GwinNETTwork: Week 9

What an amazing experience this program has been! We’re so grateful for getting to meet new people and learn new things as part of this program.

We are Angela Lau (Cornell University ’22), Jason Chen (Purdue University ’20), and David Li (Stony Brook University ’19). Our team, nicknamed GwinNETTwork, worked on the Connected Vehicle Technology Master Plan based in Gwinnett County, the second largest county in the state of Georgia. Traffic in the Atlanta metro area can get extremely congested. Therefore, the long term goal of the project was to connect vehicles and traffic signals to reduce this congestion. The portion of the project that we worked on focused on emergency vehicles under Gwinnett County Fire and Emergency Services, and we mainly focused on analyzing data from on-board sensors (vehicle location, time, speed, bearing values, etc.) positioned in the emergency vehicles themselves to see where they might experience delays when responding to emergency situations.

Upon receiving the collected data and doing initial visualizations of the data points, we wrote an initial series of Python scripts to filter out points that were deviating from fire truck routes, points that were not within range of an intersection, points receding from an intersection, and taking into account when an emergency vehicle may turn on an intersection. Finally, we took some first steps on working with the data from the traffic signal sensors. Another series of scripts were generated to obtain signal status at the time of intersection approaches by emergency vehicles, as well as to obtain the slowest approaches on each signal color (red, yellow, green) by average speed. In addition to consulting our advisor, Dr. Angshuman Guin from Civil and Environmental Engineering, we also consulted Gwinnett County firefighters midway through the summer to get their perspective as to how certain emergency scenarios may be approached.

To wrap up our program, we prepared a presentation and poster detailing what we achieved in these past ten weeks, as well as where the project can go in the future, and gave our final presentations this Wednesday night. The results are available for display at this website (, where we use two Leaflet.js bubble maps to display average speed and delays at intersections throughout the county.  In the coming weeks, we hope to continue working on associating the signal data with the GPS data before handing off the project to Gwinnett County.  This project definitely has the potential to be applicable to solving traffic problems in the entire metropolitan Atlanta area and we’re excited to see where it can potentially go.

As we finish up this final blog post of the 2019 program, we would like to thank our advisor, Dr. Angshuman Guin, and the graduate student working on the Georgia Smart Communities Corp, Zixiu Fu, for their endless help and support this summer. We would also like to thank Gwinnett County for providing this project and allowing us to visit one of the fire stations to get insight into intersection approaches that was used to help us when filtering out data points. Lastly, we would like to give a big thanks to the directors of the Civic Data Science program, Ellen and Chris, for their support and giving us the opportunity to participate in this experience. From every day working alongside each other to every project meeting to every intern outing, it’s safe to say it’s been a great ride these past ten weeks at the very least. Thank you all for such an amazing summer, and best of luck with the upcoming school year!

Fun times with our entire group!

GwinNETTwork: Week 8

Time flies—our last week is coming in soon (next week!), but we’re not ready to leave yet 🙁

This week, we wrapped up working on the histogram and began associating signal data with the fire truck data.

To wrap up the program, we designed this year’s final t-shirts and stickers.

Our t-shirt logo/program sticker design
The designs for all of the projects to be put on the back of the shirt

Meanwhile, we have been filtering through the most updated data in the server to update our website maps (speed and delay) and to generate our new histogram. In the process, we’ve run through many bugs and problems that has made the process lengthier than expected. The good thing is that we finally finished these this week!

This weekend, we’ll be taking a group trip to Sweetwater Creek State Park for a picnic and hike!

GwinNETTwork: Week 7

While the other teams were taking overnight visits to their sites earlier this week, we continued working back at Georgia Tech.

In the first half of the week, we met a couple of times with Zixiu Fu, the master’s student working on the project, to get more information on the signal data. Currently we are processing the data for him to understand how the traffic light data works, and to see if we can tell when the fire truck passed through the traffic light intersection based off of the traffic light’s behavior. Memory errors and slow runtimes, due to the size of the data, required us to create more scripts to break up these files into smaller chunks to access the data stored inside.

We also received emergency logs from Dr. Guin, showing us where the fire trucks are located. We geocoded the fire truck locations based off of the given addresses to figure out where the logged emergencies occurred. With this, we hope to connect the routes that we already analyzed to emergency responses—helping us determine when a fire truck is responding to an emergency.

On Thursday, we took a day trip to one of the Gwinnett County fire stations with Dr. Guin. At the station, we hoped to better understand the firefighter’s perspectives and procedures when responding to emergencies. We asked about how they how they handle approaching an intersection, how fast they generally travel when on the road, and how timing can affect their response to an emergency call. Overall, the emergency personnel all favored this project because it would greatly benefit them in the long run.

