Description:FORM 2
THE PATENTS ACT 1970
(39 of 1970)
&
The Patents Rules, 2003
COMPLETE SPECIFICATION
(See section 10 and rule 13)
1. TITLE OF THE INVENTION: “A DYNAMIC ROUTE OPTIMIZATION SYSTEM AND METHOD THEREOF”
2. APPLICANTS:

(A) NAME : NERVANIK AI LABS PVT. LTD.
(B) NATIONALITY : INDIAN
(C) ADDRESS : A – 1111, WORLD TRADE TOWER
OFF. S G ROAD,
B/H SKODA SHOWROOM, MAKARBA
AHMEDABAD 380 051
GUJARAT, INDIA.

PROVISIONAL
The following specification describes the invention. COMPLETE
The following specification particularly describes the invention and the manner in which it is to be performed.

Field of invention
The present invention relates to transportation and logistics, more particularly to a system and method for dynamically optimizing travel routes and schedules using real-time traffic data, historical data, environmental conditions, and predictive analytics.
Background of invention
In the contemporary domain of transportation and logistics, the need for intelligent, responsive route optimization has become increasingly critical. Existing navigation and route planning systems, though widely used, suffer from fundamental limitations. Traditional static routing systems compute routes based primarily on geographic distance or predefined travel times, without accommodating dynamic road conditions. These systems are incapable of adjusting to real-time disruptions such as sudden traffic congestion, accidents, or road closures, leading to inefficiencies in logistics operations and travel safety.
Popular real-time navigation applications, such as Google Maps and Waze, provide some level of adaptability by incorporating user-reported incidents and traffic flow data. However, these platforms largely remain reactive, depending on inconsistent or delayed data sources. They also lack depth in contextual decision-making, offering limited or no integration with broader environmental data such as real-time weather patterns, fog, flooding, landslides, or hazardous material spills. Fleet management solutions used in commercial logistics attempt to optimize delivery paths and schedules based on heuristics like cost or delivery windows but rarely include real-time risk assessment or environmental forecasting as part of their computation models.
Some systems provide weather overlays or alert icons, but these are generally visual indicators rather than actively integrated inputs influencing route generation or adjustment. Similarly, databases that log accident hotspots, construction zones, or road condition assessments tend to operate in isolation, offering planners post-hoc insights rather than real-time, actionable intelligence. Current technologies are therefore siloed and unable to cohesively respond to changing road and environmental conditions during a journey.
As transportation systems become increasingly complex and risk-prone, especially in sectors such as logistics, emergency services, and autonomous vehicles, the limitations of conventional systems become more pronounced. There is a significant gap in solutions that can unify historical route patterns, live traffic data, environmental risk indicators, and predictive analytics into a cohesive, modular, and intelligent routing engine.
The present invention addresses these deficiencies by offering a dynamic route optimization and real-time adjustment system that integrates multiple real-time and historical datasets. By leveraging machine learning and predictive modeling, the system not only recommends optimal routes but continuously adapts to evolving traffic, weather, and environmental conditions. This enables safer, more efficient, and contextually intelligent travel and delivery planning, filling a critical void left by current routing technologies.
Hence, it is needed to invent a dynamic route optimization system and method.
Object of Invention
The object of the present invention is to provide a dynamic route optimization system and method
Further object of the dynamic route optimization system and method is to provide an integrated route optimization system that unifies historical travel data, real-time traffic conditions, predictive weather information, and route-specific risk factors into a centralized platform for more accurate and intelligent route planning.
Further object of the dynamic route optimization system and method is to enable predictive and adaptive routing using trained models over past and present datasets, allowing the system to proactively recommend and update travel paths based on evolving traffic dynamics, environmental conditions, and travel behavior trends.
Another object of the dynamic route optimization system and method is to incorporate a real-time implementation and adjustment mechanism that monitors incoming data during a journey and dynamically modifies route and schedule recommendations in response to incidents such as accidents, congestion, weather disruptions, or environmental hazards.
Yet another object of the dynamic route optimization system and method is to enhance travel safety and operational efficiency by integrating alert system based on accident hotspots, assessing road quality, and identifying natural or chemical hazards, thereby reducing exposure to risky or inaccessible routes.
Yet another object of the dynamic route optimization system and method is to support diverse deployment environments and interfaces by designing the system architecture to be modular, scalable, and compatible with vehicle dashboards, mobile applications, and centralized fleet or logistics control centers via wireless communication protocols.
