Traffic congestion is rising in cities around the world driven by factors such as growing urban populations, aging infrastructure, poorly synchronized traffic signals and a lack of real-time data. Given the physical and financial limitations around building additional roads, cities must use new strategies and technologies to improve traffic conditions. One key approach is ‘Traffic Prediction’. The task of traffic prediction is to detect traffic conditions for upcoming periods such as next day, week, etc. Utilizing historical traffic data, time-of-day patterns, and other relevant factors, predictive models can anticipate traffic congestion hotspots and enable the timely deployment of resources to mitigate its impact.
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ML Tasks: Scikit-learn
Data Analysis and Visualization: Pandas, Numpy, Matplotlib, seaborn
Web Demo: Gradio
Report: LaTeX
Project Page: HTML, CSS
- Anushka Singh(B22AI008) : Anushka-1603
- Hari Shubha(B22AI021) : harishubha
- Mohanshi Jain(B22ES014) : Mohanshi04
- Pari Sharma(B22CS039) : Parisharma16
- Aastha Bhore(B22EE002) : aasthaasb
- Pranaya Jayaprakash(B22EE086) : pran9803