Search Results (3,402)

To minimize the occurrence of traffic accidents, such as vehicle rear-end collisions, while enhancing vehicle following, stability, economy, and ride comfort, a hierarchical adaptive cruise control strategy for vehicles is proposed. The upper-level controller computes the desired vehicle output acceleration based on model predictive control and switches between speed and spacing control in accordance with driving conditions. The brake/throttle opening switching model, brake control inverse model, and throttle opening inverse model in the lower-level controller of ACC are designed to obtain the desired throttle opening and braking pressure of the vehicle, thereby achieving control of the vehicle. A joint simulation platform was established using PreScan, CarSim and Matlab/Simulink. Finally, simulations for three typical working conditions were conducted in Simulink to verify the performance of the adaptive cruise control strategy. The results indicate that, in both the constant-speed cruise and vehicle-following cruise conditions, the vehicle can rapidly and stably follow the set initial speed and consistently maintain a safe distance from the preceding vehicle. Under the emergency braking condition, the vehicle can promptly respond with deceleration, ensuring driving safety. The proposed control strategy can accurately and safely track the target vehicle in diverse driving conditions and can concurrently fulfill the requirements of economy and comfort during vehicle travel. Full article

The environmental influence of seasonal freezing and thawing forces the longitudinal shear effect of the bridge abutment, and the differential settlement between the subgrade and the bridge abutment will significantly affect traffic safety. In this work, based on the finite element simulation analysis method, the longitudinal push-out effect and differential settlement of the transition section caused by cycles are systematically investigated, and the treatment results under different control measures (buffer layer thickness) are compared and analyzed. The results show that changing the thickness of the buffer material in the transition section has no significant influence on the overall temperature field of the subsurface. The longitudinal displacement of the transition region will be obvious under the condition of seasonal cycle, and its longitudinal thrust effect on the abutment shows a typical periodic law with the seasonal change. As the depth of the lower soil layer from the surface increases, the pushing effect becomes weaker and weaker. The development of the different subsoil settlements in the transition section also showed periodic changes with the passage of seasons. The differential settlement of the transition section after the buffer layer treatment can be effectively controlled, and the maximum value of the surface settlement of the roadbed after the 5 cm thick buffer material is reduced by 35%, compared with the two deformations of frostshocked bridges, where differential settlement after the buffer material treatment creates only tip deformation. After using a 15 cm thick buffer layer material treatment, the maximum settlement value of the surface settlement of the road base is reduced from 0.2 m to 0.01 m, which will not affect safety and driving comfort. The research conclusions can provide a reference for the design of road and bridge transition sections in frozen areas. Full article

In recent years, mobility as a service (MaaS) has been thought as one of the opportunities for shifting towards shared travel solutions with respect to private transport modes, particularly owned cars. Although many MaaS aspects have been explored in the literature, there are still issues, such as platform implementations, travel solution generation, and the user’s role for making an effective system, that require more research. This paper extends and improves a previous study carried out by the authors by providing more details and experiments. The paper proposes a diachronic network model for representing travel services available in a given MaaS platform by using an agent-based approach to simulate the interactions between travel operators and travelers. Particularly, the diachronic network model allows the consideration of both the spatial and temporal features of the available transport services, while the agent-based framework allows the representation of how shared services might be used and which effects, in terms of modal split, could be expected. The final aim is to provide insights for setting the architecture of an agent-based MaaS platform where transport operators would share their data for providing seamless travel opportunities to travelers. The results obtained for a simulated test case are promising. Particularly, there are interesting findings concerning the traffic congestion boundary values that would move users towards shared travel solutions. Full article

The dynamic modulus (∣$E^{*}$∣) is a fundamental mechanical parameter for studying the performance of hot mix asphalt and simulating its viscoelastic behaviour under different loading and thermal conditions. It is a primary tool to replicate road surface behaviour under vehicle traffic loading and temperature variations. Though, laboratory testing to determine this parameter is time-consuming and costly. Several predictive models have been developed to estimate the dynamic modulus, ranging from rheological to empirical regression models. This research was dedicated to studying two predictive models for determining the master curve of the dynamic modulus of hot mix asphalt used in a regular pavement binder course containing different reclaimed asphalt contents (0%, 30%, 40%, and 50%). Laboratory experiments were conducted to assess their accuracy. The results show that Witczak’s sigmoid function provided the best accuracy for the master curves, while the Generalized Huet-Sayegh (2S2P1D) model showed less accurate predictions, particularly at the range of low and high frequencies. Full article

