Description: FIELD OF INVENTION
[0001] Embodiments of the present disclosure relates to route planning systems, and more particularly to a system and a method for vehicle route optimization.
BACKGROUND
[0002] Easy online rental of vehicles has made travelling less cumbersome and cheap. With the increase in third-party transportation services, rental vehicles are always available between a source location and a target location. Any consumer can easily book the rental vehicle through one click on a computing system and start the onboarding system.
[0003] Conventionally transportation services focus on helping people to reach the target location with the pre-decided money agreement. However, the conventional transportation systems lack any planning for pick up or drop for commuters traveling together. Such planning is necessary for lowering the traveling cost or reducing traveling time. The conventional transportation services fail to focus on comfort or specific travel requirements of commuters.
[0004] Hence, there is a need for an improved system for vehicle route optimization and a method to operate the same and therefore address the aforementioned issues.
BRIEF DESCRIPTION
[0005] In accordance with one embodiment of the disclosure, a computing system for vehicle route optimization is disclosed. The computing system includes a hardware processor. The computing system also includes a memory coupled to the hardware processor. The memory comprises a set of program instructions in the form of a plurality of subsystems and configured to be executed by the hardware processor.
[0006] The plurality of subsystems includes a travel request subsystem. The travel request subsystem is configured to receive a plurality of travel requests from one or more users. The plurality of subsystems also includes a travel parameter input. The travel parameter input subsystem is configured to capture one or more travel parameters from each of the one or more users and each of one or more fleet operators.
[0007] The plurality of subsystems also includes a travel analysis subsystem. The travel analysis subsystem is configured to generate optimal demand setting for the received request based on the one or more travel parameters, current route demand, current vehicle demand, and demand from number of the one or more users. The travel analysis subsystem is configured to classify each of the one or more users into one or more travel groupings based on the generated optimal demand setting. The travel analysis subsystem is also configured to apply the captured one or more travel parameters of the classified one or more users, prestored map router information and prestored site route information onto an artificial intelligence-based vehicle route management model. The travel analysis subsystem is also configured to determine one or more travel routes between each of the one or more source locations and each of the one or more target locations based on the results of the artificial intelligence-based vehicle route management model.
[0008] The travel analysis subsystem is also configured to determine one or more available vehicles for the classified one or more users based on the captured list of vehicle information and determined one or more travel routes. The plurality of subsystems also includes a route output subsystem. The route output subsystem is configured to output the determined one or more travel routes through one or more display interfaces.
[0009] In accordance with one embodiment of the disclosure, a method for vehicle route optimization is disclosed. The method includes receiving a plurality of travel requests from one or more users. The method also includes capturing one or more travel parameters from each of the one or more users and each of one or more fleet operators. The method also includes generating optimal demand setting for the received request based on the one or more travel parameters, current route demand, current vehicle demand, and demand from number of the one or more users. The method also includes classifying each of the one or more users into one or more travel groupings based on the generated optimal demand setting. The method also includes applying the captured one or more travel parameters of the classified one or more users, prestored map router information and prestored site route information onto an artificial intelligence-based vehicle route management model.
[0010] The method also includes determining one or more travel routes between each of the one or more source locations and each of the one or more target locations based on the results of the artificial intelligence-based vehicle route management model. The method also includes determining one or more available vehicles for the classified one or more users based on the captured list of vehicle information and determined one or more travel routes. The method also includes outputting the determined one or more travel routes through one or more display interfaces.
[0011] To further clarify the advantages and features of the present disclosure, a more particular description of the disclosure will follow by reference to specific embodiments thereof, which are illustrated in the appended figures. It is to be appreciated that these figures depict only typical embodiments of the disclosure and are therefore not to be considered limiting in scope. The disclosure will be described and explained with additional specificity and detail with the appended figures.
BRIEF DESCRIPTION OF THE DRAWINGS
[0012] The disclosure will be described and explained with additional specificity and detail with the accompanying figures in which:
[0013] FIG. 1 is a block diagram illustrating an exemplary computing system for vehicle route optimization in accordance with an embodiment of the present disclosure;
[0014] FIG. 2 is a flowchart illustrating an exemplary workflow for vehicle route optimization in accordance with an embodiment of the present disclosure;
[0015] FIG. 3 is a flowchart illustrating various inputs and outputs of the computing system for determining vehicle route in accordance with an embodiment of the present disclosure;
[0016] FIG. 4 is a flowchart illustrating various inputs and outputs for working of the artificial intelligence-based vehicle route management model in accordance with an embodiment of the present disclosure; and
[0017] FIG. 5 is a process flowchart illustrating an exemplary method for vehicle route optimization in accordance with an embodiment of the present disclosure.
[0018] Further, those skilled in the art will appreciate that elements in the figures are illustrated for simplicity and may not have necessarily been drawn to scale. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the figures by conventional symbols, and the figures may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the figures with details that will be readily apparent to those skilled in the art having the benefit of the description herein.
DETAILED DESCRIPTION
[0019] For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to the embodiment illustrated in the figures and specific language will be used to describe them. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended. Such alterations and further modifications in the illustrated online platform, and such further applications of the principles of the disclosure as would normally occur to those skilled in the art are to be construed as being within the scope of the present disclosure.
[0020] The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such a process or method. Similarly, one or more devices or subsystems or elements or structures or components preceded by "comprises... a" does not, without more constraints, preclude the existence of other devices, subsystems, elements, structures, components, additional devices, additional subsystems, additional elements, additional structures or additional components. Appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but not necessarily do, all refer to the same embodiment.
[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this disclosure belongs. The system, methods, and examples provided herein are only illustrative and not intended to be limiting.
