Truck Usage Research Articles

SVD-based adaptive fuzzy for generalized transportation

This research stems from the urgent need to address the complex challenges faced in multimodal transportation within the cargo sector, particularly concerning the reduction of transportation costs and CO2 emissions. Existing transportation systems often struggle to efficiently balance economic and environmental concerns, necessitating innovative solutions. In this paper, we have introduced a novel mathematical model that integrates Singular Value Decomposition (SVD), Genetic Algorithms (GA), and the Adaptive Neural Fuzzy Inference System (ANFIS). The synergy of GA and the ANFIS forms a dynamic learning system that continually refines solutions by adapting to historical decisions and aligning with supply-demand dynamics. This integrated approach aims to optimize cost allocation and resource utilization while simultaneously minimizing CO2 emissions. Deploying the ANFIS+GA+SVD model in multimodal scenarios yields impressive results, with a minimal average percentage error (MAPE) of 0.28% for transportation costs and a remarkable coefficient of determination R2 of 0.992 for the Truck + Ship scenario. Similarly, a MAPE of 0.11% for CO2 emissions, supported by an impressive R2 of 0.989, underscores the model's accuracy. Observations of people's transportation choices in response to emission price changes reveal a significant decrease in truck usage, plummeting from 84% to below 50%. Alternative transportation methods, especially in the 3S-3 M-3 C scenario, show a substantial surge, with railways leading at a remarkable 30% increase in utilization. The combined quantity and cost comparison, utilizing the ANFIS+GA+SVD model, emphasizes the suitability of the Truck + Ship scenario for Iraq's transportation network, reinforcing the case for sustainable and efficient multimodal transportation systems.

Feasibility of Battery Electric Pickup Trucks in a State Department of Transportation Fleet

Vehicle manufacturers are beginning to introduce battery electric pickup trucks, with at least three models in production in the United States and at least six others announced. Unlike hybrid and plug-in hybrid vehicles, which use batteries to supplement an internal combustion engine, battery electric vehicles are fully reliant on their batteries and have significantly shorter ranges, fewer refueling/recharging options, and may experience shorter ranges under towing, mountain driving, and temperature extremes. This study investigated real-world pickup truck usage data from a large state department of transportation to determine whether battery electric trucks could effectively replace trucks based on manufacturer-stated range as well as early field tests of actual vehicle range under various speeds, idling scenarios, and weather conditions. The results indicated that 97% of pickup truck day trips could be completed on a standard range battery, and 99% on an extended range battery. Among tracked department of transportation fleet pickup trucks, 31% could be replaced by a standard range electric truck with no change in operation; and up to 64% of the fleet with an extended range could be replaced by an extended range electric truck. Dynamically assigning long trips to dedicated conventional engine pickup trucks could further reduce the number of nonelectric trucks required by half.

Breaking truck dominance in supply chains: Proactive freight consolidation and modal split transport

Environmental concerns and a shortage of truck drivers motivate a paradigm shift in truck-dominated supply chains. To alleviate the pressure on truck transport, one can consolidate freight with other companies. Freight consolidation can be facilitated by third-party logistics service providers, who combine shipments that require delivery to the same destination on the same day. More gains can be realized through “proactive freight consolidation”, by proactively synchronizing the timing of shipments prior to placing orders. This is facilitated by a joint replenishment policy. The truck intensity of supply chains can be further reduced by shifting freight from road towards alternative transport modes, such as train. Modal split transport combines two complementary transport modes by using both modes in parallel. We analyze how proactive freight consolidation can be combined with modal split transport. We propose a heuristic that combines a can-order joint replenishment policy to consolidate freight orders proactively via truck, with a tailored base-surge policy to coordinate shipments via train. We develop a lower bound on the optimal cost to validate our heuristic. By comparing the truck usage and cost performance of our policy against alternative replenishment strategies, we show how the combination of proactive freight consolidation and modal split transport can shift freight towards alternative transport modes, without negatively impacting costs or service.

