Bored Youth from Chennai Coming from a populated place in south India, traffic has been a pressing issue since my childhood days. It includes missing important lectures till delayed emergency treatment! I even tried to solve the problem through a few citizen-centric initiatives, but it all went in vain.
Fast-forwarding 5 years, I got the same amount of involvement when I heard Google is working on the Ghost Traffic . of course, Tom (Tom Cruise) mentioned it : ] so no backsies this time. In this project, we tried to address the potential cause of ghost trafficking by leveraging AI and mathematical techniques as a proof of concept.
Our sole inspiration is to develop a prototype that is ready to be pilot-integrable with G-Maps.
Towards a Traffic-Free Toronto
Our traffic simulation development journey presented several opportunities for learning and refinement: Data Acquisition Hurdle: The ideal scenario would have involved leveraging a pre-existing real-world traffic simulation dataset. The absence of such data necessitated the creation of a custom traffic environment from scratch. While this approach allowed us to progress with development, it inevitably limited the simulation's ability to fully capture the complexities and nuances of real-world traffic patterns. Incorporating real-world data points or partnering with traffic data providers would significantly enhance the simulation's accuracy and applicability.
Visualization Shortcomings: The current iteration of the simulation effectively depicts situations where vehicles abruptly change lanes. However, a key challenge emerged in rendering smoother lane-changing maneuvers. Ideally, the simulation should showcase a spectrum of lane-changing behaviors, ranging from cautious and gradual adjustments to more urgent maneuvers. Further development efforts will focus on refining visualization techniques to achieve a more nuanced and realistic portrayal of lane-changing behavior.
Braking Model Limitations: The current braking model, implemented using cubic Bezier functions, represents a valuable starting point. However, its reliance on these functions introduces limitations in replicating the full range of realistic braking behavior observed in the real world. Future iterations will explore more sophisticated braking models, potentially incorporating physics-based simulations, to enhance the simulation's accuracy and adaptability.
Pygame Learning Curve: As this project marked our first attempt at using Pygame for simulation development, we encountered a learning curve associated with the framework's coordinate system. The intricacies of this system may have introduced unforeseen effects on how vehicles move within the simulation environment. Investing additional time in mastering Pygame's coordinate system and exploring alternative visualization libraries will allow for a more intuitive development process and potentially lead to a more visually appealing and functionally accurate simulation.
Traffic simulation offers a powerful tool to study and improve traffic systems without the need for real-world testing. This translates to two key benefits:
Faster and Cheaper Testing: Simulations allow us to experiment with new traffic management ideas in a virtual environment, saving time and money compared to real-world trials. Data Generation for Machine Learning: Simulations can create vast amounts of customized data, essential for training machine learning models that can optimize traffic flow in the real world.