[0022] In the following specification and the claims, reference will be made to a number of terms, which shall be defined to have the following meanings. The singular forms “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise.
[0023] A computer system (standalone, client or server computer system) configured by an application may constitute a “subsystem” that is configured and operated to perform certain operations. In one embodiment, the “subsystem” may be implemented mechanically or electronically, so a subsystem may comprise dedicated circuitry or logic that is permanently configured (within a special-purpose processor) to perform certain operations. In another embodiment, a “subsystem” may also comprise programmable logic or circuitry (as encompassed within a general-purpose processor or other programmable processor) that is temporarily configured by software to perform certain operations.
[0024] Accordingly, the term “subsystem” should be understood to encompass a tangible entity, be that an entity that is physically constructed permanently configured (hardwired) or temporarily configured (programmed) to operate in a certain manner and/or to perform certain operations described herein.
[0025] FIG. 1 is a block diagram illustrating an exemplary computing system 100 for vehicle route optimization in accordance with an embodiment of the present disclosure. The computing system 100 computes optimal route by taking into considerations the following important facts such as regional or zone-based restrictions for travelling, fair pickup or drop point for all users, passenger comfort and the like.
[0026] The computing system 100 includes a hardware processor 108. The computing system 100 also includes a memory 102 coupled to the hardware processor 108. The memory 102 comprises a set of program instructions in the form of a plurality of subsystems and configured to be executed by the hardware processor 108.
[0027] The hardware processor(s) 108, as used herein, means any type of computational circuit, such as, but not limited to, a microprocessor, a microcontroller, a complex instruction set computing microprocessor, a reduced instruction set computing microprocessor, a very long instruction word microprocessor, an explicitly parallel instruction computing microprocessor, a digital signal processor, or any other type of processing circuit, or a combination thereof.
[0028] Input/output (I/O) devices 110 (including but not limited to keyboards, displays, pointing devices, etc.) can be coupled to the system either directly or through intervening I/O controllers.
[0029] The memory 102 includes a plurality of subsystems stored in the form of executable program which instructs the hardware processor 108 via bus 104 to perform the method steps. The plurality of subsystems has following subsystems: a travel request subsystem 112, a travel parameter input subsystem 114, a travel analysis subsystem 116 and a route output subsystem 118.
[0030] Computer memory elements may include any suitable memory device(s) for storing data and executable program, such as read only memory, random access memory, erasable programmable read only memory, electrically erasable programmable read only memory, hard drive, removable media drive for handling memory cards and the like. Embodiments of the present subject matter may be implemented in conjunction with program modules, including functions, procedures, data structures, and application programs, for performing tasks, or defining abstract data types or low-level hardware contexts. Executable program stored on any of the above-mentioned storage media may be executable by the hardware processor(s) 108.
[0031] The plurality of subsystems includes a registration subsystem. The registration subsystem is configured to register one or more fleet operators using respective operator data. In such embodiment, the operator data comprises personal details of the one or more fleet operators, driver personal details, one or more vehicle details, one or more garage details, rate cards, documents and billing details.
[0032] The registration subsystem is also configured to register one or more users using user data. In such embodiment, the user data includes user personal details and one or more vehicle requirement parameters.
[0033] In one exemplary embodiment, the one or more users and the one or more fleet operators, first registers in the computing system 100 with all such registration data. The one or more users and the one or more fleet operators may avail help for route management only with complete registration. In such embodiment, the one or more users may include corporate users or an individual user.
[0034] The plurality of subsystems includes a travel request subsystem 112. The travel request subsystem 112 is configured to receive a plurality of travel requests from one or more users. In one exemplary embodiment, each of the plurality of travel requests is being associated with one or more source locations and one or more target locations. In such embodiment, each of the one or more users provide a source location or a target location. The source location refers to pick up location and the target location refers to drop location.
[0035] As used herein, the term “vehicle” refers to means of road transportation such as bike, car, truck, bus and the like. Vehicles have a fixed cost(rental) and a variable cost(mileage) which vary by the type of vehicle. As used herein the “Fleet” refers to a set of vehicle types. Fleet may be a homogenous fleet or a heterogenous fleet. The homogenous fleet refers to situation where all vehicles are the same type. The heterogenous fleet refers to situation where all the vehicles are different. The quantity of each vehicle type may be finite or infinite. As used herein, the term “location” is the ordered pair (x,y) coordinates of any surface (any two dimensional space) i.e. on the Euclidean plane or the surface of earth. As used herein, the term “demand” represents the quantity of a certain vehicle supplied or stored by any depot. Demand type may be different for pickup or drop.
[0036] The plurality of subsystems also includes a travel parameter input subsystem 114. The travel parameter input subsystem 114 is configured to capture one or more travel parameters from each of the one or more users and each of one or more fleet operators. In one specific embodiment, the one or more travel parameters includes information representative of a list of the one or more source locations with geographical coordinate information, a list of the one or more target locations with geographical coordinate information, a list of vehicle information, vehicle travel constraint information and user travel constraint information.
[0037] In one specific embodiment, the vehicle travel constraint information includes vehicle sitting constraint, vehicle quantity constraint and traffic constraint. In such embodiment, the constraints are based specifically on travelling vehicles or pre-defined traffic conditions. In another specific embodiment, the user travel constraint information includes maximum route deviation and maximum trip duration. For example, each of the one or more users may pre-define maximum time duration for travelling or pre-define maximum deviation of route each of the one or more users may undertake. Constraints are specific rules which determine whether a route is acceptable or not.
