Engineering

Abstract: Highway–rail grade crossing (HRGC) incidents remain a persistent safety concern due to repeated interactions between roadway users and rail operations under varying environmental and operational conditions. Existing studies rely on raw incident counts or partial exposure measures, which do not provide a consistent basis for comparing risk across locations or accounting for spatial dependence. This study developed an exposure-normalized framework to model incident intensity at the county level using accumulated incidents per crossing (AIPC). The analysis integrated statistical distribution modeling, spatial clustering, and supervised machine learning. The study combined county-level HRGC data for the contiguous United States with infrastructure, traffic, environmental, and accessibility variables. Results showed that AIPC was consistent with a gamma distribution, indicating a continuous representation of incident intensity without discrete risk regimes. Local Moran’s I identified statistically significant high-intensity clusters in specific regions, confirming spatial dependence in incident intensity. Machine learning models achieved strong predictive performance, with the extra trees model reaching AUC = 0.907 and ensemble methods outperforming linear and kernel approaches. Feature importance analysis identified temperature, train frequency, and accessibility measures as the most influential predictors, while aggregate density measures contributed the least. The results provided consistent evidence that incident intensity was associated with environmental conditions, operational exposure, and network structure. The proposed framework supports exposure-based risk assessment and enables identification of high-intensity counties for targeted intervention. This approach provides a transparent and transferable method for improving HRGC safety analysis and prioritizing resource allocation across large geographic areas.

Abstract: The rapid growth of social media has transformed digital communication while simultaneously increasing the spread of hate speech, offensive language, and abusive online behavior. Automated hate speech detection has therefore become a critical research challenge in Natural Language Processing (NLP) and Machine Learning (ML). This paper presents a machine learning framework for hate speech detection using TF-IDF and Word2Vec feature extraction techniques combined with Logistic Regression, Support Vector Machine (SVM), Random Forest, and Naïve Bayes classifiers. Experimental evaluation demonstrates that SVM achieved the highest performance with 93.4% accuracy and 92.9% F1-score. The study further discusses contextual ambiguity, sarcasm detection challenges, feature interpretability, and future integration with transformer-based architectures such as BERT and multilingual NLP models.

Abstract: Sustainable transportation systems can remain operational while becoming functionally vulnerable when access to them deteriorates. We distinguish two dimensions of transport resilience—operational resilience, the continued running of the main transport services, and functional accessibility resilience, users’ capacity to reach them—and locate weather-induced vulnerability in the latter. We focus on weather-induced last-mile vulnerability and examine how adverse weather alters mode choice. Drawing on a discrete choice experiment with 760 respondents in Portland, we estimate a mixed logit model capturing the effects of rain. The results reveal that rain significantly increases the disutility of travel time, intensifies the burden of last-mile walking, and strongly discourages bicycle use. In contrast, no statistically significant additional average disutility is found for transit itself. Marginal willingness to pay for travel time reduction increases from $1.53 per minute in sunny weather to $2.54 in rainy weather, while the average predicted probability of choosing car rises from 34.9% to 54.4%. These findings suggest that weather sensitivity is driven less by main transit services than by the surrounding access conditions and active travel links. The study contributes to transportation resilience research by showing that strengthening last-mile access under adverse weather is essential for sustaining low-carbon mobility choices.

Abstract: This study investigates how intersection-related factors affect traffic crash severity through a comparative analysis of two major U.S. cities: Chicago and New York City (NYC). Using large-scale crash datasets, the analysis applies logistic regression and machine-learning methods to assess how intersections and temporal conditions influence injury outcomes. The results indicate that intersection-related crashes significantly increase the probability of injury in both cities, though the magnitude is substantially larger in Chicago. Nighttime conditions consistently elevate crash severity across both cities. Model evaluation using ROC curves suggests moderate predictive performance, indicating the influence of additional unobserved factors. A comparative modeling framework further reveals that the relationship between intersection-related factors and crash severity is context-dependent, varying across urban environments. These findings highlight the importance of developing location-specific traffic safety strategies and demonstrate the value of integrating statistical, machine-learning, and spatial analyses in crash severity research.