Artificial Intelligence (AI) applications are rapidly growing, and more applications are joining the market competition. As a result, the AI-as-a-service (AIaaS) model is experiencing rapid growth. Many of these AIaaS-based applications are not properly optimized initially. Once they start experiencing a large volume of traffic, different challenges start revealing themselves. One of these challenges is maintaining a profit margin for the sustainability of the AIaaS application-based business model, which depends on the proper utilization of computing resources. This paper introduces the resource award predictive (RAP) model for AIaaS cost optimization called RAP-Optimizer. It is developed by combining a deep neural network (DNN) with the simulated annealing optimization algorithm. It is designed to reduce resource underutilization and minimize the number of active hosts in cloud environments. It dynamically allocates resources and handles API requests efficiently. The RAP-Optimizer reduces the number of active physical hosts by an average of 5 per day, leading to a 45% decrease in server costs. The impact of the RAP-Optimizer was observed over a 12-month period. The observational data show a significant improvement in resource utilization. It effectively reduces operational costs from USD 2600 to USD 1250 per month. Furthermore, the RAP-Optimizer increases the profit margin by 179%, from USD 600 to USD 1675 per month. The inclusion of the dynamic dropout control (DDC) algorithm in the DNN training process mitigates overfitting, achieving a 97.48% validation accuracy and a validation loss of 2.82%. These results indicate that the RAP-Optimizer effectively enhances resource management and cost-efficiency in AIaaS applications, making it a valuable solution for modern cloud environments. Full article

The zygomatic bone, a fundamental structure in facial anatomy, is exposed to fractures in impact situations, such as traffic accidents or contact sports. The installation of zygomatic implants can also alter the distribution of forces in this region, increasing the risk of fractures. To evaluate this situation, the first step is to develop a complex anatomical model from the stomatognathic point of view so that simulations in this sense can be validated. This study uses numerical simulation using a finite-element method (FEM) to analyze the behavior of the zygomatic bone under impacts of different velocities, offering a more realistic approach than previous studies by including the mandible, cervical spine, and masticatory muscles. Methods: An FEM model was developed based on 3D scans of actual bones, and simulations were performed using Abaqus Explicit 2023 software (Dassault Systemes, Vélizy-Villacoublay, France). The impact was evaluated using a steel cylinder (200 mm length, 40 mm diameter, 2 kg weight) impacted at speeds of 5, 10, 15, and 20 km/h. Zygomatic, maxillary, and mandibular bone properties were based on dynamic stiffness parameters, and bone damage was analyzed using ductile fracture and fracture energy criteria. Results: The results show that at impact velocities of 15 and 20 km/h, the zygomatic bone suffered crush fractures, with impact forces up to 400 kg. At 10 km/h, a combination of crushing and bending was observed, while at 5 km/h, only local damage without complete fracture was detected. The maximum stresses were concentrated at the zygoma–jaw junction, with values above 100 MPa at some critical points. Conclusion: The FEM model developed offers a detailed representation of the mechanical behavior, integrating the main structures of the stomatognathic apparatus of the zygomatic bone under impact, providing valuable information to, for example, advance injury prevention and zygomatic implant design. Higher impact velocities result in severe fractures, underscoring the need for protective measures in clinical and sports settings. Full article

The poor maneuverability inherent to large ships is a non-negligible problem that restricts the development of the shipping industry, as large ships can only cruise at an excessively conservative speed when they encounter complicated traffic conditions; nevertheless, ship collision accidents still occasionally occur. In the present study, the novel concept of spoiler fins for modern large ships is proposed. In order to assess their effectiveness in enhancing ship maneuverability, a KRISO container ship (KCS) was selected to carry a pair of spoiler fins, after which a simplified simulation approach for saving the calculation resource was designed for ship collision avoidance conditions, and a full-scale numerical model, including the ship hull, fin, and fluid field domain, was established. Transient-state hydrodynamic forces were calculated during collision avoidance maneuvers using the CFD method; the pressure and velocity contours around the ship were demonstrated; and the ship motion trajectories under different initial ship speeds were simulated and predicted through the adoption of overset mesh and 6-DOF dynamic mesh techniques. Eventually, the improved course-changing performance, dependent on the spoiler fins, was validated. Full article

