FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003
COMPLETE SPECIFICATION (See Section 10 and Rule 13)
Title of the invention:
METHOD AND SYSTEM FOR TRIP PLANNING BY CLUBBING
COMMUTERS BASED ON GEOCODES AND CUSTOMIZED
HOTSPOTS
Applicant
Tata Consultancy Services Limited
A company incorporated in India under the Companies Act, 1956
Having address:
Nirmal Building, 9th floor,
Nariman Point, Mumbai 400021,
Maharashtra, India
Preamble to the description
The following specification particularly describes the invention and the manner in which it is to be performed.

TECHNICAL FIELD [001] The embodiments herein generally relate to automated trip planning for commuters and, more particularly, to a method and system for trip planning by clubbing commuters based on geocodes and customized hotspots.
BACKGROUND
[002] Conventionally, planning and scheduling of pick-drop for commuters via vehicle transportation facility has been a manual excel sheet process. This involves geography and area knowledge of transport admin to effectively utilize the existing resources. Manual inputs makes process or system dependable, as execution depends on individual’s skills, knowledge, and experience, in turn affecting consistency in performance of the objective to be achieved.
[003] System automation has been introduced in trip planning for commuters, however, complete elimination of the manual intervention has been a challenge. Most cases require an administrator to provide inputs for one or the other aspect during real time trip planning. The reason being there are many factors, resource constraints, policy constraints, and situations that arise on the fly, in real time, while planning each trip planning. Moreover, while managing all factors, commuters’ or customer’s comfort is expected to be on the top. Commuters’ comfort is understood to be in experiencing minimum travel time with shortest route taken by a drop vehicle. However, considering that the commuters always are a mixed bag of people with different expectation, while having an unpredicted variation in number of commuters, time of travel, and variation in drop location, eliminating manual intervention completely is a technical challenge for existing automated systems. Machine Learning (ML) approaches also have been used in existing systems that try to address all aspects in trip planning, but still rely on manual intervention for one or the other inputs. Furthermore, with ML approaches building location specific or application specific models, requirements of availability of abundant training data, lengthy model training time, complex computational requirements of the ML based system, etc. are obvious technical challenges in bringing complete automation in trip planning systems, when

automation is expected to provide quick, time efficient, cost effective deployment, enhancing user experience with respect to travel time and route.
SUMMARY
[004] Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems.
[005] For example, in one embodiment, a method for trip planning by clubbing commuters based on geocodes and customized hotspots is provided. The method comprises receiving a plurality of trip requests from a plurality of commuters for dropping each of the plurality of commuters from a pickup location to an associated drop location from a plurality of drop locations. A trip request from the plurality of trip requests is received for a time slot from among a plurality of predefined time slots.
[006] Further, the method comprises identifying, on receiving the trip request, a geocode of each of the plurality of commuters and a hotspot associated with the geocode from among a plurality of predefined hotspots. The geocode is pre-tagged to a commuter based on the associated drop location provided by each of the plurality of commuters, and wherein the geocode is preassigned to the hotspot based on a customized hotspot coverage radius.
[007] Furthermore, the method comprises planning a trip for each of the received plurality of trip requests based on the geocodes identified for each of the plurality of commuters. The steps of the planning comprise, A) organizing the plurality of commuters into one or more hotspot groups based on the hotspot associated with the geocode of each of the plurality of commuters. B) Determine, within each hotspot group from the one or more hotspot groups, a geo-angle between one or more drop locations associated with corresponding geocodes and the pickup location using an associated geographical latitude and longitudinal information. C) Identify, within each hotspot group of the one or more hotspot groups, a first set of commuters having the determined geo-angle within a predefined angle and a second set of commuters having the geo-angle greater than

the predefined angle. D) Apply, a vehicle assignment policy, for assigning the first set of commuters within each hotspot group, to a first set of pickup vehicles among a plurality of pickup vehicles. The vehicle assignment policy comprises sequentially accommodating commuters in each pickup vehicle from the plurality of pickup vehicles to a preset seating capacity of the pickup vehicle adhering to a set of travel constraints. E) Aggregate the second set of commuters from each of the one or more hotspot groups. F) Obtain a locality of each of the aggregated second set of commuters corresponding to the associated drop location thereof, wherein the locality is obtained via a mapping Application Programming Interface (API). G) Dynamically identify and clubbing one or more commuters from among the aggregated second set of commuters if a) the locality of the one or more commuters maps to each other, and b) the geo-angle computed for the one or more commuters is within the predefined angle, wherein the clubbing generates a plurality of clubs with each club comprising one or more commuters. The one or more commuters in each club are assigned a second set of pickup vehicles among the plurality of pickup vehicles in accordance with the vehicle assignment policy. H) Assign a gang type for remaining commuters from the first set of commuters and the second set of commuters in each of the plurality of hotspot groups that are yet to be assigned a pickup vehicle from among the plurality of pickup vehicles. The gang type is assigned based on number of the remaining commuters, and is one of a) a macro gang, b) a mini gang and c) a micro gang, wherein the number of remaining commuters in the macro gang is less than the maximum seating capacity of the pickup vehicle. I) Perform a hybrid clubbing of two or more hotspot groups based on a) the gang type identified for each of the hotspot groups and b) a hybrid clubbing criteria defining a set of hybrid clubbing constraints, The hybrid clubbing generates a plurality of hybrid hotspot groups, and wherein the number of commuters in each of the hybrid hotspot groups are within the preset seating capacity of the vehicle. J) Assign a third set of vehicles among the plurality of pickup vehicles to each commuter in each of the plurality of hybrid hotspot groups in accordance with the vehicle assignment policy, wherein the number of