Abstract: Existing unmanned aerial vehicle (UAV) urban logistics planning follows a sequential paradigm—depot siting first, routing second—that embeds a structural information loss. Straight-line distance screening systematically overestimates the feasible service radius of candidate depots, creating a blindzone of depot–demand pairs that appear reachable but prove operationally infeasible under road-network distances. We term this range-feasibility blindness and derive its analytical radius Δ=Rmax(α−1)/(2α), where α is the road-to-straight-line distance ratio. Empirical measurement across three Chinese urban districts confirms α∈[1.40,1.52] and blindzone radii exceeding 2.8 km, establishing the phenomenon as a systemic property of high-density urban road geometry. To eliminate this failure by construction, we formulate a feasibility-embedded location–routing mixed-integer linear programme (MILP) that enforces road-network range constraints simultaneously with depot-opening decisions, making blindzone configurations implicitly inadmissible. A structure-aware Adaptive Large Neighbourhood Search (ALNS) solves the model at practical scales. Benchmark experiments across all three cities show consistent cost reductions of 20.6–28.2% over sequential baselines, with gains increasing monotonically with instance scale. These results position joint optimisation as a necessary methodological shift for city-scale UAV infrastructure planning.

Abstract: Highway–rail grade crossing (HRGC) safety analysis is often based on raw incident counts or site-level models that do not control for exposure and ignore spatial dependence. This limits the ability to identify where risk is structurally concentrated across the rail network. The problem is important because misidentifying high-risk environments leads to inefficient allocation of limited safety resources and weakens corridor-level intervention strategies. This study introduces accumulated incidents per crossing (AIPX), an exposure-normalized metric that measured cumulative incident burden at the county level over a 51-year period (1975–2025). The study developed an algorithmic framework that integrates data reconciliation with spatial autocorrelation analysis, distributional modeling, and nonparametric machine learning to identify and interpret high-intensity risk environments. Global Moran’s I indicates statistically significant positive spatial autocorrelation (I = 0.359, p = 0.001), confirming that incident intensity is spatially clustered rather than random. Local indicators identify coherent high and low intensity county clusters. Distributional analysis shows that AIPX in high intensity clusters follows heavy-tailed behavior best represented by lognormal and Johnson SU distributions, indicating concentrated risk in a small subset of counties. Machine learning models achieve strong classification performance (AUC ≈ 0.85), with explainability methods consistently identifying temperature, train direction, crossing warning configuration, train composition, and track class as dominant associated features. These variables function as proxies for exposure intensity and network structure rather than causal drivers. The findings demonstrate that HRGC risk is a regional, network-driven phenomenon concentrated along freight-intensive corridors. The study provides a transparent and transferable framework that supports corridor-level prioritization of safety interventions and more effective allocation of infrastructure investments.

Abstract: Addressing the issues of insufficient adaptability and limited energy efficiency optimization capabilities in traditional tunnel lighting control methods under complex traffic conditions, this paper proposes a dynamic dimming strategy for tunnel lighting based on the Proximal Policy Optimization (PPO) algorithm.First, the tunnel lighting system is modeled as a reinforcement learning environment. A state space integrating multi-dimensional information—including traffic flow, vehicle speed, external brightness, and tunnel section location—is constructed, and a continuous action space is designed to enable precise dimming control for each functional section. Based on this, a multi-objective reward function is established that integrates brightness tracking error, energy consumption optimization, control stability, and environmental adaptability to guide the agent in learning the optimal dimming strategy.Subsequently, model training and experimental validation were conducted using actual tunnel operation data.Experimental results indicate that, compared to traditional L20 control strategies, the proposed method achieves smoother brightness regulation and higher zone control accuracy while ensuring driving safety and visual comfort, and demonstrates significant energy-saving advantages during periods of high lighting demand. In summary, the dynamic dimming strategy based on the PPO algorithm shows promising application prospects and engineering value in intelligent tunnel lighting systems.

