python assignment 9 flight tracker

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Realtime Flight Tracking with Pandas and Bokeh

Tutorial Outline

• Getting data and convert to pandas data frame
• Load basemap into bokeh
• Plot aircraft position on the map
• Add some intreactivity
• Build a realtime flight tracking application

Getting Flight Data and Convert to Pandas Data frame

Adding basemap to bokeh, plotting aircraft position on the map.

Adding Some Styles and Interactivity

Build real time flight tracking application.

• In the import section, we add some bokeh libraries such as Server, Application and  FunctionHandler (line 9-12). These libraries will be used for the server.
• In line 30, we initiate a bokeh column data source in a dict. It will be used later for streaming the updated flight data response.
• In streaming the updated data at line 50, we are using the length of data frame's row as the number of roll back property, to store only the updated data in the column data source. Otherwise, the data will be appended with the old data, causing the number of data will grow in each updating step.
• At line 53 the updated function is calling for every 1000 millisecond (1 second).
• The last three line is the server related codes, where we define application function handler, port number to be used for the application and start the server when the code is running.

 Python tracking Tutorial

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Build Your Own Map Flight Tracking Application with Python

Introduction

Visualize flight tracks on a map with Python framework (geo/moving/pandas, contextually…)

Flight data are stored as  pandas  /  GeoPandas  data frames

The trajectory is built with  movingpandas  and interpolation and projection are realized with movingpandas functions

The map background is built contextually

The final animated gif is built with PIL and matplotlib

Example: one flight from Marrakesh to Rome

Running the tests

Run the demo-flight-tracking notebook to see how to build the Marrakesh-Rome example

• pandas  – Python Data Analysis Library
• GeoPandas  – GeoPandas is an open-source project which extends the datatypes used by pandas to allow spatial operations on geometric types.
• movingpandas  – Implementation of Trajectory classes and functions built on top of GeoPandas
• contextually – Context geo-tiles in Python
• Thomas Dubot

This project is licensed under the MIT License – see the  LICENSE.md  file for details

Acknowledgments

Thanks to  Anita Graser  for her tutorials and videos on movingpandas

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Putting That Airplane On The Map – Live And With Python

Mankind’s fascination with airplanes is unbroken. Whether you’re outside with your camera, getting an actual glimpse of the aircraft, or sitting at home with your RTL-SDR dongle and have a look at them from a distance, tracking them is a fun pastime activity. Provided, of course, that you are living close by an airport or in an area with high enough air traffic. If not, well there’s always real-time tracking online to fall back to, and as [geomatics] will show you, you can build your own live flight tracking system with a few lines of Python.

As it’s usually the case with Python, a lot of functionality is implemented and readily available from external modules, which lets you focus on the actual application without having to worry too much about the details. Similarly, plenty of data can be requested from all sorts of publicly accessible APIs nowadays. If you are looking for a simple-enough example to get into both subjects with a real-world application, [geomatics]’ flight tracker uses cartopy to create a map using Open Street Map data, and retrieves the flight information from ADS-B Exchange ‘s public API.

We have seen ADS-B Exchange mentioned a few times before, for example with this ESP8266 based plane spotter and its successor . And if you’re more curious about the air traffic in your direct surroundings, it’s probably time for a DVB USB dongle .

9 thoughts on “ Putting That Airplane On The Map – Live And With Python ”

Now do this, but for trains. I can’t count the number of times I find my way to work blocked by a stopped or slow-moving train.

Living near tracks, I have wondered if my first foray into SDR could be tracking trains, or at least getting some sort of transponder signal off of them as they passed by. I don’t know if a system exists though.

What about a flock of cyclists?

During events like the Tour Down Under [a variation of the Tour de France] roads get blocked until all cyclists have passed. If you want to take photos or support them, it’s good to know where they are. If you just want to drive – that’s useful, too.

This summer, one of the stage of the Tour de France was in my area. I was able to hear the communications from the helicopters with an RTL-SDR. I kind of stumbled on it as I was listening to planes and checking on taxis. If I remember correctly, it was near 165MHz. There was multiple channels.