commuters in each of the hybrid hotspot groups are assigned a common pickup vehicle.
[008] In another aspect, a system for trip planning by clubbing commuters based on geocodes and customized hotspots is provided. The system comprises a memory storing instructions; one or more Input/Output (I/O) interfaces; and one or more hardware processors coupled to the memory via the one or more I/O interfaces, wherein the one or more hardware processors are configured by the instructions to receive a plurality of trip requests from a plurality of commuters for dropping each of the plurality of commuters from a pickup location to an associated drop location from a plurality of drop locations. A trip request from the plurality of trip requests is received for a time slot from among a plurality of predefined time slots.
[009] Further, the system comprises identifying, on receiving the trip request, a geocode of each of the plurality of commuters and a hotspot associated with the geocode from among a plurality of predefined hotspots. The geocode is pre-tagged to a commuter based on the associated drop location provided by each of the plurality of commuters, and wherein the geocode is preassigned to the hotspot based on a customized hotspot coverage radius.
[0010] Furthermore, the system comprises planning a trip for each of the received plurality of trip requests based on the geocodes identified for each of the plurality of commuters. The steps of the planning comprise, A) organizing the plurality of commuters into one or more hotspot groups based on the hotspot associated with the geocode of each of the plurality of commuters. B) Determine, within each hotspot group from the one or more hotspot groups, a geo-angle between one or more drop locations associated with corresponding geocodes and the pickup location using an associated geographical latitude and longitudinal information. C) Identify, within each hotspot group of the one or more hotspot groups, a first set of commuters having the determined geo-angle within a predefined angle and a second set of commuters having the geo-angle greater than the predefined angle. D) Apply, a vehicle assignment policy, for assigning the first set of commuters within each hotspot group, to a first set of pickup vehicles among

a plurality of pickup vehicles. The vehicle assignment policy comprises sequentially accommodating commuters in each pickup vehicle from the plurality of pickup vehicles to a preset seating capacity of the pickup vehicle adhering to a set of travel constraints. E) Aggregate the second set of commuters from each of the one or more hotspot groups. F) Obtain a locality of each of the aggregated second set of commuters corresponding to the associated drop location thereof, wherein the locality is obtained via a mapping Application Programming Interface (API). G) Dynamically identify and clubbing one or more commuters from among the aggregated second set of commuters if a) the locality of the one or more commuters maps to each other, and b) the geo-angle computed for the one or more commuters is within the predefined angle, wherein the clubbing generates a plurality of clubs with each club comprising one or more commuters. The one or more commuters in each club are assigned a second set of pickup vehicles among the plurality of pickup vehicles in accordance with the vehicle assignment policy. H) Assign a gang type for remaining commuters from the first set of commuters and the second set of commuters in each of the plurality of hotspot groups that are yet to be assigned a pickup vehicle from among the plurality of pickup vehicles. The gang type is assigned based on number of the remaining commuters, and is one of a) a macro gang, b) a mini gang and c) a micro gang, wherein the number of remaining commuters in the macro gang is less than the maximum seating capacity of the pickup vehicle. I) Perform a hybrid clubbing of two or more hotspot groups based on a) the gang type identified for each of the hotspot groups and b) a hybrid clubbing criteria defining a set of hybrid clubbing constraints, The hybrid clubbing generates a plurality of hybrid hotspot groups, and wherein the number of commuters in each of the hybrid hotspot groups are within the preset seating capacity of the vehicle. J) Assign a third set of vehicles among the plurality of pickup vehicles to each commuter in each of the plurality of hybrid hotspot groups in accordance with the vehicle assignment policy, wherein the number of commuters in each of the hybrid hotspot groups are assigned a common pickup vehicle.

