## 1 Introduction  
With the development of the economy and technology, the scenarios of people traveling and traffic flow have been changing over time. The complex multi-modal travel demand and corresponding interactions with multi-modal transportation networks bring new challenges to system-wide transportation analysis, deduction, and control. Both advanced system modeling tools and transportation big data are required to fulfill the aforementioned tasks. All along, transportation simulation has a rich accumulation in the literature [1; 2; 3; 4], and transportation data-driven studies have been attracting attention recently. Therefore, the fusion of transportation simulation and transportation big data, i.e., model and data dual-driven simulation, will make a difference. Transportation simulation is an essential approach to understanding and predicting traffic dynamics of city-wide transportation networks. It can capture the complex interactions between different elements in transportation systems, evaluate and compare the influences of different planning schemes, and assist the design of operations management strategies to seek the optimal solution to transportation problems [5; 6; 7]. Over the years,there has been a significant shift in the transportation modeling paradigm. The traditional Four-Stage Models have evolved into more advanced Activity-Based Models. Concurrently, modeling approaches have shifted from static to dynamic models [8], expanding from a macroscopic perspective to a microscopic level. Additionally, there has been a transition from discrete-event models to agent-based models [7; 9]. These transformations have led to more complex data required for transportation simulations. Consequently, the acquisition and processing of such complex data have become a major bottleneck for system modeling. Recently, the emergence of data-driven simulation has offered a promising solution, which leverages massive amounts of data generated using transportation systems to develop models that are more accurate and efficient [10]. By relying on data-driven simulations, researchers can capture the complexity of transportation systems while minimizing the need for extensive manual data collection and processing. The field of transportation planning has evolved significantly with the advent of big data. Initially, transportation planning research primarily relied on traditional travel survey data for information gathering [11], constrained by limitations such as insufficient sample sizes and poor data real-time performance. With the advancements in information technology, numerous sources of real-time data have become available for modeling purposes, such as traffic flow data from checkpoints [12], mobile signaling data [13], and public transit card swiping data [14]. Moreover, the rise of Location-Based Services (LBS) data has introduced new possibilities and advantages. LBS data provide detailed information on individuals' spatial and temporal behaviors, allowing for a more granular understanding of travel demand patterns. It gains insights into origin-destination (OD) flows, route choices, and mode selection, among other valuable information. Recently, a growing number of studies have used LBS data from telecom carriers, smartphone-based apps, and Bluetooth devices for transportation applications [15; 16]. However, due to insufficient data coverage, data-network mismatching and privacy issues, the usage of LBS data in transportation studies is still in its infancy, necessitating further dedicated research. Such issues need to be addressed to ensure data robustness and reliability. To tackle these challenges, mitigating potential data gaps and ensuring data integrity is imperative. Currently, the completion of LBS data primarily relies on statistical learning and machine learning, with no existing method for data completion through transportation simulation. This gap in the research represents a significant opportunity for exploration and innovation.