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Utility-Based Route Choice Behavior Modeling Using Deep Sequential Models

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Journal of Big Data Analytics in Transportation 2022年第2期4卷 119-133页

作者： Dong, Guimin Kweon, Yonghyeon Park, B. Brian Boukhechba, Mehdi Link Lab and Engineering Systems and Environment University of Virginia Charlottesville USA School of Data Science University of Virginia Charlottesville USA

GPS-based navigation systems have played crucial roles to improve transportation system performances. A limitation of such route guidance systems is that their route recommendation algorithms are mostly based on the shortest travel time and/or toll avoidance. To enhance the performance of route recommendation systems, it is critical to model drivers’ route choice behaviors, such that personalized route recommendations can be delivered to individual drivers or groups of drivers who share similar route choice behaviors. Previous studies have focused on game theory-based discrete choice models (e.g., Multinomial Logit) or shallow machine learning models (e.g., support vector machine) to generate the interpretations of route choice behavior by using the estimated parameters. Despite the good interpretability of these models, such interpretation could be often biased due to a small number of observations, restricted model complexity, and incapacity to capture the rich variations in route choice behaviors. In this paper, by leveraging both route level and route segment level features, we apply deep sequential models for the route data , and propose a hybrid sequence-to-one deep neural network model, named hybrid transformer-LSTM (HTL), to model the complex route choice behaviors. We utilize a large real-world route choice dataset to evaluate HTL and demonstrate its performance as compared to other state-of-the-art sequential neural network models. A model agnostic interpretability method, called SHAP, is applied to explain the route choice rationals. Finally, a benchmark clustering of route choice behavior is demonstrated to unveil the latent heterogeneous partitions in route selection decision making, based on the shapely values provided by SHAP.

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Performance Evaluation of Real-Time Object Detection for Electric Scooters

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arXiv 2024年

作者： Chen, Dong Hosseini, Arman Smith, Arik Nikkhah, Amir Farzin Heydarian, Arsalan Shoghli, Omid Campbell, Bradford Environmental Institute & Link Lab & Computer Science University of Virginia CharlottesvilleVA22903 United States System Engineering University of Virginia CharlottesvilleVA22903 United States Link Lab & Engineering Systems and Environment 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

Electric scooters (e-scooters) have rapidly emerged as a popular mode of transportation in urban areas, yet they pose significant safety challenges. In the United States, the rise of e-scooters has been marked by a concerning increase in related injuries and fatalities. Recently, while deep-learning object detection holds paramount significance in autonomous vehicles to avoid potential collisions, its application in the context of e-scooters remains relatively unexplored. This paper addresses this gap by assessing the effectiveness and efficiency of cutting-edge object detectors designed for e-scooters. To achieve this, the first comprehensive benchmark involving 22 state-of-the-art YOLO object detectors, including five versions (YOLOv3, YOLOv5, YOLOv6, YOLOv7, and YOLOv8), has been established for real-time traffic object detection using a self-collected dataset featuring e-scooters. The detection accuracy, measured in terms of mAP@0.5, ranges from 27.4% (YOLOv7-E6E) to 86.8% (YOLOv5s). All YOLO models, particularly YOLOv3-tiny, have displayed promising potential for real-time object detection in the context of e-scooters. Both the traffic scene dataset (https://***/records/10578641) and software program codes (https://***/DongChen06/ScooterDet) for model benchmarking in this study are publicly available, which will not only improve e-scooter safety with advanced object detection but also lay the groundwork for tailored solutions, promising a safer and more sustainable urban micromobility landscape. © 2024, CC BY.

关键词： Object detection

Using Statistical Models to Detect Occupancy in Buildings through Monitoring VOC, CO2 and other environmental Factors

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arXiv 2022年

作者： Varnosfaderani, Mahsa Pahlavikhah Heydarian, Arsalan Jazizadeh, Farrokh Link Lab. Department of Engineering Systems and Environment University of Virginia CharlottesvilleVA22904 United States Department of Civil and Environmental Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States

Dynamic models of occupancy patterns have shown to be effective in optimizing building-systems operations. Previous research has relied on CO2 sensors and vision-based techniques to determine occupancy patterns. Vision-based techniques provide highly accurate information;however, they are very intrusive. Therefore, motion or CO2 sensors are more widely adopted worldwide. Volatile Organic Compounds (VOCs) are another pollutant originating from the occupants. However, a limited number of studies have evaluated the impact of occupants on VOC level. In this paper, continuous measurements of CO2, VOC, light, temperature, and humidity were recorded in a 17,000 sqft open office space for around four months. Using different statistical models (e.g., SVM, K-Nearest Neighbors, and Random Forest) we evaluated which combination of environmental factors provide more accurate insights on occupant presence. Our preliminary results indicate that VOC is a good indicator of occupancy detection in some cases. It is also concluded that a proper feature selection and developing appropriate global occupancy detection models can reduce the cost and energy of data collection without a significant impact on the accuracy. Copyright © 2022, The Authors. All rights reserved.

