With the advances of new sensor technologies and the prospect of crowd-sourced big location data, it has become feasible to obtain vehicle travel times on a large temporal and spatial scale. This study presents a novel method to make use of this new rich travel time data source to re-optimize traffic signals on a continual basis without requiring turning movement counts. In this method, the traffic state — degree of saturation is first estimated for each movement based on the observed travel time distribution and the signal control parameters are then optimized accordingly. This signal retiming method is evaluated under a wide range of simulated scenarios varying by penetration rates, measurement errors, vehicle arrival patterns, and traffic demand, showing comparable results to the typical traffic count-based approach.
Continual retiming of traffic signals using big travel time data
2017-08-01
748192 byte
Conference paper
Electronic Resource
English
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