## 2 Literature Review  
The exploration of human mobility properties has become a focal point in research and industry, with various studies aiming to identify the upper limits of predictability. A seminal work by [6] utilized the Fano inequality to estimate predictive performance based on mobile phone user trajectories, revealing a potential 93% predictability for most users. Ref. [9] employed time-correlated entropy to measure time series regularity, obtaining maximum predictability. In Cote d'Ivoire, ref. [10] measured the entropy of mobile phone user trajectories and identified a theoretical maximum predictability of up to 88%. These studies primarily focused on estimating the upper limits for individual predictability based on trajectory entropy. Additionally, ref. [11] emphasized the need to consider contextual information and proposed context-shifting predictability with a higher upper limit than other predictability types. Understanding these upper limits not only provides insights into human behavior but also aids application designers in navigating constraints and assists researchers in designing effective prediction algorithms [12]. Several studies have aimed to improve human mobility forecasting by enhancing algorithm performance and analyzing key factors influencing predictive accuracy. Ref. [13] correlated predictability with user demographics, revealing significant associations with gender and age. Ref. [14] explored the factors influencing next-location prediction accuracy, emphasizing the role of exploring new locations in limiting predictability. To address data sparsity in traditional Markov models, ref. [15] proposed a time-varying Markov model using Gibbs sampling. Refs. [13; 16] developed a data-driven hybrid model, demonstrating improved mobility predictions at various spatial scales. Ref. [17] introduced a causal and spatially constrained long-term and short-term learner (CSLSL) for predicting future locations. Ref. [18] surpassed the conventional upper limit (\(>\)90%) by expanding the recurrent neural network structures, challenging the commonly used upper limit in validating mobility prediction models. They revealed that assuming an exponential decay of information in mobile trajectories, combined with coding techniques, inflated entropy and reduced the upper bound of predictability. Ref. [19] employed seven predictive models-linear regression, polynomial regression, MLP, CNN, LSTM, CNN-LSTM, and AutoML-to predict real-time fuel consumption rates. The evaluation results show that the learning-based method AutoML improves the coefficient of determination to 0.99. Ref. [20] Compared with the existing methods of SRV and ARIMA for truck traffic flow prediction, LSTM and GRU have better performance. Over the forecast period, LSTM consistently outperformed GRU, achieving an accuracy rate of 4.10% higher than that of GRU. Ref. [21] conducted a comparative study on fuel consumption prediction methods for heavy-duty diesel trucks. Their results indicated that the prediction accuracies of the decision trees, backpropagation (BP) neural networks, and random forest models were 81.38%, 83.98%, and 86.58%, respectively [22]. Using a prediction framework consisting of ConvLSTM coding and a ResNet structure, global traffic states are predicted based on the spatio-temporal location data of small-sampled trucks. The experimental results show that it outperforms general advanced baseline methods. The majority of studies on mobility predictability have focused on the general population, with limited attention given to specific groups, such as truck drivers. Ref. [23] explored the predictability limit of vehicle mobility in large-scale urban networks and found strong regularity in daily vehicle mobility. In summary, predictability is a crucial aspect for understanding human mobility characteristics; however, existing research has been predominantly centered on the general population, offering limited insights into freight transportation.