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Transportation agencies tasked with forecasting freight movements, creating and evaluating policy to mitigate transportation impacts on infrastructure and air quality, and furnishing the data necessary for performance driven investment depend on quality, detailed, and ubiquitous vehicle data. Unfortunately, commercial vehicle data is either missing or expensive to obtain from current resources. To overcome the drawbacks of existing commercial vehicle data collection tools and leverage the already heavy investments into existing sensor systems, a novel approach of integrating two existing data collection devices to gather high resolution truck data - Weigh-in-motion (WIM) systems and advanced inductive loop detectors (ILD) is developed in this dissertation. Each source provides a unique data set that when combined produces a synergistic data source that is particularly useful for truck body class modeling. Modelling truck body class, rather than axle configuration, provides more detailed depictions of commodity and industry level truck movements. Since body class is closely linked to commodity carried, drive and duty cycle, and other operating characteristics, it is inherently useful for each of the above mentioned applications. In this work the physical integration including hardware and data collection procedures undertaken to develop a series of truck body class models is presented. Approximately 35,000 samples consisting of photo, WIM, and ILD signature data were collected and processed representing a significant achievement over previous ILD signature models which were limited to around 1,500 commercial vehicle records. Three families of models were developed, each depicting an increasing level of input data and output class resolution. The first uses WIM data to estimate body class volumes of five semi-trailer body types and individual predictions of two tractor body classes for vehicles with five axle tractor trailer configurations. The trailer model produces volume errors of less than 10% while the tractor model resulted in a correct classification rate (CCR) of 92.7%. The second model uses ILD signatures to predict 47 vehicle body classes using a multiple classifier system (MCS) approach coupled with the Synthetic Minority Oversampling Technique (SMOTE) for preprocessing the training data samples. Tests show the model achieved CCR higher than 70% for 34 of the body classes. The third and most complex model combines WIM and ILD signatures using to produce 63 body class designations, 52 with CCR greater than 70%. To highlight the contributions of this work, several applications using body class data derived from the third model are presented including a time of day analysis, average payload estimation, and gross vehicle weight distribution estimation.
Presently, the Office of Transportation Data & Analysis (TDA) at the Minnesota Department of Transportation (Mn/DOT) manages 29 Vehicle Classification (VC) sites and 12 Weigh-in-Motion (WIM) sites installed on various Minnesota roadways. The data is collected 24/7 from all sites, resulting in a large amount of data. The total amount of data is expected to substantially grow with time due to the continuous accumulation of data from the present sites and future expansion of sites. Therefore, there is an urgent need to develop an efficient data management strategy for dealing with the present needs and future growth of this data. The solution proposed in this research project is to develop a centralized data warehouse from which all applications can acquire the data. The objective of this project was to develop software for creating a VC/WIM data warehouse and example applications that utilize it. This project was successfully completed by developing the software necessary to build the VC/WIM data warehouse and the application software packages that utilize the data. The main contribution of this project is that it provides a single access point for querying all of the Mn/DOT's WIM and VC data, from which many more applications can be developed without concerns of proprietary binary formats.
This book comprises the proceedings of the 8th International Conference on Advanced Composite Materials in Bridges and Structures (ACMBS) 2021. The contents of this volume focus on recent technological advances in the field of material behavior, seismic performance, fire resistance, structural health monitoring, sustainability, rehabilitation of structures, etc. The contents cover latest advances especially in applications in reinforced concrete, wood, masonry and steel structures, field application, bond development and splice length of FRB bars, structural shapes and fully composite bars, etc. This volume will prove a valuable resource for those in academia and industry.
Abstract: Over the years many vehicle classification schemes have been developed to sort passing vehicles into several classes according to their length, number of axles, axle spacing, number of units or some other combination of vehicle features. Vehicle classification is important for infrastructure management, traffic modeling, and quantifying emissions along highways. Weigh-in-motion (WIM), axle counting, and length from dual loop detectors are commonly used for vehicle classification on freeways.
Abstract: The congestion on the freeways as well as on the city roads is increasing day by day due to a continuous rise in the traffic. Better traffic systems using advanced intelligent components are urgently needed to manage this continuous growth of traffic. Currently, traffic departments in the United States utilize inductive loop technologies to provide a solution to this problem by detecting the cars to control the traffic lights. These inductive loops are placed under the road during the construction of the road, or they are placed in the road at a later point by sawing through the surface. As stated earlier, these inductive loops are primarily used for the detection of vehicles and they consume a large amount of power. What if vehicles could be classified as well as identified instead of just being detected? This would make the traffic control system much more advanced and intelligent. To achieve this, an in-node microprocessor-based vehicle classification approach is proposed in this thesis. It can analyze the vehicles’ magnetic data and determine the types of vehicles passing over a 3-axis magnetometer sensor. This is done using the J48 classification algorithm, which is implemented in Waikato Environment for Knowledge Analysis (WEKA), a machine learning software suite, and the Naïve Bayes model implemented in MATLAB (MATrix LABoratory) as well. The J48 is a decision tree machine learning algorithm derived from Quinlan's C4.5 algorithm which is based on the ID3 (Iterative Dichotomiser 3) algorithm. Features are extracted from the data collected from vehicles passing over the sensor and a decision tree model is generated based on those features. The decision tree iii model that is generated is then implemented using decision commands such as if-else in any language and on any microprocessor platform. Also, to make the sensor node reusable at different locations and in different environments, an adaptive baseline is set up to eliminate the background magnetic field from the raw data. Binary Naïve Bayes model is also used in addition to the J48 algorithm as only two major classes of vehicles are being analyzed which are Sedan and Hatchback.
Assessing the service status and maintaining the safety of existing structures are critical to the sustainable operations of various engineering and cross-industry, including civil infrastructures, railways and machinery. Static and dynamic structural characteristics play a key role in the global deterioration assessment of the structural performance, which has enabled structural monitoring and analysis technology to become an active focus in the engineering area. Meanwhile, structural control has been widely used in modern structural engineering. Structural control devices are implemented to enhance deteriorating structures and mitigate natural disasters. Through advanced structural control technology, the structural responses can be controlled. These structural control techniques include passive, active or semi-active reverse forces, which aim to modify structural stiffness, mass and damping with minimal control force. Structural control, monitoring and analysis complement each other, ensuring the safety of the structure to the greatest extent.
This report provides a summary of results from a multi-year study that includes both the use of inductive loop detectors (ILDs) and magnetoresistive sensors for in-situ vehicle classification. There were strengths and weaknesses noted in both type of sensor systems. Although the magnetoresistive array provides the best vehicle profile resolution, the standard inductive loop detector provides a significant cost, hardware and software complexity, and reliability advantage. The ILD installed base far exceeds the number of magnetoresistive sensors. Several electrical and computer engineering students participated in the study and their contributions are included in the individual chapter headings. Under my direction, these students also presented project work and Research Day conferences at MN/DOT District 1 Headquarters.