Search Results (203)

Effective communication of traffic information is critical for freeway safety. Yet, Traditional road signs and variable message displays often fail to capture drivers’ attention, as evidenced by persistent speeding-related crashes on highways. Advances in connected-vehicle technology and head-up displays (HUDs) now enable real-time, personalized in-vehicle warnings, offering new opportunities to enhance speed compliance. At the same time, these systems introduce an important design challenge: the effectiveness of warnings may depend on how messages are framed (e.g., emphasizing benefits versus risks), particularly under time pressure and varying driver characteristics. Despite its theoretical importance, the impact of message framing in real-time, HUD-based speed warning contexts remains insufficiently understood. This study proposes and evaluates a Freeway-Centered Dynamic Speed Warning System (F-DSWS) that delivers real-time speed warnings via vehicle-to-infrastructure communication and HUD interfaces. Two message-framing strategies were examined: gain-framed messages emphasizing positive and socially relevant outcomes, and loss-framed messages highlighting safety risks. A driving simulator experiment was conducted with 39 licensed drivers aged 19–78 years across multiple freeway scenarios. Results indicate that HUD-based warnings significantly outperformed traditional roadside signs in reducing speeding, lane-deviation extremes, and harsh braking. Moreover, gain-framed messages consistently produced greater improvements in driving performance than loss-framed messages across all evaluated metrics. These findings suggest that the proposed F-DSWS provides measurable safety benefits and demonstrate that framing choice is a critical design factor in personalized in-vehicle freeway traffic messaging. These results offer evidence-based guidance on framing selection within HUD-based in-vehicle freeway warnings, and support future field deployment of the proposed F-DSWS to improve freeway safety. Full article

Ensuring railway safety requires reliable monitoring of trains in critical safety areas, such as station throat zones and railway crossings. Compared with cameras, roadside LiDAR can more reliably capture the geometry of trains under low-light, high-speed, and adverse weather conditions. However, industrial LiDAR solutions still primarily use the background comparison technique, which compares each sample against a pre-recorded clean map and then applies a size-based filter. Such approaches are highly sensitive to point cloud background changes arising from varying LiDAR installation distances, train speeds, and surface materials, often resulting in fragmented clustering and missed detections. In this paper, train detection is reformulated as a point-level semantic segmentation problem. A lightweight 3D segmentation network that directly predicts train points from raw data is designed, and clustering-based post-processing is applied to generate train-level events in real time. Experiments on real railway data under various operating conditions show that the proposed method achieves higher detection accuracy and greater robustness than traditional compare-based methods and representative deep learning benchmark methods, and is therefore suitable for practical railway safety monitoring. Full article

Rural transportation corridors remain weakly instrumented for continuous low-altitude airspace monitoring. At the same time, Vehicle-to-Everything (V2X) roadside units (RSUs) are increasingly deployed for transportation safety services. This work investigates whether existing RSUs can be extended with passive, cooperative RF sensing to detect small UAVs without modifying standards-compliant ITS communications in the protected 5.9 GHz band. A calibrated simulation study evaluates corridor-scale operation under realistic propagation conditions, including terrain masking and narrowband interference. All results reported in this paper are derived from simulation and do not include field measurements or hardware prototyping. False alarm performance under diverse ISM emitters is not quantified. The results show that cooperative processing across neighboring RSUs improves epoch-level verified detection coverage compared with single-RSU sensing. Bearing variability is reduced for weak or partially masked signals. These gains result from feature-level validation across spatially separated receivers rather than deterministic signal combining. RF calibration constrains detections to physically plausible kilometer-scale ranges. The resulting angular accuracy is sufficient for early warning and track initiation, but not for precise localization. Overall, the findings indicate that existing V2X infrastructure can support supplementary early warning capability for corridor-scale airspace monitoring while preserving primary V2X safety functions. Full article

