1. A method for transportation network optimization comprising: receiving transportation data from a plurality of sources, analyzing the transportation data to identify transportation patterns, predicting future transportation demand based on the transportation patterns, and optimizing transportation resources based on the predicted transportation demand.

2. The method of claim 1, wherein the transportation data includes data related to transportation routes, transportation modes, transportation schedules, transportation capacity, transportation costs, and transportation delays.

3. The method of claim 1, wherein the transportation patterns include patterns related to transportation volumes, transportation origins and destinations, transportation frequencies, and transportation time windows.

4. The method of claim 1, wherein predicting future transportation demand comprises using machine learning algorithms to analyze historical transportation data and identify trends and patterns in transportation demand.

5. The method of claim 1, wherein optimizing transportation resources comprises allocating transportation resources based on the predicted transportation demand and minimizing transportation costs while meeting transportation service level agreements.

6. A transportation network optimization system comprising: a transportation data collection module configured to receive transportation data from a plurality of sources, a transportation analysis module configured to analyze the transportation data to identify transportation patterns and predict future transportation demand based on the transportation patterns, and a transportation optimization module configured to optimize transportation resources based on the predicted transportation demand.

7. The transportation network optimization system of claim 6, further comprising a machine learning module configured to analyze historical transportation data and identify trends and patterns in transportation demand to improve the accuracy of the predicted transportation demand.

8. The transportation network optimization system of claim 6, wherein the transportation optimization module is further configured to allocate transportation resources based on the predicted transportation demand and minimize transportation costs while meeting transportation service level agreements.

9. The transportation network optimization system of claim 6, further comprising a user interface module configured to display transportation data, transportation patterns, predicted transportation demand, and optimized transportation resources to a user. Transportation Network Optimization for Efficient and Effective Resource Allocation Abstract The patent describes a method and system for optimizing transportation networks by analyzing transportation data, predicting future demand, and allocating transportation resources accordingly. The method involves receiving transportation data from various sources, such as transportation routes, modes, schedules, capacity, costs, and delays, and analyzing this data to identify transportation patterns, such as volumes, origins and destinations, frequencies, and time windows. The method then predicts future transportation demand using machine learning algorithms and optimizes transportation resources based on the predicted demand to minimize costs while meeting service level agreements. The system includes modules for transportation data collection, analysis, prediction, and optimization, as well as a user interface for displaying relevant information. The system can also incorporate machine learning to improve the accuracy of the predicted transportation demand. , Claims:Claims :

1. A method for transportation network optimization comprising: receiving transportation data from a plurality of sources, analyzing the transportation data to identify transportation patterns, predicting future transportation demand based on the transportation patterns, and optimizing transportation resources based on the predicted transportation demand.

2. The method of claim 1, wherein the transportation data includes data related to transportation routes, transportation modes, transportation schedules, transportation capacity, transportation costs, and transportation delays.

3. The method of claim 1, wherein the transportation patterns include patterns related to transportation volumes, transportation origins and destinations, transportation frequencies, and transportation time windows.

4. The method of claim 1, wherein predicting future transportation demand comprises using machine learning algorithms to analyze historical transportation data and identify trends and patterns in transportation demand.

5. The method of claim 1, wherein optimizing transportation resources comprises allocating transportation resources based on the predicted transportation demand and minimizing transportation costs while meeting transportation service level agreements.

6. A transportation network optimization system comprising: a transportation data collection module configured to receive transportation data from a plurality of sources, a transportation analysis module configured to analyze the transportation data to identify transportation patterns and predict future transportation demand based on the transportation patterns, and a transportation optimization module configured to optimize transportation resources based on the predicted transportation demand.

7. The transportation network optimization system of claim 6, further comprising a machine learning module configured to analyze historical transportation data and identify trends and patterns in transportation demand to improve the accuracy of the predicted transportation demand.

