Large Trucks Research Articles

TO THE PROBLEM OF CREATING LARGE DUMP TRUCKS WITH ACTIVE BODIES

An analytical review of existing dump trucks with forced unloading of the body, implemented using hydraulic or mechanical devices built into its structure, as well as due to the vibration effect of automobile on-site vibration exciters fixed on the body walls, is given. The possibility of using the transporting devices with an elastic function element, developed at the Institute of Mining of the Siberian Branch of the Russian Academy of Sciences, as a false bottom of the body of large dump truck and the need to equip them with self-synchronizing inertial vibration exciters has been substantiated. The procedure and results of experimental studies of the dynamics of elastic function elements with different rigidity, equipped with two unbalanced vibration sources have been presented. Geometric and dynamic ratios that ensure the stability of synchronous operation of vibration exciters in conditions of a change in the technological load on the surface of a function element due to the release of bulk material from the body have been found. The degree of vibration isolation of vibrotransporting devices from the supporting structure of a dump truck has been experimentally determined.

The Green Logistics Idea using Vacuum Insulation Panels (VIPs)

This research aims to reduce fuel consumption in transportation systems using vacuum insulation panels (VIPs) for cold storage trucks. The material was vacuum Insulation Panels (VIPs) is a custom-made insulator using glass fibers pressed at 10 tons per square meter and the thermal conductivity conditions of the selected materials ranged between 0.0028 and 0.007 W/mK and has a size of 12 mm comparative experiment method of cold storage trucks equipped with VIPs and without VIPs of 4 model frozen cars. Which has Model 1 (4 Wheels Truck), Model 2 (4 Wheels large Truck), Model 3 (6 Wheels Truck), and Model 4 (10 Wheels Truck). The sensor installation in the cold room is distributed in every wall to achieve differentiation and distribution of cold temperatures. The results are calculated as the energy saving result, it was found Model 1 (4 Wheels Truck) have best fuel savings. This research can apply other types of cold storage trucks or research in the same way as the air wall by inserting VIPs insulation inside the external insulation to reduce heat transfer.

Optimal Biomass Transportation Model

The transportation represents a key proportion of the operational cost for the biomass industries worldwide. As biomasses are mainly carried by trucks for parts of the transportation, the focus of this paper is on the transport of treated and untreated biomass (rice husk, empty fruit bunch, and woody biomass) by large, medium and small trucks. The objectives were to formulate biomass transportation model for transporting treated and untreated biomass resources and to obtain optimal result for selecting the best transportation mode. By screening of biomass types, locations for treated and untreated biomass resources and screening of suitable transportation mode used, the important model parameters were obtained and linear programming for minimizing overall transportation costs was formulated. General Algebraic Modelling System (GAMS) software was used to solve the optimization formulations. From the optimization result obtained by using GAMS, large truck was selected to be the best transportation mode for treated, untreated and hybrid biomass since it showed minimal overall transportation cost.

A Study on Commercial Vehicle Speeds and Its Operational Characteristics


 
 
 A commercial vehicle is the backbone of the logistics industry which drives a vibrant economy of a country. Nevertheless, commercial or heavy vehicle-related accident is drawing serious attention. The height, weight and width dimension of the commercial vehicles reduce the visibility of other drivers and thereby increase the risk of an accident. Furthermore, due to the evolution of technology, larger trucks are now equipped with higher horsepower where a lot of speeding-related accidents were reported. This study is set to evaluate the speed profile for different types of commercial vehicles at different road hierarchy. The results indicate that in general most of the heavy vehicles (4.39%-98.61%) travelled fast and did not comply with the speed limits posted on different types of road hierarchy. The lower the posted speed limit the higher the percentage of non-compliance rates which means that the compliance increase as the speed limit increase. The majority of the commercial vehicles (28%-57%) occupied the middle lanes and those smaller sizes of vehicles (as compared to other sizes of commercial vehicles) had more tendencies to travel on the fast lane.
 