[0011] In yet another aspect, there are provided one or more non-transitory machine-readable information storage mediums comprising one or more instructions, which when executed by one or more hardware processors causes a method for trip planning by clubbing commuters based on geocodes and customized hotspots.
[0012] The method comprises receiving a plurality of trip requests from a plurality of commuters for dropping each of the plurality of commuters from a pickup location to an associated drop location from a plurality of drop locations. A trip request from the plurality of trip requests is received for a time slot from among a plurality of predefined time slots.
[0013] Further, the method comprises identifying, on receiving the trip request, a geocode of each of the plurality of commuters and a hotspot associated with the geocode from among a plurality of predefined hotspots. The geocode is pre-tagged to a commuter based on the associated drop location provided by each of the plurality of commuters, and wherein the geocode is preassigned to the hotspot based on a customized hotspot coverage radius.
[0014] Furthermore, the method comprises planning a trip for each of the received plurality of trip requests based on the geocodes identified for each of the plurality of commuters. The steps of the planning comprise, A) organizing the plurality of commuters into one or more hotspot groups based on the hotspot associated with the geocode of each of the plurality of commuters. B) Determine, within each hotspot group from the one or more hotspot groups, a geo-angle between one or more drop locations associated with corresponding geocodes and the pickup location using an associated geographical latitude and longitudinal information. C) Identify, within each hotspot group of the one or more hotspot groups, a first set of commuters having the determined geo-angle within a predefined angle and a second set of commuters having the geo-angle greater than the predefined angle. D) Apply, a vehicle assignment policy, for assigning the first set of commuters within each hotspot group, to a first set of pickup vehicles among a plurality of pickup vehicles. The vehicle assignment policy comprises sequentially accommodating commuters in each pickup vehicle from the plurality

of pickup vehicles to a preset seating capacity of the pickup vehicle adhering to a set of travel constraints. E) Aggregate the second set of commuters from each of the one or more hotspot groups. F) Obtain a locality of each of the aggregated second set of commuters corresponding to the associated drop location thereof, wherein the locality is obtained via a mapping Application Programming Interface (API). G) Dynamically identify and clubbing one or more commuters from among the aggregated second set of commuters if a) the locality of the one or more commuters maps to each other, and b) the geo-angle computed for the one or more commuters is within the predefined angle, wherein the clubbing generates a plurality of clubs with each club comprising one or more commuters. The one or more commuters in each club are assigned a second set of pickup vehicles among the plurality of pickup vehicles in accordance with the vehicle assignment policy. H) Assign a gang type for remaining commuters from the first set of commuters and the second set of commuters in each of the plurality of hotspot groups that are yet to be assigned a pickup vehicle from among the plurality of pickup vehicles. The gang type is assigned based on number of the remaining commuters, and is one of a) a macro gang, b) a mini gang and c) a micro gang, wherein the number of remaining commuters in the macro gang is less than the maximum seating capacity of the pickup vehicle. I) Perform a hybrid clubbing of two or more hotspot groups based on a) the gang type identified for each of the hotspot groups and b) a hybrid clubbing criteria defining a set of hybrid clubbing constraints, The hybrid clubbing generates a plurality of hybrid hotspot groups, and wherein the number of commuters in each of the hybrid hotspot groups are within the preset seating capacity of the vehicle. J) Assign a third set of vehicles among the plurality of pickup vehicles to each commuter in each of the plurality of hybrid hotspot groups in accordance with the vehicle assignment policy, wherein the number of commuters in each of the hybrid hotspot groups are assigned a common pickup vehicle.
[0015] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS
[0016] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles:
[0017] FIG. 1 is a functional block diagram of a system, for trip planning by clubbing commuters based on geocodes and customized hotspots, in accordance with some embodiments of the present disclosure.
[0018] FIGS. 2A, FIG. 2B and FIG. 2C (collectively referred as FIG. 2) is a flow diagram illustrating a method for trip planning by clubbing commuters based on the geocodes and the customized hotspots, using the system of FIG. 1, in accordance with some embodiments of the present disclosure.
[0019] It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative systems and devices embodying the principles of the present subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, and the like represent various processes which may be substantially represented in computer readable medium and so executed by a computer or processor, whether or not such computer or processor is explicitly shown.
DETAILED DESCRIPTION OF EMBODIMENTS [0020] Exemplary embodiments are described with reference to the accompanying drawings. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the scope of the disclosed embodiments.

[0021] With commuters typically being a mixed bag of people with different expectations from a transport facility and with an unpredicted variation in number of commuters, time of travel and variation in drop location, eliminating manual intervention completely is a technical challenge for existing systems. Moreover, required are faster commuter clubbing techniques providing cost effective, optimal route planning of vehicles within available resource (vehicle) constraints.
[0022] Embodiments of the present disclosure provide a method and system for trip planning, which clubs commuters based on geocodes and corresponding hotspots that are pre-identified in accordance with drop locations of the commuters. A geo-angle is computed between a pickup location to each of the drop locations, wherein commuters associated with geo-angles within a predefined angle, are clubbed for vehicle assignment. Remaining commuters from each group are aggregated and clubbed based on a locality of each of the commuter and the angle criteria. Thereafter, the further remaining commuters in each of the hotspots are classified into a plurality of gang types, wherein any two gang types from different hotspots that satisfy a set of hybrid clubbing constraints and a predefined hybrid clubbing angle constraint are clubbed. The clubbing at any step is such that the commuter experiences optimal travel time, optimal travel route with minimum zig zag time, and system providing a cost-efficient vehicle assignment approach for a transport service provider.
[0023] Referring now to the drawings, and more particularly to FIGS. 1 through 2, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and/or method.
[0024] FIG. 1 is a functional block diagram of a system 100, for trip planning by clubbing commuters based on geocodes and customized hotspots, in accordance with some embodiments of the present disclosure.
[0025] In an embodiment, the system 100 includes a processor(s) 104, communication interface device(s), alternatively referred as input/output (I/O)

