This week in Hal9, we are headed to the Fremont Bridge in Portland Oregon to visualizing bike traffic. This visualization showcases the ease in which you can utilize Hal9 to create data visualizations and show trends that span from a 24 hour period to a full decade. You can view the pipeline here.
Fremont Bridge Cross Comparison
High profile streets or avenues with heavy vehicle traffic often have designated pathways for pedestrian and bicycle use. One of the avenues that use this style of pathway is the Fremont Bridge in Portland. This bridge also tracks and registers all bicycle traffic in its database. We used Hal9 to analyze this database and track bike traffic from the west and east side by hour, along with yearly bike usage from 2012.
Lets starts by analazying the amount of traffic throughout the years.
In the visualization, the data has been divided into east and west bound traffic. The trend that emerges from the last ten years appears to be that the amount of traffic from the east bound sidewalk has maintained or increased whereas the west bound has drastically reduced in comparison.
Although we are not able to decipher why the traffic trends have adjusted in this way, being able to quickly visualize the trends can help individuals look at what deeper analysis may need to take place.
Another thing that we can see is the overall ammount of traffic throughout the years. In 2012 there is not a lot of data because the database starts counting late in the year. In 2018 and 2019 the ammount of overall traffic was increasing in significant numbers compared to previous years. This trend of increasing traffic stops in 2020, most likely due to Covid-19 crisis. In 2021, the data hasn't been completed and therefore the numbers are low.
The database also allows us to see the traffic by hour. From this data we find that in the morning, more people use the west bicycle lane, where as in the afternoon, more people utilize the east bicycle lane.
This kind of behavior was also seen in the comparison by year, in which the east lane also had a lot of traffic compared to the west one. This information tells us something interesting because now we know when people take the east lane. An assumption that can be made is that individuals are returning from work on the east lane, and more people are taking the east lane in the afternoon than the west lane in the morning. Perhaps the west lane is not a fast route when people go to work, or maybe the east lane is part of a faster route to exit the area, and that would explain why the east lane generates a lot more traffic compared to the west lane.
Hal9’s Interface provides you with various types of charts, transformations and ready-to-use AI models to analyze data with ease.
If you are interested in using AI models in your data analysis, please give hal9.ai a try and let us know what you think. If you’re ready for a bigger challenge, you can create entirely new transformations, visualizations or predictive models, and contribute them to our open source GitHub repository.
We also have a Twitter Hal9 account, worth following to learn more about Artificial Intelligence, visualizations, and data analysis.