from upstream signals, leading to restricted mobility and intensified congestion. While substantial research exists on estimating performance measures and managing oversaturated arterials, methods for consistently identifying spillovers using conventional surveillance data remain limited. The authors propose a robust methodology to detect these spillovers using data from loop detectors, aiming to provide real-time information for traffic management and to investigate the macroscopic impact of spillovers on urban network performance. The proposed methodology utilizes vehicle count and occupancy data from single inductive loop detectors, typically located upstream of intersection stop lines. Based on kinematic wave theory and a piecewise linear flow-density relationship, the authors derive a critical "blocking occupancy" threshold. When measured occupancy exceeds this threshold, it indicates that queues have extended past the detector and are blocking upstream departures, causing discharge rates to fall below saturation flow during green phases. The method accounts for variations in vehicle lengths by modeling the distribution of small and long vehicles, demonstrating that assuming a constant effective vehicle length introduces negligible error (less than 2%) in most cases. The approach was verified using real-world data from a 1.42-mile stretch of Lincoln Avenue in Los Angeles, comparing detector-derived spillover identifications with floating-car GPS speed data. Additionally, the network-level effects of spillovers were analyzed using microsimulation data from a 2.5-square-mile portion of downtown San Francisco. Results from the Los Angeles field study show that the methodology consistently identifies spillovers, which correlate with significantly reduced vehicle speeds (below 9 mi/h) and repetitive stopping during green phases. The method effectively detects critical congested intersections, whereas simple occupancy contour plots contain significant noise and fail to reliably identify blocking traffic. In the San Francisco simulation, the authors establish a clear functional relationship between the total number of vehicles involved in spillovers and network output. They find that as spillovers increase, network output decreases, confirming that spillovers are not merely local disturbances but spread to decrease overall system performance. The total number of vehicles at spillovers is identified as a key variable for characterizing urban congestion, revealing distinct macroscopic patterns rather than qualitative descriptions. The significance of this work lies in providing a practical, data-driven tool for identifying active spillovers in large urban areas using existing infrastructure. This capability supports proactive traffic management strategies, such as predicting congestion locations and restricting access to highly congested areas, rather than reacting to congestion after it occurs. Furthermore, the study contributes to the field of macroscopic traffic modeling by quantifying the detrimental impact of spillovers on network-wide mobility, offering a new metric for assessing and managing urban traffic congestion.

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• traffic density