These and other objects will be apparent based on the disclosure herein.
Summary of invention
The dynamic route optimization system and method provides a modular, intelligent route planning and adjustment system that integrates historical travel data, real-time traffic conditions, weather forecasts data, route-specific risk factors and driver’s historical performance to optimize travel decisions dynamically. The system comprises a data aggregation module that cleanses and synchronizes multi-source inputs into a unified dataset. An analysis module processes aggregated data using trained models to estimate optimal travel routes, risk and adaptive departure scheduling outcomes providing actionable recommendations. The implementation module presents ranked travel options to users, factoring in travel time, safety, and environmental impact. During active trips, the real-time adjustment module monitor evolving conditions and re-routes travel as necessary. The dynamic route optimization system and method enhances situational awareness, resilience, and proactive decision-making in transportation, making it ideal for logistics, emergency services, and autonomous vehicle applications.
Brief description of drawings
Other objects, advantages and novel features of the invention will become apparent from the following detailed description of the present embodiment when taken in conjunction with the accompanying drawings. Fig. 1 illustrates the core components and data flow within the invention.
Fig. 2 illustrates the procedural execution of the invention from data intake to real-time adjustment.
Detailed Description of Invention
Before explaining the present invention in detail, it is to be understood that the invention is not limited in its application to the details of the construction and arrangement of parts illustrated in the accompany drawings. The invention is capable of other embodiment, as depicted in different figures as described above and of being practiced or carried out in a variety of ways. It is to be understood that the phraseology and terminology employed herein is for the purpose of description and not of limitation.
It is to be also understood that the term "comprises" and grammatical equivalents thereof are used herein to mean that other components, ingredients, steps, etc. are optionally present. For example, an article "comprising" (or "which comprises") components A, B, and C can consist of (i.e., contain only) components A, B, and C, or can contain not only components A, B, and C but also contain one or more other components.
The present invention discloses a dynamic route optimization system and method designed to intelligently compute and modify travel routes using a combination of historical data, real-time traffic conditions, environmental hazards, and predictive analytics. The invention is modular in structure and employs advanced machine learning to generate context-aware, optimized travel plans that are continuously updated based on live inputs.
As used herein, a ‘processor’ includes any programmable system including systems using micro-controllers, reduced instruction set circuits (RISC), application specific integrated circuits (ASICs), logic circuits, and any other circuit or processor capable of executing the functions described herein. The above examples are for illustration only, and are thus not intended to limit in any way the definition and/or meaning of the term “processor”.
A ‘Machine learning (ML)’ is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values that resembles to human brains’ decisive capabilities.
Unless specifically stated otherwise, throughout this specification, terms such as "processing", "computing", "calculating", "selecting", "forming", "enabling", "inhibiting", "identifying", "initiating", "querying", "obtaining", "hosting", "maintaining", "representing", "modifying", "receiving", "transmitting", "storing", "authenticating", "authorizing", "hosting", "determining" and/or the like refer to actions and/or processes that may be performed by a system, such as a computer and/or other computing platform, capable of manipulating and/or transforming data which may be represented as electronic, magnetic and/or other physical quantities within the system's processors, memories, registers, and/or other information storage, transmission, reception and/or display devices.
A ‘Document or Data’ defines a digital record of some information that can be used as an authority or for reference, further analyses or study. Documentation refers to the on-going process of creating, disseminating, managing and using documents.
‘Data storage’ or ‘database’ defines the use of recording media to retain data using computers or other infrastructure (cloud). The most prevalent forms of data storage are file storage, block storage and object storage, with each being ideal for different purposes. Data storage is the recording (storing) of information (data) in a storage medium. Handwriting, phonographic recording, hard drive, magnetic tape, and optical discs are all examples of storage media.
A ‘Server’ is a computer program or device that provides a service to another computer program and its user. The Server's computer alternatively can be a workstation, minicomputer or microcomputer or other device. Although reference herein is made to information transfer via modem, it should be noted that cable, satellite, fiber optics, or other means for transferring information can also be utilized. The method of transferring the information is based on the current availability within the communities.
A ‘Method’ describes flow diagrams or otherwise, may also be executed and/or controlled, in whole or in part, by a computing platform. Method herein described as flow diagram is graphical or visual representation using a standardized set of symbols and notations to describe a business's operations through data movement.
