4,848 research outputs found
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
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