Heavy Vehicles Research Articles

Hospitalization information and burden of pediatric inpatients in transport accidents

BackgroundTransport accidents are one of the leading causes of child morbidity and mortality worldwide and represent a significant public health burden. This study aimed to investigate the hospitalization information and burden of pediatric inpatients in transport accidents in China.MethodsIn this study, we collected the cover page of the medical records of pediatric inpatients in transport accidents using the Futang Research Center of Pediatric Development (FRCPD) database from January 1, 2016 to December 31, 2021. Then, we extracted the epidemiological characteristics, including demographic characteristics, cases distribution, disease information, and hospitalization burden.ResultsAmong 36,455 included inpatients, males, aged 1–3 years, East China, July were dominant in different subgroups. In transport accidents, pedestrians were the most frequently type of injury (65.69%). Of all known lesions, craniocerebral/nerve injury was the more common results in pediatric inpatients in transport accidents (33.93%). In addition to pedal cyclists more susceptible to sport system injury, other types of injured person with transport accidents were mainly craniocerebral/nerve injury. In terms of the type of discharge, occupant of heavy transport vehicle or bus and people with craniocerebral/nerve injury had the highest mortality rate after hospitalization in all type and lesion of injured person groups, respectively. The largest hospitalization burden in the type of injured person was occupant of heavy transport vehicle or bus.ConclusionsThis study revealed that epidemiological characteristics and the main factor influencing the hospitalization information and burden of children with traffic accidents in China.

Heavy vehicle driver fatigue: Observing work and rest behaviours of truck drivers in Australia

The extant transport literature suggests that truck driver fatigue is a leading contributor in truck crashes. Various factors are known to affect a truck driver’s ability to comply with work and rest requirements, including long working hours, irregular work schedules, sleep deprivation and driving at night. In Australia, while control measures restrict the number of hours of work a truck driver may undertake without rest, they travel vast distances and work with a high level of autonomy. This makes it difficult for transport companies to monitor and manage truck drivers’ work and rest compliance; thereby relying on them to self-manage driver fatigue at designated rest-stops. However, there is little Australian research examining rest-stop behaviour of truck drivers in relation to driver fatigue compliance. To better understand how truck drivers manage driver fatigue, an exploratory participant observation study of truck drivers (N = 378) was undertaken at two Australian truck rest stops in rural New South Wales. The findings indicate that while most truck drivers comply with rest requirements regarding driver fatigue, load type, driver changeovers, access to parking, and time spent on work-related activities at the rest areas affects the time truck drivers spend at rest, and these factors are likely to increase the risk of truck drivers not complying with driver fatigue regulation. They are also likely to increase the number of truck drivers involved in fatigue related crashes.

Thermo-rheological behaviour of styrene-butadiene-styrene, hard plastics with or without graphene oxide modified bitumens

With the increase in traffic volume and heavy vehicles, as the changes in climatic conditions, in recent years the design of the road surface has completely changed because it has to guarantee the suitable performance of the pavement. To this end, in recent decades, modifiers have been introduced into the bitumen and, currently, the most performing modifier is SBS (styrene-butadiene-styrene) polymer, but in recent years the investigation on the so-called green additives, obtained from recycled materials or of natural origin is growing. The study intends to investigate, from a rheological point of view, the possibility of using waste hard plastics also in combination with graphene oxide because considered a promising material for new binder generation. For this purpose, the influence that hard plastics, with or without graphene oxide addition, have on the bitumen performance compared to standard SBS-modified bitumens has been studied. The comparison has shown that the plastomeric additive can improve the rheological properties, in the intermediate and high in-service temperature region, while the graphene oxide addition acts on the transitions from viscoelastic to viscous material at high temperature; nevertheless, the aging reduces the effect. In addition, it was found that 4 %w/w of hard plastics improve the performance of bitumen and the rheological properties are not affected by the influence of aging.

