RU2841498C2 - Method and system for distribution of vehicles by taxi orders - Google Patents

Abstract

FIELD: physics.

SUBSTANCE: invention discloses a method and a server for allocating vehicles to taxi orders. Method comprises obtaining data on a plurality of taxi orders in a given zone, determining a set of candidate vehicles for distribution across the plurality of taxi orders, formation in the set of candidate vehicles of indication to the model candidate vehicle, which corresponds to the threshold estimated time of arrival for the taxi order, the set of candidate vehicles distribution across the plurality of taxi orders, suspending the assignment of a taxi order to one of the candidate vehicles included in the set of candidate vehicles, at the current iteration of the distribution of orders when assigning a taxi order to the model vehicle candidate and the inclusion of the taxi order in the next plurality of taxi orders to be received at the next iteration of the distribution of orders after the current iteration of the distribution of orders.

EFFECT: possibility of initiating calls based on dynamic changes in the location of a taxi and the current traffic situation.

20 cl, 8 dwg

Description

Region techniques, To which refers to invention

[0001] The present technology relates generally to Internet transport platforms and, in particular, to methods and systems for distributing vehicles based on taxi orders made by users of Internet transport platforms.

Level techniques

[0002] In recent years, advances in transportation services have given rise to various online platforms that meet the needs of passengers for transportation. In particular, online transportation platforms that offer passengers on-demand transportation services have become a popular solution to meet the ever-growing demand for transportation services. The operators of such online transportation platforms are ride-sharing companies (taxi ordering services) such as Uber™, Lyft™ and Yandex.Taxi™, which offer users of the online transportation platform the opportunity to arrange a trip through a website and/or a mobile application.

[0003] Typically, a passenger orders a taxi for a trip from a pick-up location to a drop-off location. For example, a passenger may order a taxi by pressing the "Call a car" button in the passenger's mobile application on the client side of the Internet transportation platform. In response, a server on the server side of the Internet transportation platform may (1) process the taxi order and (2) select a suitable vehicle to fulfill this taxi order. The server may accept taxi orders made in a given area (e.g., in a region, city, district, etc.) iteratively, in particular, with a certain time step. The assignment of a vehicle to a specific taxi order may be performed taking into account a number of factors.

[0004] For example, such an assignment may take into account that the driver of the candidate vehicle has been driving for a long time and is currently tired. This may negatively affect the driver's ability to drive the vehicle and the passenger's experience of the trip. In another example, the assignment may be performed based on the criterion of minimizing the total Estimated Time of Arrival (ETA) for a set of current taxi orders made by passengers in a given zone in the current iteration of order distribution. However, such approaches are not always effective in terms of satisfying the needs of individual passengers, and therefore need further improvement.

[0005] Patent application US20150081362A1 "Context-aware distributive taxi cab dispatching" (published March 19, 2015, Intel Corp) discloses a cloud-based taxi dispatch service with contextual distribution of orders for registered users. A method for implementing such a service includes receiving a request for taxi information from a device of a registered user, obtaining the requested information from one or more computing devices of at least one taxi or at least one taxi company and from one or more data stores, and transmitting the received information to the user's device. The provided taxi information may depend on the user's passenger profile and include a list of taxis recommended based on this profile. The method may further include creating a schedule for the selected taxi. In addition, the method may include monitoring the user's location and updating the taxi schedule based on the user's location data. The method may also include monitoring the user's location during a taxi ride to collect data and ensure safety.

[0006] Patent application US20170206622A1 "Systems and methods for matching drivers with passengers, wherein passengers specify the price to be paid for a ride before the ride commences" (published July 20, 2017, Suol Innovations Ltd) discloses a computer method for requesting a ride at a fare specified by a passenger, implemented using a computer system comprising at least a processor and memory. The method involves receiving a taxi order from a passenger at a fare specified by the passenger, transmitting the taxi order to the driver's ride request feed, selecting a driver in response to the passenger's taxi order at the fare specified by the passenger by receiving driver requests to receive an order, determining the location of the drivers' devices, determining the driver's rating and selecting a driver based on his location and rating, and providing the results of the driver selection to the passenger, the selected driver and other drivers.

[0007] Patent application US20200160251A1 "Adaptive dispatching engine for advanced taxi management" (published May 21, 2020, International Business Machines Corp) discloses a method for organizing adaptive taxi dispatching, which provides for receiving a taxi request from a passenger, evaluating taxi drivers based on the proportion of satisfied requests, the proportion of unsatisfied requests, the idle (inactivity) period and/or the frequency of accepting orders, evaluating the passenger based on the proportion of canceled requests, the frequency of bookings, the proportion of trip refusals and/or the presence or absence from a blacklist, selecting a taxi driver for the passenger based on the ratings of the taxi driver and the passenger, assigning the passenger to the taxi driver based on a conditional average statistical proportion of satisfied requests and/or a stochastic distribution of probability constraints or a conditional risk value, and dispatching the taxi driver to the passenger.

[0008] Patent application US20200393257A1 "Casual driver ride sharing" (published December 17, 2020, Lyft Inc) discloses a system for organizing ride sharing with random driver selection, comprising an interface and a processor. The interface is operative to receive a ride request from a user, including the user's GPS data. The processor evaluates the compatibility of typical route information and the ride request, compiles a ranked list based at least in part on the compatibility evaluation, and sends a ride offer to one or more random drivers based at least in part on the ranked list.

[0009] Patent application US20210005087A1 "Server for communicating with mobile and vehicle devices" (published December 7, 2021, Verizon Patent and Licensing Inc) discloses at least one server that can implement a first communication interface for communicating with a mobile device, a second communication interface for communicating with devices related to a plurality of vehicles, and a selection algorithm. The first communication interface can receive requests from a mobile device over a network when interacting with the mobile device. At the same time, the selection algorithm is capable of obtaining weighted estimates for two or more vehicles from a plurality of vehicles and selecting one of the two or more vehicles using the selection function, which provides more efficient access to transportation services and reduces the waste of resources used by the plurality of vehicles.

[0010] Patent application US20170091677A1 "Vehicle booking technique" (published March 30, 2017, Magenta Corp Ltd) discloses a method for distributing a plurality of booking requests among a set of vehicles, providing for determining a cost for all combinations of requests and vehicles and selecting a vehicle for each request based on a criterion of minimizing the total cost of the combinations of requests and vehicles.

Disclosure inventions

[0011] The purpose of the present technology is to eliminate at least some of the disadvantages of known technical solutions. Various embodiments of the present technology make it possible to provide and/or expand the capabilities of approaches to achieving the goals and solving the problems of the present technology. The developers of the present technology have identified at least one technical problem characteristic of known technical solutions.

[0012] The developers of the present technology have established that the distribution of vehicles among taxi orders can be implemented not only taking into account the total estimated arrival time, but also taking into account the specific preferences of each passenger with respect to the planned taxi orders. In particular, in accordance with at least some non-limiting embodiments of the present technology, a server located on the server side of the Internet transport platform, at the current iteration of the distribution of orders, is capable of (1) accepting a plurality of taxi orders in a given zone, (2) forming a set of candidate vehicles in a given zone suitable for fulfilling the plurality of taxi orders, (3) determining for each of the set of candidate vehicles a corresponding initial estimated arrival time with respect to each order from the plurality of taxi orders, and (4) changing the initial estimated arrival time for a candidate vehicle based on whether this candidate vehicle corresponds to the preferences of passengers specified in the taxi order. For example, if a passenger who has ordered a taxi has declared, among other things, the availability of a child seat as a preferred feature of a candidate vehicle, the back-end server of the Internet transportation platform can ensure either (1) a decrease in the initial estimated arrival time of the candidate vehicle for the taxi order, if this candidate vehicle provides such a feature, or (2) an increase in the corresponding initial estimated arrival time otherwise. In addition, the server can ensure the distribution of the set of candidate vehicles over a plurality of taxi orders by minimizing the total estimated arrival time, determined in this case by the estimated arrival times for each of the set of candidate vehicles modified in the manner described herein. Thus, these methods and systems make it possible not only to reduce the waiting time of passengers, but also to give priority to vehicles with certain features in those taxi orders where these features are declared as the most desirable. This contributes to an increase in the degree of satisfaction of passengers using the services of the Internet transportation platform.

[0013] The developers of the present technology also took into account that the approach to distributing the set of candidate vehicles based on minimizing the total estimated arrival time may have some drawbacks. For example, at some iteration, the distribution of the set of candidate vehicles in a given zone may be such that the estimated arrival time for any particular taxi order may be relatively large, despite the fulfillment of the condition of minimizing the total estimated arrival time, which may negatively affect the degree of satisfaction of the user who made this taxi order.

[0014] Thus, the developers of the present technology took into account that more efficient execution of taxi orders is ensured by placing only those taxi orders that can be executed within a given threshold estimated arrival time. In particular, in at least some non-limiting embodiments of the present technology, before optimizing the total estimated arrival time, the server is capable of forming in the set of candidate vehicles an indication of a so-called "model" candidate vehicle that corresponds to a given threshold estimated arrival time for the taxi order. Accordingly, if after optimizing the total estimated arrival time at a particular iteration of order distribution a certain taxi order is assigned to a "model" candidate vehicle, the server is capable of suspending such a taxi order until the next iteration of order distribution, at which the server will include this taxi order in the next set of taxi orders.

[0015] In this way, the server is able to allocate some time to allow the distribution of candidate vehicles in a given zone to change, which may result in finding, at the next iteration of order distribution, a closer real candidate vehicle whose estimated arrival time when selecting a taxi order is less than the specified threshold estimated arrival time. This allows for reducing the waiting time of the user who has ordered a taxi and, accordingly, increasing the degree of his satisfaction.

