Article
Chemistry, Analytical
Youssef Chebli, Samira El Otmani, Jean-Luc Hornick, Jerome Bindelle, Jean-Francois Cabaraux, Mouad Chentouf
Summary: This study utilized advanced electronic sensors to investigate the grazing activity and protein-energy requirements of dairy goats in a Mediterranean woodland, finding that grazing time was shorter in summer and protein-energy intake often insufficient. The combination of GPS collars and accelerometers provided valuable insights for goat-feeding strategies.
Article
Transportation Science & Technology
Manon Seppecher, Ludovic Leclercq, Angelo Furno, Delphine Lejri, Thamara Vieira da Rocha
Summary: This article introduces a new method based on urban partitioning and analysis of basic vehicle trip characteristics to estimate urban traffic speed dynamics from sparse data sources. When applied to a large GPS dataset, the method yields satisfactory speed estimation results.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2021)
Article
Astronomy & Astrophysics
Christopher W. Johnson, Nicholas Lau, Adrian Borsa
Summary: The study found discrepancies in GPS station velocity estimates among different analysis centers, with vertical differences being more significant. Actual velocity uncertainties are often underestimated in the horizontal direction and may be over or underestimated in the vertical direction. Subsidence rates vary widely in different regions, with station density having a modest impact on uncertainties.
EARTH AND SPACE SCIENCE
(2021)
Article
Engineering, Civil
Zhu Xiao, Yanxun Chen, Mamoun Alazab, Hongyang Chen
Summary: This paper proposes a low-cost implementation method for acquiring large-scale private car trajectory data in urban environments, using ensemble learning based Gauss Process Regression and incremental learning to address the challenges of non-linearity, non-stationarity, and concept drifting during trajectory collection. Experimental results demonstrate the effectiveness and reliability of the proposed method in real-world urban environments.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Liping Huang, Yongjian Yang, Hechang Chen, Yunke Zhang, Zijia Wang, Lifang He
Summary: This paper proposes a context-aware road travel time estimation framework using trajectory data and third-order tensor modeling, combined with congestion levels and points of interest information to fill missing data and calculate the final travel time matrix. The effectiveness of the method was validated on two real-world datasets, showing superior accuracy performance compared to state-of-the-art methods.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Engineering, Civil
Minh Hieu Nguyen, Jimmy Armoogum, Emeli Adell
Summary: This study introduces a method to enhance purpose imputation from global positioning system data by selecting relevant features, showing that the addition of actual or predicted travel modes improves imputation performance, with actual modes having a stronger effect. The newly adopted MFVP feature contributes to better prediction results, and the purpose-imputation models utilizing all features achieve high accuracy levels on both datasets.
TRANSPORTATION RESEARCH RECORD
(2021)
Article
Computer Science, Information Systems
Seunghyeon Lee, Hong-Woo Seok, Ki-rim Lee, Hoh Peter In
Summary: In this study, a prototype system was developed to improve the reliability and data integrity of national reference point surveys by using blockchain technology to record GPS data and correction data.
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
(2022)
Article
Mechanics
Yuhang Xu, Yangyang Sha, Cong Wang, Yingjie Wei
Summary: In this study, PVNet (Pressure-Velocity Network), an improved U-shaped neural network combined with Transformer Modules and Multi-scale Fusion Modules, is proposed to predict velocity fields from pressure on the hydrofoil surface. The incorporation of position encodings into the input features improves prediction accuracy. Tests on the cavitation dataset of the NACA66 hydrofoil show that PVNet outperforms traditional models such as shallow neural networks and UNet. Furthermore, a quantitative analysis of the impact of input features on prediction performance and a comparison of different positional encodings are conducted.
Article
Engineering, Civil
Chaopeng Tan, Jiarong Yao, Keshuang Tang, Jian Sun
Summary: This study proposed a novel approach for cycle-based queue length estimation by fusing real-time and historical probe vehicle trajectory data using maximum likelihood estimation. Results show that precise estimation for cycle-based queue lengths can be achieved based on sparse vehicle trajectory data.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Review
Transportation
Adham Badran, Ahmed El-Geneidy, Luis Miranda-Moreno
Summary: With the popularity of smartphones and mobile internet, GPS data from vehicles has become widely available. This data provides a unique opportunity to automatically extract road network features and generate detailed maps for transport network models, reducing the usual resource investment. However, current studies lack systematic exploration and evaluation of road network inference methods from a transport network modelling perspective. This research aims to address this gap by conducting a systematic and reproducible literature review on the use of GPS data in transport network modelling, highlighting limitations and future work for road network representation in transport models and autonomous vehicles navigation.
