frankmengzj / ttc-subway-delay Goto Github PK

inputs

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inputs/sketches

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scripts

The scripts are good and clear.

README.md

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outputs/paper/paper.pdf

Your paper is well written. It is interesting to learn about the patterns and causes of subway delays. Your graph and tables help support your findings and visualize the data. Overall, good job on your analysis.

Here are some suggestions:

The subway system, as a part of the Toronto Transit Commission (TTC) service, plays an important role in the daily lives of commuters in Toronto .

Your introduction is clear and organized. You could provide more specific details about how the subway system impacts commuters and the city's economy. This could give the audience more background information on why you decided to analyze subway delays.

Given its critical importance, understanding why subway delays is essential to improve the system.

Here, you could elaborate more on why addressing delays is essential. Do delays have negative impacts on commuters and the economy?

By studying on the causes, we learned that there are several factors not only make the subway late but also threats the safety of passengers, it is necessary for TTC to take actions.

Good job on emphasizing the need for action. I think you could include a short paragraph about the main findings of your analysis and the structure of your paper to guide the audience about what information will be covered in each section.

The TTC Subway Delay Data @subwaydelaydata records the information of each delay they faced including time, location and reason which can be used to analyze and consequently find some insights to reduce the problems.

Where does the TTC Subway Delay Data come from? I think you could mention and cite opendatatoronto here as well.

Variables "Date", "Time" and "Day" state when the delay happens, variable "Station" shows where the delay heppens, variable "Code" indicates what caused the delay, variable "Min_Delay" records how many minutes the subway delayed, and finally "Line" shows which subway line delayed.

I think you could explain the types of delay codes included in the "Code" variable and the lines of subway in the “Line” variable. For example, what do code “MUPAA” and line “YU” mean in the first row?

There are 161 kinds of cases that caused the delay in 2023, among them, the top 16 kinds of incidents led to 6015 out of 8234 of all the delays, as they are the most important ones.

To enhance the presentation of this finding, I think you could also include a graph here to visually compare the mean delay of each 16 kinds of incidents.

@tbl-delay-by-reason showed the number of cases happened and the mean of how long they delayed.

You mentioned that Table 2 shows both the number of cases and the mean duration of delays. Considering renaming the 'n' in the table could ensure that the audience understands that it represents 'Number of Cases'.

In conclusion, by solving these problems, TTC can not only decrease the number of delays but also improve the safety of all the passengers.

For the conclusion, you could provide more details to reiterate the important insights gained from your findings and discuss how these findings could potentially impact the subway system. Moreover, I was wondering if there’re any potential limitations or next steps for future research in your analysis.

Positive Aspects
The topic is highly relevant and important, addressing the efficiency of public transportation and its impact on daily life in Toronto. This focus is commendable as it addresses a practical issue that affects many people.
The use of TTC Subway Delay Data is an excellent choice. It provides a solid foundation for the analysis and demonstrates a good application of real-world data.
The use of R and specific packages like tidyverse, here, kableExtra, and ggplot2 is appropriate for data analysis and visualization. This shows a good understanding of the tools needed for such an analysis.
The suggestions for TTC to focus on station order and consider hiring more staff are practical and actionable. Similarly, the discussion about passenger-triggered alarms and train door issues provides a clear path for potential improvements.

Areas for Improvement
Clarity in Data Presentation: While the analysis is insightful, the narrative could benefit from clearer explanation of the data. For instance, explaining the significance of the variables in the data set (like 'Code', 'Min_Delay') in layman terms could make the report more accessible to a wider audience.

The use of ggplot2 for visualizations is great, but ensuring these graphs are clearly labeled and include explanations for non-technical readers would enhance understanding. For example, explaining what 'YU' and 'BD' lines are in the fig-delay-by-line might be necessary for readers unfamiliar with Toronto's subway system.
Overall
Overall, the analysis is well-conducted with relevant data and practical insights. It demonstrates a good understanding of data analysis techniques and their application in real-world scenarios. This document could serve as a valuable resource for improving subway services in Toronto.