Abstract: To address the complex spatiotemporal dependencies and dynamically evolving spatial relationships in tunnel traffic flow prediction, a macro–micro collaborative two-stage prediction method is proposed.The Grey Wolf Optimizer (GWO) is first employed to optimize the GRU model for predicting incoming traffic flow at the tunnel entrance, providing reliable macro-level input for subsequent modeling.Based on this, a spatiotemporal graph structure is constructed, and an FSE-ST-GCN model integrating an adaptive adjacency matrix with spatial and channel attention mechanisms is developed to capture dynamic spatial dependencies and enhance key feature representation.Experiments are conducted using real-world traffic flow data collected from the Shizuizi Tunnel on the Jilin–Caoshi Expressway. The results show that the proposed method outperforms baseline models in terms of MAE, RMSE, and MAPE, achieving superior prediction accuracy and stability. This work provides effective technical support for refined tunnel traffic management and lighting control.

Abstract: Student safety during daily school transportation remains a major concern, particularly in systems that rely mainly on GPS tracking and manual supervision. Existing approaches often lack proactive safety mechanisms for monitoring both student attendance and driver condition in real time. This paper presents MUTMA’INN derived from the Arabic word “مطمئن”, meaning being reassured, at peace, or tranquil, reflecting the system’s role in ensuring the safety and security of students during transportation. The proposed system is an AI-powered school bus safety framework designed to improve the security and reliability of daily student transportation in alignment with Saudi Vision 2030’s Quality of Life Program. The proposed system consists of two integrated components: a cross-platform Flutter mobile application for parents, drivers, and school administrators, and a Python-based edge system connected to Firebase for real-time synchronization. The framework automates student attendance through facial recognition at the bus gate, reducing manual effort and the risk of human error. In addition, it monitors the driver using contactless remote photoplethysmography and facial analysis techniques to estimate heart rate and detect signs of fatigue or emotional distress. When abnormal conditions are detected, immediate alerts are sent to administrators to support timely intervention. By combining mobile computing, edge intelligence, computer vision, and cloud services into a unified platform, MUTMA’INN provides a proactive approach to school transportation safety. The proposed framework demonstrates how AI can support safer and more intelligent student transit systems.

Abstract: In this study, the traffic operational effects of a pacemaker system (PMS) on the traffic operation in the Geumnam Tunnel on the Seoul–Yangyang Expressway was evaluated herein using a before–after analysis based on long-term vehicle detection system (VDS) data. Changes in spatiotemporal traffic flow and traffic capacity, and speed improvement under different levels of service (LOS) were analyzed using data from five VDS detectors installed upstream and downstream of the tunnel. After PMS installation, (i) increased average and 25th-percentile speeds at most detector locations and decreased standard deviation of speed were observed both near the tunnel exit and the downstream sections, (ii) maximum traffic volume was increased from 1661 to 1765 veh/h/lane (~6.3% increase), (iii) LOS-based speed improvement analysis showed that mean speed and 25th-percentile speed increased by ~6.5%, indicating the alleviation of speed reduction among low-speed vehicles due to PMS. These results prove that PMS increases vehicle speed, reduces speed variability, and enhances traffic flow stability and processing capability. These findings provide empirical evidence supporting the operational effectiveness of a PMS as a practical tool for mitigating phantom congestion in highway tunnel sections and reducing the speed differences between vehicles and improve traffic stream stability.