It was quite fun to get news about the race directly from the crew !

here you are: https://spoorkaart.mwnn.nl/

Nice work fiddling with all the effort required to get the data into a usable state. That is the main reason I haven’t bothered making something for my home since I am in the area of SMF and McClellan field. I currently use the Flightradar24 app and it works well.

The Flight Aware app works perfectly for this…

The problem I see is that if you want multiple windows open, each is calling for data over the network. For example I like to have one window for above 12 kft, one for below 12 kft, and one for the airport below 6 kft. Thus LAN multicasting is the perfect solution. But you need a shim to convert unicast WAN to multicast LAN. Sort of like ADSNet ( https://github.com/srsampson/ADSNet ). Only in that solution it feeds old Air Defense Command style vector maps :-)

There is another version of aircraft tracking in python with pandas and bokeh, check it out https://www.geodose.com/2019/01/realtime-flight-tracking-pandas-bokeh-python.html

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How to Build an Airplane Tracker with Raspberry Pi

Project a map of current aircraft onto your ceiling.

Chances are that there are planes flying over your house right now. Using a Raspberry Pi , a device known as an ADS-B receiver, and a standard projector, we can create our very own “radar” that shows the real time location of these aircraft and, if we have a projector, we can project it on the ceiling. Otherwise, we can track it on a regular screen. 

About ADS-B Technology 

Lots of aircraft are outfitted with a device known as an ADS-B. Standing for “Automatic Dependant Surveillance-Broadcast”, it's a technology that allows aircraft to transmit positional information about themselves to other aircraft, ground-based stations, and even satellite-based stations. For smaller planes where it isn’t feasible to install more complicated collision-avoidance technologies, an ADS-B transmitter and receiver can do a lot to increase flight safety.

Aircraft outfitted with ADS-B transmitters (which is becoming law in more and more countries), transmit a variety of positional data like altitude, GPS coordinates, and ground speed data. Fortunately for us, all the data is transmitted on a standard frequency and it’s  unencrypted. This means with a small USB dongle and a Raspberry Pi, we can listen in to the positional information of aircraft nearby

What You’ll Need For This Project 

• Raspberry Pi 4 or Raspberry Pi 3 with power adapter
• 8 GB (or larger) microSD card withRaspberry Pi OS. See our list of best microSD cards for Raspberry Pi . 
• ADS-B Receiver Kit (Antenna and USB dongle) like this one .
• Monitor or Projector with HDMI and power cables. If you want to project on the ceiling, you’ll need a projector.

How to Track Local Airplanes with Raspberry Pi 

Before you get started, make sure that you have your Raspberry Pi OS set up. If you haven’t done this before see our article on how to set up a Raspberry Pi for the first time or how to do a headless Raspberry Pi install (without the keyboard and screen).

1. Update Raspberry Pi OS by entering the commands below at the command prompt. This almost goes without saying, but is a good practice. 

2. Install the base components we’ll need to communicate with the ADS-B receiver and display aircraft positions using Python. 

3. Clone the dump1090 repository into your home directory. Dump1090 is a decoder that will let us decode ADS-B messages into readable JSON.  

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4. Build dump1090 . This may take a bit of time depending on your type of raspberry pi. 

5. Connect your ADS-B receiver to the Raspberry Pi’s USB port.  

6. Run dump1090 from within its directory. 

You should see a table appear in your console with various rows filled with data for overhead airplanes, including their altitude and flight number.

Now that we’ve got our ADS-B decoder installed, we can download the projection code. I wrote a simple program using python and the pygame library that displays the real-time location of aircraft, as well as their flight number and altitude (all from dump1090) on your display. You’re more than welcome to modify it or build your own.

7. Clone the Raspberry Pi Flight Tracker git . 

8. Set up a virtual environment with python3 for the flight tracker. 

9. Activate the virtual environment , and install python requirements . 

10. Rename the environment.sample.sh to environment.sh and open the new file for editing.  

11. Edit the file to set the values for your current latitude and longitude, along with maximum latitude and longitude. The maximums will determine how much of the area around your location to display. An easy way to get your latitude and longitude values is to use Google Maps. First, find your location and right click it to display a menu - click the latitude and longitude values to copy them to your clipboard. 

