Search Results (271)

Skid resistance, or friction, on a road surface is a critical parameter in functional highway assessments, given its direct relationships with safety and accident frequency. Therefore, road administrations must collect friction data across their road networks to ensure safe roads for users. In addition, having a predictive model of skid resistance for each road section is essential for an efficient pavement management system (PMS). Traditionally, road authorities disregard rural roads, since they are more focused on freeways and traffic-intense roads. This study develops a model for predicting minimum-available skid resistance, which occurs in summer, measured using the Sideway-force Coefficient Routine Investigation Machine (SCRIM), on bituminous pavements in the single-carriageway road network of the Province of Gipuzkoa, Spain. To this end, traffic volume data available in the PMS of the Provincial Council of Gipuzkoa, such as the annual average daily traffic (AADT) and the AADT of heavy vehicles (AADT.HV), were uniquely used to forecast skid-resistance values collected in summer. Additionally, a methodology for eliminating outliers is proposed. Despite the simplicity of the model, which does not include information about the materials at the surface layer, a coefficient of determination (R²) of 0.439 was achieved. This model can help road authorities identify the roads for which lower skid-resistance values are most likely to occur, allowing them to focus their attention and efforts on these roads, which are key infrastructure in rural areas. Full article

This study examines the safety and cost-effectiveness of implementing shoulder rumble strips (SRS) and road lighting on Saudi Arabian freeways, providing insights into their roles in fostering sustainable transport systems. By leveraging the Highway Safety Manual (HSM) framework, this research develops localized Crash Modification Factors (CMFs) for these interventions, ensuring evidence-based and context-specific evaluations. Data were collected for two periods—pre-pandemic (2017–2019) and post-pandemic (2021–2022). For each period, we obtained traffic crash records from the Saudi Highway Patrol database, traffic volume data from the Ministry of Transport and Logistic Services’ automated count stations, and roadway characteristics and pavement-condition metrics from the National Road Safety Center. The findings reveal that SRS reduces fatal and injury run-off-road crashes by 52.7% (CMF = 0.473) with a benefit–cost ratio of 14.12, highlighting their high cost-effectiveness. Road lighting, focused on nighttime crash reduction, decreases such crashes by 24% (CMF = 0.760), with a benefit–cost ratio of 1.25, although the adoption of solar-powered lighting systems offers potential for greater sustainability gains and a higher benefit–cost ratio. These interventions align with global sustainability goals by enhancing road safety, reducing the socio-economic burden of crashes, and promoting the integration of green technologies. This study not only provides actionable insights for achieving KSA Vision 2030’s target of improved road safety but also demonstrates how engineering solutions can be harmonized with sustainability objectives to advance equitable, efficient, and environmentally responsible transportation systems. Full article

Vacuum degassing (VD) is a critical refining step in electric arc furnace (EAF) steelmaking for producing clean steel with reduced nitrogen and hydrogen content. This study develops an Effective Equilibrium Reaction Zone (EERZ) model focused on denitrogenation (de-N) by simulating interfacial reactions at the bubble–steel interface (Z1). The model incorporates key process parameters such as argon flow rate, vacuum pressure, and initial nitrogen and sulfur concentrations. A robust empirical correlation was established between de-N efficiency and the mass of Z1, reducing prediction time from a day to under a minute. Additionally, the model was further improved by incorporating a dynamic surface exposure zone (Z_eye) to account for transient ladle eye effects on nitrogen removal under deep vacuum (<10 torr), validated using synchronized plant trials and Python-based video analysis. The integrated approach—combining thermodynamic-kinetic modeling, plant validation, and image-based diagnostics—provides a robust framework for optimizing VD control and enhancing nitrogen removal control in EAF-based steelmaking. Full article

