# Vehicle Acceleration Prediction Based on Machine Learning Models and Driving Behavior Analysis
## 1 Introduction
### Data-Driven Prediction Methods  
A data-driven approach is a model with no fixed structure and no fixed parameters. For example, based on the principle of recursive Bayesian filtering, [PERSON] et al. [6] proposed a filter that estimates drivers' behavior and predicted their future trajectories, labeled as \"braking\", \"constant speed\" and \"acceleration\". [PERSON] et al. [7] divided the road network into critical paths, and modeled each critical path using a bidirectional long short-term memory neural network (Bi-LSTM NN) for network-wise traffic speed prediction. [PERSON] et al. [8] used a neural network model based on historical traffic data to predict the average speed of road segments, and then used Hidden Markov models (HMMs) to represent the statistical relationship between individual vehicle speeds and traffic speed. [PERSON] et al. [9] modeled patterns of individual driving styles using a Dirichlet process mixture model, a nonparametric Bayesian approach, which was used to predict the pedal-operation behavior of the driver during the car-following process. Data-driven models for driving behavior prediction can use datasets from multiple sources, which can improve the prediction performance. However, most of these prediction methods do not consider driving behavior classification, which may ignore the potential heterogeneity of driving behavior. We expect to improve the prediction accuracy of the model by classifying drivers into different groups according to their driving behavior characteristics.  
In order to address the above problems, this study proposes a vehicle acceleration prediction model based on driving behavior analysis. In this model, the Finite Mixture of Hidden Markov Model (MHMM) is used to analyze driving behavior data, and LSTM and GRU are used to predict acceleration. This model can take advantage of the additional information present in large datasets better. Besides this, combining driving behavior analysis and prediction allows the better fetching of potential heterogeneity in driving behavior. Unlike the traditional HMM [10], the MHMM can divide driving behavior semantics, which will obtain the dynamic driving decision-making process. The MHMM provides a better characterization of the levels for the analysis of car-following data, while adapting to the population. Moreover, the inclusion of multiple HMMs in the MHMM makes it possible to consider dependencies over time, and to improve the traditional methods based on cutoff points [11]. LSTM and GRU are used to predict the acceleration of subject vehicles. Driving data are stochastic and nonlinear, and are thus difficult to describe using a traditional linear model. In recent years, deep-learning-based methods have been widely used in time series data prediction. [PERSON] et al. [12] used LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) to predict short-term traffic flow. Experiments show that the performance of deep learning methods based on Recurrent Neural Networks (RNN), such as LSTM and GRU, is better than that of the auto-regressive integrated moving average (ARIMA) model. The main contributions of the paper can be summarized as follows:  
* Firstly, this paper predicts the vehicle acceleration based on machine learning methods and driving behavior classification. The MHMM-based driving behavior classification can identify the heterogeneity of different drivers and improve the prediction accuracy.
* Secondly, we compare the performance of the LSTM and GRU in the prediction of vehicle acceleration.  
The rest of this paper is organized as follows. The second section introduces MHMM, LSTM and GRU. In Section 3, we introduce the data sources, then perform the semantic segmentation of driving behavior and similarity evaluation. Section 4 predicts acceleration using LSTM and GRU, and discusses the results. Finally, Section 5 provides conclusions and future work for this research.