A Bee Colony Optimization (BCO) and Type-2 Fuzzy Approach to Measuring the Impact of Speed Perception on Motor Vehicle Crash Involvement

Abstract

This paper examines how a driver’s perception of various speed levels, as well as driver’s speed perception from different positions, affect the propensity for motor vehicle crashes (MVCs). Data collection is performed in twelve experiments. 178 young drivers assessed the speed level from four positions; three of them relate to the speed perception of other vehicles on the road, while the remaining one represents the assessment of own speed. At each position, three speed levels were assessed: 30, 50, and 70 km/h. To process data, seven Type-2 fuzzy inference systems (T2FISs) are designed and tested in a sense of compliance with the empirical data. As a result, a relationship between the various forms of speed perception and participation in MVCs can be quantified. To examine the initial conclusions, the optimization of each of these T2FISs is performed by implementing the bee colony optimization (BCO) metaheuristic. The BCO based algorithm proposed in this paper achieved an average improvement of 21.17% in the performance of the initial T2FIS structures. The final results indicate that the drivers whose speed perception of the vehicle they are looking at from the rear side, as well as of the own vehicle, is poor have an elevated risk toward participation in MVCs compared to other forms of speed perception. The best-found T2FIS structures can be used as a decision-making tool that quantifies the driver propensity for MVCs, which can be useful in various educational and recruitment procedures in the field of transportation and traffic safety.