[0016] In particular, according to a first broad aspect of the present technology, a computer method for distributing vehicles among taxi orders is implemented. The method is implemented by a processor. The processor is configured to communicate with the vehicles. At the current iteration of distributing orders from a plurality of iterations of distributing orders, the method involves receiving by the processor data on a plurality of taxi orders in a given zone, determining by the processor a set of candidate vehicles, each of which corresponds to an estimated value of the arrival time at a location associated with a taxi order from a plurality of taxi orders, for distribution among a plurality of taxi orders, forming by the processor in the set of candidate vehicles an indication of a model candidate vehicle that corresponds to a threshold estimated arrival time for a taxi order, distributing by the processor the set of candidate vehicles among a plurality of taxi orders with minimization of the total estimated arrival time determined by the estimated values of the arrival time corresponding to each of the set of candidate vehicles, including the model candidate vehicle, at the destinations for taxi orders from a plurality of taxi orders. In case of assigning a taxi order from a plurality of taxi orders to a model candidate vehicle, the method additionally provides for the processor to suspend the assignment of a taxi order to one of the candidate vehicles included in the set of candidate vehicles at the current iteration of order distribution and for the processor to include this taxi order in the next plurality of taxi orders that must be received at the next iteration of order distribution after the current iteration of order distribution from the plurality of order distribution iterations. In case of assigning a taxi order to a candidate vehicle from the set of candidate vehicles that is not a model candidate vehicle, the method provides for the processor to initiate sending the candidate vehicle according to the taxi order.

[0017] In some embodiments of the method, before generating an indication of a model candidate vehicle, the method further comprises determining by the processor for the candidate vehicle an assessment characterizing the vehicle features associated with the candidate vehicle, and applying by the processor this assessment to the estimated arrival time in order to obtain a modified estimated arrival time for the candidate vehicle, wherein the distribution of the set of candidate vehicles across a plurality of taxi orders is based on the modified estimated arrival time values corresponding to each of the set of candidate vehicles.

[0018] In some embodiments of the method, the vehicle features of a candidate vehicle include (1) a safety parameter associated with the candidate vehicle, (2) a number of candidate vehicles in a set of candidate vehicles, and/or (3) properties of the candidate vehicle.

[0019] In some embodiments of the method, the safety parameter characterizes the probability of a traffic accident involving a candidate vehicle on the route to the location associated with the taxi order.

[0020] In some embodiments of the method, it further provides for the processor to determine a safety parameter associated with the candidate vehicle based on (1) a safety assessment of the driver of the candidate vehicle and/or (2) a route safety assessment associated with the route to the location associated with the taxi order.

[0021] In some embodiments of the method, it further provides for determining a driver safety assessment based on (1) the driver's characteristics, (2) the driver's driving experience, (3) the driver's driving style, (4) the number of road traffic accidents involving the driver, and/or (5) a driver-related traffic compliance indicator.

[0022] In some embodiments of the method, the properties of the candidate vehicle include the make and model of the candidate vehicle, the year of manufacture of the candidate vehicle, the volume of the luggage compartment of the candidate vehicle, and/or the presence of a child seat in the candidate vehicle.

[0023] In some embodiments of the method, in addition to including the taxi order in the next set of taxi orders, it provides for the processor to increase the threshold estimated arrival time for this taxi order at the next iteration of order distribution.

[0024] In some embodiments of the method, increasing the threshold estimated arrival time involves increasing the threshold estimated arrival time such that the longer the buffering period before including a taxi order in the next set of taxi orders, the longer the threshold estimated arrival time for this taxi order in the next iteration of order distribution.

[0025] In some embodiments of the method, increasing the threshold estimated arrival time includes an exponential increase in the threshold estimated arrival time depending on the buffering period.

[0026] In some embodiments of the method, the increase in the threshold estimated time of arrival is carried out according to the following formula:

ETA threshold = d + a ⋅ e ^b+c ⋅ ^{buffering period} ,

where d is the initial value of the threshold estimated arrival time before the increase, the buffering period is the period of time before the taxi order is included in the next set of taxi orders, a, b and c are constant coefficients.

[0027] In some embodiments of the method, it further comprises determining by the processor a threshold estimated time of arrival based on environmental conditions in a given area and/or estimated arrival times for each of the set of candidate vehicles.

[0028] In some embodiments of the method, the threshold estimated time of arrival is the same for all orders from a plurality of taxi orders in a given zone until the threshold estimated time of arrival for a taxi order is increased.

[0029] In some embodiments of the method, a different threshold estimated arrival time value is used for taxi orders in another zone that differs in geographic location from the specified zone.

[0030] In some embodiments of the method, the same threshold estimated time of arrival is used in zones that differ in geographical location.

[0031] According to a second broad aspect of the present technology, a server is implemented for distributing vehicles for taxi orders. The server comprises a processor and physical machine-readable memory for storing commands. When executing commands at the current iteration of distributing orders from a plurality of iterations of distributing orders, the processor is capable of receiving data on a plurality of taxi orders in a given zone, determining a set of candidate vehicles, each of which corresponds to an estimated value of the arrival time at a location associated with a taxi order from a plurality of taxi orders, for distributing over a plurality of taxi orders, generating in the set of candidate vehicles an indication of a model candidate vehicle to which a threshold estimated arrival time for a taxi order corresponds, distributing the set of candidate vehicles over a plurality of taxi orders with minimization of the total estimated arrival time determined by the estimated values of the arrival time corresponding to each of the set of candidate vehicles, including the model candidate vehicle, at destinations for taxi orders from a plurality of orders, in the case of assigning a taxi order from a plurality of taxi orders to a model candidate vehicle, suspending the assignment of a taxi order to one of the candidate vehicles included in the set of candidate vehicles, at the current iteration of order distribution and include this taxi order in the next set of taxi orders that are to be received at the next iteration of order distribution after the current iteration of order distribution from the set of iterations of order distribution, and in case of assigning a taxi order to a candidate vehicle from the set of candidate vehicles that is not a model candidate vehicle, dispatch the candidate vehicle according to the taxi order.

[0032] In some embodiments of the server, the processor, before generating an indication of a model candidate vehicle, is further configured to determine for the candidate vehicle an estimate characterizing vehicle features associated with the candidate vehicle and apply this estimate to the estimated arrival time in order to obtain a modified estimated arrival time for the candidate vehicle, wherein the distribution of the set of candidate vehicles across a plurality of taxi orders is based on the modified estimated arrival times corresponding to each of the set of candidate vehicles.

[0033] In some embodiments of the server, in addition to including the taxi order in the next set of taxi orders, the processor is configured to increase the threshold estimated arrival time for that taxi order in the next iteration of order distribution.

[0034] In some embodiments of the server, the processor is capable of implementing such an increase in the threshold estimated arrival time, in which the longer the buffering period before including a taxi order in the next set of taxi orders, the greater the threshold estimated arrival time for this taxi order in the next iteration of order distribution.

[0035] In some embodiments of the server, the processor is configured to implement an exponential increase in the threshold estimated arrival time depending on the buffering period.

[0036] In the context of the present description, a "server" is a computer program executed by appropriate hardware and capable of receiving requests over a network (e.g., from client devices) and executing or initiating the execution of these requests. Such hardware may be implemented as a single physical computer or a single physical computer system, which is not essential for the present technology. In this context, when using the expression "server", it is not implied that any particular task or all tasks (e.g., received commands or requests) are received, executed or started on the same server (i.e., by the same software and/or hardware), but it is meant that any number of software or hardware may participate in receiving/transmitting, executing or initiating the execution of any tasks or requests or the results of any tasks or requests, and that all of these software and hardware may represent a single server or several servers, and the expression "at least one server" covers both of these cases.

[0037] In the context of the present description, an "electronic device" is any computer hardware capable of running software suitable for performing a given task. In the context of the present description, the term "electronic device" implies that the device can function as a server for other electronic and client devices, although this is not a requirement for the present technology. Thus, non-limiting examples of electronic devices include personal computers (desktop computers, laptops, netbooks, etc.), smartphones and tablets, as well as network equipment such as routers, switches and gateways. It should be understood that in this context, the fact that equipment functions as an electronic device does not mean that it cannot function as a server for other electronic devices. The use of the expression "electronic device" does not exclude the use of multiple client devices for receiving/transmitting, performing or initiating the execution of any tasks or requests, the results of any tasks or requests, or the steps of any method described herein.

[0038] In the context of the present description, a "client device" is any computer hardware capable of running software suitable for performing a given task. In the context of the present description, the term "client device" is generally associated with a user of the client device. Thus, non-limiting examples of client devices are personal computers (desktop computers, laptops, netbooks, etc.), smartphones and tablets, as well as network equipment such as routers, switches and gateways. It should be noted that a device acting as a client in this context does not lose the ability to act as a server for other client devices. The use of the expression "client device" does not exclude the use of several client devices for receiving/transmitting, performing or initiating the execution of any tasks or requests, the results of any tasks or requests, or the steps of any method described herein.

[0039] In the context of this description, the expression "information" includes information of any kind and type capable of being stored in a database. Thus, information includes, among other things, audiovisual works (images, films, sound recordings, presentations, etc.), data (location data, numerical data, etc.), text (reviews, comments, questions, messages, etc.), documents, spreadsheets, etc.

[0040] In the context of this description, the expression "software component" means software (in the context of specific hardware) necessary and sufficient to perform the specific functions mentioned.

[0041] In the context of this description, the expression "computer storage medium" (also referred to as "storage medium") is intended to refer to media of any kind and type, including, but not limited to, RAM, ROM, disks (CD-ROM, DVD, floppy disks, hard disks, etc.), USB drives, solid state drives, tape drives, etc. A computer storage medium may be a combination of several elements, including two or more elements of the same type and/or two or more elements of different types.

[0042] In the context of this description, the term "database" means any structured set of data, regardless of its specific structure, software for managing the database, and computer hardware for storing such data, using it, or otherwise enabling its use. The database may be located in the same hardware where the process that ensures the storage or use of information stored in the database is implemented, or in separate hardware, such as a dedicated server or group of servers.

[0043] As used herein, the numerals "first," "second," "third," etc., serve merely to distinguish between the nouns to which they refer, and do not describe any particular relationship between those nouns. Thus, it should be understood that, for example, the terms "first database" and "third server" do not imply any particular order, type, chronology, hierarchy, or ranking of servers, and the use of these terms (in and of themselves) does not necessarily imply the presence of any "second server" in any particular situation. Furthermore, as used herein in other contexts, references to a "first" element and a "second" element do not exclude the possibility that the two elements may in fact be the same element. For example, in some cases the “first” and “second” servers may represent the same software components and/or hardware elements, while in other cases they may represent different software components and/or hardware elements.

[0044] Each embodiment of the present technology relates to at least one of the above-mentioned objectives and/or to one of the above-mentioned aspects, but not necessarily to all of them. It should be understood that some aspects of the present technology associated with an attempt to achieve the above-mentioned objective may not correspond to this objective and/or may correspond to other objectives not explicitly mentioned herein.