interface(s) 106, and one or more data storage devices or a memory 102 operatively coupled to the processor(s) 104. The system 100 with one or more hardware processors is configured to execute functions of one or more functional blocks (not shown) of the system 100.
[0026] Referring to the components of system 100, in an embodiment, the processor(s) 104, can be one or more hardware processors 104. In an embodiment, the one or more hardware processors 104 can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the one or more hardware processors 104 are configured to fetch and execute computer-readable instructions stored in the memory 102. In an embodiment, the system 100 can be implemented in a variety of computing systems including laptop computers, notebooks, hand-held devices such as mobile phones, workstations, mainframe computers, servers, and the like.
[0027] The I/O interface(s) 106 can include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface to trip plans for each commuter and the like and can facilitate multiple communications within a wide variety of networks N/W and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular and the like. In an embodiment, the I/O interface (s) 106 can include one or more ports for connecting to a number of external devices or to another server or devices such as mobile phones of the commuters to provide notifications on assigned vehicles, routes and estimated time.
[0028] The memory 102 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random-access memory (SRAM) and dynamic random-access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes.
[0029] Further, the memory 102 includes a database 108 that stores a hotspot matrix maintaining distance and travel time among the hotspots. Further, the

database may include a database cache for storing information extracted via the map APIs. Further, the memory 102 may comprise information pertaining to input(s)/output(s) of each step performed by the processor(s) 104 of the system100 and methods of the present disclosure. In an embodiment, the database 108 may be external (not shown) to the system 100 and coupled to the system via the I/O interface 106. Functions of the components of the system 100 are explained in conjunction with flow diagram of FIG. 2.
[0030] FIGS. 2A, FIG. 2B and FIG. 2C (collectively referred as FIG. 2) is a flow diagram illustrating a method for trip planning by clubbing commuters based on geocodes and customized hotspots, using the system of FIG. 1, in accordance with some embodiments of the present disclosure. In an embodiment, the system 100 comprises one or more data storage devices or the memory 102 operatively coupled to the processor(s) 104 and is configured to store instructions for execution of steps of the method 200 by the processor(s) or one or more hardware processors 104. The steps of the method 200 of the present disclosure will now be explained with reference to the components or blocks of the system 100 as depicted in FIG. 1 and the steps of flow diagram 200 as depicted in FIG. 2. Although process steps, method steps, techniques or the like may be described in a sequential order, such processes, methods, and techniques may be configured to work in alternate orders. In other words, any sequence or order of steps that may be described does not necessarily indicate a requirement that the steps to be performed in that order. The steps of processes described herein may be performed in any order practical. Further, some steps may be performed simultaneously.
[0031] Referring to the steps of the method 200, at step 202 of the method 200, the one or more hardware processors 104 receive a plurality of trip requests from a plurality of commuters for dropping each of the plurality of commuters from a pickup location to an associated drop location from a plurality of drop locations. A trip request received is for a time slot selected by a commuter from among a plurality of predefined time slots available for drop transport facility provided by a transport department of an organization. In typical examples, where the commuters

are employees working in the organization, the time slot refer to work shifts of the organization.
[0032] On receiving the trip requests, at step 204 of the method 200, the one or more hardware processors 104 identify a geocode of each of the plurality of commuters and a hotspot assigned to the geocode from among a plurality of predefined hotspots. The geocode referred herein is defined as a unique code assigned to each unique drop location (geo location) identifiable using map applications via personal digital devices such as smartphones of the commuter. The geocodes that are identified for each commuter for the drop location are pre-tagged to the associated drop location or an address provided by the commuter. Further, the system 100 has a plurality of hotspots identified, covering maximum possible known drop locations within the scope of a larger area such as a city, identified by the transportation facility. Unlike ‘locality’ used by conventional approaches, the hotspot is an area custom defined by configuring in the system 100. The hotspot is a custom defined radius (for example, 2 Kilometer (km), 5km, etc.), also referred as customized hotspot coverage radius, identifying multiple geocodes of drop locations within the radius. Thus, as soon as the commuter is tagged to the geocode, the system automatically groups or identifies the specific geocode to corresponding hotspot. The geocode tagging can be initiated by the commuter for any drop location of interest or can be automated by the system once user confirms his address in organizations database. The distances between drop locations or geocodes is precomputed in terms of distances between corresponding hotspots, along with travel time between the hotspots in a hotspot matrix that is used by the system during clubbing of commuters. Thus, the precomputed matrix provides ready to use information during the real time clubbing process. However, the use of hotspot for distance and travel time calculation, rather than using geocodes, reduces the matrix size, while still serving the purpose for travel time and distance comparison between the drop locations. It can be understood that as multiple geocodes withing a same radius can be approximately represented by the hotspot and assumed to be approximately having a common drop location. Further, during the hotspot-to-