Rainstorms and flooding are among the most common natural disasters, which have a number of impacts on the transport system. This reality highlights the importance of understanding resilience—the ability of a system to resist disruptions and quickly recover to operational status after damage. However, current resilience assessments often overlook transport network functions and lack dynamic spatiotemporal analysis, posing challenges for comprehensive disaster impact evaluations. This study proposes an SR-PR-FR comprehensive resilience evaluation model from three dimensions: structure resilience (SR), performance resilience (PR), and function resilience (FR). Moreover, a simulation model based on Geographic Information System (GIS) and Simulation of Urban MObility (SUMO) is developed to analyze the dynamic spatial–temporal effects of a rainstorm on traffic during Xi’an’s evening rush hour. The results reveal that the southwest part of Xi’an is most prone to being congested and slower to recover, while downtown flooding is the deepest, severely affecting emergency services’ efficiency. In addition, the road network resilience returns to 70% of the normal values only before the morning rush the next day. These research results are presented across both temporal and spatial dimensions, which can help managers propose more targeted recommendations for strengthening urban risk management. Full article

Complex microscopic simulation models of strategic Air Traffic Management (ATM) performance assessment and decision-making are hindered by several factors. One of the most important is the existence of hidden parameters—such as aircraft take-off weight (TOW) and the selected cost index (CI)—which, if known, would allow for more effective performance modeling methodologies for assessing Key Performance Indicators (KPIs) at various levels of abstraction/detail, e.g., system-wide, or at the level of individual flights. This research proposes a data-driven methodology for the estimation of flights’ hidden parameters combining mechanistic and advanced Artificial Intelligence/Machine Learning (AI/ML) models. Aiming at microsimulation models, our goal is to study the effect of these estimations on the prediction of flights’ KPIs. In so doing, we propose a novel methodology according to which data-driven methods are trained given optimal trajectories (produced by mechanistic models) corresponding to known hidden parameter values, with the aim of predicting hidden parameters’ values of unseen trajectories. The results show that estimations of hidden parameters support the accurate prediction of KPIs regarding the efficiency of flights: fuel consumption, gate-to-gate time and distance flown. Full article

This research introduces a cutting-edge approach to traffic infrastructure digitization, integrating UAV oblique photography with LiDAR point clouds for high-precision, lightweight 3D road modeling. The proposed method addresses the challenge of accurately capturing the current state of infrastructure while minimizing redundancy and optimizing computational efficiency. A key innovation is the development of the TJYRoad-Net model, which achieves over 85% mIoU segmentation accuracy by including a traffic feature computing (TFC) module composed of three critical components: the Regional Coordinate Encoder (RCE), the Context-Aware Aggregation Unit (CAU), and the Hierarchical Expansion Block. Comparative analysis segments the point clouds into road and non-road categories, achieving centimeter-level registration accuracy with RANSAC and ICP. Two lightweight surface reconstruction techniques are implemented: (1) algorithmic reconstruction, which delivers a 6.3 mm elevation error at 95% confidence in complex intersections, and (2) template matching, which replaces road markings, poles, and vegetation using bounding boxes. These methods ensure accurate results with minimal memory overhead. The optimized 3D models have been successfully applied in driving simulation and traffic flow analysis, providing a practical and scalable solution for real-world infrastructure modeling and analysis. These applications demonstrate the versatility and efficiency of the proposed methods in modern traffic system simulations. Full article

Background: Urban mobility management faces growing challenges that require the analysis and optimization of sustainable solutions. Digital twins (DTs) have emerged as innovative tools for this assessment, but their implementation requires standardized procedures and languages; Methods: As part of a broader methodology for continuous DT validation, this study focuses on the conceptual validation phase, presenting a conceptualization approach through formalization using Specification and Description Language (SDL), agnostic to simulation tools. The conceptual validation was achieved through stakeholder engagement in the Bolzano context, producing 41 SDL diagrams that define both elements common to different urban realities and specific local data collection procedures; Results: The feasibility of implementing this stakeholder-validated conceptualization was demonstrated using Simulation of Urban MObility (SUMO) for traffic simulation and optimization criteria calculation, and its framework SUMO Activity GenerAtion (SAGA) for generating an Activity-Based Modeling (ABM) mobility demand that can be improved through real sensor data; Conclusions: The SDL approach, through its graphical representation (SDL/GR), enables conceptual validation by enhancing stakeholder communication while defining a framework that, while adapting to the monitoring specificities of different urban realities, maintains a common and rigorous structure, independent of the chosen implementation tools and programming languages. Full article