Utilization of GPS Data of Logistic Truck as a Delivery Reporting System

We propose a tracking system that can provide information from the truck coordinate analysis. Firstly, GSM will transmit the coordinates of the truck in real time via HTTP protocol to a website that has been hosted using shared hosting and displays a travel map on the Map Box API application. We also use MYSQL as a storage of truck coordinates. Reports will be generated after the truck arrives at the company. For system accuracy level, we set the delivery duration to the database every 10 seconds so that the generated path is closer to the original path. We compared our system with google maps to observe how accurate the system was built. The length difference of the track is 0.01 Km. The system also shows a very small deviation of starting point is 0.111322 meters and at the end point is 0.729938354 meters. While, the report contains truck ID, driver's identity, departure time, exact time, arrival time, distance, type of goods, quantity of goods, origin, destination, service, date, initial odometer, used odometer which later the results can be printed into a final report of truck usage.

Coordinated routing system for fire detection by patrolling trucks with drones

Vehicle routing problem with drones (VRPD) is an extension of classical vehicle routing, which involves the usage of trucks and drones. Nowadays, bushfires are taken into account as a major global challenging subject. Indeed, forests are very vulnerable to fires, and the classical patrol systems cannot patrol all areas of the forest and pastures due to the difficult crossing of some routes.The literature survey demonstrated that the mathematical models are an efficient approach for planning forest and rangeland patrols. Drones and trucks are used in cooperation for forest monitoring in this study, with drones acting as an efficient and accurate means of monitoring routes that are impassable to ground vehicles. However, this novel approach deals with several substantial challenges, including limitation in the drone's battery and the routing problem in an integrated truck-drone system based on considering hard-to-reach areas and creating a trade-off between time and cost of the patrol process. These issues make significant differences between the proposed novel approach and the classical vehicle routing problem. Thus, it is essential to develop a comprehensive approach to resolve these crucial issues. The present study develops a multi-objective mathematical programming model as mixed-integer programming for simultaneous patrolling of trucks and drones. The augmented epsilon constraint (AEC) method has been considered to solve the two-objective mathematical programming model. Besides, Benders' decomposition (BD) algorithm has been employed to evaluate the achieved results in large-size problems. The performance of the BD algorithm has been confirmed in this study. The findings indicated that the proposed model efficiently protected the forest via monitoring all areas, including impassable areas where ground patrolling is impossible (e.g., the lack of roads due to dense vegetation). Also, it was proved that this system was a novel, cost-effective patrol, which prevents uncontrollable fires in the shortest possible time.

Techno-economic and life cycle greenhouse gas emissions assessment of liquefied natural gas supply chain in China

This study assessed the techno-economic performance and life cycle greenhouse gas (GHG) emissions for various liquefied natural gas (LNG) supply chains in China in order to find the most efficient way to supply and use LNG. This study improves current literature by adding supply chain optimization options (cold energy recovery and hydrogen production) and by analyzing the entire supply chain of four different LNG end-users (power generation, industrial heating, residential heating, and truck usage). This resulted in 33 LNG pathways for which the energy efficiency, life cycle GHG emissions, and life cycle costs were determined by process-based material and energy flow analysis, life cycle assessment, and production cost calculation, respectively. The LNG and hydrogen supply chains were compared with a reference chain (coal or diesel) to determine avoided GHG emissions and GHG avoidance costs. Results show that NG with full cryogenic carbon dioxide capture (FCCC) is most beneficial pathway for both avoided GHG emissions and GHG avoidance costs (70.5–112.4 g CO2-e/MJLNG and 66.0–95.9 $/t CO2-e). The best case was obtained when NG with FCCC replaces coal-fired power plants. Results also indicate that hydrogen pathways requires maturation of new technology options and significant capital cost reductions to become attractive.

