ADAPTIVE DYNAMIC TRANSPORTATION AND SIGNALING SYSTEM FOR REAL- TIME TRAFFIC MANAGEMENT AND ENVIRONMENTAL OPTIMIZATION

Abstract

The Dynamic Transportation & Signaling System (DTSS) is an adaptive, real-time traffic management solution comprising a Traffic Signal Network (100), Vehicle Hardware Unit (110), and Central AI/ML Software Platform (120) that processes real-time data to dynamically adjust signal timings, optimizing urban traffic flow and reducing congestion. The system includes an Emergency Vehicle Detection Module (140) to prioritize emergency vehicles, an Incident Detection and Response Module (180) to reroute traffic around obstructions, and an Environmental Sensing Unit (150) that minimizes emissions in sensitive zones. Commuters access real-time updates through a User Information Interface (170), while city planners utilize a customizable settings dashboard (230) to adjust parameters and analyze behavioral insights. The Renewable Energy Integration (220) powers DTSS sustainably, and an Adaptive Learning System (190) refines algorithms over time, making DTSS a comprehensive, efficient, and eco-friendly solution for modern urban traffic management. Fig.1 Claim.1-10

Specification

Description:FIELD OF INVENTION

[001] The present invention relates to adaptive traffic management systems, specifically for real-time signal optimization and congestion reduction in urban transportation networks.

BACKGROUND

[001] Urban areas worldwide face escalating challenges due to traffic congestion, which leads to significant delays, increased fuel consumption, and air pollution. With the continuous growth in urban populations and vehicle ownership, traditional traffic management systems struggle to handle the increased volume of vehicles, resulting in inefficient traffic flow and substantial productivity losses. These issues impact not only the efficiency of urban transportation but also contribute to economic losses as delayed deliveries, prolonged commutes, and reduced access to services hinder both individuals and businesses.

[002] Current traffic control systems predominantly rely on fixed signal timings or limited adaptive measures, which are insufficient to address the dynamic nature of modern urban traffic. These static systems are incapable of adjusting in real time to sudden changes in traffic patterns, such as peak-hour congestion, emergency vehicle routing, or traffic incidents. Consequently, they fail to optimize traffic flow efficiently, often exacerbating delays during high-traffic periods or emergency situations. The inability of conventional systems to adapt swiftly further limits the efficiency of emergency responses, compromising public safety.

[003] Additionally, the environmental impact of traffic congestion is profound. Vehicles idling in traffic produce higher levels of carbon dioxide and other pollutants, which degrade air quality and contribute to climate change. Existing traffic management solutions have minimal capability to reduce emissions by regulating traffic flow effectively, resulting in deteriorated urban air quality and associated public health concerns. The lack of real-time adaptability in current systems thus not only impacts travel efficiency but also has a long-term impact on environmental sustainability and urban living conditions.

[004] In view of the foregoing disadvantages inherent in the known systems in the prior art, the present invention provides a novel method and system.

OBJECT OF THE INVENTION
[001] The principal objective of the present invention is to address urban traffic congestion through an adaptive and intelligent system that optimizes traffic signal timings in real time to reduce delays and improve overall traffic flow efficiency.
[002] Yet another objective is to dynamically classify vehicles, including emergency vehicles and VIP convoys, allowing for prioritized passage and minimizing response times for critical services.
[003] Yet another objective is to enhance environmental sustainability by reducing idle time and emissions from vehicles, thereby contributing to improved air quality in urban areas.
[004] Yet another objective is to provide commuters with accurate, real-time information on expected wait times at traffic junctions, empowering them to make informed route decisions and enhancing the commuting experience.
[005] Yet another objective is to integrate multi-modal transportation options, such as buses and metro systems, with the urban traffic network, facilitating seamless connectivity and encouraging the use of public transport.
[006] Yet another objective is to incorporate advanced sensor technologies and predictive algorithms that allow the system to preemptively adjust to anticipated traffic conditions, improving response to peak traffic hours, weather changes, and road incidents.
[007] Yet another objective is to establish an interconnected network of traffic signals across the city, allowing for coordinated signal adjustments that optimize traffic flow over larger urban areas.
[008] Ultimately, the invention seeks to provide a comprehensive traffic management system that enhances urban mobility, prioritizes critical traffic, reduces environmental impact, and improves the overall quality of urban transportation.

SUMMARY

[001] The present invention, known as the Dynamic Transportation & Signaling System (DTSS), offers a groundbreaking solution to urban traffic congestion and environmental challenges. DTSS is an adaptive, intelligent traffic management system that leverages advanced AI/ML algorithms, predictive analytics, and real-time data from multiple integrated modules to dynamically optimize urban traffic flow. This system addresses various traffic scenarios, including peak-hour congestion, emergency vehicle prioritization, and multi-modal transport integration, enhancing commuter experiences and overall urban mobility.

[002] At the core of DTSS is the Central AI/ML Software Platform (120), which receives real-time data from several components, including the Vehicle Hardware Unit (110) for vehicle classification and the Environmental Sensing Unit (150) to monitor air quality. The Predictive Algorithm Module (130) anticipates congestion patterns, enabling the system to proactively adjust traffic signals within the Traffic Signal Network (100), ensuring smoother traffic flow. In the event of emergencies, the Emergency Vehicle Detection Module (140) prioritizes emergency vehicles, expediting their routes through automated signal adjustments.

[003] DTSS also features a Public Transport Integration Module (160), which synchronizes traffic signals with public transit schedules to promote efficient multi-modal connectivity. The User Information Interface (170) provides commuters with real-time traffic updates, optimal route suggestions, and eco-friendly travel options. The system's Incident Detection and Response Module (180) further enhances safety by rerouting traffic during unexpected events, such as accidents or road blockages.

[004] The Adaptive Learning System (190) continuously improves DTSS performance through feedback and historical data analysis, making the system increasingly efficient over time. Additional features, such as the Data Security Module (200), ensure secure handling of all data transmissions, while the Behavioral Insights and Social Impact Analysis Module (210) offers valuable analytics to city planners. The system also integrates renewable energy support through the Renewable Energy Integration (220) and provides a customizable dashboard for policymakers through the City Planner Customizable Settings Dashboard (230).

[005] In summary, DTSS not only improves urban traffic flow and reduces congestion but also addresses environmental concerns and facilitates emergency responses, creating a safer, more efficient, and sustainable transportation ecosystem for modern cities.


