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Decentralised Motion Planning for Autonomous Mining Haulage Trucks using Prioritised Multi-Agent MPPI

IFAC-PapersOnLine 2024年第10期58卷 154-161页

作者： Kenta Matsui Fahmy Adan Panagiotis Angeloudis Transport Systems and Logistics Laboratory Department of Civil and Environmental Engineering Imperial College London London SW7 2AZ U.K Komatsu Ltd. 2-3-6 Akasaka Minato-ku Tokyo 107-8414 Japan

This paper introduces a decentralised motion planning method for Autonomous Haulage systems (AHS) using Prioritised Multi-Agent Model Predictive Path Integral Control (P-MA-MPPI). Our approach incorporates vehicle prioritisation, addressing a challenge that has previously limited decentralised MPPI frameworks, to enhance navigation safety and efficiency by exchanging future trajectories among agents. The experimental findings indicate a reduction in collision and deadlock rates when compared to an existing MPPI-based approach, alongside about a 20% increase in energy efficiency in complicated scenarios where three vehicles simultaneously enter an intersection. This research contributes to scalable and sustainable operation strategies, improving the efficiency and the environmental impact of future mine automation.

关键词： Transportation systems Fleet Control Mining Autonomous Vehicles Decentralised Motion Planning

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Sequentially modeling household accommodation, destination, and departure time choices

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International Journal of Mass Emergencies & Disasters 2023年第2-3期41卷 223 - 240页

作者： Ruijie “Rebecca” Bian Pamela Murray-Tuite Joseph E Trainor Praveen Edara Konstantinos Triantis Louisiana Transportation Research Center Louisiana State University Baton Rouge LA USA Glenn Department of Civil Engineering Clemson University Clemson SC USA Biden School of Public Policy and Administration Disaster Research Center University of Delaware Newark DE USA Department of Civil and Environmental Engineering University of Missouri Columbia MO USA Grado Department of Industrial and Systems Engineering Virginia Tech Falls Church VA USA

During evacuations, households make a number of important, related choices including accommodation type, destination, and departure time. They may make trade-offs among these choices where one decision affects the others. The analysis models the linkages among these three aforementioned choices using data from a household behavioral intention survey conducted in 2017 in the Hampton Roads, VA area. Statistical tests and a theoretical basis show that the approach that best fits the dataset was to estimate the three choices in a sequence, where the first decision serves as an independent variable in the next choice process. To model the sequence, we began by modeling accommodation choice using a multinomial logit (MNL) model. Next, the accommodation choice decisions were used with other control variables to estimate destination choice in a second MNL model. Last, evacuation distance (related to destination decisions) was used in a Cox proportional-hazards model to estimate departure time choices. The models that provide the best estimates included the following control variables that help explain the sequence of decisions residents in the Hampton Roads area expect to make: (1) a variable expressing residential stability helps explain accommodation choice; (2) prior evacuation experience, the geographic location of a household, and the duration of living in the area help predict the destination choice; and (3) distance to the chosen destination helps predict departure time. Findings from this study provide evidence that the decisions associated with these three choices influence each other and help emergency managers identify additional actions that potentially can improve the evacuation experiences of local residents.

关键词： Accommodation destination departure time hurricane choice behavioral models

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Predicting the impact of the weather conditions on transportation agility: A case study of an Australian Maritime Port 51

Predicting the impact of the weather conditions on transport...

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51st International Conference on Computers and Industrial engineering, CIE 2024

作者： Ordibazar, Amir Hossein Hussain, Omar Chakrabortty, Ripon K. Irannezhad, Elnaz Saberi, Morteza School of Business The University of New South Wales Northcott Drive CanberraACT2600 Australia School of Systems & Computing The University of New South Wales Northcott Drive CanberraACT2600 Australia School of Civil and Environmental Engineering The University of New South Wales High Street SydneyNSW2052 Australia School of Computer Science University of Technology Sydney Ultimo SydneyNSW2007 Australia

