Search Results (82)

In response to the current severe traffic congestion issues, highly reliable traffic flow prediction serves as a fundamental prerequisite for optimizing municipal road networks and mitigating systemic vehicular congestion. Aiming to elevate the precision of short-term traffic flow prediction, this paper first addresses the low precision of the Dung Beetle Optimizer (DBO) algorithm by introducing an exponential adaptive weight in the way of position update for the ball-rolling dung beetle, along with incorporating a Cauchy–Gaussian mutation strategy. We propose the Multi-strategy improved Dung Beetle Optimizer (MDBO), which is validated using eight benchmark test functions, demonstrating that MDBO outperforms common optimization algorithms in solution accuracy. Secondly, we adopt a combined prediction model, Traffic Flow Temporal-Spatio Network (TFTSNet), which constructs spatial feature modules and temporal feature modules in parallel fusion. Finally, we achieve short-term traffic flow prediction by optimizing the TFTSNet combined prediction model using MDBO. The experiment evaluated model performance using publicly available traffic flow datasets. The results demonstrate that, compared to other state-of-the-art models, the proposed joint prediction model based on MDBO-optimized TFTSNet achieves substantial enhancements in both prediction precision and generalization capability. Root mean square error (RMSE) decreased by 8.7–35.7%, mean absolute error (MAE) decreased by 6.6–40.0%, and R² reached 0.975, showcasing robust predictive capabilities and engineering reference value. Full article

Autonomous ground vehicles operating in structured and semi-structured environments—such as urban roads, parking lots, and logistics warehouses—require fast, reliable, and collision-free path planning on occupancy grid maps. Existing metaheuristic planners often suffer from premature convergence, insufficient population diversity, and poor feasibility maintenance, limiting their deployment in safety-critical vehicular navigation. This paper proposes a multi-strategy enhanced Crested Porcupine Optimizer (MSCPO) that systematically addresses these limitations through four coordinated enhancements: chaos-opposition initialization with feasibility repair to ensure high-quality and diverse initial routes; a diversity-coupled adaptive mechanism for dynamic strategy scheduling throughout the search; elite-guided differential Lévy perturbation to escape local optima and accelerate convergence; and a two-stage safety-aware objective with elite local refinement to sharpen final solution precision. Experiments on four representative grid maps with varying obstacle densities, conducted over 30 independent runs per algorithm, demonstrate that MSCPO consistently outperforms state-of-the-art metaheuristic planners and deterministic baselines in path length, smoothness, and convergence speed. Statistical analysis via Wilcoxon rank-sum and Friedman tests confirms the significance of the improvements. An ablation study quantifies the individual contribution of each enhancement module, confirming the practical effectiveness of MSCPO for autonomous vehicle navigation tasks. Full article

Road transportation is a major contributor to global CO₂ emissions, yet the influence of road geometry on vehicular emissions remains insufficiently quantified under real-world conditions. This study investigates the effects of horizontal and vertical alignments on CO₂ emissions of a light-duty gasoline passenger vehicle using Portable Emissions Measurement System (PEMS) data collected along a 62.4 km highway section. Six geometric parameters longitudinal grade, cross slope, horizontal curve radius, horizontal curve length, vertical curve radius, and vertical curve length were analyzed in combination with second-by-second vehicle dynamics. The results indicate that transient CO₂ emissions exhibit substantial variability, with instantaneous emission rates exceeding 7.0 g/s under high-load conditions. Longitudinal slope gradient shows the strongest linear association with emission rate (r = 0.63), while speed and acceleration exhibit weaker but statistically significant correlations (r = 0.21 and r = 0.28, respectively). Vehicle Specific Power (VSP), representing integrated tractive power demand, demonstrates stronger association with instantaneous CO₂ emissions than individual kinematic variables. In contrast, cross slope and horizontal curvature parameters display minimal direct correlations under the tested highway conditions. A nonlinear polynomial regression model modestly improves explanatory performance relative to a linear formulation (R² = 0.21 versus 0.15; RMSE approximately 56 g/km), although a substantial portion of variability remains unexplained, reflecting the complexity of transient real-world processes. Overall, vertical alignment and transient driving conditions dominate CO₂ emission variability, while horizontal parameters play supplementary roles. These findings provide empirical evidence for refining emission models and highlight the importance of incorporating vertical alignment into sustainable roadway design and carbon reduction strategies. Full article