The widespread application of road deicing salts in northern regions has led to elevated salinity in roadside soils and adjacent watersheds. Phytoremediation offers a cost-effective and sustainable approach for mitigating salt contamination, but its success depends on utilizing plant species that can both tolerate and remove salt under roadside conditions. To systematically identify high-potential candidates from the large inventory of salt-tolerant plants in North America, we developed a quantitative decision matrix incorporating criteria related to ecological safety, establishment potential on disturbed soils, aboveground biomass production, biomass use-value, and salt uptake capacity. Thirteen of the highest-ranked species were subsequently evaluated for sodium (Na⁺) and chloride (Cl⁻) uptake in a controlled greenhouse study under saline and non-saline conditions. The greatest total salt uptake was observed in common sunflower (Helianthus annuus) (35.6 mg Na⁺ and 100.2 mg Cl⁻ plant⁻¹) and pitseed goosefoot (Chenopodium berlandieri) (18.6 mg Na⁺ and 76.0 mg Cl⁻ plant⁻¹), while perennial species including tall fescue turfgrass (Lolium arundinaceum), showy goldenrod (Solidago speciosa), and weeping alkaligrass (Puccinellia distans) also demonstrated substantial uptake combined with greater long-term suitability for roadside management. Overall, this study presents a quantitative framework for phytoremediation species selection and provides experimental evidence supporting both annual and perennial species for mitigating deicing salt contamination through environmentally sustainable, low-input roadside management strategies. Full article

Traffic accidents involving Vulnerable Road Users (VRUs) frequently occur at unsignalized intersections on Japanese community roads. To prevent such accidents, collision avoidance systems need to predict VRUs’ movements throughout the entire road network while explicitly handling uncertainty degraded by sparse observations and frequent route branching at intersections. Based on this motivation, this study proposes a Vehicle-to-Network (V2N)-based comprehensive safety framework for estimation of VRU movement and prediction of future intersection entry for community roads. The framework integrates estimation results provided from Roadside Edges and Vehicle Edges at a Central Server. In addition, road geometry from map information is incorporated as pseudo-observations into the estimation, and multiple route hypotheses are explicitly managed to represent route branching at intersections. For intersection-entry prediction, entry certainty is calculated by integrating a predicted distribution. For evaluation of the proposed framework, we conduct Monte Carlo simulations on simplified grid road networks. The results show that the proposed framework maintains conservative estimation under sparse observations and improves prediction when additional observation information from surrounding vehicles becomes available. Furthermore, a simulation-based case study using an actual community road-network geometry shows the feasibility of the proposed framework for cooperative collision avoidance on actual community roads. Full article

The Intelligent Road Side Unit (RSU) is a crucial component of Intelligent Transportation Systems (ITSs), where roadside LiDAR are widely utilized for their high precision and resolution. However, water droplets and atmospheric particles in fog significantly attenuate and scatter LiDAR beams, posing a challenge to multi-target tracking and ITS safety. To enhance the accuracy and reliability of RSU-based tracking, a collaborative RSU method that integrates denoising and tracking for multi-target tracking is proposed. The proposed approach first dynamically adjusts the filtering kernel scale based on local noise levels to effectively remove noisy point clouds using a modified bilateral filter. Subsequently, a multi-RSU cooperative tracking framework is designed, which employs a particle Probability Hypothesis Density (PHD) filter to estimate target states via measurement fusion. A multi-target tracking system for intelligent RSUs in Foggy scenarios was designed and implemented. Extensive experiments were conducted using an intelligent roadside platform in real-world fog-affected traffic environments to validate the accuracy and real-time performance of the proposed algorithm. Experimental results demonstrate that the proposed method improves the target detection accuracy by 8% and 29%, respectively, compared to statistical filtering methods after removing fog noise under thin and thick fog conditions. At the same time, this method performs well in tracking multi-class targets, surpassing existing state-of-the-art methods, especially in high-order evaluation indicators such as HOTA, MOTA, and IDs. Full article

The rapid advancement of connected and autonomous vehicles (CAVs) has the potential to revolutionize road transportation, promising significant improvements in safety, efficiency, and sustainability. However, traditional CAV architectures are predominantly modular and rule-based. They struggle with interaction, cooperation, and adaptability in complex mixed-traffic environments. Moreover, the substantial infrastructure investment required and the absence of compelling killer applications have limited large-scale deployment of CAVs and roadside units (RSUs), resulting in insufficient penetration to realize the full safety benefits of CAV applications and creating a deployment stalemate. To address the above challenges, this paper proposes an innovative connected autonomous vehicle system, termed AQ-CAV, which leverages recent advances in AI agents and QR codes. AI agents are employed to enable cooperative, self-adaptive, and intelligent vehicular behavior, while QR codes provide a cost-effective, accessible, robust, and scalable mechanism for supporting CAV deployment. We first analyze existing CAV systems and identify their fundamental limitations. We then present the architectural design of the AQ-CAV system, detailing the components and functionalities of vehicle-side and infrastructure-side agents, inter-agent communication and coordination mechanisms, and QR code-based authentication for AQ-CAV operations. Representative applications of the AQ-CAV system are investigated, including a case study on emergency response. Preliminary results demonstrate the feasibility and effectiveness of the proposed system, which achieves significant safety improvements at low system cost. Finally, we discuss the key challenges faced by AQ-CAV and outline future research directions that require exploration to fully realize its potential. Full article

