TRA-942: IMPUTATION OF MISSING CLASSIFIED TRAFFIC DATA DURING WINTER SEASON

Highway agencies collect traffic data to calculate traffic parameters such as Annual Average Daily Traffic (AADT), Design Hourly Volume (DHV) and then to use as input in the planning, operation and management of their highway systems. The traffic data are usually collected through traffic monitoring programs. In particular, the Weigh-in-Motion (WIM) system is one of data collection systems to capture configuration patterns of vehicle travelling on the detection area. It is learned from literatures that traffic monitoring devices are prone to be in malfunctioning and, consequently, providing erroneous or missing traffic data due to the adverse weather conditions in which they operate. It is very critical for transportation agencies to be able to estimate classified missing traffic data in high accuracy level because the truck traffic plays a crucial role in developing pavement design and evaluation long term pavement performance. Several imputation methods have been cited in the literature but none of them have been designed to impute classified traffic data missed during severe winter weather conditions. To do this, winter weather model is structured and then calibrated to relate classified traffic volume variation to weather factors (snowfall and temperature) with traffic data collected from WIM stations located on highway network of Alberta, Canada and weather data collected from weather stations nearby WIM stations. Performance of the developed weather model is compared with a nonparametric regression method namely k-Nearest Neighbour (k-NN) method in terms of several error measures. It is concluded that winter weather models show better performance in terms of error measures than k-NN method while imputing the missing classified traffic data

Skylab experiments. Volume 6: Mechanics

Skylab program activities are presented in a form adapted to instruction of high school students. The overall goals of the program are discussed. The specific accomplishments of the mechanics investigations are described. The subjects involved are as follows: (1) evaluation of mobility aids, (2) mass measurement devices, and (3) space guidance crew/vehicle disturbances

Development and Implementation of Hay Yield Monitoring Technology

Three independent technologies related to hay yield monitoring were developed and studied. One technology involved comparison of infrared and ultrasonic sensors on a self-propelled forage harvester. Sensor response related to grass height and was used to estimate yield. Plots that were harvested ranged from 20-40 ft. in length while a bin mounted on the back of the mower collected the crop for weighing and sampling. The infrared sensors data demonstrated accuracies across plots between 5.9% and 9.5% error. The infrared sensors quickly deteriorated and eventually proved to be useless for data acquisition. As for the ultrasonic sensors, they demonstrated similar accuracy to the infrared sensors but the sensor response did not deteriorate like that of the infrared sensors. It was concluded that the standing crop method of yield monitoring would be difficult to adapt for commercial adoption but could potentially be beneficial in crop and machinery research. The same model sensors that were used on the self-propelled forage harvester were installed on a boom that was mounted to the tongue of a round hay baler to measure windrow height, which was then used to estimate mass flow rate and therefore crop yield. The infrared sensors proved to not be suitable for the environment from the beginning of testing due to the dusty atmosphere. Each hay bale was individually weighed by placing them on a platform that was sitting on truck scales. Samples were also taken from bales and dried to calculate moisture content. In year one, 59 bales of a Tifton 85 and Coastal mix were baled along with 57 bales of Tifton 85 and 9 bales of alfalfa. Average absolute error as calculated from sensor data acquired from the ultrasonic sensors ranged from 3.1% to 23.86%. Although the range is large, most of the average absolute errors stayed around approximately 10%. Year two data was also collected from ultrasonic sensors of a different model. The average absolute errors for those ultrasonic sensors ranged from 5.11% to 9.27%. In the third technology presented, a pressure transducer was installed on the hydraulic bale kicker circuit on two different round balers. The pressure transducer data was collected and correlated to bale weight to provide on-the-go bale weight estimates. Analyses were conducted to compare different size bales and bales that use different methods of wrapping. Average absolute errors for comparison of wrapping methods ranged from 1.1% to 7.28%. When combining the two methods, average absolute errors ranged from 2.44% to 9.46%. Average absolute errors ranged from 1.1% to 5.79% when data was analyzed within particular bale sizes

Probabilistic Bridge Weigh-in-Motion

Conventional bridge weigh-in-motion (BWIM) uses a bridge influence line to find the axle weights of passing vehicles that minimize the sum of squares of differences between theoretical and measured responses. An alternative approach, probabilistic bridge weigh-in-motion (pBWIM), is proposed here. The pBWIM approach uses a probabilistic influence line and seeks to find the most probable axle weights, given the measurements. The inferred axle weights are those with the greatest probability amongst all possible combinations of values. The measurement sensors used in pBWIM are similar to BWIM, containing free-of-axle detector (FAD) sensors to calculate axle spacings and vehicle speed and weighing sensors to record deformations of the bridge. The pBWIM concept is tested here using a numerical model and a bridge in Slovenia. In a simulation, two hundred randomly generated 2-axle trucks pass over a 6 m long simply supported beam. The bending moment at mid-span is used to find the axle weights. In the field tests, seventy-seven pre-weighed trucks traveled over an integral slab bridge and the strain response in the soffit at mid-span was recorded. Results show that pBWIM has good potential to improve the accuracy of BWIM

Multi-Purpose ESS/ITS Data Collection Sites, SPR72-00-0003-042, 2014

This document presents the results of a state-of-practice survey of transportation agencies that are installing intelligent transportation sensors (ITS) and other devices along with their environmental sensing stations (ESS) also referred to as roadway weather information system (RWIS) assets

Canadian Civil Justice: Relief in Small and Simple Matters in an Age of Efficiency

Canada is in the midst of an access to justice crisis. The rising costs and complexity of legal services in Canada have surpassed the need for these services. This article briefly explores some obstacles to civil justice as well as some of the court-based programmes and initiatives in place across Canada to address this growing access to justice gap. In par- ticular, this article explains the Canadian civil justice system and canvasses the procedures and programmes in place to make the justice system more efficient and improve access to justice in small and simple matters. Although this article does look briefly at the impact of the global financial crisis on access to justice efforts in Canada, we do not provide empirical data of our own on this point. Further, we conclude that there is not enough existing data to draw correlations between austerity measures in response to the global crisis and the challenges facing Canadian civil justice. More evidence-based research would be helpful to understand current access to justice challenges and to make decisions on how best to move forward with meaningful innovation and policy reform. However, there is reason for optimism in Canada: innovative ideas and a national action plan provide reason to believe that the country can simplify, expedite, and increase access to civil justice in meaningful ways over the coming years

Modelling Truck Weigh Stations’ Locations based on Truck Traffic Flow and Overweight Violation: A Case Study in Bosnia and Herzegovina

The number of registered commercial freight vehicles is constantly increasing, increasing therefore as well the traffic load on the roads in Bosnia and Herzegovina. A significant part of freight vehicles moving along the main and regional roads are overloaded and cause significant damage to road infrastructure, affect road safety and result in an increase of emissions of harmful gases for people and the environment. The overloading rate is extremely high, in particular with 5-axle trucks representing 58.7%. The research showed that the increased overload level ranges from 10-20% of the maximum permissible weight. The importance of load limits was recognized early in the history of road development. This interrelation led directly to limitations on vehicle loads, and laws were enacted in many countries to establish the maximum allowable motor vehicle sizes and weights. Strict enforcement of motor vehicle size and weight laws is a step toward reducing motor vehicle size and weight violations, heavy truck accidents, and, even more, improving road maintenance, rehabilitation expenditures and road safety. Thus, based on the applied model the objective of this paper is to evaluate and optimize the locations of truck weigh stations on the road network of Bosnia and Herzegovina.</p