Adaptive Neural Network Control of Heavy Vehicle Air Suspension with Uncertainties

Adaptive Neural Network Control of Heavy Vehicle Air Suspension with Uncertainties

Smart Traffic Signaling Using Machine Learning and IoT

The project, titled “Smart Traffic Signaling using Machine Learning and IoT," introduces an innovative solution for optimizing traffic signal control. By harnessing the power of image processing, IoT, and machine learning, this project will be a real-time system that accurately assesses vehicle density at intersections. The project focuses on training a machine learning model to recognize various vehicle types, including bikes, cars, trucks, and heavy vehicles. This adaptive control mechanism aims to enhance traffic flow efficiency, reduce congestion, and contribute to the advancement of intelligent transportation systems. systems. Key Words: Machine learning, IoT, Image processing, Smart Traffic.

Reinforcement learning-based heuristic planning for optimized energy management in power-split hybrid electric heavy duty vehicles

In this work, we systematically integrate relevant expertise, specifically on the optimal brake-specific fuel consumption (BSFC) curve, battery characteristics and terrain information into the development of an energy management plan for heavy-duty power-split hybrid electric vehicles. We utilize deep deterministic policy gradient (DDPG) algorithm as one of the most sophisticated reinforcement learning technique. Initially, we begin by explaining the vehicle configuration's system modeling. Subsequently, we then present an energy management approach based on deep Q-learning concepts. A novel algorithm, Deep Deterministic Policy Gradient, for energy management control, have been created to combat the “curse of dimensionality” in reinforcement learning. The new AMSGrad optimization technique is used by DDPG algorithm to update the weight of neural networks. We robustly train the proposed control system in realistic driving environment. The Knowledge Incorporation (KI) based DDPG based system is compared systematically to the conventional DDDPG methodology and the benchmark Dynamic Programming (DP) method, the latter of which usually uses the RMSProp Optimizer in its formulation. The results show that as compared to the typical DQL policy, deep reinforcement learning approaches, notably expert Knowledge incorporation KI-DDPG and terrain information with the AMSGrad optimizer, achieve faster training speeds and reduced fuel consumption while maintaining the terminal state of charge (SOC).

Optimal Torque Control of the Launching Process with AMT Clutch for Heavy-Duty Vehicles

When launching a heavy-duty vehicle, torque and position control during automatic clutch engagement is critical, and the driver’s intention to launch and changes in the vehicle’s launching resistance make clutch control more complex. This paper analyses the automatic engagement process of automated mechanical transmission (AMT) clutches and proposes an optimal control of the clutch torque for launching heavy-duty vehicles. Firstly, a fuzzy neural network (FNN)-based vehicle launching states recognition (LSR) system is designed for distinguishing the driver’s launching intention and the vehicle’s launching equivalent moment of resistance. Secondly, jerk, friction work, and launching reserve power are taken as the performance indexes for clutch torque optimization, the weight coefficients of each performance index are adjusted according to the LSR results, and the optimal clutch torque is solved by using the minimum value principle based on the shooting method. Finally, simulations and tests are conducted to validate the strategy of optimizing clutch torque, and the impact of torque optimization on the position change during the engagement process is analyzed. The results indicate that under different driver’s intentions, vehicle masses, and road gradient conditions, the jerk, friction work, and slipping time of heavy vehicles during the launching process are improved by applying the optimization strategy.

СВІТОВА ПРАКТИКА ПРОЄКТУВАННЯ БЕТОННОГО ПОКРИТТЯ СТОЯНОК ВЕЛИКОВАГОВИХ ТРАНСПОРТНИХ ЗАСОБІВ І ЗОН КАНАЛІЗОВАНОГО ТА МАНЕВРОВОГО РУХУ

Introduction. The introduction to the paper emphasizes the performance of pavements for the traffic areas of channelized/maneuvering traffic zones and heavy vehicles parking lots and highlights the advantages of concrete pavements for the said objects. Problem statement. The issues of the paper concern the design of various types of concrete pavements for the traffic areas of channelized/maneuvering traffic zones and heavy vehicles parking lots. Purpose. The goal of the paper is to compare the main provisions of the designated concrete pavements design in terms of various types of the monolithic pavement concrete slabs, and usage of types of concrete which differ in their laydown methods, and also regarding various principles of joints construction with an accent made on the contraction joints. Results. The main principles of the said concrete pavements designing were reviewed through separation the input design data such as the geotechnical information, the parameters of loading, the pavement service life and the characteristics of the material. The pavement design regarding the requirements for the subgrade/subbase parameters was analyzed more detailed concerning the appointment of the pavement layers thicknesses to reach the required bearing capacity in terms of a modulus of subgrade reaction.