Deep Reinforcement Learning (DRL) algorithms often exhibit significant performance variability across different training runs, even with identical settings. This paper investigates the hypothesis that a key contributor to this variability is the divergence in the observation spaces explored by individual learning agents. We conducted an empirical study using Proximal Policy Optimization (PPO) agents trained on eight Atari environments. We analyzed the collected agent trajectories by qualitatively visualizing and quantitatively measuring the divergence in their explored observation spaces. Furthermore, we cross-evaluated the learned actor and value networks, measuring the average absolute TD-error, the RMSE of value estimates, and the KL divergence between policies to assess their functional similarity. We also conducted experiments where agents were trained from identical network initializations to isolate the source of this divergence. Our findings reveal a strong correlation: environments with low-performance variance (e.g., Freeway) showed high similarity in explored observation spaces and learned networks across agents. Conversely, environments with high-performance variability (e.g., Boxing, Qbert) demonstrated significant divergence in both explored states and network functionalities. This pattern persisted even when agents started with identical network weights. These results suggest that differences in experiential trajectories, driven by the stochasticity of agent–environment interactions, lead to specialized agent policies and value functions, thereby contributing substantially to the observed inconsistencies in DRL performance. Full article

In the past few decades, extensive research has been conducted on the modeling of cascading failures and their recovery processes in freeway networks. In practice, the restoration of functionality and structure in complex networks that suffer large-scale cascading failures may involve a series of repair operations. In this paper, we first propose a cascading failure model for freeway networks, which considers load redistribution by taking travelers’ choice behavior into account. Specifically, we use the Stochastic User Equilibrium (SUE) as a method for redistribution in the model. Next, we propose a recovery strategy focused on critical edges, with their importance ranked through the integration of the network’s topological features and traffic characteristics. This ranking then serves as the foundation for the edge-recovery process. This model considers the operational mechanisms of complex freeway networks. In the experiment, we used the freeway network in Hunan Province as a case study to validate the effectiveness of our model. Traffic volume data were collected from toll stations on the freeway network, and the topological structure of the network was combined with these data to construct a complex weighted freeway network. The evolution of network cascading failures was analyzed under various scenarios of attacks caused by traffic incidents. Subsequently, the failed network was recovered, and the results indicate that the proposed recovery strategy demonstrates better performance compared to other traditional methods. This research provides theoretical and methodological support for the management of freeway networks. Full article

Sustainable traffic management relies on accurate traffic flow prediction to reduce congestion, fuel consumption, and emissions and minimise the external environmental impacts of traffic operations. This study contributes to this objective by developing and evaluating advanced machine learning models that leverage multisource data to predict traffic patterns more effectively, allowing for the deployment of proactive measures to prevent or reduce traffic congestion and idling times, leading to enhanced eco-friendly mobility. Specifically, this paper evaluates the impact of multisource sensor inputs and spatial detector interactions on machine learning-based traffic flow prediction. Using a dataset of 839,377 observations from 14 detector stations along Melbourne’s Eastern Freeway, Bidirectional Long Short-Term Memory (BiLSTM) models were developed to assess predictive accuracy under different input configurations. The results demonstrated that incorporating speed and occupancy inputs alongside traffic flow improves prediction accuracy by up to 16% across all detector stations. This study also investigated the role of spatial flow input interactions from upstream and downstream detectors in enhancing prediction performance. The findings confirm that including neighbouring detectors improves prediction accuracy, increasing performance from 96% to 98% for eastbound and westbound directions. These findings highlight the benefits of optimised sensor deployment, data integration, and advanced machine-learning techniques for smart and eco-friendly traffic systems. Additionally, this study provides a foundation for data-driven, adaptive traffic management strategies that contribute to sustainable road network planning, reducing vehicle idling, fuel consumption, and emissions while enhancing urban mobility and supporting sustainability goals. Furthermore, the proposed framework aligns with key United Nations Sustainable Development Goals (SDGs), particularly those promoting sustainable cities, resilient infrastructure, and climate-responsive planning. Full article

