Abstract
Time-Space Relationship Analysis Model on the Bus Driving Characteristics of Different Drivers Based on the Traffic Performance Index System
Highlights
The driving characteristics of different bus drivers directly influence the safe and stable performance of buses
A time-space analysis model of the driving characteristics of different drivers based on traffic performance index is established through fuzzy association rules and a type-2 fuzzy set prediction algorithm
In the aspect of the research on aided driving, He et al [1] established the fuzzy PID driver model according to the characteristics and complementation of two control methods, namely, fuzzy intelligent control and traditional PID control; Liu [2] divided the driver model into a driver model based on people–vehicle–environment closed-loop system vehicle handling stability, driving characteristic model of different drivers based on intelligent traffic system, and driver fatigue model based on traffic safety, as well as analyzed and discussed the shortages of various driver models; Xie et al [3] studied a driver model based on OCC (Ortony–Clore–Collins) in simplified road conditions, Markov model for the emotional state spontaneous transfer process, and Hidden Markov model, HMM [4], for emotional state stimulation transfer
Summary
The driving characteristics of different bus drivers directly influence the safe and stable performance of buses. In the aspect of automobile driving safety, Song et al [5] established safe and reliable quantitative screening analysis data model of automobile drivers by the average of a random group testing algorithm, which provides theoretical basis for selecting automobile drivers. On the basis of traffic performance index based on GIS, in this paper, a time–space relationship analysis method on driving characteristics of different bus drivers is proposed to describe the influence of time–space and traffic performance conditions on the driving model. Fuzzy association rules are based on driver quantitative index, and the traffic performance index prediction is realized through type-2 fuzzy set, effectively solving the problem of delayed release of traffic performance index because of massive calculations for floating vehicle data
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