Next, zoom out from your current location. Pick a spot north of your current location and copy the value to your clipboard. Then copy the first value (latitude) into your environment.sh file as LAT_MAX (shown below as 43.680222). Do the same with a spot south of your current location, and fill in the first value in your environment.sh file as LAT_MIN. These values represent how far tracking extends north and south of your location. 

Next, pick a spot west of your current location, and copy the coordinates to the clipboard. Use the second value (viewed above as -79.49174) to fill in the LON_MAX value. Do the same with a spot east of your location, and LON_MIN. These values represent how far tracking extends east-west from your current location.

When completed, your environment.sh file should look something like this (with your coordinates).

12. Start the dump1090 server and the projection code using the same command: 

If all goes well, after a moment you’ll be greeted with a blank screen with a dot in the center indicating your current position, and aircraft around you will show up as moving dots across the screen as their signal appears.

If you’re having trouble getting a signal, try moving  your antenna to where it has a clear view of the sky, like an upstairs window.

13. If you’re using a projector, point it at the ceiling and line up the top of the screen with your magnetic north.

And there you have it. Your own personal aircraft “radar” system.

Ryder Damer is a Freelance Writer for Tom's Hardware US covering Raspberry Pi projects and tutorials.

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• Suncoast Cyclist Amazing! I live very close to a fairly large airport and was able to see several planes landing and taking off in addition to the ones just going overhead. It was even more fun to enter the flight number into a flight tracker program and see the origin and destination info. Even my wife thought it was interesting. Reply

Admin said: Project a map of current aircraft onto your ceiling. How to Build an Airplane Tracker with Raspberry Pi : Read more

Benni00x said: Hello guys, if I type in bash entrypoint.sh, a window starts to open for a second. Then I get a message like "FileNotFoundError: No such file or directory : '/home/pi/raspberry_pi_flight_tracker/src/../data'

Suncoast Cyclist said: The same thing happened to me the first time I tried it so I just manually created the missing 'data' directory and now everything works.

• NightTripper Where did you place the folder and with what permissions? Reply

NightTripper said: Where did you place the folder and with what permissions?

• OrbisGoose Trying to build, but in can't locate the packages for limesuite, liblimesuite-dev, ..... Can you point me in the correct location(s)? Reply

Suncoast Cyclist said: The error message I received had the path '/home/pi/raspberry_pi_flight_tracker/src/../data' . The data directory should be in the main project directory '/home/pi/raspberry_pi_flight_tracker' Since this is apparently for simple data files I didn't worry about permissions and just took the defaults. Once the directory is created, the programs will be able to create all the data files they need to run.

OrbisGoose said: Trying to build, but in can't locate the packages for limesuite, liblimesuite-dev, ..... Can you point me in the correct location(s)?

• equati0n Even after creating the data directory, the file aircraft.json cannot be found. Cool project, but this tutorial is not working after following the directions. Reply
• View All 12 Comments

Most Popular

How to Build a Real-Time Model to Predict Flight Delays

Have you ever tried to build a model that can handle new data in real time? We set out to do just that, and discovered that there aren’t a lot of tutorials about training real-time machine learning models, so we made this one!

Editor’s Note: This project placed 1st for the Fall ‘23 MScADS Hackathon co-hosted by the UChicago Data Science Institute and Rotational Labs .

Our goal for the hackathon was to develop a real-time flight delay prediction system to address the challenges faced by the aviation industry in mitigating the impact of flight delays, caused by combination of missed/slow connections, in-air delays, and scheduling disruptions. Based on personal experience, we know that consultants and others who travel often for work often disproportionately suffer from these delays, and we hoped to build something that would help those travelers.

We used Ensign , which is a cloud-based asynchronous data streaming tool developed by Rotational Labs to allow for real-time data pipelines through a publish/subscribe model . Our real-time flight delay predictor was an ideal problem to solve with Ensign, since it allows users to train event-driven ML models (such as through River ) for practical use cases. It has a convenient Python SDK and is made by an awesome team of veteran data scientists with a lot of experience.

First, let me introduce the members of our hackathon team, The Three Musketeers :

• Jason Chandra
• Kevin Sianto

For more information, our presentation can be found here .