[0038] The plurality of subsystems also includes a travel analysis subsystem 116. The travel analysis subsystem 116 is configured to generate optimal demand setting for the received request based on the one or more travel parameters, current route demand, current vehicle demand, and demand from number of the one or more users.
[0039] To generate optimal demand setting for the received request based on the one or more travel parameters, the current route demand, the current vehicle demand, and the demand from number of the one or more users, the travel analysis subsystem 116 is configured to create a partition for the one or more users based on the source location of each of the one or more users. Furthermore, in such embodiment, the travel analysis subsystem 116 is configured to determine vehicle capacity required for the created partition for the one or more users. The travel analysis subsystem 116 generates an optimal demand splitting of the created partition for the one or more users based on the determined vehicle capacity. The optimal demand splitting includes subgroup of the one or more users assigned to one or more vehicles based on the determined vehicle capacity, the source location and the one or more target locations.
[0040] The travel analysis subsystem 116 is configured to classify each of the one or more users into one or more travel groupings based on the generated optimal demand setting. In one specific embodiment, the one or more travel groupings are classified as cluster-based routing, zone-based routing, back-to-back pick up assessment and preferential routing.
[0041] To classify each of the one or more users into one or more travel groupings based on the generated optimal demand setting, the travel analysis subsystem 116 is configured to determine whether there exists a common point between the source location and the target location associated with each of the one or more users based on the one or more travel parameters. In such embodiment, the travel analysis subsystem 116 classifies the one or more users as cluster-based routing if there exists the common point between the source location and the target location associated with each of the one or more users.
[0042] In one specific embodiment, each of the one or more users is classified based on cluster of pick up or drop locations. For example, the computing system 100 classifies the one or more users into two groups based on cluster around two drop locations or pickup locations.
[0043] To classify each of the one or more users into one or more travel groupings based on the generated optimal demand settings, the travel analysis subsystem 116 is configured to determine whether the source location and the target location belongs to separate zonal areas. In such embodiment, the travel analysis subsystem 116 classifies the one or more users as zone-based routing if the source location and the target location belong to separate zonal areas. For example, the computing system 100 classifies the one or more users into two groups based on two travelling zones.
[0044] To classify each of the one or more users into one or more travel groupings based on the generated optimal demand setting, the travel analysis subsystem 116 is configured to determine dead distance covered from last drop to next first pickup based on past travel routes stored in a database. In such embodiment, the travel analysis subsystem 116 classifies the one or more users as back-to-back pick up assessment based on the determined dead distance. In such embodiment, such classification ensures less wastage of time as users nearby are pickup at same time.
[0045] To classify each of the one or more users into one or more travel groupings based on the generated optimal demand setting, the travel analysis subsystem 116 is configured to generate a preferential index based on previous trip details associated with each of the one or more user. The travel analysis subsystem 116 classifies the one or more users as preferential routing based on the generated preferential index. In such embodiment, the computing system 100 classifies the one or more users based on preferential route information stored in database 106. As used herein, the preference index is a history-based method that considers the entire history of choices made by each of the one or more users.
[0046] The travel analysis subsystem 116 is also configured to apply the captured one or more travel parameters of the classified one or more users, prestored map router information and prestored site route information onto an artificial intelligence-based vehicle route management model. The prestored site route information includes type of partitioning, escort constraints, traffic constraints, constraint relaxation and the like.
[0047] The travel analysis subsystem 116 is also configured to determine one or more travel routes between each of the one or more source locations and each of the one or more target locations based on the results of the artificial intelligence-based vehicle route management model.
[0048] To determine the one or more travel routes between each of the one or more source locations and each of the one or more target locations based on the results of the artificial intelligence-based vehicle route management model, the travel analysis subsystem 116 is configured to receive job data from each of the one or more users. The job data includes job type details, fleet details, user details, user defined constraints, vehicle capacity details, vehicle quantity details, user identification details, office pick up location details, drop location details, gender details, maximum route deviation details, maximum trip deviation details, login details and logout details.
[0049] The travel analysis subsystem 116 is also configured to determine site specific data. The site-specific data includes type of partitioning details, escort constraint details, traffic constraint details and constraint relaxation details. The travel analysis subsystem 116 processes the job data, site specific data and the mapping information using the artificial intelligence-based vehicle route management model.
[0050] The travel analysis subsystem 116 generates one or more travel routes between each of the one or more source locations and each of the one or more target locations based on the results of the artificial intelligence-based vehicle route management model. Furthermore, the travel analysis subsystem 116 generates trip information based on the generated one or more travel routes. The generated trip information includes details about unassigned users, details of users with pick up and drop times, trip distance details and tip duration details.
[0051] The artificial intelligence-based vehicle route management model uses vehicle routing problem algorithm to provide one or more travel routes. Furthermore, some of the other algorithm used are capacitated vehicle routing problem, vehicle routing problem with time windows, travelling salesman problem, Chinese postman problem and generalization of the Chinese postman problem.
[0052] The capacitated vehicle routing problem is similar to the vehicle routing problem. The capacitated vehicle routing problem have a single depot (main hub for vehicles) instead of multiple depots. Another generalization is that the each of the one or more fleet operators has only one type of vehicle with same maximum loading capacity and a quantity which can be either finite or infinite. Each of the one or more users have unique identification, a unique location, and a demand but no time window or service time.
[0053] The capacitated vehicle routing problem has implicit constraints such as a vehicle cannot carry total demand exceeding its payload capacity, no user from the one or more users should be visited more than once, or by more than one vehicle, each of the one or more users must be visited, and the same vehicle cannot have more than one trip.