 

Analysis of Severe Injuries in Crashes Involving Large Trucks Using K-Prototypes Clustering-Based GBDT Model

The unobserved heterogeneity in traffic crash data hides certain relationships between the contributory factors and injury severity. The literature has been limited in exploring different types of clustering methods for the analysis of the injury severity in crashes involving large trucks. Additionally, the variability of data type in traffic crash data has rarely been addressed. This study explored the application of the k-prototypes clustering method to countermeasure the unobserved heterogeneity in large truck-involved crashes that had occurred in the United States between the period of 2016 to 2019. The study segmented the entire dataset (EDS) into three homogeneous clusters. Four gradient boosted decision trees (GBDT) models were developed on the EDS and individual clusters to predict the injury severity in crashes involving large trucks. The list of input features included crash characteristics, truck characteristics, roadway attributes, time and location of the crash, and environmental factors. Each cluster-based GBDT model was compared with the EDS-based model. Two of the three cluster-based models showed significant improvement in their predicting performances. Additionally, feature analysis using the SHAP (Shapley additive explanations) method identified few new important features in each cluster and showed that some features have a different degree of effects on severe injuries in the individual clusters. The current study concluded that the k-prototypes clustering-based GBDT model is a promising approach to reveal hidden insights, which can be used to improve safety measures, roadway conditions and policies for the prevention of severe injuries in crashes involving large trucks.

Investigating Head-On Crash Severity Involving Commercial Motor Vehicles in Kentucky

This study examines various features affecting the severity associated with commercial motor vehicle (CMV, i.e., large truck and bus) head-on collisions on Kentucky highways. Recent five-year (2015–2019) crash data and variables rarely explored before (e.g., presence of centerline rumble strips, type of passing zone, and terrain type) were collected and prepared using Google Maps. A total of 378 CMV-related head-on collisions were analyzed. The generalized ordered probit (GOP) model was employed to identify the significant factors affecting the severity level resulting from CMV head-on collisions. The model allows the coefficients to vary across the injury severity categories for reliable parameter estimations. From the preliminary investigation, rolling terrains had the highest share of severe CMV head-on crashes (62% and 71% for multilane and two-lane roadways, respectively). The presence of centerline rumble strips could reduce severe crash outcomes along multilane and two-lane facilities. The GOP model identified various significant predictors of minor and severe injuries from CMV head-on crashes. Occupants wearing seatbelt were 39.3% less likely to sustain severe head-on crash injuries. From the roadway characteristics, presence of median cable and concrete barriers could significantly reduce the probability of severe head-on crash injuries, with median cables being more effective. With regard to the driver characteristics, drug impairment and speeding increased the risk of sustaining fatal/serious injuries by 39.5% and 26.4%, respectively. Necessary safety recommendations are proposed to reduce the severity of CMV head-on-related collisions. One example is installing median cable barriers along roadway stretches with a history of head-on CMV-related crashes.

Examining Driver Injury Severity in Single-Vehicle Road Departure Crashes Involving Large Trucks

Road departure (RD) crashes are among the most severe crashes that can result in fatal or serious injuries, especially when involving large trucks. Most previous studies neglected to incorporate both roadside and median hazards into large-truck RD crash severity analysis. The objective of this study was to identify the significant factors affecting driver injury severity in single-vehicle RD crashes involving large trucks. A random-parameters ordered probit (RPOP) model was developed using extensive crash data collected on roadways in the state of Kentucky between 2015 and 2019. The RPOP model results showed that the effect of local roadways, the natural logarithm of annual average daily traffic (AADT), the presence of median concrete barriers, cable barrier-involved collisions, and dry surfaces were found to be random across the crash observations. The results also showed that older drivers, ejected drivers, and drivers trapped in their truck were more likely to sustain severe single-vehicle RD crashes. Other variables increasing the probability of driver injury severity have included rural areas, dry road surfaces, higher speed limits, single-unit truck types, principal arterials, overturning-consequences, truck fire occurrence, segments with median concrete barriers, and roadside fixed object strikes. On the other hand, wearing seatbelt, local roads and minor collectors, higher AADT, and hitting median cable barriers were associated with lower injury severities. Potential safety countermeasures from the study findings include installing median cable barriers and flattening steep roadside embankments along those roadway stretches with high history of RD large-truck-related crashes.

