Modeling And Operation Of Leader Follower Autonomous Vehicle System For Work Zone Maintenance
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Modeling and Operation of Leader-follower Autonomous Vehicle System for Work Zone Maintenance
The emerging technology of the Autonomous Truck Mounted Attenuator (ATMA) system, also known as the Autonomous Impact Protection Vehicle (AIPV) system, utilizes connected and automated vehicle (CAV) capabilities to enhance safety for transportation infrastructure maintenance in work zones. While the ATMA system has been promoted by the Federal Highway Administration and several State Departments of Transportation (DOTs), its deployment faces significant challenges due to the lack of practical and implementable guidance from the Manual on Uniform Traffic Control Devices (MUTCD), federal regulations, and national standards presents a significant challenge in the deployment. The primary objective of this dissertation is to establish a theoretical modeling framework that serves as a foundation for the deployment of the ATMA system. This theoretical modeling framework aims to enhance the safety of DOT workers in work zones while minimizing the efficiency trade-offs within the traffic system. Firstly, the performance of the ATMA system is assessed through the analysis of the field-testing results. The system's performance is quantitatively evaluated using statistical models and hypothesis testing. The numeric analysis demonstrates that the ATMA system meets expectations and performs acceptably according to predefined criteria. The results of the hypothesis test indicate consistent and stable performance across multiple testing iterations, demonstrating stability and repeatability. Secondly, a set of practical operation guidelines for ATMA system operators is developed, focusing on crucial decision-making scenarios. These guidelines address determining appropriate car-following distances, critical lane-changing gap distances, and intersection clearance times. To model the driving behaviors of ATMA vehicles at these critical decision-making locations, modifications have been made to Newell's simplified car-following model and the classic lane-changing behavior model. These modified models are calibrated and validated using data collected from field-testing. The modelling outputs suggest important thresholds for ATMA system operators to adhere to. For instance, on a freeway with a speed limit of 70 mph and ATMA operating speed of 10 mph, the leader vehicle should maintain a car-following distance of at least 75 ft, while the follower vehicle should maintain a distance of 100 ft. The critical lane-changing gap distance is determined to be 912 ft, and a minimum intersection clearance of 15 seconds is recommended. These thresholds significantly exceed the requirements for general vehicles. Furthermore, the Operational Design Domain (ODD), indicated by the annual average daily traffic (AADT) in this dissertation, of the ATMA system is identified by developing traffic models for both two-lane and multilane highways. On two-lane highways, the presence of ATMA vehicles leads to the formation of queues behind them, resulting in an increase in the percent-time-spent-following (PTSF) for general vehicles. By employing queuing theory, an analytical model is developed to estimate the PTSF by identifying the arrival rate and service rate. This analytical approach establishes a linkage between AADT and the level of service (LOS). Numerical analysis reveals that roadway performance is influenced by factors such as the K factor, D factor, and the operating speed of the ATMA system. For a desired design objective of LOS=C, an appropriate AADT threshold to consider would be around 11,000 vehicles per day. On multilane highways, the capacity reduction or effective discharge rate is analytically derived using a simple mathematical expression based on the fundamental diagram with moving coordinates. Microscopic traffic flow models are then employed to calculate vehicle delay and density, as key indicators of a multilane highway's LOS. The numerical results demonstrate that roadway performance is sensitive to the K factor and D factor, and the operating speed of the ATMA system. If a LOS=C is the desirable design objective, a suitable AADT threshold to consider would be around 40,000 vehicles per day. Lastly, this dissertation focuses on identifying the optimal routing for the ATMA system to achieve transportation infrastructure maintenance at the lowest system cost. Different routes selections by the ATMA system lead to varying patterns of time-varying capacity drops, which can impact the overall traffic system. To address this, a queuing-based traffic assignment approach is developed, incorporating a queuing-based time-dependent (QBTD) travel time function that considers the capacity drop. The queueing-based traffic assignment problem, following the user equilibrium (UE) principle, is formulated and solved using a modified path-based algorithm. The numerical analysis conducted demonstrates that when the capacity drop is disregarded, the corrected relative gap in large-scale network experiences an average increase of 11.77%, deviating significantly from the user equilibrium. These findings suggest that the shortest path for ATMA maintenance routing is not necessarily optimal for the entire traffic system. Furthermore, sensitivity analysis reveals that variations in traffic demand and travel speed have minimal impact on the optimal route for ATMA vehicles.
Springer Handbook of Automation
Author: Shimon Y. Nof
language: en
Publisher: Springer Science & Business Media
Release Date: 2009-07-16
Automation is undergoing a major transformation in scope and dimension and plays an increasingly important role in the global economy and in our daily lives. Engineers combine automated devices with mathematical and organizational tools to create complex systems for a rapidly expanding range of applications and human activities. This handbook incorporates these new developments and presents a widespread and well-structured conglomeration of new emerging application areas of automation. Besides manufacturing as a primary application of automation, the handbook contains new application areas such as medical systems and health, transportation, security and maintenance, service, construction and retail as well as production or logistics. This Springer Handbook is not only an ideal resource for automation experts but also for people new to this expanding field such as engineers, medical doctors, computer scientists, designers. It is edited by an internationally renowned and experienced expert.
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This book constitutes the thoroughly refereed proceedings of the First International Conference on Machine Learning for Networking, MLN 2018, held in Paris, France, in November 2018. The 22 revised full papers included in the volume were carefully reviewed and selected from 48 submissions. They present new trends in the following topics: Deep and reinforcement learning; Pattern recognition and classification for networks; Machine learning for network slicing optimization, 5G system, user behavior prediction, multimedia, IoT, security and protection; Optimization and new innovative machine learning methods; Performance analysis of machine learning algorithms; Experimental evaluations of machine learning; Data mining in heterogeneous networks; Distributed and decentralized machine learning algorithms; Intelligent cloud-support communications, resource allocation, energy-aware/green communications, software defined networks, cooperative networks, positioning and navigation systems, wireless communications, wireless sensor networks, underwater sensor networks.