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Data Science Blog > Big Data > Data Analysis of COVID-19 and NYC Taxis

Data Analysis of COVID-19 and NYC Taxis

Brian Calderon
Posted on Apr 16, 2022

The skills the authors demonstrated here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

Introduction:


With the onset of the pandemic in 2020 many industries experienced sever setbacks, the for hire industry was not the exception. Within this industry it was reported that for hire vehicles (FHV's) experienced an 84% reduction in demand during March 2020 with only 26% of drivers still on the road by April of that year [1]. For the daring FHV's that decided to continue working their earnings had declined by ~26% based on data [1].

data

From the above figure we can see that in 2020 the number of reported trips in April has severely declined as compared to previous years. We can also see that there's a steady increase in trips until the end of 2020 with the increase continuing in 2021.

The below box plot shows that median number of trips reported in 2021 is higher than in 2016 however we are still no where near pre-pandemic levels which reported ~170k in 2019.

data

With this background in mind we now see that the FHV industry has not yet completely recovered from the COVID pandemic and perhaps one factor of this recovery is due to a lag in TLC driver application approvals.

Goal:


Its a known fact that various industries related to the FHV industry suffered major backups during the pandemic. The TLC depends highly on the DMV in order to schedule drivers test and written exams, however the DMV also experienced high levels of saturation of work that seem to be lasting until today [2].

The goal of this study is to understand how the pandemic has effected the ability of the TLC to complete new driver applications using data. The goal is to recommend actions for hiring more personnel based on relevant statistics that show how much lag there is.

Data Overview:


All the data used in the present analysis was taken from NYC Open Data . The major data sets were:

  • TLC New Driver Application Status: Rows: 7212 ; Columns: 12. Contains historical data of driver applicants grouped into three main categories:
    • Completed: TLC driver license approved
    • Needed: Needs one or more requirements prior to granting license
    • N/A: Not applicable.

*NOTE: An extra dataset was used and merged with the above dataset since the above dataset only had values going back to 2020. This data set can be found here and contains values going back to 2016.

  • FHV Base Aggregate Report: Rows: 41.4k ; Columns: 9. Contains data of monthly trips completed by NYC taxi base stations. This was used to understand the decline in taxi usage historically.

Data Analysis:


Having identified a clear correlation between demand and the covid pandemic (see introduction) we not delve into how this impacted the new TLC driver applications.

data

The above bar chart shows that historically the total number of applications which have completed the driver exam for a new TLC license has been declining concurrently with the total number of "Needed" (aka Incomplete). The driver exam was choosen as a significant figure of merit (FOM) for this study since it was observed to be the most impacted part of the TLC application process due to the inability to socialize during the pandemic.

In 2021 we have completed nearly 4x the driver exams as compared to 2020, however, we also an uptick in the needed category.

data

The above line plot shows a more descriptive statistic. Here we plot the percentage of Completed, Needed and Not Applicable TLC driver applications as a function of the total applications present any given year. We can see that there was a clear decline in 2020, however, in 2021 both the Completed as well as the Needed percentages are comparable to the highest pre-pandemic year in 2018. Although this shows that we have clearly made significant efforts to recover in 2021, the data from the 1st quarter of 2022 shows that for the first time there a more Needed applications than there are completed, suggesting a large potential backlog for 2022.

data

Diving deeper into the data, we plot the percent applications per month. historically we can see that February is indeed a month were applications pile up and more personnel is needed. This coincides with the fact that the 1st quarter data for 2022 shows a large backlog as well.

Conclusion


The TLC is clearly suffering from the pandemic effects. Efforts have clearly been made to catch up the new driver applications that were lagging from 2020 as the numbers were greatly improved in 2021, however these efforts need to continue since in 2022 there are allready more incomplete applications overall than complete.

It is recommended that more personnel should be added for the 2022 calendar year to continue the efforts demonstrated in 2021.

Overall, a quarterly hiring trend can also  be implemented  in the future since it was observed that the 1st quarters of every year suffer from a large amount of incomplete applications. This hiring trend could help sustain a steady completion number of applications.

Future Work


Although we were able to demonstrate the need for additional hiring in 2022, this was merely a descriptive view. Gathering data on number of personnel as a function of time can enable us to give a more quantitative answer to how many people need to be hired.

Although the pandemic was one of the largest factor that affected the TLC driver applications, other emerging can also be studied such as the hike in gas prices in 2022. Other potential factors could be the amount of drivers deciding to drive outside of NYC (where no special TLC license is needed).

Thank you for reading through my post, and comments or suggestions are very welcome. Linked-In

References:

[1] 2020, "CCOVID-19 Impact on the NYC For-Hire Industry", Report.

[2] "COVID-19 DMV Delays and Their Impact", Online source.

My Full Blog Profile: 

About Author

Brian Calderon

HI I'm Brian, my background is in Electrical Engineer and Device Physics. For my day job I work with large sets of electrical data which I try to untangle and understand any deviations then subsequently correlate them to...
View all posts by Brian Calderon >

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