This study addresses the limitations of conventional highway visibility monitoring under rain–fog conditions, where fixed stations and visibility sensors provide limited spatial coverage and unstable accuracy. Considering that drivers’ visual fields are jointly affected by global fog and local spray-induced mist, a dynamic visibility recognition and risk assessment framework is proposed using roadside monocular CCTV (Closed-Circuit Television) imagery. The method integrates the Koschmieder scattering model with the dark channel prior to estimate atmospheric transmittance and derives visibility through lane-line calibration. A Monte Carlo-based coupling model simulates local visibility degradation caused by tire spray, while a safety potential field defines the low-visibility risk field force (LVRFF) combining dynamic visibility, relative speed, and collision distance. Results show that this approach achieves over 86% accuracy under heavy rain, effectively captures real-time visibility variations, and that LVRFF exhibits strong sensitivity to visibility degradation, outperforming traditional safety indicators in identifying high-risk zones. By enabling scalable, infrastructure-based visibility monitoring without additional sensing devices, the proposed framework reduces deployment cost and energy consumption while enhancing the long-term operational resilience of highway systems under adverse weather. From a sustainability perspective, the method supports safer, more reliable, and resource-efficient traffic management, contributing to the development of intelligent and sustainable transportation infrastructure. Full article

Accurate scene understanding from 3D point cloud data is fundamental to intelligent transportation systems and geospatial digital twins. However, point clouds acquired from lidar sensors in urban environments suffer from incompleteness due to occlusions and limited sensor resolution, presenting significant challenges for precise object localization and geometric reconstruction—critical requirements for traffic safety monitoring and autonomous navigation. To address these point cloud processing challenges, we propose a Center-guided Feature Fusion Completion Network (CFFCNet) that enhances vehicle representation through geometry-aware point cloud completion. The network incorporates a Branch-assisted Center Perception (BCP) module that learns to predict geometric centers while extracting multi-scale spatial features, generating initial coarse completions that account for the misalignment between detection centers and true geometric centers in real-world data. Subsequently, a Multi-scale Feature Blending Upsampling (MFBU) module progressively refines these completions by fusing hierarchical features across multiple stages, producing accurate and complete vehicle point clouds. Comprehensive evaluations on the KITTI dataset demonstrate substantial improvements in geometric accuracy, with localization mean absolute error (MAE) reduced to 0.0928 m and length MAE to 0.085 m. The method’s generalization capability is further validated on a real-world roadside lidar dataset (CUG-Roadside) without fine-tuning, achieving localization MAE of 0.051 m and length MAE of 0.051 m. These results demonstrate the effectiveness of geometry-guided completion for point cloud scene understanding in infrastructure-based traffic monitoring applications, contributing to the development of robust 3D perception systems for urban geospatial environments. Full article

Drinking water contamination during the treatment process remains a major problem for decision-makers responsible for the collection and supply of water to recipients. This article presents measurements of 33 parameters of drinking water quality in the years 2009–2023, taken from the Dobromierz reservoir in Poland, with particular emphasis on the stages of raw water, water undergoing treatment, and utility water. The results showed that the raw water tested is contaminated microbiologically (presence of coliform bacteria), organoleptically (worse turbidity, odor, color), and chemically (increased PAHs, nitrites, benzo(α)pyrene). This indicates improper maintenance of the areas around the reservoir, i.e., agricultural areas (the existing nutrient runoff), residential areas (the lack of stringent records of cesspools and septic tanks), and roadside (improper maintenance of ditch slopes). In most cases, water at the treatment stage and at the end recipients was effectively purified (in most cases, the analyzed parameters achieved a degree of compliance with drinking water standards of at least 95%). Only for the turbidity in the network, the standards did not reach the adopted minimum level. This suggests the need to conduct systematic investment activities in order to reduce failures in the network (average system failure rate of 34%). Moreover, the statistical analysis of the results showed significant changes in the parameters between raw water and water in the water supply network and at end recipients (p < 0.05). Therefore, it is necessary to focus on protecting the quality of raw water resources for more effective treatment and ensuring human health safety. Full article