НОВАЦІЇ У ВІТЧИЗНЯНІЦЙ НОРМАТИВНІЙ БАЗІ З ПИТАНЬ БУДУВАННЯ АВТОМОБІЛЬНИХ ДОРІГ ІЗ ЦЕМЕНТОБЕТОННИМ ДОРОЖНІМ ПОКРИВОМ

Introduction. The article deals with the issues of expanding the regulatory and technical base of the road sector for the construction of roads with cement concrete pavement. The article covers the issues of improving the existing and developing new requirements for the construction of cement concrete pavements with pin-connected slabs, reinforced slabs with pin connection, with continuous reinforcement and using rolled cement concrete mixtures using modern technologies. Improvement of the national regulatory framework is carried out on the basis of the analysis of existing legal acts, regulatory documents, technical literature related to the objects of standardization and, of course, taking into account the requirements of safety and environmental protection and the factors and features established by the current martial law in Ukraine. The work concerns the developed national standards for the installation of cement concrete pavements, namely those innovations that have already been agreed upon and are planned to be submitted to the National Standardization Body for adoption in the near future. Problem statement. Implementation of a unified technical policy to ensure the reliability and durability of roads, increase the efficiency of capital investments, implementation of scientific and technical achievements of science and best domestic and international practices in the practice of construction of roads with cement concrete pavement, issues of saving material, labor, financial and energy resources, improving the quality of construction work, labor safety and health conditions, rational use of land and other types of natural resources. The fact of constant growth in the intensity of heavy vehicles traffic, which is associated with the transportation of heavy machinery, agricultural products, metallurgical products, etc., significantly affects the reduction of the service life of roads with non-rigid pavement. This necessitates the search for alternative solutions for the construction of more durable pavement structures, one of which is the improvement of existing and introduction of new technologies for the construction of cement concrete pavements.

ЗАБЕЗПЕЧЕННЯ ПІДВИЩЕНОЇ ДОВГОВІЧНОСТІ КОНСТРУКЦІЇ ДОРОЖНЬОГО ПОКРИТТЯ НА ЗАЛІЗОБЕТОННИХ АВТОДОРОЖНІХ МОСТАХ

Introduction. The pavement on reinforced concrete road bridges is one of the main structural elements of the structure, the technical condition of which affects the safety of the structure as a whole. The durability of the asphalt pavement is influenced by a number of factors, including the type of construction of the designed pavement, including the waterproofing coating, as well as the resistance of the structure to climatic factors and the effects of traffic. Problemstatement. Most of the highway bridges were built in the 60s and 80s of the 20th century. The artificial structures were designed in accordance with the regulations and technical specifications in force at the time, taking into account the prospects for the development of transport infrastructure and calculating the estimated traffic intensity of up to 20 thousand vehicles per day. However, over the last decade, the intensity and speed of road traffic has increased significantly, as well as the movement of heavy vehicles (mostly uncontrolled), especially in summer, when the air temperature fluctuates above +28 °C. In turn, this leads to excessive load on the bridge deck structures, including the asphalt pavement, which leads to its destruction (formation of cracks, potholes, sagging, rutting and other damage). Results. A technological scheme for the construction of a pavement with increased durability on reinforced concrete road bridges is presented.

Assessment of the surface water quality and primary health risk in urban wastewater and its receiving river Kathajodi, Cuttack of eastern India.