[0054] The capacitated vehicle routing problem takes a list of one or more user’s location as input, where each location is a unique point in the euclidean plane, and each location n has a demand, which can be thought of as the quantity requirement of a particular item. A location is provided as input, and a finite/infinite set of homogeneous vehicles with a fixed load capacity “C” for each vehicle. Each vehicle starts from the depot, visits some of the one or mor users to meet their demands and then returns back to the depot.
[0055] The capacitated vehicle routing problem predicts assignment of vehicles to registered one or mor users where all the one or more users are visited and their demands are met, the total demand serviced by a vehicle does not exceed “C”, and each of the one or more users is serviced by one and only one vehicle. Such an assignment may happen in many ways, but an optimal assignment should have the sum of total distance travelled by each vehicle to be the least.
[0056] The vehicle routing problem with time windows has an additional constraint of time windows, and demand of each of the one or more users be serviced only during their specified time window. Time window represents the window between a specified Timestart and a specified Timeend, within which a demand has to be serviced, and Timeend >= Timestart.
[0057] The traveling salesman problem is an algorithmic problem tasked with finding the shortest route between a set of points and locations that must be visited. The traveling salesman problem is obtained from capacitated vehicle routing problem by setting the vehicle quantity to one and removing the demand constraint. While traveling salesman problem is about finding a Hamiltanion cycle with least cost, the Chinese postman problem is about finding minimum cost eulerian path. In graph theory, a Eulerian trail (or Eulerian path) is a trail in a finite graph that visits every edge exactly once (allowing for revisiting vertices).
[0058] The travel analysis subsystem 116 is also configured to determine one or more available vehicles for the classified one or more users based on the captured list of vehicle information and determined one or more travel routes. In such embodiment, to determine the one or more available vehicles for the classified one or more users, the travel analysis subsystem 116 is configured to identify number of users in each of the classified one or more users and identify number of available vehicles from the captured list of vehicles.
[0059] In such embodiment, the travel analysis subsystem 116 applies identified number of users, identified number of available vehicles, captured vehicle travel constraint information, captured user travel constraint information and the determined one or more travel routes on an artificial intelligence-based vehicle route management model. The travel analysis subsystem 116 determines the one or more available vehicles for the classified one or more users based on result of the artificial intelligence-based vehicle route management model.
[0060] In one specific embodiment, from a specific pick-up point, the number of one or more users travelling may be more than the assigned vehicle loading capacity. In such embodiment, the travel analysis subsystem 116 determines the one or more available vehicles needed for optimal travelling. The travel analysis subsystem 116 uses integer linear programming for optimal splitting of the vehicle numbers. For example, at particular pick-up point, only 4 sitter vehicle or 6 sitter vehicles are available for 11 users. The computing system 100 determines how to optimally balance the vehicle loading capacity after taking into consideration all the vehicle and user constraints. One solution would be to provide three vehicles with 4, 3, 4 sitter facilities.
[0061] The plurality of subsystems also includes a route output subsystem 118. The route output subsystem 118 is configured to output the determined one or more travel routes through one or more display interfaces. In one embodiment, the one or more display interfaces may include personal computers, any handheld devices, a smart device and the like.
[0062] FIG. 2 is a flowchart illustrating an exemplary workflow 200 for vehicle route optimization in accordance with an embodiment of the present disclosure. In step 202, users are registered into the computing system 100 with one or more travel parameters. The one or more travel parameters includes information representative of a list of the one or more source locations with geographical coordinate information, a list of the one or more target locations with geographical coordinate information, a list of vehicle information, vehicle travel constraint information and user travel constraint information.
[0063] In step 204, users are partitioned or classified into one or more travelling groups. The one or more travel groupings are classified as cluster-based routing, zone-based routing, back-to-back pick up assessment and preferential routing. Such partitions help the computing system 100 to handle a larger number of users in a short period of time.
[0064] In step 206, partitioned one or more travelling groups are processed. For some of the partitioned one or more travelling groups, vehicle counts are determined. In step 210, processed one or more travelling groups are applied artificial intelligence-based vehicle route management model for route management based on the one or more travel parameters. In step 212, a new optimized route is provided.
[0065] In step 208, the one or more travel groupings user is split, to assign optimal number of vehicles to distribute user load. In step 214, algorithm is applied for such determination. In step 216, the number of fleets are calculated and provided to the algorithm. In step 218, all constraints related to the one or more travelling groups are listed and provided to the algorithm for assigning of vehicles. In step 220, the output is stored and provided back to the computing system 100 for processing.
[0066] FIG. 3 is a flowchart 310 illustrating various inputs and outputs of the computing system for determining vehicle route in accordance with an embodiment of the present disclosure. In step 308, a map router information is provided to the computing system 100. In step 302, all data related to the job of vehicle route optimization is provided to the computing system 100. In step 304, site specific data is provided to the computing system 100. In step 306, optimised rote information is computed and displayed to the user. In such embodiment, job signifies the task of route optimization.
[0067] All data 302 related to the job of vehicle route optimization includes job type, fleet, users, user defined constraints, vehicle, vehicle siting capacity, vehicle quantity available, user id, office pick location, drop location, gender, user defined constraints, maximum route deviation, maximum trip duration, computing system login, log out, and the like. Site specific data 304 includes type of partitioning, escort constraints, traffic constraints, constraint relaxation and the like. Job solution data 306 includes trips, unassigned users, list of users with pick up and drop times, trip distance and trip duration, users who could not fit in any trip and the like.
