Multi-Horizon Explainable Queue Forecasting and Resource-Aware Decision Support for Intelligent Border Control Using LightGBM

Varvara Mama; Fani D. Christidi; Nikolaos C. Kapsalis; Anargyros T. Baklezos; Christos D. Nikolopoulos

cs.AIOct 11, 2026

Multi-Horizon Explainable Queue Forecasting and Resource-Aware Decision Support for Intelligent Border Control Using LightGBM

Authors: Varvara Mama, Fani D. Christidi, Nikolaos C. Kapsalis, Anargyros T. Baklezos, Christos D. Nikolopoulos

Organizations: Department of Electronic Engineering, Hellenic Mediterranean University · School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece

Abstract

Border crossing points are stochastic service systems in which traffic demand, traveler categories, registration status, inspection requirements, staffing, and lane availability interact on multiple time scales. This paper extends an earlier multimodal queue-management framework by introducing an implementation-oriented, multi-horizon forecasting and decision-support architecture based on Light Gradient Boosting Machine (LightGBM) models. In contrast to a single forecasting block, the proposed approach constructs horizon-specific regressors for 15, 30, 60, and 120 min forecasts and jointly estimates future waiting time and queue lengths. The models operate on temporally lagged traffic, service, density, and queue features, while an operational layer incorporates lane compatibility, officer availability, possibly degraded resources, traveler category, preregistration information, and human-approval constraints. Quantile LightGBM models provide 95% prediction intervals for waiting-time forecasts. Beyond prediction, the framework produces machine-readable recommendations, alerts, aggregate key performance indicators, confidence information, and integration outputs suitable for border resource-management and decision-support systems. The results establish a reproducible synthetic-data baseline and a practical interface for subsequent validation with operational border-crossing data.

Figures & tables

Sampling interval	5 min
Total duration	35 days
Queue streams	5
Total observations	50,400
Training period	First 28 days
Training observations	40,320
Test period	Subsequent 7 days

TABLE I: Synthetic Dataset Protocol

Fig. 1: Representative synthetic queue-length profiles for the five operational streams during one day of the unseen test period. The figure illustrates the heterogeneous temporal dynamics generated for cars, buses, and truck/customs processing. Thirty-minute plotting intervals are used for readability; the underlying data resolution is five minutes.

Fig. 2: Unseen-test prediction error as a function of forecast horizon: (a) waiting-time MAE and RMSE; (b) queue-length MAE and RMSE. Both error measures increase with horizon, illustrating the progressive uncertainty of longer-range border-queue forecasting.

Horizon	Waiting Time			Queue Length
(min)	MAE (min)	RMSE (min)	$R^{2}$	MAE (veh.)	RMSE (veh.)	$R^{2}$
15	5.007	10.186	0.9870	0.784	1.182	0.9995
30	5.705	11.435	0.9836	1.108	1.704	0.9990
60	6.588	13.074	0.9785	1.609	2.557	0.9977
120	8.472	16.729	0.9648	2.468	3.926	0.9946
Overall	6.438	13.079	0.9785	1.490	2.558	0.9977

TABLE II: Unseen Test Performance by Forecast Horizon

Fig. 3: Representative 30-min-ahead waiting-time forecast for the trucks_customs stream during one day of the strictly unseen test period. The graph compares the future observed waiting time with the LightGBM prediction and illustrates both normal tracking and transient forecast deviations.

Fig. 4: Predicted versus observed 30-min-ahead waiting time across all queue streams in the unseen test set. Every tenth point is displayed for visual clarity; all test samples are retained in the numerical metrics.

Fig. 5: Relative change in unseen-test MAE under feature ablation, referenced to the full LightGBM model: (a) waiting-time prediction and (b) queue-length prediction. Removing cyclical temporal encoding produces the largest degradation, particularly for queue-length forecasting.

Configuration	Waiting Time			Queue Length
	MAE (min)	RMSE (min)	$R^{2}$	MAE (veh.)	RMSE (veh.)	$R^{2}$
Full model	6.438	13.079	0.9785	1.490	2.558	0.9977
Without lagged history	6.436	13.078	0.9785	1.511	2.599	0.9976
Without demand/context	6.414	13.048	0.9786	1.504	2.573	0.9977
Without service rate	6.582	13.407	0.9774	1.510	2.576	0.9977
Without temporal encoding	7.284	14.542	0.9734	2.102	3.937	0.9946

TABLE III: Feature Ablation Study Under the Same Unseen Hold-Out Protocol

Study	Application	Models/approach	Forecast setting	Reported result	Relation to present work
Zhang and Haghani [ 4 ]	Freeway travel time	Gradient boosting regression trees; ensemble/statistical baselines	Multi-step ahead	GBM reported advantageous prediction accuracy and interpretability	Establishes gradient boosting as a suitable nonlinear, interpretable travel-time predictor.
Berliotti et al. [ 15 ]	Road traffic flow, 14 sensors	LightGBM, XGBoost, CatBoost, Random Forest	Hourly traffic; two unseen one-week periods	First test week: LightGBM MAE 0.0679; XGBoost 0.0649; CatBoost 0.0637 (normalized task scale)	Demonstrates that no boosting method is universally dominant and motivates same-data model comparison.
Wang et al. [ 3 ]	209 urban signalized intersections	Dynamically Weighted LightGBM versus LightGBM, XGBoost, LSTM, GRU, RF, SVM, KNN	Hourly traffic-volume forecasting	DW-LGBM RMSE 5.54, $R^{2}=0.9996$ ; standard LightGBM RMSE 118.13, $R^{2}=0.6574$	Supports temporal weighting/encoding and shows the importance of feature architecture around LightGBM.
Hari et al. [ 16 ]	Multi-location pedestrian/crowd demand	LightGBM, CatBoost, LSTM and other models	Multiple prediction windows	Best model changes with location and horizon; reported $R^{2}$ ranges from negative values at difficult low-volume sites to $>0.5$ at busier sites	Reinforces horizon- and location-dependent evaluation rather than reliance on one aggregate metric.
Present work	Border queue waiting time and queue length	Horizon-specific LightGBM + quantile LightGBM + resource-aware decision layer	15/30/60/120 min; strict 28-day train/7-day unseen test	Wait: MAE 6.438 min, RMSE 13.079 min, $R^{2}=0.9785$ ; Queue: MAE 1.490 veh., RMSE 2.558 veh., $R^{2}=0.9977$	Adds border-specific segmentation, service/resource constraints, uncertainty intervals, JSON interoperability, and operational recommendations.

TABLE IV: Positioning Against Selected Published Forecasting Studies

Time Series Forecasting

Time series forecasting predicts future values from ordered observations using statistical, neural, and probabilistic models to support decisions under uncertainty.

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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+1

Department of Electronic Engineering, Hellenic Mediterranean University, Chania, Greece, Crete, Greece · School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece, Athens, Greece

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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.

Abstract

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