DESCRIPTION

[001] The present invention, referred to as the Dynamic Transportation & Signaling System (DTSS), is designed to alleviate traffic congestion, prioritize emergency vehicle movement, and optimize urban mobility through a network of interconnected elements. DTSS operates via real-time data collection, advanced predictive algorithms, and adaptive learning to dynamically adjust traffic signals across urban areas. The core components and interactions of DTSS are described below.
[002] Traffic Signal Network (100): The Traffic Signal Network (100) consists of interconnected traffic lights positioned across various junctions in a city. Each signal is equipped with hardware to receive and execute timing adjustments based on commands from the Central AI/ML Software Platform (120). The Traffic Signal Network (100) serves as the operational backbone of DTSS, allowing for real-time control of signal timings across multiple intersections to reduce congestion and improve traffic flow.
[003] Vehicle Hardware Unit (110): The Vehicle Hardware Unit (110) is integrated within vehicles to capture essential data, such as vehicle type (e.g., 2-wheeler, 4-wheeler, ambulance, VIP convoy) and location. This unit uses advanced sensors to classify vehicles and transmits data to the Central AI/ML Software Platform (120) for analysis. The real-time data collected by the Vehicle Hardware Unit (110) enables DTSS to dynamically prioritize traffic based on vehicle types and traffic density.
[004] Central AI/ML Software Platform (120): The Central AI/ML Software Platform (120) is the core processing unit of DTSS, responsible for receiving, analyzing, and interpreting data from the Vehicle Hardware Unit (110) and other sources. This platform utilizes AI and machine learning algorithms to predict traffic flow and determine optimal signal timings. By analyzing real-time traffic conditions, the Central AI/ML Software Platform (120) adjusts the Traffic Signal Network (100) to minimize congestion and improve traffic efficiency.
[005] Predictive Algorithm Module (130): Embedded within the Central AI/ML Software Platform (120), the Predictive Algorithm Module (130) processes historical and real-time traffic data to anticipate congestion patterns. By accounting for factors such as peak hours, weather conditions, and local events, this module allows DTSS to proactively adjust signal timings to prevent traffic build-ups, thereby improving travel times and reducing vehicle idle time.
[006] Emergency Vehicle Detection Module (140): The Emergency Vehicle Detection Module (140) plays a critical role in enabling rapid response for emergency services. When an emergency vehicle, such as an ambulance, is detected within the network, this module prioritizes its movement by adjusting traffic signals to create a clear path. The Emergency Vehicle Detection Module (140) allows DTSS to ensure that emergency vehicles reach their destinations faster by dynamically altering traffic flows to facilitate quick passage.
[007] Environmental Sensing Unit (150): The Environmental Sensing Unit (150) monitors environmental parameters such as air quality and pollution levels. Located within sensitive zones (e.g., near schools or hospitals), this unit gathers data on CO2 emissions and particulate matter. When pollution levels are high, the Central AI/ML Software Platform (120) can modify signal timings to reduce congestion in these areas, thereby contributing to environmental sustainability and improved urban air quality.
[008] Public Transport Integration Module (160): The Public Transport Integration Module (160) connects DTSS with public transportation schedules, such as buses and metro systems. This module enables synchronization of traffic signals with public transit timings, allowing for smoother movement of public transportation vehicles through intersections. By prioritizing public transport, DTSS encourages the use of eco-friendly commuting options and helps reduce individual vehicle use in urban areas.
[009] User Information Interface (170): The User Information Interface (170) provides real-time traffic updates to commuters via a mobile or web application. Through this interface, users can access estimated wait times at junctions, eco-friendly route suggestions, and alerts about incidents or emergencies. By empowering users with traffic information, the User Information Interface (170) allows for more informed route planning, reducing unnecessary congestion and improving travel experiences.
[010] Incident Detection and Response Module (180): The Incident Detection and Response Module (180) identifies incidents such as accidents, stalled vehicles, or road blockages. Upon detection, this module reroutes traffic around the affected area by adjusting signal timings and notifying nearby drivers through the User Information Interface (170). This capability enhances safety and reduces delays by proactively managing incidents within the traffic network.
[011] Adaptive Learning System (190): The Adaptive Learning System (190) continuously refines DTSS performance based on historical data, real-time feedback, and user input. This system allows DTSS to adapt to evolving traffic patterns and urban changes over time, improving the accuracy of its predictions and the efficiency of its signal adjustments. The Adaptive Learning System (190) is key to ensuring DTSS remains responsive and effective in dynamic urban environments.
[012] Data Security Module (200): The Data Security Module (200) safeguards all data transmitted within DTSS, protecting user privacy and ensuring compliance with data protection regulations. This module applies encryption protocols to secure vehicle data, traffic information, and environmental metrics. By incorporating robust data security measures, DTSS maintains the integrity and confidentiality of sensitive data across its network.
[013] Behavioral Insights and Social Impact Analysis Module (210): The Behavioral Insights and Social Impact Analysis Module (210) analyzes commuter behavior, such as peak travel times, common routes, and eco-friendly choices. The insights generated are valuable for city planners seeking to understand urban mobility trends and to make data-driven decisions regarding infrastructure and policy. This module supports social impact assessments that align with city planning goals.
[014] Renewable Energy Integration (220): The Renewable Energy Integration (220) enables DTSS to operate on renewable energy sources, such as solar panels installed at traffic signals. This feature ensures DTSS can continue functioning during power outages and contributes to the sustainability of urban infrastructure. By integrating renewable energy, DTSS reduces its dependency on traditional energy sources, enhancing its environmental impact.
[015] City Planner Customizable Settings Dashboard (230): The City Planner Customizable Settings Dashboard (230) provides an administrative interface that allows city planners to set parameters within DTSS, such as prioritizing environmental zones or customizing peak traffic responses. This dashboard gives policymakers flexibility to tailor the DTSS operation according to urban planning goals and to address specific community needs.
[016] The DTSS system, with its multifaceted approach, not only enhances real-time traffic flow and reduces congestion but also supports emergency response, environmental sustainability, public transport efficiency, and data-driven urban planning. By integrating these diverse elements into a single cohesive system, DTSS offers a robust solution to the complex challenges faced by modern cities in managing urban transportation.
Detailed Interaction Between Components
[0017] In the Dynamic Transportation & Signaling System (DTSS), seamless data flow and interaction among the core components are crucial to achieving efficient traffic management. The Vehicle Hardware Unit (110), Traffic Signal Network (100), and Central AI/ML Software Platform (120) work in harmony, each performing specific tasks that collectively drive the system's functionality.
[0018] The Vehicle Hardware Unit (110) continuously gathers data on vehicle type, speed, and location using an array of sensors, including GPS and vehicle classification sensors. This data is transmitted to the Central AI/ML Software Platform (120) through a secure communication channel. The transmission is configured to be near real-time, allowing for minimal latency and ensuring that the software platform has the most current data on vehicle distribution and movement patterns across the traffic network.
[0019] Upon receiving this data, the Central AI/ML Software Platform (120) processes it through its Predictive Algorithm Module (130), which assesses both current and anticipated traffic conditions. This module interprets the incoming vehicle data, analyzing patterns such as vehicle density, speed variances, and clustering at intersections. Based on this analysis, the platform calculates optimal signal timings to minimize congestion and prevent bottlenecks at key junctions.
[0020] Once optimal timings are calculated, the Central AI/ML Software Platform (120) sends command signals to the Traffic Signal Network (100). Each traffic signal within this network is equipped with hardware that allows it to receive and implement these dynamic timing adjustments. The signals adapt their green and red phases based on the platform's recommendations, creating a responsive traffic system that adjusts continuously in real time to the volume and flow of vehicles.
[0021] Additionally, the Incident Detection and Response Module (180) within the software platform plays a pivotal role in handling anomalies, such as accidents or unexpected road blockages. If an incident is detected, the module immediately relays this information to the Traffic Signal Network (100), prompting a reconfiguration of signal timings to divert traffic away from the affected area. Simultaneously, the User Information Interface (170) notifies nearby drivers, advising them of the incident and suggesting alternative routes.
[0022] This multi-layered data flow between the Vehicle Hardware Unit (110), Central AI/ML Software Platform (120), and Traffic Signal Network (100) enables DTSS to act as a coordinated and adaptive system. By leveraging real-time interactions and rapid decision-making, DTSS ensures that urban traffic is managed dynamically, with each component continuously informing and adjusting the system for optimal traffic flow.