[0068] FIG. 4 is a flowchart 400, 402 illustrating various inputs and outputs for working of the artificial intelligence-based vehicle route management model in accordance with an embodiment of the present disclosure. At step 404, a user submits a job to web application. In one embodiment, the web application related to computing system 100. Web application persists the job of vehicle route optimization in database. Web application calls the rest API server and passes the information about new job of vehicle route optimization.
[0069] At step 406, the rest API server calls vehicle router service passing information about the job of vehicle route optimization. At step 408, the vehicle router pulls the entire job from database and starts algorithm. In such embodiment, the algorithm is related artificial intelligence-based vehicle route management model. At step 412, the vehicle router queries map router for getting the route released data. At step 416, upon job completion, the vehicle router updates the database with output. The web application displays the vehicle route to different users.
[0070] FIG. 5 is a process flowchart illustrating an exemplary method 500 for vehicle route optimization in accordance with an embodiment of the present disclosure. At step 502, a plurality of travel requests is received from one or more users. In one aspect of the present embodiment, the plurality of travel requests is received from one or more users by a travel request subsystem 112. Each of the plurality of travel requests is being associated with one or more source locations and one or more target locations. travel parameter input subsystem 112.
[0071] At step 504, one or more travel parameters are captured from each of the one or more users and each of one or more fleet operators. In one aspect of the present embodiment, the one or more travel parameters are captured by a travel parameter input subsystem 114. In such embodiment, the one or more travel parameters includes information representative of a list of the one or more source locations with geographical coordinate information, a list of the one or more target locations with geographical coordinate information, a list of vehicle information, vehicle travel constraint information and user travel constraint information.
[0072] At step 506, an optimal demand setting is generated for the received request based on the one or more travel parameters, current route demand, current vehicle demand, and demand from number of the one or more users. In one aspect of the present embodiment, the optimal demand setting is generated by a travel analysis subsystem 116.
[0073] In one specific embodiment, in generating optimal demand setting for the received request based on the one or more travel parameters, current route demand, current vehicle demand, and demand from number of the one or more users, the method 500 includes creating a partition for the one or more users based on the source location of each of the one or more users. The method 500 also includes determining vehicle capacity required for the created partition for the one or more users. Further, the method 500 includes generating an optimal demand splitting of the created partition for the one or more users based on the determined vehicle capacity. The optimal demand splitting includes subgroup of the one or more users assigned to one or more vehicles based on the determined vehicle capacity, the source location and the one or more target locations.
[0074] In another specific embodiment, for classifying each of the one or more users into one or more travel groupings based on the generated optimal demand setting, the method 500 includes determining whether there exists a common point between the source location and the target location associated with each of the one or more users based on the one or more travel parameters. In such embodiment, the method 500 includes classifying the one or more users as cluster-based routing if there exists the common point between the source location and the target location associated with each of the one or more users.
[0075] In yet another embodiment, for classifying each of the one or more users into one or more travel groupings based on the generated optimal demand setting, the method 500 includes determining whether the source location and the target location belongs to separate zonal areas. In such embodiment, the method 500 includes classifying the one or more users as zone-based routing if the source location and the target location belongs to separate zonal areas.
[0076] In one embodiment, for classifying each of the one or more users into one or more travel groupings based on the generated optimal demand setting, the method 500 includes generating a preferential index based on previous trip details associated with each of the one or more user. In such embodiment, the method 500 includes classifying the one or more users as preferential routing based on the generated preferential index.
[0077] In another embodiment, for classifying each of the one or more users into one or more travel groupings based on the generated optimal demand setting, the method 500 includes determining dead distance covered from last drop to next first pickup based on past travel routes stored in a database. In such embodiment, the method also includes classifying the one or more users as back-to-back pick up assessment based on the determined dead distance.
[0078] At step 508, each of the one or more users are classified into one or more travel groupings based on the generated optimal demand setting. In one aspect of the present embodiment, each of the one or more users are classified by a travel analysis subsystem 116. In such embodiment, the one or more travel groupings are classified as cluster-based routing, zone-based routing, back-to-back pick up assessment and preferential routing.
[0079] At step 510, the captured one or more travel parameters are applied of the classified one or more users, prestored map router information and prestored site route information onto an artificial intelligence-based vehicle route management model. In one aspect of the present embodiment, the captured one or more travel parameters are applied by the travel analysis subsystem 116.
[0080] At step 512, one or more travel routes between each of the one or more source locations and each of the one or more target locations are determined based on the results of the artificial intelligence-based vehicle route management model. In one aspect of the present embodiment, one or more travel routes between each of the one or more source locations and each of the one or more target locations are determined by the travel analysis subsystem 116.
[0081] In one specific embodiment, for determining the one or more travel routes between each of the one or more source locations and each of the one or more target locations based on the results of the artificial intelligence-based vehicle route management model, the method 500 includes receiving job data from each of the one or more users. The job data includes job type details, fleet details, user details, user defined constraints, vehicle capacity details, vehicle quantity details, user identification details, office pick up location details, drop location details, gender details, maximum route deviation details, maximum trip deviation details, login details and logout details. The method 500 also includes determining site specific data. The site-specific data includes type of partitioning details, escort constraint details, traffic constraint details and constraint relaxation details.
[0082] The method 500 also includes retrieving mapping information from a map router. The method 500 also includes processing the job data, site specific data and the mapping information using the artificial intelligence-based vehicle route management model. Furthermore, the method 500 generates one or more travel routes between each of the one or more source locations and each of the one or more target locations based on the results of the artificial intelligence-based vehicle route management model. The method 500 also generates trip information based on the generated one or more travel routes. The generated trip information comprise details about unassigned users, details of users with pick up and drop times, trip distance details and tip duration details.