Concrete and other cementitious materials are among the most widely used construction materials worldwide. However, their high embodied carbon emissions and energy-intensive manufacturing processes pose significant environmental challenges. This study assesses the carbon emissions, cost implications, and circularity potential of a novel concrete mix, Tex-crete, which incorporates recycled textile and cardboard fibres as sustainable alternatives to conventional reinforcement and cementitious materials in concrete. The study employs a cradle-to-gate life cycle assessment (LCA) approach to compare carbon emissions and costs across different mix designs, using two case studies: a temporary construction site compound and a footpath. Experimental results indicate that Tex-crete, particularly the KFT mix design (including 2.5% textile fibres with treated kraft fibres), achieves comparable compressive and tensile strength to traditional concrete while demonstrating a net reduction in both carbon emissions (3.38%) and production costs (2.56%). A newly introduced circularity index (CI) further evaluated the reuse, repair, and recycling potential of the novel mix, revealing that KFT exhibits the highest circularity score (0.44). Parametric analysis using Monte Carlo simulations highlighted transportation distance and energy consumption during fibre processing as key factors influencing emissions. The findings provide valuable insights for industry stakeholders seeking sustainable concrete solutions aligned with circular economy principles, offering an optimized balance between environmental performance, structural integrity, and cost-effectiveness. Full article

As autonomous vehicles (AVs) are gradually integrated into existing traffic systems, understanding their impact on freeway operations becomes essential for effective infrastructure planning and policy design. This study explores how AV penetration rates, behavior profiles, and freeway geometry interact to influence traffic performance and safety. Using microscopic simulations in VISSIM (a high-fidelity traffic simulation tool), four typical freeway segment types—basic sections, weaving zones, on-ramp merging areas, and AV-exclusive lanes—were modeled under diverse traffic demands and AV behavior settings. The findings indicate that, while AVs can improve flow stability in simple environments, their performance may deteriorate in complex merging scenarios without supportive design or behavior coordination. AV-exclusive lanes offer some mitigation when AV share is high. These results underscore that AV integration requires context-specific strategies and cannot be universally applied. Adaptive, behavior-aware traffic management is recommended to support a smooth transition toward mixed autonomy. Full article

As human-driven vehicles (HDVs) and autonomous vehicles (AVs) coexist on the road, the asymmetry between their driving behaviors, decision-making processes, and responses to traffic scenarios introduces new safety challenges, especially in complex merging areas where frequent interactions occur. The existing traffic safety analysis of mixed traffic is mainly to analyze each safety index separately, lacking comprehensive evaluation. To investigate the safety risk more broadly, this study proposes a comprehensive safety evaluation framework for mixed traffic flows in merging areas from the perspective of traffic conflicts, emphasizing the asymmetry between HDVs and AVs. Firstly, an indicator of Emergency Lane Change Risk Frequency is introduced, considering the interaction characteristics of the merging area. A safety evaluation index system is established from lateral, longitudinal, temporal, and spatial dimensions. Then, indicator weights are determined using a modified game theory approach that combines the entropy weight method with the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method, ensuring a balanced integration of objective data and expert judgment. Subsequently, a cloud model enhanced with the fuzzy mean value method is then developed to evaluate comprehensive safety. Finally, a simulation experiment is designed to simulate traffic operation of different traffic scenarios under various traffic flow rates, AV penetration rates, and ramp flow ratios, and the traffic safety of each scenario is estimated. Moreover, the evaluation results are compared against those derived from the fuzzy comprehensive evaluation (FCE) method to verify the reliability of the comprehensive evaluation model. The findings indicate that safety levels deteriorate with increasing total flow rates and ramp flow ratios. Notably, as AV penetration rises from 20% to 100%, safety conditions improve significantly, especially under high-flow scenarios. However, at AV penetration rates below 20%, an increase of the AV penetration rate may worsen safety. Overall, the proposed integrated approach provides a more robust and accurate assessment of safety risks than single-factor evaluations, providing deeper insights into the asymmetries in traffic interactions and offering valuable insights for traffic management and AV deployment strategies. Full article