As used herein, a ‘module’ refers to sections of large software packages to define its functionality. In the present invention module is used to make the software easier to use for a specific purpose or to define where boundaries exist in a program. The modules described minutely with sub-modules which merely differentiates detailed aspect of said module work to serve the purpose.
A dynamic route optimization system (10) may be deployed in vehicle and fleet management command centre for route optimization and schedule recommendation based on various inputs from the associated parameters for effective adaptation.
As shown in fig. 1, a dynamic route optimization system (10) comprises a data collection module (100) may be configured to retrieve and store data being collected from various sensors and databases. The data collection module (100) fetches a historical data (101) from historical data storage (301) which consists past travel route information, including route selection patterns, average travel time, delays, and previous route preferences. The historical data storage (301) also comprises various datasets including, but not limited to, a historical datasets of driver performance, route behavior, recurring events of route, traffic behavior, road condition, road blockages that are retrieved and stored in structured database for future referencing and training predictive models using machine learning. Said historical datasets are used as a foundation for predictive modeling and machine learning training, allowing the system to learn from past traffic behaviors, driver performance under various weather conditions and recurring bottlenecks in various routes.
A real-time traffic data (102) being collected by the data collection module (100) consist a data of live traffic being collected from IoT (Internet of Things) traffic sensors, navigation APIs, and vehicular telematics. Information such as current congestion levels, speed averages, lane closures, and reported accidents are necessary to detect dynamic changes in road conditions. The real-time traffic data (102) ensures the system remains aware of immediate road constraints during route planning and execution.
A weather forecast data (103) being collected by the data collection module (100) is a real-time and short-term weather forecast data from meteorological sources. The weather forecast data (103) includes parameters like precipitation, temperature, visibility, fog, storms, and snow conditions. These inputs are critical for predicting weather-induced travel delays or hazards and for planning safe travel schedules.
A Route Risk Data (104) is an inclusive data being collected by the data collection module (100) that provides risk-related inputs through three dedicated datasets, including accident hotspot identification (104a), a road conditions assessment (104b) and environmental hazard detection (104c). The accident hotspot identification (104a) is data related to accident history databases and live incident feeds to map zones prone to frequent accidents. The road conditions assessment (104b) is data of pavement quality, construction zones, potholes, and maintenance alerts sourced from road agencies or crowd-sourced platforms. The environmental hazard detection (104c) is detection and evaluation of risks such as floods, fog, landslides, and chemical spills using sensors, government hazard feeds, and integration with GIS-based hazard mapping tools.
A data aggregation module (201) may be configured to receive, format, and synchronize input data from the data collection module (100). It includes a data cleaning engine to handle missing values, outlier correction, and temporal alignment of datasets. The module provides an aggregated data (105). The aggregated data (105) is a unified, high-quality dataset ready for analysis.
An analysis module (203) may be configured to form the computational core of the invention that analyzes the aggregated data (105). It employs supervised and unsupervised learning techniques (e.g., regression, clustering, neural networks) trained on historical and real-time data to predict route travel time, risk scores, and route viability. The analysis module (203) also evaluates correlations between input variables (e.g., weather and traffic flow) to improve prediction accuracy and make real-time adjustments. Said module processes historical data collected from historical data storage (301) to assess drivers' historical performance under various conditions, factoring in attributes like adaptability to unforeseen challenges, driving proficiency under adverse weather, and adherence to delivery timelines. Hence, tailored approach factoring individual strength provides driver-centric solutions for optimized efficiency and significantly improving reliability. Said module continuously learns using newly aggregated data and updates its internal weights accordingly. The analysis module (203) generates recommendations (106) for suitable travel route and estimated schedule. Said module may be configured to, but not limited to, present results via user interfaces such as mobile apps, dashboard screens, or control room terminals. The analysis module (203) ranks routes based on travel time, safety, fuel efficiency, driver habits and environmental impact. Each route includes reasoning (e.g., "Route A avoids flooded area X") enabling the operator of a vehicle or fleet management command centre to take informed decisions. The analysis module (203) also facilitates "adaptive departure scheduling”. Said module dynamically recalibrates departure times based on live data streams from various sources. For example, a driver departing 15 minutes can possibly avoid a developing storm or congested traffic may achieve a safer and faster delivery. The analysis module (203) also considers sustainability by minimizing fuel consumption and reducing carbon emissions through smarter route and schedule planning.