The European Union’s regulatory mandate requirements for vehicular components include the integrity of sealing performance, mitigating leaks from fuel tanks and transmission systems in order to guard against environmental pollution. Non-compliance can result in significant costs for the OEM and their supplier base. The majority of the reported research regarding leakage from radial lip seals focuses on static analysis of leakage under a given set of laboratory conditions. However, in practice, seal conjunctions are often subjected to significant excitations due to vehicular vibration. In the current study, the case of a front-wheel drive vehicle, equipped with three-axle accelerometers and subjected to a comprehensive road test, is used as the basis for the development of a realistic representative test rig. The test rig is developed using bespoke components from the vehicle under investigation to assess the impact of the encountered natural frequencies on sealing performance in controlled laboratory conditions, when the system is subjected to controlled excitation. Experiments are conducted to evaluate leakage at the transmission interface, focusing specifically on the sealing system’s performance. The influence of driveshaft manufacturing processes using corundum grinding and subsequent surface topography upon leakage performance are also considered. Identified modal response frequencies are imposed upon the test rig using a shaker, whilst the seal leakage is measured. The importance of shaft roughness characteristics, such as topographical skewness upon seal leakage rate under various resonant conditions, are ascertained. The results indicate potentially significant leakage rates under excitation conditions, with a non-optimised shaft roughness profile. Full article

Vehicular Ad Hoc Networks (VANETs) enable Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications for enhancing road safety. However, reliable driver stress assessment remains challenging due to noisy sensing, inter-driver variability, and context dynamics. This paper proposes a Fuzzy-based Driver Stress Detection System (FDSDS) that employs an Interval Type-2 Fuzzy Logic System (IT2FLS) to model uncertainty. The FDSDS considers four complementary inputs—Heart Rate Variability (HRV), Galvanic Skin Response (GSR), Steering Angle Variation (SAV), and Traffic Density (TD)—to estimate Driver Stress Level (DSL). Extensive simulations (14,641 test points) show monotonic associations between DSL and the inputs, which reveal that physiological indicators dominate average influence (finite-difference sensitivity: GSR 0.357, SAV 0.239, TD 0.239, HRV 0.235). Under severe physiological conditions (HRV = 0.1, GSR = 0.9), the system consistently outputs high stress (mean DSL = 0.813; range 0.622–0.958), while favorable physiological conditions (HRV = 0.9, GSR = 0.1) yield low stress even in challenging traffic (range 0.044–0.512). The IT2FLS uncertainty bands widen for intermediate conditions, aligning with the inherent ambiguity of moderate stress states. These results indicate that combining physiological, behavioral, and environmental factors with IT2FLS yields interpreted, uncertainty-aware stress estimates suitable for real-time VANET applications. Full article

Over the past few decades, road traffic has grown significantly, bringing with it increasing safety concerns. These concerns range from short-term issues like accidents and congestion to long-term challenges such as deteriorating air quality and health problems caused by prolonged exposure to harmful emissions. Various policies and regulations have been implemented to address these problems with varying degrees of success. While past efforts primarily focused on the industrial sector, private individuals are now the main contributors to road traffic challenges. In densely populated cities, vehicular traffic plays a major role in individuals’ emission footprint and heightens risks for both drivers and pedestrians. In this article, a new blockchain-driven gamification method for the improvement of road traffic safety and emission regulation is proposed, tested, and implemented. For this method, different types of driving sessions are recorded and analyzed, and based on the parameters received from an OBD-II (onboard diagnostic) device connected to the vehicle, data is collected through a smartphone app and recorded on a private Ethereum blockchain. The gamification component computes a score for each drive, based on the analysis of the data. For the regulation of emissions (CO₂, CO, and NOx), different methods of emission estimation are analyzed and compared, based on the OBD-II data. Full article

The advancement of Artificial Intelligence (AI) and the Internet of Things (IoT) has accelerated the development of Intelligent Transportation Systems (ITS) in smart cities, playing a crucial role in optimizing traffic flow, enhancing road safety, and improving the driving experience. With urban traffic becoming increasingly complex, timely detection and response to congestion and accidents are critical to ensuring safety and situational awareness. This paper presents Passable, an intelligent and adaptive traffic light control system that monitors traffic conditions in real time using deep learning and computer vision. By analyzing images captured from cameras at traffic lights, Passable detects road incidents and dynamically adjusts signal timings based on current vehicle density. It also employs wireless communication to alert drivers and update a centralized dashboard accessible to traffic management authorities. A working prototype integrating both hardware and software components was developed and evaluated. Results demonstrate the feasibility and effectiveness of designing an adaptive traffic signal control system that integrates incident detection, instantaneous communication, and immediate reporting to the relevant authorities. Such a design can enhance traffic efficiency and contribute to road safety. Future work will involve testing the system with real-world vehicular communication technologies on multiple coordinated intersections while integrating pedestrian and emergency vehicle detection. Full article