Abstract: Floating belt conveyor routes, consisting of serially arranged belt conveyors, the end parts of which are mechanically attached to floating bodies, are designed for the continuous transport of extracted granular materials from the water. The paper deals with the analytical determination of the position of the centre of gravity of the buoyancy force, the coordinates of which change depending on the longitudinal deflection of the floating body from the equilibrium state, which acts as a supporting element of individual conveyor belts. The analysis of the individual phases of deflection of the floating body, consisting of a pair of floats with a circular cross-section, shows that the complete submergence of one of the floats occurs at a higher value of the angle of inclination in the case when the floats are initially submerged under the surface to exactly half of their diameter. On the realized experimental device the buoyancy force was detected using strain gauges during the deflection of the floating body from the equilibrium position for three defined levels of immersion. The floating body of the experimental device consists of a pair of floats with a circular cross-section with a diameter of 80 mm. The output is a structured methodological procedure for determining the position of the centre of gravity of the displacement (centre of buoyancy) of a floating body when it deviates from the equilibrium position and a methodology for calculating the stability arm, which is a key parameter for assessing the buoyancy and stability of the body. On the basis of the laboratory measurements, the magnitude of the buoyancy force can be quantified as a function of the immersion depth of the floating body. It was found that the buoyancy force remains constant when the body deflects only when the immersion corresponds to half the diameter of a float with a circular cross-section. If the depth of the immersion is less than the radius of the float, the buoyancy force increases during deflection; on the contrary, if the immersion is greater than the radius of the float, the buoyancy force decreases.

Abstract: Temperature control during hot-mix asphalt production and paving is critical to construction quality and emissions. However, conventional thermometry is susceptible to dust and vibration. This study proposes an indirect temperature monitoring system using electronic nose technology, exploiting the nonlinear correlation between asphalt VOC odor fingerprints and temperature. The system integrates a MOS sensor array with IoT feedback, with a proof-of-concept study involving three asphalt types at five temperature levels. Leave-One-Out Cross-Validation (LOOCV) was employed to mitigate overfitting. The hybrid modeling framework significantly outperformed the Multiple Linear Regression (MLR) baseline, achieving 88.9% three-class accuracy and a regression RMSE of ±6.2℃. Drift compensation improved accuracy by 16.4%. These results show the feasibility of multi-dimensional odor patterns for quantitative temperature prediction, offering a new paradigm for closed-loop, non-contact temperature control in smart, low-carbon pavement engineering.

Abstract: Short-term rider demand forecasting is a foundational operational capability for Mobility-on-Demand (MoD) systems, enabling proactive vehicle pre-positioning, dynamic pricing, and service-level optimization across ride-hailing, bike-sharing, carsharing, and demand-responsive transit platforms. Despite a rapidly growing body of literature, the field lacks a comprehensive and critically structured synthesis of methodological developments, input feature practices, evaluation standards, and unresolved research gaps. This paper presents a Systematic Literature Review (SLR) conducted in accordance with the PRISMA protocol, encompassing 291 peer-reviewed studies published between 2016 and 2025 across transportation engineering, intelligent transportation systems, and machine learning venues—the most comprehensive corpus assembled for this topic to date. The review identifies a clear five-generation methodological succession—from classical statistical and machine learning models through recurrent and convolutional deep learning architectures to Graph Neural Networks and transformer-based models—with signal decomposition methods and probabilistic architectures emerging as distinct 2023–2025 trends. Most significantly, we identify a seventeen-dimension research gap matrix that involves research gaps such as probabilistic demand forecasting, cross-city transfer, decision-focused predict-and-optimize frameworks, etc. Further, six concrete research directions grounded in these gaps are proposed, each accompanied by specific methodological proposals rather than general aspirational statements. The findings underscore the need for standardized benchmarking protocols, open dataset releases with documented preprocessing, and a fundamental reorientation of model evaluation from statistical accuracy metrics toward composite operational, probabilistic, and equity-aware performance objectives.