hotspot distance and travel time computation, a geocode at center of the hotspot can be a reference point for each hotspot.
[0033] When the geocode is generated for a new entry (new commuter) aerial distance of the commuters geocode to the geocode of the hotspots currently present is computed. If the new commuter falls within a particular predefined distance range (for example, 2 km or 5 km) of existing hotspots, top 5 least aerial distance geocodes of hotspots are selected and individually the road distance of the commuter to the hotspot geocode is computed. Thereafter, the nearest hotspot geocode is tagged to the new commuter. However, if none of the existing hotspots fall withing the predefined distance range, the geocode of the commuter is added as a new hotspot. The hotspot generation is completely automatic and does not require manual intervention.
[0034] Each hotspot covers or includes the geocodes that lie within the geographical area covered by the hotspot defined by a customized hotspot coverage radius. The radius of each hotspot may be uniform or non-uniform and can be predetermined by the system 100 based on predicted number of commuters from a part of a geographical area.
[0035] At step 206 of the method 200, the one or more hardware processors 104 plan a trip for each of the received plurality of trip requests based on the geocodes identified for the plurality of commuters. The trip planning steps are listed below:
a) Step 206a - Organizing the plurality of commuters into one or more hotspot groups based on the hotspot associated with the geocode of each of the plurality of commuters.
b) Step 206b - Determining within each hotspot group from the one or more hotspot groups, the geo-angle between one or more drop locations associated with corresponding geocodes and the pickup location using an associated geographical latitude and longitudinal information. Angle is created by joining a line from the latitude and longitude, and north pole. The geo-angle is defined as an angular difference between angles formed by the drop location and the

pickup location with geographic North Pole. The angles are computed by joining a line from the associated geographical latitude and longitudinal information of the geocodes associated with the drop location and the pickup location with the geographic North Pole..
c) Step 206c - Identifying, within each hotspot group, a first set of commuters having the determined geo-angle within a predefined angle and a second set of commuters having the geo-angle greater than the predefined angle. Experimentally computed predefined angle of 30 degrees, is identified to provide best clubbing with least discomfort to commuters. The geo-angle is important parameter for clubbing of commuters because it ensures that whenever multiple commuters are tagged to single trip in same vehicle, they should be travelling in the same direction and will not be moving haphazardly. The geo-angle disclosed herein, ensures that the method 200 never tags commuters way off from each other in the same trip ensuring time saving and comfort.
d) Step 206d - Applying a vehicle assignment policy, for assigning the first set of commuters within each hotspot group, to a first set of pickup vehicles among a plurality of pickup vehicles. The vehicle assignment policy comprises sequentially accommodating commuters in each pickup vehicle, interchangeably referred to as vehicle, to a preset seating capacity of the vehicle adhering to a set of travel constraints. The vehicle assignment policy can be executed by a rule engine during every vehicle assignment to the clubbed commuter, for example the first set of commuters, wherein the constraints such as number of vehicles available, maximum seating capacity of the vehicle, guidelines on male female clubbing based of whether the last drop is male or female can be defined in the rule engine. Thus, vehicle assignment policy can address multiple constraints to ensure comfortable clubbing of associates.

Travel Time constraint: 110min (Employee level) -- Post clubbing
of hotspots , the total travel time should not exceed 110min.
Angle Constraint: All the employees clubbed in a trip should be
within 30 degree of each other.
Social Constraint : In case of a last female drop/ pick up between
night hours, a security escort is automatically assigned to the
Trip for ease of admin, so that he knows how many security escorts
would be needed for each shift.
Vehicle Capacity Constraint : the total passengers in a vehicle
cannot exceed its defined capacity
e) Step 206e - Aggregating the second set of commuters from each hotspot group from the one or more hotspot groups.
f) Step 206f - Obtaining a locality of each of the aggregated second set of commuters corresponding to the associated drop location of each of the aggregated second set of commuters. The locality is obtained via a mapping Application Programming Interface (API). The information obtained in every hit of the mapping API is stored in a database cache and reused for acquiring localities in successive iterations to reduce repeated requests to the mapping API, and effectively the charge per service request.
g) Step 206g - Dynamically identifying and clubbing one or more commuters from among the aggregated second set of commuters if

a) the locality of the one or more commuters maps to each other, and
b) the geo-angle computed for the one or more commuters is within the predefined angle. The clubbing generates a plurality of clubs with each club comprising one or more second commuters. Further, the one or more commuters in each club are assigned a second set of vehicles among the plurality of pickup vehicles in accordance with the vehicle assignment policy.
h) Step 206h - Assigning a gang type for remaining commuters from the first set of commuters and the second set of commuters in each

