# Bus Travel Time Prediction Based on the Similarity in Drivers' Driving Styles
## 2 Related Works
### Research on Driver's Driving Style  
A driver's driving style can be understood as the way a driver operates the vehicle controls in driving scenarios and external conditions [12]. Driving style is largely dependent on traffic conditions, vehicle performance, the driver's personality, and control devices at traffic intersections [13]. Driving style is manifested mainly in a driver's control over the vehicle's speed and acceleration, as well as their lane change behavior and progress during driving [14]. For analyses of driving behavior, it is important to select appropriate test cycles, test routes, test vehicles, and test drivers [14]. Vehicle positioning can be provided by GPS, which is an indirect measurement method of speed and acceleration at the same time.  
The most common aggregate driving behavior parameters used in the articles studied include the mean speed, acceleration, deceleration, driving duration, number of acceleration and deceleration events, relative positive speed, and idle time [14]. Speed and acceleration/deceleration were used by [PERSON] and [PERSON] to identify driver characteristics [15]. [PERSON] et al. [16] categorized drivers' driving styles according to the speed at which a driver accelerated and decelerated. Moreover, they proposed that driving style is a short-lived behavior; specifically, a driver can be aggressive over a period of time, while their performance might be normal during another period of time. [PERSON] and [PERSON] [17] used standard deviation and the average acceleration ratio to classify driving styles. If the ratio was greater than 100%, the driving style was classified as aggressive, if the ratio was between 50% and 100%, the driving style was classified as normal, and if the ratio was less than 50%, the driving style was classified as calm.  
The classification algorithms for the driving styles of drivers are usually divided into three categories in these papers. The first category is those that are implemented by a set of rules, also known as threshold-based algorithms. It is the simplest method for recognizing driving styles [17; 18]. This type of algorithm is simple to use, as well as easy to explain and implement, but it limits the number of parameters that can be managed. Therefore, its accuracy is quite limited. The second category is model-based classification algorithms, which describe driving styles through a set of predefined feature equations. However, the main disadvantage of driving style modeling by drivers is that it is difficult to prove the accuracy of the results. For model verification, its results must be compared with those of real drivers, which requires a lot of data collection [15; 19]. The third category is recognizing drivers' driving styles through machine learning algorithms, including the hierarchical cluster analysis [20], analysis of principal components [20], Gaussian mixture model [19], k-nearest neighbor [21; 22], artificial neural networks [22; 23] and other machine learning algorithms.  
Current research generally divides the driving style of drivers into two or three categories. In [15; 18; 21; 22], driving style was divided into two categories, i.e., aggressive and non-aggressive. [PERSON] et al. [24] divided driving styles into three categories, including aggressive, mild, and medium driving styles. [PERSON] et al. [16] divided driving styles into four categories by analyzing the jerk profile of the driver, including calm driving, normal driving, aggressive driving, and no speed. [PERSON] et al. [20] proposed five to seven levels of drivers, covering a range from non-aggressive to aggressive. However, this complicated the algorithm's development and the interpretation of the classes themselves. [PERSON] [23] classified drivers using a range within (\(-1\), \(1\)), from the mild driver to the aggressive driver. Despite a fruitful line of empirical findings from previous scholarship, the impact of bus drivers' driving styles on travel time has not yet been fully investigated.