Our primary objective was to create a real-time flight delay prediction system tailored for consultants, helping them identify flights with expected delays, providing confidence intervals for predictions, and offering specific departure and arrival airport pair predictions to make informed travel decisions.

Given departure and arrival times for most frequent daily flights are the same, we decided to use historical airport/aircraft data, and flight path to predict the likelihood of delay, and update it through real-time tracking from our data pipeline.

We planned on retrieving live data from multiple sources, engineering a live data pipeline that could output ~5 relevant flights per second, with 30 pieces of info per flight. This data could then be fed into our event-driven model, with the goal of providing a continuously-updating predicted arrival time with a confidence interval backed by historical data and live updates.

Data Pipeline

One of the keys to our success in the hackathon was that we designed our data architecture for our solution before we dove into the machine learning steps.

First, we utilised Ensign’s flight data from OpenSky Network’s live API to grab basic flight data. Each sample in the dataset represents a specific flight with various attributes related to that flight at one point in time (updated in 15-second intervals), such as a flight’s callsigns and ICAO24 registration, in-air info (velocity, altitude, horizontal/vertical headings, etc.), and flight type (aircraft model, passenger vs. cargo, etc.). We can pull 1000+ flights across the continental US per second with this model, although most flights are irrelevant and untracked by our pipeline.

Next, we published this info into an intermediary topic, and subscribed with a pipeline file that filtered and processed this data. If a flight is “tracked”, our pipeline will take more detailed info from FlightRadar24’s API , grabbing airport-level data and detailed historical data to supplement each flight’s profile. This info is then published into a final topic, and subscribed to with our model to fetch real-time events for further training.

Finally, our model - a basic regression model - is then updated through incremental training. We conducted feature engineering through data selection (time-based features, historical data, and airport data were prioritised), scaling, and pre-processing. Model performance is validated through data splitting (80/10/10 split) and cross-validation, then using evaluation metrics such as RMSE, MAE and R-squared.

Given more time post-hackathon, we may try more complex versions of the model (e.g. polynomial regression, decision trees, random forests, etc.) and compare through k-cross validation performance.

Our team had a great experience! We spent lots of time bouncing ideas off one another, and spent a lot of time encouraging and coding together for a common goal. As new data scientists, it allowed us to apply our rigorous academic takeaways to a practical goal, and tackle the challenges that arose from fitting theory into practice.

We learnt the importance of data engineering in practical data science work - it took up 75% of our time! Rotational Labs encouraged us to explore data engineering (which tends to be underappreciated), and we are much stronger at data engineering as a result.

Getting familiar with Ensign was a challenge for our team, and we wasted lots of time on pursuing solutions that fit with Ensign. The best solutions are only as good as the data quality/tools allow for, and these solutions tend to play to the strengths and integrate-ability of our various tools.

In the context of our flight delay prediction project, Ensign stands out (compared to Google Pub/Sub, Kafka, etc) due to its user-friendly interface, making it accessible for both beginners and experienced users. Its intuitive design and easy-to-navigate features allow fast adoption, enabling our team to efficiently utilize the tool without extensive training. Moreover, Ensign’s capability for rapid Minimum Viable Product (MVP) development is noteworthy, allowing us to swiftly prototype and iterate our real-time flight delay prediction system. Additionally, its built-in end-to-end encryption ensures data security, a critical aspect when handling sensitive flight information, enhancing our project’s integrity and compliance measures. These attributes collectively make Ensign a well-suited tool for our project, balancing usability, speed of development, and data security requirements.

Limitations

Given the short time duration of the hackathon (less than 2 weeks), we were heavily hampered by server-side unreliability issues from both APIs. OpenSky’s API was heavily rate-limited and frequently timed out our pipeline (as seen below), and FlightRadar24’s API could handle only ~1/50 of Ensign’s input, creating a bottleneck that forced us to implement arbitrary rate limiters in our pipeline - leading to decreased data quality (unpredictable/missing/incorrect rows) and poor data consistency.

The APIs could not integrate perfectly - OpenSky’s API only outputted airline registration info (24-bit ICAO/Callsign), while FlightRadar24’s API required aircraft registration info (tail number). Our creative solution was to create a tiny (~0.5 sq. mile) bounding box around each tracked aircraft, and identify the correct flight in each bounding box to retrieve flight details. This method definitely slowed our pipeline down, and did not always result in finding our flight due to data collection inaccuracies. If given more time, we would be able to come up with a more elegant solution.

