When Do Hartford Crashes Happen?

by Theodora Tatsi

Last updated on May 1, 2023

for Data Visualization for All
with Prof. Jack Dougherty
Trinity College, Hartford CT, USA


Last year was reported to be one of the deadliest years for drivers in Connecticut.

According to CT Insider ,, in 2022 Connecticut had the most pedestrian deaths since 1988, with 357 fatal crashes. Officials and other groups are trying to understand why those crashes happen and how we can prevent more from happening.

Since March 2020, when the first lockdown in Connecticut happened, traffic did increase, fatalities on the road however increased according to CT Insider . This is one more reason why officials have started paying attention to the issue of road safety. For instance, the Federal Highway Administration increased their funding towards road safety, in an attempt to protect pedestrians and bikers on the streets.

Our partners, The Planning Division, City of Hartford are focusing on the city of Hartford and are trying to find out ways to make the streets of Hartford safer, and asked our help. In order to tackle any major issue such as the one of crashes in Hartford, one must address the three major questions: Where? When? and Why? In this project I will be focusing on the question of “when?”.

To answer the question of “when do crashes in Hartford happen?”, using data from the years 2018-2022 provided by the UConn Connecticut Crash Data Repository, I decided to focus on three different variables and try to establish some meaningful patterns. I examined how many crashes happened per month, per day and then per hour of the day. For the first section, which is the part focused on the months, I wanted to answer the question “Which month has the most crashes in Hartford?”. My findings were surprising, since despite my hypothesis that winter months will have more crashes, the results show that the average number of crashes per year is somewhat constant. Secondly, I questioned which day of the week has the most crashes. My initial assumption was that weekends would show a higher number of crashes, however, the findings showed that most crashes in Hartford actually happen on Fridays. Lastly, I wanted to inspect the specific times of the day when most crashes happen. Doing so, I found out that most crashes happen in the afternoon.

These findings are important to consider when trying to find ways to make the roads of Hartford safer. Having this information will allow our partners to focus on specific months, days and times in their attempt to make the roads of Hartford safer

Findings and Evidence

1. Which month has the most crashes?

One of the first questions that arise when examining when do crashes happen, is if there are any specific times during the year that are more dangerous, and more specifically what months do most crashes happen during the years? Differences in the weather, the duration of daylight or temperature could be factors that affect the frequency of crashes. Thus, my hypothesis would be that winter months, where the weather conditions are harsher and the sun goes down earlier in the day, like October to March would show a higher number of crashes. It is interesting to see however, that even though there are some fluctuations, the average number of crashes per month seems to be relatively stable. At the same time, each year shows variations in the rate of crashes per month that are irrelevant to the weather conditions. For example, in 2019 during the months of May to July, there was a big increase in the number of crashes. Meanwhile, April of 2020 has significantly less crashes than all other months during the period of 2018-2022.

The line chart below (Figure 1) shows the number of crashes each month for each year (2018-2022) separately, and also the avarage of all years combined.

Figure 1: Explore the interactive chart line chart depecting number of crashes per month for years 2018-2022 with data from the UConn Connecticut Crash Data Repository.

Although, paying attention to road safety is important during the winter months, Figure 1 shows that on average there is no big increase on crashes during the winter months. This means that more safety measures focused on those months are unnecessary. It appears, that other factors such as the Covid lockdowns play a bigger role in the number of crashes than one would expect. Winters in Hartford can be harsh, and driving is more challenging, but focusing solely on this may prevent us from seeing the bigger picture, as we can tell from Figure 1 above.

2. Which day of the week has the most crashes?

Examining the number of crashes in Hartford in relation to the months did not show any significant pattern. In order to establish some sort of pattern, we have to look further, now focusing on specific days rather than months. First and foremost, Friday appears to be the day with the most crashes throughout all four years. What I found surprising is that Saturday and Sunday appeared to have significantly lower crashes. I found this interesting since I would expect weekends to be more high-risk due to the amount of people that go out during the night and probably drive intoxicated. The findings show that workdays are significantly more dangerous than weekends.