The feasibility of heavy battery electric trucks

Summary Research on the decarbonization of transport often concludes that heavy battery electric trucks are infeasible due to the incompatibility of long driving distance with high energy use and low specific energy and high costs of batteries. However, emphasis is often placed on battery electric range matching that of diesel trucks, instead of overall competitiveness. We model battery electric trucks that use high-power fast charging, enabling smaller batteries and showing that the economics of battery electric trucks per ton-kilometer improves with greater weight, driven by increasing load capacity as well as increased energy savings as a function of weight. Furthermore, we show that previous findings that the competitiveness per kilometer is worse for heavy trucks than for lighter trucks are very sensitive to assumptions about the battery cost per kWh and lifetime of the battery pack. Given the rapid development of batteries, we conclude that the economic feasibility of heavy battery electric trucks might have been generally underestimated.

Control scheme of electric driving system for large electric wheel mine-used truck

Control scheme of electric driving system for large electric wheel mine-used truck

The association between crashes and safety-critical events: Synthesized evidence from crash reports and naturalistic driving data among commercial truck drivers

The past decade has witnessed continuous growth of naturalistic driving studies (NDSs). When analysing NDS data, safety-critical events (SCEs) are commonly used as surrogates for safety, since actual crashes are very rare. However, the association between SCEs and crashes is not consistent in previous studies and has not been verified among commercial truck drivers. Based on routinely collected kinematic data from 31,828 truck drivers in a large commercial trucking company, this paper examines the association between four types of SCEs (headway, hard brake, collision mitigation, and rolling stability) and crashes, as well as injuries and fatalities. Compared to existing studies on the subject, that are based on up to about 2 million miles driven, our study involves an estimated 2.3 billion miles driven. Bayesian negative binomial models were applied to examine the association between three outcomes (crashes, injuries, and fatalities) and the four SCEs. It was found that a unit increase in the number of any type of SCEs per 10,000 miles was associated with 8.4% (95% credible interval (CI): 8.0–8.8%) increase in crashes per mile and 8.7% (95% CI: 4.8–13.6%) increase in the number of injuries per mile. The increase was different in different types of SCEs: 3.3% (95% CI: 2.6–4%) for headways, 8.1% (95% CI: 7.5–8.7%) for hard brakes, 50.4% (95% CI: 41.4–60%) for rolling stability, and 22.2% (95% CI: 19.8–24.5%) for collision mitigation. The results are consistent when stratified by different business units and driver types. This study provides statistically strong and robust evidence that SCEs are positively associated with crashes and injuries among commercial truck drivers. NDS and kinematic data routinely collected by trucking companies provide a promising opportunity for future data analytic research.

Effectiveness of front crash prevention systems in reducing large truck real-world crash rates

Objective: Forward collision warning (FCW) and automatic emergency braking (AEB) have the potential to prevent or mitigate many large truck crashes. While these systems are known to be effective in passenger vehicles, less is known about their effectiveness in large trucks. The objective of this study was to estimate the effectiveness of these systems in reducing real-world crash rates of large trucks. Methods: Data on Class 8 trucks operating on limited-access highways during 2017–2019 were obtained from SmartDrive Systems. Detailed data on exposure measures and crash circumstances were extracted from video footage by both automated means and manual coding. Crash rates were compared by front crash prevention technology (FCW, AEB, neither), both for all police-reportable crashes overall and for relevant crash types. Results: FCW was associated with a statistically significant 22% reduction in the rate of police-reportable crashes per vehicle miles traveled, and a significant 44% reduction in the rear-end crash rate of large trucks. AEB also was associated with significant reductions—12% overall and 41% for rear-end crashes. Warnings were issued in 31% of rear-end crashes for FCW-equipped trucks. AEB intervened in 43% of rear-end crashes; about two thirds of these interventions involved autobrake activations. On average, speed was reduced by over half between the time of the intervention and impact for both systems. Observed reductions in same-direction sideswipe and roadway departure crashes per mile traveled were smaller in magnitude than those of rear-end crashes; these were consistent with other crash avoidance technologies suspected to be bundled with FCW/AEB in some cases, and very few front crash prevention interventions occurred in these types of crashes. Conclusions: FCW and AEB are effective countermeasures for crashes in which large trucks rear-end other vehicles. Large truck safety is expected to improve as new trucks are increasingly equipped with these systems. FCW has the advantage that some of these systems can be retrofitted to existing trucks, so benefits can be realized sooner and with less investment.

