A system for optimizing traffic signals in real time

Abstract

A real-time traffic signal optimization system that includes: (a) a traffic data acquisition module that collects real-time traffic data from sensors, cameras, GPS and vehicle-to-infrastructure communication; b) a machine learning-based traffic prediction engine that analyses historical and real-time data to predict congestion patterns; c) an adaptive signal control unit that dynamically adjusts traffic light switching times based on real-time traffic flow and predictive insights; (d) an emergency and priority vehicle management system that overrides normal signal operation to give priority to emergency vehicles and public transport vehicles; (e) a central traffic monitoring and control dashboard for real-time monitoring, manual interventions and system configuration; and (f) a vehicle-to-infrastructure communication interface enabling data exchange between traffic signals and connected vehicles to improve driver awareness and traffic coordination.

Description

[0001] The present invention relates to intelligent transportation systems, particularly to real-time traffic signal optimization. It uses machine learning, sensor data, and adaptive algorithms to dynamically adjust traffic signals for better traffic flow and reduced congestion. The system improves urban mobility, minimizes delays, and optimizes fuel efficiency.

[0002] Traffic congestion is a major problem in urban areas, resulting in longer travel times, higher fuel consumption, and increased environmental pollution. Conventional traffic lights operate on predefined timers that do not adapt to real-time traffic conditions, often leading to inefficiencies such as unnecessary waiting times and bottlenecks at intersections. The lack of dynamic control mechanisms results in poor traffic flow management, which is frustrating for commuters and negatively impacts economic productivity.

[0003] Existing traffic management solutions, such as manually controlled signals and traditional fixed-time traffic lights, are unable to cope with unpredictable traffic patterns. While some cities have implemented sensor-based or semi-adaptive traffic control systems, these methods still cannot effectively optimize traffic flow in real time. They lack either advanced data analytics or predictive decision-making, making them less able to respond to sudden changes in traffic density, accidents, or the movement of emergency vehicles.

[0004] To address these challenges, this invention introduces an intelligent real-time traffic signal optimization system. By leveraging machine learning algorithms, sensor networks, and real-time traffic data, the system dynamically adjusts signal timing to reduce congestion and improve vehicle movement efficiency. It provides a scalable and adaptive approach to traffic management that ensures smoother traffic flow, reduces carbon dioxide emissions, and improves overall urban mobility. One objective of the present disclosure is to develop an adaptive traffic signal system that dynamically adjusts timing to reduce congestion and improve vehicle movement.

[0005] Another objective of this disclosure is to implement AI-driven predictive analytics to anticipate and avoid bottlenecks and reduce unnecessary waiting times at intersections.

[0006] Another objective of this disclosure is to enable ambulances, fire engines and public buses to pass through more quickly by granting them priority signals at intersections.

[0007] Another objective of the present disclosure is to optimize traffic flow to reduce idle time, resulting in lower fuel consumption and less pollution.

[0008] Another objective of this disclosure is to use real-time data and intelligent signals to prevent sudden stops, reduce collisions, and increase pedestrian safety at intersections.

[0009] Another objective of this disclosure is to develop a scalable and IoT-enabled system that seamlessly integrates with urban traffic management and smart infrastructure.

[0010] Another objective of this disclosure is to provide transport authorities with a central dashboard for real-time monitoring and analysis, as well as for manual intervention when necessary.

[0011] Another objective of this disclosure is to establish a vehicle-to-infrastructure communication framework for better coordination with connected and self-driving cars.

[0012] The present invention relates to an intelligent system for real-time traffic signal optimization, designed to improve traffic flow in cities using AI, sensors, and adaptive control mechanisms. It collects real-time traffic data from cameras, radar, and connected vehicles to analyze congestion patterns and predict future traffic conditions. Machine learning algorithms dynamically adjust traffic signals to reduce delays and optimize road efficiency. The system prioritizes emergency vehicles and public transport and is connected to a central dashboard for real-time monitoring and control. Furthermore, vehicle-to-infrastructure (V2I) communication improves driver awareness and supports the integration of autonomous vehicles. This intelligent traffic management solution reduces congestion, lowers emissions, and increases overall road safety.

[0013] The present invention relates to a real-time traffic signal optimization system that uses AI and sensor-based modules to improve traffic flow and reduce congestion. It consists of six key modules: a traffic data acquisition module, a machine learning-based traffic prediction engine, an adaptive signal control unit, an emergency and priority vehicle management system, a centralized traffic monitoring and control system, and a vehicle-to-infrastructure (V2I) communication interface. These modules work together to dynamically adjust signal timing, prioritize emergency vehicles, and integrate with the smart city infrastructure to ensure efficient urban mobility. Traffic data acquisition module

[0014] The traffic data acquisition module serves as the foundation of the system and collects real-time traffic information from various sources such as cameras, radar sensors, GPS devices, and vehicle-to-infrastructure (V2I) communication. This data includes vehicle count, speed, lane occupancy, and pedestrian movement, ensuring a comprehensive understanding of traffic flow. By continuously monitoring road conditions, this module provides accurate data for adjusting traffic signals, allowing the system to dynamically respond to changing congestion patterns. Machine learning-based traffic prediction engine