8. The transportation network optimization system of claim 6, wherein the transportation optimization module is further configured to allocate transportation resources based on the predicted transportation demand and minimize transportation costs while meeting transportation service level agreements.

9. The transportation network optimization system of claim 6, further comprising a user interface module configured to display transportation data, transportation patterns, predicted transportation demand, and optimized transportation resources to a user.

Description:Transportation Network Optimization for Efficient and Effective Resource Allocation
Field of the Invention
[0001] The present invention relates to transportation network optimization for efficient and effective resource allocation. More specifically, the invention relates to methods and systems for analyzing transportation data, identifying transportation patterns, predicting transportation demand, and optimizing transportation resources to meet transportation service level agreements while minimizing transportation costs.
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] The transportation industry plays a critical role in the global economy by facilitating the movement of people and goods. With the increasing demand for transportation services, transportation companies are facing challenges in meeting service level agreements while minimizing costs. One of the major challenges is the optimization of transportation resources, such as vehicles, drivers, and fuel. Inefficient resource allocation can lead to increased transportation costs, longer delivery times, and reduced customer satisfaction.
[0004] To address these challenges, transportation companies have started using technology solutions to optimize their transportation networks. These solutions typically involve the collection and analysis of transportation data to identify transportation patterns and predict transportation demand. However, many of these solutions are limited in their ability to optimize transportation resources efficiently and effectively.
[0005] There is therefore a need for improved methods and systems for transportation network optimization that can efficiently and effectively allocate transportation resources while meeting transportation service level agreements and minimizing transportation costs. The present invention addresses this need by providing novel methods and systems for transportation network optimization that can analyze transportation data, identify transportation patterns, predict transportation demand, and optimize transportation resources to achieve these objectives.
[0006] All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.
[0007] It also shall be noted that as used herein and in the appended claims, the singular forms “a”, “an”, and “the” include plural referents unless the context clearly dictates otherwise. This invention can be achieved by means of hardware including several different elements or by means of a suitably programmed computer. In the unit claims that list several means, several ones among these means can be specifically embodied in the same hardware item. The use of such words as first, second, third does not represent any order, which can be simply explained as names.
Summary
[0008] Various objects, features, and advantages of the disclosed subject matter can be more fully appreciated with reference to the following detailed description of the disclosed subject matter when considered in connection with the following drawings, in which like reference numerals identify like elements.
[0009] The present invention relates to transportation network optimization for efficient and effective resource allocation. More specifically, the invention relates to methods and systems for analyzing transportation data, identifying transportation patterns, predicting transportation demand, and optimizing transportation resources to meet transportation service level agreements while minimizing transportation costs.
[00010] The present invention relates to a method and system for optimizing transportation networks. The system and method involve receiving transportation data from various sources, analyzing the data to identify transportation patterns, predicting future transportation demand based on those patterns, and then optimizing transportation resources based on the predicted demand.
[00011] The transportation data may include information related to transportation routes, modes, schedules, capacity, costs, and delays. The transportation patterns may include transportation volumes, origins and destinations, frequencies, and time windows.
[00012] The system may include a transportation data collection module for receiving data, a transportation analysis module for analyzing the data to identify patterns, and a transportation optimization module for optimizing resources based on the predicted demand. Additionally, the system may include a machine learning module for analyzing historical data to improve the accuracy of the predicted demand and a user interface module for displaying data and results to the user.
[00013] The method may involve using machine learning algorithms to analyze historical transportation data to identify trends and patterns in demand. The optimization module may allocate transportation resources based on the predicted demand while minimizing costs and meeting transportation service level agreements.
[00014] In operation, the transportation network optimization system receives transportation data from various sources, such as transportation companies and government agencies. The data is analyzed to identify patterns related to transportation volumes, origins and destinations, frequencies, and time windows. Historical data is analyzed using machine learning algorithms to improve the accuracy of the predicted transportation demand.