MODELING THE LOT CARGOES TRANSPORTATION SYSTEM IN CITIES

The share of the transport component that is taken into account in the prices formation for final products reaches 50%, so the cost transport services has a great impact on the final goods price, and the need to carry out transportation in a tight time frame, take into account minimal costs require the emergence of new scientifically methods of their organization. The research purpose is to build the lot cargoes automobile transportations in cities system model. The research object is the lot cargoes automobile transportations in cities process. As the analysis result of lot cargoes automobile transportations rational technology formation in the cities, such basic problem at the transportation process organization is allocated - it is irrational transportation routes. It leads to a large overall truck mileage, as well as low transportation speed, which is unacceptable in the current competition. During the literature sources analysis, the most popular existing transportation routing methods were considered, by means of which in research it is planned to carry out transport routes building in cities and it was found out that the routes developed by means of these methods have average links. As the existing criteria analysis result of lot cargoes automobile transportations rational technology formation in the cities the transportations cost is chosen as a criterion. After analyzing the all modeling methods advantages and disadvantages, taking into account the scientific literature recommendations, it was decided to use mathematical modeling due to its results versatility and accuracy. After formalizing the research object, developed a model of "white box", in which for the selected input parameters (delivery points number, car load, the average consignment size delivered to one point) built the lot cargoes automobile transportations rational technology formation in the cities mathematical model, at which the transportation cost will be minimal.

Investigating factors affecting severity of large truck-involved crashes: Comparison of the SVM and random parameter logit model

Introduction: Reducing the severity of crashes is a top priority for safety researchers due to its impact on saving human lives. Because of safety concerns posed by large trucks and the high rate of fatal large truck-involved crashes, an exploration into large truck-involved crashes could help determine factors that are influential in crash severity. The current study focuses on large truck-involved crashes to predict influencing factors on crash injury severity. Method: Two techniques have been utilized: Random Parameter Binary Logit (RPBL) and Support Vector Machine (SVM). Models have been developed to estimate: (1) multivehicle (MV) truck-involved crashes, in which large truck drivers are at fault, (2) MV track-involved crashes, in which large truck drivers are not at fault and (3) and single-vehicle (SV) large truck crashes. Results: Fatigue and deviation to the left were found as the most important contributing factors that lead to fatal crashes when the large truck-driver is at fault. Outcomes show that there are differences among significant factors between RPBL and SVM. For instance, unsafe lane-changing was significant in all three categories in RPBL, but only SV large truck crashes in SVM. Conclusions: The outcomes showed the importance of the complementary approaches to incorporate both parametric RPBL and non-parametric SVM to identify the main contributing factors affecting the severity of large truck-involved crashes. Also, the results highlighted the importance of categorization based on the at-fault party. Practical Applications: Unrealistic schedules and expectations of trucking companies can cause excessive stress for the large truck drivers, which could leads to further neglect of their fatigue. Enacting and enforcing comprehensive regulations regarding large truck drivers’ working schedules and direct and constant surveillance by authorities would significantly decrease large truck-involved crashes.

Pioneers of the plantation economy: militarism, dispossession and the limits of growth in the Wa State of Myanmar

The characteristic mobility of highland populations in Southeast Asia relied to a large extent on their particular adaption to an ecological environment: swidden cultivation of tubers on mountain slopes. This ecology corresponded to cosmologies in which potency was limitless, or at least had no fixed and delimited precinct (as did the rice paddies and Buddhist realms in the valleys). Military state building, modern transport, and new crops and agricultural technologies have effectively ended swidden cultivation. In this article, I follow the pioneers of the plantation economy in the Wa State of Myanmar, who dispossess local populations of their land and employ them as plantation labour. The limits of growth and potency they encounter are (a) in the natural environment and (b) in the resistance of local populations. Yet, even though there are such limits, the potency to which these pioneers aspire is still limitless. It is however channelled through a new economy of life, epitomised in the plantation, nourished in excessive feasting, and maintained by the kinship dynamics of capture and care.