In this paper, we investigate whether greedy algorithms, traditionally used for pedestrian-based crowdsensing, remain effective in the context of vehicular crowdsensing (VCS). Vehicular crowdsensing leverages vehicles equipped with sensors to gather and transmit data to address several urban challenges. Despite its potential, VCS faces issues with user engagement due to inadequate incentives and privacy concerns. In this paper, we use a dynamic incentive mechanism based on a recurrent reverse auction model, incorporating vehicular mobility patterns and realistic urban scenarios using the Simulation of Urban Mobility (SUMO) traffic simulator and OpenStreetMap (OSM). By selecting a representative subset of vehicles based on their locations within a fixed budget, our mechanism aims to improve coverage and reduce data redundancy. We evaluate the applicability of successful participatory sensing approaches designed for pedestrian data and demonstrate their limitations when applied to VCS. This research provides insights into adapting greedy algorithms for the particular dynamics of vehicular crowdsensing. Full article

(1) Background: As cycling gains popularity as a mode of transportation, the frequency of accidents involving cyclists also rises. This has become a major concern for traffic safety, sustainability, and city planning. Identifying the risk factors that contribute to bicycle road accidents remains a significant challenge. This study aims to figure out which risk factors make some road segments more dangerous for cyclists than others. (2) Methods: This study introduces the use of a bicycle simulator to test different road segments involving thirty-nine participants. The impact of demographics and some risk factors related to infrastructure were analyzed in terms of their influence on the perceived level of risk through pre- and post-surveys. (3) Results: The findings showed that the bicycle facility type affects the perceived level of risk. Shared-use roads were ranked as riskiest, while separated bike lanes were least risky. Bicycle roads with no separated safety barriers had higher risks. Heavy traffic jams increased danger among cyclists. Women gave higher risk ratings than men. The perceived levels of risk were then compared with the previously developed risk index and they correlated well. (4) Conclusions: This confirms that the risk index can reliably evaluate the degree of risk of each road segment. Full article

Automated driving (AD) is a new technology that aims to mitigate traffic accidents and enhance driving efficiency. This study presents a deep reinforcement learning (DRL) method for autonomous vehicles that can safely and efficiently handle highway overtaking scenarios. The first step is to create a highway traffic environment where the agent can be guided safely through surrounding vehicles. A hierarchical control framework is then provided to manage high-level driving decisions and low-level control commands, such as speed and acceleration. Next, a special DRL-based method called deep deterministic policy gradient (DDPG) is used to derive decision strategies for use on the highway. The performance of the DDPG algorithm is compared with that of the DQN and PPO algorithms, and the results are evaluated. The simulation results show that the DDPG algorithm can effectively and safely handle highway traffic tasks. Full article

With growing air travel demand, weather disruptions cost millions in flight delays and cancellations. Current resilience analysis research has been focused on airports and airlines, rather than the en-route waypoints, and has failed to consider the impact of disruption scenarios. This paper analyses the resilience of the United Kingdom (UK) air traffic network to weather events that disrupt the network’s high-traffic areas. A Demand and Capacity Balancing (DCB) model is used to simulate adverse weather and re-optimise the cancellation, delay, and rerouting of flights. The model’s feasibility and effectiveness were evaluated under 20 concentrated and randomly occurring extreme disruption scenarios, lasting 2 h and 4 h. The results show that the network is vulnerable to extended weather events that target the network’s most central waypoints. However, the network demonstrates resilience to weather disruptions lasting up to two hours, maintaining operational status without any flight cancellations. As the scale of disruption increases, the network’s resilience decreases. Notably, there exists a threshold beyond which further escalation in disruption scale does not significantly impair the network’s performance. Full article