Exposure therapy approaches are recognized as effective treatments for specific phobias; however, certain phobias, such as fear of driving on urban highways, present unique challenges in order to expose the patient to the triggering stimuli in a safe, accessible, and controlled manner. In this context, we developed a virtual reality (VR) computerized driving simulator based on a local urban highway, and an accompanying clinical protocol to provide exposure therapy for patients with observed fear of driving on urban highways. We recruited eleven patients for this pilot study, where safety and tolerability as well as clinical and functional improvement were explored. We found that the driving simulator was safe and well tolerated by patients, with a notable 82% of patients successfully completing in vivo exposure, and there being a consistent trend in reduced anxiety scores using standardized testing. Nine patients successfully engaged in live exposures in a real freeway after participating in this VR-based exposure therapy protocol. The creation of an immersive and realistic VR environment based on a local urban highway for treating this phobia proved feasible and well-tolerated by participants. The intervention’s ability to engage patients who might otherwise have avoided traditional exposure therapies is noteworthy. Future research should aim to replicate this study with a larger and more diverse sample to enhance the generalizability of the findings. Full article

Due to complex traffic conditions, freeway curves are associated with higher crash rates, particularly for trucks, which poses significant safety risks. Predicting truck crash rates on curves is essential for enhancing freeway safety. However, geometric design consistency indicators (GDCIs) are limited in terms of their ability to evaluate safety levels. To address this, this study identifies key factors influencing truck crash rates on curves and proposes a new safety evaluation indicator, the mean speed change rate (MSCR). A vague set, as an extension of the fuzzy set, was employed to integrate the MSCR and GDCI to identify high-risk curves. The factors contributing to differences in crash rates between the curves to the left and right are also analyzed. To assess the proposed approach, a case study was conducted using truck traffic data extracted from floating car data (FCD) collected on 32 freeway curves. The results demonstrate that the deflection angle, radius, and deflection direction are key contributions to truck crash risks. Importantly, the recognition accuracy of the MSCR indicator for crash risks on curves to the left and right is improved by 11.8% and 18.2% compared with GDCIs. Combining the proposed MSCR indicator with GDCIs can more comprehensively evaluate the safety of curves, with recognition accuracy rates of 88.2% and 27.3%, respectively. The indicator change value of the curves to the left are always larger, and the difference is more obvious as the geometric indicator changes. The MSCR indicator provides a more comprehensive curve safety assessment method than existing indicators, which is expected to promote the formulation of curve safety management strategies and further achieve sustainable development goals. Full article

As an essential component of China’s comprehensive transportation network, freeways play an irreplaceable role in promoting regional economic integration, improving logistics efficiency, and serving public travel. However, the development of freeways faces challenges such as the underutilization of road resources, significant financial pressure for construction and maintenance, and imbalanced revenue and expenditure leading to heavy debt burdens, which severely impact the sustainable development of freeways. Optimizing freeway toll rates is an effective measure to alleviate these issues, playing a crucial role in enhancing the operational efficiency of the road network and increasing the revenue of freeway operating enterprises. Existing studies have focused on finding the optimal toll rates for freeways based on bi-level programming models, neglecting the dynamic relationships among individual travel behavior preferences, toll rates, travel demand, and toll revenue. Grounded in bounded rationality theory, the research employs microscopic traffic simulation technology to analyze the dynamic relationships among freeway toll rates, travel demand, and toll revenue. The results confirm that travel demand decreases as toll rates increase, while toll revenue exhibits asymmetric “synchronization” and “asynchronization” phases, peaking at CYN 58.9 thousand (USD 8246) when the toll rate reaches CYN 0.45/km (USD 0.06/km). Additionally, users’ rationality levels significantly affect the stabilization time of toll revenue, and the speed difference between freeways and parallel roads demonstrates a threshold effect on travel demand and revenue. These findings provide theoretical and technical support for optimizing freeway toll strategies, enhancing operational efficiency, and promoting sustainable transportation development. Full article

