Source-linked AI summary
A Multi-Modal AI Framework for Real-Time Queue Prediction, Management and Optimisation in Intelligent Border Control Systems
Varvara Mama, Eleni Veroni, Nikolaos Kapsalis, Christos D. Nikolopoulos, Anargyros T. Baklezos
TL;DR
Border-control queue management must handle dynamic, uncertain traffic that traditional historical-data and rule-based approaches do not adequately adapt to. The paper proposes a multi-modal framework combining data fusion, LSTM forecasting, and MPC-based optimization. On synthetic realistic-traffic scenarios, it reports lower prediction error and waiting time, higher throughput, and stable performance under extreme conditions, while noting that real-world operational validation remains necessary.
Problem
Border-control traffic is dynamic and uncertain, while traditional methods rely on historical data and have limited ability to adapt or integrate heterogeneous sources.
Method
The framework fuses multi-source data, uses LSTM networks for queue forecasting, and applies MPC and scheduling to generate control actions.
Results
The proposed framework reports consistently stronger predictive and operational performance than baseline methods, including lower prediction error, reduced waiting time, and increased throughput.
Takeaways & Limitations
The results support combining multi-modal AI with optimization for proactive and adaptive border-traffic management.
Takeaways & Limitations
Evaluation relies primarily on synthetic traffic scenarios, so further validation with operational land and maritime border datasets is needed.
Abstract
from arXiv · showhide
In the present work an efficient border control management procedure is proposed. Compared to operational queue management systems, whose operations are based on mostly static data, the proposed work takes into account dynamic traffic conditions, thus enabling optimal performance, even in cases of uncertainty. To this end, we are proposing a multi-modal Artificial Intelligence (AI) framework, tailored to th needs of border control systems, which enables real-time queue prediction, management, and resource optimization. The novel proposed approach integrates heterogeneous data sources and presents them through a unified representation by employing Long Short-Term Memory (LSTM) networks for queue forecasting. Furthermore, it leverages Model Predictive Control (MPC) and scheduling optimization to derive actionable control policies, which in turn can be presented to border control officers. The proposed work has been evaluated using synthetic data simulating realistic traffic. The evaluation results demonstrate that the proposed method reduces queue prediction error by up to 35% and average waiting time by 30%. Accordingly, the average throughput increases by nearly 20%, compared to ARIMA and rule-based methods. The abovementioned results show the effectiveness and efficiency of combining AI architectures with optimization techniques for proactive and adaptive border traffic management.
I. INTRODUCTION
Border control faces highly variable traffic, while traditional queue-management methods rely on historical data and separate prediction from control. The proposed framework combines heterogeneous-data integration, LSTM prediction, and optimization for adaptive queue management.
- Border traffic is dynamic and rapidly fluctuating because of geopolitical, economic, and seasonal factors.
- Traditional statistical and rule-based methods rely on historical data and cannot adequately adapt to real-time fluctuations or heterogeneous inputs.
- Existing approaches often treat queue prediction and control as separate problems, limiting their effectiveness in real-time management.
- The proposed framework combines multi-source data integration with predictive and optimization capabilities for border control.
- LSTM models estimate traffic variables using temporal and environmental factors, including vehicle arrivals, queue lengths, and vehicle-density distributions.
- The queue is formulated as a dynamic multi-server system whose evolution depends on time-varying arrivals and lane service rates.
- The optimization objective is to minimize congestion through control decisions such as lane allocation.
B. System Architecture and Data Representation
The system represents border-control operations through a multi-modal input space and forecasts future queue lengths from a temporal input window. This representation supports queue prediction from heterogeneous vehicle-related data.
- The system operates over a multi-modal input space containing real-time vehicle-related data.
- The input vector includes historical data, pre-registration information, and environmental context.
- The prediction task maps a temporal input window x_t−T:t to predicted queue lengths over the future horizon t+1:t+H.
C. Multi-Source Data Fusion
The framework fuses heterogeneous traffic sources into a unified data ecosystem using temporal alignment and feature harmonization. This fusion reconciles expected and observed traffic patterns and improves predictive accuracy relative to isolated sources.
- The system integrates computer-vision counts, historical traffic, pre-registration, ferry-ticketing, and environmental or contextual data.
- Temporal alignment and feature harmonization fuse heterogeneous inputs into a unified data ecosystem.
- The architecture presents data ingestion, LSTM predictive modelling, MPC and scheduling optimization, and decision support as connected stages from inputs to control actions.
D. LSTM-Based Queue Prediction
The queue-prediction component uses LSTM networks to model temporal dependencies and produce predicted queue lengths. Its training and recurrent computation are expressed using vector-valued states and a defined loss function.