Next week, we will collaborate with Zixiu to integrate our GPS data with the signal data. Specifically, we hope to start optimizing a query that will analyze a traffic intersection and obtain the signal status so that we can see how it matches up with the existing data that we have. We want to visualize the number of different fire trucks meeting at certain intersections, since it’s common for different stations to cross paths during emergency responses.

GwinNETTwork: Week 6

It was a shorter week than usual with Independence Day yesterday, but we were still able to work efficiently. We revised our website to display two bubble maps, one displaying the speed around intersections, and a new bubble map displaying the delay at each of the intersections. Additionally, we gave the maps a new visual look, including easier to read color-coding for each of the data points. We were able to get our dataset limited near the intersections. This week we also got new data onto the maps.

A screenshot of the newest speed map (

Currently, we are working to improve our scripts to continue to identify and group the types of movements (left turn, right turn, through) at each intersection This way, we can make the map even more precise and specific.

We also obtained log files for fire truck routes from four fire stations, and are currently working to export them into CSV files so that we can make use of them in our project.

Finally, we started working on the signal data with Zixiu Fu, the Georgia Smart Communities Corp grad student, who is also working on this project! Hopefully, we can start getting the algorithm for connecting the signals to the vehicles soon.

Hope everyone had a great Fourth of July!

GwinNETTwork: Week 5

We just passed week 5! Wow, time flies. We have been finishing up the last of the data visualization this week.

This Tuesday, the Civic Data Science teams presented our Mid-Term Presentations to a few of our advisors and other GT faculty members and their graduate students. The presentation was centered around our progress thus far for the last 5 weeks.

For the rest of the week, we have been finishing up the Python script for associating data points with the coordinates of the traffic lights, and refining the bubble map up to its final form.

Jason has been in charge of the script; he has constantly been improving the logic in the code and refining it so that we can eventually get the points associated to one of the 730+ intersections. We are mainly interested in the data points only within 1000 foot square of an intersection. Therefore, Jason has been writing a code that can combine data points that are approaching an intersection. This has been proving tricky because writing logic that can associate a coordinate point with another coordinate requires some finessing with the speed and bearing sections.

In the meantime, we have greatly improved upon the bubble map since last week. We managed to get the hover option running (displaying coordinate and speed when a cursor hovers over the bubble), as well as get started on adding filtering and layering functionality.  The level of complexity that we hope to reach with the map still seems distant. We need the intersection logic from the Python script running first so that we can start aggregating data on the bubble map and display the visualization that we want. We’ve finalized that we want the map to display average speed and average delay at the intersection boundaries.

This week, we started working with a graduate student on our team, Zixiu Fu, on the final objective of this project: connecting emergency vehicles to traffic lights. We also met with another graduate student, Somdut Roy, working with Dr. Guin for help with developing our Leaflet map due to his experience. In the coming weeks, we hope to improve our visual map and improve on our scripts such as to filter our data points to being near intersections. We also hope to visit the site in Gwinnett County and gather more information from firefighters as to what procedures are generally followed when approaching an intersection.

GwinNETTwork: Week 4

This Tuesday, one representative of each of our Civic Data Science teams traveled with our advisor, Professor Chris Le Dantec, to Macon to attend the press release of the new Georgia Smart Community Challenge winners of 2019.

Angela was able to go and represent our Gwinnett team. At the event, we learned that Milton, Woodstock, Macon, and Columbus would be the four new cities awarded with the funds and resources provided by Georgia Tech! We also found out that our current project advisor, Dr. Angshuman Guin, will also continue to be a head researcher in the coming year with the Milton team!

Kutub Gandhi (from the Chatham team), Olivia Fiol (from the Albany team), and Angela Lau (from the Gwinnett team) with the Georgia Smart Community Corps and GT President Peterson in Macon, Georgia!

Back at Georgia Tech, David and Jason continued to make progress in the project. Before Angela left to represent our group in the Smart Community Challenges event, all of us came together to discuss what the next version of the website would look like. We were thinking about grouping sets of points near each other into one big bubble that was clickable. When the user clicks on the bubble, the javascript code we have designed using leaflet would zoom into that location and display information about that bubble. Here is a rough draft of the website design we came up with:

We hope to design and implement more advanced features as time goes on. David and Angela continued to make progress on developing the next version of the website. Jason, on the other hand, was coding and debugging a script that would help analyze the firetruck data and outputting meaningful data onto the same file. Specifically, he looked at how the fire truck locations behaved within a 1000 feet box of each traffic light in Gwinnett County. What Jason was looking for was what points were near intersection location, whether the fire trucks at those locations were approaching or receding from the intersection, and what type of movement did the firetruck performed once they reached that intersection. Did they turn left, turn right, or continue straight. We hope to determine if these actions have an effect on the delay the firetruck.

