ATLAS

Adaptive Traffic Learning and Analysis System

Team Name

Traffic Enablers

Team Members

1. Chidambaram Aditya Somasundaram
2. Mohor Banerjee
3. Ronak Pahwa
4. Sunkara Bhargavi
5. Zhu Yu Hao

Project Description:

In the bustling urban landscapes of Singapore, traffic congestion emerges as a predominant challenge, causing significant delays and affecting the daily commutes of thousands. This issue is compounded by the underutilization of vast amounts of data generated by urban traffic systems, which, if leveraged appropriately, could significantly enhance decision-making processes in traffic management. Additionally, urban planners are often constrained by the limitations inherent in the traffic environment when devising solutions to alleviate congestion.

Objective:

The "Adaptive Traffic" project aims to harness the capabilities of Large Language Models (LLMs) to derive detailed insights into traffic congestion points within the urban environment of Singapore. By analyzing data collected from loop detectors and other traffic monitoring tools, the project seeks to identify bottlenecks and propose informed suggestions to improve traffic flow on specific routes.

Methodology:

The project employs a novel approach by integrating Open Street Map data to replicate the layout of streets and traffic signals for chosen locations. This data is then converted into a network XML file compatible with the Simulation of Urban MObility (SUMO) software, an open-source traffic simulation tool. SUMO facilitates the simulation of real-time traffic conditions by mapping demand traffic directly onto the virtual network using existing traffic data. Further, the project utilizes available APIs from SUMO to extract vehicle positioning information, which is preprocessed to determine the mean and standard deviation of traffic flow. These statistical insights serve as the basis for LLMs to propose optimizations for identified congestion points.

Outcome and Impact:

The recommendations generated by LLMs are intended to assist urban planners and traffic managers in making informed decisions regarding traffic routing and the implementation of new road infrastructure. These suggestions can then be integrated back into the SUMO software for further evaluation and refinement. Through this innovative integration of technology and data analysis, the "Adaptive Traffic" project aims to provide a scalable and efficient solution to mitigate traffic congestion in Singapore, ultimately leading to improved urban mobility and enhanced quality of life for its residents.

Conclusion:

The "Adaptive Traffic" initiative by Traffic Enablers represents a forward-thinking approach to urban traffic management, leveraging advanced technologies to address a pressing societal issue. By optimizing traffic flow and reducing congestion, this project stands to make a significant contribution to the efficiency of urban transportation networks.

Files Description -

1. test.net.xml - is the network definition file of our sample road network for SUMO.
2. test.rou.xml - is the route file, which contains information about vehicle routes within the simulated environment.
3. s-test.sumocfg - is the SUMO configuration file containing the parameters and settings for simulation scenario.
4. script.py - runs the s-test.sumocfg file and stores the vehicle's co-ordinates, mean and standard deviations in the simulation_data csv file.
5. DataProcessing.ipynb - we have extracted the timestamps at which the standard deviation is less ( belonging to the bottom 10 percentile ), suggesting that the vehicles are close to each other, thereby congested - meaning more traffic at these points and saved them into the congestionpoints.csv file
6. LLM-model.ipynb - consists of the LLM model that takes in an additional prompt along with the data points in the congestionpoints.csv file and suggests structural changes that can be made to reduce the traffic congestion.