Kathajodi, the principal southern distributary of the Mahanadi River, is the vital source of irrigation and domestic water use for densely populated Cuttack city which receives anthropogenic wastes abundantly. This study assesses the contamination level and primary health status of urban wastewater, and its receiving river Kathajodi based on the physicochemical quality indices employing inductively coupled plasma mass spectroscopy and aligning with guidelines from the United States Environmental Protection Agency (USEPA) and WHO. The high WQI, HPI, and HEI in the catchment area (KJ2, KJ3, and KJ4) indicate poor water quality due to the influx of domestic waste through the primary drainage system and effluents of healthcare units. A high BOD (4.33-19.66mg L-1) in the catchment indicates high organic matter, animal waste, bacteriological contamination, and low DO, resulting in deterioration of water quality. CR values beyond limits (1.00E - 06 to 1.00E - 04) in three locations of catchment due to higher Cd, Pb, and As indicate significant carcinogenic risk, while high Mn, Cu, and Al content is responsible for several non-carcinogenic ailments and arsenic-induced physiological disorders. The elevated heavy metals Cd, Cu, Fe, Mn, Ni, and Zn, in Kathajodi, could be due to heavy coal combustion, vehicle exhaust, and industrial waste. On the other hand, Cu, Fe, K, and Al could be from agricultural practices, weathered rocks, and crustal materials. Positive significant (p ≤ 0.05) Pearson correlations between physicochemical parameters indicate their common anthropogenic origin and similar chemical characteristics. A strong correlation of PCA between elements and physiological parameters indicates their role in water quality deterioration. Assessing the surface water quality and heavy metal contents from this study will offer critical data to policymakers for monitoring and managing public health concerns.

Impact of Aggressive HGV Platoons and Human-Driven Heavy Goods Vehicles on Signalized Intersections Performance

This paper presents a study on the performance and environmental impacts of aggressive heavy goods vehicle (HGV) platoons in comparison to human-driven HGVs at a random signalized intersection under varying traffic volumes between 500 and 1500 HGVs. A total of 12 scenarios have been developed, 6 for each of the vehicular behaviors, to quantify the emissions (CO, NOX, VOC) and fuel consumption, travel time, and delays. The analysis is implemented using the PTV VISSIM microscopic traffic flow model. Realization that the majority of the differences in the performance of the two vehicular controls seems to be different as the traffic volume increases was the realization. In most cases, aggressive HGV platoons were found to have lower emissions, in comparison to fuel consumption, while the flow and delay of aggressive HGV platoons were comparatively better against the case with human-driven HGVs. The results have thus provided new avenues for the incorporation of aggressive HGV platoons into urban traffic systems, more so in scenarios that involve a high level of traffic at intersections, as a potentially effective tool for augmenting the efficiency of an intersection and cutting down its environmental impacts. The present study strongly recommends the advancement of traffic management strategies that can capture the dynamics between the two heterogenous traffic flows induced by autonomous and semi-autonomous vehicle technologies.

Shape Optimization of Heavy Vehicles Chassis

The main objective of this project is to modify the design to decrease the deformation of chassis.

Development of a heavy vehicle torque rod applying continuous fiber reinforced thermoplastic composite materials instead of forged steel material: Design, analysis and optimization

The torque rod is an important component of the suspension system that connects the axle to the chassis in heavy commercial vehicles. The main motivation of this study is the development of a torque rod made of (1) plus cross-section, (2) continuous carbon and glass fiber reinforced hybrid thermoplastic composites, which can replace a forged steel torque rod used in heavy vehicles, has superior mechanical properties, provides minimum cost and weight. This study aims to develop a torque rod before its production within the framework of integration with Computer Aided Design (CAD), Finite Element Analysis (FEA), and Multi-Criteria Decision Making (MCDM). In this study, the design data and the properties of the composite materials were chosen as control factors. The minimum displacement, the best mechanical properties such as tensile stress, compressive stress, torsional shear stress, maximum critical buckling load, and the minimum part weight minimum production cost per piece were selected as quality characteristics. As a result of the FEA study, considering the experimental set of the Taguchi Method L25 orthogonal array, the data on mechanical properties, weight, and production cost per piece were subjected to the MCDM process using the Entropy Weighted TOPSIS method. As a result of the MCDM study, a torque rod made of continuous fiber-reinforced thermoplastic composite material, instead of a torque rod produced by forged steel, had the highest mechanical properties produced, the weight of the torque rod can be reduced by 64.76%, and the production costs per piece can be reduced by 37.5%. This study’s findings have shown that the torque rod produced from continuous fiber-reinforced thermoplastic composite materials in a new geometry with a plus cross-section can be substituted for the torque rod produced by steel forging. Thus, it will contribute to reducing the fixed vehicle weight, especially in heavy commercial vehicles, reducing CO2 emissions, and increasing the range of the vehicles.