[0015] This component uses machine learning algorithms to analyze historical and real-time traffic data and predict traffic patterns and congestion. Leveraging artificial intelligence (AI), the system anticipates traffic spikes, sudden peaks due to incidents or accidents, and overall traffic flow dynamics. These predictive insights enable proactive optimization of traffic signals, reducing bottlenecks and improving road efficiency before critical congestion occurs. The system's self-learning capability ensures its accuracy increases over time. Adaptive signal control unit

[0016] The adaptive signal controller is responsible for dynamically adjusting traffic light timing based on real-time data and predictive models. Instead of relying on fixed signal cycles, this unit modifies the duration of red, yellow, and green lights to optimize traffic flow at intersections. It prioritizes high-density routes, ensures smoother pedestrian crossings, and reduces unnecessary stops. This real-time adaptability significantly improves mobility and minimizes travel delays. Emergency and Priority Vehicle Management System

[0017] To ensure the expedited passage of emergency vehicles, public transport, and high-priority traffic, this component is connected to emergency services and traffic networks. When an ambulance, fire engine, or police vehicle approaches an intersection, the system temporarily overrides normal traffic light operation to give them a green signal. Similarly, buses and public transport can be given priority signaling to encourage the use of public transport and thus reduce overall traffic congestion. Central dashboard for traffic monitoring and control

[0018] A cloud-based or on-site traffic monitoring dashboard provides authorities with a real-time overview of traffic conditions, system performance, and congestion trends. This dashboard allows traffic managers to intervene manually in critical situations, analyze historical data, and configure system settings. It also enables remote monitoring of multiple intersections, ensuring efficient traffic management across an entire city. Visual analytics and AI-driven recommendations assist with long-term traffic planning and infrastructure improvements. Vehicle-to-Infrastructure (V2I) communication interface

[0019] The V2I communication interface facilitates seamless interaction between vehicles and traffic infrastructure, enabling real-time data exchange. Connected vehicles can receive live updates on signal changes, estimated waiting times, and alternative routes, increasing driver awareness and reducing sudden braking or acceleration. This component also supports the future integration of autonomous vehicles by enabling self-driving cars to coordinate with traffic signals to ensure optimal travel. Improving connectivity between vehicles and road systems increases overall traffic safety and efficiency.

[0020] The invention is explained again below with reference to the figures. In the following, Fig. : Illustration of the real-time traffic signal optimization system

[0021] Fig.demonstrates how the real-time traffic signal optimization system uses an integrated network of sensors, AI-driven analytics, and adaptive control mechanisms to dynamically improve traffic flow. First, the traffic data acquisition module continuously collects real-time information from cameras, radar sensors, GPS devices, and connected vehicles, providing key data such as vehicle count, speed, and lane occupancy.

[0022] This data is processed by the machine learning-based traffic forecasting unit, which analyzes historical patterns and current road conditions to predict congestion and peak traffic times. Based on these insights, the adaptive signal control unit dynamically adjusts traffic light durations at intersections to prioritize routes with higher traffic volumes and avoid unnecessary stops. In parallel, the Emergency and Priority Vehicle Management System detects approaching emergency vehicles or public transport and overrides normal signal cycles to give them a clear path, thus minimizing delays for essential services. Traffic authorities monitor and manage the entire system through the Centralized Traffic Monitoring and Control Dashboard, which provides real-time data visualization, AI-driven insights, and manual overrides for unexpected situations.Finally, the vehicle-to-infrastructure (V2I) communication interface ensures a seamless connection between the traffic system and the vehicles, allowing drivers to receive live updates on signal changes, estimated wait times, and alternative routes. By integrating these components, the system continuously learns and adapts to ensure smoother traffic flow, reduce congestion, lower carbon emissions, and improve overall urban mobility.

Claims

[1] A real-time traffic signal optimization system comprising: (a) a traffic data acquisition module that collects real-time traffic data from sensors, cameras, GPS and vehicle-to-infrastructure communication; b) a machine learning-based traffic prediction engine that analyses historical and real-time data to predict congestion patterns; c) an adaptive signal control unit that dynamically adjusts traffic light switching times based on real-time traffic flow and predictive insights; (d) an emergency and priority vehicle management system that overrides normal signal operation to give priority to emergency vehicles and public transport vehicles; (e) a central traffic monitoring and control dashboard for real-time monitoring, manual interventions and system configuration; and (f) a vehicle-to-infrastructure communication interface enabling data exchange between traffic signals and connected vehicles to improve driver awareness and traffic coordination. [2] The system of claim 1, wherein the traffic data acquisition module integrates AI-based image processing to capture the number of vehicles, lane occupancy, and pedestrian movement in real time. [3] The system of claim 1, wherein the machine learning-based traffic prediction module continuously updates and improves its prediction accuracy by training with real-time and historical traffic data. [4] The system of claim 1, wherein the adaptive signal control unit dynamically adjusts the traffic light duration by prioritizing routes with high traffic density and reducing unnecessary waiting times at intersections. [5] The system of claim 1, wherein the emergency and priority vehicle management system communicates with emergency units and public transport to enable automatic green light passage in critical situations. [6] The system of claim 1, wherein the vehicle-to-infrastructure (V2I) communication interface provides real-time signal updates to connected vehicles and autonomous vehicles and optimizes their speed and route selection for smoother traffic flow.

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