This paper presents an optimized vehicular reordering methodology designed to minimize energy consumption within heterogeneous cohorts operating at constant velocity on limited-access highways. The approach addresses the challenge of optimizing vehicle sequencing by considering both aerodynamic drag reduction benefits and the energy costs of reconfiguring a cohort from a stochastic initial state. This study provides empirical validation through on-road vehicle tests, demonstrating significant energy savings, achieving up to 10% reduction in axle energy for optimally configured cohorts compared to independent operation. A System of Systems (SoS) simulation environment, integrating micro-traffic, validated powertrain, and aerodynamic drag reduction models, was developed to simulate complex reconfiguration maneuvers and quantify associated energy expenditures. The methodology examines how powertrain characteristics influence optimal arrangements and quantifies the impact of individual vehicle placement on overall cohort efficiency. Findings indicate that while reconfiguration incurs a minor energy cost (typically <0.45% of total trip energy for a 20 km trip), the net energy savings over relevant travel distances are substantial. The study also highlights the sensitivity of drag reduction estimators for heterogeneous platoons and the current limitations in available models. Ultimately, a predictive optimization framework is proposed that leverages connectivity-enabled information to select the most energy-efficient cohort configuration, considering factors such as distance to destination and reconfiguration energy, thereby offering a practical strategy for enhancing fuel economy in future connected and automated transportation systems. Full article

This paper addresses the challenge of optimizing path planning for autonomous vehicles in urban environments by considering both traffic and bandwidth variability on the road. Traditional path planning methods are inadequate for the needs of interconnected vehicles that require significant real-time data transfer. We propose a reinforcement learning approach for path planning, formulated to use road traffic conditions and bandwidth availability. This approach optimizes routes by minimizing travel time while maximizing data transfer capability. We create a realistic simulation environment using GraphML, incorporating real-world map data and vehicle mobility patterns to evaluate the effectiveness of our approach. Through comprehensive testing against various baselines, our reinforcement learning model demonstrates the ability to adapt and find optimal paths that significantly outperform conventional strategies. These results emphasize the feasibility of using reinforcement learning for dynamic path optimization and highlight its potential to improve both the efficiency of travel and the reliability of data-driven decisions in autonomous vehicular networks. Full article

With the continuous development of assisted driving technology, the autonomous emergency braking (AEB) system has emerged as a critical innovation in preventing collisions and improving vehicular safety. In this paper, to test the performance of the AEB system efficiently and reliably in real-world driving scenarios, four typical test scenarios for the AEB system were constructed, and five comprehensive performance evaluation indices, including braking parking distance, braking deceleration, collision warning time, speed variation, and accident collision avoidance rate, were proposed for the first time. Subsequently, the Comprehensive Performance Evaluation Index Model (CPEIM) for the AEB system and scoring rules for typical test scenarios were established, which were applied to analyze data obtained from road testing, thereby enabling comprehensive testing and evaluation for AEB system performance. The results showed that the Tesla Model Y and Volvo S90 scored 1.8857 and 2.0433, respectively. Under conditions of dry pavement, across a range of test scenarios, the AEB system of both the Tesla Model Y and Volvo S90 were capable of averting collisions at speeds not exceeding 35 km/h and 45 km/h, respectively. Full article

Establishing a safe and stable routing path for a source–destination pair is necessary regardless of the weather conditions. The reason for this is that vehicles can improve safety on the road by exchanging messages and updating each other on the current conditions of both roads and vehicles. This paper intends to solve the problem of when foggy roads make it difficult for drivers to travel, especially when people encounter emergency situations and have no other option but to drive in foggy weather. Although the literature offers few solutions to the problem, no one has considered integrating software-defined networking into vehicular networks for foggy roads to create an optimal routing path. Moreover, it is of significance to mention that vehicles in adverse weather conditions travel following each other and maintaining a constant safety distance, which leads to the formation of a platoon. Considering this, we propose a heterogeneous communication protocol in a software-defined vehicular network to establish an optimal routing path using platoons on foggy highways. Different cases were tested to show how platoons behave in high connectivity and sparsity, achieving a maximum delivery ratio of 99%, a delay of 2 ms, an overhead of 55%, and an acceptable number of hops compared to reference schemes. Full article

