Convincing Features
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Modern cities face significant challenges in managing urban mobility, including traffic congestion, inefficient public transportation, and environmental concerns. By leveraging big data, city authorities can analyze transportation patterns, optimize public transit, and reduce congestion. Data from GPS devices, ride-sharing apps, IoT sensors, and social media can provide insights to improve urban mobility.
For this assignment, you will explore the applications of big data in smart cities, focusing on transportation optimization. You are required to conduct research on this topic, select a realworld application, and complete the following tasks. You can use Google Scholar to find relevant scholarly articles (published between 2020 and 2025) to support your discussion.
Your report must explain how you performed each task in detail and must adhere to formal academic writing standards.
The following tasks need you to analyze and discuss how big data analytics can be used to optimize urban mobility and improve smart transportation systems.
1. Introduction: (10 Marks)
➢ Briefly introduce the role of smart transportation systems in urban mobility.
➢ Discuss the significance of big data in improving transportation efficiency and sustainability.
2. Data Sources and Collection: (10 Marks)
➢ Identify and analyze different data sources used in smart transportation (e.g., GPS data, IoT sensors, ride-sharing platforms).
➢ Discuss challenges in collecting and integrating data from multiple sources.
3. Big Data Technologies for Smart Transportation: (20 Marks)
➢ Recommend big data tools and frameworks (e.g., Apache Spark, Hadoop,
Kafka, etc.) for handling transportation data.
➢ Justify why these tools are suitable for large-scale urban mobility analysis. 4. Ethical and Privacy Considerations: (10 Marks)
➢ Discuss the ethical implications of using big data for urban mobility analysis.
➢ Address concerns such as data privacy, security, and regulatory compliance in smart cities.
(50 Marks)
From the scholastic articles, select one study that proposes an existing smart transportation application that uses big data technologies and implements a data-driven solution for optimizing urban mobility. Based on the selected study address the following tasks:
5. System Architecture and Workflow: (20 marks)
➢ Discuss the high-level architecture and the workflow of the proposed smart
transportation solution by the selected study.
➢ Describe the main components of the architecture and their relationships.
6. Dataset Introduction and Preprocessing: (10 marks)
➢ Introduce and describe the dataset used, including key attributes.
➢ Discuss preprocessing steps such as data cleaning, normalization, and feature engineering.
7. Findings, Limitations, and Recommendations: (20 marks)
➢ Discuss key findings from your implementation and evaluation.
➢ Identify the limitations of your approach and suggest improvements.
Notes:
Your submission must include:
1. A well-structured report with formal academic writing and proper citations.
2. A detailed introduction and justification of the dataset and methods used.
3. Diagrams and visualizations to illustrate concepts and comparisons.
4. A critical discussion on ethical considerations, challenges, and recommendations.
