Electric vehicle (EV) charging stations are critical in promoting EV adoption and mitigating global warming by reducing reliance on fossil fuels. However, a comprehensive understanding of the latent characteristics of these charging stations remains limited. Unveiling these latent features is essential for enhancing predictive tasks such as utilization prediction, demand forecasting, and strategic infrastructure planning. In this paper, we conduct a comprehensive investigation into methods for extracting embedding vectors of charging stations based on userstation interactions. We explore a spectrum of techniques—from traditional approaches like non-negative matrix factorization to advanced machine learning models such as neural collaborative filtering—to effectively capture these latent features. Through extensive experiments and analyses, we evaluate the quality and effectiveness of the generated embeddings in improving predictive modeling tasks related to charging station usage. Our findings demonstrate that incorporating these embeddings significantly enhances the performance of predictive models, leading to more accurate demand forecasts and better utilization predictions. To the best of our knowledge, this is the first study to delve deeply into extracting and analyzing embedding vectors of charging stations derived from user interaction data. The insights gained from this research provide valuable guidance for optimizing EV charging infrastructure and can inform future developments in the field, ultimately supporting the broader adoption of electric vehicles.

The public commute is essential to all urban centers and is an efficient and environment-friendly way to travel. Transit systems must become more accessible and user-friendly. Since public transit is majorly designed statically, with very few improvements coming over time, it can get stagnated, unable to update itself with changing population trends. To better understand transportation demands and make them more usable, efficient, and demographic-focused, we propose a fast, multi-layered transit simulation that primarily focuses on public transit simulation (BTE-Sim). BTE-Sim is designed based on the population demand, existing traffic conditions, and the road networks that exist in a region. The system is versatile, with the ability to run different configurations of the existing transit routes, or inculcate any new changes that may seem necessary, or even in extreme cases, new transit network design as well. In all situations, it can compare multiple transit networks and provide evaluation metrics for them. It provides detailed data on each transit vehicle, the trips it performs, its on-time performance and other necessary factors. Its highlighting feature is the considerably low computation time it requires to perform all these tasks and provide consistently reliable results.

Traffic simulators have many parameters that describe vehicle characteristics and driving behaviors. However, driving behaviors differ across urban, suburban, and rural areas. Even in the same area, driving behavior can be affected by the time of day or weather conditions. Therefore, it is difficult to get an accurate parameter set that is suitable for all scenarios. As a result, default parameters of simulators are usually determined only for a specific test case. To simulate a traffic scenario, researchers need to perform calibration to determine a suitable parameter set, which can provide more reliable simulated traffic than the default parameter set. A popular approach is manual calibration using human experience, but it is usually not effective due to the huge space of possible parameters. Although some studies proposed automated methods using evolutionary algorithms, implementing these methods is a time-consuming job. In this paper, we introduce a toolkit for researchers to easily conduct calibration for their own traffic scenarios. The toolkit supports several state-of-the-art algorithms and is designed to run in parallel to utilize the power of high performance computers. Moreover, by using this toolkit, we conduct in-depth experiments to understand which factors affect the calibration performance.