关键词： Volatile organic compounds

Learning Enabled Fast Planning and Control in Dynamic environments with Intermittent Information

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arXiv 2022年

作者： Cleaveland, Matthew Yel, Esen Kantaros, Yiannis Lee, Insup Bezzo, Nicola The Department of Computer Science and Precise Center University of Pennsylvania PhiladelphiaPA19104 United States The Department of Engineering Systems and Environment University of Virginia CharlottesvilleVA22904 United States The Department of Electrical and Computer Engineering Link Lab University of Virginia CharlottesvilleVA22904 United States

This paper addresses a safe planning and control problem for mobile robots operating in communication- and sensor-limited dynamic environments. In this case the robots cannot sense the objects around them and must instead rely on intermittent, external information about the environment, as e.g., in underwater applications. The challenge in this case is that the robots must plan using only this stale data, while accounting for any noise in the data or uncertainty in the environment. To address this challenge we propose a compositional technique which leverages neural networks to quickly plan and control a robot through crowded and dynamic environments using only intermittent information. Specifically, our tool uses reachability analysis and potential fields to train a neural network that is capable of generating safe control actions. We demonstrate our technique both in simulation with an underwater vehicle crossing a crowded shipping channel and with real experiments with ground vehicles in communication-and sensor-limited environments. Copyright © 2022, The Authors. All rights reserved.

关键词： Robot programming

environment-Adaptive Multiple Access for Distributed V2X Network: A Reinforcement Learning Framework

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arXiv 2021年

作者： Kim, Seungmo Kim, Byung-Jun Park, B. Brian Department of Electrical and Computer Engineering Georgia Southern University StatesboroGA United States Department of Mathematical Sciences Michigan Technological University HoughtonMI United States Link Lab & Department of Engineering Systems and Environment University of Virginia CharlottesvilleVA United States

Cellular vehicle-to-everything (C-V2X) communications amass research interest in recent days because of its ability to schedule multiple access more efficiently as compared to its predecessor technology, i.e., dedicated short-range communications (DSRC). However, the foremost issue still remains: a vehicle needs to keep the V2X performance in a highly dynamic environment. This paper proposes a way to exploit the dynamicity. That is, we propose a resource allocation mechanism adaptive to the environment, which can be an efficient solution for air interface congestion that a V2X network often suffers from. Specifically, the proposed mechanism aims at granting a higher chance of transmission to a vehicle with a higher crash risk. As such, the channel access is prioritized to those with urgent needs. The adaptation is implemented based on reinforcement learning (RL). We model the RL framework as a contextual multi-armed bandit (MAB), which provides efficiency as well as accuracy. We highlight the most superb aspect of the proposed mechanism: it is designed to be operated at a vehicle autonomously without need for any assistance from a central entity. Henceforth, the proposed framework is expected to make a particular fit to distributed V2X network such as C-V2X mode 4. © 2021, CC BY.

关键词： Reinforcement learning

Practical guidance for using mesoscopic simulation tools

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Transportation Research Procedia 2020年 48卷 764-776页

作者： Bingrong Sun Justice Appiah B. Brian Park National Renewal Energy Laboratory Golden CO 80401 USA Virginia Transportation Research Council 530 Edgemont Road Charlottesville VA 22903 USA Link Lab & Department of Engineering Systems and Environment Univiersity of VirginiaCharlottesville VA 22904 USA

Mesoscopic simulation has many advantages such as requiring less effort for network building and calibration than microscopic simulation and generating more detailed simulation results in terms of traffic conditions than macroscopic simulation. As more and more transportation practitioners become interested in it, this paper aims at providing practical guidance by demonstrating the capabilities of mesoscopic simulation tools. In order to provide practical guidance, two commercially available mesoscopic simulation software, AIMSUN and VISSIM were chosen and a testbed network consisting of a system of freeways in Richmond, Virginia was prepared. The testbed was calibrated and used to evaluate the potential impacts of various levels of High Occupancy Vehicle (HOV) lane usage on system operations. The goal of the HOV lane implementation was to demonstrate practical guidance of utilizing mesoscopic simulation package as a tool for evaluating traffic management strategies. Results of the demonstration study, using both AIMSUN and VISSIM, were deemed to be reasonable. Some lessons learned during the study as well as through communications with the software vendors are also shared in the paper. This paper should provide transportation practitioners with practical guidance when selecting and/or conducting mesoscopic simulation.