Transportation of the products is the primary connection between the different parts of the supply chain network (SCN), and the agility of the movements smooths the SCN operations. Potential risks (e.g., delay, road closure, and vehicle failure) disrupt the transportation process and negatively impact SCN operations. Accurately identifying the potential risks is the first step in Supply Chain Risk management (SCRM). All the factors, especially the external events outside of the SCN boundaries that cannot be controlled, need monitoring. In this paper, we propose a data-driven model to analyse the impact of weather status, as an external event, on the agility of the transportation system. A case study of a maritime port in Australia is considered to validate the model's applicability. For categorising the transportation based on agility, first, the speed of the movements was calculated by dividing distance by time, then by comparing the transportation speed with a defined threshold, the records of the dataset were divided into"Fast" and'Slow". For feature selection, First, seven weather-related attributes for the time of the dataset were collected, and then four criteria were calculated for each attribute. Secondly, by calculating the correlation of the features, the highly correlated ones were removed or justified, and the weather features were aggregated to the SCN dataset. Finally, after the data had been cleaned, Machine Learning (ML) models were used to train and test the aggregated dataset. The results showed 92.7% F1-score and 92.12% accuracy for the XGBoost classifier as the best one. Moreover, the weather features were recognised to be highly influential in predicting the agility of the transportation process. © 2024 Computers and Industrial engineering. All rights reserved.

关键词： Risk management

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The role of green buildings in achieving the sustainable development goals

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International Journal of Thermofluids 2025年 25卷

作者： Olabi, Abdul Ghani Shehata, Nabila Issa, Usama Hamed Mohamed, O.A. Mahmoud, Montaser Abdelkareem, Mohammad Ali Abdelzaher, M.A. Sustainable Energy & Power Systems Research Centre RISE University of Sharjah P.O. Box 27272 Sharjah United Arab Emirates Environmental Science and Industrial Development Department Faculty of Postgraduate Studies for Advanced Sciences Beni-Suef University Beni-Suef Egypt Department of Civil Engineering College of Engineering Taif University Taif21944 Saudi Arabia Chemical Engineering Department Minia University Elminia Egypt

The Sustainable Development Goals (SDGs) are a group of 17 objectives established by the United Nations (UN) and specified in resolutions adopted by the UN general assembly on September 25, 2015, and January 1, 2016. These broad aims, which collectively encompass 169 goals, are interconnected even though they have their own unique objectives. Numerous environmental, social, and economic development challenges are covered under the SDGs. This paper discusses the role of green buildings in achieving the UN's SDGs and investigates the issues related to their design and barriers. It is clearly demonstrated that the reliance on renewable energy resources is one of the key elements of green buildings. Additionally, the integration between these resources is considered a promising approach to achieving net zero energy consumption. This paper also aims to emphasize the positive effects that these green buildings have on people, the environment, and the economy, which ensures their survival. It also discusses several aspects related to green buildings, including materials, site evaluation, and greening existing buildings. © 2024

关键词： Green development

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Investigating the Relationship Between a Driver’s Psychological Feelings and Biosensor Data 23rd

Investigating the Relationship Between a Driver’s Psycholog...

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23rd International Conference on Human-Computer Interaction , HCII 2021

作者： Mostowfi, Sara Kim, Jung Hyup Buttlar, William G. Department of Architectural Studies University of Missouri Columbia United States Department of Industrial and Manufacturing Systems Engineering University of Missouri Columbia United States Department of Civil and Environmental Engineering University of Missouri Columbia United States

ISBN: (纸本)9783030909659

Recent studies show that emotional changes can influence driving behavior. For example, positive valence could enable better takeover performance in regaining vehicle control. On the other hand, a state of anger has been shown to degrade driver situational awareness and performance compared to a neutral state. These emotional states may result from different factors like a vehicle’s internal or external environment. The roadside environment is one of the external factors which might influence driver behavior and performance. Hence, it is necessary to understand how emotional conditions affect driving actions to design safer road environments. However, research involving the use of emotions can be challenging due to methodological issues associated with measuring emotions. Different methods can be used to measure emotion both subjectively and objectively. These could include the use of reliable sources for assessing evoked emotions, since subjective emotional experiences are the essence of a feeling. An objective manifestation of emotion can be captured as a representation of our inner experience. Also, the objective manifestation of emotion is the representation of our inner experience. In this study, we conducted an eye-integrated human-in- the- loop (HTIL) simulation experiment to analyze the driver’s emotional state by comparing both subjective and objective data to find the relation between drivers’ physiologic feelings (i.e., Kansei engineering method) and biosensor data (i.e., facial expression and eye movement). © 2021, Springer Nature Switzerland AG.

