Integrating bridge influence surface and computer vision for bridge weigh-in-motion in complicated traffic scenarios

Complicated traffic scenarios, including random change of vehicles’ speed and lane, as well as the simultaneous presence of multiple vehicles on bridge, are main obstacles that prevents bridge weigh-in-motion (BWIM) technique from reliable and accurate application. To tackle the complicated traffic problems of BWIM, this paper develops a novel BWIM method which integrates deep-learning-based computer vision technique and bridge influence surface theory. In this study, bridge strains and traffic videos are recorded synchronously as the data source of BWIM. The computer vision technique is employed to detect and track vehicles and corresponding axles from traffic videos so that spatio-temporal paths of vehicle loads on the bridge can be obtained. Then a novel method is proposed to identify the strain influence surface (SIS) of the bridge structure based on the time-synchronized strain signals and vehicle paths. After the SIS is identified, the axle weight (AW) and gross vehicle weight (GVW) can be identified by integrating the SIS, time-synchronized bridge strain, and vehicle paths. For illustration and verification, the proposed method is applied to identify AW and GVW in scale model experiments, in which the vehicle-bridge system is designed with high fidelity, and various complicated traffic scenarios are simulated. Results confirm that the proposed method contributes to improve the existing BWIM technique with respect to complicated traffic scenarios.

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.

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.

A Brief of the Economics of ADAS for Full Size Light Duty Pickup Trucks: Relating Effectiveness to Cost

Advanced Driver Assistance Systems (ADAS) have become increasingly common in vehicles in the last decade. The majority of studies have focused on smaller vehicles with gross vehicle weight rating (GVWR) under 5,000lbs, predominantly sedans, for their ADAS evaluations. While it is sensible to use this style of vehicle because it is ubiquitous worldwide for a typical vehicle body style, these studies neglect full-size light-duty pickup trucks, GVWR 5,000 – 10,000lbs, which are abundant on the roads in the United States. The increase in mass, higher center of gravity, and utilitarianism of the vehicles allows for unique conditions for studying the effects of ADAS. This work evaluates the effectiveness of ADAS in full-size light-duty pickup trucks across brands, representing 18% of registered vehicles in the US, at reducing severity of injury for occupants during accidents involving fatalities relative to expense of the ADAS technology. This work will illustrate the cost benefit of ADAS at reducing the severity of injuries for occupants of full-size light-duty pickup trucks for multiple different brands.

Analysis and Determination of Axle Load Spectra and Traffic Input for Pavement Design

This study examines the vehicle class distribution, hourly distribution factors, weekly distribution factors, monthly distribution factors, axle load spectra for each vehicle class, and each axle of each vehicle class for the WIM station installed on the N-55 highway to aid analysis and design of new Mechanistic-Empirical Pavement Design Guide. The maximum, minimum and permissible load limit for the different vehicle class, average gross vehicle weight (GWV) and permissible load limits also being incorporated. The directional distribution for north bound and south bound traffic were observed to be almost 50% for both directions, except for 5 axle trucks which was 74% for north bound and 26% for south bound. The truck class most prevalent on the highway were identified to be 3-axle tandem truck (47.50%) and also it was observed that 94.1% of this vehicle class carried load above permissible limits. Keywords: Traffic characteristics, Load distribution factor, Axle Load Spectra.

Minimizing the Cost of Automotive Accidents by Optimizing the Design of Advanced Driver Assist Systems: An Empirical Study Based on a Full-Size Light-Duty Pickup Truck

Advanced Driver Assistance Systems (ADAS) have become increasingly common in vehicles in the last decade. The majority of studies has focused on smaller vehicles with gross vehicle weight rating (GVWR) under 5,000lbs, predominantly sedans, for their ADAS evaluations. While it is sensible to use this style of vehicle because it is ubiquitous worldwide for a typical vehicle body style, these studies neglect full-size light-duty pickup trucks (FSLDPTs), GVWR 5,000 – 10,000lbs, which are abundant on the roads in the United States, 18% of vehicles. The increase in mass, higher center of gravity, and utilitarianism of the vehicles allows for unique conditions for studying the effects of ADAS. This work determines the best and worst location to be hit in a full-size light-duty pickup truck based on data for the industry sales leader in this class of vehicles. The objective is to use these results for future designs of ADAS technologies and their placement on the FSLDPT. While these methods could be applied to any vehicle, the FSLDPT sales leader will be investigated as it represents about 9% of registered vehicles in the United States. The results will be optimized with respect to cost in terms of initial up-front purchasing cost and post-accident vehicle repair cost.

A Novel Acceleration-Based Moving Force Identification Algorithm to Detect Global Bridge Damage

This paper presents a new moving force identification (MFI) algorithm that uses measured accelerations to infer applied vehicle forces on bridges. Previous MFI algorithms use strain or deflection measurements. Statistics of the inferred forces are used in turn as indicators of global bridge damage. The new acceleration-based MFI algorithm (A-MFI) is validated through numerical simulations with a coupled vehicle-bridge dynamic interaction model programmed in MATLAB. A focussed sensitivity study suggests that results are sensitive to the accuracy of the vehicle velocity data. The inferred Gross Vehicle Weight (GVW), calculated by A-MFI, is proposed as the bridge damage indicator. A real weigh-in-motion database is used with a simulation of vehicle/bridge interaction, to validate the concept. Results show that the standard deviation of inferred GVWs has a good correlation with the global bridge damage level.

Evaluation of the Impact of Timber Truck Configuration and Tare Weight on Payload Efficiency: An Australian Case Study

The forest industry tends to plan, and model transportation costs based on the potential payload benefits of increased legal gross vehicle weight (GVW) by deploying different configurations, while payload benefits of a configuration can be significantly influenced by the vehicle design tare weight. Through this research the relative benefit of increased legal GVW of different configurations is compared across Australia over a 13-year period from 2006 to 2019, by examining data collected post operation across multiple operations. This approach is intended to offer realistic insight to real operations not influenced by observation and thus reflect long-term operating behaviour. The inclusion of the three most common configuration classes in Australian forestry over a 13-year period has also allowed the exploration of load management between configurations and potential trends over time. When considering the legal GVW and the tare weight impacts across the fleets, the semi-trailer has an 8 t payload disadvantage compared to B-Doubles and 19.6 t disadvantage compared to road trains.

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.