# Travel Time Prediction on Long-Distance Road Segments in Thailand
## 2 Literature Review
### Travel Time Prediction Techniques  
In general, researchers have organized the problem into time-series analytics, then adopted time-series forecasting techniques to create travel time prediction models, such as the ARIMA [7; 8; 9], SVR [10; 11; 12], LSTM [4; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22], and ConvLSTM [14; 22] models. Some studies proposed improvements using attentions [13; 14; 16] and filters [8; 23; 24]. These methods are reviewed and summarized as follows:  
Auto-Regressive Integrated Moving Average (ARIMA) is a well-known time-series prediction technique that is commonly used in the domain of finance, especially for data that are seasonal. This technique has been applied for traffic prediction challenges, such as urban roadway travel time prediction from Bluetooth data [7], real-time road traffic state prediction [8], and the prediction of changes in travel time [9]. The ARIMA uses statistical data points to forecast future points. In more detail, the ARIMA has three basic parameters, which are \(p\), \(d\), and \(q\), where \(p\) is the number of lag observations (the lag order); \(d\) is the number of non-seasonal differences observed from the raw data (the degree of differencing), and \(q\) is the moving average window size (the order of the moving average). Support Vector Regression (SVR) is a regression technique that utilizes the concept of a well-known classification method: the Support Vector Machine (SVM). In our review, we found studies of prediction models using SVR, including travel time of freight transport [10], urban main road travel time [11], and traffic flow prediction [12]. The SVM uses a kernel, which is a mathematical function, to transform data points to aid a hyperplane separation of the data in higher-dimensional space. SVR uses a hyperplane to define decision boundaries to maximize the number of included data points [25]. On the basis of this mechanism, the SVR model can be applied as a time-series forecasting technique. However, an appropriate kernel must be chosen to fit with the characteristics of the data. Long Short-Term Memory (LSTM) is a type of artificial Recurrent Neural Network (RNN) architecture. Some recent research studies have utilized this method as follows:  
* [PERSON] et al. [13] used LSTM and extension to predict travel time in an English road network. * [PERSON] et al. [4] used LSTM analytics on travel time on freeways using data from inductive loop detectors. [PERSON] et al. [14] predicted bus journey times using LSTM. * [PERSON] et al. [15] adopted LSTM to estimate bus arrival times and performed an evaluation using MAE. * [PERSON] and [PERSON] [16] used LSTM with self-attention to predict travel time by grouping the time into hours. * [PERSON] et al. [17] created a travel time prediction model using LSTM and trip plans from historical GPS data and road networks. * [PERSON] et al. [18] made a travel time prediction using LSTM with multiple lane levels. * [PERSON] et al. [19] used LSTM for bus travel time prediction by having distance, speed, and start time as the parameters. * [PERSON] et al. [20] made a travel time prediction model using different methods and found that the LSTM provided the highest performance. * [PERSON] and [PERSON] [21] used LSTM to predict travel time on five routes whose average distances were about 16 km. * [PERSON] and [PERSON] [22] performed an experiment on Convolutional LSTM (ConvLSTM) and LSTM to predict travel time and found that the LSTM with trip plans had a better performance than that of the ConvLSTM in their dataset. In more detail, the LSTM uses the connection of the feedback to analyze the sequences of data [26], so it is a well-known technique for time-series prediction. As depicted in Figure 1, LSTM architecture usually contains a sequence of units corresponding to window size. One unit has an input and output, and inside a unit, there is a cell state, a forget gate, an input gate, and an output gate.