Economic effects of predation by scaup on baitfish and sportfish farms

AbstractFish‐eating birds have been found to consume baitfish and sportfish raised on farms in the United States. Understanding the on‐farm economic effects of such wildlife conflicts is essential for wildlife management agencies to make informed decisions. Lesser scaup, while not widely considered a fish‐eating bird, will consume farmed fish. Baitfish and sportfish farms in Arkansas (the major baitfish and sportfish producing‐state in the U.S.) were surveyed to gather data on the cost of protecting farm crops from scaup. The values of lost sales revenue from the various species of baitfish and sportfish consumed by scaup were estimated based on a concomitant field study on the abundance, distribution, and dietary habits of scaup that visited Arkansas baitfish and sportfish farms during winters of 2016–2017 and 2017–2018. Economic effects were estimated for golden shiner, fathead minnow, sportfish, and goldfish farms. Total annual costs to scare birds from baitfish and sportfish farms were $622 ± 742 per ha. The greatest components of bird‐scaring costs were manpower (56%), truck usage (32%), levee upkeep for vehicle access to scare birds (9%), firearms and ammunition (2%), and pyrotechnic devices (1%). The combined annual economic losses, calculated as reduced revenue from fish losses to scaup plus expenditures to scare birds, averaged $683/ha for golden shiners, $695/ha for fathead minnows, $663/ha for sportfish, and $673/ha for goldfish across the two study years. The fish losses to scaup alone averaged $1.06 million per year ($0.09 million in Year 1 and $2.03 million in Year 2) for the Arkansas baitfish industry. Total direct negative economic effects on the Arkansas baitfish industry were estimated to be, on average, $5.5 million per year ($4.6 in Year 1 and $6.3 million in Year 2).

Finite Element Analysis of Automotive Truck Chassis

The transportation industries at present world play a chief role in field of current commercial economy and developed countries. The usage of trucks is intensely increasing to carry the loads and materials. To enterprise a truck chassis many features to be considered including material selection, packaging, strength-to-weight ratio, stiffness. This paper mostly reviews on most research works and focuses on stress study of the truck chassis using four Finite Element Analysis (FEA) namely ANSYS. The result of this research paper gives the researcher instantaneous solution on modern and present developments in truck chassis field using FEA.

Design and experiment verification of a novel analysis framework for recognition of driver injury patterns: From a multi-class classification perspective

Detecting driver injury patterns is a typical classification problem. Crash data sets are highly skewed where fatalities and severe injuries are often less represented compared to other events. The severity prediction performance of the existing models is poor due to the highly imbalanced samples of different severity levels within a given dataset. This paper proposes a machine learning based analysis framework from a multi-class classification perspective for accurate recognition of the driver injury patterns. The proposed framework includes preprocessing, classification, evaluation and application of a given dataset. This framework is verified based on the three years single-vehicle ROR (run-off-road) crash records collected in Washington State from 2011 to 2013. At first, thirteen most important safety-related variables are recognized through random forests. Then, the four driver’s injury severity levels viz., fatal/serious injury, evident injury, possible injury, and no injury are predicted by integrating the decomposed binary neural network models to achieve better performance. Finally, a sensitivity analysis is carried out to interpret variables’ impacts on the decomposed injury severity levels. The study shows that lack of restraint, female drivers, truck usage, driver impairment, driver distraction, vehicle overturn (rollover), dawn/dusk, and overtaking are the leading factors contributing to the driver fatalities or severe injuries in a single-vehicle ROR crash. Most of the findings are consistent with the previous studies. The experimental results validate the effectiveness of the proposed framework which can be further applied for pattern recognition in traffic safety research.

The Energy Paradox and the Adoption of Energy-Saving Technologies in the Trucking Industry

Several federal benefit-cost analyses report an energy paradox among firms in competitive markets and conclude that firms would benefit from mandates to increase the use of energy-saving technologies. Such findings appear incompatible with neoclassical views that private firms in competitive markets minimize costs. The Environmental Protection Agency, for example, presumes that owners of trailers pulled by tractors belonging to others underinvest in energy-saving technologies because trailer owners incur the costs while tractor owners get the benefits. We test this hypothesis by collecting data and modeling the use of energy-saving technologies as a function of fleet size, the intensity of truck usage, and proxies for management quality. We find effects consistent with conventional models but no evidence that different ownership of tractors and trailers is associated with reduced use of energy-saving technologies on trailers. Regulators should refrain from making claims that firms underuse energy-saving technologies without first rigorously evaluating evidence for such claims.