[00015] The transportation resources are then allocated based on the predicted demand to optimize the network while minimizing costs and meeting transportation service level agreements. The results of the optimization may be displayed to the user via the user interface module, which may include transportation data, transportation patterns, predicted transportation demand, and optimized transportation resources.
[00016] In summary, the present invention provides a method and system for optimizing transportation networks by predicting future demand and allocating resources accordingly. The use of machine learning algorithms to analyze historical data improves the accuracy of the predictions, and the optimization module minimizes costs while meeting service level agreements. The user interface module provides a way for users to view data and results.
Brief Description of the Drawings
[00017] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00018] FIG. 1 denotes a representative transportation network optimization system, according to some embodiments of the present disclosure.
[00019] FIG. 2 shows an exemplary flowchart exemplifying a method for transportation network optimization, according to some embodiments of the present disclosure.
Detailed Description
[00020] The following is a detailed description of exemplary embodiments to illustrate the principles of the invention. The embodiments are provided to illustrate aspects of the invention, but the invention is not limited to any embodiment. The scope of the invention encompasses numerous alternatives, modifications and equivalent; it is limited only by the claims.
[00021] In view of the many possible embodiments to which the principles of the present discussion may be applied, it should be recognized that the embodiments described herein with respect to the drawing figures are meant to be illustrative only and should not be taken as limiting the scope of the claims. Therefore, the techniques as described herein contemplate all such embodiments as may come within the scope of the following claims and equivalents thereof.
[00022] Throughout the present disclosure, the term “network” relates to an arrangement of interconnected programmable and/or non-programmable components that are configured to facilitate data communication between one or more electronic devices and/or databases, whether available or known at the time of filing or as later developed. Furthermore, the network may include, but is not limited to, one or more peer-to-peer network, a hybrid peer-to-peer network, local area networks (LANs), radio access networks (RANs), metropolitan area networks (MANS), wide area networks (WANs), all or a portion of a public network such as the global computer network known as the Internet, a private network, a cellular network and any other communication system or systems at one or more locations.
[00023] Throughout the present disclosure, the term “process”* relates to any collection or set of instructions executable by a computer or other digital system so as to configure the computer or the digital system to perform a task that is the intent of the process.
[00024] Throughout the present disclosure, the term ‘Artificial intelligence (AI)’ as used herein relates to any mechanism or computationally intelligent system that combines knowledge, techniques, and methodologies for controlling a bot or other element within a computing environment. Furthermore, the artificial intelligence (AI) is configured to apply knowledge and that can adapt it-self and learn to do better in changing environments. Additionally, employing any computationally intelligent technique, the artificial intelligence (AI) is operable to adapt to unknown or changing environment for better performance. The artificial intelligence (AI) includes fuzzy logic engines, decision-making engines, preset targeting accuracy levels, and/or programmatically intelligent software.
[00025] The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different instances in the description and the figures may indicate similar or identical items.
[00026] The present invention relates to transportation network optimization for efficient and effective resource allocation. More specifically, the invention relates to methods and systems for analyzing transportation data, identifying transportation patterns, predicting transportation demand, and optimizing transportation resources to meet transportation service level agreements while minimizing transportation costs.
[00027] One embodiment of the method 200 (shown in Fig. 2) for transportation network optimization includes; at step 202 receiving transportation data from a plurality of sources. The transportation data may include data related to transportation routes, transportation modes, transportation schedules, transportation capacity, transportation costs, and transportation delays. The transportation data may be collected from various sources such as transportation companies, public transportation authorities, and traffic management systems.The method also involves at step 204, analyzing the transportation data to identify transportation patterns. The transportation patterns may include patterns related to transportation volumes, transportation origins and destinations, transportation frequencies, and transportation time windows. The transportation patterns may be identified using data mining techniques and statistical analysis.At stepm 206, once the transportation patterns have been identified, the method involves predicting future transportation demand based on the transportation patterns. This may involve using machine learning algorithms to analyze historical transportation data and identify trends and patterns in transportation demand. The predicted transportation demand may be used to forecast the required transportation resources in the future.At step 208, the method also involves optimizing transportation resources based on the predicted transportation demand. This may involve allocating transportation resources based on the predicted transportation demand and minimizing transportation costs while meeting transportation service level agreements. The transportation resources may include vehicles, drivers, and fuel. The optimization of transportation resources may be achieved using optimization algorithms, such as linear programming or genetic algorithms.
