Abstract
Bicycle is one of the main factors that affects the traffic safety and capacity on pedestrian-bicycle mixed traffic sections. It is important for implementing the warning of bicycle safety and improving the active safety to identify the cyclists’ intention in the mixed traffic environments under the condition of the “Internet of Things”. The phase-field coupling theory has been developed in this paper to comprehensively analyse the generation, spring up, increase, transfer, regression and reduction method of the traffic phase. The adaptive genetic algorithm based on the information entropy has been used to extract feature vectors of different types of cyclists for intention identification from the reduced pedestrian-bicycle traffic phase, and the theory of evidence has been provided here to build the identification model. The experimental verification shows that the extraction method of cyclists’ intention feature vector and identification model are scientific and reasonable. The theoretical basis can be applied to establishing the pedestrian-bicycle interactive security system.
Highlights
With the rapid development of automotive industry and the improvement of people’s living standards, more and more people have cars
Bicycle traffic was mainly studied using the car-following model, cellular automata model and simulation model based on two-dimensional space
The concept of pedestrian-bicycle traffic phase is proposed for the pedestrian-bicycle mixed traffic system based on Ginzburg–Landau theory of phase transitions and the concept of traffic situation in literature [12]
Summary
With the rapid development of automotive industry and the improvement of people’s living standards, more and more people have cars. Bicycle traffic was mainly studied using the car-following model, cellular automata model and simulation model based on two-dimensional space. [1] proposed a mixed traffic network simulation model (MIXNETSIM model). In this model, the bicycle movement was decomposed into two-dimensional coordinates, and the car-following model was used to analyse the longitudinal following movement of the bicycle. The bicycle movement was decomposed into two-dimensional coordinates, and the car-following model was used to analyse the longitudinal following movement of the bicycle It would provide a theoretical basis for the study on the longitudinal motion of bicycle under the condition of cyclist’s overtaking, avoiding, etc. It can be used to simulate the complex traffic state through simple rules
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