The Plan:

For this project, I wanted to determine if I could find in the data, in a way that was not indicated elsewhere, if the driver had been temporarily blinded by the sun. Two events inspired this hypothesis. First, a boating accident I was a part of many years ago, when, to our complete surprise, on the open sea, we managed to hit another boat. We had never seen that boat coming, and neither had we experienced reduced vision. Instead, it was later determined that the crash had happened because the other boat had, unbeknownst to it, approached us from the direction of the sun. This had made it practically invisible to us. I was reminded of this experience during the recent eclipse when it dawned on me just how strong the natural aversion to looking at the sun is. How automatically do we avoid even looking in its general direction? It is, therefore, completely possible to be “blinded” by our natural aversion to looking at the sun without ever realizing we have been blinded.

In order to see if this could be detected in the crash data from DataMontogmery, I first tried to use the directional data, with the cardinal directions that the vehicle was going or turning. First, I tried to use the cardinal directions to see if there was an increase in east/west crashes when the sun was low. Any evidence was, however, drowned by the noise of the commuting traffic, and since this data does not include the number of cars on the road that do not end up in crashes, it proved beyond my skill level to bring out any correlations besides the overwhelming correlation between commuting times and car crashes.

As my first leaflet plot illustrates, the data is, unfortunately, a little too broad and essentially picked up every car going in a gross approximation of the cardinal directions rather than anything specifically useful. However, it is an excellent springboard for looking at potential future avenues of investigation.

My final alluvial plots were designed to investigate how the crash reasons might vary over a day.

Exciting research has already been done on the effect of bright sunlight on vehicle crashes here in Maryland(Redelmeier, D. A., & Rasa, S. 2017). What is interesting about that study is that it takes an entirely different approach. This may be due to my own northern biases. I expect that the brightest, most blinding point during the day would be during sunset or sunrise when the sun is low and that direct sunlight would be the most blinding. However, that is not necessarily the case, and in a place as far south as Maryland and with as many clear weather days, it is midday sun, which might pose the most significant risk.

Currently, the overall public transit budget as a proportion of the highway and roads budget comes in at a measly 2%. This forces a great many people who are easily distracted or are otherwise reduced in capacity to commute by car. The plot showing the number of cars going north and south outstripping both west and east suggests these numbers indicate commuters going in and out of the city. Unsurprisingly, accidents increase when there are more cars on the road—accidents of more or less all kinds, except for alcohol impairment. The added accidents during the return from work also suggest that the wear of the working day may be at work.

However, this analysis underscores the inherent challenges and risks of driving. While measures like reflective surfaces and correctly colored dashboards can mitigate the impact of light on drivers, the data suggests that long-term solutions may lie in alternative approaches. Specifically, public transit emerges as a promising avenue for enhancing safety and reducing the reliance on individual driving.

In addition to greater investment in public transit, there my advice would also be for data montgomery to collect if it can, the vector of the direction of travel rather than a simple east, north, west and south designation. This would allow us to do a whole lot more data analysis on what is happening both to the driver and potentially around the driver. In addition, collecting data on the general start and end point of the driver’s journey(possibly in terms of categories) could also shed light on some really interesting relationships.

The Sources:

Redelmeier, D. A., & Raza, S. (2017). Life-threatening motor vehicle crashes in bright sunlight. Medicine, 96(1), e5710. https://doi.org/10.1097/MD.0000000000005710

transit advocates weigh in on Maryland’s $2B transportation shortfall. (n.d.). https://ggwash.org/view/92186/with-maryland-facing-2-billion-in-transportation-cuts-advocates-say-transit-needs-to-be-prioritized

Commentary, G. (2024, January 25). Commentary: Transportation budget should better reflect Moore administration and legislature’s priorities - Maryland Matters. Maryland Matters. https://www.marylandmatters.org/2024/01/25/commentary-transportation-budget-should-better-reflect-moore-administration-and-legislatures-priorities/

Zawodny, D. (2024, March 5). Maryland’s transportation budget is in trouble. Here are 5 ways the shortfall could shake out. The Baltimore Banner. https://www.thebaltimorebanner.com/community/transportation/maryland-dot-maryland-transit-administration-state-highway-administration-CL52QSM3VFHVTN3HGIJAKIDRDQ/

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