Search Results (414)

Traffic congestion in large-scale road networks significantly impacts urban sustainability. Traditional traffic signal control methods lack adaptability to dynamic traffic conditions. Recently, deep reinforcement learning (DRL) has emerged as a promising solution for optimizing signal control. This study proposes a Multi-Agent Deep Reinforcement Learning (MADRL) framework for large-scale traffic signal control. The framework employs spatio-temporal attention networks to extract relevant traffic patterns and a hierarchical reinforcement learning strategy for coordinated multi-agent optimization. The problem is formulated as a Markov Decision Process (MDP) with a novel reward function that balances vehicle waiting time, throughput, and fairness. We validate our approach on simulated large-scale traffic scenarios using SUMO (Simulation of Urban Mobility). Experimental results demonstrate that our framework reduces vehicle waiting time by 25% compared to baseline methods while maintaining scalability across different road network sizes. The proposed spatio-temporal multi-agent reinforcement learning framework effectively optimizes large-scale traffic signal control, providing a scalable and efficient solution for smart urban transportation. Full article

The strategic allocation of advertising billboards has become a critical aspect of urban planning and resource management. While previous studies have explored site selection based on road network and population data, they have often overlooked the diminishing marginal returns of overlapping coverage and neglected to efficiently process large-scale urban datasets. To address these challenges, this study proposes two complementary optimization methods: an enhanced greedy algorithm based on geometric modeling and spatial acceleration techniques, and a reinforcement learning approach using Proximal Policy Optimization (PPO). The enhanced greedy algorithm incorporates population-weighted road coverage modeling, employs a geometric series to capture diminishing returns from overlapping coverage, and integrates spatial indexing and parallel computing to significantly improve scalability and solution quality in large urban networks. Meanwhile, the PPO-based method models billboard site selection as a sequential decision-making process in a dynamic environment, where agents adaptively learn optimal deployment strategies through reward signals, balancing coverage gains and redundancy penalties and effectively handling complex multi-step optimization tasks. Experiments conducted on Wuhan’s road network demonstrate that both methods effectively optimize population-weighted billboard coverage under budget constraints while enhancing spatial distribution balance. Quantitatively, the enhanced greedy algorithm improves coverage effectiveness by 18.6% compared to the baseline, while the PPO-based method further improves it by 4.3% with enhanced spatial equity. The proposed framework provides a robust and scalable decision-support tool for urban advertising infrastructure planning and resource allocation. Full article

This paper presents a data-driven energy management scheme for fuel cell and battery electric vehicles, formulated as a constrained optimal control problem. The proposed method employs a co-state network trained using real-time measurements to estimate the control law without requiring prior knowledge of the system model or a complete dataset across the full operating domain. In contrast to conventional reinforcement learning approaches, this method avoids the issue of high dimensionality and does not depend on extensive offline training. Robustness is demonstrated by treating uncertain and time-varying elements, including power consumption from air conditioning systems, variations in road slope, and passenger-related demands, as unknown disturbances. The desired state of charge is defined as a reference trajectory, and the control input is computed while ensuring compliance with all operational constraints. Validation results based on a combined driving profile confirm the effectiveness of the proposed controller in maintaining the battery charge, reducing fluctuations in fuel cell power output, and ensuring reliable performance under practical conditions. Comparative evaluations are conducted against two benchmark controllers: one designed to maintain a constant state of charge and another based on a soft actor–critic learning algorithm. Full article

In this study, the thermal effectiveness of thermally conductive concrete pavements (TCPs) using silicon carbide (SiC) as a fine aggregate replacement was investigated, compared with that of ordinary Portland cement pavements (OPCPs). The most important purpose of this study is to improve the thermal performance of concrete pavement. Additionally, this study utilized improved thermal properties to enhance the efficiency of pavement heating to prevent icing and snow stacking. Both mixtures met the Korean standards for air content (4.5–6%) and slump (80–150 mm), demonstrating adequate workability. TCP exhibited a higher mechanical performance, with average compressive and flexural strengths of 42.88 MPa and 7.35 MPa, respectively, exceeding the required targets of a 30 MPa compressive strength and a 4.5 MPa flexural strength. The improved strength was mainly attributed to the filler effect and partly due to the van der Waals interactions of the SiC particles. Thermal conductivity tests showed a significant improvement in the TCP (3.20 W/mK), which was approximately twice that of OPCP (1.59 W/mK), indicating an enhanced heat transfer efficiency. In winter field tests, TCP effectively maintained high surface temperatures, overcoming heat loss and outperforming the OPCP. In the site experiment, thermal efficiency was clearly shown in the temperature at the center of the TCP, which was 3.5 °C higher than at the center of the OPCP at the coldest time. These improvements suggest that SiC-reinforced concrete pavements can be practically utilized for effective snow removal and ice mitigation in road systems. Full article