Investigating and modeling motorcycle crash rates at urban unsignalized intersections in India

Objective The present study focuses on understanding the behavior of motorized 2-wheeler (MTW) riders at urban unsignalized intersections in India. In the Indian context, over 60% of road crash fatalities are attributed to vulnerable road users, with MTWs serving as the predominant contributors, accounting for 44% of total fatalities. Notably, unsignalized intersections have emerged as critical sites for accidents involving vulnerable road users. Methods Postencroachment time is used to assess traffic conflicts of MTW users. Furthermore, the study employs the exceedance property of extreme value theory to calculate crash probabilities. Tobit and grouped random parameters Tobit regression models are developed to model crash probabilities, incorporating variables such as traffic volume, traffic composition, gap acceptance time, intersection characteristics, and intersection conflict area at 4 urban unsignalized intersections in Surat, India. Results MTW riders have the lowest gap acceptance time among vehicles in the traffic stream. Cars and other heavy vehicles readily accept gaps when MTWs are in the conflicting stream at unsignalized intersections, which increases traffic conflicts. MTWs have the highest crash rates in the traffic stream. Among the developed models, the grouped random parameters Tobit regression captures the spatial unobserved heterogeneity of the study sites and outperforms the simple Tobit regression model. The results also indicate that MTW riders are exposed to a higher risk of crashes while turning at unsignalized intersections. The presence of a central traffic island has varied implications; it raises crash rates at 3-legged intersections but lowers them at 4-legged intersections for 2-wheelers. Conclusion The study concludes that MTW crash rates are influenced by traffic and intersectional factors. Increased gap acceptance time correlates with lower crash rates. Countermeasure selections require detailed investigations, because it was observed that the presence of central traffic islands has varied effects on crash rates at 3-legged and 4-legged unsignalized intersections.

Traffic noise measurement, mapping, and modeling using soft computing techniques for mid-sized smart Indian city

The present study presents an investigation into the utilization of artificial neural networks (ANN) and multiple linear regression model (MLR) for the prediction of traffic noise levels in various locations of Dhanbad city at varying intervals. Traffic noise indices measurements were carried out using sound pressure level meter (SLM). The prediction of equivalent A-weighted sound level (LAeq) and the sound level exceeding 10 percent of the time (L10) is carried out using various influencing factors such as number of cars, number of 2 wheelers, number of 3 wheelers, number of heavy vehicle, number of medium commercial vehicles, and traffic speed. The findings demonstrate the ANN model's proficiency in comparison of MLR model for providing precise prediction accuracy of traffic noise indices with a R2 of 0.94 for LAeq and 0.91 for L10. Furthermore, the frequency spectrum analysis reveals that high peaks were observed at lower frequencies ranging from 31.5 Hz to 50 Hz, middle frequencies from 500 to 800 Hz and higher frequencies from 3.5 kHz to 5 kHz. The noise maps at varying intervals revealed that most of the locations are having higher noise levels due to increase in vehicular movement. The study emphasizes on the potential significance of the proposed neural network-based prediction model in collaboration with noise mapping as a vital tool for the anticipation of traffic noise levels and the formulation of noise mitigation strategies in context of the mid-sized smart city like Dhanbad, India.

Analyzing the transition from two-vehicle collisions to chain reaction crashes: A hybrid approach using random parameters logit model, interpretable machine learning, and clustering

Chain reaction crashes (CRC) begin with a two-vehicle collision and rapidly intensify as more vehicles get directly involved. CRCs result in more extensive damage compared to two-vehicle crashes and understanding the progression of a two-vehicle collision into a CRC can unveil preventive strategies that have received less attention. In this study, to align with recent research direction and overcome the limitations of econometric and machine learning (ML) modelling, a hybrid approach is adopted. Moreover, to tackle the existing challenges in crash analysis, addressing unobserved heterogeneity in ML, and exploring random parameter effects and interactions more precisely, a new approach is proposed. To achieve this, a hybrid random parameter logit model and interpretable ML, joint with prior latent class clustering is implemented. Notably, this is the first attempt at using a clustering with hybrid modeling. The significant risk factors, their critical values, distinct effects, and interactions are interpreted using both marginal effects and the SHAP (SHapley Additive exPlanations) method across clusters. This study utilizes crash, traffic, and geometric data from eleven suburban freeways in Iran collected over a 5-year period. The overall results indicate an increased risk of CRC in congested traffic, higher traffic variation, and on horizontal curves combined with longitudinal slopes. Some parameters exhibit distinct or fluctuating effects, which are discussed across different conditions or considering interactions. For instance, during nighttime, heightened congestion on 2-lane freeways, increased traffic variation in less congested conditions, and adverse weather combined with horizontal curves and slopes pose risks. During daytime, increased traffic variation within highly congested sections, higher proportion of heavy vehicle traffic in moderately congested sections, and two lanes in each direction coupled with curves, elevate the levels of risk. The results of this study provide a better understanding of risk factors impact across different conditions, which are usable for policy makers.

