## 2 Literature Review
### Data Sparsity Problem in e-Trans Charging Network  
As mentioned earlier, enhancing the infrastructure for e-trans is a critical task in the decarbonization process. Deploying charging networks can be costly if completed under conditions of sparse data availability. Therefore, recent research has shown considerable interest in addressing this issue when establishing charging networks for e-trans infrastructure. To optimize the existing charging station network by eliminating redundant charging stations, for times and stations with sparse data, [PERSON] et al. [7] utilize a method of context aware tensor collaborative decomposition to infer the circumstances. In another study, an XGBoost regression model is utilized, which is a tree-based boosting model tailored to address sparse data when diagnosing battery charging capacity using extensive real-world EV operational data [16]. A classification system of charging patterns and user profiles for electric vehicles is derived from an analysis of 4.9 million charging transactions spanning from January 2017 to March 2019, involving 27,000 users across 7079 charging points in the public level two charging network across the four largest cities and metropolitan areas of the Netherlands [8]. [PERSON] et al. [17] examine and assess the pricing data related to charging stations sourced from PlugShare, an EV charging data platform that relies on crowd-sourced information. A typical study investigates the charging behavior of electric vehicles (EVs) and seeks to identify the most effective method for predicting it to optimize EV charging schedules [18]. The study observes that prediction errors increase with higher data entropy or lower data sparsity. Hence, it introduces the entropy/sparsity ratio (R) to account for both indicators. When R is low, support vector regression (SVR) and random forest (RF) regression demonstrate higher accuracy in predicting stay duration and energy consumption, respectively. Conversely, when R is high, a diffusion-based kernel density estimator (DKDE) performs better for both predictions. These three methods are combined into the proposed ensemble predicting algorithm (EPA) to enhance prediction performance by reducing prediction errors by 11% for the duration and 22% for the energy consumption. The prediction results are then utilized in an optimal EV charging scheduling algorithm to minimize load variance and reduce EV charging costs. [PERSON] et al. [19] employ a trip chain model to evaluate the charging demands for battery electric trucks (BETs) during long-distance operations in Europe by the year 2030. The proposed model considers truck driving regulations as well as various types of stops. The results indicate that the quantity of overnight chargers (50-100 kW) necessary is 4-5 times greater than the number of megawatt chargers (0.7-1.2 MW) required to accommodate a 15% adoption rate of BETs in long-haul operations.  
To the best of our knowledge, previous models have not focused extensively on highlighting cross-behavioral comparative and sequence-based cross-behavioral aspects, as well as the issue of data sparsity in selecting charging sites. To fill this research gap, this paper provides the following contributions. Firstly, a thorough investigation into user behavior and embedding it into the training model can help the system separate behaviors more accurately. This is a significant contribution of the model. Secondly, through integrating a range of techniques for behavior separation into the local and global structural layer-based model during training, along with optimizing the distance from the user to the target site, user choice prediction becomes more accurate and optimal. Finally, based on the training and prediction results, the issue of sparse data is also addressed and reinforced. Consequently, this can assist in establishing additional charging stations.