High Accuracy Weigh-in-Motion Systems for Direct Enforcement

In many countries, work is being conducted to introduce Weigh-In-Motion (WIM) systems intended for continuous and automatic control of gross vehicle weight. Such systems are also called WIM systems for direct enforcement (e-WIM). The achievement of introducing e-WIM systems is conditional on ensuring constant, known, and high-accuracy dynamic weighing of vehicles. WIM systems weigh moving vehicles, and on this basis, they estimate static parameters, i.e., static axle load and gross vehicle weight. The design and principle of operation of WIM systems result in their high sensitivity to many disturbing factors, including climatic factors. As a result, weighing accuracy fluctuates during system operation, even in the short term. The article presents practical aspects related to the identification of factors disturbing measurement in WIM systems as well as methods of controlling, improving and stabilizing the accuracy of weighing results. Achieving constant high accuracy in weighing vehicles in WIM systems is a prerequisite for their use in the direct enforcement mode. The research results presented in this paper are a step towards this goal.

Trajectory Tracking and Load Monitoring for Moving Vehicles on Bridge Based on Axle Position and Dual Camera Vision

Monitoring traffic loads is vital for ensuring bridge safety and overload controlling. Bridge weigh-in-motion (BWIM) technology, which uses an instrumented bridge as a scale platform, has been proven as an efficient and durable vehicle weight identification method. However, there are still challenges with traditional BWIM methods in solving the inverse problem under certain circumstances, such as vehicles running at a non-constant speed, or multiple vehicle presence. For conventional BWIM systems, the velocity of a moving vehicle is usually assumed to be constant. Thus, the positions of loads, which are vital in the identification process, is predicted from the acquired speed and axle spacing by utilizing dedicated axle detectors (installed on the bridge surface or under the bridge soffit). In reality, vehicles may change speed. It is therefore difficult or even impossible for axle detectors to accurately monitor the true position of a moving vehicle. If this happens, the axle loads and bridge response cannot be properly matched, and remarkable errors can be induced to the influence line calibration process and the axle weight identification results. To overcome this problem, a new BWIM method was proposed in this study. This approach estimated the bridge influence line and axle weight by associating the bridge response and axle loads with their accurate positions. Binocular vision technology was used to continuously track the spatial position of the vehicle while it traveled over the bridge. Based on the obtained time–spatial information of the vehicle axles, the ordinate of influence line, axle load, and bridge response were correctly matched in the objective function of the BWIM algorithm. The influence line of the bridge, axle, and gross weight of the vehicle could then be reliably determined. Laboratory experiments were conducted to evaluate the performance of the proposed method. The negative effect of non-constant velocity on the identification result of traditional BWIM methods and the reason were also studied. Results showed that the proposed method predicted bridge influence line and vehicle weight with a much better accuracy than conventional methods under the considered adverse situations, and the stability of BWIM technique also was effectively improved. The proposed method provides a competitive alternative for future traffic load monitoring.

Perceptions of Log Truck Weight Regulations Among Loggers and Forest Industry in Georgia, USA

Georgia and other southern states have far lower gross vehicle weight (GVW) limits for log trucks than other US regions and other countries. Low GVW limits result in high hauling costs and truck traffic. In 2020, including tolerances, five-axle log tractor-trailers were allowed 38,102 kg (84,000 lb) GVW in Georgia. Telephone surveys of 30 loggers and 32 forest industry representatives from the state of Georgia were conducted to measure perceptions of weight regulations and assess support for alternative weights and configurations. The four alternatives included five axles, 39,916 kg (88,000 lb); six axles, 41,277 kg (91,000 lb); six axles, 45,359 kg (100,000 lb); and seven axles, 45,359 kg (100,000 lb) GVW. The majority of loggers and forest industry representatives stated that GVW limits for log trucks were too low. The average preferred GVW limits were 39,621 kg (87,350 lb) and 40,545 kg (89,387 lb) for loggers and forest industry, respectively. Loggers and forest industry supported the five-axle 39,916 kg (88,000 lb) configuration whereas many loggers opposed both 45,359 kg (100,000 lb) configurations. Loggers, forest industry, and policymakers should work to modernize weight laws to reduce hauling costs, maintain or improve safety, and protect public infrastructure. Study Implications Increasing gross vehicle weight (GVW) limits in combination with adding axles to tractor-trailers has been demonstrated to reduce both timber transportation costs and damage to public roads. This study found that loggers and forest industry supported additional GVW but were hesitant to support configurations that would necessitate upgrading log truck fleets. If Georgia is to make its weight limits competitive regionally and internationally, it will be necessary to clearly communicate the benefits of heavier trucks with more axles to skeptical loggers.