This paper proposes a speed-adaptive autonomous driving path-tracking framework based on the soft actor–critic (SAC) and pure pursuit (PP) methods, named the SACPP controller. The framework first analyzes the obstacles around the vehicle and plans an obstacle-free reference path with the minimum curvature using the hybrid A* algorithm. Next, based on the generated reference path, the current state of the vehicle, and the vehicle motor energy efficiency diagram, the optimal speed is calculated in real time, and the vehicle dynamics preview point at the future moment—specifically, the look-ahead distance—is predicted. This process relies on the learning of the SAC network structure. Finally, PP is used to generate the front wheel angle control value by combining the current speed and the predicted preview point. In the second layer, we carefully designed the evaluation function in the tracking process based on the uncertainties and performance requirements that may occur during vehicle driving. This design ensures that the autonomous vehicle can not only quickly and accurately track the path, but also effectively avoid surrounding obstacles, while keeping the motor running in the high-efficiency range, thereby reducing energy loss. In addition, since the entire framework uses a lightweight network structure and a geometry-based method to generate the front wheel angle, the computational load is significantly reduced, and computing resources are saved. The actual running results on the i7 CPU show that the control cycle of the control framework exceeds 100 Hz. Full article

This study investigates the dynamic relationship between maritime trade and cultural convergence between China and Saudi Arabia, with a particular focus on the roles of creative goods and information and communication technology (ICT) exports as proxies for sociocultural integration. Utilizing quarterly data from 2012 to 2021, the analysis employs the Toda–Yamamoto Granger causality approach within a Vector Autoregression (VAR) framework. This methodology offers a robust means of testing causality without requiring data stationarity or cointegration, thereby reducing estimation bias and enhancing applicability to real-world economic data. The empirical model examines causal interactions among maritime trade, creative goods exports, ICT exports, and population, the latter serving as a control variable to account for demographic scale effects on trade dynamics. The results indicate statistically significant bidirectional causality between maritime trade and both creative goods and ICT exports, suggesting a reciprocal reinforcement between trade and cultural–technological exchange. In contrast, the relationship between maritime trade and population is found to be unidirectional. These findings underscore the strategic importance of cultural and technological flows in shaping maritime trade patterns. Furthermore, the study contextualizes its results within broader policy initiatives, notably China’s Belt and Road Initiative and Saudi Arabia’s Vision 2030, both of which aim to promote mutual economic diversification and regional integration. The study contributes to the literature on international trade and cultural economics by demonstrating how cultural convergence can serve as a catalyst for strengthening bilateral trade relations. Policy implications include the promotion of cultural and technological collaboration, investment in maritime infrastructure, and the incorporation of cultural dimensions into trade policy formulation. Full article

Urban road traffic congestion poses significant challenges to sustainable mobility in smart cities. Traditional traffic light systems, reliant on static or semi-fixed timers, fail to adapt to dynamic traffic conditions, exacerbating congestion and limiting inclusivity. To address these limitations, this paper proposes H-ATLM (a hybrid adaptive traffic lights management), a system utilizing the deep deterministic policy gradient (DDPG) reinforcement learning algorithm to optimize traffic light timings dynamically based on real-time data. The system integrates advanced sensing technologies, such as cameras and inductive loops, to monitor traffic conditions and adaptively adjust signal phases. Experimental results demonstrate significant improvements, including reductions in congestion (up to 50%), increases in throughput (up to 149%), and decreases in clearance times (up to 84%). These findings open the door for integrating accessibility-focused features such as adaptive signaling for accessible vehicles, dedicated lanes for paratransit services, and prioritized traffic flows for inclusive mobility. Full article

Traffic congestion has increased significantly in today’s rapidly urbanizing world, influencing people’s daily lives. Traffic signal control systems (TSCSs) play an important role in alleviating congestion by optimizing traffic light timings and improving road efficiency. Yet traditional TSCSs neglected pedestrians, cyclists, and other non-monitored road users, degrading traffic signal optimization (TSO). Therefore, this framework proposes a multi-object-based traffic flow analysis and intensity estimation model for efficient TSO using Upper Confidence Bound Multi-agent Reinforcement Learning Cubic Spline Fuzzy Logic (UCB-MRL-CSFL). Initially, the real-time traffic videos undergo frame conversion and redundant frame removal, followed by preprocessing. Then, the lanes are detected; further, the objects are detected using Temporal Context You Only Look Once (TC-YOLO). Now, the object counting in each lane is carried out using the Cumulative Vehicle Motion Kalman Filter (CVMKF), followed by queue detection using Vehicle Density Mapping (VDM). Next, the traffic flow is analyzed by Feature Variant Optical Flow (FVOF), followed by traffic intensity estimation. Now, based on the siren flashlight colors, emergency vehicles are separated. Lastly, UCB-MRL-CSFL optimizes the Traffic Signals (TSs) based on the separated emergency vehicle, pedestrian information, and traffic intensity. Therefore, the proposed framework outperforms the other conventional methodologies for TSO by considering pedestrians, cyclists, and so on, with higher computational efficiency (94.45%). Full article