Use of Advanced Sensor Technologies

[0023] The Vehicle Hardware Unit (110) in the Dynamic Transportation & Signaling System (DTSS) employs advanced sensor technologies to gather precise data on vehicle characteristics and surrounding conditions. These sensors include, but are not limited to, LiDAR, radar, GPS, and environmental sensors, each contributing to accurate vehicle classification, speed monitoring, and location tracking. The integration of these technologies ensures that DTSS can effectively manage traffic flow in various environmental and lighting conditions.

[0024] LiDAR sensors provide detailed, high-resolution spatial data, enabling the system to classify vehicles based on their size, shape, and distance from other objects. This level of detail allows the Central AI/ML Software Platform (120) to distinguish between different vehicle types, such as 2-wheelers, 4-wheelers, and emergency vehicles, with high accuracy. Additionally, LiDAR sensors are particularly effective at detecting non-motorized participants, such as pedestrians and cyclists, further enhancing DTSS's ability to manage mixed traffic.

[0025] Radar sensors are incorporated to capture real-time speed data, especially in high-speed traffic zones where precise speed measurements are essential for adjusting signal timings. Radar's ability to detect movement regardless of lighting conditions makes it ideal for nighttime and low-visibility situations, ensuring that DTSS maintains reliable data collection around the clock. The speed data collected by radar is sent to the Predictive Algorithm Module (130), which uses this information to anticipate congestion and make preemptive adjustments to signal timings.

[0026] GPS sensors embedded in the Vehicle Hardware Unit (110) provide continuous location tracking, which is crucial for understanding vehicle density and distribution within the traffic network. The GPS data allows DTSS to create real-time traffic maps, identifying areas with high congestion levels and distributing this information to the Traffic Signal Network (100) for optimized signal management. GPS tracking also enables the system to prioritize certain vehicles, such as public transport or emergency services, by dynamically adjusting routes or signal timings to facilitate their passage.

[0027] Environmental sensors are deployed to monitor factors like air quality and noise levels in sensitive urban zones, such as residential areas, school zones, and hospitals. These sensors feed data to the Environmental Sensing Unit (150), allowing DTSS to respond dynamically to environmental conditions. For example, if high pollution levels are detected near a school, DTSS may reduce signal wait times for vehicles passing through that zone, minimizing idling and emissions. This feature allows DTSS to actively contribute to urban sustainability efforts by reducing the environmental impact of traffic.

[0028] By integrating these advanced sensor technologies, DTSS enhances its accuracy and responsiveness, allowing the system to function effectively across diverse urban conditions. The combined capabilities of LiDAR, radar, GPS, and environmental sensors enable DTSS to gather a comprehensive data set that informs real-time decisions, promotes efficient traffic flow, and addresses environmental concerns. This robust sensor infrastructure ensures that DTSS is equipped to handle the complexities of modern urban transportation.

Specific Algorithms for Signal Optimization

[0029] The Predictive Algorithm Module (130) within the Central AI/ML Software Platform (120) employs advanced machine learning algorithms to analyze real-time and historical traffic data, enabling dynamic optimization of traffic signals. These algorithms are tailored to recognize traffic patterns, identify potential congestion points, and predict future traffic flows based on a variety of data inputs, such as vehicle density, average speed, time of day, and environmental conditions.

[0030] The module incorporates supervised learning algorithms that have been trained on extensive traffic datasets, allowing DTSS to recognize patterns like peak traffic hours and high-demand intersections. By understanding these patterns, the supervised learning models can adjust signal timings preemptively, mitigating congestion before it occurs. For example, during peak hours, the algorithms may allocate longer green phases to major arterial roads, reducing the likelihood of traffic jams.

[0031] Reinforcement learning algorithms are also embedded in the Predictive Algorithm Module (130) to enable adaptive signal control based on real-time feedback. Reinforcement learning allows the system to "learn" optimal signal timings by continuously interacting with the traffic network and receiving feedback on the results of previous adjustments. This method ensures that DTSS remains highly responsive to real-time changes, such as sudden increases in vehicle volume, accidents, or adverse weather conditions.

[0032] Additionally, the module uses deep learning algorithms for multi-variable analysis, accounting for complex factors like the interaction between pedestrian and vehicle flows, weather impact on traffic, and special event traffic surges. Deep learning models process this data to create more accurate traffic predictions, allowing DTSS to optimize signals based on not only immediate needs but also anticipated demands, such as increased traffic near venues during events.

[0033] For emergency situations, the Predictive Algorithm Module (130) integrates priority-based algorithms that recognize and prioritize emergency vehicles detected by the Emergency Vehicle Detection Module (140). When an emergency vehicle is identified, the system calculates the quickest possible route and dynamically adjusts signal timings along that route to facilitate an unobstructed path. The priority-based algorithm also minimizes the disruption to surrounding traffic by selectively adjusting signals in a localized area.

[0034] The Predictive Algorithm Module (130) also employs environmental impact algorithms that adjust traffic signals to reduce emissions in high-sensitivity zones. These algorithms work closely with the Environmental Sensing Unit (150), receiving real-time air quality data to determine optimal signal adjustments for reducing vehicle idling and minimizing emissions in polluted areas. The environmental impact algorithms thus contribute to a more sustainable urban traffic system, aligning with eco-friendly urban planning initiatives.

[0035] Together, these machine learning and AI-driven algorithms enable DTSS to deliver a highly efficient, adaptive, and predictive traffic management solution. By leveraging supervised learning, reinforcement learning, deep learning, priority-based algorithms, and environmental impact models, the Predictive Algorithm Module (130) ensures that DTSS continuously optimizes traffic flow, reduces congestion, and meets the complex demands of modern urban transportation.
Emergency Vehicle Prioritization Process

[0036] The Emergency Vehicle Detection Module (140) is a critical feature of the Dynamic Transportation & Signaling System (DTSS) that facilitates swift movement of emergency vehicles through busy intersections by prioritizing their routes. This module is designed to recognize emergency vehicles, such as ambulances, fire trucks, and police vehicles, and provide real-time adjustments to traffic signals, ensuring these vehicles face minimal delay.
[0037] Emergency vehicles are detected using multiple methods. The Vehicle Hardware Unit (110) installed in emergency vehicles may send a direct signal to nearby traffic signals, indicating its presence and requiring prioritization. Additionally, the system may use other sensors, such as radio frequency identification (RFID) and GPS, to confirm the presence and location of emergency vehicles within the network, providing accurate and timely information to the Central AI/ML Software Platform (120).

[0038] Once an emergency vehicle is detected, the Emergency Vehicle Detection Module (140) communicates this information to the Predictive Algorithm Module (130), which calculates the optimal route and determines necessary adjustments to signal timings. The system adjusts green phases to create a clear path along the vehicle's route, allowing it to move through intersections without stopping. This process may involve temporarily extending green lights and coordinating multiple intersections to ensure a continuous flow for the emergency vehicle.

[0039] The prioritization process is carefully managed to minimize disruption to surrounding traffic. The Predictive Algorithm Module (130) selectively adjusts only those signals that are immediately relevant to the emergency vehicle's path, rather than altering the entire network. This localized adjustment prevents excessive delays for non-emergency traffic while still facilitating a swift passage for the emergency vehicle.

[0040] In situations where multiple emergency vehicles are detected, the Emergency Vehicle Detection Module (140) assigns priority based on the type of emergency and proximity to critical destinations, such as hospitals. For instance, an ambulance with a critical patient may be given precedence over other emergency vehicles. This prioritization helps manage multiple emergency situations effectively within a congested urban area.

[0041] After the emergency vehicle has passed, the system quickly reverts the signal timings to normal settings. The Adaptive Learning System (190) records the event, analyzing how the emergency prioritization affected overall traffic flow and making adjustments to improve future emergency responses. This feedback loop helps the system learn from each event, enhancing the accuracy and speed of future prioritizations.

