Traffic bottleneck detection and classification on a transportation network graph

Abstract

Traffic congestion detection, classification and identification includes analysis of link-speed data representative of vehicular speed and capacity on one or more roadway segments to determine non-linear, multi-segment traffic bottlenecks in a transportation network graph. Link-speed data is processed to detect bottleneck conditions, classify bottlenecks and bottleneck-like traffic features according to their complexity, and identify sustained or recurring bottlenecks. Such a system and method of traffic congestion detection, classification and identification provides a framework for using this link-speed data to detect the head and queue of bottlenecks on a directed graph representing the transportation network, classify the resulting bottlenecks and bottleneck-like traffic features according to the shape of their queue, and identify and measure sustained or recurrent bottlenecks even when the location, or head, of the bottleneck varies slightly across multiple time periods or across multiple days.