Abstract: Accurate per-wagon occupancy accounting at freight stations — knowing which wagon entered or exited which track and when — is a prerequisite for automated shunting management, yet existing technologies — axle counters, RFID, computer vision, and LPWAN IoT — each provide only a subset of the required information and depend on dedicated infrastructure or favourable conditions. This paper investigates whether two fixed BLE gateways, combined with Eddystone-TLM beacon nodes proposed for mounting on freight wagon bodies, can classify passage direction from RSSI signals without training data, site-specific calibration, or track modification. The enabling mechanism is wagon-body attenuation: as a wagon passes between the receivers, its metallic body creates a temporal asymmetry in the RSSI envelopes that encodes travel direction. We present a five-stage online pipeline at O (1) memory per packet: a two-sided CUSUM detector with adaptive per-event baseline estimation segments the RSSI stream; a three-stage validation filter rejects partial passes, lateral paths, and near-gateway reversals; and direction is classified by the normalized Temporal Centroid shift — a speed-invariant feature requiring no training data — with a cascade fallback for ambiguous short windows. Combined with the beacon MAC address as a wagon identifier, the system generates structured occupancy events directly consumable by station management systems. Validated on 151 labelled events across eight scenario categories at Urtaul freight station and the TSTU test polygon, the pipeline achieves 96.7% accuracy (95% Wilson CI: [92.5%, 98.6%]) zero wrong-direction predictions across all 84 directional events (exact Clopper-Pearson 95% CI for the wrong-direction rate: [0%, 3.5%])", a Random Forest baseline on the same features confirms supervised learning adds no measurable benefit over the training-free approach within this feature space.

Abstract: As cities worldwide face challenges of rapid urbanization and declining public transit ridership, traditional fixed-route systems often fail to meet evolving mobility needs. Urban planning issues, such as suburban sprawl and fragmented land use, exacerbate these limitations, leading to underutilized services, higher operational costs, and accessibility gaps, particularly for underserved communities. Demand-Responsive Transit (DRT) systems have emerged as an effective solution, offering flexible, on-demand services that dynamically adjust routes based on user demand. This review synthesizes insights from 65 studies, including 20 real-world implementations, examining DRT's potential to enhance accessibility, cost efficiency, and environmental sustainability. Key findings demonstrate that DRT systems reduce operational costs by 25-35% while increasing ridership up to 300%. Integration of AI-driven routing algorithms improves service reliability by 90-98% and reduces travel times by 35-50%. Multiple booking interfaces increase adoption by 40-60%, while multimodal integration expands service coverage by 100-150%. However, significant barriers persist, with 58% of DRT system models requiring subsidies and 51% facing equity challenges. The study proposes hybrid funding models, integrated multimodal platforms, and inclusive design approaches to address these challenges. By aligning with urban design principles and leveraging advanced technologies, DRT systems can enhance urban resilience while promoting sustainable development.

Abstract: With the expansion of offshore oil and gas exploration into deep-water regions, the efficient scheduling of Platform Supply Vessels (PSVs) is critical to offshore operations. The Platform Supply Vessel Routing and Scheduling Problem (PSVRSP) is an NP-hard combinatorial optimization problem, which is further complicated by uncertainty in offshore demand. Existing studies reveal a methodological gap: heuristic approaches cannot guarantee optimality, while exact algorithms often ignore demand uncertainty. To address this gap, this study proposes a Branch-and-Price (B&P) method for the Platform Supply Vessel Routing and Scheduling Problem with Uncertain Demand (PSVRSP-UD). A scenario-based Mixed-Integer Linear Programming (MILP) model is formulated, in which demand uncertainty is captured using Latin Hypercube Sampling (LHS) combined with Cholesky Decomposition and Sample-Based Reduction (SBR). Based on Dantzig–Wolfe Decomposition, the proposed B&P algorithm integrates NG-Route labeling and a two-level branching strategy to achieve global optimization. Computational experiments show that the B&P algorithm outperforms CPLEX in both computational efficiency and solution quality. Sensitivity analyses examine the impacts of scenario number, demand fluctuation, and weight coefficients on the results. The new results in this study can provide a practical decision-support tool for offshore logistics operations.

