# Estimation Method for Road Link Travel Time Considering the Heterogeneity of Driving Styles
## 1 Introduction
In recent years, the problem of link TTE has been extensively studied [15]. According to the types of data used, existing methods can be roughly divided into two categories. The first category of methods focuses on modeling link-based data from various sources [18; 19; 20; 21], and the other category focuses on modeling emerging trip-based data to estimate link TT [22; 23; 24]. [PERSON] et al., proposed a TT distribution estimation method considering variance. The TT distribution is the sum of the deterministic link TT and the random turn delay at the intersection [25]. [PERSON] et al. used the data obtained by the point-to-point detector to analyze the traffic state of vehicles before the signal and estimate the arterial travel time on a more microscopic level [26]. [PERSON] et al. came up with an improved Markov chain method to estimate TT. This method considers the temporal and spatial correlation between road links and performed better when traffic conditions change [27]. Nonetheless, the model's mobility is poor, and the details on the road section are not considered. [PERSON] et al. recommend a tensor-based urban TTE model based on GPS data. A third-order tensor model is established to model the TT of different road sections under other traffic conditions in a specific period [28]. The missing items in the tensor model are estimated by tensor decomposition. [PERSON] et al. used large-scale trip data to estimate TT between origins and destinations in a very efficient manner. These adjacent trips were used to estimate TT, and experiments were performed on two large, real datasets [24]. [PERSON] et al. proposed a Trip Information Maximizing Generative Adversarial Network (T-InfoGAN) based on deep learning [29]. Through the literature review study on the estimation of the road link travel time in the past, we found that the ANPR data mainly focus on commuter vehicle recognition or trajectory recognition. Although the ANPR data contain ample information about traffic conditions, there is merely limited attention to the quality of the data [13]. Secondly, the use of ANPR data for TTE mainly focuses on mathematical methods for processing and less consideration of individual driver heterogeneity. Few studies on traffic guidance provided TTE based on user driving styles. In this paper, we propose a road link TTE method that considers the heterogeneity of driving styles. The DBSCAN algorithm uses tensor-based urban personalized travel time modeling, and interpolation methods supplement the missing travel time items. TTE uses only limited information, i.e., urban ANPR data [30]. While performing DBSCAN on the tensor model, we estimate the travel time of road links in different periods. This method combines the spatial correlation between different road links and the heterogeneity among individual driving styles of other drivers. In addition, the variability caused by the inherent uncertainty of the urban road network is also considered. The rest of the paper is organized as follows: Section 1 presents related work. Section 2 introduces tensor model establishment, travel time cluster analysis, and outlier supplementation. Section 3 presents a specific case study of TTE in Ningbo City, China. Finally, Section 4 summarizes the results of this work and puts forward the implication for future research.