[0083] At step 514, one or more available vehicles for the classified one or more users are determined based on the captured list of vehicle information and determined one or more travel routes. In one aspect of the present embodiment, one or more available vehicles for the classified one or more users are determined by the travel analysis subsystem 116. In such embodiment, classifying each of the one or more users includes classification based on the captured list of one or more source locations and the captured list of one or more target locations.
[0084] In such embodiment, for determining one or more available vehicles for the classified one or more users, the method 500 includes identifying number of users in each of the classified one or more users and identifying number of available vehicles from the captured list of vehicles. The method also includes applying identified number of users, identified number of available vehicles, captured vehicle travel constraint information, captured user travel constraint information and the determined one or more travel routes on an artificial intelligence-based vehicle route management model. The method also includes determining the one or more available vehicles for the classified one or more users.
[0085] At step 516, the determined one or more travel routes is outputted through one or more display interfaces. In one aspect of the present embodiment, the determined one or more travel routes is outputted by a route output subsystem 118.
[0086] The method 500 also includes registering one or more fleet operators using respective operator data. In such embodiment, the operator data includes personal details of the one or more fleet operators, driver personal details, vehicle details, one or more garage details, rate cards, documents and billing details.
[0087] The method 500 also includes registering one or more users using user datal in such embodiment, the user data includes user personal details and one or more vehicle requirement parameters.
[0088] Various embodiments of the present disclosure relate to vehicle route optimization process. The disclosed computing system 100 provides a route optimization based on the important facts like regional or zone-based restrictions for travelling, fair pickup or drop point for all users, passenger comfort and the like.
[0089] The computing system 100 divides the one or more users based prestored cluster-based routing information, prestored zone-based routing information, prestored back-to-back pick up information and prestored preferential routing-based information. Further the system predicts the optimal route taking into account all the user as well as vehicle constraints.
[0090] The written description describes the subject matter herein to enable any person skilled in the art to make and use the embodiments. The scope of the subject matter embodiments is defined by the claims and may include other modifications that occur to those skilled in the art. Such other modifications are intended to be within the scope of the claims if they have similar elements that do not differ from the literal language of the claims or if they include equivalent elements with insubstantial differences from the literal language of the claims.
[0091] The embodiments herein can comprise hardware and software elements. The embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, etc. The functions performed by various modules described herein may be implemented in other modules or combinations of other modules. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
[0092] The medium can be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system (or apparatus or device) or a propagation medium. Examples of a computer-readable medium include a semiconductor or solid-state memory, magnetic tape, a removable computer diskette, a random-access memory (RAM), a read-only memory (ROM), a rigid magnetic disk and an optical disk. Current examples of optical disks include compact disk-read only memory (CD-ROM), compact disk-read/write (CD-R/W) and DVD.
[0093] Input/output (I/O) devices (as shown in FIG. 1) (including but not limited to keyboards, displays, pointing devices, etc.) can be coupled to the system either directly or through intervening I/O controllers. Network adapters may also be coupled to the system to enable the data processing system to become coupled to other data processing systems or remote printers or storage devices through intervening private or public networks. Modems, cable modem and Ethernet cards are just a few of the currently available types of network adapters.
[0094] A representative hardware environment for practicing the embodiments may include a hardware configuration of an information handling/computer system in accordance with the embodiments herein. The system herein comprises at least one processor or central processing unit (CPU). The CPUs are interconnected via system bus to various devices such as a random-access memory (RAM), read-only memory (ROM), and an input/output (I/O) adapter. The I/O adapter can connect to peripheral devices, such as disk units and tape drives, or other program storage devices that are readable by the system. The system can read the inventive instructions on the program storage devices and follow these instructions to execute the methodology of the embodiments herein.
[0095] The system further includes a user interface adapter that connects a keyboard, mouse, speaker, microphone, and/or other user interface devices such as a touch screen device (not shown) to the bus to gather user input. Additionally, a communication adapter connects the bus to a data processing network, and a display adapter connects the bus to a display device which may be embodied as an output device such as a monitor, printer, or transmitter, for example.
[0096] A description of an embodiment with several components in communication with each other does not imply that all such components are required. On the contrary, a variety of optional components are described to illustrate the wide variety of possible embodiments of the invention. When a single device or article is described herein, it will be apparent that more than one device/article (whether or not they cooperate) may be used in place of a single device/article. Similarly, where more than one device or article is described herein (whether or not they cooperate), it will be apparent that a single device/article may be used in place of the more than one device or article, or a different number of devices/articles may be used instead of the shown number of devices or programs. The functionality and/or the features of a device may be alternatively embodied by one or more other devices which are not explicitly described as having such functionality/features. Thus, other embodiments of the invention need not include the device itself.
[0097] The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed.
[0098] The figures and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, order of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts need to be necessarily performed. Also, those acts that are not dependant on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples.