[0042] The Emergency Vehicle Detection Module (140), combined with DTSS's real-time data processing and adaptive algorithms, provides an efficient solution for emergency vehicle prioritization. By ensuring fast and unobstructed passage for emergency vehicles, DTSS improves response times for critical services and enhances overall urban safety without significantly disrupting regular traffic flow.

Incident Detection and Response Logic

[0043] The Incident Detection and Response Module (180) is a vital component of the Dynamic Transportation & Signaling System (DTSS) that enhances urban safety by identifying and managing unexpected events, such as accidents, stalled vehicles, or road blockages. This module monitors real-time traffic data and uses advanced detection algorithms to recognize anomalies that may indicate an incident within the traffic network.

[0044] Incident detection is primarily achieved through data collected from the Vehicle Hardware Unit (110), which continuously reports vehicle speed, position, and movement patterns. If a vehicle is stationary in a high-speed lane for an extended period or if a sudden drop in speed is detected across multiple vehicles within the same area, the system flags a potential incident. Additionally, sensors within the Traffic Signal Network (100), such as cameras or infrared detectors, contribute visual or motion-based information that helps confirm the presence and nature of an incident.

[0045] Once an incident is detected, the Incident Detection and Response Module (180) communicates with the Central AI/ML Software Platform (120) to determine the most effective traffic management response. The Predictive Algorithm Module (130) evaluates the incident's location, traffic density in the surrounding area, and alternate routes. Based on this analysis, DTSS adjusts signal timings at nearby intersections to reroute traffic around the affected area, minimizing congestion and preventing additional delays.

[0046] The User Information Interface (170) immediately alerts drivers in the vicinity of the incident, providing them with real-time updates and suggested detours. These alerts help drivers make informed decisions to avoid the impacted area, further reducing congestion and enhancing safety. Notifications are sent to drivers through mobile applications or integrated in-vehicle displays, ensuring the information is easily accessible.

[0047] In addition to rerouting traffic, DTSS can coordinate with local authorities, such as traffic management centers or emergency response teams, through the Data Security Module (200). This secure channel allows DTSS to share incident details, facilitating a faster and more efficient response. For example, the module may notify law enforcement to manage the scene or deploy tow services if a vehicle breakdown has caused the blockage.

[0048] The Adaptive Learning System (190) continuously monitors and records incident data, analyzing the effectiveness of the response and identifying patterns related to common incident types and locations. This historical data helps DTSS refine its incident response strategies over time, leading to more accurate detection and faster reactions in future events.
[049] By leveraging the Incident Detection and Response Module (180), DTSS provides a proactive approach to incident management that not only enhances road safety but also minimizes traffic disruptions. The module's ability to detect incidents quickly, reroute traffic efficiently, and alert drivers contributes to a safer and more resilient urban transportation system.

Adaptive Learning Capabilities

[050] The Adaptive Learning System (190) is an integral part of the Dynamic Transportation & Signaling System (DTSS), designed to continuously refine the system's performance through real-time feedback and historical data analysis. This module enables DTSS to learn from past traffic patterns, incidents, user behaviors, and system adjustments, making the overall system more responsive and efficient over time.
[0051] The Adaptive Learning System (190) utilizes a combination of machine learning techniques, including reinforcement learning, supervised learning, and data clustering, to analyze patterns in traffic flow and signal adjustments. By examining the outcomes of previous signal timing changes, incident responses, and environmental adjustments, the system identifies which strategies were most effective in reducing congestion and improving traffic flow under similar conditions.
[0052] One of the key features of the Adaptive Learning System (190) is its ability to recognize seasonal and time-based traffic patterns. For instance, the system identifies predictable changes, such as increased traffic during holiday seasons, peak commuting hours, or weekends. This temporal awareness allows DTSS to preemptively adjust its algorithms and signal timings to accommodate these recurring patterns, ensuring optimal traffic flow during high-demand periods.

[053] The Adaptive Learning System (190) also leverages user feedback from the User Information Interface (170) to understand commuter behavior and preferences. For example, if users frequently choose certain eco-friendly routes or detours during incidents, the system takes note of these trends to improve its route recommendations in the future. This user-centric learning approach enhances the accuracy of DTSS in delivering routes that align with user needs, further reducing congestion.

[054] Additionally, the Adaptive Learning System (190) tracks the effectiveness of the Incident Detection and Response Module (180) and Emergency Vehicle Detection Module (140) in handling specific scenarios. By analyzing incident types, locations, and frequency, the system improves its incident detection algorithms and response times, creating a more resilient urban traffic management solution. The system can even learn to prioritize common emergency routes based on historical data, ensuring faster response times in future events.

[055] Through adaptive learning, the system becomes more proficient at integrating data from diverse sources, including vehicle telemetry from the Vehicle Hardware Unit (110), air quality readings from the Environmental Sensing Unit (150), and behavior data from the Behavioral Insights and Social Impact Analysis Module (210). This holistic approach to data integration allows the Adaptive Learning System (190) to make smarter, more nuanced adjustments to traffic management strategies based on a comprehensive understanding of urban mobility dynamics.

[056] The Adaptive Learning System (190) further incorporates a feedback loop that allows city planners to provide input on system performance via the City Planner Customizable Settings Dashboard (230). Planners can assess system reports, evaluate the effectiveness of different traffic management strategies, and make adjustments to parameters such as emergency vehicle prioritization thresholds or eco-friendly zone sensitivities. This collaborative learning model between DTSS and city planners enables continuous system improvements aligned with urban planning goals.

[057] By enabling DTSS to learn from past data and adjust in real time, the Adaptive Learning System (190) ensures that the system remains responsive to evolving traffic conditions, seasonal shifts, and user needs. This capability not only enhances traffic flow and safety but also allows DTSS to remain adaptable and effective in addressing the complex challenges of modern urban transportation networks.

