TrafficPulse is software project leveraging neural networks and computer vision technology to provide real-time traffic data for cities. The objective is to streamline the traditional process of traffic counting, which relies heavily on manual labor, and instead automate it using machine learning.

TrafficPulse is designed to accurately count and differentiate various types of vehicles including cars, bikes, and other modes of transportation. By using real-time video feeds from traffic cameras, the system is capable of identifying and tracking vehicles, subsequently storing this data into a comprehensive database.

The amassed data becomes a vital resource for traffic analysis, city planning, and congestion management. With the potential for integration into larger smart city infrastructure, Traffic Pulse aims to support data-driven decisions, facilitating improvements in urban transportation systems. Furthermore, this data can be utilized by data scientists to conduct in-depth research and analysis, contributing to advancements in traffic management and planning.

This software stands to greatly reduce the manual effort involved in traffic monitoring, while increasing the accuracy and timeliness of the data collected. The end goal is to promote efficient and sustainable city planning, making our urban spaces more livable and less congested.