## 1 Introduction  
The development of online freight exchange (OFEX) platforms, facilitated by emerging technologies such as the Internet of Things (IoT), has led to increased autonomy and intelligence in the ecological structure and organization of the freight industry. Through recommendation and location prediction algorithms, these platforms efficiently match truck drivers with freight, integrating demand and services. However, unlike cargo transportation fleets that adhere to strict operational strategies, self-employed truck drivers often prioritize valuable cargo upon entering the freight network [1]. Consequently, the location of truck drivers is determined by their transportation habits, contributing significantly to the complexity of mobility and the uncertainty of truck locations, particularly when self-employed individuals constitute the majority of the trucker population [2]. For instance, in China, the number of freight truck drivers exceeds 30 million, with less than 10% belonging to professional fleets of enterprises, while over 90% are self-employed [3].  
To improve the efficiency of matching trucks with cargo and reduce transaction costs, it is essential to investigate the predictability of truck mobility and variations in predictability based on different modes of operation.  
Predicting truckers' locations is crucial for modeling human travel behavior, especially for understanding urban evolution, estimating migratory flows, and modeling epidemics. Existing research has focused on capturing the intrinsic nature of human activities rather than solely improving predictive accuracy [4; 5]. The state-of-the-art method involves using entropy as a fundamental measure of predictability, as demonstrated by [6], who achieved 93% potential predictability of mobility. However, challenges arise in comprehending the factors influencing predictability in individual mobility trajectories due to compression algorithms. Ref. [7] enhances interpretability by incorporating regularity and smoothness, and utilizing contextual information like weather and time of day to improve predictability. Previous studies mistakenly conflated smoothness estimation with next-location prediction, influencing predictability outcomes. Introducing a natural length scale for individual next-location predictability yields a result of 71.1%, which is lower than the 93% in [6], indicating the impact of spatial and temporal scales on human mobility smoothness. Notably, ref. [8] investigated truckers' predictability using GPS trajectories, revealing 83% predictability on a raster map, suggesting differences in mobility patterns between truck drivers and the general population. The question remains: Are there variations in predictability among different groups of truck drivers?  
This paper investigates the predictability of truck drivers' mobility using extensive data from the Chinese truck operation supervision platform. This study develops a measurement index system with ten indicators across space, operation intensity, and stability dimensions. Utilizing the Fuzzy c-means clustering algorithm, four distinct truck groups with varying operational patterns are identified. Prediction models (KNN, BP neural network, and LSTM) are employed based on stability patterns, revealing a close relationship between stability and predictability. Despite not reaching the prediction algorithm limits, the focus is on exploring variations in predictability among truck groups and understanding the contributing internal factors. The aim is to identify more predictable truck driver groups to enhance the overall predictability of truck locations.