, Claims: WE CLAIM:
1. A system (100) for vehicle route optimization, the system (100) comprising:
a hardware processor (108); and
a memory (102) coupled to the hardware processor (108), wherein the memory (102) comprises a set of program instructions in the form of a plurality of subsystems, configured to be executed by the hardware processor (108), wherein the plurality of subsystems comprises:
a travel request subsystem (112) is configured to receive a plurality of travel requests from one or more users, wherein each of the plurality of travel requests is being associated with one or more source locations and one or more target locations;
a travel parameter input subsystem (114) configured to capture one or more travel parameters from each of the one or more users and each of one or more fleet operators,
wherein the one or more travel parameters comprises information representative of a list of the one or more source locations with geographical coordinate information, a list of the one or more target locations with geographical coordinate information, a list of vehicle information, vehicle travel constraint information and user travel constraint information;
a travel analysis subsystem (116) configured to:
generate optimal demand setting for the received request based on the one or more travel parameters, current route demand, current vehicle demand, and demand from number of the one or more users;
classify each of the one or more users into one or more travel groupings based on the generated optimal demand setting, wherein the one or more travel groupings are classified as cluster-based routing, zone-based routing, back-to-back pick up assessment and preferential routing;
apply the captured one or more travel parameters of the classified one or more users, prestored map router information and prestored site route information onto an artificial intelligence-based vehicle route management model;
determine one or more travel routes between each of the one or more source locations and each of the one or more target locations based on the results of the artificial intelligence-based vehicle route management model; and
determine one or more available vehicles for the classified one or more users based on the captured list of vehicle information and determined one or more travel routes; and
a route output subsystem (118) configured to output the determined one or more travel routes through one or more display interfaces.
2. The system (100) as claimed in claim 1, further comprising a registration subsystem configured to:
register one or more fleet operators using respective operator data, wherein the operator data comprises personal details of the one or more fleet operators, driver personal details, vehicle details, one or more garage details, rate cards, documents and billing details; and
register one or more users using user data, wherein the user data comprises user personal details and one or more vehicle requirement parameters.
3. The system (100) as claimed in claim 1, wherein to determine one or more available vehicles for the classified one or more users, the travel analysis subsystem (116) is configured to:
identify number of users in each of the classified one or more users;
identify number of available vehicles from the captured list of vehicles;
apply the identified number of users, the identified number of available vehicles, the captured vehicle travel constraint information, the captured user travel constraint information and the determined one or more travel routes onto an artificial intelligence-based vehicle route management model; and
determine the one or more available vehicles for the classified one or more users based on result of the artificial intelligence-based vehicle route management model.
4. The system (100) as claimed in claim 1, wherein to generate optimal demand setting for the received request based on the one or more travel parameters, current route demand, current vehicle demand, and demand from number of the one or more users, the travel analysis subsystem (116) configured to:
create a partition for the one or more users based on the source location of each of the one or more users;
determine vehicle capacity required for the created partition for the one or more users; and
generate an optimal demand splitting of the created partition for the one or more users based on the determined vehicle capacity, wherein the optimal demand splitting comprises subgroup of the one or more users assigned to one or more vehicles based on the determined vehicle capacity, the source location and the one or more target locations.
5. The system (100) as claimed in claim 1, wherein to classify each of the one or more users into one or more travel groupings based on the generated optimal demand setting, the travel analysis subsystem (116) configured to:
determine whether there exists a common point between the source location and the target location associated with each of the one or more users based on the one or more travel parameters; and
classify the one or more users as cluster-based routing if there exists the common point between the source location and the target location associated with each of the one or more users.
6. The system (100) as claimed in claim 1, wherein to classify each of the one or more users into one or more travel groupings based on the generated optimal demand settings, the travel analysis subsystem (116) configured to:
determine whether the source location and the target location belong to separate zonal areas; and
classify the one or more users as zone-based routing if the source location and the target location belong to separate zonal areas.
7. The system (100) as claimed in claim 1, wherein to classify each of the one or more users into one or more travel groupings based on the generated optimal demand setting, the travel analysis subsystem (116) configured to:
generate a preferential index based on previous trip details associated with each of the one or more user; and
classify the one or more users as preferential routing based on the generated preferential index.
8. The system (100) as claimed in claim 1, wherein to classify each of the one or more users into one or more travel groupings based on the generated optimal demand setting, the travel analysis subsystem (116) configured to:
determine dead distance covered from last drop to next first pickup based on past travel routes stored in a database; and
classify the one or more users as back-to-back pick up assessment based on the determined dead distance.
9. The system (100) as claimed in claim 1, wherein to determine the one or more travel routes between each of the one or more source locations and each of the one or more target locations based on the results of the artificial intelligence-based vehicle route management model, the travel analysis subsystem (116) configured to:
receive job data from each of the one or more users, wherein the job data comprises job type details, fleet details, user details, user defined constraints, vehicle capacity details, vehicle quantity details, user identification details, office pick up location details, drop location details, gender details, maximum route deviation details, maximum trip deviation details, login details and logout details;
determining site specific data, wherein the site-specific data comprises type of partitioning details, escort constraint details, traffic constraint details and constraint relaxation details;
retrieving mapping information from a map router module;
processing the job data, site specific data and the mapping information using the artificial intelligence-based vehicle route management model;
generating one or more travel routes between each of the one or more source locations and each of the one or more target locations based on the results of the artificial intelligence-based vehicle route management model; and
generating trip information based on the generated one or more travel routes, wherein the generated trip information comprises details about unassigned users, details of users with pick up and drop times, trip distance details and tip duration details.
10. The system (100) as claimed in claim 1, wherein the vehicle travel constraint information comprises vehicle sitting constraint, vehicle quantity constraint and traffic constraint.
11. The system (100) as claimed in claim 1, wherein the user travel constraint information comprises maximum route deviation and maximum trip duration.
12. The system (100) as claimed in claim 1, wherein the prestored site route information comprises type of travel bookings, escort constraints, traffic constraints and relaxation constraints.