[0045] Additional and/or alternative features, aspects and advantages of embodiments of the present technology are contained in the following description, in the attached drawings and in the claims.

Brief description drawings

[0046] These and other features, aspects and advantages of the present technology are contained in the following description, in the appended claims and in the following drawings.

[0047] Fig. 1 is a schematic representation of an example of a computer system suitable for implementing certain non-limiting embodiments of the present technology.

[0048] Fig. 2 is a schematic representation of a networked computing environment comprising the computer system shown in Fig. 1 and suitable for use with certain non-limiting embodiments of the present technology.

[0049] Fig. 3 shows a section of a road map in a given area illustrating an example of an approach to distributing vehicles for taxi orders by the server of the network computer environment shown in Fig. 2 at the current iteration of order distribution in accordance with some non-limiting embodiments of the present technology.

[0050] Fig. 4 shows a section of a road map in a given area illustrating another example of an approach to distributing vehicles for taxi orders by the server of the network computer environment shown in Fig. 2 at the current iteration of order distribution in accordance with some non-limiting embodiments of the present technology.

[0051] Fig. 5 shows a section of a road map in a given area illustrating another example of an approach to distributing vehicles for taxi orders by the server of the network computer environment shown in Fig. 2 at the current iteration of order distribution in accordance with some non-limiting embodiments of the present technology.

[0052] Fig. 6 shows a graph of the dependence of a given threshold estimated arrival time for a taxi order that remains unassigned at the current iteration of order distribution in a given zone on the buffering period (assignment suspension) of the taxi order until its processing at the next iteration of order distribution after the current iteration of order distribution, in accordance with non-restrictive embodiments of the present technology.

[0053] Fig. 7 depicts a section of a road map in a given area illustrating an example of an approach to assigning by a server from the network computer environment shown in Fig. 2 a taxi order that remains unassigned after the current iteration of order distribution to one of the vehicles in the next iteration of order distribution in accordance with non-limiting embodiments of the present technology.

[0054] Fig. 8 is a block diagram of a method for distributing taxi orders by vehicles by a server from the network computer environment shown in Fig. 2 in accordance with some non-limiting embodiments of the present technology.

Implementation inventions

[0055] The examples and conventional language provided herein are intended to provide a better understanding of the principles of the present technology, and not to limit its scope to such specifically provided examples and conditions. It should be understood that those skilled in the art are able to develop various methods and devices that are not explicitly described or shown, but implement the principles of the present technology within its spirit and scope.

[0056] To facilitate better understanding, the following description may contain simplified embodiments of the present technology. Those skilled in the art will understand that various embodiments of the present technology may be significantly more complex.

[0057] In some cases, supposedly useful examples of modifications of the present technology are given. They are intended only to facilitate understanding and also do not define the scope or boundaries of the present technology. The presented list of modifications is not exhaustive and a person skilled in the art can develop other modifications within the scope of the present technology. In addition, if in some cases modifications are not described, this does not mean that they are impossible and/or that the description contains the only possible embodiment of a particular element of the present technology.

[0058] The description of the principles, aspects and embodiments of the present technology, as well as specific examples thereof, are intended to cover their structural and functional equivalents, regardless of whether they are currently known or will be developed in the future. For example, those skilled in the art should understand that all block diagrams provided herein correspond to conceptual representations of illustrative circuit diagrams implementing the principles of the present technology. It should also be understood that all block diagrams, process diagrams, state transition diagrams, pseudocodes, etc. correspond to various processes that can be represented on a computer-readable information carrier and can be executed by a computer or processor, regardless of whether such a computer or processor is shown explicitly or not.

[0059] The functions of the various elements shown in the drawings, including all functional blocks designated as a "processor", may be implemented using dedicated hardware as well as hardware capable of supporting the execution of corresponding software. If a processor is used, these functions may be performed by a single dedicated processor, a single shared processor, or multiple separate processors, some of which may be shared. Furthermore, the explicit use of the term "processor" or "controller" should not be construed as referring exclusively to hardware capable of supporting the execution of software, and may include, among other things, hardware of a digital signal processor (DSP), a network processor, an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), ROM for storing software, RAM, and non-volatile memory. Other hardware, general purpose and/or custom, may also be included.

[0060] Software modules or simply modules, the implementation of which is supposed to be based on software, can be represented here in the form of any combination of flowchart elements or other elements indicating the execution of process steps and/or containing a text description. Such modules can be executed by hardware, shown explicitly or implied.

[0061] Taking into account the above principles, some non-limiting examples are discussed below to illustrate various embodiments of aspects of the present technology.

Computer system

[0062] Fig. 1 shows a computer system 100 suitable for use in some embodiments of the present technology. The computer system 100 includes various hardware elements, including one or more single-core or multi-core processors, collectively represented by a processor 110, a graphics processor 111, a solid-state drive 120, RAM 130, a display interface 140, and an input/output interface 150.

[0063] Communication between elements of the computer system 100 may be accomplished via one or more internal and/or external buses 160 (such as a PCI bus, a USB bus, an IEEE 1394 FireWire bus, a SCSI bus, a Serial-ATA bus, etc.) to which various hardware elements are electronically connected.

[0064] The input/output interface 150 may be connected to a touch screen 190 and/or to one or more internal and/or external buses 160. The touch screen 190 may also be referred to as a screen, such as the screen (not specifically indicated) of the electronic device 210 shown in Fig. 2. In the embodiments shown in Fig. 1, the touch screen 190 comprises touch means 194 (e.g., touch elements embedded in the display layer and providing for recording the physical interaction between the user and the display) and a touch input/output controller 192 providing communication with the display interface 140 and/or with one or more internal and/or external buses 160. In some non-limiting embodiments of the present technology, the input/output interface 150 may be connected to a keyboard (not separately shown), a mouse (not separately shown), or a touch pad (not separately shown), which provide for user interaction with the computer system 100 in addition to or instead of the touch screen 190.

[0065] It should be noted that in some non-limiting embodiments of the present technology, some elements of the computer system 100 may be missing. For example, a keyboard and mouse (not shown separately) may be missing, particularly in cases (but not limited to) where the computer system 100 is implemented as a compact electronic device, such as a smartphone.

[0066] According to embodiments of the present technology, the solid-state drive 120 stores software instructions suitable for loading into the RAM 130 and execution by the processor 110 and/or the graphics processor 111. For example, the software instructions may be part of a library or an application.

Network computer Wednesday

[0067] Fig. 2 shows a networked computer environment 200 suitable for use with some non-limiting embodiments of the present technology. The networked computer environment 200 includes a server 250 connected via a communication network 270 (1) to a plurality of passenger devices 210, such as a passenger device 215 associated with a passenger 217, and (2) to a plurality of on-board devices 230, such as an on-board device 235 associated with a vehicle 237.

[0068] According to some non-limiting embodiments of the present technology, a transportation Internet platform 260 may be hosted on the server 250, which may be accessed by a plurality of passenger devices 210 and a plurality of on-board devices 230. In general, the transportation Internet platform 260 is a web resource that provides ridesharing services (taxi ordering service) to passengers by selecting for their taxi orders a plurality of vehicles involved in work on the transportation Internet platform 260, such as vehicle 237. Non-limiting examples of the transportation Internet platform 260 are the transportation Internet platforms Uber™, Lyft™ and Yandex.Taxi™.

[0069] In particular, in some non-limiting embodiments of the present technology, the Internet transportation platform 260 is capable of (1) receiving from the passenger device 215 data about a taxi order 245 from the passenger 217 for making a trip from the pick-up point to the drop-off point, (2) selecting from among a plurality of vehicles (not separately shown) connected to the Internet transportation platform 260 via corresponding devices from the plurality of on-board devices 230, a suitable vehicle, for example, a vehicle 237, for fulfilling the taxi order 245, and (3) transmitting data about the taxi order 245 to the on-board device 235 associated with the vehicle 237 for fulfilling the taxi order 245.

[0070] Below is a more detailed description of how the passenger device 215, the on-board device 235, and the server 250 may be implemented in accordance with some non-limiting embodiments of the present technology.

Passenger device

[0071] According to some non-limiting embodiments of the present technology, the passenger device 215 may include suitable logic, circuitry, interfaces, and/or code executable by the circuitry that are capable of performing one or more operations for the purpose of transmitting the taxi order 245 to the server 250 for subsequent processing. To this end, the passenger device 215 may include some or all of the elements of the computer system 100 shown in Fig. 1.

[0072] In particular, in some non-limiting embodiments of the present technology, the passenger device 215 is capable of executing a software application associated with the online transportation platform 260 (e.g., a passenger application thereof) and capable of displaying various graphical user interfaces that allow the passenger 217 to interact with the online transportation platform 260 to place an order 245 for a taxi. For example, the passenger device 215 may display various graphical user interfaces for bookings and notifications (not shown) that may be rendered by the online transportation platform 260. For example, the passenger application of the online transportation platform 260 executed on the passenger device 215 may display a graphical user interface for bookings that, in particular, allows the passenger 217 to (1) specify pick-up and drop-off locations, (2) specify desired vehicle properties related to the vehicle 237, and (3) provide the necessary consent to place an order 245 for a taxi.

[0073] According to some non-limiting embodiments of the present technology, the properties of the vehicle 237 may include, among other things, (1) the make and model of the vehicle 237, (2) the year of manufacture of the vehicle 237, (3) the body type of the vehicle 237, such as a sedan, hatchback, station wagon, minivan, etc., (4) the type of vehicle 237 in terms of control, such as traditional (driver-operated) or self-driving (also referred to as an "unmanned vehicle" or "autonomous vehicle"), (5) the luggage compartment volume of the vehicle 237 taking into account the cargo compartment available in the vehicle 237, in particular small (e.g., less than 3680 L), medium (e.g., from 3680 to 4500 L) or large (e.g., more than 4530 L), (6) the ability to travel with children, i.e., the presence of a child seats in the vehicle 237, (7) the possibility of traveling for passengers with special needs, such as passengers in wheelchairs, elderly passengers and pregnant women, (8) the possibility of traveling with pets.