The design parameters improvement of wide-grip sprinkler machines of circular action

During the process of irrigation of wide-coverage sprinklers (WS) by the interaction of the wheels with the soil, the soil is pressed. In this case, the WS wheels impact the soil with a certain specific pressure. Specific pressure depends on a number of factors such as the length of vehicle, span length, vehicle weight, the diameter of water line, wheel contact area, determined by wheel geometry, pressure and tire type. The article carries out theoretical investigations determining the specific pressure of the wheel on the soil. It also given some recommendations connected with a number of wheels to be installed on the WS body compared with the calculated specific pressure of the designed vehicle with the standard specific pressure.

Analysis of the Effect of Mass Parameters on Motorcycle Vibration and Stability

This paper presents a vibration analysis method and an example of its application to evaluate the influence of mass parameters on torsional vibration frequencies in the steering system of a motorcycle. The purpose of this paper is to analyze to what extent vibration frequencies can change during their daily operation. These changes are largely due to the ratio of vehicle weight to driver and load. The complex dynamics make it very difficult to conduct research using simple models. It is difficult to observe the influence of individual parameters because they are strongly interrelated. This paper provides a description of the vibration analysis method, and the results are presented in the form of Bode diagrams and tables. On this basis, it was found that the driver, deciding on the way of using the vehicle and introducing modifications in it, influences the resonant frequencies of the steering system. Typical exploitation factors, on the other hand, do not cause significant changes, although they may contribute to increasing the sensitivity of the system to vibrations. The conducted analysis also showed some nonlinear changes in the dynamics of the system with linear changes of the parameter values.

Key Benefits of Cloud-Based Internet of Vehicle (IoV)-Enabled Fleet Weight Management System

The fleet industry is flourishing at extreme levels with a wide acceptance of IoV-powered methods in various verticals. Fuel consumption, tire pressure readings, driver’s driving habits, cargo monitoring, load measurement, and many other applications have now become possible in the fleet sector with the implementation of IoV and cloud based applications. Most of the industries heavily rely on transportation to carry loads and other different industrial products. The overall cost of vehicle maintenance involves a significant portionof total operating costs. Vehicle weight plays a major role in transporting goods from one place to the other. Many places have stringent rules regarding load transportation that only a certain amount of weight is allowed through a particular passage or road. This could result in heavy fines if all the regulations are not met and create unwanted expenses. It also creates unnecessary delays in product deliveries, which hampers the reputation of businesses. Hence, a smart solution can be integrated with the fleet sector to avoid extra costs during transit. In this paper, we will highlight the key benefits of IoV-based fleet weight management smart solution for fleet sector

The Effect of Vehicle and Road Conditions on Rollover of Commercial Heavy Vehicles during Cornering: A Simulation Approach

Heavy vehicles make up a relatively small percentage of traffic volume on Malaysian roads compared to other vehicle types. However, heavy vehicles have been reported to be involved in 30,000–40,000 accidents yearly and caused significantly more fatalities. Rollover accidents may also incur cargo damages and cause environmental or human disasters for vehicles that carry hazardous cargos if these contents are spilled. Thus, in this paper, a comprehensive study was conducted to investigate the effects of vehicle and road conditions on rollover of commercial heavy vehicles during cornering at curved road sections. Vehicle conditions include the heavy vehicle class (based on the axle number and vehicle type), speed and gross vehicle weight, while road conditions include the cornering radius and coefficient of friction values. In order to reduce the risks involved in usage of actual heavy vehicles in crash experiments, a simulation approach using a multi-body vehicle dynamic software was applied in this study, where the verified virtual heavy vehicle model was simulated and the output results were extracted and analyzed. The results showed that a maximum of 40% and a minimum of 23% from the total number of simulations resulted in an unsafe condition (indicated as failed) during the simulations. From the unsafe conditions, two types of rollover accidents could be identified, which were un-tripped and tripped rollovers. The heavy vehicle speed was also found to have a strong correlation to the lateral acceleration (to cause a rollover), followed by gross vehicle weight, coefficient of friction and cornering radius, respectively.