Project Milestone 1 – Project Ideas

Team Members:

Melisa Cen Xie –
Ayesha Iftikhar –
Hoi Hin Lau (Edwin) –
Chin Chia Leong (Mendel) –

Course Number & Section: 
CIS 9440 UTA

Project Proposals

Proposal #1:  Influence on commodity due to Covid-19 from a business perspective

Project Description:

This project is mainly focusing on the change of traditional human behavior (primarily on shopping) extending to future business perspectives. Assuming most people are staying at home due to the pandemic, our hypothesis is that the change in human behavior is going to have both positive and negative impacts on different industries. Along with the frequency of people going outside with respect to the trips traveled by distance in a larger scale or daily transit ridership on a smaller scale, we could construct a relationship with the adjustment of business. From the business perspective, sales of different industries, shopping behavior, habits, and preferences could be some of the measurements we expected. For example, what effect do we have towards the apparel industry because of the rise of online shopping, e.t.c


  • Date
  • Demographic Location
  • Freight activityPotential Dataset: 

    Potential sources: 

    • Foreign trade in goods
    • import/ export on commodity
    • USITC

    Proposal #2: COVID-19 & Impact on the Taxi & Limousine Commission (TLC) Industry 

    The focus on this idea of the project is to analyze and understand the effects in the TLC industry due to the COVID-19 pandemic. As businesses are opening back up, more people started to commute back to work. Many are also going back to their pre-COVID living lifestyles. Living in NYC, the majority of people may need to take different modes of transportation to commute to their workplace or elsewhere, whether that is through subways, ride-hailing apps (also known as for-hire vehicles), or yellow/green cabs. We can use our datasets to determine how many trips were completed throughout the months, which pick up locations are common by boroughs, compared to pre-COVID and during COVID, and travel time between pickup and drop off. 


    • Frequency of trips
    • Trip distance
    • Number of drivers/vehicles 
    • Type of ride-hailing
    • Location (pickup/drop-off)Datasets:
      1. For-Hire Vehicle Base Aggregate Report
      2. Monthly Data Report on Yellow, Green, and FHV 
      3. TLC Trip Record Data (Including locations of pick up and drop-offs)


      Proposal #3 – The impact of COVID-19 on Job losses in different industries

      During the pandemic, many people have lost their jobs. However, some people or specific industries are doing well during this severe period. For now, experts and governors keep telling us the pandemic isn’t over yet, and we still need to prevent the surge. As we are all graduating next year, finding a job that is relatively steady, which means a job in an industry that was still performing well in profits and was still hiring in the job market, is crucial to us. 

      Reference reports:

      Potential Datasets:


      Opportunity Insights real-time Economic Tracker US –

      GDP and Unemployment data of various countries –


      Proposal #4 – Remapping the Last Mile of the Urban Supply Chain

      With increasing urbanization, it is estimated that a quarter of world’s population will reside in its 600 largest cities, accounting for 453 million people. These urban centers constitute a huge market for a broad range of products. But a major hurdle to tap the market’s full potential is the last mile delivery. The final segment of supply chains where products are delivered to these customers. Among many other factors, limited infrastructure, traffic issues, complicated city maps and regulations disrupt last-mile operations. The segmentation of demand, stimulated by the dramatic growth in e-commerce, adds more complexity. New delivery technologies are not a solution to this problem on their own. The more underlying need is how companies mine and model their data. We will gather such data from a company and run analysis to help them to improve the design and management of urban delivery services, their Last Mile Delivery Challenges through our analysis.

      Potential Datasets: The source will be companies ready to share their data. The main datasets would include GPS data derived from smartphones carried aboard vehicles, vehicle details, route details, transactional data, census and geo-spatial data, and information on driver activities.


      • Time
      • Cost
      • Vehicle Efficiency
      • Delivery Routes
      • Demand



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