Article
Computer Science, Artificial Intelligence
Chao Li, Zhanwen Liu, Nan Yang, Wenqian Li, Xiangmo Zhao
Summary: In this paper, a regional attention network for multi-modal trajectory prediction is proposed, which can accurately predict the future trajectory of vehicles, improving the performance of existing methods.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Remote Sensing
Asmamaw Yehun, Tsegaye Gogie, Martin Vermeer, Addisu Hunegnaw
Summary: The study estimated the integrated precipitable water vapor in the atmosphere using GPS and LEO data, revealing significant variations in water vapor content across different regions in Ethiopia, with high correlation to other forecasting systems. Analyzing changes across different time scales, the study identified substantial variations in precipitable water vapor in the study area.
INTERNATIONAL JOURNAL OF REMOTE SENSING
(2021)
Article
Engineering, Mechanical
George D. Pasparakis, Ketson R. M. dos Santos, Ioannis A. Kougioumtzoglou, Michael Beer
Summary: A methodology based on compressive sampling and l1-norm minimization is developed for wind time-histories reconstruction and extrapolation, suitable for wind turbine monitoring and environmental hazard modeling in structural system performance optimization. However, the computational cost is prohibitive for two spatial dimensions, leading to the introduction of a method based on low-rank matrices and nuclear norm minimization.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Remote Sensing
Rui Wang, Doris Becker, Thomas Hobiger
Summary: To improve the centimeter-level positioning accuracy of real-time kinematic (RTK), an enhanced Kalman filtering procedure is proposed in this study. By adaptively estimating the variance-covariance matrix of double-differenced measurements based on a posteriori residuals, instead of approximating it by an empirical function using satellite elevation angle, the success rate of ambiguity resolution and the reliability of positioning results are enhanced. Moreover, a stochastic model based on robust Kalman filtering is introduced to compute the variance-covariance matrix of double-differenced measurement noise empirically, using a modified version of the IGG III method, for outlier detection and identification, improving the filter performance.
Article
Physics, Multidisciplinary
Tatsuro Mukai, Yuichi Ikeda
Summary: This study develops a method for evaluating the mobility of people in a city using GPS data, including evaluating human mobility using temporal networks and searching for the shortest path by solving the time-dependent traveling salesman problem. The results show that considering congestion leads to more realistic estimations.
FRONTIERS IN PHYSICS
(2022)
Article
Engineering, Civil
Ziran Wang, Guoyuan Wu, Matthew J. Barth
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2020)
Article
Thermodynamics
Zhiming Gao, Tim J. LaClair, Kashif Nawaz, Guoyuan Wu, Peng Hao, Kanok Boriboonsomsin, Mike Todd, Matt Barth, Abas Goodarzi
Summary: A comprehensive forward-looking powertrain model with an efficiency-based control strategy was developed for real-time optimization of plug-in hybrid electric buses while considering drivability and practical operation under real driving conditions. Results showed that the innovative powertrain control improved energy savings by 10%-30% compared to the baseline strategy.
ENERGY CONVERSION AND MANAGEMENT
(2022)
Article
Engineering, Civil
Zhengwei Bai, Peng Hao, Wei Shangguan, Baigen Cai, Matthew J. Barth
Summary: This study proposes a hybrid reinforcement learning framework to support connected eco-driving in mixed traffic. Through experiments, it shows that the proposed method can reduce energy consumption and save travel time compared to a state-of-the-art model-based approach.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Chao Wang, Peng Hao, Guoyuan Wu, Xuewei Qi, Matthew J. Barth
Summary: This paper proposes a data-driven method to identify intersection areas and map stop bar positions. The method analyzes the entropy of vehicles' moving directions to identify intersections, and estimates the number, coordinates, and directions of stop bars by evaluating the upstream vehicles' stopping locations. The empirical results prove the applicability and robustness of the method for handling data at an urban regional scale.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Xishun Liao, Ziran Wang, Xuanpeng Zhao, Kyungtae Han, Prashant Tiwari, Matthew J. Barth, Guoyuan Wu
Summary: A cooperative ramp merging system utilizing Digital Twin technology has been designed to assist vehicles in cooperating before merging, addressing safety and environmental sustainability issues in ramp merging scenarios.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Automation & Control Systems
Xishun Liao, Xuanpeng Zhao, Ziran Wang, Kyungtae Han, Prashant Tiwari, Matthew J. Barth, Guoyuan Wu
Summary: A game theory-based ramp merging strategy is proposed in the article for optimal merging coordination of CAVs in mixed traffic, which enhances the safety and efficiency of the merging process. Simulation results demonstrate significant improvements in average speed, fuel consumption, and driving stability.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Engineering, Civil
Zhensong Wei, Peng Hao, Matthew Barth, Kanok Boriboonsomsin
Summary: As the travel demand and the number of eligible vehicles for HOV lanes increase, the performance degradation of HOV lanes has become a more prevalent issue in many regions. In this study, the impact of adding a contraflow HOV lane was evaluated using traffic microsimulation. The evaluation results showed that the contraflow HOV lane design can significantly reduce traffic delay and increase the average speed of the currently degraded HOV lane.
