An Enhanced Analysis of Traffic Intelligence in Smart Cities Using Sustainable Deep Radial Function

Smart cities have revolutionized urban living by incorporating sophisticated technologies to optimize various aspects of urban infrastructure, such as transportation systems. Effective traffic management is a crucial component of smart cities, as it has a direct impact on the quality of life of residents and tourists. Utilizing deep radial basis function (RBF) networks, this paper describes a novel strategy for enhancing traffic intelligence in smart cities. Traditional methods of traffic analysis frequently rely on simplistic models that are incapable of capturing the intricate patterns and dynamics of urban traffic systems. Deep learning techniques, such as deep RBF networks, have the potential to extract valuable insights from traffic data and enable more precise predictions and decisions. In this paper, we propose an RBF based method for enhancing smart city traffic intelligence. Deep RBF networks combine the adaptability and generalization capabilities of deep learning with the discriminative capability of radial basis functions. The proposed method can effectively learn intricate relationships and nonlinear patterns in traffic data by leveraging the hierarchical structure of deep neural networks. The deep RBF model can learn to predict traffic conditions, identify congestion patterns, and make informed recommendations for optimizing traffic management strategies by incorporating these rich and diverse data To evaluate the efficacy of our proposed method, extensive experiments and comparisons with real world traffic datasets from a smart city environment were conducted. In terms of prediction accuracy and efficiency, the results demonstrate that the deep RBF based approach outperforms conventional traffic analysis methods. Smart city traffic intelligence is enhanced by the model capacity to capture nonlinear relationships and manage large scale data sets.Comment: 25 pages, 6 figures, and 3 Table

The Parking Problem At Loyola Marymount University

The goal of this research proposal is to address and propose a solution to the ongoing parking problems at Loyola Marymount University (LMU). Research in this paper will indicate a direct correlation between alternative transit usage at universities and a decrease in parking-related issues. Consequently, this proposal will explore the viability of implementing advertisements that highlight the ideal qualities of public transit to potential users — students and faculty — as a means of increasing university public transit usage. This proposal will suggest that similar research be conducted at LMU so as to increase public transit usage to combat LMU’s parking problems. Lastly, this paper will outline the timeline and required funding for this research to be conducted appropriately

Beyond Financial Aid: How Colleges Can Strengthen the Financial Stability of Low-income Students and Improve Student Outcomes - 2018

This report is a compendium of best practices for assisting low-income students. It highlights work that has been underway for years but has not always been implemented at scale, especially within institutions that enroll significant numbers of low-income students. This toolkit offers leaders five concrete strategies they can use in two ways to increase student success: (1) it can help determine how, and how well, their institutions are serving low-income students; and (2) it can help them devise and implement plans to improve, expand and better coordinate services for greater impact. The report is divided in three sections: (1) Five Strategies to Increase the Success of Low-Income Students; (2) BFA Institutional Self-Assessment Guide; and (3) BFA Implementation Guide. Two appendices are included

Online Predictive Optimization Framework for Stochastic Demand-Responsive Transit Services

This study develops an online predictive optimization framework for dynamically operating a transit service in an area of crowd movements. The proposed framework integrates demand prediction and supply optimization to periodically redesign the service routes based on recently observed demand. To predict demand for the service, we use Quantile Regression to estimate the marginal distribution of movement counts between each pair of serviced locations. The framework then combines these marginals into a joint demand distribution by constructing a Gaussian copula, which captures the structure of correlation between the marginals. For supply optimization, we devise a linear programming model, which simultaneously determines the route structure and the service frequency according to the predicted demand. Importantly, our framework both preserves the uncertainty structure of future demand and leverages this for robust route optimization, while keeping both components decoupled. We evaluate our framework using a real-world case study of autonomous mobility in a university campus in Denmark. The results show that our framework often obtains the ground truth optimal solution, and can outperform conventional methods for route optimization, which do not leverage full predictive distributions.Comment: 34 pages, 12 figures, 5 table

INSPIRE Newsletter Fall 2018

https://scholarsmine.mst.edu/inspire-newsletters/1003/thumbnail.jp