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ABOUT.Rmd
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---
output:
github_document:
pandoc_args: --webtex
html_preview: false
params:
actor_id: "roten"
data_date: "2020-03-26"
sha: "sha"
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/ABOUT-",
out.width = "100%"
)
```
## Metro Freeway Travel Trends
Metropolitan Council researchers have developed an interactive tool that allows users to explore traffic count trends on Twin Cities metro area freeway system. Trends are compared to a historic, pre-pandemic baseline. Users can view daily and hourly trends at more than 2,000 individual traffic monitoring stations or view summarized trends for freeway corridors and the entire metropolitan area.
*A line graph of traffic volumes relative to typical traffic (the horizontal line at zero). The blue line shows traffic trends for the metro area freeway system. Metro area freeway traffic was down as much as 70% during the stay-at-home order (gray rectangle) but rebounded steadily over time.*
<img src="predicted_actual_plot.png" alt="A line graph of traffic volumes relative to typical traffic. A blue line shows traffic trends for the metro area freeway system, relative to a horizontal line at zero that represents “typical traffic.” Metro area freeway traffic was down as much as 70% during the stay-at-home order but rebounded steadily over time." style="width: 85%; margin-left: 12.5%">
Met Council researchers used a modeling approach that relies on historical traffic data from January 2018 - March 2020 to estimate typical travel. Freeway traffic was almost 70% below typical levels early in the pandemic, but gradually rose over the course of the year. By the middle of 2021, total freeway traffic counts were nearing typical levels -- and in some places on the freeway system, traffic was even greater than expected.
### What this tool can do
This tool can be used to view broad traffic trends across the Twin Cities freeway network. Trends are summarized at varying time scales (daily, day part, hourly) and three different levels of geography (station, corridor, and system).
Station-level trends: This is the most detailed level of spatial grouping in our tool. A “station” is usually a group of 2-4 sensors that count traffic across each lane of the freeway. Station-level trends are available at daily and day part (morning peak, mid-day, evening peak) time scales.
Corridor-level trends: Corridors are groups of stations along a freeway (e.g., I-494). Corridor-level trends are available at the hourly level, for each hour of the day.
System-level trends: The freeway system represents all available stations in the MnDOT freeway sensor network. System-level trends are available at daily level and by day part (morning peak, mid-day, and evening peak).
### What this tool cannot do
These data become less trustworthy, and more sensitive to sensor malfunction and other sources of error, at finer time scales and smaller geographies. Users should use caution when drawing conclusions from individual stations represented on the map and reach out to our data team with questions.
### Data sources
Traffic count data are provided by the Minnesota Department of Transportation (MnDOT). MnDOT maintains a network of sensors at approximately half-mile increments on every lane, exit ramp and entry ramp across the metro area freeway system. These sensors are a mix of simple magnetic field “loop” detectors and more sophisticated Weigh-in-Motion (WIM) sensors.
MnDOT serves data at thirty-second increments, which is more detail than is needed for trend analysis. Council researchers process the raw data feeds, aggregate data to fifteen-minute increments, and perform several quality checks using their open-source R package, [tc.sensors](https://github.com/Metropolitan-Council/tc.sensors). These cleaned, fifteen-minute data are stored in a council-maintained database.
To make the data useful to those most interested in regional and long-term trends, council researchers aggregate the data from multiple sensors (lanes) to the station-level and roll up to the hourly and daily level. Read more about the traffic monitoring system maintained by MnDOT [here](https://www.dot.state.mn.us/roadway/data/index.html).
### Data analysis
To estimate typical traffic in a way that is robust to weekly and seasonal fluctuations in travel, we used statistical modeling that relies on pre-pandemic traffic count data from January 2018 to March 1, 2020. The model relies on generalized additive models, or GAMs. GAMs are commonly used to analyze data with a strong seasonal or cyclical trend – especially when that trend does not follow a straight line. Some GAMs that people might already be familiar with are those that climate scientists use to estimate temperature trends within and across years.
The Council’s traffic trend GAMs consider three trends. One trend happens over the span of a year: in most places, travel increases in the summer months and decreases in the winter months. A second trend occurs over the span of a week: travel tends to be highest on Fridays, and lowest on Sundays. Finally, for hourly models, we examined the variations in traffic that happen every twenty-four hours.