Data Security and Privacy Measures
[058] The Data Security Module (200) in the Dynamic Transportation & Signaling System (DTSS) is designed to protect the integrity and confidentiality of all data processed and transmitted within the system. Given the sensitive nature of traffic data, including vehicle location and environmental metrics, this module ensures that the system adheres to stringent data privacy standards and complies with local and international data protection regulations.
[059] The Data Security Module (200) employs advanced encryption protocols, such as AES-256, for data transmitted between the Vehicle Hardware Unit (110), Central AI/ML Software Platform (120), and Traffic Signal Network (100). This encryption protects data during transit and prevents unauthorized access, ensuring that only authorized components within DTSS can interpret and utilize the information. Additionally, all data stored within the system, including historical traffic and incident data, is encrypted to maintain data privacy.
[060] To further enhance security, the Data Security Module (200) utilizes multi-factor authentication (MFA) and access control mechanisms for administrative interfaces, such as the City Planner Customizable Settings Dashboard (230). Only authorized personnel, such as city planners or traffic management officials, can access and modify system settings, minimizing the risk of unauthorized interference.
[061] The module also includes data anonymization techniques, particularly for commuter information received via the User Information Interface (170). Personally identifiable information (PII) is stripped from user data before it is stored or analyzed, ensuring that DTSS can deliver optimized routes and insights without compromising individual privacy. This anonymization process complies with regulatory standards, such as the General Data Protection Regulation (GDPR).
[062] In the event of a potential data breach, the Data Security Module (200) is equipped with automated alert systems that notify administrators immediately. These alerts allow for swift incident response, ensuring that any vulnerability is addressed and mitigated before it impacts the system's operations. The module also provides a comprehensive audit trail, logging all access and modifications within the system for transparency and accountability.
[063] By incorporating robust data security and privacy measures, DTSS ensures that sensitive traffic and environmental data are managed responsibly. The Data Security Module (200) upholds the integrity of the system and instills confidence in users and authorities that DTSS operates in a secure, privacy-conscious manner.
Behavioral Insights and Social Impact Analysis
[064] The Behavioral Insights and Social Impact Analysis Module (210) in the Dynamic Transportation & Signaling System (DTSS) provides valuable analytics on commuter behavior, traffic patterns, and social impact. This module helps city planners and traffic authorities gain a deeper understanding of urban mobility trends, enabling them to make informed decisions that align with long-term urban development goals.
[065] The Behavioral Insights and Social Impact Analysis Module (210) collects and processes anonymized data from the User Information Interface (170), as well as from historical traffic patterns and environmental metrics. By analyzing data related to peak travel times, popular routes, and commuter preferences, the module identifies recurring patterns and behaviors. These insights are invaluable for planners looking to optimize infrastructure, reduce congestion, and encourage eco-friendly commuting.
[066] One of the key functionalities of this module is its ability to identify high-demand zones and corridors where traffic flow is consistently heavy. For example, the module can pinpoint intersections or routes frequently congested during rush hours or near popular commercial areas. By identifying these traffic hotspots, DTSS enables city planners to focus resources and attention on improving these critical areas, either through additional infrastructure, targeted policy changes, or specialized signal timing adjustments.
[067] The Behavioral Insights and Social Impact Analysis Module (210) also tracks user engagement with eco-friendly route suggestions provided via the User Information Interface (170). If a significant portion of commuters opts for eco-friendly routes, the module recognizes this pattern and may prioritize these routes in future recommendations. This feedback loop encourages sustainable commuting behaviors, as users are guided towards routes that minimize emissions and reduce congestion in sensitive areas.
[068] In addition to commuter behavior, the module provides social impact insights that highlight the effects of DTSS on air quality, noise pollution, and travel efficiency. By correlating traffic patterns with environmental data from the Environmental Sensing Unit (150), the system can generate reports on how reduced congestion impacts air quality indices or lowers noise levels in residential zones. These social impact metrics are essential for demonstrating the environmental and health benefits of DTSS to local governments and the public.
[069] The City Planner Customizable Settings Dashboard (230) allows city planners to view these behavioral insights and social impact reports in a user-friendly interface. Planners can access visualizations and detailed analytics that show trends over time, such as seasonal traffic variations or shifts in commuter behavior following policy changes. This data empowers planners to make evidence-based decisions that improve urban mobility, reduce emissions, and enhance quality of life for residents.
[070] Through the Behavioral Insights and Social Impact Analysis Module (210), DTSS not only optimizes traffic flow but also fosters a more sustainable, community-focused approach to urban transportation management. By promoting eco-friendly practices, identifying high-demand areas, and measuring the system's social impact, DTSS contributes to creating more livable and environmentally friendly cities.

Renewable Energy Integration

[071] The Renewable Energy Integration (220) feature in the Dynamic Transportation & Signaling System (DTSS) is designed to enhance the sustainability of urban traffic management by leveraging renewable energy sources, such as solar and wind power, to operate traffic signals and system components. This feature reduces DTSS's dependence on traditional energy sources, aligns with eco-friendly city initiatives, and ensures uninterrupted functionality, even in areas prone to power outages.

[072] Each traffic signal within the Traffic Signal Network (100) is equipped with solar panels or small-scale wind turbines as part of the Renewable Energy Integration (220). These renewable energy sources provide a dedicated power supply for the traffic lights, signal controllers, and communication hardware. Solar panels are particularly beneficial in urban settings, where sunlight is typically abundant, while wind turbines offer an alternative source of power in areas with consistent wind flow. This dual approach helps to ensure that DTSS components remain powered regardless of weather variations.

[073] In addition to providing a direct power source, Renewable Energy Integration (220) includes battery storage systems connected to each traffic signal. During sunny or windy days, excess energy generated is stored in batteries and used during low-light or low-wind conditions, maintaining a continuous power supply. This built-in storage capability is crucial for maintaining reliable operation during nights, overcast days, or power grid failures, making the system more resilient and capable of operating independently of the main electrical grid.

[074] The Central AI/ML Software Platform (120) monitors energy levels across the network to ensure optimal use of available power. If an area within the network experiences low energy reserves due to prolonged adverse weather, the system can adjust traffic signal timings in that area to a low-power mode. For instance, during off-peak hours, the system can reduce signal intensity or dim indicator lights, conserving energy while maintaining essential functionality.
[075] The Renewable Energy Integration (220) also supports reporting and data collection, allowing city planners to track the environmental benefits of DTSS through the City Planner Customizable Settings Dashboard (230). This dashboard displays metrics such as the amount of energy saved, the reduction in carbon emissions achieved through renewable energy use, and the percentage of DTSS operations powered by renewable sources. These insights help city planners assess the impact of the system and showcase the city's commitment to sustainable urban infrastructure.

[076] Through Renewable Energy Integration (220), DTSS contributes to urban sustainability by minimizing its carbon footprint and supporting resilient infrastructure. By integrating renewable energy sources and battery storage, DTSS not only operates in an eco-friendly manner but also ensures continuous operation in the event of power outages or grid fluctuations, reinforcing its reliability in a modern urban setting.

City Planner Customizable Settings Dashboard
[077] The City Planner Customizable Settings Dashboard (230) is an administrative interface within the Dynamic Transportation & Signaling System (DTSS) that enables city planners and traffic management officials to tailor DTSS operations according to specific urban planning objectives. This dashboard offers a user-friendly interface where authorized personnel can adjust parameters, access system analytics, and make data-driven decisions to optimize traffic flow and improve urban mobility.
[078] Through the dashboard, city planners can set priorities for different zones within the city. For example, they can designate specific areas as high-priority zones for environmental protection, such as school districts or hospital zones, and the Environmental Sensing Unit (150) will adjust signal timings in these areas to minimize vehicle idling and reduce emissions. Similarly, city planners can allocate additional resources or adjust signal timings for major commercial or residential areas, helping to alleviate congestion during peak hours.
[079] The City Planner Customizable Settings Dashboard (230) also includes options for adjusting emergency response priorities. Planners can define key emergency routes, such as pathways to hospitals or fire stations, and establish pre-configured settings for the Emergency Vehicle Detection Module (140) to ensure rapid response in these critical areas. By adjusting these parameters, city planners have greater control over how DTSS prioritizes emergency vehicles in different parts of the city, supporting faster and more efficient emergency response times.
[080] Additionally, the dashboard provides settings for implementing eco-friendly traffic policies. City planners can activate eco-friendly route recommendations, encouraging users of the User Information Interface (170) to take routes that minimize emissions or avoid high-sensitivity zones. These options allow planners to guide commuter behavior in a way that aligns with city-wide sustainability goals, promoting greener commuting habits across the urban population.
[081] The City Planner Customizable Settings Dashboard (230) also offers advanced reporting capabilities, providing visualizations and data analytics on traffic patterns, environmental impact, and system performance. Planners can access historical data on congestion trends, monitor the effectiveness of recent adjustments, and track key performance indicators (KPIs) such as average commute times, reduction in emissions, and the success rate of emergency vehicle prioritization. These insights support long-term strategic planning, enabling city officials to assess the impact of DTSS over time.
[082] Furthermore, the dashboard allows city planners to schedule regular or situational adjustments based on known events, such as sports games, parades, or construction projects. In anticipation of these events, planners can configure DTSS to temporarily prioritize certain routes, extend green light durations, or redirect traffic flow to minimize disruptions. This flexibility is especially valuable for managing traffic in cities with frequent events or seasonal congestion surges.
[083] To ensure security, access to the City Planner Customizable Settings Dashboard (230) is restricted to authorized personnel, with multi-factor authentication and role-based permissions controlled by the Data Security Module (200). This secure access protects the integrity of DTSS operations and ensures that only designated officials can make adjustments to system parameters.
[084] By offering city planners a high degree of customization and control, the City Planner Customizable Settings Dashboard (230) empowers urban authorities to shape DTSS according to their specific objectives, addressing unique traffic challenges and enhancing overall city infrastructure. The dashboard serves as an essential tool for optimizing DTSS to meet the diverse needs of a growing urban environment, making it a versatile and responsive component of the system.