Abstract: Concept-phase planning of diesel-engined hybrid vehicles requires rapid engine synthesis, including brake specific fuel consumption (BSFC) estimation, with minimal input data. Fuel savings from hybridization arise partly through engine downsizing and engine-off operation, so trade studies depend on knowing the dependence of BSFC on engine sizing and speed and load conditions. This paper presents a method for synthesizing hypothetical modern diesel engines of any given size for the purpose of trade studies, while matching the performance and efficiency capabilities of commercially available units. Relationships are developed between rated power, rated speed, peak torque, displacement and cylinder count for four vehicle application classes. Together with a BSFC estimation method, these relationships form a complete engine synthesis chain from rated power to a full torque curve and BSFC map, with provision for substituting known data, including minimum BSFC, where available. The method supports continuous scaling.

Abstract: The rapid evolution of urban environments and the growing demand for efficient transportation systems have accelerated the transition toward smart cities. In this context, traffic modeling and urban mobility analysis play a critical role in understanding, predicting, and optimizing complex transportation dynamics. This study explores contemporary approaches to traffic modeling, integrating data-driven methodologies, simulation techniques, and intelligent transportation systems to enhance urban mobility in Petrosani city from Romania. Emphasis is placed on the use of big data, Internet of Things (IoT) technologies, and machine learning algorithms for real-time traffic monitoring, demand forecasting, and adaptive traffic management. The paper examines the interaction between traditional modeling frameworks and emerging smart city infrastructures, highlighting how advanced analytics can improve congestion mitigation, reduce environmental impact, and support sustainable mobility solutions. Furthermore, it discusses multimodal transportation integration, user behavior analysis, and policy implications for urban planners and decision-makers. A conceptual framework is proposed to bridge the gap between theoretical models and practical implementations within smart city ecosystems. The findings suggest that the convergence of digital technologies and traffic modeling significantly enhances the resilience, efficiency, and sustainability of urban mobility systems. The study contributes to the ongoing discourse by identifying key challenges, opportunities, and future research directions in the development of intelligent, data-driven transportation networks.

Abstract: Urban intersection traffic signals play a crucial role in managing traffic flow and ensuring road safety. However, traditional actuated signal controllers make phase-switching decisions based on limited local traffic information, without leveraging network-wide context from navigation services. In this paper, we propose CATS, a Context-Aware Traffic Signal control system that jointly optimizes intersection signal control and road navigation for Connected and Automated Vehicles (CAVs). CATS integrates two key components: a Best-Combination CTR (BC-CTR) scheme and the Self-Adaptive Interactive Navigation Tool (SAINT). BC-CTR enhances the original Cumulative Travel-time Responsive (CTR) scheme by selecting the phase with the highest cumulative travel time (CTT) first and then identifying the compatible phase combination with the greatest group CTT, allowing more accurate response to real-time intersection demand. SAINT provides congestion-aware route guidance via a congestion aware mechanism, directing vehicles away from congested segments while signal timings simultaneously adapt to incoming traffic. By comparing with other baselines, our simulation results show that under moderate-to-heavy traffic conditions, CATS reduces mean end-to-end travel time by up to 23.72% and improves throughput by up to 93.19% over the baselines, confirming that the co-design of navigation and signal control produces complementary benefits.

Abstract: Inadequate vehicle maintenance management is one of the main causes of road accidents and elevated operating costs in light vehicles. This paper addresses this problem through the development and implementation of a low-cost integrated system for preventive maintenance management and alerts. The device, based on an open-hardware architecture (Arduino Mega 2560), integrates Global Positioning System (GPS) and mobile communication (GSM/LTE) modules to monitor distance traveled in real time and notify the user via SMS about the proximity of critical services such as oil changes, brake inspections, and timing-belt replacements. Experimental validation was conducted in the city of Guayaquil using a 2012 Hyundai Accent. Field tests were carried out in three scenarios: a dense urban route, a peripheral road, and interurban routes. Results showed satisfactory accuracy with a global average percentage error of 3.98% compared to the vehicle’s odometer, and 100% effectiveness in sending alerts. It is concluded that the proposed system is a viable and reliable technological solution to mitigate the "forgetfulness factor" among private drivers, improving road safety and vehicle lifespan.