## Background

New York City is notorious for being a “car-congested” place to live in. The trains can feel just as congested, too. People often opt for alternative ways of commuting, when possible. Recently, the number of options for ‘alternative’ means of commuting have increased! Bikes, mopeds. e-bikes. e-scooters are all fair game.

This is great! But, with more bikes, scooters, mopeds and the like out in the streets, we wanted to know more about how, if at all, the number of incidents involving these alternative means commuting has changed.

We were also particularly interested to see how the shelter-in-place due to COVID-19 impacted the rate of incidents. How do the number of incidents post-COVID19 compare to ‘COVID19’ times?

## Datasets

With this in mind, we took to the New York City Motor-Vehicle-Collisions-Crashes data to look into this further.

Here’s a sneak peak of what our main data looks like:

crash_dat = read.csv("./data/crash_dat.csv")

crash_dat %>% select(date, crash_time, borough,number_of_persons_injured, contributing_factor_vehicle_1) %>% head(6) %>% knitr::kable()
date crash_time borough number_of_persons_injured contributing_factor_vehicle_1
2017-01-01 19:52:00 BROOKLYN 1 Failure to Yield Right-of-Way
2017-01-01 18:00:00 QUEENS 1 Driver Inattention/Distraction
2017-01-01 00:00:00 NA 0 Driver Inattention/Distraction
2017-01-01 00:00:00 MANHATTAN 2 Driver Inattention/Distraction
2017-01-01 16:30:00 BROOKLYN 0 Unspecified
2017-01-01 18:45:00 BROOKLYN 1 Passing or Lane Usage Improper

We also used data on bike count, which gave us the daily total of bikes that were involved in crashes. Source:

We also incorporate weather data from NOAA

## Focus of our Project

Our project focuses on the use of particular types of alternate means of transformations, namely: bicycles and microvehicles.

We all know what bikes are, but what exactly are Microvehicles:

The ITF report, defined Microvehicles as: “: vehicles with a mass of no more than 350 kg (771 lb) and a design speed no higher than 45 km/h. Here’s ITF’s report if you’re interested.

Here are some gifs (pronounced ‘jif’, of course) to show you what we mean:

## Main Questions

We wanted to ask some of the big questions on incidents involving Bikes and Microvehicles:

• Where in NYC do we see the most incidents involving Bikes and Microvehicles?

• What does the trend of incidents involving Bikes and Microvehicles look like?

• What are some factors that contribute to these incidents?

• In particular we also wanted to see how the these incidents may have changed in the context of COVID-19 and the shelter in place?

• Do we see any statistically significant differences in incident rates comparing pre-COVID to COVID times?

• Lastly, we wanted to see if we could build a predictive model using weather data, considering factors such as maximum/minimum temperature and daily precipitation to try an predict the number of incidents involving these bikes and microvehicles?

## Take aways

• Bike and microvehicles incidents tend to display seasonality, just like the weather.
• The top contributing factor to these incidents is: Driver Inattention.
• The Poisson regression analysis we carried out showed that were no significant differences in the rate of incidents involving bikes or microvehicles.
• Downtown Manhattan saw the most crashes within the last 2 years.

## Fun facts

• Folks that use microvehicles are referred to as ‘micromotorists’