Advantages of the Dynamic Transportation & Signaling System (DTSS)

[085] The Dynamic Transportation & Signaling System (DTSS) presents numerous advantages that address the pressing challenges of urban traffic management. The system's ability to dynamically adapt to real-time traffic conditions, integrate renewable energy sources, prioritize emergency vehicles, and provide valuable analytics makes it a comprehensive solution for modern cities. These advantages highlight the transformative impact DTSS has on urban mobility, safety, and sustainability.

[086] One of the primary advantages of DTSS is its capacity to reduce traffic congestion through intelligent signal timing adjustments. By utilizing real-time data and predictive algorithms, DTSS minimizes waiting times at intersections, ensuring smoother traffic flow across the city. This improvement in traffic efficiency not only reduces commuter frustration but also leads to shorter travel times, enhancing the overall commuting experience.

[087] Another significant advantage of DTSS is its environmental impact. The system's ability
to monitor and respond to air quality data from the Environmental Sensing Unit (150) allows it to prioritize eco-friendly traffic patterns in sensitive zones, such as residential areas or near schools. Additionally, the integration of renewable energy sources, such as solar and wind, through the Renewable Energy Integration (220) reduces the carbon footprint of the traffic management system itself, contributing to a more sustainable urban environment.

[088] DTSS also provides enhanced safety through its Emergency Vehicle Detection Module (140), which prioritizes the movement of emergency vehicles. By adjusting traffic signals dynamically to clear the path for ambulances, fire trucks, and other emergency responders, DTSS significantly reduces response times in critical situations. This functionality supports public health and safety by ensuring that emergency services can reach their destinations quickly and efficiently.

[089] The Incident Detection and Response Module (180) further contributes to safety by proactively identifying and managing incidents, such as accidents or road obstructions. By rerouting traffic around the incident area and alerting nearby drivers through the User Information Interface (170), DTSS prevents secondary incidents and reduces delays, ensuring a safer and more resilient traffic network.

[090] The Adaptive Learning System (190) allows DTSS to continuously evolve, becoming more efficient over time. By analyzing historical traffic data, user behavior, and system performance, DTSS improves its response to recurring traffic patterns, seasonal fluctuations, and high-demand events. This adaptability ensures that DTSS remains responsive to changing urban conditions and continues to deliver optimal traffic management as cities grow and evolve.

[091] Another advantage of DTSS is the comprehensive data and insights it provides to city planners through the City Planner Customizable Settings Dashboard (230). This dashboard offers real-time data visualizations, performance reports, and behavioral analytics, enabling planners to make informed decisions based on actual traffic patterns and environmental impact. The dashboard's customizable settings also allow planners to adapt the system to specific city priorities, such as reducing emissions in certain areas or enhancing traffic flow during major events.

[092] Additionally, DTSS promotes eco-friendly commuting practices by providing commuters with real-time route recommendations and eco-friendly route options through the User Information Interface (170). By guiding drivers toward routes that reduce idling and emissions, DTSS fosters environmentally conscious behavior among commuters, supporting long-term sustainability goals for the city.

[093] DTSS also contributes to resilient urban infrastructure by operating independently of the main power grid through Renewable Energy Integration (220). This capability ensures uninterrupted functionality of traffic signals and system components during power outages or grid failures, making DTSS a reliable system for cities prone to power interruptions.

[094] In summary, the Dynamic Transportation & Signaling System (DTSS) offers substantial advantages, from reducing congestion and improving safety to promoting sustainability and supporting urban planning. By addressing the complex needs of modern cities, DTSS represents a comprehensive solution for transforming urban transportation, making it more efficient, resilient, and environmentally friendly.

Examples of Application Scenarios
[095] The Dynamic Transportation & Signaling System (DTSS) can be deployed across various urban settings, each presenting unique challenges and opportunities. These examples illustrate how DTSS operates effectively under different scenarios, showcasing its adaptability and multifaceted functionality.

[096] Urban Traffic Management: In a densely populated city center, DTSS can be deployed to manage high traffic volumes, particularly during peak commuting hours. By using real-time data and predictive algorithms, DTSS adjusts signal timings dynamically to optimize traffic flow. For example, during the morning rush, DTSS may allocate longer green lights to major inbound routes, while in the evening, it prioritizes outbound routes. This real-time optimization reduces waiting times, prevents bottlenecks, and enhances commuting efficiency.

[097] Emergency Response Optimization: DTSS is particularly beneficial in areas where rapid emergency response is critical, such as near hospitals, fire stations, and police departments. The Emergency Vehicle Detection Module (140) identifies ambulances or fire trucks and creates a clear path by adjusting traffic signals along the emergency vehicle's route. For instance, if an ambulance is approaching a busy intersection, DTSS temporarily extends the green light on its path, allowing it to pass without delays. This functionality significantly improves response times, potentially saving lives in critical situations.

[098] Public Transportation Synchronization: DTSS can be integrated with public transportation systems in cities with high commuter reliance on buses, trams, or metro services. The Public Transport Integration Module (160) syncs traffic signals with transit schedules, prioritizing public transport routes during peak hours. For instance, if a bus is running behind schedule, DTSS may extend green signals at upcoming intersections to allow it to catch up. This synchronization minimizes delays for public transportation and encourages more residents to use eco-friendly transit options.

[099] Environmental Impact Reduction in Sensitive Zones: In areas with strict air quality regulations, such as school zones, residential neighborhoods, and hospital vicinities, DTSS can help reduce emissions by managing traffic flow to minimize idling. The Environmental Sensing Unit (150) continuously monitors air quality, and DTSS adjusts signal timings accordingly. For example, if air pollution levels near a school reach a threshold, DTSS may prioritize signal timings to reduce congestion, lowering emissions in the vicinity and protecting public health.

[100] Event Management and Traffic Surge Handling: During large public events, such as concerts, sports games, or parades, DTSS adapts to handle temporary traffic surges. By pre-configuring DTSS to accommodate increased traffic volumes around event venues, city planners can prevent congestion. For example, DTSS may allocate additional green light time on major routes leading to an event venue before the start and direct traffic away afterward. This proactive management of event-related traffic improves flow and minimizes disruptions to regular commuting routes.

[101] Construction Zone Management: DTSS is valuable in managing traffic around construction zones, where roadblocks and detours are common. The Incident Detection and Response Module (180) detects slowdowns or obstructions in these areas and reroutes traffic accordingly. For example, if a lane is closed for roadwork, DTSS will adjust signal timings to divert traffic to alternative routes, reducing congestion near the construction site and improving traffic flow.