[0074] In some non-limiting embodiments of the present technology, a graphical user interface for booking (not shown) may provide a passenger 217 with a geographic map and a first set of selection elements. The first set of selection elements may include text fields, a navigation cursor, a "go now" button, a "go later" button, a button for determining the current location, a distance scale, and the like. The text fields may be used by the passenger 217 to enter addresses of pick-up and drop-off locations in text format. The navigation cursor may be moved around the geographic map via touch input performed by the passenger 217 on the passenger device 215 and may be used to indicate pick-up and drop-off locations on the geographic map. The "go now" button may be used by the passenger 217 when the passenger 217 wants the taxi order 245 to begin immediately. The "go later" button may be used by the passenger 217 when the passenger 217 wants the taxi order 245 to begin at a certain time in the future. The current location determination button can be used to move the navigation cursor to a point corresponding to the current location of the passenger device 215. The distance scale can be used by the passenger 217 to increase or decrease the scale of the geographic map.

[0075] In addition, in accordance with some non-limiting embodiments of the present technology, the graphical user interface of the reservation may provide the passenger 217 with a second set of options corresponding to the features of the vehicle 237. In these embodiments, the second set of options may be implemented, for example, as a menu with check boxes.

[0076] In addition, as indicated above, the passenger device 215 may further provide for the transmission via the communication network 270 of the taxi order 245 to the server 250. In this case, the server 250 may provide for the display on the passenger device 215 of a graphical user interface providing the passenger 217 with the destination information received from the server 250, including data on the vehicle assigned to fulfill the taxi order 245 and the estimated time of arrival of this vehicle at the pick-up location specified in the taxi order 245.

Onboard device

[0077] According to some non-limiting embodiments of the present technology, the on-board device 235 associated with the vehicle 237 of the plurality of vehicles may be implemented similarly to the passenger device 215 and, accordingly, may also include some or all of the elements of the computer system 100 shown in Fig. 1.

[0078] In some non-limiting embodiments of the present technology, the on-board device 235 is capable of executing a software application associated with the transportation internet platform 260 (e.g., its driver application) and capable of displaying various graphical user interfaces that allow a driver (not shown) of the vehicle 237 to interact with the transportation internet platform 260.

[0079] For example, in some non-limiting embodiments of the present technology, in response to server 250 assigning vehicle 237 to taxi order 245 from passenger 217, on-board device 235 may receive order information from server 250 and display a notification graphical user interface on the display of on-board device 235. The notification graphical user interface allows the driver of vehicle 237 to view a message containing order information 245.

[0080] In some non-limiting embodiments of the present technology, the information about the taxi order 245 may include the first and last name of the passenger 217, the number of seats ordered in the taxi order 245, the pick-up location, the drop-off location, the pick-up time, the estimated drop-off time, the cost of the trip, etc. The graphical user interface of the notification may further include a navigation map that displays the pick-up location, the drop-off location, and the estimated route from the pick-up location to the drop-off location. The graphical user interface of the notification may also further include a number of selection elements, such as the menu items "confirm booking" and "reject booking", which may be selected by the driver of the vehicle 237 after receiving information about the order 245. The menu item "confirm booking" is selected by the driver when the driver needs to confirm the order 245. The menu item "reject booking" is selected by the driver when the driver needs to reject the order 245. In the latter case, the server 250 may assign another vehicle to fulfill the order 245.

[0081] Furthermore, based on the assignment information and/or in response to the confirmation of the taxi order 245 by the driver, the on-board device 235 can provide a display of a navigation graphical user interface, indicating to the driver of the vehicle 237 a route to the pick-up location and thereby dispatching the vehicle 237 there to ensure the pick-up of the passenger 217. The navigation graphical user interface can further provide a display of a route from the pick-up location to the drop-off location, indicating to the driver of the vehicle 237 a route from the pick-up location to the drop-off location to drop off the passenger 217. The on-board device 235 is further capable of tracking the location of the vehicle 237 in real time and transmitting information about the real-time tracked location to the server 250. Based on this information, the server 250 is capable of determining (1) whether the vehicle 237 is currently executing any current taxi order or (2) whether it is currently ready to execute the taxi order 245. The on-board device 235 is further capable of transmitting to the server 250 data from one or more sensors (not shown) installed on the vehicle 237. Examples of such one or more sensors include, among other things, an accelerometer, an odometer, a traffic flow sensor, a global navigation system (GPS) sensor, a temperature sensor, cameras, etc.

[0082] It is obvious that, without departing from the essence and scope of the present technology, such embodiments can also be implemented in which the vehicle 237 is a self-driving vehicle, and the on-board device 235 is additionally capable of controlling the operation of the vehicle 237 along a given trajectory of its movement. In these embodiments, the on-board device 235 is capable of automatically confirming or rejecting the taxi order 245, for example, depending on the rating of the passenger 217 who made the taxi order 245, the current technical condition of the vehicle 237, the traffic situation, etc. Moreover, in these embodiments, the server 250 is capable of sending the vehicle 237 according to the taxi order 245 by transferring to the on-board device 235 associated with the vehicle 237 the control of the vehicle 237 during its movement along the route to the pick-up point according to the taxi order 245.

Net connections

[0083] In some non-limiting embodiments of the present technology, the communication network 270 is the Internet. In other non-limiting embodiments, the communication network 270 may be implemented as any suitable local area network (LAN), wide area network (WAN), private communication network, etc. It is obvious that the embodiments of the communication network 270 are given for illustrative purposes only. The method of implementing the communication lines (not separately indicated) between the passenger device 215, the on-board device 235, the server 250 and the communication network 270 depends, among other things, on the implementation of the passenger device 215, the on-board device 235 and the server 250. Just by way of example, it can be noted that, among other things, in those non-limiting embodiments of the present technology, where the passenger device 215 and/or the on-board device 235 is implemented as a wireless communication device, such as a smartphone or a navigation device, the communication line can be implemented as a wireless communication line. Examples of wireless communication lines can be, among other things, a 3G communication network channel, a 4G communication network channel, etc. In the communication network 270, a wireless connection to the server 250 can also be used.

Server

[0084] According to some non-limiting embodiments of the present technology, the server 250 may include suitable logic, circuitry, interfaces, and/or code executable by the circuitry that are capable of performing one or more operations (e.g., executing programs, procedures, scripts, etc. stored in memory) for distributing vehicles in response to receiving taxi orders from passengers, such as taxi order 245 from passenger 217. As noted above, according to some non-limiting embodiments of the present technology, the server 250 is capable of receiving taxi order 245 from passenger device 215 via communication network 270. Based on taxi order 245, the server 250 is capable of selecting and assigning a suitable vehicle (e.g., vehicle 237) to taxi order 245. The server 250 is further capable of transmitting the assignment information to the passenger device 215 to notify the passenger 217 of the assignment of the vehicle 237. The server 250 may be implemented based on various web technologies, such as, among others, the Java web application platform, the .NET platform, the PHP (hypertext preprocessor) platform, and other web application platforms.

[0085] In some non-limiting embodiments of the present technology, the server 250 may be implemented as a conventional computer server and, thus, may include some or all of the elements of the computer system 100 shown in Fig. 1. In one non-limiting example, the server 250 is implemented as a Dell™ PowerEdge™ server running a Microsoft™ Windows Server™ operating system, but it may also be implemented on any other suitable types of hardware, software, and/or firmware, or a combination thereof. In the presented non-limiting embodiments of the present technology, the server 250 is a single server. In other non-limiting embodiments of the present technology, the functions of the server 250 may be distributed among multiple servers (not shown).

[0086] In some non-limiting embodiments of the present technology, the server 250 may be operated by the same organization that provides the Internet transportation platform 260. For example, if the Internet transportation platform 260 is the Yandex.Taxi™ Internet transportation platform, the server 250 may be operated by Yandex LLC, 119021, Russia, Moscow, Leo Tolstoy St., Bldg. 16. In other non-limiting embodiments of the present technology, the server 250 may be operated by an organization other than the organization that provides the Internet transportation platform 260.

[0087] In some non-limiting embodiments of the present technology, the passenger device 215 and/or the on-board device 235 may communicate with the server 250 to receive one or more updates. The updates may include, among other things, updates to the software applications for passengers and drivers of the transportation Internet platform 260, running on the passenger device 215 and the on-board device 235, respectively, map updates, route updates, weather data updates, and the like. In some non-limiting embodiments of the present technology, the passenger device 215 and/or the on-board device 235 are also capable of sending to the server 250 some operational data, such as information about the routes taken, traffic data, operational data, and the like.

[0088] Thus, in some non-limiting embodiments of the present technology, the server 250 is capable of storing in its database (not shown) various data used for the purpose of selecting vehicles for fulfilling taxi orders. In particular, in some non-limiting embodiments of the present technology, the database of the server 250 can provide for storing and managing (1) data on past trips of passengers (for example, passenger 217) who made trips on one or more vehicles from a plurality of vehicles used by the organization that manages the Internet transportation platform 260, and (2) data on past trips of drivers (for example, the driver of vehicle 237) who drove vehicles from a plurality of vehicles involved in work on the Internet transportation platform 260.

[0089] According to various non-limiting embodiments of the present technology, the data about the past trips of the passenger 217 may comprise information about past taxi orders made by the passenger 217 previously and related to vehicles involved in work on the Internet transportation platform 260. The information about the taxi orders made by the passenger 217 may include, among other things, past pick-up and drop-off locations, the frequency of taxi orders for trips between past pick-up and drop-off locations, the desired properties of the vehicle that the passenger 217 indicated in past taxi orders, ratings given to the passenger 217 by the drivers of the Internet transportation platform 260 after fulfilling past taxi orders, which characterize the degree of satisfaction of the drivers with the trips provided to the passenger 217, etc.

[0090] In some non-limiting embodiments of the present technology, the data on past trips of a specific driver executing taxi orders made on the Internet transportation platform 260, for example, the driver of the vehicle 237, may contain information on past taxi orders assigned to the driver of the vehicle 237 by the server 250 in the past. The information on past taxi orders assigned to the driver of the vehicle 237 may include past pick-up and drop-off locations, the duration of each trip, the driver's work schedule, ratings given to the driver by passengers of the Internet transportation platform 260 after executing past taxi orders, characterizing the degree of satisfaction of passengers with the trips performed by the driver, etc.