[00028] In the method of transportation network optimization, the transportation data includes various factors such as transportation routes, transportation modes, transportation schedules, transportation capacity, transportation costs, and transportation delays. This data is obtained from various sources, including transportation companies, logistics providers, and other transportation-related entities.
[00029] The transportation patterns identified by analyzing the transportation data can include transportation volumes, transportation origins and destinations, transportation frequencies, and transportation time windows. By identifying these patterns, the transportation network optimization system can predict future transportation demand.
[00030] The prediction of future transportation demand is achieved by using machine learning algorithms to analyze historical transportation data and identify trends and patterns. By analyzing the transportation data, the system can identify trends such as seasonality, spikes in demand, and changes in transportation routes, among others.
[00031] The optimization of transportation resources involves allocating transportation resources based on the predicted transportation demand while minimizing transportation costs and meeting transportation service level agreements. The transportation optimization module achieves this by creating transportation plans that allocate transportation resources such as vehicles and drivers based on predicted demand, optimizing routes to reduce costs and improve efficiency, and considering service level agreements to ensure customer satisfaction. Transportation networks are an essential component of modern societies, enabling people and goods to move quickly and efficiently. The optimization of transportation networks is crucial to ensure the efficient use of transportation resources, minimize congestion, reduce travel time, and increase safety. A transportation network optimization system is a software tool that helps to optimize transportation resources by analyzing transportation data and predicting future transportation demand.
[00032] Fig. 1 shows a transportation network optimization system 100 comprises three main components: a transportation data collection module 102, a transportation analysis module 104, and a transportation optimization module 106.
[00033] The transportation data collection module is designed to receive transportation data from a variety of sources, including traffic sensors, GPS devices, toll booth data, public transportation schedules, and other data sources. The transportation data collection module collects and aggregates data from various sources and stores it in a centralized database for analysis.
[00034] The transportation analysis module is configured to analyze the transportation data to identify transportation patterns and predict future transportation demand based on the transportation patterns. The transportation analysis module uses advanced analytics techniques such as machine learning, statistical analysis, and data visualization to identify transportation patterns, including traffic flow, travel time, and congestion.
[00035] The transportation optimization module is designed to optimize transportation resources based on the predicted transportation demand. The transportation optimization module uses advanced algorithms to determine the most efficient and cost-effective transportation routes and modes of transportation. The transportation optimization module considers a range of factors, including travel time, distance, cost, and environmental impact.
[00036] Embodiments of the transportation network optimization system include a real-time transportation network optimization system. The real-time transportation network optimization system uses real-time transportation data to optimize transportation resources. The system continuously collects and analyzes transportation data to update transportation models and predict future transportation demand.
[00037] Another embodiment of the transportation network optimization system is a demand-responsive transportation network optimization system. The demand-responsive transportation network optimization system is designed to optimize transportation resources based on real-time transportation demand. The system uses real-time data to optimize transportation routes and modes of transportation based on current demand.
[00038] Another embodiment of the transportation network optimization system is a multimodal transportation network optimization system. The multimodal transportation network optimization system optimizes transportation resources across multiple modes of transportation, including cars, buses, trains, and bicycles. The system uses advanced algorithms to determine the most efficient and cost-effective transportation routes and modes of transportation based on user preferences and transportation demand.