An implementation module (205) facilitates effective means of implementing the recommendations (106) provided by the analysis module (203). The stake holders including but not limited to a driver, a supervisor, a monitor, and a fleet manager can implement the recommendations (106) by various means. For autonomous vehicles it can be implemented by automation or on demand commands. For manually driven vehicles it enables authorities to communicate and instruct the driver to take recommended route or schedule for efficient road travel, while driver himself can decide to take actions based on recommendations. The system may be configured to be integrated with the vehicle in such a way that it can perform various tasks like switching turning traction control, hill hold control, emergency breaking, tyre change recommendations based on road condition and tyre pressure.
An adjustment module (207) monitors live data feeds from data aggregation module (201) and implementation module (205) during an ongoing trip and provides adjustment data (107) to the analysis module (203) that adjusts the recommendations (106) in real-time. The adjustments (108) are stored in historical data storage (301) in form of structured data for future referencing and training predictive models using machine learning. For example, if a new traffic jam or roadblock is detected, the module re-triggers the predictive engine and reroutes the journey. It can override previous recommendations based on severity thresholds and notify users through alerts. The module ensures dynamic adaptability throughout the travel lifecycle.
As shown in fig. 2, the process begins by following mentioned steps (300) in sequence as listed below
• Collecting (301) data from various sources through data collection module (100) including historical travel logs, live traffic updates, weather forecasts, and route-specific risks. Each source contributes to building a comprehensive understanding of current and potential travel conditions.
• Aggregating (303) historical data from historical data storage (301) with real-time data through the aggregation module (201) that merges, cleanses, and prepares input streams for downstream processing. Temporal alignment is particularly critical here, as data must be synchronized to maintain coherence.
• Analyzing (305) data through analysis module (203) models using various machine learning models hosted in said module for performing certain tasks. The analysis module (203) processes aggregated data (105) inputs to estimate optimal paths based on speed, reliability, safety, and compliance with constraints (e.g., delivery deadlines, restricted zones). Predicting potential risks and route are computed and provides recommendations (106). Based on computed insights, the analysis module (203) proposes recommendations (106) in form of a ranked list of travel routes and corresponding schedules.
• Implementing (307) plan selected from recommendations using the implementation module (205). Said module (205) facilitates users to review route recommendations via connected interfaces. The selected travel plan is activated, and instructions are sent to the driver, vehicle navigation system, or logistics control center. Feedback channels remain active to monitor journey execution.
• Adjusting (309) the recommendations (106) based on live input as new data from aggregation module (201). The adjustment module (207) dynamically recalculates alternatives based on critical data such as an accident on the planned route and provides adjustment data (107). The adjustment data (107) along with the reasoning are also stored in the historical data storage (301) in form of structured data for future referencing and training predictive models using machine learning.
Hence, said method ensures that the system remains responsive and resilient under unforeseen circumstances.
A dynamic route optimization system and method not only provides route optimization but also improves transportation resilience, situational awareness, and risk-informed mobility. Unlike conventional systems that respond passively to disruptions, the proposed invention offers an active decision-making engine with environmental and predictive foresight. It is suitable for applications in logistics, emergency response, public transport routing, and autonomous vehicle networks. Its modular design allows for extensibility by introducing additional data layers such as fuel pricing, carbon footprint metrics, or driver fatigue indexes can be integrated in future iterations.
The invention has been explained in relation to specific embodiment. It is inferred that the foregoing description is only illustrative of the present invention and it is not intended that the invention be limited or restrictive thereto. Many other specific embodiments of the present invention will be apparent to one skilled in the art from the foregoing disclosure.
All substitution, alterations and modification of the present invention which come within the scope of the following claims are to which the present invention is readily susceptible without departing from the invention. The scope of the invention should therefore be determined not with reference to the above description but should be determined with reference to appended claims along with full scope of equivalents to which such claims are entitled.