LCTC Ships Concept Design in the North Europe- Mediterranean Transport Scenario Focusing on Intact Stability Issues

In late years, the size of RoRo cargo ships has continuously increased, leading to the so-called Large Car Truck Carriers (LCTC). The design of these vessels introduced new challenges that shall be considered during the ship design since the conceptual stage, which has a very strong impact on the technical and economic performances of the vessel during all its life-cycle. In this work, the concept design of an LCTC is presented based on Multi-Attribute Decision Making (MADM). A large set of design alternatives have been generated and compared in order to find out the most promising feasible designs. The proposed approach is based on a Mathematical Design Model (MDM) capable to assess all the main technical and economic characteristics for each design. Among the others, here focus has been done on the ship stability to assure the compliance with statutory rules within the MDM. A new stability metamodel has been developed capable to define the cross curves of stability at the concept design stage. The proposed MADM methodology has been applied to North Europe-Mediterranean transport scenario highlighting the impact of main particulars describing hull geometry on the technical and economic performances of an LCTC ship.

Traffic Flow Statistics Method and Practice Based on Roadside Video with YOLO_V3

Temporary traffic flow observation plays a pivotal role in transportation planning, consultation, and decision-making, and it supplements fixed long-term traffic observation data. Although substantial automated technology has been used for traffic observation, such technology is limited by the temporariness and uncertainty of observation points; moreover, these methods are difficult to use in temporary observations. In this study, the YOLO_V3 algorithm, which is based on deep learning, is used for vehicle detection based on roadside videos of temporary observations. Moreover, a secondary detection framework based on vehicle detection and traffic counting regions is proposed. Then, a traffic counting pattern with Kalman filter, Hungarian allocation, and perspective projection transformation is established. In addition, by collecting multiple sets of actual video data, the effectiveness of the method under different conditions is analyzed in terms of three indicators: camera intersection angle with the road, erection height, and road traffic density. Results show that at a camera height of 3 m and a roadside angle of 30°, the accuracy is approximately 95%. However, traffic flow accuracy drops to approximately 90% when vehicles are blocked by large buses and trucks during detection. In this study, the algorithm is tested for execution efficiency using 1080p video streams on a Windows 10 x 64 operating system, a 2080Ti graphics card, a 64 GB RAM, and an i7-7820X CPU. Results show that camera installation angle and height have no considerable effect on operation efficiency. Under low-density traffic, the frames per second (FPS) value is approximately 44; under high-density traffic, the FPS value drops to approximately 33, indicating that the method still has high execution efficiency and can be used for real-time video traffic counting.

Recursive Bivariate Probit Analysis of Injury Severity and Non-Truck Improper Actions in Large Truck-Related Crashes on Florida Suburban Roads

This study investigated the hierarchical connection among injury severity, non-truck improper actions, and contributing factors in large-truck-involved crashes. Data for 4 years (2011–2014) of crashes that involved a large truck (≥ 10,000 lb) and a non-truck vehicle were collected from suburban roads in Florida, U.S. A recursive bivariate probit model was fitted with collected data to identify the cause-effect chain, including contributing factors influenced by improper actions, the effects of improper actions on injury severity, and contributing factors indirectly affecting injury severity in large-truck-related crashes. Study results indicate that non-truck vehicle improper actions, such as excessive speed, careless driving, failure to yield right-of-way, and others, significantly increase the likelihood of fatal and severe injury in large-truck crashes, and factors such as crash month, darkness, intersection-related, surface and shoulder width, truck parking, truck driver age, non-truck driver age, and non-truck alcohol/drug impaired indirectly influence injury severity through their impacts on non-truck improper actions. Two factors—truck right-turn and non-truck driver physical defects—affect injury severity and non-truck improper actions simultaneously. Other factors, including crash year, annual average daily traffic, speed limit, crash type, truck type, truck speed, truck alcohol/drug-impaired, and motorcycle involvement, directly contribute to injury severity in large-truck crashes and have no influence on non-truck improper actions.