The sustainable development of geotechnical infrastructure necessitates using durable, efficient, and environmentally resilient reinforcement materials. This study investigates the pullout performance of geostraps to assess their potential as a sustainable alternative to conventional geosynthetics. This study focuses on the pullout performance of geostraps, flexible, polymeric reinforcement materials. There has not been a thorough study of their pullout resistance, which directly affects the stability and durability of reinforced soil structures. Pullout tests were conducted on sandy soil in a controlled environment. The experimental findings from the pullout test were then validated in a numerical model. The model was used to determine the pullout resistance of different grades of geostraps for comparative analysis. This helped to identify the possible application areas based on the pullout capacity of various grades. The results obtained for the geostraps were then compared with those in the established literature on geogrids. Initially, the pullout resistance of the M65 geostrap was up to 20% higher than that of a biaxial geogrid. This makes it a suitable option for reinforced earth applications. However, the maximum pullout resistance of geogrids was up to 8% higher than that of geostraps when subjected to a surcharge of 17 kN m⁻² in poorly graded sand. This study highlights the potential of geostraps as reinforcement materials, particularly in challenging environments where conventional geosynthetics may underperform. Future research may explore their behaviour with different soil types and other controlled environmental factors to establish their broader applicability and design charts. Full article

The road construction sector urgently requires environmentally friendly, low-carbon, and high-performance base materials. Traditional materials exhibit issues of high energy consumption and carbon emissions, making it difficult for them to align with sustainable development requirements. While slag- and fly ash-based geopolymers demonstrate promising application potential in civil engineering, research on their application in road-stabilized soils remains insufficient. To address the high energy consumption and carbon emissions associated with conventional road base materials and to fill this research gap, this study investigated the utilization of industrial solid wastes through slag-based geopolymer and fly ash as stabilizers, systematically evaluating the pavement performance of two distinct soil types. Unconfined compressive strength tests and freeze–thaw cycling tests were conducted to elucidate the effects of stabilizer dosage, fly ash co-stabilization, and compaction degree on mechanical properties. The results demonstrated that the compressive strength of both stabilized soils increased significantly with higher slag-based geopolymer content, achieving peak values of 5.2 MPa (soil sample 1) and 4.5 MPa (soil sample 2), representing a 30% improvement over cement-stabilized soils with identical mix proportions. Fly ash co-stabilization exhibited more pronounced reinforcement effects on soil sample 2. At a 98% compaction degree, soil sample 1 maintained a stable 50% strength enhancement, whereas soil sample 2 displayed a dose-dependent exponential strength increase. Freeze–thaw resistance tests revealed the superior performance of soil sample 1, showing a loss of compressive strength (BDR) of 78% with 8% geopolymer stabilization alone, which improved to 90% after fly ash co-stabilization. For soil sample 2, the BDR increased from 64% to 80% through composite stabilization. This study confirms that slag/fly ash-based geopolymer-stabilized soils not only meet the strength requirements for heavy-traffic subbases and light-traffic base courses, but also demonstrates its great potential as a low-carbon and environmentally friendly material to replace traditional road base materials. Full article

To obtain waterborne polymer-modified emulsified asphalt materials with better comprehensive performance, waterborne polymer modifiers including waterborne epoxy resin (WER)-reinforced styrene–butadiene–styrene block copolymer (SBS), waterborne acrylate (WA) or styrene butadiene rubber (SBR) emulsion were prepared. The mechanical strength, toughness, adhesion and impact resistance of these waterborne polymers were evaluated. Furthermore, the correlation between the performance indicators of the waterborne polymers was analyzed. Based on Fourier transform infrared (FTIR) spectroscopy and thermogravimetric (TG) analysis, the mechanism of WER-modified SBS and WA was characterized. The results show that adding 10%–15% WER can significantly improve the mechanical properties of the waterborne polymer. The performances of modified SBS and WA are better than that of modified SBR. When the content of WER is 10%, the tensile strength, elongation at break and pull-off strength of WER-modified SBS and WA are 4.80–6.38 MPa, 476.3%–579.6% and 1.62–1.70 MPa, respectively. The mechanical strength and breaking energy of the waterborne polymers show a significant linear correlation with their application properties such as adhesion, bonding and impact resistance. FTIR and TG analyses indicate that WER-modified SBS or WA prepared via emulsion blending undergo primarily physical modifications, enhancing thermal stability while promoting crosslinking and curing. Full article