All team members were new to data science (particularly ML), and had little coding experience. We struggled massively in building event-driven models, and had to learn real-time engineering/machine learning from scratch - this was a great experience! If given the chance to rebuild again with our current knowledge, we would have gone a lot further in the hackathon!

Further Expansions

Parallel to our primary focus on Ensign and real-time flight delay prediction, our project’s expansion ventured into leveraging Google Cloud Platform (GCP) and big data technologies, notably employing PySpark for advanced analytics. Through this expansion, we delved into two pivotal areas:

• Association Mining for Targeted Marketing: Identify traveler behavior patterns by uncovering correlations between airports and preferred airlines, aiding airline marketing teams in tailoring strategies for route planning and passenger engagement.
• Graph-Based Analysis for Delay Reduction: Utilize delay-integrated network representation to analyze delay propagation across airports and airlines, helping airline operations teams identify influential nodes and routes to minimize delays and enhance operational efficiency.

Our full exploration can be found Here for results and Here for the python code

Association rule mining

Association rule mining like FP-Growth used here, emphasizes discovering frequent patterns within transactions, beneficial for identifying strategic airline route planning rather than directly pinpointing airlines with the lowest delays for specific routes.

Specifically, we filter and analyze frequent itemsets and association rules based on high lift and support, centered on departures from ORD or MDW, aiding airlines in optimizing flight schedules and operations.

Our outcomes were the following:

MDW (Chicago Midway International Airport) and Southwest Airlines: The association between MDW and Southwest Airlines reveals a very high confidence level (93.15%) and a significant lift value (4.52), indicating an extremely strong connection between Southwest Airlines and flights departing from MDW.

Strong Association with ORD (Chicago O’Hare Airport): American Airlines demonstrates a moderate confidence level (16%) with ORD, indicating a significant association with the airport, along with other airlines like SkyWest Airlines and United Airlines. The high lift values (>1) suggest these airlines are more frequently associated with ORD than expected randomly.

Connection between Airports (e.g., ORD and LGA): The association rule between LGA (LaGuardia Airport) and ORD (Chicago O’Hare Airport) with American Airlines shows a high confidence level (41.3%), indicating a strong association between these airports in terms of flights operated by American Airlines. The notably high lift value (3.07) indicates a substantial influence or relationship between American Airlines’ flights from LGA to ORD.

Graph Theory

For traveler segments aiming to reduce delays and explore diverse route options, like consultants seeking efficient travel, we’re exploring an alternative strategy beyond association mining.

Our new approach involves leveraging graph theory, where airports are represented as nodes in a network, connected by edges that signify relationships based on shared passengers or historical delay occurrences. What makes this approach innovative is how we’re integrating delay information into this network structure. Each connection’s strength is determined by historical delay data, factoring in both departure and arrival times.

By using this graph-based model, our goal is to create a more comprehensive analysis. This way, we don’t just identify commonly used routes but also consider historical delay trends. It’s all about providing a refined framework that caters to travelers looking for smoother, less-delayed travel experiences and a wider array of travel choices.

There are 3 different applications that can be used here:

• Exploration of Indirect Flight Paths (JFK to SFO) by Top Airlines Consultants would benefit from this analysis if there are no direct flights between JFK and SFO as it helps identify popular indirect routes. Airlines, by leveraging this analysis, can identify market gaps, optimize their flight schedules, and potentially introduce new routes based on the demand for indirect flights between these two airports.
• BFS (Breadth-First Search) algorithm considering delays in a graph (representing airline connections) The algorithm computes levels (or distances) between nodes (airports) while factoring in delays as weights on edges (routes). Levels are determined by incrementally adding delays to the current level as the algorithm traverses the graph, helping airlines understand the impact of delays on the connectivity and distances between airports in their network.
• Airline Route Optimization: Leveraging High Connectivity Airports for Enhanced Efficiency Understanding the principles of in-degree and out-degree is essential: in-degree represents flights arriving at an airport, while out-degree signifies flights departing from an airport. For instance, let’s take a look at Burbank Airport (BUR). It displays a total degree of 35,051, attributed to its 17,549 incoming and 17,502 outgoing flights, indicating significant connectivity. This information presents a potential strategy for airlines to refine their flight schedules efficiently. By prioritizing routes linked to airports with substantial connectivity, airlines can optimize their flight networks, potentially streamlining operations and offering improved travel options.

