# Estimation Method for Road Link Travel Time Considering the Heterogeneity of Driving Styles
## 1 Introduction  
The continuous advancement of urbanization has caused many urban road transportation systems to face increasing congestion, which threatens the environment and transport efficiency [1]. To address issues such as traffic congestion, understanding the traffic state is critical at many levels of traffic management and traffic policy. With the rapid development of computer science and the progress of traffic system sensors, the collection of massive traffic data has shown its advantages in traffic decision-making [2]. ANPR is one of the decisive components of intelligent transportation systems [3]. It is often used in traffic big data analysis such as travel time estimation, OD (Origin-Destination) estimation, commuting recognition, etc. The ANPR uses image processing technology to collect vehicle information at the time of the shooting, such as shooting time, vehicle type, vehicle license plate, etc., which can obtain a large amount of traffic information [4]. There is much helpful traffic information in these data, and some characteristics related to traffic flow can be obtained through traffic data mining technology [5]. Thereby, traffic big data can be converted into readable information for traffic information prediction and management control [6]. By mining ANPR data, researchers can obtain the travel characteristics of travelers and judgethe status of the transportation network, which can provide traffic managers with a reliable basis for policy design and implementation [7]. Many researchers have conducted research on ANPR data, mainly in the fields of OD estimation, travel time estimation, road traffic state estimation, etc. [8]. [PERSON] et al. proposed a dynamic OD prediction method for ANPR data and combined it with the Kalman filter framework for prediction [9]. [PERSON] et al. studied the non-commuter travel demand of car commuters by using ANPR data. Suggestions were made for traffic demand and management after COVID-19 [7]. [PERSON] et al. carried out real-time OD matrix estimation based on large-scale ANPR data and proposed an OD estimation method based on the Trip-Vehicle Matrix [10]. [PERSON] et al. used real-time ANPR data to estimate the real-time traveler information system (RTIS) [11]. In the absence of real-time AVI data, the travel time of other road links can be inferred through the spatial variance-covariance between the historical travel time and the road network. [PERSON] et al. proposed an online and historical travel time prediction method based on the Extended Kalman Filter (EKF). This method uses ANPR data to quickly identify the traffic state when congestion appears or dissipates [12]. However, this method does not consider the correlation between different road links. [PERSON] et al. pointed out a wavelet analysis method to identify travel time outliers extracted from Changsha ANPR data. A conclusion is drawn by comparing the two data processing methods [13]. [PERSON] et al. proposed a linear relationship model between travel time and traffic characteristics such as standard deviation and skewness. Statistical analysis was performed on simulated data from a delay distribution model and real data from an ANPR camera [14]. Road link (a single segment of a road) travel time (TT) is becoming more and more essential in urban traffic travel information in nowadays society. Travel time estimation (TTE) has become a very crucial requirement. Accurate TTE is one of the core tasks of traffic modeling and is of great significance in many fields [15]. For example, providing accurate TTE can help travelers arrange travel routes reasonably, and the government and traffic management departments can perceive traffic conditions and ensure the smooth operation of traffic flow [16]. Driving styles vary widely among different drivers, so personalized TTE is meaningful for different styles of drivers. With information and personalized recommendations, individuals and fleet management companies can arrange their trips more accurately and improve the efficiency of the system [17].