Optimizing Traffic Control with Real-time Data: A HERE API-Based Approach

Abstract

Traffic congestion is a pervasive issue in urban areas, leading to increased commute times, environmental pollution, and reduced overall quality of life. To address this challenge, this paper introduces a dynamic traffic control system that leverages the HERE API for real-time traffic data. The system provides a web- based interface with user authentication and allows users to input the address of a signal they wish to control. Utilizing the HERE API, it fetches live traffic information, including jam factor and free flow speed, for all four roads at the specified signal. This data serves as the foundation for an intelligent traffic management system that dynamically allocates green signal times based on the intensity of traffic, with roads experiencing higher congestion receiving longer green time. The system's backend is built using Python and the Flask framework, while the frontend features HTML and CSS for a user-friendly interface. Authentication ensures that only authorized users can access and utilize the system. Geocoding is employed to convert user-provided addresses into geographic coordinates, enabling precise data retrieval from the HERE API. Once the traffic data is collected, an analysis is performed to identify the road with the highest traffic intensity. The dynamic allocation of green signal times is a fundamental feature, allowing for real-time traffic optimization and a reduction in congestion. The paper explores the development, implementation, and testing of this dynamic traffic control system, emphasizing its potential to significantly improve urban mobility. Furthermore, considerations for scalability, security, and maintenance are addressed to ensure the system's practicality in real-world scenarios. This project contributes to the growing field of intelligent transportation systems by showcasing the benefits of data-driven decision-making in traffic signal control. By utilizing the HERE API, this system not only provides a solution to mitigate traffic congestion but also represents a step towards more sustainable and efficient urban transportation systems. Key Words: Traffic control, HERE API, Dynamic signal allocation, Real-time traffic data, Urban mobility, Traffic congestion, Traffic optimization, Intelligent transportation systems, Geocoding, User authentication, Python backend, Flask framework, User interface, Green signal time, Scalability, Security, Maintenance, Sustainable transportation.