Rear-end collisions involving maintenance vehicles remain a critical source of severe injuries and fatalities in highway work zones. Existing studies on Rear Impact Guards (RIGs) and Truck-Mounted Attenuators (TMAs) have primarily relied on vehicle-based acceleration metrics or low-speed tests, leaving uncertainty regarding their performance under high-energy impact conditions. This study investigates occupant injury risk and vehicle crash behavior through full-scale frontal impact tests conducted at 80 km/h using a 2002 Renault SM520 passenger car against (1) a truck equipped with a RIG and (2) the same truck equipped with a TMA. Hybrid III 50th percentile ATDs, high-speed imaging, and multi-axis accelerometers were employed to measure occupant kinematics and injury responses. Occupant Risk Indices (THIV (Theoretical Head Impact Velocity), ASI (Acceleration Severity Index), PHD (Post-impact Head Deceleration), and ORA (Occupant Ridedown Acceleration)) and the ATD-based HIC36 were evaluated to assess crash severity. The RIG test exhibited severe underride, resulting in an HIC36 value of 1810, far exceeding the FMVSS 208 limit. In contrast, the TMA significantly reduced occupant injury risk, lowering HIC36 by 83.5%, and maintained controlled vehicle deceleration without compartment intrusion. Comparisons between FSM-based indices and ATD-measured injury responses revealed discrepancies in impact timing and occupant motion, highlighting limitations of current evaluation methodologies. The findings demonstrate the necessity of high-speed testing and ATD-based injury assessment for accurately characterizing RIG/TMA performance and provide evidence supporting improvements to roadside safety hardware standards and work-zone protection strategies. Full article

As an essential highway safety facility, roadside W-beam guardrails effectively prevent errant vehicles from entering hazardous zones or causing secondary collisions by blocking and redirecting them, thereby reducing accident severity. With the rapid development of the automotive industry, the front bumper height of small passenger cars generally ranges between 405 mm and 485 mm. However, the lower edge height of the current Chinese Class A W-beam guardrail is 444 mm above the ground, which leads to a high risk of “underride” during collisions, resulting in elevated occupant injury risks. To address this issue, this paper proposes an optimized guardrail structure composed of a double W-beam and a C-type beam, aiming to reduce the underride risk for small passenger cars while accommodating multi-vehicle protection needs. In this design, the double W-beam is installed at a height of 560 mm and the C-type beam at 850 mm, connected to circular posts using a regular hexagonal anti-obstruction block. The beam thickness is uniformly 3 mm, while the thickness of other components is 4 mm. To systematically evaluate the impact of material strength on both safety performance and cost, two material configurations are proposed: Scheme 1 uses Q235 carbon steel for all components; Scheme 2 reduces the thickness of the C-type beam to 2.5 mm and employs Q355 high-strength low-alloy steel, with the thickness of the connected anti-obstruction block reduced to 3.5 mm, while the other components retain Q235 steel and unchanged structural dimensions. Using finite element simulation, collisions involving small passenger cars, medium trucks, and buses are simulated, and performance comparisons are conducted based on vehicle trajectory and guardrail deformation. For the small passenger car scenario, risk quantification indicators—Acceleration Severity Index (ASI), Theoretical Head Impact Velocity (THIV), and Post-impact Head Deceleration (PHD)—are introduced to assess occupant injury. The results demonstrate that Scheme 2 not only meets the required protection level but also significantly reduces occupant risk for small passenger cars, lowering the injury rating from Class C to Class B. Moreover, the overall structural mass is reduced by approximately 1407 kg per kilometer, with material costs decreased by about RMB 10,129, demonstrating favorable economic efficiency. The proposed structural optimization not only effectively mitigates small car underride and improves multi-vehicle protection performance but also provides the industry with a novel guardrail geometric design directly applicable to engineering practice. The technical approach of enhancing material strength and reducing component thickness also offers a feasible reference for lightweight design, material savings, and cost optimization of guardrail systems, contributing significantly to improving the safety and sustainability of road transportation infrastructure. Full article