*Line graphs show generalized trends at the annual, weekly and hourly level based on January 2018-March 2020 traffic data for a subset of the most reliable traffic stations.*
<img src="annual-weekly-hourly-trend-illustration.png" alt="Line graphs show generalized trends at the annual, weekly and hourly level based on January 2018-March 2020 traffic data for a subset of the most reliable RTMC traffic nodes. The leftmost panel shows a busy line graph with lowest traffic volumes in late January, with highest traffic volumes in late May. The middle panel shows hourly trends across an entire seven day week. Traffic is lower on weekends than on weekdays. On weekdays (Monday-Friday), two distinct peaks in traffic are visible. These hourly trends are even clearer in the right panel, where traffic peaks around 7-8AM, and again from 4-6PM, with lowest traffic counts from 2-4AM." style="width: 85%; margin-left: 12.5%">
To allow the shapes of these yearly and weekly trends to vary specific to location, we created separate models for each traffic monitoring station – each station might be made up of multiple lanes of traffic. We eliminated stations with poor data coverage across the historic baseline period from our analyses. We used our GAMs to generate predictions of expected or “typical” traffic for every station, day of the year, and hour of the day. We then estimate the difference from expected traffic as a percentage:
</br>
<center>
$$
\frac{Observed_{station} - Expected_{station}}{Expected_{station}} * 100
$$
</center>
</br>
Negative values show where traffic counts are lower than expected; zero values indicate that traffic is at typical levels; and positive values greater than zero indicate that traffic counts are greater than baseline. To generate system-level trend estimates, or those across an entire corridor, we calculate the total observed and expected traffic for all working sensors on that day.
Expressing differences from normal as a percentage, rather than in raw numbers of vehicles, allows for more robust comparisons of traffic over time when sensors malfunction or go offline due to construction.
#### Limitations of the tool
Because our estimates of typical traffic rely on more than two years of data with good coverage, stations that went offline for an extended period during the historic baseline time frame (e.g., due to construction) will not be shown on the map. Similarly, recent road closures and projects may not always be detected and removed by our data quality check process. Data at the individual station level is less reliable than data at the whole-system or corridor level. Data errors can be reported to [Ashley Asmus](mailto:[email protected]) or by submitting an issue to our tool’s [GitHub page](https://github.com/Metropolitan-Council/loop-sensor-trends).
Additionally, our GAMs do not account for holidays, special events or weather – except where they might have a strong effect on seasonal traffic trends (e.g., lower travel in the winter due to poorer driving conditions). Daily trend results should be considered with this in mind. Future iterations of the GAM models may account for these extra-seasonal effects.
### Effects of COVID-19 on Freeway Travel
Our analysis of traffic data during the COVID-19 Pandemic showed that Minnesotans reduced their travel rapidly in the days following the first COVID-19 case in Minnesota, with a slow and steady rebound to near-typical levels by the following year. This graph shows the daily relative decrease in travel over time across the Twin Cities’ metro area freeways (blue dots and lines) and on traffic sensors placed primarily outside the metro area (black dots and lines) for the period between March 1, 2020 and March 1, 2021. Points that fall below the zero-line represent decreases in travel relative to typical travel on that day of the year and day of the week.
*A line graph of traffic volumes relative to typical traffic (the horizontal line at zero). The blue line shows traffic trends for the metro area freeway system. The black line shows traffic trends on the MnDOT Automated Traffic Recorder System. Metro area traffic was consistently more reduced than statewide averages throughout the COVID-19 Pandemic.*
<img src="covid_plot.png" alt="A line graph of traffic volumes relative to typical traffic (the horizontal line at zero). The blue line shows traffic trends for the metro area freeway system. The black line shows traffic trends on the MnDOT Automated Traffic Recorder System. Metro area traffic was consistently more reduced than statewide traffic averages throughout the COVID-19 Pandemic." style="width: 100%;">
In the two weeks before the stay-at-home order was put in place on March 28, traffic volumes plummeted. Travel across the region’s freeways declined by up to 70% in mid-March 2020. By the time the stay-at-home order was lifted on May 18, 2020, freeway traffic was only 32% below typical levels.
Data also show how weather plays a part in residents’ decision whether to travel, especially on weekends. This is most clear on April 27, 2020 when an Easter Sunday snowstorm dropped traffic 71%, lower even than when the Governor issued the stay-at-home order. Heavy rain has had a similar effect as seen on May 17 when a spring storm dumped over two inches of rain in the metro area.
### Contributors
- Liz Roten, app development (Metropolitan Council)
- Ashley Asmus, data and model development (Metropolitan Council)
- Brian Kary, data development and ongoing consultation (MnDOT)
- Ian Vaagenes, data and model development (MnDOT)
- Jonathan Ehrlich, project management (Metropolitan Council)
### Contact
Email [Ashley Asmus](mailto:[email protected])
For app bug reports and new feature requests, feel free to open an Issue on our [GitHub page](https://github.com/Metropolitan-Council/loop-sensor-trends).
<right style="font-size: 1rem; text-align: right; display: block;">
*Last updated `r Sys.Date()`*
Build ID: `r Sys.Date()`.`r params$actor_id`.`r params$sha`
</right>