关键词： Mesoscopic Simulation AIMSUN VISSIM High Occupancy Vehicles

Enabling smartphone-based estimation of heart rate

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arXiv 2019年

作者： Homdee, Nutta Boukhechba, Mehdi Feng, Yixue W. Kramer, Natalie Lach, John Barnes, Laura E. Link Lab University of Virginia CharlottesvilleVA United States Department of Electrical and Computer Engineering University of Virginia CharlottesvilleVA United States Department of Engineering Systems and Environment University of Virginia CharlottesvilleVA United States Department of Computer Science University of Virginia CharlottesvilleVA United States Sports Medicine University of Virginia CharlottesvilleVA United States

Continuous, ubiquitous monitoring through wearable sensors has the potential to collect useful information about users' context. Heart rate is an important physiologic measure used in a wide variety of applications, such as fitness tracking and health monitoring. However, wearable sensors that monitor heart rate, such as smartwatches and electrocardiogram (ECG) patches, can have gaps in their data streams because of technical issues (e.g., bad wireless channels, battery depletion, etc.) or user-related reasons (e.g. motion artifacts, user compliance, etc.). The ability to use other available sensor data (e.g., smartphone data) to estimate missing heart rate readings is useful to cope with any such gaps, thus improving data quality and continuity. In this paper, we test the feasibility of estimating raw heart rate using smartphone sensor data. Using data generated by 12 participants in a one-week study period, we were able to build both personalized and generalized models using regression, SVM, and random forest algorithms. All three algorithms outperformed the baseline moving-average interpolation method for both personalized and generalized settings. Moreover, our findings suggest that personalized models outperformed the generalized models, which speaks to the importance of considering personal physiology, behavior, and life style in the estimation of heart rate. The promising results provide preliminary evidence of the feasibility of combining smartphone sensor data with wearable sensor data for continuous heart rate monitoring. Copyright © 2019, The Authors. All rights reserved.

关键词： Heart

The global spectrum of tree crown architecture

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Nature Communications 2025年第1期16卷 1-16页

作者： Jucker, Tommaso Fischer, Fabian Jörg Chave, Jérôme Coomes, David A. Caspersen, John Ali, Arshad Loubota Panzou, Grace Jopaul Feldpausch, Ted R. Falster, Daniel Usoltsev, Vladimir A. Jackson, Toby D. Adu-Bredu, Stephen Alves, Luciana F. Aminpour, Mohammad Angoboy Ilondea, Bhely Anten, Niels P. R. Antin, Cécile Askari, Yousef Ayyappan, Narayanan Banin, Lindsay F. Barbier, Nicolas Battles, John J. Beeckman, Hans Bocko, Yannick E. Bond-Lamberty, Ben Bongers, Frans Bowers, Samuel van Breugel, Michiel Chantrain, Arthur Chaudhary, Rajeev Dai, Jingyu Dalponte, Michele Dimobe, Kangbéni Domec, Jean-Christophe Doucet, Jean-Louis Dupuy Rada, Juan Manuel Duursma, Remko A. Enríquez, Moisés van Ewijk, Karin Y. Farfán-Rios, William Fayolle, Adeline Ferretti, Marco Forni, Eric Forrester, David I. Gilani, Hammad Godlee, John L. Haeni, Matthias Hall, Jefferson S. He, Jie-Kun Hemp, Andreas Hernández-Stefanoni, José L. Higgins, Steven I. Holdaway, Robert J. Hussain, Kiramat Hutley, Lindsay B. Ichie, Tomoaki Iida, Yoshiko Jiang, Hai-Sheng Joshi, Puspa Raj Kaboli, Hasan Kazempour Larsary, Maryam Kenzo, Tanaka Kloeppel, Brian D. Kohyama, Takashi S. Kunwar, Suwash Kuyah, Shem Kvasnica, Jakub Lin, Siliang Lines, Emily R. Liu, Hongyan Lorimer, Craig Loumeto, Jean-Joël Malhi, Yadvinder Marshall, Peter L. Mattsson, Eskil Matula, Radim Meave, Jorge A. Mensah, Sylvanus Mi, Xiangcheng Momo, Stéphane T. Moncrieff, Glenn R. Mora, Francisco Muñoz, Rodrigo Nissanka, Sarath P. Nur Hajar, Zamah Shari O’Hara, Kevin L. Pearce, Steven Pelissier, Raphaël Peri, Pablo L. Ploton, Pierre Poorter, Lourens Pour, Mohsen Javanmiri Pourbabaei, Hassan Ribeiro, Sabina C. Ryan, Casey Sanaei, Anvar Sanger, Jennifer Schlund, Michael Sellan, Giacomo Shenkin, Alexander Sonké, Bonaventure Sterck, Frank J. Svátek, Martin Takagi, Kentaro Trugman, Anna T. Vadeboncoeur, Matthew A. Valipour, Ahmad Vanderwel, Mark C. Vovides, Alejandra G. Waldner, Peter Wang, Weiwei Wang, Li-Qiu Wirth, Christian Woods, Murray Xiang, Wenhua de Aquino Ximenes, Fabiano Xu, Yaozhan Yamada, Toshihi School of Biological Sciences University of Bristol Bristol BS8 1TQ United Kingdom UMR5300 Centre de Recherche sur la Biodiversité et l’Environnement CNRS INPT IRD Université de Toulouse Bât 4R1 118 route de Narbonne Toulouse 31062 France Conservation Research Institute University of Cambridge Cambridge CB2 3EA United Kingdom Institute of Forestry and Conservation University of Toronto 33 Willcocks Street Toronto M5S 3B3 ON Canada Forest Ecology Research Group College of Life Sciences Hebei University Hebei Baoding 071002 China Institut Supérieur des Sciences Géographiques Environnementales et de l’Aménagement (ISSGEA) Université DENIS SASSOU-N’GUESSO Kintélé Congo Laboratoire de Biodiversité de Gestion des Ecosystèmes et de l’Environnement (LBGE) Faculté des Sciences et Techniques Université Marien NGOUABI BP 69 Brazzaville Brazzaville Congo Faculty of Environment Science and Economy University of Exeter Exeter EX4 4QE United Kingdom Evolution & Ecology Research Centre University of New South Wales Sydney Sydney NSW Australia Department of Forest Mensuration and Management Ural State Forest Engineering and Economic University Yekaterinburg Russian Federation Forestry Research Institute of Ghana Council for Scientific and Industrial Research University Kumasi Ghana Center for Tropical Research Institute of the Environment and Sustainability University of California Los Angeles Los Angeles CA United States Natural Recourses and Watershed Management Office West Azerbaijan Province Urmia Iran Institut National pour l’Etude et la Recherche Agronomiques Kinshasa BP 2037 Democratic Republic Congo Université Pédagogique Nationale Kinshasa-Ngaliema BP 8815 Democratic Republic Congo Center for Crop Systems Analysis Wageningen University & Research Wageningen Netherlands AMAP lab Montpellier University IRD CIRAD CNRS INRAE Montpellier France Research Division of Natural Resources Kohgiluyeh and Boyerahmad Agriculture and Natural Resources Resea