Lab-based Simulation of Carton Clamp Truck Handling - Preliminary FEA and Analysis of Handling Test Courses

Carton clamp truck is widely perceived as the high-efficient handling equipment of factory premises and warehouse by its capability of palletless handling. Therefore, the significance of a lab- based handling simulation is becoming higher with the growth of clamp truck usage. In this study, preliminary FEA and design of handling test courses for the lab-based simulation of carton clamp truck handling were performed, and the PSD analyses were performed for the modified one for the test course proposed by Park et al. (2017) as well as ASTM D 6055 and ISTA 3B standards. For the vibration in all directions, the vibration energy intensity analyzed by ISTA 3B standard showed higher than that by the other two cases. A FEA was performed for the handling operation of the sudden stop of the clamps after lifting the target HCP (heavyweight refrigerator corrugated package, w=180 kgf) up to the specified height. The slip distance between the clamp arm and the target HCP was 0.85 mm. The simulation result of 0.85 mm was 3.7 times lower than the experimental result (3.2 mm) obtained by Park et al. (2017), and it was estimated that the deviation comes from both the experimental error by weight imbalance of target HCP, and excessive simplification during the FE modelling of target HCP.

Developing an Ecological Passport for an Open-Pit Dump Truck to Reduce Negative Effect on Environment

Expanding the open-pit dump truck usage areas and the need to transport more and more minerals results in producing more and more powerful open-pit dump trucks, and this all is about environmental problems and potential health risks for the personnel. Harmful gas concentrations in working areas became threatening enough to have the work in some areas completely halted, until the contents of harmful substances in the air, as well as visibility on the roads, get back to norm. The article represents the new methodology for assessing comparatively the efficiency of modern transportation systems with performance and ecology characteristics taken into account, by developing an ecological passport for machines, facilitating design improvements and reducing pollution during operation.

Differential evolution algorithms for scheduling raw milk transportation

This paper focuses on determining routes for raw milk collection from collection centers to dairy factories with the objective of minimizing the total costs, considering fuel costs and costs of cleaning and sanitizing raw milk tanks on vehicles. This problem is considered to be a special case of the vehicle routing problem (VRP) but it is complex compared to the general VRP, especially since each vehicle contains more than one tank with heterogeneous capacity to collect raw milk and raw milk from different collection centers cannot be transferred into the same compartment. In this paper, a DE metaheuristic was used to solve the problem. In order to improve the solution quality, five modified DE algorithms with two additional steps, reincarnation and survival processes, were proposed. In addition, the skipped customer and multi-route attributes are also developed in the decoding process in order to obtain a shorter traveling distance and lower truck usage in the system, especially if they are used together with the reincarnation and survival processes. The computational results reveal that the modified DE algorithms yield higher relative improvement (RI) on the total costs and also the RI on the number of vehicles used.

Multidimensional Data and Model Uncertainties in Comparing Heterogeneous Benefits of Distributed Transportation Projects

AbstractInvestments in transportation projects are typically justified by diverse potential benefits, including safety, environmental, energy savings, congestion mitigation, and others. There is a need for such benefits to be comparable early in transportation programming, to allocate scarce resources to preliminary engineering. This paper discusses quantitative methods to aid in prioritizing locations of future highway projects. The paper adopts 15 quantitative metrics including crash rate, emergency route access, environmental issues, level of service (LOS), volume-to-capacity ratio, traffic flow, intermodal access, heavy truck usage, unemployment rate, right-of-way use, use of alternative transportation modes, bridge sufficiency rating, and cost effectiveness. This effort contributes to real-world transportation programming and priority setting via analysis of the crash avoidance and other benefits and costs that are expected before project designs are available. The innovation of this paper is in two ...

American Automobile and Light Truck Statistics Update

Given that transportation is an essential topic in any Physics and Society or Energy course, it is necessary to have useful statistics on transportation in order to have a reasoned discussion on this topic. And a major component of the transportation picture is the automobile. This paper presents updated transportation statistics for American automobile and light truck usage. These data are a continuation of the original statistics presented in my paper “A Physicist's View of the Automobile Engine,” published almost 10 years ago.1

The Impact of Automated Truck Lanes on Intercity Expressways: A Study Using Hybrid Simulator

Automated truck lanes (ATL) is a future technology for alleviating high proportion of truck usage. This paper proposes a hybrid microscopic-mesoscopic traffic simulator that can be used to precisely estimate the changes in traffic flows that would result from introduction of ATL on large-scale road networks. An integration framework is designed to ensure consistency of route choice and synchronization between mesoscopic and microscopic simulators. Microscopic car-following and lane-changing models are extended to reflect detailed interactions between cars and trucks. Three cases are simulated to evaluate the impact of ATL on traffic flows, proportion of trucks, travel time, and performance in total travel time saving. Finally, an analysis of safety and the effect of road incidents on travel time are also conducted using the hybrid simulator. Simulation results indicate that ATL can change traffic volumes not only on the expressway itself (New Tomei-Meishin Expressway) but also on the other expressways in the network, leading to shorter travel times and improved safety. For a more precise evaluation, the ATL system should be analyzed as part of a large-scale road network.