This study evaluates low-strength concrete incorporating recycled rubber aggregates from waste tires and polymer fiber for use as “forgiving” safety barriers that enhance road safety while promoting environmental sustainability. Incorporating the rubber and fiber enables recycling the tires close to the source where they were originally used—the road. These barriers are designed to absorb collision energy, reduce vehicle deceleration, and minimize the severity of accidents. The key requirements for such concrete are low strength, low elastic modulus, high ductility, high toughness, and minimal dispersion of large fragments upon failure. The study investigated various concretes containing different percentages of recycled rubber (0–20% by volume) and polymer fibers (0–1.2% by volume). We conducted compression, flexural, and dynamic impact tests to assess the effects of these additions on the properties of the concrete. Dynamic tests were carried out in a cantilever loading scheme with strain rates of 2.5–3 s⁻¹, to emulate barrier loading during car crush. Key findings include indications that recycled rubber decreases concrete strength, while its contribution to energy absorption is limited. In contrast, polymer fibers enhance the concrete’s elongation and toughness, increasing energy absorption. The quantity of fibers present in the fracture area is critical for energy absorption. Notably, energy absorption under dynamic loads is more significant than that under quasi-static loads; however, the difference between these results diminishes as the fiber percentage increases. Furthermore, quasi-static tests on fiber-reinforced concrete can effectively evaluate its response to impact loads. In conclusion, the combined use of recycled rubber and polymer fibers in low-strength concrete offers a sustainable solution for developing safer and more environmentally responsible roadside infrastructure by repurposing waste materials and reducing the ecological footprint of construction. Careful attention should be paid to the distribution of fibers within the concrete, as this significantly influences energy absorption. Full article

Traffic safety is a critical issue in highway operation management, where accurate identification of accident hotspots enables proactive risk prevention and facility optimization. Traditional methods relying on historical statistics often fail to capture macro-level environmental patterns and micro-level dynamic variations. To address this challenge, we propose a Dual-Branch Feature Adaptive Gated Fusion Network (DFAGF-Net) that integrates satellite remote sensing imagery with traffic flow time-series data. The framework consists of three components: the Global Contextual Aggregation Network (GCA-Net) for capturing macro spatial layouts from remote sensing imagery, a Sequential Gated Recurrent Unit Attention Network (Seq-GRUAttNet) for modeling dynamic traffic flow with temporal attention, and a Hybrid Feature Adaptive Module (HFA-Module) for adaptive cross-modal feature fusion. Experimental results demonstrate that the DFAGF-Net achieves superior performance in accident hotspot recognition. Specifically, GCA-Net achieves an accuracy of 84.59% on satellite imagery, while Seq-GRUAttNet achieves an accuracy of 82.51% on traffic flow data. With the incorporation of the HFA-Module, the overall performance is further improved, reaching an accuracy of 90.21% and an F1-score of 0.92, which is significantly better than traditional concatenation or additive fusion methods. Ablation studies confirm the effectiveness of each component, while comparisons with state-of-the-art models demonstrate superior classification accuracy and generalization. Furthermore, model interpretability analysis reveals that curved highway alignments, roadside greenery, and varying traffic conditions across time are major contributors to accident hotspot formation. By accurately locating high-risk segments, DFAGF-Net provides valuable decision support for proactive traffic safety management and targeted infrastructure optimization. Full article

Agricultural soils near roadways are increasingly contaminated with presumably contaminating elements (PCEs), raising concerns for food safety and health risks in Bangladesh. This study quantified Mn, As, Co, Cr, Zn, Ni, Cu, Cd and Pb in roadside agricultural farm soils at three depths (0–5, 5–10, 10–15 cm) across industrial, peri-urban, and research areas using ICP-MS. The average mass fractions ranked as Mn > Zn > Cr > Ni > Cu > Pb > Co > As > Cd with peri-urban soils exhibiting the elevated levels of Cr (80.48 mg.kg⁻¹ and Ni (65.81 mg.kg⁻¹). Contamination indices indicated Cd (Contamination Factor: 2.01–2.53) and Ni (Contamination Factor: up to 2.27) as the most enriched elements, with all sites showing a Pollution Load Index (PLI) >1 (1.07–1.66), reflecting cumulative soil deterioration. Cd posed moderate ecological risk (Er: 60.3–75.9), whereas other PCEs were low risk. Health risk assessment showed elevated non-carcinogenic hazard indices (HI: 7.87–10.5 for children; 3.72–4.78 for adults), with Mn, Cr, and Co as major contributors. Cumulative carcinogenic risk (CCR) values were dominated by Cr, reaching 7.22 × 10⁻⁴ in industrial areas and 3.98 × 10⁻⁴ in peri-urban areas, exceeding the acceptable range (10⁻⁶–10⁻⁴). Metal mass fractions were consistently higher in surface soils (0–5 cm) than at deeper layers, indicating anthropogenic deposition from traffic and industry. Multivariate analysis distinguished geogenic (Cr-Ni-Cu; Mn-Co-As) from anthropogenic (Cd-Pb-Zn) sources. These findings identify Cd and Cr as priority pollutants, highlighting the need for soil management and pollution control near roadways in Bangladesh. Full article