A Quantitative and Qualitative Analysis on the Improvement of Public Transportation in Santa Clara County

2025-10-05T13:05:23+08:00

This research investigates the possibilities of improving Santa Clara County’s public transit system using various modalities of public transit service. In the recent past, many individual projects have been pursued, funded, and constructed on the county’s transit network, yet as a whole failed to attract significant ridership. The county’s fundamental issue of car-dependency and congestion remains and is worsening. Previous research has established plans for specific routes, corridors, or modalities of transit, but a comprehensive analysis and proposal by a third party outside of the transit operator for future public transportation service and development in the county has not yet been laid out. We review contemporary and historical literature on the topic, public transportation guides and best practices, and the unique situation in Santa Clara County which has contributed to its current congestion and overall transportation challenges. We compare three feasible modalities for future construction and expansion and the ways in which this could impact ridership. From the results of our data and general concepts in the literature, we propose a solution combining a greatly expanded bus rapid transit system and improvements to current bus and light rail networks, particularly in time-competiveness, route linearity, and zoning. Through these improvements, we aim to produce a proposal where transit service that is more time-efficient and ultimately better fits the diverse needs of the Santa Clara County community. We hope that we may outline a possible method to significantly increase ridership while decreasing environmental impact, congestion, and maintaining cost-effectiveness, both in the future construction and operation of transit. We recommend further study with regards to BRT construction in addition to a continuation and expansion of projects to improve the efficiency and potential ridership of existing bus and light rail routes.

An Ensemble Model Based on Fusion of Multiple Machine Learning Algorithms for Remaining Useful Life Prediction of Lithium Battery in Electric Vehicles

2025-06-06T15:43:48+08:00

As the demand for renewable energy solutions increases, electric vehicles (EVs) have become a critical component of sustainable transportation. Lithium-ion batteries, the core of EVs, determine vehicle performance and efficiency. Accurate Remaining Useful Life (RUL) prediction of these batteries is essential for effective battery management, reducing unexpected failures, and supporting sustainability goals. Traditional RUL prediction methods often fail to capture the complex degradation processes of batteries. To address these limitations, we propose a novel machine learning framework combining Artificial Neural Networks (ANN) for feature extraction and ensemble modeling with Random Forest (RF), K-Nearest Neighbors (KNN), and Gradient Boosting Decision Tree (GBDT). The final ensemble fusion further refines predictions by leveraging the complementary strengths of the models. Experimental results demonstrate that the proposed framework significantly improves prediction accuracy, with the final ensemble model achieving an R² of 0.92 and reducing MAE and RMSE to 74.52 and 110.07, respectively. While promising, the framework faces challenges in real-world generalization and computational efficiency, highlighting the need for further research.

Innovations in Applied Engineering and Technology

A Quantitative and Qualitative Analysis on the Improvement of Public Transportation in Santa Clara County

An Ensemble Model Based on Fusion of Multiple Machine Learning Algorithms for Remaining Useful Life Prediction of Lithium Battery in Electric Vehicles