of the plurality of hotspot groups that are yet to be assigned a pickup vehicle from among the plurality of pickup vehicles. The gang type is assigned based on number of the remaining second commuters, and is one of a) a macro gang, b) a mini gang and c) a micro gang. The number of remaining commuters in the macro gang is less than the maximum seating capacity of the vehicle. For example, for 4-seater vehicles or cabs, the macro gang is ‘Gang’ of 3 commuters, the mini gang is ‘Couple’ of 2 commuters, and the micro gang is ‘Solo’ of single commuter. Thus, the commuters in each gang type can be predefined based on the vehicle capacity and type. i) Step 206i - Performing a hybrid clubbing of two or more hotspot groups based on a) the gang type identified for each of the hotspot groups and b) a hybrid clubbing criteria defining a set of hybrid clubbing constraints. The hybrid clubbing generates a plurality of hybrid hotspot groups, wherein the number of commuters in each of the hybrid hotspot groups are within the preset seating capacity of the vehicle. The hybrid clubbing comprises at least one of a) adjacent hotspots clubbing, and b) on-route hotspots clubbing, in accordance with the hybrid clubbing criteria. The hybrid clubbing criteria is based on a set of hybrid clubbing constraints comprising a) maximum allowed additional forced travel time limit for each commuter, b) minimum zig zag route traversing by the commuter to reach the drop location, c) minimum number of vehicles from among the plurality of vehicles to be engaged in dropping the plurality of commuters, d) a precomputed distance and travel time among the plurality of hotspots and e) a predefined hybrid clubbing angle. The precomputed distance and travel time is stored in the hotspot matrix (database 108) and accessed while applying the hybrid clubbing criteria during the hybrid clubbing. The distance and travel time computation uses known techniques for generating the hotspot matrix. Experimentally it is determined that the hybrid clubbing

angle of 20 degrees provides maximum comfort to commuters in terms of travel time and minimum zigzag route.
For example, for a 4-seater cab, the hybrid clubbing constraints can be
[predefined hybrid clubbing angle: (20 degree){5,10,15,20},max distance (8KM){2,4,6,8} in incremental order]. The clubbing is performed as below ensuring vehicles are assigned to the last commuter:
Gang + Solo {3+1}----{5,10,15,20}{2,4,6,8} Couple + Couple {2+2}----{5,10,15,20}{2,4,6,8} Couple + Solo {2+1}----{5,10,15,20}{2,4,6,8} Solo +Solo {1+1}----{5,10,15,20}{2,4,6,8} j) Step 206j - Assigning a third set of vehicles among the plurality of pickup vehicles to each commuter in each of the plurality of hybrid hotspot groups in accordance with the vehicle assignment policy. The number of commuters in each of the hybrid hotspot groups are assigned a common vehicle. employees who are along the way of each other to be clubbed programmatically. With this method, commuters such as employees who can travel with others on the via route are clubbed, without causing discomfort to each other which allows better occupancy of cabs and reduces cab count for the overall shift. A custom defined factor is used wherein the increase in distance and time is calculated for each trip for each employee post clubbing and increase in travel time is maintained within a threshold value of max 20min or 40 whichever is lower thereby reducing single trips and providing better packing of vehicles. [0036] Suppose there are 100 employees (commuters) for a particular shift, of which 40 people belong to hotspot1, 40 belong to hotspot2 ,10 belong to hotspot3, 10 belong to hotspot4. Ideally then ten 4 Seaters would be assigned to hotspot1, 10 to hotspot2, 2 to hotspot3 and hotspot4. Of the remaining 4 employees of hotspot3 and 4, the system tries to combine them and see if their individual travel time does

not exceed 20min or 40 of their individual time whichever is lower reducing a cab cost, else assign separate cab to them.
[0037] The method and system disclosed herein provide faster commuter clubbing techniques with cost effective, optimal route with minimum zig-zag travel for each commuter within available resource (vehicle) constraints, while simultaneously ensuring optimal travel time. The method enables time efficient clubbing for large number of commuters, having varying constraints. The optimized clubbing enables using minimum possible pickup vehicles rather than randomly assigning separate vehicles for individual commuters that could not get clubbed in initial clubbing process at hotspot level or locality level. Reduction in pick vehicles required for transport saves time and effort of drivers, reduces cost per pickup vehicle such as fuel cost, rent of pick vehicles and the like.
[0038] Provided below is are results from experiments conducted during real time implementation of the system and method disclosed herein.
1. For Servicing 867 employees, the method took 12 seconds, whereas
manual planning would require 5-10 min of acute Human Attention.
2. For Servicing 1925 employees, , the method took 36, whereas manual planning would require 15-20 min of acute Human Attention.
3. A single map API hit can take around 250ms, so for a 1000 hits it would take around 4minutes just to calculate distance and time in real time which is reduced by using Hotspots.
[0039] 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.
[0040] It is to be understood that the scope of the protection is extended to such a program and in addition to a computer-readable means having a message therein; such computer-readable storage means contain program-code means for

implementation of one or more steps of the method, when the program runs on a server or mobile device or any suitable programmable device. The hardware device can be any kind of device which can be programmed including e.g. any kind of computer like a server or a personal computer, or the like, or any combination thereof. The device may also include means which could be e.g. hardware means like e.g. an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a combination of hardware and software means, e.g. an ASIC and an FPGA, or at least one microprocessor and at least one memory with software processing components located therein. Thus, the means can include both hardware means, and software means. The method embodiments described herein could be implemented in hardware and software. The device may also include software means. Alternatively, the embodiments may be implemented on different hardware devices, e.g. using a plurality of CPUs.
[0041] 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 components described herein may be implemented in other components or combinations of other components. 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.
[0042] 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. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such

alternatives fall within the scope of the disclosed embodiments. Also, the words “comprising,” “having,” “containing,” and “including,” and other similar forms are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise.
[0043] Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.
[0044] It is intended that the disclosure and examples be considered as exemplary only, with a true scope of disclosed embodiments being indicated by the following claims.

