## 1 Introduction  
As the fuel crisis escalates and environmental worries mount, governmental bodies and automobile manufacturers have unveiled diverse strategic initiatives aimed at advancing the adoption of fuel-efficient vehicles with reduced carbon emissions [1]. The resilience and remaining e-trans infrastructure stand as pillars of paramount importance, commanding attention from both academia and the transportation infrastructure constructing corporation. Hence, bolstering the fast-charging infrastructure to facilitate more frequent vehicle rendering represents a viable strategy for upholding the sustainability of e-trans infrastructure [2]. An investigation was implemented of the scenario where the company opts to invest in the necessary charging infrastructure to facilitate a cost-minimizing strategic transition, either due to the current inadequacy of public charging infrastructure or for strategic purposes [3]. To model and optimize the public charging network and thus, sustain the e-trans infrastructure, data on vehicle user behaviors play a crucial role in establishing the charging station network [4]. Furthermore, the resilience of e-trans infrastructure is closely intertwined with the broader concept of digital transformation. As businesses increasingly rely on digital channels to conduct transactions and engage with customers, the importance of maintaining robust and resilient e-trans platforms cannot be overstated. Whether it is ensuring the availability of charging sites, streamlining payment processing systems, or safeguarding customer data, the resilience of the e-trans infrastructure is essential for fostering trust, reliability, and continuity in the digital platform. The internet holds an immense amount of information, continuously expanding to a degree where humans are unable to manage the overwhelming volume of data manually or through conventional tools [5]. Numerous studies indicate that data sparsity poses a significant barrier to deploying public charging site networks. The data sparsity and the cold start problem present a significant hurdle in datasets where each user provides ratings for only a limited number of sites [6]. In addition, electric vehicle trajectory data are sparse, resulting in a sparse tensor [7]. The implications of the shortage of data go far beyond the field of infrastructure development, especially when it comes to the construction of networks for electric vehicle (EV) charging. There are several issues associated with inadequate data availability, from imprecise demand forecasts to inefficient resource distribution and infrastructure design. Fundamentally, insufficient data makes it difficult to determine where charging stations would be most appropriate, which causes uneven coverage and accessibility problems for electric vehicle customers. This may lead to overworked facilities in some locations and underutilized infrastructure in others, which would ultimately impair the convenience and dependability of EV charging networks. A log10 transform on the DBS (distance between sessions) was deployed to avoid broad difficulty due to the sparsity of data at high distances [8]. Moreover, the lack of data makes it more difficult to create demand forecasting prediction models, which are necessary to figure out the right size and capacity of infrastructure for charging EVs in the future. Infrastructure planners may find it difficult to deploy resources effectively in the absence of precise forecasts, which could lead to either an overinvestment or an underinvestment in charging infrastructure. Data gaps also make it more difficult to apply load management techniques and dynamic pricing schemes, both of which are essential for maximizing the efficiency of charging networks and reducing the burden on the electrical grid. It is difficult to put into practice efficient demand-side management techniques and incentives to promote off-peak charges without detailed data on consumption trends, periods of peak demand, and user behavior.