List of Reference Numerals
10 Dynamic route optimization system
100 Data collection module
101 Historical Data
102 Real-Time Traffic Data
103 Weather Forecast Data
104 Route Risk Data
104a Accident Hotspot Identification
104b Road Conditions Assessment
104c Environmental Hazard Detection
105 Aggregated Data
106 Recommendations
107 Adjustment data
201 Data Aggregation Module
203 Analysis Module
205 Implementation Module
207 Adjustment Module
301 Historical Data Storage
, Claims:We Claim:
1. A dynamic route optimization system (10), comprising;
a data collection module (100) configured for collecting historical data (101) stored in historical data storage (301) to retrieve driver behavior on past travel routes and travel time metrics, a real-time traffic data (102) to receive live traffic congestion, speed, and incident information, a weather forecast data (103) to acquire predictive meteorological data including temperature, precipitation, and visibility; and a route risk data (104);
a data aggregation module (201) configured to clean, normalize, and synchronize time-stamped data feeds to maintain consistency across all inputs data received from the data collection module (100) to generate an aggregated data (105);
an analysis module (203) configured to process the aggregated data (105) and generate recommendations (106) providing optimal route and travel schedule;
a implementation module (205) configured to implement the recommendations (106);
an adjustment module (207) configured to continuously update travel plans based on incoming real-time data from the data aggregation module (201);
characterized in that,
the dynamic route optimization system (10) assesses driver's historical performance under various conditions, factoring in attributes like adaptability to unforeseen challenges, driving proficiency under adverse weather, and adherence to delivery timelines and provides assignments that are optimized for efficiency and tailored to individual strengths for significantly improving reliability while also providing adaptive departure scheduling for dynamically recalibrating departure times based on combination of continuously updating recommendations (106) through real-time adjustment data (107).
2. The dynamic route optimization system as claimed in claim 1, wherein the route risk data (104) includes an accident hotspot identification (104a) for detecting frequent accident zones based on historical and real-time reports, a road conditions assessment (104b) for identifying deteriorated, under-maintenance, or closed routes, an environmental hazard detection (104c) configured to recognize natural and chemical hazards including fog, landslides, and chemical spills.
3. The dynamic route optimization system as claimed in claim 1, wherein the analysis module (203) utilizes supervised and unsupervised learning models trained on travel history, traffic flow dynamics, weather impacts, drivers' historical performance and route risk patterns.
4. The dynamic route optimization system as claimed in claim 1, wherein the environmental hazard detection (104c) comprises a sensor interface layer and API integration for detecting and reporting weather-induced and chemical threats on the route.
5. The system as claimed in claim 1, wherein the implementation module (108) is configured to interface with vehicle dashboards, mobile devices, or fleet control centers through wireless communication protocols.
6. The dynamic route optimization system as claimed in claim 1, wherein the adjustment module (207) provides flexibility to override previously suggested routes upon detection of significant deviations in traffic conditions, weather alerts, or accident reports, notify the user or vehicle control system of the updated optimal route in real-time, and store adjustment data (107) along with reasoning in historical data storage (301) for future referencing and training predictive models.
7. A method of optimizing and adjusting travel routes in real-time, the method comprising:
collecting historical data (101) from historical data storage (301), real-time traffic data (102), weather forecast data (103), and route risk data (104) through data collection module (100);
aggregating the collected data through a data aggregation module (201) generating an aggregated data (106);
analyzing the aggregated data (106) through machine learning and predictive models embedded within a analysis module (203) for generating a recommendations (106) of route and travel schedule;
implementing the recommendations (106) based on selected route and travel schedule; and
adjusting the recommendations (106) through real-time adjustment data (107) based on newly received data as live input;
wherein, drivers' historical performance under various conditions is assessed, factoring in attributes like adaptability to unforeseen challenges, driving proficiency under adverse weather, and adherence to delivery timelines and provides assignments that are optimized for efficiency and tailored to individual strengths for significantly improving reliability while also providing adaptive departure scheduling for dynamically recalibrating departure times based on combination of continuously updating recommendations (106) through real-time adjustment data (107).
8. The method as claimed in claim 7, wherein the data aggregation comprises cleaning, normalizing, and timestamp-aligning data streams to ensure consistency across data communication between modules.
9. The method as claimed in claim 7, wherein the machine learning models used in the analysis module (203) are periodically retrained using continually updated aggregated data (105).
10. The method as claimed in claim 7, wherein the real-time adjustment step comprises identifying a triggering condition including road closure, unexpected congestion, or environmental hazard, and modifying the route accordingly by switching to alternate routes, modifying estimated arrival times, re-ranking route risks in real-time and provide Adjustment data (107) to analysis module (203) and stores adjustment data (107) along with reasoning in historical data storage (301) for future referencing and training predictive models.
Dated this on 04th August, 2025.