Modeling actual transportation costs in supplier selection and order quantity allocation decisions

Given the impact of transportation costs in both supplier selection and inventory management decisions in today’s enterprises, this article addresses both problems simultaneously by developing a mixed integer nonlinear programming model to properly allocate order quantities to the selected set of suppliers while taking into account the purchasing, inventory, and transportation costs under suppliers’ capacity and quality constraints. In particular, we focus on the usage of trucks as a means of transporting goods and the option of the full-truck-load versus less-than-truckload (LTL) is studied. Since actual LTL transportation costs are neither differentiable nor convex, they are modeled with a piecewise linear function using binary variables. It is shown that, considering actual transportation costs in inventory planning, not only affects the order quantities shipped from selected suppliers but also the actual selection of suppliers.

Testing of a Heavy Heavy-Duty Diesel Engine Schedule for Representative Measurement of Emissions

The Advanced Collaborative Emissions Study (ACES) program required the use of representative heavy-duty diesel engine activity. This need resulted in an engine test schedule creation program, and a schedule of engine modes representative of modern truck usage was developed based on data collected from engines in trucks operated through the heavy heavy-duty diesel truck (HHDDT) chassis schedule. The ACES test schedule included four active modes of truck operation including creep, transient, cruise, and high-speed cruise (HHDDT_S). This paper focuses on Phase 2 of the program, which was to validate and demonstrate the use of the ACES modes in a test cell. Preliminary testing was performed using a 1992 Detroit Diesel Corporation heavy heavy-duty diesel engine (HHDDE) on only the transient mode. On the basis of these results, each mode was modified slightly to suit implementation in a test cell. The locations of “closed throttle” points in the modes were determined through careful examination of the data. These closed throttle points were simulated during testing by adding negative set point torque values to the input file. After modification, all modes were tested during a final ACES modes demonstration period using a 2004 Cummins ISM HHDDE, obtaining three runs for each mode. During testing, carbon monoxide (CO), carbon dioxide (CO2), nitrogen oxides (NOx), particulate matter (PM), and hydrocarbon (HC) emissions were measured, and engine control unit (ECU) data were recorded. The new ACES modes did not adopt the Federal Test Procedure (FTP) regression criteria. New regression criteria for acceptability of a run were determined for each mode using the data obtained during testing.

The Truth About Light Trucks: Despite Critics' Claims, SUVs are Saving Lives

To determine the relationship between light trucks and motor vehicle fatalities, we formulated a simultaneous equations model that considered the effect that light truck usage and other variables had on fatality rates over the period 1994 to 1997. We discovered that there is a negative relationship between light truck registration and the motor vehicle fatality rate in both single-vehicle and multiple-vehicle accidents. Our elasticity estimates indicate that the five-percent increase in light truck registrations in the United States over the time period 1994 to 1997 lowered single-vehicle fatalities per driver by 7.5 percent and multiple-vehicle fatalities per driver by two percent. These figures translate into about 2,000 lives saved.

The economic impacts of a divisible-load permit system for heavy vehicles

A methodology is demonstrated for analyzing the economic impacts of various weight limits for heavy vehicles through an application to New York State. Truck usage data were gathered from truck operators in 1990–1991 through three seasonal mail surveys, which allowed the collection of sensitive truck usage data while guaranteeing anonymity to the respondents. The benefits of this permit system are primarily lower business costs for those operators who hold permits; in the long-run, part of the savings realized by the truck operators flow to most sectors of the state's economy. On the cost side, increased infrastructure damage is assumed to result primarily from increased pavement damage. We find that direct benefits of the permit system (to the transportation industry and its users) exceed its costs (to society) by a factor of 17 to 1. An important finding of this study is the surprising level of non-compliance with permitted weight limits that was reported voluntarily. This may be due to the complexity of the New York state permit system and to the enforcement levels of the weight limits by state and local authorities.