Wireless charging modules for electric vehicles are being increasingly studied. Previous research has focused on developing more effective wireless-charging modules for electric vehicles in order to pave the way for a more sustainable urban transportation. The objectives of the study were to identify the social structure of the field by mapping of research collaborations among authors and countries, measure the influence of authors and sources, identify the interactions between different researchers and the most influential authors, sources, documents and organizations. To achieve these objectives, a bibliometric search in the SCOPUS database was conducted using a combination of keywords and Boolean operators. The initial keyword search returned 2163 documents. The documents retrieved were manually filtered for further analysis. A scientometric analysis was carried out on the remaining 1367 documents using co-authorship, co-citation, and citation analyses for a number of measurement units. The results showed that “object detection” and “shielding effectiveness” were the most current research topics. Authors who were widely cited did not generally produce a large number of papers or collaborate with other authors. Authors from China, the United States, and the United Kingdom have all co-authored published works on the topic, indicating that they have all contributed considerably to the field’s achievements. This strongly highlighted the amount of funding localized in developed countries towards such technologies. The number of international co-authored studies conducted was low. This is most significant with no research conducted in this field in the less developed world. The most cited and influential scholars were G. A. Covic, J. T. Boys, and C. C. Mi. The most influential sources were IEEE Trans. on Power Electronics and IEEE Trans. on Induction Electronics, while the most productive sources were Energies and IEEE Access. The most influential documents were those by Covic G.A. (2013a) and Covic G.A. (2013b). Finally, emerging trends in charging and energy storage in electric vehicles were also discussed.

Traffic accidents on highways are attributed mostly to the "invisibility" of oncoming traffic and road signs. "Speeding" also causes drivers to reduce the effective radius of the vehicle path in the curve, thus trespassing into the lane of the oncoming traffic. The main aim of this paper was to develop a multisensory obstacle-detection device that is affordable, easy to implement and easy to maintain to reduce the risk of road accidents at blind corners. An ultrasonic sensor module with a maximum measuring angle of 15° was used to ensure that a significant portion of the lane was detected at the blind corner. The sensor covered a minimum effective area of 0.5 m² of the road for obstacle detection. Yellow light was employed to signify caution while negotiating the blind corner. Two photoresistors (PRs) were used as sensors because of the limited number of pins on the microcontroller (Arduino Uno). However, the device developed for this project achieved obstacle detection at blind corners at relatively low cost and can be accessed by all road users. In real-world applications, the use of piezoelectric accelerometers (vibration sensors) instead of PR sensors would be more desirable in order to detect not only cars but also two-wheelers.

Fatal accidents remain a significant challenge to road safety globally, particularly for human-controlled vehicles (SAE Level 1 and 2). Despite advancements in vehicle safety technologies, understanding and addressing edge cases — scenarios where conventional safety measures might fail — are crucial for enhancing safety standards. This study offers method for identification and a detailed analysis of edge cases in accident fatalities. Using a dataset of 39,221 accident cases from 2022, the Angle-Based Outlier Detection (ABOD) method was employed to identify 4,429 edge cases. The dataset underwent thorough preprocessing and feature engineering to accurately reflect the complexities of real-world accidents, including encoding categorical variables, normalizing numerical features, and applying ABOD technique to pinpoint anomalous data points. The effectiveness of the method was evaluated using statistical measures such as ROC AUC scores and confusion matrix. The study highlights critical factors contributing to fatalities in edge cases, revealing previously underappreciated aspects of vehicle safety. The implications for improving road safety are discussed in detail. This research lays the groundwork for future studies on advanced safety systems and automated vehicle technologies, utilizing outlier detection technique to generate actionable insights for enhancing road safety.

With rising urban populations, optimizing passenger group-to-vehicle allocation (PGVA) is critical for enhancing ride-sharing efficiency, particularly when integrated with transit networks. Existing PGVA methods often underperform by overlooking arithmetic compatibility between passenger group sizes and vehicle capacities. Traditional approaches prioritize spatial or temporal factors but neglect structural relationships inherent in passenger group-vehicle matching. This study introduces the Greatest Common Divisor (GCD) method, a novel framework leveraging number-theoretic principles to optimize resource allocation. The GCD-based method addresses PGVA problem by decomposing passenger group sizes and vehicle capacities into prime factors, ensuring mathematically rigorous compatibility while minimizing wasted capacity and computational complexity. Under the tested simulation conditions, the GCD-based method demonstrated superior performance in reducing eVMT and VMT compared to the benchmark algorithms. It significantly reduced empty and total vehicle miles travelled by over 70% and 85% respectively, compared to the Hungarian algorithm, while avoiding the inefficiencies of a first-come-first-served strategy. The GCD-based compatibility score successfully encodes the qualitative notion of a “good fit”, leading to more efficient resource utilization and directly contributing to the model’s performance. While relatively computationally more intensive, the proposed GCD-based model solves problems of realistic scale within a timeframe that is practical for operational deployment in modern ride-sharing platforms. The method bridges a critical gap in ridesharing optimization and aligns with sustainability goals through inherent resource efficiency. This study supports data-driven strategies for passenger-centric mobility systems that balance demand, capacity, and environmental impact by prioritizing arithmetic alignment.

With rising urban populations, optimizing passenger group-to-vehicle allocation (PGVA) is critical for enhancing ride-sharing efficiency, particularly when integrated with transit networks. Existing PGVA methods often underperform by overlooking arithmetic compatibility between group sizes and vehicle capacities. Traditional approaches prioritize spatial or temporal factors but neglect structural relationships inherent in group-vehicle matching. This study introduces the Prime Factor Tree (PFT) method, a novel framework leveraging number-theoretic principles—prime factorization and greatest common divisor (GCD)—to optimize resource allocation. The PFT method addresses PGVA by decomposing passenger group sizes and vehicle capacities into prime factors, ensuring mathematically rigorous compatibility while minimizing wasted capacity and computational complexity. Unlike heuristic or integer programming approaches, PFT achieves polynomial-time scalability. Computational experiments demonstrate a 12% increase in ridership, 15% reduction in vehicle miles travelled (VMT), 7% decrease in empty VMT, and 13% shorter passenger wait times compared to non-optimized baseline. The method bridges a critical gap in ridesharing optimization, aligning with sustainability goals through inherent resource efficiency. For academia, it offers a novel integration of logical compatibility metrics into transportation models, advancing theoretical foundations for sustainable mobility. Practically, it provides ride-hailing firms and urban planners with a scalable tool to reduce operational costs and improve service efficiency. This study supports data-driven strategies for passenger-centric mobility systems that balance demand, capacity, and environmental impact by prioritizing arithmetic alignment.