Improve our model performance (as stated in the limitation section)

Further steps could involve real-time implementation and integration with travel platforms, enhancing the user experience for consultants and travelers.

Future extensions: expanding the system to cover a broader range of flights and regions. Or, we could integrate additional features, such as weather data, to enhance prediction accuracy and further empower air travelers.

Turning it into a web app. Rotational Labs recently posted an article here that would allow us to deploy this into an app - which we are currently exploring right now.

Photo by NASA Commons CD99-0095-1.1

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A walk-through of our approach to building a real-time flight delay tracker in Python using event-driven machine learning and Ensign for data streaming.

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google-flight-analysis 1.2.0

pip install google-flight-analysis Copy PIP instructions

Released: Jun 11, 2023

Scraping flight data from Google Flights and analyzing.

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License: MIT License

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Project description

Flight Analysis

This project provides tools and models for users to analyze, forecast, and collect data regarding flights and prices. There are currently many features in initial stages and in development. The current features (as of 5/25/2023) are:

• Detailed scraping and querying tools for Google Flights
• Ability to store data locally or to SQL tables
• Base analytical tools/methods for price forecasting/summary

The features in development are:

• Models to demonstrate ML techniques on forecasting
• Querying of advanced features
• API for access to previously collected data

Table of Contents

Updates & new features.

• Real Usage 😄

Flight price calculation can either use newly scraped data (scrapes upon running it) or cached data that reports a price-change confidence determined by a trained model. Currently, many features of this application are in development.

The web scraping tool is currently functional only for scraping round trip flights for a given origin, destination, and date range. It can be easily used in a script or a jupyter notebook.

Note that the following packages are absolutely required as dependencies:

• selenium (make sure to update your ChromeDriver !)

You can easily install this by running either installing the Python package google-flight-analysis :

or forking/cloning this repository. Upon doing so, make sure to install the dependencies and update ChromeDriver to match your Google Chrome version.

The main scraping function that makes up the backbone of most other functionalities is Scrape() . It serves also as a data object, preserving the flight information as well as meta-data from your query. For Python package users, import as follows:

For GitHub repository cloners, import as follows from the root of the repository:

Here is some quick starter code to accomplish the basic tasks. Find more in the documentation .

A Scrape object represents a Google Flights query to be run. It maintains flights as a sequence of one or more one-way flights which have a origin, destination, and flight date. The above object for a round-trip flight from JFK to IST is a sequence of JFK --> IST, then IST --> JFK. We can obtain the data as follows:

You can also scrape for one-way trips:

You can also scrape chain-trips, which are defined as a sequence of one-way flights that have no direct relation to each other, other than being in chronological order.

You can also scrape perfect-chains, which are defined as a sequence of one-way flights such that the destination of the previous flight is the origin of the next and the origin of the chain is the final destination of the chain (a cycle).

You can read more about the different type of trips in the documentation. Scrape objects can be added to one another to create larger queries. This is under the conditions:

• The objects being added are the same type of trip (one-way, round-trip, etc)
• The objects being added are either both unqueried or both queried

Performing a complete revamp of this package, including new addition to PyPI. Documentation is being updated frequently, contact for any questions.

Here are some great flights I was able to find and actually booked when planning my travel/vacations:

• NYC ➡️ AMS (May 9), AMS ➡️ IST (May 12), IST ➡️ NYC (May 23) | Trip Total: $611 as of March 7, 2022

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Jun 11, 2023

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Given a origin and destination airport, tracks the cheapest flight for a given date range. Progress is being tracked using Kanban board in Notion.

asexton7/Python-Flight-Price-Tracker

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######################### PYTHON FLIGHT WEB SCRAPER #########################

This project will track prices from an origin to a destination airport, plot the historical prices, output to a chart, and notify the user when a new all time low price has been found.

• Python 100.0%