Winter road safety is significantly compromised by ice formation, leading to increased vehicular accidents due to reduced friction. Traditional anti-icing strategies, such as chemical deicers, present environmental and structural drawbacks, necessitating innovative solutions. This study evaluates methyl methacrylate (MMA)-based resin composites for anti-icing and skid-resistant applications. These composites are particularly intended for application on asphalt and concrete pavements in urban roads, highways, and other high-traffic areas prone to icing during winter. MMA composites exhibit excellent mechanical properties, including tensile strength of up to 10 MPa and compressive strength of 34 MPa under optimized formulations. These composites are specifically developed for application on asphalt and concrete pavements commonly found in urban roads, highways, and other high-traffic areas, where icing and skid resistance are critical challenges during winter conditions. Anti-icing performance was enhanced by incorporating additives like magnesium chloride hexahydrate, achieving a freezing point reduction to −12.9 °C and a heat of solution of 0.429 kJ/g. Laboratory tests revealed that increasing anti-icing additives reduced ice adhesion and melting time, with a trade-off in compressive strength, which decreased from 30 MPa (unmodified) to 16 MPa at higher additive concentrations. Skid resistance was improved through the addition of high-friction aggregates, ensuring durability under icy and wet conditions. These results highlight MMA composites as a sustainable and cost-effective alternative to traditional deicing methods, offering enhanced road safety and reduced environmental impact. Further research is recommended to optimize formulations and validate performance through field trials under varying climatic conditions. Full article

The deployment of cluster mailboxes (CMs) in the U.S. has raised safety concerns for passengers in potential vehicular crashes involving CMs. This study investigated the crashworthiness of two types of CMs through nonlinear finite element simulations. Two configurations of CM arrangements were considered: a single- and a dual-unit setup. These CM designs were tested on flat-road conditions with and without a curb. A 2010 Toyota Yaris and a 2006 Ford F250, both in compliance with the Manual for Assessing Safety Hardware (MASH), were employed in the analysis. The simulations incorporated airbag models, seatbelt restraint systems, and a Hybrid III 50th percentile adult male dummy. The investigations focused on evaluating the safety of vehicle occupants in 32 impact scenarios and under MASH Test Level 1 conditions (with an impact speed of 50 km/h). The simulation results provided insights into occupant risk and determined the primary failure mode of the CMs. No components of the mailboxes were found intruding into the vehicle’s occupant compartment. For all considered cases, the safety factors remained within allowable limits, indicating only a marginal risk of potential injury to occupants posed by the considered CMs. Full article

This study aims to identify the most polluted areas and sites using the magnetic signal of ornamental plant leaves as an indicator of environmental pollution. Systematic sampling was conducted with 98 sampling sites described according to urban land use, such as road hierarchy and road surface, soil group, collected plant species, and municipality. The magnetic parameters analyzed were low- and high-frequency magnetic susceptibility and the isothermal remanent magnetization acquisition curves in order to calculate the magnetic enhancement factor. For the analysis of variance, a Kruskal–Wallis test was performed to compare urban land uses. Subsequently, the magnetic enhancement factor in dust and surface soil was used to prepare maps of environmental pollution for each urban area. Analyses of the different magnetic parameters of the dust deposited on leaves show that low-coercivity ferrimagnetic minerals dominated the magnetic signal, probably magnetite of anthropic origin, and were closely linked to vehicular traffic and, to a lesser extent, industrial activities. Full article

Security is a crucial matter, and when it comes to road safety, barriers are increasingly needed to protect assets and pedestrians from intentional and accidental vehicular impacts. Hollow steel tubes are commonly used to produce bollards; however, their impact resistance and energy absorption are limited. Hence, the aim of this study is to investigate whether the addition of honeycomb and auxetic cellular cores can improve the energy absorption and protection level of existing bollards. Hollow bollard, a honeycomb–core bollard and an auxetic-core bollard were numerically modeled and tested (using Simulia Abaqus software, version 2019) against the impact of M1-class vehicles (of 1500 kg mass) at five different speeds (following PAS 68:2013 British standard). Hence, 15 cases/numerical models were considered, with 5 cases for each bollard type. The results revealed that the addition of an auxetic cellular core to the bollard system could increase its energy dissipation by 52% compared to the hollow steel bollard. Moreover, the proposed auxetic anti-ram bollard system was capable of stopping an M1-class vehicular impact of 64 km/h compared to only 32 km/h when using a hollow steel bollard. To the authors’ knowledge, the use of an auxetic core, explicitly for anti-ram bollards, can be considered the novel part of this research. Full article