[102] Tourist and Visitor Traffic Control: In cities with high tourist activity, DTSS can help manage traffic near popular attractions and historical sites. The system can adapt to daily visitor peaks by prioritizing traffic signals around these areas. Additionally, the User Information Interface (170) can guide tourists on optimal routes and parking options, reducing unnecessary congestion and enhancing the visitor experience.

[103] Smart City and IoT Integration: DTSS integrates seamlessly with other smart city infrastructures, such as CCTV, weather sensors, and centralized traffic management systems. In this scenario, DTSS receives data from various Internet of Things (IoT) devices, enhancing its ability to manage traffic. For instance, during a rainstorm, DTSS could receive weather updates, prompting it to adjust signal timings to accommodate slower driving speeds, thereby enhancing safety.

[104] Data-Driven Urban Planning: By continuously collecting and analyzing traffic and environmental data, DTSS provides valuable insights for city planners. Through the City Planner Customizable Settings Dashboard (230), planners can access reports on congestion trends, air quality impacts, and incident frequencies. This data helps in making strategic infrastructure improvements, such as adding lanes, adjusting speed limits, or setting up new traffic signals in high-demand areas.

[105] These application scenarios demonstrate DTSS's versatility in managing urban traffic effectively across various environments and conditions. By adapting to specific needs-whether improving emergency response, reducing environmental impact, or handling event-related traffic surges-DTSS proves to be a robust and flexible solution for modern urban traffic management challenges.

Enablement of the Invention
[106] The Dynamic Transportation & Signaling System (DTSS) is enabled through the integration of multiple hardware and software components that operate in coordination to dynamically manage traffic flow, optimize signal timings, and reduce urban congestion. Each element of DTSS has been carefully designed to perform specific functions within the system, ensuring that it meets the requirements for real-time traffic management, environmental monitoring, emergency vehicle prioritization, and adaptive learning.
[107] The Traffic Signal Network (100) is enabled through the deployment of interconnected traffic lights, each equipped with programmable signal controllers capable of receiving and executing timing adjustments in response to real-time commands from the Central AI/ML Software Platform (120). Traffic signals within this network are installed at key intersections across the urban area, and they are configured to communicate with the central software platform to receive timing adjustments. This setup allows DTSS to operate as a cohesive system, with the Traffic Signal Network (100) responding dynamically to optimize traffic flow based on real-time conditions.
[108] The Vehicle Hardware Unit (110) is designed with GPS, LiDAR, radar, and environmental sensors that collect data on vehicle location, speed, type, and surrounding environmental conditions. This unit is installed within vehicles that participate in the DTSS network, such as public transport vehicles, emergency services, and commercial fleets. The vehicle hardware unit is configured to transmit data securely to the central software platform, enabling DTSS to analyze traffic density, identify vehicle classifications, and prioritize traffic flow based on real-time conditions. This hardware integration ensures accurate data collection essential for the system's decision-making process.
[109] The Central AI/ML Software Platform (120) operates as the core of DTSS, processing data from the Vehicle Hardware Unit (110), Traffic Signal Network (100), and Environmental Sensing Unit (150). The software platform includes the Predictive Algorithm Module (130), which utilizes machine learning techniques, such as supervised and reinforcement learning, to predict traffic patterns and adjust signal timings accordingly. This platform is configured with an adaptive learning capability, allowing DTSS to improve its performance based on historical data, real-time feedback, and evolving urban traffic patterns. The central platform is implemented on a secure, cloud-based or dedicated server, allowing it to scale and adapt as urban needs grow.
[110] The Emergency Vehicle Detection Module (140) is enabled through the use of RFID, GPS, and vehicle-to-infrastructure (V2I) communication technology that identifies emergency vehicles and prioritizes their movement within the traffic network. This module is designed to work in tandem with the predictive algorithm module, calculating the optimal path for emergency vehicles and adjusting signal timings to clear a passage along their route. By employing real-time data from emergency vehicle hardware, the module can quickly adapt to ensure minimal delay for emergency services, enhancing public safety and response times.
[111] The Environmental Sensing Unit (150) is enabled by a network of air quality and noise sensors installed at key locations, such as near schools, hospitals, and high-traffic zones. These sensors continuously collect data on pollution levels, which is sent to the Central AI/ML Software Platform (120) for processing. The environmental data influences the system's decisions on signal timing, reducing idling and emissions in sensitive areas. The sensing unit is designed to withstand urban environmental conditions and provides reliable data critical for eco-friendly traffic management.
[112] The Public Transport Integration Module (160) is enabled through communication protocols that allow DTSS to sync with public transportation schedules. This integration is achieved through direct data exchange with public transit systems, which provides the DTSS platform with schedule data and vehicle locations. The module is programmed to prioritize public transport vehicles by adjusting traffic signals to reduce delays along their routes, encouraging the use of eco-friendly commuting options and improving public transit reliability.
[113] The User Information Interface (170) is enabled as a mobile or web application accessible to city commuters. This interface provides users with real-time traffic updates, eco-friendly route suggestions, and alerts about incidents or emergency vehicle prioritization. The User Information Interface (170) is designed with an intuitive layout, allowing users to interact easily and receive timely information that improves their commuting experience. The application is integrated with the Central AI/ML Software Platform (120) to provide commuters with accurate, up-to-the-minute information based on current traffic conditions.
[114] The Incident Detection and Response Module (180) is enabled through anomaly detection algorithms that monitor traffic flow and identify potential incidents, such as accidents or road obstructions. This module processes real-time data from vehicle hardware and signal network sensors, flagging unusual patterns that may indicate an incident. Once detected, the module communicates with the central platform to adjust nearby signal timings and reroute traffic, minimizing congestion and delays caused by the incident.
[115] The Adaptive Learning System (190) is enabled through continuous data collection and machine learning models that analyze past system performance. This adaptive learning capability allows DTSS to identify recurring traffic patterns, optimize signal timings based on historical data, and improve incident response strategies. The system's learning capability is implemented on the Central AI/ML Software Platform (120), providing it with the resources to adapt and refine its algorithms based on ongoing data inputs.
[116] The Data Security Module (200) is enabled through encryption protocols, such as AES-256, multi-factor authentication, and access controls. This module protects data integrity, ensuring that all transmissions between DTSS components are secure. Data security measures comply with regulatory standards, providing a reliable and secure traffic management solution that safeguards user privacy and system integrity.
[117] The City Planner Customizable Settings Dashboard (230) is enabled as a secure administrative interface, allowing city planners to customize DTSS parameters, access data analytics, and monitor system performance. This dashboard provides planners with control over traffic prioritization, eco-friendly zones, emergency response protocols, and system-wide adjustments during large events. The dashboard's customizable features allow for fine-tuning of DTSS operations, ensuring the system remains aligned with urban planning goals.
[118] Through the integration of these components, DTSS operates as a comprehensive, adaptive, and resilient system for urban traffic management. The enablement of DTSS is achieved through advanced data processing, machine learning, secure data handling, and user-centered design, making it a fully functional and deployable solution for cities aiming to optimize traffic flow, enhance public safety, and support sustainability initiatives.
Alternative Embodiments
[119] The Dynamic Transportation & Signaling System (DTSS) can be adapted for diverse urban environments, including suburban and rural settings where traffic density and infrastructure requirements differ. For instance, in smaller cities or suburban areas with lower traffic volumes, DTSS may be deployed with a simplified sensor network and fewer predictive algorithms, optimizing for cost-effectiveness while maintaining essential traffic flow benefits. Additionally, DTSS can be configured as a temporary system in construction zones, special event venues, or other transient locations where dynamic traffic management is required. In these scenarios, DTSS could operate in a streamlined mode, adjusting signal timings based on real-time data to ensure safe and efficient passage.
Redundancy and Failover Mechanisms
[120] To maintain uninterrupted service, DTSS is equipped with redundancy and failover mechanisms designed to handle unexpected hardware or software malfunctions. Backup power sources, such as rechargeable batteries connected to solar panels, ensure that DTSS can continue functioning during power outages. Moreover, data backup servers support continuous data storage and analysis, safeguarding against data loss. In cases where certain sensors or network connections fail, the Central AI/ML Software Platform (120) is programmed to compensate by relying on alternative data sources or activating backup protocols. This redundancy ensures that DTSS remains operational and dependable, even under challenging conditions.
Implementation Examples
[121] The scalability of DTSS allows it to be implemented in cities of varying sizes, from dense urban centers to smaller municipalities. In large metropolitan areas, DTSS would deploy extensive sensor networks, high-performance computing systems, and advanced predictive algorithms to handle complex traffic patterns and large data volumes. In contrast, smaller cities might require fewer sensors and less computational power, with simplified algorithms that prioritize cost-effectiveness. Additionally, DTSS's modular architecture facilitates phased implementation, allowing cities to gradually add functionalities such as emergency vehicle prioritization, environmental sensing, and public transport integration as budgets permit. This flexibility makes DTSS adaptable and scalable across different urban landscapes.
Maintenance and Upgrades
[122] DTSS is designed to support ongoing maintenance and upgrades, ensuring that it remains effective and up-to-date over time. Regular maintenance of physical components, such as traffic signals, vehicle hardware units, and environmental sensors, is facilitated through a predictive maintenance schedule managed by the Central AI/ML Software Platform (120). Additionally, software updates, including machine learning model improvements and algorithm refinements, can be deployed remotely, allowing DTSS to evolve alongside advancements in traffic management technology. This maintenance and upgradeability ensures that DTSS remains robust and adaptable, delivering consistent performance as urban needs and technologies change.
Benefits and Expected Outcomes
[123] Deploying DTSS in an urban environment brings measurable improvements to traffic efficiency, public safety, and environmental impact. By dynamically optimizing traffic flow, DTSS reduces average commute times and minimizes idling, leading to lower emissions and improved air quality. Additionally, the prioritization of emergency vehicles results in faster response times, enhancing public safety. With its eco-friendly route suggestions and renewable energy integration, DTSS also supports sustainable city initiatives, contributing to lower carbon footprints. City planners can utilize the comprehensive data analytics provided by DTSS to make informed, data-driven decisions that shape the city's future infrastructure and transportation policies.
Definitions of Terms
[124] For clarity, certain terms used throughout the DTSS patent document are defined as follows. "V2X" refers to Vehicle-to-Everything communication, a protocol enabling data exchange between vehicles and DTSS infrastructure. "Adaptive learning" denotes DTSS's machine learning capability to adjust its algorithms based on real-time and historical data analysis. "Eco-friendly zones" indicate designated areas where DTSS prioritizes emission reduction through traffic control. "Predictive Algorithm Module" represents the AI component responsible for forecasting traffic flow and adjusting signal timings. Lastly, "User Information Interface" is the mobile or web application interface that provides commuters with real-time traffic updates and routing suggestions. These definitions clarify key components and functions within the DTSS system, ensuring comprehensive understanding of the technology.