TRANSPORTATION RESEARCH RECORD
(2023)
Article
Engineering, Electrical & Electronic
Xuanpeng Zhao, Ahmed Abdo, Xishun Liao, Matthew Barth, Guoyuan Wu
IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE
(2022)
Article
Engineering, Civil
Haishan Liu, Peng Hao, Yejia Liao, Kanok Boriboonsomsin, Matthew Barth
Summary: The market for on-demand food delivery has experienced significant growth during the COVID-19 pandemic. Understanding the impact of on-demand delivery on travel patterns and pollutant emissions is crucial for transportation and environmental agencies. This research proposes a comprehensive framework to quantify the vehicle-miles traveled (VMT) and emissions generated by on-demand food delivery. The framework includes a simulation scenario, an efficient order dispatching and routing algorithm, and an emission evaluation model. The study evaluates both short-term and long-term impacts of the COVID-19 pandemic and concludes that on-demand food delivery has the potential to reduce dining-related VMT and emissions, especially with the involvement of electric vehicles.
TRANSPORTATION RESEARCH RECORD
(2023)
Article
Engineering, Electrical & Electronic
Zhengwei Bai, Guoyuan Wu, Xuewei Qi, Yongkang Liu, Kentaro Oguchi, Matthew J. Barth
Summary: Endowed with automation and connectivity, connected and automated vehicles (CAVs) will be a revolutionary promoter for cooperative driving automation (CDA). Authentic perception (AP) information based on high-fidelity sensors via a cost-effective platform is crucial for enabling CDA-related research, and a cyber mobility mirror (CMM) co-simulation platform is designed for providing AP information.
IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE
(2023)
Article
Engineering, Civil
Zhengwei Bai, Saswat P. Nayak, Xuanpeng Zhao, Guoyuan Wu, Matthew J. Barth, Xuewei Qi, Yongkang Liu, Emrah Akin Sisbot, Kentaro Oguchi
Summary: In this paper, a next-generation real-world object perception system, Cyber Mobility Mirror (CMM), is proposed for enabling Cooperative Driving Automation (CDA). The CMM system utilizes roadside sensors to perform 3D object detection, tracking, localization, and reconstruction. Field tests demonstrate that the CMM prototype system achieves high precision and recall for object detection and tracking, as well as accurate geo-localization and real-time traffic condition display.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Review
Engineering, Mechanical
Fuad Un-Noor, Guoyuan Wu, Harikishan Perugu, Sonya Collier, Seungju Yoon, Mathew Barth, Kanok Boriboonsomsin
Summary: While electric vehicles have made significant progress in on-road transportation, similar advancements in the off-road equipment sector are relatively lacking. This paper reviews the current state of technology in electrifying off-road equipment used in construction and agricultural applications, discussing the advantages, challenges, and potential solutions for overcoming those challenges and facilitating electrification.
Article
Computer Science, Artificial Intelligence
Nigel Williams, Guoyuan Wu, Matthew Barth
Summary: This paper proposes a cooperative merging application that can function even with degraded vehicle positioning accuracy and when vehicles are free to change lanes, and it demonstrates safety, efficiency, and environmental benefits for the overall traffic stream.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2022)
Article
Transportation Science & Technology
Peng Hao, Kanok Boriboonsomsin, Chao Wang, Guoyuan Wu, Matthew Barth
Summary: The research developed a truck eco-approach and departure system based on signal controllers and road grade information, showing significant energy savings compared with a baseline algorithm on different terrains.
SAE INTERNATIONAL JOURNAL OF COMMERCIAL VEHICLES
(2021)