Two-wheeler seepage behavior—the aggressive lane-splitting, lateral maneuvering and small-gap acceptance in dense mixed traffic—poses a significant challenge for traffic simulation and autonomous vehicle development in South Asian urban environments. Despite the dominance of two-wheelers in these regions, existing microscopic traffic simulators rely on generic, largely Western-calibrated parameters that fail to reproduce realistic seepage dynamics almost entirely: the default minimum-gap parameter (2.5 m) exceeds the observed mean Indian seepage gap (0.906 m) by 176%, making most real seepage opportunities physically infeasible in simulation. This study proposes a novel real-to-simulation data-driven calibration framework for modeling two-wheeler seepage behavior using 310,110 video frames from the IDD and TIAND real-world traffic datasets. Utilizing a multi-stage quality filtering based on detection confidence, traffic density, and gap realism criteria, we yield 13,219 algorithmically validated seepage events (retained through algorithmic quality scoring across confidence, density, and gap-geometry criteria) with empirical distributions of gap acceptance, lateral positioning, and seepage maneuver types. A percentile-based strategy maps aggressive gap-acceptance thresholds to SUMO simulator parameters, replacing conventional mean-based estimation. Default SUMO produces only 132 seepage events versus 80,553 from the calibrated model—a 610 × increase. The calibration targets seepage emergence at scale and faithful reproduction of the aggressive-tail threshold ( P 10 = 0.272 m) rather than distributional equality across all percentiles. The calibrated mean gap (0.609 m) undershoots the observed mean (0.906 m), a trade-off inherent to parameterized car-following models. On a geo-referenced OpenStreetMap network of Hyderabad the calibrated model generates 49% more seepage events and reproduces the empirical P 10 within 1%. This work provides the first comprehensive, data-driven characterization of two-wheeler seepage behavior and establishes a replicable calibration and validation methodology for realistic mixed-traffic heterogeneous traffic simulation. The framework provides a building block for routing optimization and fleet management where seepage dynamics materially affect travel times and network throughput in different types of traffic environments.

This work was supported by the project ‘‘[DISCOVERY]: Distributed Smart Communications with Verifiable EneRgy-optimal Yields’’ PID2023-148716OB-C32 (Agencia Estatal de Investigación, Spain, Ministerio de Ciencia e Innovación); also by the project ‘‘MultiMO: Datos MultiSectoriales para la Movilidad Obligada’’ TSI-100123-2024-60 (Ministerio de transformación digital y de la función pública, NextGenerationEU); also, by predoctoral scholarship for the training of research personnel associated with the ‘‘Generación de Conocimiento’’ Project PRE2021-099830.

Electric vehicles (EVs) offer significant potential for reducing emissions, yet their expansion is constrained by long charging times, limited charging infrastructure, and inefficient charging station (CS) utilization. This study proposes an intelligent platform that explicitly supports drivers in multiple aspects: predicting EV energy consumption (EVEC), estimating the remaining energy at the destination, and determining the remaining driving range, thereby assisting drivers in deciding whether to continue their trip or stop for recharging. The system also recommends the most appropriate CS by considering driver preferences. Using SUMO simulations with OpenStreetMap data to prepare realistic traffic scenarios, the platform combines ensemble machine learning (ML) models with federated learning (FL) to optimize EV charging decisions. Through the integration of Bi-LSTM + XGBoost for EVEC prediction and FFNN + XGBoost for the optimal CS selection, the proposed framework significantly outperforms conventional methods by minimizing total travel time and resulting in mean absolute error (MAE) values ranging from 2.3 to 4.5 minutes under varying traffic conditions.

The coordinated scheduling of electric vehicle (EV) charging is a critical challenge for smart cities, particularly in high-density infrastructure such as Mobility Hubs (MHs). This paper evaluates and compares two prominent approaches to the EV Charging Scheduling Problem (CSP): Mixed-Integer Linear Programming (MILP) and Reinforcement Learning (RL). We formulate a shared problem framework and apply both strategies under two structured scenarios: a small-scale deterministic benchmark and a medium-scale, realistic deployment with higher heterogeneity. Results show that MILP achieves optimal cost and state of charge SoC compliance in tractable cases but struggles with scalability. RL, based on Proximal Policy Optimization (PPO), achieves near-optimal performance while scaling to 100 EVs with minimal computation time. Despite occasional SoC deviations, the RL agent exhibits robust and adaptive behavior under dynamic conditions. This study offers actionable insights for selecting and deploying EV scheduling strategies in real-world urban environments.