[00039] Another embodiment of the transportation network optimization system is a sustainable transportation network optimization system. The sustainable transportation network optimization system optimizes transportation resources while minimizing environmental impact. The system considers factors such as energy consumption, emissions, and carbon footprint when optimizing transportation routes and modes of transportation.
[00040] Another embodiment of the transportation network optimization system is a smart city transportation network optimization system. The smart city transportation network optimization system is designed to optimize transportation resources in a smart city environment. The system uses advanced analytics techniques such as artificial intelligence, machine learning, and data visualization to optimize transportation resources and improve mobility in smart cities.
[00041] In conclusion, the transportation network optimization system is a software tool that helps to optimize transportation resources by analyzing transportation data and predicting future transportation demand. The system comprises three main components: a transportation data collection module, a transportation analysis module, and a transportation optimization module. The transportation network optimization system has several embodiments, including a real-time transportation network optimization system, a demand-responsive transportation network optimization system, a multimodal transportation network optimization system, a sustainable transportation network optimization system, and a smart city transportation network optimization system. These embodiments enable the transportation network optimization system to optimize transportation resources efficiently and effectively in various environments and contexts.
[00042] An exemplary use case scenario for the transportation network optimization system could be a large logistics company that operates a fleet of trucks and a network of warehouses across the country. The company wants to optimize its transportation network to reduce costs, improve delivery times, and increase customer satisfaction.
[00043] The transportation data collection module receives data from various sources, including GPS trackers on the trucks, warehouse inventory systems, and customer order data. The transportation analysis module analyzes this data to identify transportation patterns, such as the most common delivery routes, the busiest warehouses, and the most popular delivery time windows.
[00044] Using machine learning algorithms, the transportation analysis module predicts future transportation demand based on the identified patterns and historical data. The transportation optimization module then allocates transportation resources, such as trucks and drivers, based on the predicted demand and minimizes costs while meeting service level agreements.
[00045] For example, if the analysis shows that certain delivery routes are frequently congested during rush hour, the optimization module could allocate more resources to those routes during off-peak hours. Alternatively, if certain warehouses have excess inventory, the optimization module could schedule more deliveries to those warehouses to reduce storage costs.
[00046] The system provides a user interface that displays transportation data, patterns, predicted demand, and optimized resources to the logistics company's operations team. With this information, the team can make informed decisions about resource allocation and adjust the transportation network to meet changing demand patterns.
[00047] By optimizing its transportation network, the logistics company can reduce costs, improve delivery times, and increase customer satisfaction, ultimately leading to a more profitable business.
[00048] The above description is intended to be illustrative, and not restrictive. Although the present disclosure has been described with references to specific illustrative examples and implementations, it will be recognized that the present disclosure is not limited to the examples and implementations described. The scope of the disclosure should be determined with reference to the following claims, along with the full scope of equivalents to which the claims are entitled.
[00049] Modifications, additions, or omissions may be made to the systems and apparatuses described herein without departing from the scope of the disclosure. The components of the systems and apparatuses may be integrated or separated. Moreover, the operations of the systems and apparatuses may be performed by more, fewer, or other components. Additionally, operations of the systems and apparatuses may be performed using any suitable logic comprising software, hardware, and/or other logic. As used in this document, “each” refers to each member of a set or each member of a subset of a set.
[00050] The term “memory,” as used herein relates to a volatile or persistent medium, such as a magnetic disk, or optical disk, in which a computer can store data or software for any duration. Optionally, the memory is non-volatile mass storage such as physical storage media. Furthermore, a single memory may encompass and in a scenario wherein computing system is distributed, the processing, memory and/or storage capability may be distributed as well.
[00051] Throughout the present disclosure, the term ‘server’ relates to a structure and/or module that include programmable and/or non-programmable components configured to store, process and/or share information. Optionally, the server includes any arrangement of physical or virtual computational entities capable of enhancing information to perform various computational tasks.

