Article
Thermodynamics
Gokturk Poyrazoglu, Elvin Coban
Summary: The stochastic value estimation tool is developed for electrical and financial valuation of electric vehicle charging points, serving as a planning and research tool with detailed data on station and vehicle usage. The analysis of a case study at one of the biggest shopping malls in Istanbul, Turkey, provides insights on station performance metrics and related indicators.
Article
Ergonomics
Marco Dozza, Alessio Violin, Alexander Rasch
Summary: This article introduces a data-driven evaluation framework for micro-mobility vehicles, comparing bicycles and e-scooters in field tests. The preliminary results suggest that e-scooters may have advantages in maneuverability and comfort, although they require longer braking distances. The data collected from e-scooters can facilitate policy making and further research on the integration of micro-mobility vehicles.
JOURNAL OF SAFETY RESEARCH
(2022)
Article
Environmental Sciences
Ka Ho Tsoi, Becky P. Y. Loo, Xiangyi Li, Kai Zhang
Summary: Traffic noise is a significant threat to urban populations, and can lead to negative health consequences. This study examines the impact of electric vehicles, particularly electric buses, on traffic noise levels in a highly urbanized city. The results show that electric buses have a greater potential to reduce traffic noise, resulting in health co-benefits for a significant portion of the population.
ENVIRONMENT INTERNATIONAL
(2023)
Article
Transportation Science & Technology
Fatemeh Fakhrmoosavi, Ehsan Kamjoo, Mohammadreza Kavianipour, Ali Zockaie, Alireza Talebpour, Archak Mittal
Summary: Connected and autonomous vehicle technologies are expected to revolutionize transportation systems by improving mobility, safety, and reducing emissions. However, there is limited understanding of the impacts of these technologies at large scales. This study develops a stochastic framework and optimization algorithm to determine the optimal market shares of connected and autonomous vehicles in a mixed traffic environment, considering uncertainties in various parameters. The framework and algorithm are successfully applied to a large-scale network in Chicago, providing valuable insights for policymakers.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2022)
Article
Engineering, Electrical & Electronic
Onur Kadem, Jongrae Kim
Summary: This paper presents a novel technique for constructing the SoC-OCV relation in batteries and converting it into a single parameter estimation problem. The Kalman filter is implemented to estimate the SoC and related states in batteries using the proposed parameter estimation and SoC-OCV construction technique. Numerical simulations demonstrate accurate parameter estimation and SoC estimation error below 2%. Experimental results validate the algorithm with an SoC estimation error remaining within 2.5%.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Transportation Science & Technology
Zhandong Xu, Yiyang Peng, Guoyuan Li, Anthony Chen, Xiaobo Liu
Summary: This paper studied the impact of range anxiety among electric vehicle drivers on traffic assignment. Two types of range-constrained traffic assignment problems were defined based on discrete or continuous distributed range anxiety. Models and algorithms were proposed to solve the two types of problems. Experimental results showed the superiority of the proposed algorithm and revealed that drivers with heightened range anxiety may cause severe congestion.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Ergonomics
Norris Novat, Emmanuel Kidando, Boniphace Kutela, Angela E. Kitali
Summary: Automated vehicle technology is promising for improving traffic efficiency and reducing emissions. This study compares automated vehicles and conventional vehicles in different types of collisions, finding that automated vehicles are more likely to be involved in rear-end crashes but less likely to be involved in other types of collisions. Safety aspects of automated vehicles need improvement.
JOURNAL OF SAFETY RESEARCH
(2023)
Article
Multidisciplinary Sciences
Andrii Kashkanov, Andriy Semenov, Anastasiia Kashkanova, Natalia Kryvinska, Oleg Palchevskyi, Serhii Baraban
Summary: The paper analyzes methods for evaluating vehicle braking parameters and introduces a mathematical model that can consider various types of parameter uncertainty, reducing possible modeling errors by 39%. Experimental results show an average relative error of 4.58% and a maximum error of 7.82%. Studying the stability of electric vehicles during emergency braking and utilizing specialized computer programs can reduce type I errors by 2-19% and type II errors by 43-68%.
SCIENTIFIC REPORTS
(2023)
Article
Energy & Fuels
Follivi Kloutse Ayevide, Sousso Kelouwani, Ali Amamou, Mohsen Kandidayeni, Hicham Chaoui
Summary: This study proposes a hybrid machine learning approach to estimate the battery output power and remaining driving range of a battery electric vehicle, which has been demonstrated to provide more accurate predictions compared to traditional model-based methods through real-world testing.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Energy & Fuels
Morteza Taiebat, Samuel Stolper, Ming Xu
Summary: The range and lifetime cost of battery electric vehicles (BEVs) should not be significant barriers to widespread adoption in the ride-hailing sector. Providing moderate subsidies, information interventions, and targeting programs to ride-hailing drivers who can benefit the most from BEVs will promote a faster transition in this industry. Driver-targeted outreach, provision of information about the benefits of electric vehicles, and expansion of charging infrastructure and fast charging rates through local and federal policies are valuable steps to encourage ride-hailing electrification.