关键词： Eye tracking

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Impact of Road Infrastructure and Traffic Scenarios on E-scooterists’ Riding and Gaze Behavior

arXiv

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arXiv 2024年

作者： Chen, Dong Hosseini, Arman Smith, Arik Zheng, Zeyang Xiang, David Heydarian, Arsalan Shoghli, Omidreza Campbell, Brad Agriculture and Biomedical Engineering Mississippi State University Mississippi StateMS39762 United States Systems Engineering and Computer Science University of Virginia CharlottesvilleVA22903 United States Civil and Environmental Engineering University of Virginia CharlottesvilleVA22903 United States Civil Engineering Technology and Construction Management University of North Carolina at Charlotte CharlotteNC28223 United States Link Lab & Computer Science University of Virginia CharlottesvilleVA22903 United States

The growing adoption of e-scooters has raised significant safety concerns, particularly due to a surge in injuries and fatalities. This study explores the relationship between road infrastructure, traffic scenarios, and e-scooterists’ riding and gaze behaviors to improve road safety and user experience. A naturalistic study was conducted using instrumented e-scooters, capturing gaze patterns, fixation metrics, and head movement data across various road layouts and traffic scenarios. Key findings reveal that bike lanes offer a stable environment with reduced horizontal head movement and focused attention on the road, while shared roads and sidewalks lead to more dispersed gaze and increased head movement, indicating higher uncertainty and complexity. Interactions with other road users—such as navigating intersections, passing buses, riding near cars, and descending on downhill paths—demand greater cognitive load. Intersections re- quire heightened visual focus and spatial awareness, reflected in increased horizontal eye and head movements. Interactions with vehicles prioritize visual scanning over head movement to maintain stability and avoid collisions, while high-speed and downhill riding demand focused attention on obstacles and the road surface. The results provide insights into e-scooter riders’ behavior and physiological response analysis, paving the way for safer riding experiences and improved understanding of their needs. © 2024, CC BY.

关键词： Eye movements

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Optimal Speed Limit Control for Network Mobility and Safety: A Twin-Delayed Deep Deterministic Policy Gradient Approach

SSRN

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SSRN 2024年

作者： Afifah, Fatima Guo, Zhaomiao Department of Civil Environmental and Construction Engineering Resilient Intelligent and Sustainable Energy Systems Center University of Central FloridaFL32766 United States 4353 Scorpius Street OrlandoFL32816-0120 United States

Variable speed limit control (VSLC) has emerged as a promising approach for improving traffic safety and reducing congestion. However, existing research primarily focuses on intersection, link, or corridor-level improvements, overlooking the broader impacts of VSLC on the network due to driver rerouting. To address this gap, this study proposes a deep reinforcement learning (DRL) model to improve mobility and safety over a small-scale interconnected network considering rerouting behavior. The proposed DRL-based VSLC controller is designed to handle a large number of possible speed limits at each time step by utilizing a deep actor-critic framework. The study also experiments with different reward functions to characterize network mobility, safety, and traffic oscillation. Additionally, we investigate the sensitivity of the control algorithm across different traffic patterns, driving behavior, and VSLC locations. This study contributes to the understanding of the impacts of VSLC on transportation networks and provides insights into effective ways of implementing VSLC to improve network mobility and safety. © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Call for Papers for a Special Issue on "Ocean Health"

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environmental SCIENCE & TECHNOLOGY LETTERS 2024年第10期11卷 1016-1016页

