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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.
Transportation agencies in the U.S. use devices such as loop detectors, automatic traffic recorders (ATR), or weigh-in- motion (WIM) sensors to monitor the performance of traffic network for planning, forecasting, and traffic operations. With a limited number of ATR and WIM sensors deployed throughout the state roadways, temporary double tubes are often deployed to get axle-based vehicle classification counts. An inductive loop signature technology previously developed by a Small Business Innovation Research (SBIR) program sponsored by the US Department of Transportation is used to classify vehicles using existing loops. This technology has the potential to save time and money while providing the state, counties or cities more data especially in the metro area where loop detectors have already been installed. This research leveraged the outcomes from previous development to validate the classification accuracy with video data. A loop signature system was initially installed at a traffic station in Jordan, MN, to evaluate its performance. The system was later moved to another location on US-52 near Coates, MN, to validate its classification accuracy with more heavy- vehicle traffic. Individual vehicle records were manually verified and validated with ground-truth video data using both the 13 and 7-bin classification schemes from the Federal Highway Administration (FHWA) and the Highway Performance Monitoring System (HPMS). The combined results from both test sites indicated that the loop signature technology had an overall classification accuracy of 93% and 96% using the FHWA and HPMS schemes, respectively. The classification performance can be further improved by including additional vehicle signatures from the state to the classification library.
"This synthesis will be of interest to planners, pavement designers, administrators, and others interested in knowing the actual weights of vehicles using the highways. Information is presented on current uses of weigh-in-motion systems that can obtain the data needed to properly plan and design highways."--Avant-propos.
With AI advancements eliciting imminent changes to our transport systems, this enlightening Handbook presents essential research on this evolution of the transportation sector. It focuses on not only urban planning, but relevant themes in law and ethics to form a unified resource on the practicality of AI use.
Trucks contribute disproportionally to traffic congestion, emissions, road safety issues, and infrastructure and maintenance costs. In addition, truck flow patterns are known to vary by season and time-of-day as trucks serve different industries and facilities. Therefore, truck flow data are critical for transportation planning, freight modeling, and highway infrastructure design and operations. However, the current data sources only provide partial truck flow or point observations. This dissertation developed a framework for estimating path flows of trucks by tracking individual vehicles as they traverse detector stations over long distances. Truck physical attributes and inductive waveform signatures were collected from advanced point detector systems and used to match vehicles between detector locations by a Selective Weighted Bayesian Model (SWBM). The key feature variables that were the most influential in distinguishing vehicles were identified and emphasized in the SWBM to efficiently and successfully track vehicles across road networks.The initial results showed that the Bayesian approach with the full integration of two complementary detector data types – advanced inductive loop detectors and Weigh-in-Motion (WIM) sensors – could successfully track trucks over long distances (i.e., 26 miles) by minimizing the impacts of measurement variations and errors from the detection systems. The network implementation of the model demonstrated high coverage and accuracy, which affirmed the capability of the tracking approach to provide comprehensive truck travel patterns in a complex network. Specifically, the model was able to successfully match 90 percent of multi-unit trucks where only 67 percent of trucks observed at a downstream site passed an upstream detection site.A strategic plan to identify optimal sensor locations to maximize benefits from the truck tracking model was also proposed. A decision model that optimally locates sensors to capture the maximum truck OD and route flow was investigated using a goal programming approach. This approach suggested optimal locations for tracking implementation in a large truck network considering a limited budget. Results showed that sensor locations from a maximum-flow-capturing approach were more advantageous to observe truck flow than a conventional sensor location approach that focuses on OD and route identifiability.
The objective of this research was to evaluate low cost weigh-in-motion systems. The three systems evaluated were (1) a capacitance weigh mat system, (2) a bridge weighing system, and (3) a piezoelectric cable sensor system. All three systems have a two-lane capability. An evaluation was made of (1) the quality of the data, (2) the performance of the equipment, (3) the applications of the equipment and its ease ofuse, and (4) the format of the data and its usefulness. Although objective data were used when possible, the majority of the evaluation is subjective. The quality of the data from each of the three systems is about the same. The piezoelectric cable system provides slightly lower quality data than the other two systems. The equipment of the capacitance weigh mat performed well; that of the bridge system was adequate; and there was concern about the durability of the piezoelectric cable system. Because of the tradeoffs between the capacitance weigh mat system and the bridge system, it is difficult to rank them. The piezoelectric cable system's sensors are permanently installed; therefore, it is not as portable as the other two systems. With regard to the format of the data and its usefulness (which are dependent mostly on the software and not the sensors), the capacitance weigh mat system is flexible and provides individual truck records in two formats, the bridge system provides the most comprehensive tables, and the piezoelectric cable system is limited and depends on other software to generate additional tables. Suggestions are made about how to use the systems and how to improve their performance.