- LSTM networks evaluate temporal dependencies in the queue-prediction component.
- The LSTM formulation uses vector-valued variables, recurrent and input weights, zero initial states, and element-wise Hadamard products.
- The predicted queue length is calculated at each time step from the LSTM computation.
- Model training uses a defined loss function to support queue-prediction learning.
E. Predictive Queue Management System (PQMS)
The PQMS uses predictive outputs to detect congestion, forecast lane-specific queues, and recommend routing and lane-allocation actions. It supports a shift from reactive to proactive border-traffic management.
- The PQMS detects congestion hotspots and forecasts queue lengths for individual lanes.
- The system recommends traffic routing and lane allocation through a decision-support interface for border operators.
- Predicted per-lane queues and binary control decisions support predictive rather than reactive management.
F. Ferry Terminal Optimisation
The optimization component adapts border-control operations to land and ferry environments. Land-border optimization emphasizes continuous flow, while ferry-terminal optimization addresses burst traffic around scheduled arrivals and departures.
- The framework adapts optimization to land borders and ferry terminals according to their distinct traffic characteristics.
- Land Border Optimisation: Land-border optimization focuses on continuous flow by maximizing lane utilization and minimizing queues.
- Ferry Terminal Optimisation: Ferry-terminal optimization focuses on burst traffic associated with scheduled ferry arrivals or departures.
- The objective is defined over vehicles V(t) and their arrival times τ_i.
- The optimization is performed over a horizon H.
H. Joint Optimisation with Resource Allocation
The evaluation compares the proposed approach with historical-average, ARIMA, and static rule-based baselines. The supplied passage identifies the benchmark setup but does not provide metric values.
- The simulated border-crossing datasets are compared against Historical average, ARIMA, and Static rule-based queue-management baselines.
- The benchmark includes realistic vehicle arrivals, seasonal and peak-hour variations, and synthetic pre-registration and weather data.
- The evaluation defines a set of benchmark metrics, but their values are not specified in the supplied passage.
A. Experimental Protocol
The experimental protocol simulates border traffic with a calibrated discrete-event model under varying demand and service conditions. Experiments cover normal, peak, and disruption scenarios and are repeated independently for statistical reporting.
- The border-control environment is simulated with a discrete-event traffic model calibrated using realistic parameters.
- Traffic Model: Vehicle arrivals follow a non-homogeneous Poisson process with a time-varying rate.
- Traffic Model: Peak periods are represented by Gaussian demand surges.
- Traffic Model: Service rates use lane-dependent stochastic processing times.
- Experimental Design: Experiments cover normal, peak, and disruption conditions, with 10 independent runs per experiment and mean values reported with standard deviation.
IV. RESULTS AND DISCUSSION
The proposed framework combines LSTM queue prediction with MPC-based optimisation and multi-modal data fusion, improving predictive and operational performance across variable traffic conditions. Evaluation reports lower prediction error, reduced waiting time, increased throughput, and stable performance under extreme conditions.
- Prediction Accuracy: 25–35% lower MSE than ARIMA is achieved by the proposed LSTM model, including robust performance under peak conditions.The comparison is presented in Table I against baseline approaches.
- Prediction Accuracy: Approximately 35% lower MSE than ARIMA demonstrates the model’s ability to capture temporal dependencies and non-linear traffic patterns.
- Operational Performance: ∼30% reduced waiting time and ∼20% increased throughput are reported on operational performance datasets after integrating prediction with optimisation.Resource utilisation also improved significantly.
- Data Modality Contribution: Multi-modal data fusion significantly improves prediction accuracy, while the framework consistently outperforms baseline methods across evaluation metrics.The ablation study in Table III evaluates the contribution of each data modality.
- Predictive Optimisation: Predictive modelling with MPC-based optimisation anticipates congestion and proactively adjusts system parameters, reducing queue accumulation and improving throughput.The control loop fuses multi-source data, predicts the next queue state with LSTM, solves an MPC problem, and applies actions such as lane allocation and routing.
- Robustness: Less than 10% degradation in prediction accuracy is maintained under sudden traffic surges and partial sensor failures, compared with over 25% degradation for baseline models.The evaluation uses synthetic traffic scenarios designed to emulate realistic border crossing conditions.
V. CONCLUSION
The paper presents a unified border-control framework combining multi-modal data fusion, deep learning, and control-theoretic optimisation. It reports improvements in predictive accuracy and operational performance while supporting proactive, adaptive traffic management.
- The framework combines multi-modal data fusion, deep learning, and control-theoretic optimisation for intelligent queue prediction and optimisation.
- The proposed approach demonstrates substantial improvements in predictive accuracy and operational performance.
- The framework enables proactive and adaptive traffic management in border control systems.