Figure 2 below shows the number of crashes per day for years 2018-2019.

Figure 2: Explore the interactive chart line chart depecting number of crashes per month for years 2018-2022 with data from the UConn Connecticut Crash Data Repository.

According to Figure 2, the number of crashes that happen on Fridays is 16.75% higher than the average number of crashes that happen through the rest of the weekdays (Monday to Thursday). What this information tells us is that when deciding when to impose stricter safety measures in order to decrease the number of crashes, we should focus on the weekdays, and especially Fridays.

3. What times do most crashes happen?

Having information on the months and days when crashes occur, we can take our research one step further and examine the hours of the days during which most crashes appear to happen. Prior to this project, I would assume that late at night, more crashes happen due to the light conditions and the lower energy levels of drivers. From the previous findings however, I now suspect that rush hours are actually more dangerous, and the data shows just that. It looks like the very early hours of the morning are the safest for drivers, and the evening hours are the times when most crashes happen. More specifically,25% of the crashes in Harford happen between 15:00 and 17:00 o’clock!

Figure 3 below demonstrates just that. The rows highlighted in green are the times with the least crashes through the years while the rows highlighted in red showthe times with the most crashes.

Figure 3: Explore the interactive chart line chart depecting number of crashes per month for years 2018-2022 with data from the UConn Connecticut Crash Data Repository.

Once again, this information is very useful when trying to find ways to reduce the number of crashes in Hartford. Knowing the times which most crashes happen, in this case the hours of the afternoon can help our partners focus on those specific time periods.


The data used to create the charts above is not normalized. This means that the data was not organized or adjusted to be proportional. This means that the number of crashes was not calculated in proportion to the number of cars on the road. For instance, during Covid traffic was significantly lower, thus the number of crashes at that time were proportionally more.

Moreover, factors such as the decrease in police reports during Covid, the number of crashes that are not on record, and the possibility of human error are all things that could affect the accuracy of the results.

Sources and methods

Sources and methods :

For this project, I was given data (2018-2022 Hartford crashes UConn) with information about all the crashes that happened in Hartford in the years 2018 to 2022. This google sheet did include a lot of information, but in order to make sense of it we need to somehow process this information. What does this mean and why does it matter?

To find any pattern in the data, I needed to focus on the parts of the data that were interesting to me and relevant to the question “When?”. To isolate different variables and create more specific tables I made some pivot tables (learn how to create pivot tables here ) where I experimented and concluded three different ones. One which showed me the crashes each month for each year, one which showed me the crashes each day for each year, and one which showed me the crashes each hour of the day for each year.

Making sense of the information without some visual aid is almost impossible so I jumped straight into creating some graphs. Finding out which chart is more suitable for each set of data can be a challenging task, so I got help from the book “Data Visualization for All”. (view the chapter on charts here ). I wanted my charts to be easily understood for everyone, so I went with the option that seemed more easy to comprehend.

It is important to note that I did not collect the data on my own, nor had any means to check the validity of the data I used. Through the project, I used the raw data and did not normalise them. Moreover, human error, both on my part as well as from the individuals collecting or reporting the data is always possible.

Works Cited

Backus, Lisa. “2022 Among Deadliest for CT Drivers and Pedestrians in Decades, Data Shows.” CT Insider, 23 Dec. 2022, https://www.ctinsider.com/news/article/CT-2022-fatal-crashes-pedestrians-wrong-way-driver-17663854.phpCT

Dougherty, Jack, and Ilya Ilyankou. Introduction: Why Data Visualization? | Hands On Data Visualization. handsondataviz.org, https://handsondataviz.org/introduction.html. Accessed 17 Apr. 2023.

Katy Golvala, Dave Altimari. “Roads Are Deadlier than Ever. Figuring out Why Is Complicated.” CT Mirror, 30 June 2022, https://ctmirror.org/2022/02/06/connecticut-traffic-deaths-fatalities-dui/.

Data Collected from the UConn Connecticut Crash Data Repository, https://www.ctcrash.uconn.edu/QueryTool2.action?qid=156713.