[0091] Furthermore, according to some non-limiting embodiments of the present technology, the database of the server 250 may also be capable of storing information about passengers (e.g., about passenger 217) of the Internet transportation platform 260, information about drivers (e.g., about the driver of vehicle 237), and information about each of a plurality of vehicles (e.g., about vehicle 237) that are engaged in fulfilling taxi orders made on the Internet transportation platform 260. For example, the information about passenger 217 may include certain passenger characteristics associated with passenger 217, including, among other things, (1) the first and last name of passenger 217, (2) a contact telephone number of passenger 217, (3) an e-mail address of passenger 217, (4) an overall rating of passenger 217 on the Internet transportation platform 260, determined, for example, based on the ratings of passenger 217 posted by drivers after completing previous taxi orders for passenger 217, and/or other related information about passenger 217.

[0092] In this case, the information about the driver of the vehicle 237 may include (1) some characteristic features of the driver of the vehicle 237, including, for example, the first and last name of the driver, the contact telephone number of the driver, the email address of the driver, the gender and age of the driver, the driver's identification card, and (2) professional features of the driver of the vehicle 237, including, for example, among other things, the driving style of the driver, the driving experience (driving experience) of the driver, the experience of accident-free driving (accident-free driving experience) of the driver, the number of traffic accidents in the past and/or the indicator of compliance with traffic rules associated with the driver. In addition, in those embodiments where the driver is the owner of the vehicle 237 involved in work on the Internet transportation platform 260, the information about the driver may additionally include the number of the vehicle 237.

[0093] According to some non-limiting embodiments of the present technology, the driving style of the driver may be determined, for example, based on past data on the nature of acceleration and braking received from the vehicle 237 when the driver performed past taxi orders. The server 250 may provide receiving such data from the accelerometer of the vehicle 237 during the process of the driver performing past taxi orders. Thus, based on such data, the server 250 is able to evaluate the driving style of the driver, for example, as (1) an aggressive driving style, (2) an irresponsible and dangerous driving style, (3) a nervous driving style, or (4) a careful driving style. In this case, the indicator of compliance with traffic rules characterizes how strictly the driver of the vehicle observes traffic rules when performing taxi orders, for example, speed limits, stop signs and rules for stopping and parking vehicles in a given area. The server 250 is capable of determining the indicator of compliance with the traffic rules of the driver by tracking the movements and movement parameters (for example, speed and acceleration) of the vehicle 237 based on information received from the sensors of the vehicle 237, and comparing them with data on the routes taken by the driver in the vehicle 237 when performing past taxi orders, on a road map.

[0094] Finally, the information about the vehicle 237 may include, for example, the registration number of the vehicle 237, the date of the last maintenance of the vehicle 237, the odometer reading of the vehicle 237, as well as the properties of the vehicle 237 listed above in a non-exhaustive manner.

[0095] In addition, in some non-limiting embodiments of the present technology, the server 250 may have access (local and/or remote) to information representing a road map. In general, the road map is a map of roads located in a city, region and/or other areas where the Internet transportation platform 260 provides ridesharing services. For example, a section of the road map may contain, among other things, information such as the presence of roads in this section, the number of traffic lanes on these roads, the presence of intersections, etc.

[0096] In some non-limiting embodiments of the present technology, the server 250 may have access (locally and/or remotely) to information characterizing traffic rules associated with the road map. In general, traffic rules are traffic laws adopted by an authority with the relevant authority and in effect on at least some sections of the road map. For example, traffic rules on a particular section of the road map may be represented, among other things, by such means ensuring compliance with the traffic laws in effect on this section of the road map as the established direction of traffic on each lane of this section of the road map, traffic signs regulating traffic on this section of the map, including stop signs, give way signs, lane signs/markings, speed limit signs, other types of signs provided for by traffic rules, etc.

[0097] In addition, in some non-limiting embodiments of the present technology, the server 250 is capable of receiving real-time traffic data characterizing traffic on roads from a road map. According to some non-limiting embodiments of the present technology, the real-time traffic data may include, among other things, (1) the average speed of traffic on a road from a road map, (2) data on the locations of traffic accidents on the road, such as vehicle collisions, traffic accidents involving pedestrians and animals, and the like, (3) data on foreign objects on the road, such as cargo that has fallen from vehicles traveling on this road, traffic cones, dead animals, and the like, (4) data on construction work on the road from a road map, (5) data on the presence of police on the road and/or (6) the number of free and closed traffic lanes on the road, and the like. According to some non-limiting embodiments of the present technology, the server 250 is capable of receiving real-time traffic situation data by analyzing data coming from sensors of a plurality of vehicles and/or their on-board devices 230, such as GPS sensors of a plurality of vehicles. However, in other non-limiting embodiments of the present technology, the server 250 is capable of receiving real-time traffic situation data from drivers of a plurality of vehicles through an application for drivers of the Internet transportation platform 260. In these embodiments, the server 250 is capable of generating and then issuing to the corresponding on-board devices of the plurality of on-board devices 230, for example, to the on-board device 235, questions to drivers regarding the current traffic situation in their vicinity, such as "Is there a traffic jam ahead?", "Are the police 250 m away. Have they already left the place or are they still there?" and the like. Accordingly, the server 250 is capable of causing the on-board device 235 to display appropriate interface elements (e.g., Yes/No controls) to enable the driver to answer the questions asked.

[0098] Thus, in accordance with some non-limiting embodiments of the present technology, upon receipt of a taxi order 245, the server 250 is capable, based on the road map data and the data received in real time from one or more sensors of each of the plurality of vehicles and/or the plurality of their on-board devices 230, of (1) determining the estimated time of arrival of each of the plurality of vehicles at the pick-up location associated with the taxi order 245, and (2) selecting a vehicle to fulfill the taxi order 245 based on the estimated time of arrival values associated with the plurality of vehicles.

[0099] With reference to Figs. 3-7, it is described below how the server 250 is capable of selecting vehicles to fulfill taxi orders from passengers of the Internet transportation platform 260, such as the taxi order 245 from the passenger 217, based on the estimated arrival times, in accordance with some non-limiting embodiments of the present technology.

Distribution transport funds By orders Taxi

[0100] As shown in Fig. 2, according to some non-limiting embodiments of the present technology, the server 250 is capable of processing taxi orders made by passengers at a given point in time, including the taxi order 245, in batches, such as the taxi order batch 240. According to some non-limiting embodiments of the present technology, the server 250 is capable of receiving the taxi order batch 240 from a given area in which the Internet transportation platform 260 provides ridesharing services. In general, the given area is a territory of a given area, for example, 5, 10, 25 or even 100 or 300 square kilometers. In some non-limiting embodiments of the present technology, the given area may be a corresponding unit of administrative-territorial division in the country where the Internet transport platform 260 operates, such as a block, a district, a populated area, a city, a region (such as a district, a region, a territory, etc.), etc. In other non-limiting embodiments of the present technology, the given area may be defined differently, such as a given shape of certain dimensions, including, for example, a square, a rectangle, or a circle, on a road map to which the server 250 has access. In some non-limiting embodiments of the present technology, the dimensions of the given area may be determined taking into account various factors, such as, among other things, the population density living in the given area, the availability of public transportation in the given area, the rate of movement of residents in the given area, etc.

[0101] In addition, according to some non-limiting embodiments of the present technology, the server 250 is capable of receiving data about each batch of taxi orders made in a given area, with time iterations, for example, with a set time step. In particular, in the current iteration of order distribution, the server 250 is capable of receiving a batch of taxi orders, such as a batch 240 of taxi orders, collected in a time equal to a set time step, after the completion of the previous iteration of order distribution. In this case, there are no restrictions on the choice of the time step. In some non-limiting embodiments of the present technology, the set time step of the iterations of distributing orders in a given zone may be selected based on (1) the demographic characteristics of the given zone, including its area and the number of residents in it, (2) the average demand for vehicles, i.e. the average number of taxi orders made over a certain period, such as a day, week, month, etc., (3) the number of vehicles from the plurality of vehicles involved in work on the Internet transport platform 260, ready to fulfill taxi orders in a given zone, and/or other factors. For example, depending on such factors, the time step may be selected in the range from 30 seconds to 20 minutes.

[0102] In some non-limiting embodiments of the present technology, the set time step for a given zone remains unchanged. In other non-limiting embodiments of the present technology, the set time step may vary. For example, the set time step may have different values depending on the time of day, in particular, during peak hours (e.g., from 7:00 to 9:00 and from 16:00 to 19:00) it may be smaller than at other times of the day. In another example, the set time step may vary depending on the days of the week, for example, on weekdays it may be smaller than on weekends and holidays. In yet another example, the set time step may vary depending on the season, for example, in the winter season it may be smaller than in other seasons.

[0103] Thus, according to some non-limiting embodiments of the present technology, the server 250, after receiving data about a batch 240 of taxi orders in a given area in a current iteration of order distribution, is able to (1) form a set of candidate vehicles in the given area that are available at a given time, (2) determine for each of the set of candidate vehicles their estimated values of arrival times at pick-up locations associated with each order in the batch 240 of taxi orders, such as taxi order 245, (3) distribute the set of candidate vehicles among the batch 240 of taxi orders based on the estimated values of arrival times, and (4) based on this distribution, dispatch each of the set of candidate vehicles according to a corresponding order in the batch 240 of taxi orders. In some non-limiting embodiments of the present technology, the server 250 is capable of determining, based on real-time traffic data in a given area, the estimated arrival time of a vehicle from a set of candidate vehicles as being equal to the minimum possible, i.e., corresponding to the fastest route to the pick-up location for ordering 245 a taxi.

[0104] Furthermore, no restrictions are imposed on the manner in which the server 250 processes those taxi orders from the batch 240 of taxi orders that have not been assigned to any of the set of candidate vehicles at the current iteration of order distribution, for example, due to the fact that the number of vehicles in the set of candidate vehicles at the current iteration of order distribution was insufficient to fulfill the entire batch 240 of taxi orders. In one example, the server 250 is capable of transferring the remaining taxi orders to the next iteration of order distribution after the current iteration of order distribution. However, in another example, the server 250 is capable of canceling all pending taxi orders from the batch 240 of taxi orders that have not been assigned to any of the vehicles at the current iteration of order distribution.

[0105] According to some non-limiting embodiments of the present technology, the server 250 is configured to distribute the set of candidate vehicles across the batch 240 taxi orders such that the total estimated arrival time, which is the sum of the estimated arrival times of all vehicles in the set of candidate vehicles for the corresponding taxi orders in the batch 240 taxi orders, is minimized.