GwinNETTwork: Week 3

It’s been another fun week for us. The first couple of days we got some insight into the fields of data science and machine learning research at the Machine Learning in Science and Engineering conference, where we got to go to many talks from professors and students at universities around the country. In addition, we were also able to mingle and network with some graduate students, professors, and even industry professionals during poster sessions in the afternoon.

Even while attending the MLSE conference, we were still able to have a productive week. We met with Dr. Guin again, where we went over several things including:

  • With the webpage with the Leaflet.js heatmap getting closer to completion, we went over how to use the Windows Remote Desktops that could be used to SSH/SCP into the Linux box that would be used to host the heatmap
  • Improved the filtering by getting rid of firetruck location based off of the speed. The threshold of getting rid of the firetruck location was if the firetruck was moving at a speed less than 7 minutes per hour for more than a period of 4 minutes. We understand that the threshold might change as new methods of filtering get introduced over time.

This is a sample of the heat map we have completed:

As a result of the filtering, this is the image we were able to come up with in QGIS 3:

From the above mapping, the blue and red dots represent firetruck locations before the filtered was applied, but only the blue dots remained after the filtering script was applied.

We are nearing completion of producing initial visualizations of one fire station’s set of data. Next week, we will start rolling this process out to the other 15+ sensors among 6 fire stations along the Peachtree Industrial Boulevard corridor and upload this heat map onto the website! Afterwards, we will hopefully start collaborating with other researchers on this project and connect the fire truck data with the traffic light/sensor data in order to eventually optimize routes for emergency vehicles in the future.

A challenge that we continue to face is QGIS being slow, unreliable, or crashing at random times, causing unnecessary frustration. As far as the data points themselves go, we are still working on edge cases for filtering out the irrelevant points. Additionally, we are also wondering if and how it might be possible to improve upon the current Leaflet.js heat map that we have.

GwinNETTwork: Week 2

This week has been extremely busy and productive for us. We were in a bit of a rut during the first week because of our lack of experience with QGIS and Leaflet, as well as confusion on the project direction. We are just starting to get the hang of things this week—settling in and learning the ropes of these new applications (and learning new languages for some of us!). Our first meeting of the week with our mentor, Dr. Angshuman Guin further clarified some of our initial stumps.

The main short-term goal right now for our CDS team is to generate a visualization (mainly a heatmap) with the first batch of emergency vehicle sensor data for the research scientists and first responders to analyze. We have had more of a drive and direction this week, which has proven quite productive and fruitful for us. A sample of our work this week is shown below.

So far we have:

  • Combined multiple CSV files of the vehicles sensors using Windows commands
  • Used the combined files to generate heat maps and graduated maps in QGIS (an example of a graduated map shown below)

  • Started writing a Python script to filter out unnecessary data points in the CSV files
  • Created an initial draft of our eventual webpage to display the heatmap—using HTML and Leaflet (in the screenshot below)

The first couple of weeks of work will hopefully produce initial visualizations of one fire station’s set of data. Next week, we will start rolling this process out to the other 15+ fire stations along the Peachtree Industrial Boulevard corridor and finish this portion of the project out soon. Afterwards, we will hopefully start collaborating with other researchers on this project and connect the fire truck data with the traffic light/sensor data in order to eventually optimize routes for emergency vehicles in the future.


Hi! We are Angela Lau, Jason Chen, and David Li. This summer, our team is working with Dr. Angshuman Guin from Georgia Tech’s School of Civil and Environmental Engineering on the Connected Vehicle Technology Master Plan based in Gwinnett County (a project of the Georgia Smart Communities Challenge).

The project vision is to:

  • Improve traffic congestion and reduce crashes
  • Set the standard for the application of connected vehicle technology
  • Have broad applicability across the Atlanta region and country
  • Support goals of the recent Comprehensive Transportation Plan, Connect Gwinnett Transit Plan, and Intelligent Transportation Systems Master Plan update

Our team will be focusing mainly on evaluating the potential for improvements in safety and operations of emergency response vehicles in and around Peachtree Industrial Boulevard corridor with Connected Vehicle technology deployment.

Over the course of this summer, we hope to analyze the reported data from the current fire truck deployments. We will employ data visualization, then filter the data, implement data fusion, and finally create path optimization in last 6 weeks. We hope to eventually display data publicly on a website in order to inform researchers and the public about where emergency vehicles experience the most delays/congestion.

Available signalized intersections for deployment on Peachtree Industrial Boulevard