DRAWINGS


1. Traffic Signal Network - (100)
2. Vehicle Hardware Unit - (110)
3. Central AI/ML Software Platform - (120)
4. Predictive Algorithm Module - (130)
5. Emergency Vehicle Detection Module - (140)
6. Environmental Sensing Unit - (150)
7. Public Transport Integration Module - (160)
8. User Information Interface - (170)
9. Incident Detection and Response Module - (180)
10. Adaptive Learning System - (190)
11. Data Security Module - (200)
12. Behavioral Insights and Social Impact Analysis - (210)
13. Renewable Energy Integration - (220)
14. City Planner Customizable Settings Dashboard - (230)

, C , Claims:Claim 1
A dynamic transportation and signaling system (DTSS) for real-time traffic management, comprising:
a. a traffic signal network (100) configured to adjust signal timings in response to command signals;
b. a vehicle hardware unit (110) configured to collect data on vehicle type, speed, and location, and to transmit this data to a central AI/ML software platform (120);
c. a central AI/ML software platform (120) configured to receive data from the vehicle hardware unit (110) and environmental sensing unit (150), analyze traffic patterns, and send timing adjustments to the traffic signal network (100);
d. a predictive algorithm module (130) integrated within the central AI/ML software platform (120) to forecast traffic congestion based on real-time and historical data; and
e. an emergency vehicle detection module (140) configured to identify emergency vehicles and prioritize their passage by adjusting signal timings along their route,
wherein the DTSS dynamically optimizes traffic flow by adjusting signal timings in response to real-time data inputs.

Claim 2
The system as claimed in claim 1, further comprising an environmental sensing unit (150) configured to monitor air quality and noise levels, wherein the central AI/ML software platform (120) adjusts signal timings in response to data from the environmental sensing unit (150) to reduce vehicle emissions in high-sensitivity zones.

Claim 3
The system as claimed in claim 1, wherein the central AI/ML software platform (120) further comprises an adaptive learning system (190) configured to refine predictive algorithms based on real-time feedback, historical data, and user behavior patterns, enhancing future traffic flow predictions and incident responses.

Claim 4
The system as claimed in claim 1, further comprising a public transport integration module (160) configured to synchronize the traffic signal network (100) with public transportation schedules, thereby prioritizing public transport vehicles during peak hours.
Claim 5
The system as claimed in claim 1, further comprising a user information interface (170) configured to provide real-time traffic updates, eco-friendly route suggestions, and incident alerts to commuters.

Claim 6
The system as claimed in claim 1, further comprising an incident detection and response module (180) configured to detect traffic incidents, such as accidents and road blockages, and to reroute traffic by adjusting signal timings and providing detours through the user information interface (170).

Claim 7
The system as claimed in claim 1, wherein the emergency vehicle detection module (140) is configured to identify emergency vehicles using vehicle-to-infrastructure (V2I) communication protocols, such as RFID and GPS, to dynamically adjust signal timings along the vehicle's route.

Claim 8
The system as claimed in claim 1, further comprising a renewable energy integration unit (220) configured to power the traffic signal network (100) and vehicle hardware unit (110) using renewable energy sources, with battery storage to maintain continuous operation during low-light or low-wind conditions.

Claim 9
The system as claimed in claim 1, further comprising a city planner customizable settings dashboard (230) configured to allow authorized personnel to adjust DTSS parameters, including eco-friendly zone settings, emergency response prioritization, and public transport prioritization.
Claim 10
A method for dynamic traffic management in an urban environment, comprising:
a. collecting real-time vehicle and environmental data using a vehicle hardware unit (110) and an environmental sensing unit (150);
b. processing collected data through a central AI/ML software platform (120) that utilizes a predictive algorithm module (130) to forecast traffic congestion;
c. adjusting signal timings in a traffic signal network (100) based on the forecasted congestion and real-time traffic conditions; and
d. prioritizing emergency vehicles by detecting their presence through an emergency vehicle detection module (140) and dynamically adjusting traffic signals along their route,
wherein the method improves urban traffic flow and reduces congestion through real-time data analysis and signal adjustment.

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