[0106] Fig. 3 is a schematic representation of a section 300 of a road map corresponding to a given zone in the current iteration of order distribution, which illustrates an example of distributing a set of candidate vehicles across a batch 240 of taxi orders with minimization of the total estimated arrival time in accordance with some non-limiting embodiments of the present technology.

[0107] In the embodiments shown in Fig. 3, a batch 240 of taxi orders in the current iteration of distributing orders in a given area includes a first taxi order made by a first passenger 301 and a second taxi order made by a second passenger 303. In this regard, the server 250 is able to determine in the plurality of vehicles a set of candidate vehicles suitable for fulfilling the batch 240 of taxi orders in a given area, where the set of candidate vehicles includes a first candidate vehicle 302 and a second candidate vehicle 304. It is obvious that the first and second candidate vehicles 302, 304 can be implemented similarly to the vehicles 237 described above.

[0108] As indicated above, the server 250 is capable of determining for each of the first and second vehicles 302, 304 estimated arrival times to the respective pick-up locations (not separately indicated) for each of the first and second taxi orders. In particular, the server 250 can, based on the road map data described above and the real-time traffic data received, determine that the first candidate vehicle 302 can (1) arrive at the pick-up location for the first taxi order in an estimated arrival time of t ¹ ₁ = 2 minutes and (2) arrive at the pick-up location for the second taxi order in an estimated arrival time of t ¹ ₂ = 6 minutes. Similarly, server 250 may determine that second candidate vehicle 304 can (1) arrive at the pickup location for the first taxi order in an estimated arrival time of t ² ₁ = 8 min and (2) arrive at the pickup location for the second taxi order in an estimated arrival time of t ² ₂ = 10 min.

[0109] By minimizing the total estimated arrival time of the first and second vehicles 302, 304, the server 250 may assign the first candidate vehicle 302 to the first taxi order and the second candidate vehicle 304 to the second taxi order, as shown by the bold arrows in Fig. 3. It is obvious that in this case the total estimated arrival time of 12 min is the minimum among all possible combinations of estimated arrival times that ensure the boarding of each of the first and second passengers 301, 303.

[0110] In some non-limiting embodiments of the present technology, the server 250 is configured to perform an optimization algorithm to pairwise match vehicles from the set of candidate vehicles with taxi orders from the batch 240 of taxi orders such that the total estimated arrival time is minimized. There is no limitation on the method for implementing the optimization algorithm, and in some non-limiting embodiments of the present technology, the optimization algorithm may be a combinatorial optimization algorithm, such as a "Hungarian" optimization algorithm. Obviously, other optimization algorithms may be used to minimize the total estimated arrival time without departing from the spirit and scope of the present technology.

[0111] Also according to some non-limiting embodiments of the present technology, the server 250 is configured to change the estimated arrival time of a candidate vehicle from the set of candidate vehicles to the pick-up location for a taxi order from the batch 240 of taxi orders based on vehicle features associated with the candidate vehicle, such as one of the first or second candidate vehicles 302, 304. In particular, according to some non-limiting embodiments of the present technology, the server 250 is configured to (1) determine a vehicle rating based on the vehicle features associated with the candidate vehicle, and (2) apply the vehicle rating to the estimated arrival time of the candidate vehicle for the taxi order from the batch 240 of taxi orders to determine a corresponding modified estimated arrival time.

[0112] According to some non-limiting embodiments of the present technology, the vehicle properties may include, among other things, (1) a safety parameter associated with a candidate vehicle in a taxi order from the batch 240 of taxi orders, (2) a number of candidate vehicles in the set of candidate vehicles available in the current iteration of order distribution, and/or (3) desired vehicle properties specified above in relation to vehicle 237 and specified in a taxi order from the batch 240 of taxi orders.

[0113] According to some non-limiting embodiments of the present technology, the safety parameter associated with the candidate vehicle characterizes the likelihood of an accident involving the candidate vehicle on a given route, such as on a route to at least one of the pick-up and drop-off locations associated with a taxi order from the batch 240 of taxi orders. In various non-limiting embodiments of the present technology, the server 250 is configured to determine the safety parameter based on (1) information about the driver of the candidate vehicle described above with respect to the driver of the vehicle 237 and/or (2) information about the route to at least one of the pick-up and drop-off locations associated with a taxi order from the batch 240 of taxi orders.

[0114] According to some non-limiting embodiments of the present technology, the information about a given route on a road map may include (1) an average speed on the route, (2) an average number of traffic lanes on the route, (3) an average throughput of the route, which may be expressed, for example, in the number of vehicles traveling on the route per day, (4) the length of the route, and/or (5) the number of traffic accidents that occurred on the route over a given period of time, such as a week, month, or year.

[0115] In particular, to determine a safety parameter associated with a given route, in some non-limiting embodiments of the present technology, the server 250 is configured to determine (1) a driver safety rating for a driver of a candidate vehicle based on driver information associated with the driver of the candidate vehicle, (2) a route safety rating for the route based on route information associated with the route, and (3) a likelihood of a traffic accident involving the candidate vehicle on a route to at least one of the pick-up and drop-off locations associated with a taxi order from the batch 240 of taxi orders based on the driver safety rating and the route safety rating.

[0116] Moreover, there are no restrictions on the methods for determining by the server 250 the driver safety score and the route safety score based on the above data, as well as the probability of a traffic accident, and in some non-restrictive embodiments of the present technology, the server 250 is capable of using the machine learning algorithm described in the patent application “Methods and servers for generating a prediction score by a machine learning algorithm” of the same applicant with the patent attorney case number 106848-027 (this patent application has not yet been published), which was filed with the US Patent Office simultaneously with the present application and the contents of which are fully incorporated herein by reference.

[0117] In some non-limiting embodiments of the present technology, the server 250 is also configured to (1) compare a particular value of a vehicle feature with a specified threshold value of the feature to obtain a vehicle score and (2) increase or decrease the vehicle score for the candidate vehicle by a specified increment, such as 1, 5, or 10, depending on the result of such comparison.

[0118] For example, if the server 250 has determined that the safety parameter associated with a candidate vehicle on the corresponding route is 0.7, and the threshold value of the feature for the safety parameter is selected to be 0.5, then the server 250 is configured to decrease the vehicle score for this candidate vehicle by a specified increment. In another example, if, in the current iteration of order distribution, the set of candidate vehicles includes two candidate vehicles, and the threshold value of the feature for the number of candidate vehicles is selected to be 5, then the server 250 is configured to increase the vehicle score for the candidate vehicle by a specified increment. In another example, if a passenger, such as one of the first and second passengers 301, 303, has indicated the presence of a child seat as a desirable feature of the vehicle in his or her taxi order, and the candidate vehicle does not have such a feature, then the server 250 is able to decrease the vehicle rating for the candidate vehicle by a specified increment.

[0119] In addition, in some non-limiting embodiments of the present technology, the server 250 is configured to apply a predetermined increment value to a vehicle feature from the plurality of vehicle features with a corresponding weighting factor, thereby increasing or decreasing the priority of this vehicle feature when determining the rating of the candidate vehicle. For example, if a safety parameter for a candidate vehicle on the corresponding route exceeds a predetermined threshold value of the feature, the server 250 may decrease the vehicle rating of the candidate vehicle by a predetermined increment value taking into account a weighting factor of 2. In another example, if the candidate vehicle has all of the desirable vehicle features specified in the taxi order from the batch 240 of taxi orders, such as the presence of a child seat or a large luggage compartment volume, then the server 250 may increase the vehicle rating of the candidate vehicle by a predetermined increment value taking into account a weighting factor of 1.5. In addition, in some non-limiting embodiments of the present technology, the server 250 is capable of converting the vehicle estimate to a given time scale by applying a corresponding coefficient to the vehicle estimate and thereby converting it into units of time.

[0120] The server 250 is also capable of applying the vehicle rating to the estimated arrival time of the candidate vehicle for ordering a taxi from the batch 240 of taxi orders with the opposite sign. In particular, if the vehicle rating is negative, the server 250 is capable of increasing the estimated arrival time of the candidate vehicle for ordering a taxi from the batch of taxi orders, and otherwise, decreasing the estimated arrival time.

[0121] In some non-limiting embodiments of the present technology, the server 250 is configured to apply the vehicle estimate to the estimated arrival time of the candidate vehicle using the following formula:

where ETA ^M is the modified estimated time of arrival of the candidate vehicle obtained by applying the corresponding vehicle rating to the estimated time of arrival, ETA is the (initial) estimated time of arrival of the candidate vehicle at the pick-up location according to the taxi order from the batch of 240 taxi orders, distance is the distance value to the pick-up location, rating is the vehicle rating in units of time (e.g., in seconds), a, b are coefficients.

[0122] Fig. 4 is a schematic representation of a road map section 300 corresponding to a given zone in the current iteration of order distribution, which illustrates another example of distributing a set of candidate vehicles across a batch 240 of taxi orders with minimization of the total estimated arrival time, in accordance with some non-limiting embodiments of the present technology.

[0123] It is obvious that the example of Fig. 4 differs from the example of Fig. 3 in that in the first case, the server 250 ensures the distribution of the set of candidate vehicles across the batch 240 of taxi orders based on the modified estimated arrival times determined as described above. In particular, since the first candidate vehicle 302 has the desired vehicle feature (the presence of a child seat) indicated by the second passenger 303 in the second taxi order, the server 250 can reduce the estimated arrival time of the first candidate vehicle 302 for the taxi order t ¹ ₂ = 6 min to the modified estimated arrival time t ^1m ₂ = 4 min, leaving the estimated arrival time for the first taxi order unchanged. In addition, since the second candidate vehicle 304 does not have the desired vehicle feature (the presence of a child seat) indicated by the second passenger 303, the server 250 may increase the estimated arrival time of the second candidate vehicle 304 for the taxi order t ² ₂ = 10 min to a modified estimated arrival time t ^2m ₂ = 14 min, leaving the estimated arrival time for the first taxi order unchanged.

[0124] It is apparent that by minimizing the total estimated arrival time by executing the optimization algorithm as described above, the server 250 at the current iteration of order distribution may (1) assign the first candidate vehicle 302 to the second taxi order from the batch 240 taxi orders and (2) assign the second candidate vehicle 304 to the first taxi order from the batch 240 taxi orders.