Highway exit ramps play a crucial role in ensuring the safe and efficient operation of road networks. As automated vehicles progressively integrate into highways, it is essential to investigate whether these exit ramps can accommodate the safe and efficient operation of heterogeneous traffic flows. This study constructed a basic simulation test using the SUMO simulation platform to analyze the adaptability of motorway exit ramps in a heterogeneous traffic environment. The simulation model incorporated the Krauss car-following model for the longitudinal dynamics of manual-driving vehicles, the ACC/CACC car-following model for automated vehicles, the LC2013 lane-changing model for manual-driving vehicles, and the game-theoretic lane-changing model for automated vehicles. The results reveal that in the absence of automated vehicles, the comprehensive cost is minimized with a deceleration lane length of 215 m, offering superior adaptability compared to the current standard of 180 m. As the proportion of automated vehicles gradually increases to surpass 40%, the rate of improvement in traffic flow, operational speed, and overall operational costs diminishes. Under these conditions, heterogeneous traffic flows exhibit limited adaptability to the existing road infrastructure. However, when the deceleration lane is extended to 200 m, the exit ramp shows optimal adaptability for heterogeneous traffic flows. Full article

As a large-scale transportation infrastructure project, the construction of a freeway will consume a large amount of high-energy and high-density raw material products and emit a large amount of carbon dioxide. Selecting route options with lower carbon emissions during the preliminary design phase of a project is one effective way to mitigate carbon emission pressure. This study collected 124 highway construction cases and calculated the carbon emissions generated during the construction of each case. By utilizing the grey relational analysis method, we assessed the degree of association between various indicators and carbon emissions, identifying the primary indicators influencing carbon emissions. Furthermore, we integrated multiple strategies to improve the northern goshawk optimization algorithm and optimize the BP neural network, thereby establishing a carbon emission prediction model for the highway construction phase. Using this model, we predicted the carbon emission data per kilometer of two different highway route options, which were 2.2959 t and 4.3009 t, respectively, and recommended the route option with lower carbon emissions. This model addresses the challenge faced by highway construction units in quantifying carbon emissions for different route options during the preliminary design phase, providing a basis for adjusting and comparing route options from a low-carbon perspective. Full article

The rapid development of Connected and Autonomous Vehicles (CAVs) presents challenges in managing mixed traffic flows. Previous studies have primarily focused on mixed traffic flow involving CAVs and Human-Driven Vehicles (HDVs), or on the combination of trucks and cars. However, these studies have not fully addressed the heterogeneous mixed traffic flow consisting of CAVs and HDVs, including trucks and cars, influenced by varying human driving styles. Therefore, this study investigates the influences of the market penetration rate (MPR) of CAVs, truck proportion, and driving style on operational characteristics in heterogeneous mixed traffic flow. A total of 1105 events were extracted from highD dataset to analyze four car-following types: car-following-car (CC), car-following-truck (CT), truck-following-car (TC), and truck-following-truck (TT). Principal Component Analysis (PCA) and clustering techniques were employed to categorize distinct driving styles, while the Intelligent Driver Model (IDM) was calibrated to represent the various car-following behaviors. Subsequently, microscopic simulations were conducted using the Simulation of Urban Mobility (SUMO) platform to evaluate the impact of CAVs on sustainable traffic operations, including road capacity, stability, safety, traffic oscillations, fuel consumption, and emissions under various traffic conditions. The results demonstrate that CAVs can significantly enhance road capacity, improve emissions, and stabilize traffic flow at high MPRs. For instance, when the MPR increases from 40% to 80%, the road capacity improves by approximately 25%, while stability enhances by approximately 33%. In contrast, higher truck proportions lead to reduced capacity, increased emissions, and decreased traffic flow stability. In addition, an increased proportion of mild drivers reduces capacity, raises emissions per kilometer, and improves stability and safety. However, a high proportion of mild human drivers (e.g., 100% mild drivers) may negatively impact traffic safety when CAVs are present. This study provides valuable insights into evaluating heterogeneous traffic flows and supports the development of future traffic management strategies for more sustainable transportation systems. Full article