Modern urban mobility systems are increasingly dependent on precise and scalable simulation platforms to facilitate the design, assessment, and optimization of intelligent transportation systems (ITS). This paper sets forth a thorough benchmarking investigation of three extensively utilized traffic simulation platforms: SUMO (Simulation of Urban MObility), CityFlow, and MATSim, with an emphasis on their computational efficacy within large-scale synthetic traffic scenarios. We conduct an evaluation of these platforms across vehicle volumes ranging from 10 to 10 million vehicles, assessing total runtime, CPU and GPU utilization, and memory consumption on high-performance computing infrastructures. Our results reveal notable architectural trade-offs: SUMO exhibits predictable linear scaling but becomes constrained by CPU limitations at elevated vehicle counts, CityFlow encounters memory limitations beyond 10,000 vehicles, whereas MATSim necessitates meticulous JVM (Java Virtual Machine) optimization to effectively manage large-scale scenarios. This investigation provides essential guidance for researchers and practitioners in the selection of suitable simulation tools for urban-scale traffic modeling and identifies critical computational challenges associated with the scaling of simulations for smart city initiatives.

Non-technical losses represent a significant challenge for energy distribution companies due to their economic impact. These losses typically arise from irregularities at supply points and fraudulent customer behavior. In Cuba, electricity meter readings are performed manually, and consumption data is processed using spreadsheets that combine weighted criteria to generate alerts for potential anomalies. This procedure is prone to vulnerabilities such as manual data entry errors, incorrect key assignments, and human mistakes made by field readers, which compromise the reliability of the analysis. In this paper we present an anomaly detection system for electricity consumption, developed using artificial intelligence techniques to identify irregularities based on monthly reports from residential users. Various machine learning methods were evaluated, with eXtreme Gradient Boosting (XGBoost) standing out for its effectiveness in handling imbalanced datasets. Additionally, a web application was implemented using Flask to process consumption data and provide real-time predictions, optimizing the management of nontechnical losses. The results confirm that, with real consumption data, the algorithms achieve high accuracy in fraud detection, even in scenarios with severe class imbalance, validating the robustness of the system. Beyond its application in the energy sector, this solution is adaptable to other contexts requiring anomaly detection in transactional data. This proposal contributes to reducing economic losses, improving operational efficiency, and laying the groundwork for future research in intelligent monitoring systems.

Traffic congestion in urban areas has intensified due to the rapid growth of vehicles, inadequate infrastructure planning, and unsynchronized traffic signals. This study presents an adaptive traffic signal control strategy called Gap-Fuzzy, which combines the Mamdani fuzzy logic controller with a gapout detection mechanism. The system dynamically adjusts the duration of the green light based on real-time traffic data, including queue length and arrival rate. Furthermore, the green phase is terminated early if prolonged gaps in vehicle flow are detected. We evaluated the proposed Gap-Fuzzy system using the SUMO microscopic traffic simulator. The results indicate that it reduced vehicle waiting times by up to 70% and CO2emissions by 25% compared to fixed-time control. In addition, it outperformed the SUMO actuated controller under low, medium, and high traffic conditions while maintaining comparable performance under very high demand. These findings highlight the effectiveness of integrating fuzzy logic with gap-out detection to enhance traffic flow and minimize environmental impact.

We are witnessing a profound shift in societal and political attitudes, driven by the visible consequences of climate change in urban environments. Urban planners, public transport providers, and traffic managers are urgently reimagining cities to promote sustainable mobility and expand green spaces for pedestrians, bicycles, and scooters. To design more sustainable cities, urban planners require realistic simulation tools to optimize mobility, identify location for car chargers, convert streets to pedestrian zones, and evaluate the impact of alternative configurations. However, realistic traffic profiles are essential to produce meaningful simulation results. Addressing this need, we propose a traffic generator based on deep reinforcement learning integrated with the SUMO simulator. This tool learns to generate an instantaneous number of vehicles throughout the day, aligning closely with the target profiles observed at the traffic monitoring stations. Our approach generates accurate 24-hour traffic patterns for any city using minimal statistical data, achieving higher accuracy compared to existing alternatives. In particular, our proposal demonstrates a highly accurate 24-hour traffic adjustment, with the generated traffic deviating only by about 5% from the real target traffic. This performance significantly exceeds that of current SUMO tools like RouteSampler, which struggle to accurately follow the total daily traffic curve, especially during peak hours when severe traffic congestion occurs.