Trees can differ enormously in their crown architectural traits, such as the scaling relationships between tree height, crown width and stem diameter. Yet despite the importance of crown architecture in shaping the structure and function of terrestrial ecosystems, we lack a complete picture of what drives this incredible diversity in crown shapes. Using data from 374,888 globally distributed trees, we explore how climate, disturbance, competition, functional traits, and evolutionary history constrain the height and crown width scaling relationships of 1914 tree species. We find that variation in height–diameter scaling relationships is primarily controlled by water availability and light competition. Conversely, crown width is predominantly shaped by exposure to wind and fire, while also covarying with functional traits related to mechanical stability and photosynthesis. Additionally, we identify several plant lineages with highly distinctive stem and crown forms, such as the exceedingly slender dipterocarps of Southeast Asia, or the extremely wide crowns of legume trees in African savannas. Our study charts the global spectrum of tree crown architecture and pinpoints the processes that shape the 3D structure of woody ecosystems. © The Author(s) 2025.

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Model-based dynamic optimization with openmodelica and CasADi

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Model-based dynamic optimization with openmodelica and CasAD...

7th IFAC Symposium on Advances in Automotive Control, AAC 2013

作者： Shitahun, Alachew Ruge, Vitalij Gebremedhin, Mahder Bachmann, Bernhard Eriksson, Lars Andersson, Joel Diehl, Moritz Fritzson, Peter PELAB-Programming Environment Lab Dept. Computer Science Linköping University SE-581 83Linköping Sweden Dept. Mathematics and Engineering University of Applied Sciences D-33609 Bielefeld Germany Vehicular Systems Dept. Electrical Engineering Linköping University SE-581 83 Linköping Sweden K.U. Leuven Belgium

ISBN: (纸本)9783902823434

This paper demonstrates model-based dynamic optimization through the coupling of two open source tools: OpenModelica, which is a Modelica-based modeling and simulation platform, and CasADi, a framework for numerical optimization. The coupling uses a standardized XML format for exchange of differential-algebraic equations (DAE) models. OpenModelica supports export of models written in Modelica and the optimization language extension using this XML format, while CasADi supports import of models represented in this format. This allows users to define optimal control problems (OCP) using Modelica and optimization language specification, and solve the underlying model formulation using a range of optimization methods, including direct collocation and direct multiple shooting. The proposed solution has been tested on several industrially relevant optimal control problems, including a dieselelectric power train.

关键词： Optimization