13. A method (500) for vehicle route optimization, the method (500) comprising:
receiving, by a processor (108), a plurality of travel requests from one or more users (502), wherein each of the plurality of travel requests is being associated with one or more source locations and one or more target locations;
capturing, by the processor (108), one or more travel parameters from each of the one or more users and each of one or more fleet operators (504), wherein the one or more travel parameters comprises information representative of a list of the one or more source locations with geographical coordinate information, a list of the one or more target locations with geographical coordinate information, a list of vehicle information, vehicle travel constraint information and user travel constraint information;
generating, by the processor (108), optimal demand setting for the received request based on the one or more travel parameters, current route demand, current vehicle demand, and demand from number of the one or more users (506);
classifying, by the processor (108), each of the one or more users into one or more travel groupings based on the generated optimal demand setting, wherein the one or more travel groupings are classified as cluster-based routing, zone-based routing, back-to-back pick up assessment and preferential routing (508);
applying, by the processor (108), the captured one or more travel parameters of the classified one or more users, prestored map router information and prestored site route information onto an artificial intelligence-based vehicle route management model (510);
determining, by the processor (108), one or more travel routes between each of the one or more source locations and each of the one or more target locations based on the results of the artificial intelligence-based vehicle route management model (512);
determining, by the processor (108), one or more available vehicles for the classified one or more users based on the captured list of vehicle information and determined one or more travel routes (514); and
outputting, by the processor (108), the determined one or more travel routes through one or more display interfaces (516).
14. The method (500) as claimed in claim 13, further comprising registering one or more fleet operators using respective operator data, wherein the operator data comprises personal details of the one or more fleet operators, driver personal details, vehicle details, one or more garage details, rate cards, documents and billing details.
15. The method (500) as claimed in claim 13, further comprising registering one or more users using user data, wherein the user data comprises user personal details and one or more vehicle requirement parameters.
16. The method (500) as claimed in claim 13, for determining one or more available vehicles for the classified one or more users, the method (500) comprises:
identifying number of users in each of the classified one or more users;
identifying number of available vehicles from the captured list of vehicles;
applying identified number of users, identified number of available vehicles, captured vehicle travel constraint information, captured user travel constraint information and the determined one or more travel routes onto an artificial intelligence-based vehicle route management model; and
determining the one or more available vehicles for the classified one or more users based on result of the artificial intelligence-based vehicle route management model.
17. The method (500) as claimed in claim 13, wherein in generating optimal demand setting for the received request based on the one or more travel parameters, current route demand, current vehicle demand, and demand from number of the one or more users, the method (500) comprises:
creating a partition for the one or more users based on the source location of each of the one or more users;
determining vehicle capacity required for the created partition for the one or more users; and
generating an optimal demand splitting of the created partition for the one or more users based on the determined vehicle capacity, wherein the optimal demand splitting comprises subgroup of the one or more users assigned to one or more vehicles based on the determined vehicle capacity, the source location and the one or more target locations.
18. The method (500) as claimed in claim 13, wherein for classifying each of the one or more users into one or more travel groupings based on the generated optimal demand setting, the method (500) comprises:
determining whether there exists a common point between the source location and the target location associated with each of the one or more users based on the one or more travel parameters; and
classifying the one or more users as cluster-based routing if there exists the common point between the source location and the target location associated with each of the one or more users.
19. The method (500) as claimed in claim 13, wherein for classifying each of the one or more users into one or more travel groupings based on the generated optimal demand setting, the method (500) comprises:
determining whether the source location and the target location belongs to separate zonal areas; and
classifying the one or more users as zone-based routing if the source location and the target location belongs to separate zonal areas.
20. The method (500) as claimed in claim 13, wherein for classifying each of the one or more users into one or more travel groupings based on the generated optimal demand setting, the method (500) comprises:
generating a preferential index based on previous trip details associated with each of the one or more user; and
classifying the one or more users as preferential routing based on the generated preferential index.
21. The method (500) as claimed in claim 13, wherein for classifying each of the one or more users into one or more travel groupings based on the generated optimal demand setting, the method (500) comprises:
determining dead distance covered from last drop to next first pickup based on past travel routes stored in a database; and
classifying the one or more users as back-to-back pick up assessment based on the determined dead distance.
22. The method (500) as claimed in claim 13, wherein for determining the one or more travel routes between each of the one or more source locations and each of the one or more target locations based on the results of the artificial intelligence-based vehicle route management model, the method (500) comprises:
receiving job data from each of the one or more users, wherein job data comprises job type details, fleet details, user details, user defined constraints, vehicle capacity details, vehicle quantity details, user identification details, office pick up location details, drop location details, gender details, maximum route deviation details, maximum trip deviation details, login details and logout details;
determining site specific data, wherein the site-specific data comprises type of partitioning details, escort constraint details, traffic constraint details and constraint relaxation details;
retrieving mapping information from a map router;
processing the job data, site specific data and the mapping information using the artificial intelligence-based vehicle route management model;
generating one or more travel routes between each of the one or more source locations and each of the one or more target locations based on the results of the artificial intelligence-based vehicle route management model; and
generating trip information based on the generated one or more travel routes, wherein the generated trip information comprises details about unassigned users, details of users with pick up and drop times, trip distance details and tip duration details.
23. The method (500) as claimed in claim 13, wherein classifying, by the processor, each of the one or more users comprises classification based on the captured list of one or more source locations and the captured list of one or more target locations.
24. The method (500) as claimed in claim 13, wherein the vehicle travel constraint information comprises vehicle sitting constraint, vehicle quantity constraint and traffic constraint.
25. The method (500) as claimed in claim 13, wherein the user travel constraint information comprises maximum route deviation and maximum trip duration.
26. The method (500) as claimed in claim 13, wherein the prestored site route information comprises type of travel bookings, escort constraints, traffic constraints and relaxation constraints.
Dated this 29th day of April 2022

Vidya Bhaskar Singh Nandiyal
Patent Agent (IN/PA-2912)
Agent for applicant