Skip to content

Latest commit

 

History

History
29 lines (20 loc) · 3.83 KB

Project_Proposal.md

File metadata and controls

29 lines (20 loc) · 3.83 KB

LATrafficCollisions

Traffic Collisions within the City of Los Angeles Pre and During the COVID-19 Pandemic

Research Questions

How has traffic collisions change as a result of the COVID-19 pandemic?

  • Has the rate of traffic collisions increased since COVID-19 began?
  • Are these collisions occuring in the same areas or spreading to new areas?
  • Has the severity of the collisions changed?
  • How has the type of collisions changed?

Background

In March of 2020, the novel coronavirus spread throughout the country, resulting in widespread stay-at-home orders. As the stay-at-home order continued, people began searching for ways to move about in a socially distant manner, including active transportation modes, such as walking and biking. The creation of slow streets (residential streets that have been marked with temporary barricades and signage to reduce traffic flow) encouraged active transportation by designating safer areas that were car-free. With this mode shift occuring and combined with the stay-at-home orders encouraging people to work from home if possible, there was a decrease in daily commuters. However, with fewer cars on the road, data has shown that there has been an increase in reckless driving, such as speeding. Together, these transportation shifts create the possibility for greater traffic collisions between vehicles and pedestrians, and cyclists, leading to our research questions noted above.

Previous work has shown that traffic incidents occur in a small percentage of the overall road network, typically occuring in lower-income communities. However, since the start of the COVID-19 stay-at-home order, we are curious to see if this relationship between traffic accidents and locations has changed when compared to pre-COVID levels. This analysis is critical, especially with some slow streets becoming permanent, to ensure the safety and efficiency for pedestrians and cyclists. We hope to visualize the difference between collision severity, whether the collision involved a pedestrian or cyclist, and average number of collisions per day against demographics in the city.

For this analysis we are focusing on the City of Los Angeles, separating by neighborhoods (as defined by L.A. Times). Using these spatial scopes will allow us to factor in demographics to determine if there are any relationships. The maps will use demographics as a base layer, using a color gradient to demonstrate demographical differences, and pinpoint locations of collisions using a three-dimensional component.

Data Sources

As for data sources,

  1. City of L.A. Traffic Collisions 2010 to Present

    This dataset allows us to track traffic collisions in the City of Los Angeles. We were able to use dates from 1/1/2019-3/18/2020 as Pre-COVID-19 data, and 3/19/2020 onwards as COVID-19 data. (The Stay-at-Home order was executed March 19th, 2020.)

  2. City of L.A. Neighborhoods

    This dataset allows us to crosscheck L.A. neighborhoods with the collision data.

Conclusion

With this data, we expect to confirm the relationship between traffic collisions and lower-income and disadvantaged communties. We also predict a slight increase in collisions due to cars traveling at higher speeds and the increase of pedestrians/cyclists. We predict collisions to spread through new areas. We also predict the severity of the collisions to increase, and the type of collisions to shift. Through this project, we hope to gain an understanding of whether designated slow streets can improve safety by reducing collisions or if the number of incidents near designated slow streets will increase (approx. 1/2 mile to 1 mile radius). Using our findings, we hope to provide policy recommendations to create a safer active transportation network.