We Claim:
1. A processor implemented method (200) for trip planning, the method comprising:
receiving, via one or more hardware processors, a plurality of trip requests from a plurality of commuters for dropping each of the plurality of commuters from a pickup location to an associated drop location from a plurality of drop locations (202), wherein a trip request from the plurality of trip requests is received for a time slot from among a plurality of predefined time slots;
identifying, via the one or more hardware processors, on receiving the trip request, a geocode of each of the plurality of commuters and a hotspot associated with the geocode from among a plurality of predefined hotspots (204), wherein the geocode is pre-tagged to a commuter based on the associated drop location provided by each of the plurality of commuters, and wherein the geocode is preassigned to the hotspot based on a customized hotspot coverage radius; and
planning, via the one or more hardware processors, a trip for each of the received plurality of trip requests based on the geocodes identified for each of the plurality of commuters (206), the planning comprising:
organizing (206a) the plurality of commuters into one or more hotspot groups based on the hotspot associated with the geocode of each of the plurality of commuters;
determining (206b), within each hotspot group from the one or more hotspot groups, a geo-angle between one or more drop locations associated with corresponding geocodes and the pickup location using an associated geographical latitude and longitudinal information;
identifying (206c), within each hotspot group of the one or more hotspot groups, a first set of commuters having the determined geo-angle within a predefined angle and a second set of commuters having the geo-angle greater than the predefined angle;

applying (206d), a vehicle assignment policy, for assigning the first set of commuters within each hotspot group, to a first set of pickup vehicles among a plurality of pickup vehicles, wherein the vehicle assignment policy comprises sequentially accommodating commuters in each pickup vehicle from the plurality of pickup vehicles to a preset seating capacity of the pickup vehicle adhering to a set of travel constraints;
aggregating (206e) the second set of commuters from each of the one or more hotspot groups;
obtaining (206f) a locality of each of the aggregated second set of commuters corresponding to the associated drop location thereof, wherein the locality is obtained via a mapping Application Programming Interface (API);
dynamically identifying (206g) and clubbing one or more commuters from among the aggregated second set of commuters if
a) the locality of the one or more commuters maps to each other, and
b) the geo-angle computed for the one or more commuters is within the predefined angle, wherein the clubbing generates a plurality of clubs with each club comprising one or more commuters, and wherein the one or more commuters in each club are assigned a second set of pickup vehicles among the plurality of pickup vehicles in accordance with the vehicle assignment policy;
assigning (206h) a gang type for remaining commuters from the first set of commuters and the second set of commuters in each of the plurality of hotspot groups that are yet to be assigned a pickup vehicle from among the plurality of pickup vehicles, wherein the gang type is assigned based on number of the remaining commuters, and is one of a) a macro gang, b) a mini gang and c) a micro gang, wherein the number of remaining commuters in the macro gang is less than the maximum seating capacity of the pickup vehicle;

performing a hybrid clubbing (206i) of two or more hotspot groups based on a) the gang type identified for each of the hotspot groups and b) a hybrid clubbing criteria defining a set of hybrid clubbing constraints, wherein the hybrid clubbing generates a plurality of hybrid hotspot groups, and wherein the number of commuters in each of the hybrid hotspot groups are within the preset seating capacity of the vehicle; and
assigning (206j) a third set of vehicles among the plurality of pickup vehicles to each commuter in each of the plurality of hybrid hotspot groups in accordance with the vehicle assignment policy, wherein the number of commuters in each of the hybrid hotspot groups are assigned a common pickup vehicle.
2. The method as claimed in claim 1, wherein the geo-angle is defined as an angular difference between angles formed by the drop location and the pickup location with geographic North Pole, wherein the angles are computed by joining a line from the associated geographical latitude and longitudinal information of the geocodes associated with the drop location and the pickup location with the geographic North Pole.
3. The method as claimed in claim 1, wherein the hybrid clubbing comprises at least one of a) adjacent hotspots clubbing, and b) on-route hotspots clubbing, in accordance with the hybrid clubbing criteria, wherein the hybrid clubbing criteria is based on a set of hybrid clubbing constraints comprising a) maximum allowed additional forced travel time limit for each commuter, b) minimum zig zag route traversing by the commuter to reach the drop location, c) minimum number of vehicles from among the plurality of vehicles to be engaged in dropping the plurality of commuters, d) a precomputed distance and travel time among the plurality of hotspots and e) a predefined hybrid clubbing angle.