Development of Multilayer-Based Map Matching to Enhance Performance in Large Truck Fleet Dispatching

Spatial information technology has been widely used for vehicles in general and for fleet management. Many studies have focused on improving vehicle positioning accuracy, although few studies have focused on efficiency improvements for managing large truck fleets in the context of the current complex network of roads. Therefore, this paper proposes a multilayer-based map matching algorithm with different spatial data structures to deal rapidly with large amounts of coordinate data. Using the dimension reduction technique, the geodesic coordinates can be transformed into plane coordinates. This study provides multiple layer grouping combinations to deal with complex road networks. We integrated these techniques and employed a puncture method to process the geometric computation with spatial data-mining approaches. We constructed a spatial division index and combined this with the puncture method, which improves the efficiency of the system and can enhance data retrieval efficiency for large truck fleet dispatching. This paper also used a multilayer-based map matching algorithm with raster data structures. Comparing the results revealed that the look-up table method offers the best outcome. The proposed multilayer-based map matching algorithm using the look-up table method is suited to obtaining competitive performance in identifying efficiency improvements for large truck fleet dispatching.

Evaluating the Effects of Improving Forest Road Standards on Economic Value of Forest Products

Forest roads are the key infrastructures that provide access to forest areas for sustainable management, protection, and utilization of forest resources. In order to benefit from the important functions of forest roads, they should be built in with adequate technical road standards. The road network with low technical standards require more frequent major repairs to ensure continues access to forest resources. In addition, only small trucks with low load capacity can move on the low standard roads. Furthermore, the low road standards limit the truck speed that increases vehicle travel time. These negative effects increase the transportation costs which are an important part of the timber production costs in Turkey. Thus, improving the road standards and developing forest transportation planning should be evaluated together in the most appropriate way. Large logging trucks with high load capacity are generally preferred for efficient transportation of wood-based forest products. In Turkey, large logging trucks, however, cannot operate on the most of the forest roads because insufficient technical road standards (road width, curve radius, surface materials, road structures) limit the maneuverability of large logging trucks. In this study, the objective is to determine the effects of improving forest road standards on total net profit of forest products by using the NETWORK 2000 program, a heuristic approach for solving forest transportation problems. Three Forest Enterprise Chiefs (FECs) located in Mustafakemalpaşa Forest Enterprise Directorate (FED) in Bursa Forest Regional Directorate were selected as the study area. The digital data layers for forest road network was generated by using ArcGIS 10.4 software. In the solution process, firstly, the optimum routes that minimize the transportation cost and maximize the total net profit of forest products on existing forest road networks were investigated for a truck type (15 ton) currently used in the region. In the second case, forest transportation was planned for the high load capacity truck (29 ton) moving on the forest roads with improved standards. In the first case, the transportation costs and annual major repair costs were considered in the calculation of the net profit of forest products, while one time cost of road improvement activities (i.e. road improvement construction, road structure installation, road surface construction) and annual maintenance costs were considered along with transportation costs in the second case. In both cases, the costs of other forest operations (i.e. felling, logging, etc.) were not considered since it was assumed that they do not vary with the forest transportation alternatives. As a result of the transportation plan developed for high load capacity truck, the annual transportation cost decreased by 46.85% comparing to the local logging trucks with low load capacity. Using improved road standards, the total road costs computed for the time period of 30 years (i.e. the average expected life cycle of forest roads) were reduced by 4.64%. The total net profit of forest products transported by using a high load capacity truck was 473,340 Euro more than that of using low load capacity truck on the existing forest road network. Thus, improving road standards might result in some additional costs in the road construction stage, but total net profit of forest products increase since transportation costs along with maintenance and repair costs considerably decrease in the long term.

A Convergent Investigation on the Structural Analysis of Leaf Spring at Large Truck

A Convergent Investigation on the Structural Analysis of Leaf Spring at Large Truck