This paper is devoted to determining the influence of some risk elements on the asphalted surfaces of bridge ramps, in order to detect possible damages or potential collapses of the embankment. The main factors will be characterized by (a) movements of floating reinforced concrete (r.c.) slab over the embankment connected to the border of the bridge; (b) longitudinal cracks on the asphalt produced by small sliding deformations; (c) emerging vegetation from the slope of the ramps. The authors propose a set of possible techniques to determine level of risk indicators, illustrating a set of case studies related to several asphalt roads approaching r.c. bridges. Full article

Traffic congestion constitutes a substantial global issue, adversely impacting economic productivity and quality of life, with associated costs estimated at approximately 2% of GDP in various nations. This systematic review investigates the application of machine learning (ML) in traffic congestion forecasting from 2010 to 2024, adhering to the PRISMA 2020 guidelines. A comprehensive search of three major databases (IEEE Xplore, SpringerLink, and ScienceDirect) yielded 9695 initial records, with 115 studies meeting the inclusion criteria following rigorous screening. Data extraction encompassed methodological approaches, ML techniques, traffic characteristics, and forecasting periods, with quality assessment achieving near-perfect inter-rater reliability (Cohen’s $\kappa$ = 0.89). Deep Neural Networks were the predominant technical approach (47%), with supervised learning being the most prevalent (57%). Classification tasks were the most common (42%), primarily addressing recurrent congestion scenarios (76%) and passenger vehicles (90%). The quality of publications was notably high, with 85% appearing in Q1-ranked journals, demonstrating exponential growth from minimal activity in 2010 to 18 studies in 2022. Significant research gaps persist: reinforcement learning is underutilized (8%), rural road networks are underrepresented (2%), and industry–academia collaboration is limited (3%). Future research should prioritize multimodal transportation systems, real-time adaptation mechanisms, and enhanced practical implementation to advance intelligent transportation systems (ITSs). This review was not registered because it focused on mapping the research landscape rather than intervention effects. Full article

The On-Board Diagnostics II (OBD-II) system, driven by a wide range of embedded sensors, has revolutionized the automotive industry by enabling real-time monitoring of key vehicle parameters such as engine load, vehicle speed, throttle position, and diagnostic trouble codes. Concurrently, recent advancements in machine learning (ML) have further expanded the capabilities of OBD-II applications, unlocking advanced, intelligent, and data-centric functionalities that significantly surpass those of conventional methodologies. This paper presents a comprehensive investigation into ML-based applications that leverage OBD-II sensor data, aiming to enhance sustainability, operational efficiency, safety, and security in modern vehicular systems. To this end, a diverse set of ML approaches is examined, encompassing supervised, unsupervised, reinforcement learning (RL), deep learning (DL), and hybrid models intended to support advanced driving analytics tasks such as fuel optimization, emission control, driver behavior analysis, anomaly detection, cybersecurity, road perception, and driving support. Furthermore, this paper synthesizes recent research contributions and practical implementations, identifies prevailing challenges, and outlines prospective research directions. Full article

This paper proposes a novel navigation approach for Unmanned Tracked Vehicles (UTVs) using prior-based ensemble deep reinforcement learning, which fuses the policy of the ensemble Deep Reinforcement Learning (DRL) and Dynamic Window Approach (DWA) to enhance both exploration efficiency and deployment safety in unstructured off-road environments. First, by integrating kinematic analysis, we introduce a novel state and an action space that account for rugged terrain features and track–ground interactions. Local elevation information and vehicle pose changes over consecutive time steps are used as inputs to the DRL model, enabling the UTVs to implicitly learn policies for safe navigation in complex terrains while minimizing the impact of slipping disturbances. Then, we introduce an ensemble Soft Actor–Critic (SAC) learning framework, which introduces the DWA as a behavioral prior, referred to as the SAC-based Hybrid Policy (SAC-HP). Ensemble SAC uses multiple policy networks to effectively reduce the variance of DRL outputs. We combine the DRL actions with the DWA method by reconstructing the hybrid Gaussian distribution of both. Experimental results indicate that the proposed SAC-HP converges faster than traditional SAC models, which enables efficient large-scale navigation tasks. Additionally, a penalty term in the reward function about energy optimization is proposed to reduce velocity oscillations, ensuring fast convergence and smooth robot movement. Scenarios with obstacles and rugged terrain have been considered to prove the SAC-HP’s efficiency, robustness, and smoothness when compared with the state of the art. Full article