4. The method as claimed in claim 3, wherein the precomputed distance and travel time is stored in a hotspot matrix and accessed while applying the hybrid clubbing criteria during the hybrid clubbing.
5. The method as claimed in claim 1, wherein the information obtained in every hit of the mapping API is stored in a database cache and reused for acquiring localities in successive iterations to reduce repeated requests to the mapping API.
6. A system (100) for trip planning, the system (100) comprising: a memory (102) storing instructions;
one or more Input/Output (I/O) interfaces (106); and
one or more hardware processors (104) coupled to the memory (102) via the one or more I/O interfaces (106), wherein the one or more hardware processors (104) are configured by the instructions to:
receive a plurality of trip requests from a plurality of commuters for dropping each of the plurality of commuters from a pickup location to an associated drop location from a plurality of drop locations, wherein a trip request from the plurality of trip requests is received for a time slot from among a plurality of predefined time slots;
identify, on receiving the trip request, a geocode of each of the plurality of commuters and a hotspot associated with the geocode from among a plurality of predefined hotspots, wherein the geocode is pre-tagged to a commuter based on the associated drop location provided by each of the plurality of commuters, and wherein the geocode is preassigned to the hotspot based on a customized hotspot coverage radius; and
plan a trip for each of the received plurality of trip requests based on the geocodes identified for each of the plurality of commuters, the planning comprising:

organizing the plurality of commuters into one or more hotspot groups based on the hotspot associated with the geocode of each of the plurality of commuters;
determine, within each hotspot group from the one or more hotspot groups, a geo-angle between one or more drop locations associated with corresponding geocodes and the pickup location using an associated geographical latitude and longitudinal information;
identify, within each hotspot group of the one or more hotspot groups, a first set of commuters having the determined geo-angle within a predefined angle and a second set of commuters having the geo-angle greater than the predefined angle;
apply, a vehicle assignment policy, for assigning the first set of commuters within each hotspot group, to a first set of pickup vehicles among a plurality of pickup vehicles, wherein the vehicle assignment policy comprises sequentially accommodating commuters in each pickup vehicle from the plurality of pickup vehicles to a preset seating capacity of the pickup vehicle adhering to a set of travel constraints;
aggregate the second set of commuters from each of the one or more hotspot groups;
obtain a locality of each of the aggregated second set of commuters corresponding to the associated drop location thereof, wherein the locality is obtained via a mapping Application Programming Interface (API);
dynamically identify and clubbing one or more commuters from among the aggregated second set of commuters if a) the locality of the one or more commuters maps to each other, and b) the geo-angle computed for the one or more commuters is within the predefined angle, wherein the clubbing generates a plurality of clubs with each club comprising one or more commuters, and wherein the

one or more commuters in each club are assigned a second set of pickup vehicles among the plurality of pickup vehicles in accordance with the vehicle assignment policy;
assign a gang type for remaining commuters from the first set of commuters and the second set of commuters in each of the plurality of hotspot groups that are yet to be assigned a pickup vehicle from among the plurality of pickup vehicles, wherein the gang type is assigned based on number of the remaining commuters, and is one of a) a macro gang, b) a mini gang and c) a micro gang, wherein the number of remaining commuters in the macro gang is less than the maximum seating capacity of the pickup vehicle;
perform a hybrid clubbing of two or more hotspot groups based on a) the gang type identified for each of the hotspot groups and b) a hybrid clubbing criteria defining a set of hybrid clubbing constraints, wherein the hybrid clubbing generates a plurality of hybrid hotspot groups, and wherein the number of commuters in each of the hybrid hotspot groups are within the preset seating capacity of the vehicle; and
assign a third set of vehicles among the plurality of pickup vehicles to each commuter in each of the plurality of hybrid hotspot groups in accordance with the vehicle assignment policy, wherein the number of commuters in each of the hybrid hotspot groups are assigned a common pickup vehicle.
7. The system as claimed in claim 6, wherein the geo-angle is defined as an angular difference between angles formed by the drop location and the pickup location with geographic North Pole, wherein the angles are computed by joining a line from the associated geographical latitude and longitudinal information of the geocodes associated with the drop location and the pickup location with the geographic North Pole.

8. The system as claimed in claim 6, wherein the hybrid clubbing comprises at least one of a) adjacent hotspots clubbing, and b) on-route hotspots clubbing, in accordance with the hybrid clubbing criteria, wherein the hybrid clubbing criteria is based on a set of hybrid clubbing constraints comprising a) maximum allowed additional forced travel time limit for each commuter, b) minimum zig zag route traversing by the commuter to reach the drop location, c) minimum number of vehicles from among the plurality of vehicles to be engaged in dropping the plurality of commuters, d) a precomputed distance and travel time among the plurality of hotspots and e) a predefined hybrid clubbing angle.
9. The system as claimed in claim 8, wherein the precomputed distance and travel time is stored in a hotspot matrix and accessed while applying the hybrid clubbing criteria during the hybrid clubbing.
10. The system as claimed in claim 6, wherein the information obtained in every hit of the mapping API is stored in a database cache and reused for acquiring localities in successive iterations to reduce repeated requests to the mapping API.