Abstract

The high frequency of red-light running and complex driving behaviors at the yellow onset at intersections cannot be explained solely by the dilemma zone and vehicle kinematics. In this paper, the author presented a red-light running prevention system which was based on artificial neural networks (ANNs) to approximate the complex driver behaviors during yellow and all-red clearance and serve as the basis of an innovative red-light running prevention system. The artificial neural network and vehicle trajectory are applied to identify the potential red-light runners. The ANN training time was also acceptable and its predicting accurate rate was over 80%. Lastly, a prototype red-light running prevention system with the trained ANN model was described. This new system can be directly retrofitted into the existing traffic signal systems.

Highlights

  • Motorists face indecisiveness during the yellow and allred clearance at signalized intersections

  • The red-light running is a leading cause for severe crashes at intersections and it has been assumed that the dilemma zone is the major reason for the RLR occurrence

  • Recent research has revealed that the RLR occurrence is caused by not solely the dilemma zone and many other factors

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Summary

Introduction

Motorists face indecisiveness during the yellow and allred clearance at signalized intersections. It is a composite result of the incompatible reactions to the changes of signal indicators and random safety perceptions among motorists. Such indecisiveness is a leading cause for signal violations at intersections. In order to prevent the signal-related accidents, it is necessary to study the driver behaviors at intersections. Compared to the driver behaviors at other road segments (e.g., freeway or arterial links), the complexity of driver behaviors around intersections is that, during the yellow and all-red clearances, a driver will have to respond to signal changes and interact with other adjacent vehicles to ensure safe maneuvers. The driver behaviors at intersections can hardly be represented with analytical models

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