[0125] Thus, when pairwise comparing candidate vehicles with taxi orders, the server 250 is able to give priority to vehicles with certain features in those taxi orders where such features are required, and/or, for taxi orders in general, give priority to vehicles characterized by a lower risk of getting into a traffic accident when fulfilling a taxi order. In this regard, some non-restrictive embodiments of the present technology make it possible to increase the degree of satisfaction of passengers of the Internet transport platform 260, as well as the overall level of safety of the ridesharing services provided thereon.

[0126] It is obvious that in some non-limiting embodiments of the present technology, the modified estimated arrival time of the candidate vehicle may be expressed not only in units of time, but also in other units, for example, dimensionless, and in some cases may be negative. In such embodiments, after minimizing the total estimated arrival time determined based on the modified estimated arrival times, for distributing the set of candidate vehicles across the batch 240 of taxi orders, the server 250 is further configured to again distribute all vehicles from the set of candidate vehicles based on the initial estimated arrival times determined as described above.

[0127] In addition, the developers of the present technology have determined that in some cases it is advisable not to distribute the entire batch 240 taxi orders in the current iteration of order distribution, even if the set of candidate vehicles contains a sufficient number of candidate vehicles. In particular, some non-restrictive embodiments of the present technology are aimed at suspending the execution of those taxi orders from the batch 240 taxi orders that cannot be executed by any of the set of candidate vehicles within a given threshold estimated arrival time. Thus, the present methods, by suspending the execution of taxi orders that remain unassigned after the current iteration of order distribution, provide a certain time for a natural change in the coverage of a given area by the available vehicles, which in the future (in the next iteration of order distribution), can make it possible to select for a taxi order that remains unassigned a candidate vehicle whose estimated arrival time for this taxi order is less than the given threshold estimated arrival time. Accordingly, some non-restrictive embodiments of this technology make it possible to reduce the waiting time of passengers for vehicles when fulfilling their taxi orders.

[0128] To this end, in some non-limiting embodiments of the present technology, the server 250, at the current iteration of order distribution, is capable of generating in the set of candidate vehicles available at this iteration of order distribution, an indication of a model candidate vehicle, i.e., a virtual vehicle with the required driving characteristics, such as a specified threshold estimated time of arrival at the pick-up location, determined for all taxi orders from the batch 240 of taxi orders.

[0129] Fig. 5 is a schematic representation of a road map section 300 corresponding to a given zone in the current iteration of order distribution, which illustrates another example of distributing a set of candidate vehicles, including a model candidate vehicle 502, across a batch 240 of taxi orders with a minimum total estimated arrival time in accordance with some non-limiting embodiments of the present technology.

[0130] It is obvious that the server 250 is capable of generating for the first taxi order from the batch 240 of taxi orders an indication of the model candidate vehicle 502 in such a way that its estimated time of arrival at the pick-up location according to the first taxi order corresponds to the specified threshold estimated time of arrival for ensuring the pick-up of the first passenger 301, which in the example in Fig. 5 is equal to 4 minutes.

[0131] According to some non-limiting embodiments of the present technology, the predetermined threshold estimated time of arrival may be selected based on parameters of a predetermined area, such as, among other things, (1) the demographic characteristics of the predetermined area, (2) the average demand for vehicles therein, (3) the number of vehicles deployed on the Internet transportation platform 260 in the predetermined area, (4) the current environmental conditions in the predetermined area, such as weather conditions, air pollution levels, time of day, etc., (5) estimated arrival times and/or modified estimated arrival times for each of the set of candidate vehicles as described above, (6) marketing factors, etc. For example, if the visibility in a predetermined area is reduced due to current environmental conditions, such as snow or smog, the predetermined threshold estimated time of arrival may be increased. In another example, a given threshold estimated arrival time may be selected for a taxi order from a batch of 240 taxi orders based on an average estimated arrival time for a set of candidate vehicles for the taxi order from the batch of 240 taxi orders.

[0132] Thus, depending on at least some of the specified factors, the specified threshold estimated time of arrival can be selected for a specified zone to be, for example, 3, 4, 5 or 10 minutes. Moreover, in some non-limiting embodiments of the present technology, the specified threshold estimated time of arrival in several zones covered by the ridesharing services provided by the Internet transport platform 260, which differ in geographical location, can be different. In particular, in those embodiments where the specified zone is a city, the specified threshold estimated time of arrival for a passenger, for example, in Moscow can be 5 minutes, and in Montreal, which is smaller than Moscow, it can be 3 minutes. Moreover, without deviating from the essence and scope of the present technology, such embodiments can also be implemented in which the specified threshold estimated time of arrival in zones that differ in geographical location is the same.

[0133] In addition, in some non-limiting embodiments of the present technology, each taxi order from a batch of 240 taxi orders in a given zone may be associated with a separate threshold estimated time of arrival, and the server 250 is capable of generating in the set of candidate vehicles a separate model candidate vehicle associated with a separate estimated time of arrival at the pick-up location for the taxi order from the batch of 240 taxi orders.

[0134] Thus, continuing with the example shown in Fig. 5, by minimizing the total estimated arrival time using the optimization algorithm as described above, the server 250 may (1) assign the first candidate vehicle 302 to the second taxi order from the second passenger 303 of the batch 240 taxi orders, and (2) assign the model candidate vehicle 502 to the first taxi order from the first passenger 301 of the batch 240 taxi orders.

[0135] In addition, according to some non-limiting embodiments of the present technology, the server 250, after assigning the first taxi order to the model candidate vehicle 502, may further suspend the assignment of the first taxi order to one of the candidate vehicles included in the set of candidate vehicles in the current order distribution iteration until the start of the next order distribution iteration. In the next order distribution iteration, the server 250 may (1) include the first taxi order that remains unassigned after the current order distribution iteration in the next batch of taxi orders accumulated for the next order distribution iteration, and (2) select an actual vehicle for the first taxi order, as described above. Obviously, if in the next iteration of order distribution the server 250 fails to assign the first taxi order to the real vehicle and the first taxi order is again assigned to the model candidate vehicle 502, the server 250 may also suspend the assignment of the first taxi order to the vehicle in this iteration of order distribution and postpone the first taxi order to the next iteration of order distribution after the next iteration of order distribution.

[0136] In some non-limiting embodiments of the present technology, in order to increase the probability of assigning the first taxi order remaining after the current iteration of order distribution to a real vehicle in the next iteration of order distribution, the server 250 may, in addition to including the first taxi order in the next batch of taxi orders, increase a predetermined threshold estimated arrival time of the model candidate vehicle 502. In accordance with some non-limiting embodiments of the present technology, the server 250 is able to increase the predetermined threshold estimated arrival time based on a buffering period before including the first taxi order in the next batch of taxi orders. For example, in some non-limiting embodiments of the present technology, the server 250 is able to increase the predetermined threshold estimated arrival time of the model candidate vehicle 502 in each successive iteration of order distribution by a predetermined increment value, for example, equal to 1, 2 or 5 minutes. In another example, server 250 is configured to increase a predetermined threshold estimated arrival time of model candidate vehicle 502 at each successive iteration of order distribution by a predetermined number of times, such as by 1.5, 2.0, or 3.0 times.

[0137] In some non-limiting embodiments of the present technology, the server 250 is configured to exponentially increase the predetermined threshold estimated arrival time depending on the buffering period before including the first taxi order that remains unassigned after the current iteration of order distribution into the next batch of taxi orders received at the next iteration of order distribution. In particular, in such embodiments, the server 250 is configured to increase the predetermined threshold estimated arrival time according to the following formula:

where d is the initial value of the specified threshold estimated arrival time before the increase, which in the example in Fig. 5 is equal to 4 min, the buffering period is the period of time before the server 250 includes the first taxi order in the next batch of taxi orders, a, b and c are constant coefficients.

[0138] According to some non-limiting embodiments of the present technology, all of the constant factors may be determined empirically, such that, for example, the first taxi order that remains unassigned after the current iteration of order distribution is assigned during a certain number of subsequent iterations of order distribution with a given probability value. In another example, the server 250 is capable of adjusting the constant factors in real time, such as based on real-time traffic data received by the server 250 from a given area, as described above.

[0139] For example, during peak hours when there is a shortage of vehicles in a given area, according to some non-limiting embodiments of the present technology, the server 250 is able to increase the constant coefficient c , as a result of which the given threshold estimated arrival time responds less strongly to an increase in the buffering period, as shown in the graph of the dependence of the given threshold estimated arrival time on the buffering period according to the above formula (2), shown in Fig. 6.

[0140] Fig. 7 is a schematic representation of a section 300 of a road map corresponding to a given zone in the next iteration of order distribution, according to some non-limiting embodiments of the present technology.

[0141] In the next iteration of order distribution, the server 250 may set a predetermined threshold estimated arrival time for the first taxi order that remains unassigned after the current iteration of order distribution to 5 minutes. Then, using an optimization algorithm to minimize the total estimated arrival time, the server 250 may assign the first taxi order from the first passenger 301 to a third candidate vehicle 702 whose estimated arrival time to the pick-up location associated with the first taxi order is 4 minutes, which is less than the predetermined threshold estimated arrival time for the next iteration of order distribution.

[0142] As shown in Fig. 5 and 7, it is obvious that in some cases, by suspending the execution of the first taxi order until the next iteration of order distribution, by changing the coverage of a given area with available vehicles, the server 250 ensures the selection of a vehicle that is capable of arriving at the pick-up location associated with the first taxi order faster than the vehicle available at the current iteration of order distribution, that is, the selection of a third candidate vehicle 702 with a modified estimated arrival time for the first passenger 301 t ^3m ₁ = 4 min instead of a second candidate vehicle 304 with a modified arrival time for the first passenger 301 t ^2m ₁ = 8 min. Thus, some non-limiting embodiments of the present technology make it possible to reduce the waiting time for vehicles by passengers when fulfilling their taxi orders.

[0143] In addition, as indicated above, the server 250 is capable of sending the third candidate vehicle 702 according to the first taxi order, transmitting to the driver of the third candidate vehicle 702 through the corresponding graphical user interface of the driver application of the Internet transportation platform 260 a corresponding notification and data on the route to the pick-up location associated with the first taxi order.

Way

[0144] Taking into account the above-described architecture and the given examples, it is possible to implement a method for distributing vehicles among taxi orders, for example, a set of vehicles, among a batch 240 of taxi orders, as described above with reference to Figs. 3-7. Fig. 8 shows a flow chart of a method 800 in accordance with some non-limiting embodiments of the present technology. The method 800 can be performed by a server 250.

