The article addresses the challenges of modernizing Kazakhstan’s railway infrastructure under conditions of technological dependence on foreign automation systems and obsolete relay-based equipment. These factors pose significant risks to economic and information security and limit the throughput capacity of level crossings. A digital system, KZ-DALCS-AI, is proposed, based on a multi-level safety architecture and the integration of artificial intelligence into monitoring and control processes. A key component is an obstacle detection and classification algorithm that considers object types (vehicles, humans and animals, foreign objects, and environmental factors) and enables intelligent real-time decision making using the KZ-ODC-AI controller with data from video surveillance, microwave sensors, and inductive loops. The system architecture, operational logic, and level crossing control algorithm are developed, including optimization of closing time by minimizing the deviation between calculated and actual values. The results of the performed calculations confirm the effectiveness of the proposed notification algorithm, ensuring the required level of safety while reducing unnecessary delays for road traffic. The implementation of the system improves throughput, reduces operational costs, enhances reliability, and minimizes the impact of the human factor.

Mobile-health systems generate repeated, time-stamped observations, but future-risk forecasts must be reliable rather than merely discriminative. We evaluate Temporal Calibrated Explainable Risk Forecasting (T-CERF) for irregular ecological momentary assessment histories. Study A used 5726 forecasting examples from 542 Corona Health participants, a 56-day history window, and a 28-day primary horizon. On the fixed participant-independent test set, T-CERF achieved a PR-AUC of 0.867, ROC-AUC of 0.946, and Brier score of 0.083. Random Forest matched its ROC-AUC (0.947 versus 0.946), while the Persistence baseline yielded a higher F1-score (0.786 versus 0.761). The PR-AUC difference from the matched current-only model was +0.023, but its paired participant-level bootstrap interval included zero. Across five additional participant-level splits, ΔPR-AUC averaged +0.0106 (SD 0.0262; range −0.0298 to +0.0399), with one split favoring the current-only model. Thus, a consistent discriminative benefit from temporal history was not established. Temperature Scaling improved calibration, while ensemble uncertainty supported selective prediction. A separate HBSC Kazakhstan analysis remains exploratory. T-CERF is therefore presented as a calibrated, uncertainty-aware mobile-health forecasting framework rather than as a hardware or network-level IoT implementation.