Title A Study of Using Deep Learning Method(Long Short-Term Memory) to Build Motorcycle Moving Behavior on Urban Mixed Lane
Year 2018
Degree Master
School Transportation Science, Tamkang University
Author
Summary Motorcycle is one of the Taiwan main transportation vehicles, they cannot follow the specific route, and always frequently parallel or overtake in the identical traffic lane. In the past research about motorcycle moving behavior that tring to use a single mathematical model to explain the Motorcycle moving behavior models and the moving behaviors of continuous overtaking. However, this paper considered that it’s difficult to explain and simulate the changeable motorcycles behavior pattern by a single model, and it's unable to reflect the true situation of the mixed urban road in Taiwan. Therefore, this paper is aimed at special driving state of the Motorcycles in Taiwan and behavior pattern in the mixed road of the general urban road is the main research subject.This paper builds a predictive model of motorcycles moving behavior patterns, using the arithmetic structure with deep learning method. Establish the corresponding relationship between input and output variables of the motorcycles' forward direction and various influence parameters affecting the behavior. To obtain the complete traffic coordinates, using the coordinates of motorcycles with the camera coordinate conversion method, in order to make the prediction result approach the numerical coordinate prediction, using the special arithmetic structure of deep learning integrate coordinate and lattice concept. In the study, the motorcycles moving behavior is calculated in detail according to the coordinates of 25 propulsion modes ,with 25 influencing behavior parameters, establish a relational matrix in a continuous time series and design the operational structure rules for deep learning .The model result shows that through the establishment of the mode, the motorcycles moving behavior was successfully divided into 25 travel modes, finally to perform pattern verification, divide 25% section of the data ,and verify another 75% . The mode prediction degree can reach 84%, which proves that the model predicts the motorcycles moving behavior is indeed feasible. Subsequent research can collect complete traffic information and perform simulation training based on the results of this research to establish a complete vehicle flow characteristic database.
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