Оценка влияния различных газов на давление в шинах при выделении тепла

Transportation of minerals during open-pit mining works is mainly provided by heavy vehicles. One of the most dangerous accidents during the operation of vehicles in open-pit mines is the destruction of large-scale tires. The main cause of the destruction of tires is heating of rubber causing the increase of pressure of injected gas and change of properties of tire material. Apart from friction processes and rubber deformation causing heating of wheels, the temperature of tires can increase when affected by external heat sources. At coal mines, sources of auto-ignition of fossil fuels often located along the road shoulders and sometimes on the road surface can be additional sources of heat for the vehicles. According to the calculations, the increase in tire pressure does not depend on the type of gas injected. The temperature rise in tires, however, is significantly affected by the specific heat capacity and the density of the gas used. According to the conducted studies, carbon dioxide is a more effective gas compared with air or nitrogen. With a certain amount of heat emitted in the tire, air or nitrogen heats up from 0 to 100 °С, while the temperature of carbon dioxide will increase only to 72 °С. Due to heating, the air and nitrogen pressure in the tires will increase from 6 to 8.2 atm, whereas carbon dioxide pressure will increase only to 7.5 atm. Unlike air, carbon dioxide is not involved in the combustion and oxidation of heated rubber. Methane and its homologs are even more effective. For example, propane injected into tires, when emitting the same amount of heat, will heat up from 0 only to 28 °С, and its pressure will increase from 6 to 6.6 atm. The combustion of these hydrocarbons can be prevented by using inhibitors. Thus, adding 2–3 % of freons will make these gases non-flammable and explosion-proof.

Retrofitting pedestrian and cyclist collision warning for improved safety of heavy trucks employed in urban areas: a case study in Florence and Pistoia, Italy

The increasing use of heavy vehicles for services such as public transport, waste collection and logistics in densely populated urban areas causes numerous collisions with serious consequences every year, particularly for pedestrians, cyclists and motorcyclists. Advanced Driver Assistance Systems (ADAS) have proven to be a solution because they can significantly reduce the occurrence and consequences of road crashes. Although many ADAS technologies will be mandatory on new trucks in 2024, it will take more than a decade before the benefits are realised due to the average fleet renewal time. To accelerate this process, the most promising solution is therefore the introduction of aftermarket ADAS (also referred to as ‘retrofit’ ADAS) on the circulating fleet. This study aims to identify a tailored solution for retrofitting ADAS capable of providing pedestrian collision warnings on heavy vehicle fleets currently circulating in urban areas. The study involves a sample of vehicles and drivers operating in the metropolitan areas of Florence and Pistoia for waste collection services. The company owner of the vehicles integrated a commercial camera-based ADAS system with three additional cameras installed on the frontal bumper and edges of the vehicle, linked with three screens positioned in the vehicle cabin. In this study, a field test campaign was performed integrating trials in a proving ground closed to traffic with testing and monitoring in urban areas. This allowed to define a set of recommended parameters depending on vehicle speed that represent a good balance between alert frequency, minimizing false positives and ensuring that the alerts indicate real potential hazards to the driver. The recommendations provided in this study can also be extended to other vehicle fleets so that rapid and substantial improvements in the safety of heavy vehicles operating in cities can be achieved.

Differential Steering Control Based on CNF and ADRC Techniques for Distributed-Drive Articulated Heavy Vehicles

Differential Steering Control Based on CNF and ADRC Techniques for Distributed-Drive Articulated Heavy Vehicles