Claims
I/We Claim:
1. A method for transportation network optimization comprising: receiving transportation data from a plurality of sources, analyzing the transportation data to identify transportation patterns, predicting future transportation demand based on the transportation patterns, and optimizing transportation resources based on the predicted transportation demand.

2. The method of claim 1, wherein the transportation data includes data related to transportation routes, transportation modes, transportation schedules, transportation capacity, transportation costs, and transportation delays.

3. The method of claim 1, wherein the transportation patterns include patterns related to transportation volumes, transportation origins and destinations, transportation frequencies, and transportation time windows.

4. The method of claim 1, wherein predicting future transportation demand comprises using machine learning algorithms to analyze historical transportation data and identify trends and patterns in transportation demand.

5. The method of claim 1, wherein optimizing transportation resources comprises allocating transportation resources based on the predicted transportation demand and minimizing transportation costs while meeting transportation service level agreements.

6. A transportation network optimization system comprising: a transportation data collection module configured to receive transportation data from a plurality of sources, a transportation analysis module configured to analyze the transportation data to identify transportation patterns and predict future transportation demand based on the transportation patterns, and a transportation optimization module configured to optimize transportation resources based on the predicted transportation demand.

7. The transportation network optimization system of claim 6, further comprising a machine learning module configured to analyze historical transportation data and identify trends and patterns in transportation demand to improve the accuracy of the predicted transportation demand.

8. The transportation network optimization system of claim 6, wherein the transportation optimization module is further configured to allocate transportation resources based on the predicted transportation demand and minimize transportation costs while meeting transportation service level agreements.

9. The transportation network optimization system of claim 6, further comprising a user interface module configured to display transportation data, transportation patterns, predicted transportation demand, and optimized transportation resources to a user.

Transportation Network Optimization for Efficient and Effective Resource Allocation
Abstract
The patent describes a method and system for optimizing transportation networks by analyzing transportation data, predicting future demand, and allocating transportation resources accordingly. The method involves receiving transportation data from various sources, such as transportation routes, modes, schedules, capacity, costs, and delays, and analyzing this data to identify transportation patterns, such as volumes, origins and destinations, frequencies, and time windows. The method then predicts future transportation demand using machine learning algorithms and optimizes transportation resources based on the predicted demand to minimize costs while meeting service level agreements. The system includes modules for transportation data collection, analysis, prediction, and optimization, as well as a user interface for displaying relevant information. The system can also incorporate machine learning to improve the accuracy of the predicted transportation demand. , Claims:Claims
I/We Claim:
1. A method for transportation network optimization comprising: receiving transportation data from a plurality of sources, analyzing the transportation data to identify transportation patterns, predicting future transportation demand based on the transportation patterns, and optimizing transportation resources based on the predicted transportation demand.

2. The method of claim 1, wherein the transportation data includes data related to transportation routes, transportation modes, transportation schedules, transportation capacity, transportation costs, and transportation delays.

3. The method of claim 1, wherein the transportation patterns include patterns related to transportation volumes, transportation origins and destinations, transportation frequencies, and transportation time windows.

4. The method of claim 1, wherein predicting future transportation demand comprises using machine learning algorithms to analyze historical transportation data and identify trends and patterns in transportation demand.

5. The method of claim 1, wherein optimizing transportation resources comprises allocating transportation resources based on the predicted transportation demand and minimizing transportation costs while meeting transportation service level agreements.

6. A transportation network optimization system comprising: a transportation data collection module configured to receive transportation data from a plurality of sources, a transportation analysis module configured to analyze the transportation data to identify transportation patterns and predict future transportation demand based on the transportation patterns, and a transportation optimization module configured to optimize transportation resources based on the predicted transportation demand.

7. The transportation network optimization system of claim 6, further comprising a machine learning module configured to analyze historical transportation data and identify trends and patterns in transportation demand to improve the accuracy of the predicted transportation demand.

8. The transportation network optimization system of claim 6, wherein the transportation optimization module is further configured to allocate transportation resources based on the predicted transportation demand and minimize transportation costs while meeting transportation service level agreements.

9. The transportation network optimization system of claim 6, further comprising a user interface module configured to display transportation data, transportation patterns, predicted transportation demand, and optimized transportation resources to a user.