In this paper, a probabilistic fuzzy logic control is developed for the signalized control of a diamond interchange to improve the trafficflow on the surface streets and highways. The signal phasing, green-time exten...
详细信息
In this paper, a probabilistic fuzzy logic control is developed for the signalized control of a diamond interchange to improve the trafficflow on the surface streets and highways. The signal phasing, green-time extension, and ramp metering are decided in response to real-time traffic conditions. The probabilistic fuzzy logic for diamond interchange (PFLDI) includes the following three modules: probabilistic fuzzy phase timing that controls the green-time extension process of the current running phase, phase-selection logic that decides the next phase based on the presetup phase logic by the local transport authority, and probabilistic fuzzy ramp metering that determines the on-ramp-metering rate based on the traffic conditions of the arterial streets and highways. The advanced interactive microscopic simulator for urban and nonurban network software was used to model the diamond interchange and measure the effectiveness of the proposed PFLDI algorithm. The performance of the PFLDI was compared with that of an actuated diamond interchange (ADI) control based on the asservissement lineaire d'entre autoroutiere (ALINEA) algorithm and a conventional fuzzy logic control for a diamond interchange (FLDI) algorithm. simulation results show that the PFLDI lowers system total travel time and average delay, improves the downstream average speed, and lowers the downstream average delay compared with ADI and FLDI.
Computational dual-task models of driving with a secondary task can help compute, simulate, and predict driving behavior in dual task situations. These models can thus help improve the process of developing in-vehicle...
详细信息
Computational dual-task models of driving with a secondary task can help compute, simulate, and predict driving behavior in dual task situations. These models can thus help improve the process of developing in-vehicle devices by reducing or eliminating the need for conducting driver experiments in the early stage of the development. Further, these models can help improve trafficflowsimulation. This article develops a dual-task model of driving with a visual distraction task using the Queuing Network model of driver lateral control and a logistic regression model. The comparison between the model simulation data and the human data from drivers in a driving simulator shows that this computational model can perform driving with a secondary visual task well and its performance is consistent with the driver data.
暂无评论