Article
Urban Studies
Boniphace Kutela, Christian Mbuya, Suleiman Swai, Delphine Imanishimwe, Neema Langa
Summary: This study used Bayesian Networks to analyze the stated preferences of residents in Gilbert City, Arizona for Autonomous vehicles (AVs), and explored the association between AVs and other mobility options. The results showed that approximately 66% of respondents expressed a desire to use AVs. Respondents who were interested in using EV charging stations were more likely to want and use AVs. Among those who had differences between their intention and actual use of AVs, respondents who did not use dockless bikes had the largest percentage difference.
Review
Transportation
Pedro Fernandes, Jorge M. Bandeira, Margarida C. Coelho
Summary: This study evaluated the traffic and environmental performance of scenarios with shared, electric, and automated vehicles in an intercity corridor in Portugal using macro simulation modeling. It found that while increased shared mobility and electrification are generally positive, they may lead to potentially adverse emission effects in some network sections.
JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Chemistry, Multidisciplinary
Jingyu Li, Shiyuan Tian, Na Zhang, Guangchen Liu, Zhaoyuan Wu, Wenyi Li
Summary: This study establishes a dynamic road network model to analyze the impacts of electric vehicle travel and charging behaviors on power grid operation and traffic congestion. By improving the optimal path algorithm, the energy consumption of electric vehicles can be accurately captured, reducing travel time costs effectively.
APPLIED SCIENCES-BASEL
(2023)
Article
Physics, Multidisciplinary
Xuedong Hua, Weijie Yu, Wei Wang, De Zhao
Summary: Cooperative adaptive cruise control (CACC) has the potential to improve traffic stability and safety, but the current communication system is vulnerable to cyberattacks, posing security risks to the transportation system. However, there is limited research on the realistic impact of cyberattacks on inter-vehicle communication and vehicle dynamics, and little attention has been given to adaptive traffic operations and the stochastic nature of cyberattacks.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2023)
Article
Chemistry, Analytical
Jiahe Zhang, Yongsheng Qian, Junwei Zeng, Xuting Wei, Haijun Li
Summary: This paper proposes an improved cellular automata model that considers the physical properties of vehicles to investigate the impact of electric vehicles on the traffic flow characteristics of traditional fuel vehicles. Through sensitivity analysis and actual data, the model parameters are quantified to simulate the electric-fuel vehicle mixed traffic flow. The results show that the improved model can reproduce various traffic phenomena and is closer to the measured data compared to other models. The increase of electric vehicle permeability leads to increased flow rate and speed, reduced congestion near critical density, and improved traffic safety. However, at high density, congestion initially decreases, then increases, and then decreases again, with lower safety than homogeneous traffic flow. The speed fluctuation of fuel vehicles is enlarged, and the overall traffic flow speed shows an upward trend. Furthermore, the TIT curve can identify traffic flow state changes, and electric vehicles in high-density mixed flow cause greater disturbance to traffic flow speed and reduce traffic safety. These results provide a basis for further research on electric-fuel vehicle hybrid traffic flow.
MICROCHEMICAL JOURNAL
(2023)
Article
Materials Science, Multidisciplinary
Gal Shmuel, Adam Thor Thorgeirsson, Kaushik Bhattacharya
Article
Computer Science, Software Engineering
Adam Thor Thorgeirsson, Moritz Vaillant, Stefan Scheubner, Frank Gauterin
Summary: The article discusses the intelligent deployment of accurate range estimation using machine learning techniques. Through simulation analysis of system architecture and module placement, it is found that a cloud-based distributed system significantly reduces latency, decreases network usage, and enhances user experience.
SOFTWARE-PRACTICE & EXPERIENCE
(2021)
Article
Physics, Multidisciplinary
Adam Thor Thorgeirsson, Frank Gauterin
Summary: The text discusses the importance of probabilistic predictions in machine learning applications and the challenges with Bayesian learning methods in terms of computational cost. It introduces a method to incorporate predictive uncertainty in federated learning by treating local weights as a posterior distribution for global model weights. By comparing this approach with state-of-the-art Bayesian and non-Bayesian algorithms, it demonstrates similar performance to benchmarks in a non-distributed setting when evaluated with proper scoring rules.
Article
Engineering, Electrical & Electronic
Adam Thor Thorgeirsson, Stefan Scheubner, Sebastian Fuenfgeld, Frank Gauterin
IEEE OPEN JOURNAL OF VEHICULAR TECHNOLOGY
(2020)
Proceedings Paper
Transportation Science & Technology
Patrick Petersen, Adam Thor Thorgeirsson, Stefan Scheubner, Stefan Otten, Frank Gauterin, Eric Sax
PROCEEDINGS OF THE 5TH INTERNATIONAL CONFERENCE ON VEHICLE TECHNOLOGY AND INTELLIGENT TRANSPORT SYSTEMS (VEHITS 2019)
(2019)
Proceedings Paper
Nanoscience & Nanotechnology
Gal Shmuel, Adam Thor Thorgeirsson, Kaushik Bhattacharya
TMS 2014 SUPPLEMENTAL PROCEEDINGS
(2014)