# Bus Travel Time Prediction Based on the Similarity in Drivers' Driving Styles
## 2 Related Works
### Research on Prediction of Bus Travel Time  
In previous studies, various complex models and algorithms have been developed to predict bus travel time by using AVL/APC/GPS data. In recent years, with the rapid growth of data and the expansion of Intelligent Transportation Systems (ITS), machine learning has become an important tool for solving complex problems, such as the prediction, analytics, and patterns of large amounts of data [25]. The following models are mainly used for applying machine learning to predict the travel time of buses. Multivariate linear regression models use multivariate statistical techniques to examine the linear correlation between a group of independent variables and a single dependent variable [1]. [PERSON] et al. [26] used multiple linear regression models to predict the arrival time of buses at a target bus stop. This kind of model can reflect which independent variables are important for travel time prediction and which independent variables are relatively less important for travel time prediction. This model has a small number of calculations and can get satisfactory results in some relatively simple cases. However, the variables in a transportation system are interrelated, so the applicability of the regression model is generally limited [27]. An artificial neural network (ANN) is produced by simulating the intelligent data processing ability of the human brain. Due to its outstanding advantages in solving complex non-linear problems, ANNs are very popular for travel time prediction [28; 29]. An ANN model was used to predict bus arrival times in Jeong and Rilett [1]. Their results show that the ANN model is better than the regression model in terms of prediction accuracy. [PERSON] et al. [30] concluded that although the performance of ANN models is worse than that of SVMs, ANN models are better than k-NN and LR models. [PERSON] and [PERSON] [2] used only GPS data to predict bus travel times and concluded that the ANN model is superior to the historical average (HA) model and the Kalman filter model in terms of its overall accuracy and the robustness of its prediction. The support vector machine (SVM) model is similar to an ANN. An SVM has a strong learning ability and better generalization ability than a neural network. It is easy to balance the degree of fitting and the level of generalization. It shows many unique advantages in solving small-sample, nonlinear, and high-dimensional pattern recognition problems [31]. [PERSON] et al. [31] first applied a support vector regression model to the prediction of travel time on a highway, proving that the SVR is suitable for traffic data analyses and demonstrated the feasibility of applying SVRs in the prediction of travel time. [PERSON] and [PERSON] [32] compared several different travel time prediction methods, including a historical method, time series analysis, ANN, and SVM. Their results show that the SVM is a feasible alternative to a short-term prediction problem when the amount of data is essentially small or noisy. [PERSON] et al. [33] used an SVM to predict bus arrival times, to study the feasibility and applicability of SVMs in the field of predicting bus travel time.s Then [PERSON] et al. [30] compared the SVM, ANN, k-NN algorithm, and LR regression models. Their results show that the SVM model is the most accurate model among the four models. [PERSON] et al. [7] compared the ANN, KNN, and SVM bus travel time prediction models through experiments, and their results show that the performance of the SVM is better than that of the other two algorithms. The Kalman filter (KF) is a linear recursive prediction updating algorithm, which is used to estimate the parameters of the process model. By using dynamic AVL (Automatic Vehicle Location) and APC (Automatic Passenger Counting) data, [PERSON] and [PERSON] [34] tried to use the KF bus travel time model to provide real-time information on the arrival and departure times of buses. [PERSON] et al.