作者： Dachs, Jordi Boehm, Alexandria Sunderland, Elsie Deiner, Kristy Martin, Jonathan W. Schlenk, Daniel Brooks, Bryan Department of Environmental Chemistry IDAEA-CSIC 08034 Barcelona Catalonia Spain Department of Civil and Environmental Engineering Stanford University Stanford California 94305 United States Harvard John A. Paulson School of Engineering and Applied Sciences and Department of Environmental Health Harvard T. H. Chan School of Public Health Harvard University Boston Massachusetts 02134 United States Department of Environmental Systems Science ETH Zurich 8092 Zurich Switzerland Department of Environmental Science Science for Life Laboratory Stockholm University 114 18 Stockholm Sweden Department of Environmental Sciences University of California Riverside Riverside California 92521 United States Department of Environmental Science Baylor University Waco Texas 76706 United States

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Eco-driving Incentive Mechanisms for Mitigating Emissions in Urban Transportation

arXiv

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arXiv 2024年

作者： Umar Niazi, M.B. Cho, Jung-Hoon Dahleh, Munther A. Dong, Roy Wu, Cathy Laboratory for Information and Decision Systems Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology StockholmSE-100 44 Sweden Laboratory for Information and Decision Systems Department of Civil and Environmental Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Department of Industrial and Enterprise Systems Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States

This paper proposes incentive mechanisms that promote eco-driving in transportation networks with the overarching objective of minimizing emissions. The transportation system operator provides the drivers with energy-efficient driving guidance throughout their trips, and their eco-driving levels are measured by how closely they follow this guidance via vehicle telematics. Drivers choose their eco-driving levels to optimize a combination of their travel times and their emissions. To obtain optimal budget allocation and recommendations for the incentive mechanism, the system operator gathers drivers’ preferences, or types, to assess each driver’s trip urgency and natural willingness to eco-drive. In a setting where drivers truthfully report their types, we introduce the first-best incentive mechanism and show that the obedience condition holds (i.e., drivers find it optimal to comply with the system operator’s recommendations) when the recommended eco-driving profile constitutes a Nash equilibrium. Moreover, in a setting where drivers can strategically report their types, we introduce the second-best incentive mechanism and show that the proposed mechanism is incentive-compatible (i.e., drivers find it optimal to be truthful). Under this mechanism, we also show that all equilibrium outcomes are at least as good as the recommended eco-driving profile in terms of the system operator’s objective. Overall, this work offers a framework for designing eco-driving incentive mechanisms while considering both the strategic behavior of individual drivers and the network effects of collective decision-making. © 2024, CC BY.

关键词： Budget control

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The use of Google community mobility reports to model residential waste generation behaviors during and after the COVID-19 lockdown

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Sustainable Cities and Society 2023年 99卷

作者： Mahmud, Tanvir Shahrier Ng, Kelvin Tsun Wai Ray, Sagar Lyu, Linxiang An, Chunjiang Environmental Systems Engineering University of Regina 3737 Wascana Parkway ReginaSKS4S 0A2 Canada Department of Building Civil and Environmental Engineering Concordia University 1455 De Maisonneuve Blvd. W. MontrealQCH3G 1M8 Canada

This paper proposed an original analytical approach to quantify waste generation and recycling behaviors in Regina, a Canadian capital city, using Google's Community Mobility Reports (GCMR) mobility data and ANN modeling. Residential waste collection rate (RWCR) in Regina appears to have changed abruptly during the pandemic. Compared to the pre-COVID-19 lockdown period, the correlation between the percent changes in RWCR and GCMR mobility became increasingly stronger and statistically significant during and after COVID-19 lockdown. It appears that the government imposed public health measures improved the consistency of waste generation and recycling behaviors. All 16 models predicting RWCR performed satisfactorily, with R2 near or over 0.70. The univariate Model A along with multivariate Model B and Model G performed better, with MAE of 11.5 to 15.1 tonnes/day and RMSE of 14.5 to 19.7 tonnes/day. The prediction scores of Model A using a Simple Additive Weighting (SAW) based ranking system seems to be negatively influenced by the distribution of the training and testing sets. There is no distinctive trend in the SAW-based prediction scores among the univariate model and the multivariate models. Instead of absolute values, percent changes and SAW relative ranking are adopted, making the proposed approach appropriate for cross-jurisdictional studies. © 2023 Elsevier Ltd

关键词： COVID-19

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