Step 802: receiving data about a plurality of taxi orders in a given area by the processor.

[0145] In step 802, according to some non-limiting embodiments of the present technology, the server 250 in the current iteration of order distribution determined as described above is configured to receive from the passenger devices of the plurality of passenger devices 210 via the communication network 270 data about a batch 240 of taxi orders in a given area. As described above with reference to Fig. 3, according to some non-limiting embodiments of the present technology, the batch 240 of taxi orders in a given area in the current iteration of order distribution may include a first taxi order from a first passenger 301 and a second taxi order from a second passenger 303.

[0146] As noted above, the first and second passengers 301, 303 have the opportunity to make the first and second taxi orders, respectively, using the graphical user interface of the booking in the passenger application of the Internet transportation platform 260, executed on the passenger devices of the plurality of passenger devices 210.

[0147]-The next step in method 800 is step 804.

Step 804: determining, by the processor, a set of candidate vehicles, each of which corresponds to an estimated value of the arrival time at a location associated with a taxi order from a plurality of taxi orders, for distribution among the plurality of taxi orders.

[0148] In step 804, according to some non-limiting embodiments of the present technology, the server 250 is capable of determining, for example, based on road map data and sensor data from a plurality of on-board devices 235 in a given area, a set of candidate vehicles for fulfilling a batch 240 of taxi orders. In the example of Fig. 3, the server 250 may determine a set of candidate vehicles that includes the first and second candidate vehicles 302, 304.

[0149] In addition, as indicated above with reference to Fig. 3, the server 250 is capable of determining, for each of the first and second candidate vehicles 302, 304 included in the set of candidate vehicles, estimated arrival times to the pick-up locations (not separately indicated) for each of the first and second taxi orders. In particular, the server 250 is capable, based on the road map data described above and the real-time traffic data received, of determining that the first candidate vehicle 302 can (1) arrive at the pick-up location for the first taxi order in an estimated arrival time of t ¹ ₁ = 2 minutes and (2) arrive at the pick-up location for the second taxi order in an estimated arrival time of t ¹ ₂ = 6 minutes. In a similar manner, server 250 is able to determine that second candidate vehicle 304 can (1) arrive at the pick-up location for the first taxi order in an estimated arrival time of t ² ₁ = 8 min and (2) arrive at the pick-up location for the second taxi order in an estimated arrival time of t ² ₂ = 10 min.

[0150] Also according to some non-limiting embodiments of the present technology, the server 250 is configured to change the estimated arrival time of a candidate vehicle from the set of candidate vehicles, such as one of the first and second candidate vehicles 302, 304, at a pick-up location for a taxi order from the batch 240 of taxi orders, based on vehicle features associated with the candidate vehicle. In particular, according to some non-limiting embodiments of the present technology, the server 250 is configured to (1) determine a vehicle rating based on the vehicle features associated with the candidate vehicle, and (2) apply this vehicle rating to the estimated arrival time of the candidate vehicle for a taxi order from the batch 240 of taxi orders to determine the changed estimated arrival time.

[0151] According to some non-limiting embodiments of the present technology, the vehicle features may include, among other things, (1) a safety parameter associated with a candidate vehicle for a taxi order from the batch 240 taxi orders, (2) a number of candidate vehicles in the set of candidate vehicles available in the current iteration of order distribution, and/or (3) desirable vehicle properties specified in a taxi order from the batch 240 taxi orders.

[0152] According to some non-limiting embodiments of the present technology, the safety parameter associated with the candidate vehicle characterizes the likelihood of an accident involving the candidate vehicle on a given route, such as on a route to at least one of the pick-up and drop-off locations associated with a taxi order from the batch 240 of taxi orders. In various non-limiting embodiments of the present technology, the server 250 is configured to determine the safety parameter based on (1) the information about the driver of the candidate vehicle described above with respect to the driver of the vehicle 237 and/or (2) information about the route to at least one of the pick-up and drop-off locations associated with a taxi order from the batch 240 of taxi orders.

[0153] According to some non-limiting embodiments of the present technology, information about a given route on a road map may include (1) an average speed on the route, (2) an average number of traffic lanes on the route, (3) an average capacity of the route, which may be expressed, for example, in the number of vehicles traveling on this route per day, (4) the length of the route and/or (5) the number of traffic accidents that occurred on the route over a given period of time, such as per week, month, or year.

[0154] In particular, to determine a safety parameter associated with a given route, in some non-limiting embodiments of the present technology, the server 250 is configured to (1) determine a driver safety rating for a driver of a candidate vehicle based on driver information associated with the driver of the candidate vehicle, (2) determine a route safety rating for the given route based on route information associated with the route, and (3) a probability of a traffic accident involving the candidate vehicle on a route to at least one of the pick-up and drop-off locations associated with a taxi order of the batch 240 of taxi orders based on the driver safety rating and the route safety rating.

[0155] Thus, as described above with reference to Fig. 4, the server 250 may, based on the desired vehicle features for each of the first and second candidate vehicles 302, 304, decrease the estimated arrival time of the first candidate vehicle 302 for the second taxi order t ¹ ₂ = 6 min to a modified estimated arrival time of t ^1m ₂ = 4 min. In addition, the server 250 is able to increase the estimated arrival time of the second candidate vehicle 304 for the second taxi order t ² ₂ = 10 min to a modified estimated arrival time of t ^2m ₂ = 14 min.

[0156] The next step in method 800 is step 806.

Step 806: generating by the processor in the set of candidate vehicles an indication of a model candidate vehicle that corresponds to the threshold estimated time of arrival for ordering a taxi.

[0157] At step 806, in accordance with some non-limiting embodiments of the present technology, the server 250 is configured to generate in the set of candidate vehicles an indication of a model candidate vehicle that corresponds to a specified threshold estimated time of arrival at the pick-up location for each taxi order from the batch 240 taxi orders.

[0158] For example, as described above with reference to Fig. 5, server 250 may generate for the first taxi order of the batch 240 taxi orders an indication of a candidate model vehicle 502 with a given threshold estimated arrival time t ⁰ ₁ = 4 min.

[0159] According to some non-limiting embodiments of the present technology, the predetermined threshold estimated arrival time may be selected based on parameters of a predetermined area, such as, among other things, (1) the demographic characteristics of the predetermined area, (2) the average demand for vehicles therein, (3) the number of vehicles deployed on the Internet transportation platform 260 in the predetermined area, (4) the current environmental conditions in the predetermined area, such as weather conditions, air pollution levels, time of day, etc., (5) estimated arrival times and/or modified estimated arrival times for each of the set of candidate vehicles as described above, (6) marketing factors, etc.

[0160] Thus, depending on at least some of the specified factors, the specified threshold estimated time of arrival can be selected for a specified zone to be, for example, 3, 4, 5 or 10 minutes. Moreover, in some non-limiting embodiments of the present technology, the specified threshold estimated time of arrival in several zones covered by ridesharing services provided by the Internet transport platform 260, which differ in geographical location, can differ. In particular, in those embodiments where the specified zone is a city, the specified threshold estimated time of arrival for a passenger, for example, in Moscow can be 5 minutes, and in Montreal, which is smaller than Moscow, it can be 3 minutes. Moreover, without deviating from the essence and scope of the present technology, such embodiments can also be implemented in which the specified threshold estimated time of arrival in zones that differ in geographical location is the same.

[0161] The next step in method 800 is step 808.

Step 808: distributing by the processor the set of candidate vehicles among the plurality of taxi orders with minimization of the total estimated arrival time determined by the estimated arrival time values corresponding to each of the set of candidate vehicles, including the model candidate vehicle, to the destinations of the taxi orders from the plurality of taxi orders.

[0162] As indicated above with reference to FIG. 5, in some non-limiting embodiments of the present technology, the server 250 is configured to distribute the set of candidate vehicles, including the model candidate vehicle 502, across the batch 240 of taxi orders such that the total estimated arrival time for the set of candidate vehicles is minimized. As indicated above, in some non-limiting embodiments of the present technology, the server 250 is configured to determine the total estimated arrival time based on the (original) estimated arrival times for each of the set of candidate vehicles. In other non-limiting embodiments of the present technology, the server 250 is configured to determine the total estimated arrival time based on the modified estimated arrival times for each of the set of candidate vehicles determined as described in step 804.

[0163] In addition, the server 250 is capable of executing an optimization algorithm to distribute the set of candidate vehicles based on the criterion of minimizing the total estimated arrival time. According to some non-limiting embodiments of the present technology, the optimization algorithm may be a combinatorial optimization algorithm, such as a "Hungarian" optimization algorithm. Obviously, other optimization algorithms for minimizing the total estimated arrival time may be used without departing from the spirit and scope of the present technology.

[0164] Thus, continuing with the example shown in Fig. 5, by minimizing the total estimated arrival time at the current iteration of order distribution, server 250 may (1) assign first candidate vehicle 302 to the second taxi order made by second passenger 303, and (2) assign model candidate vehicle 502 to the first taxi order made by first passenger 301.

[0165] The next step in method 800 is step 810.

Step 810: when assigning a taxi order from a set of taxi orders to a model candidate vehicle, the processor suspends the assignment of the taxi order to one of the candidate vehicles included in the set of candidate vehicles at the current iteration of order distribution and includes this taxi order by the processor in the next set of taxi orders that are to be received at the next iteration of order distribution after the current iteration of order distribution from the set of order distribution iterations.

[0166] In addition, when the server 250 assigns a taxi order from the batch 240 of taxi orders to the model candidate vehicle, for example, when assigning the first taxi order to the model candidate vehicle 502, the server 250 is further configured, at step 810, to suspend the assignment of the first taxi order to one of the candidate vehicles included in the set of candidate vehicles in the current iteration of order distribution until the next iteration of order distribution. In the next iteration of order distribution, the server 250 is configured to (1) include the first taxi order that remains unassigned after the current iteration of order distribution in the next batch of taxi orders accumulated by the next iteration of order distribution, and (2) select a real vehicle for the first taxi order, as described above. Obviously, if at the next iteration of order distribution the server 250 fails to assign the first taxi order to the real vehicle and the first taxi order is again assigned to the model candidate vehicle 502, the server 250 is further capable of suspending the assignment of the first taxi order to the vehicle at the next iteration of order distribution and transferring the first taxi order to the next iteration of order distribution after the next iteration of order distribution.

[0167] In some non-limiting embodiments of the present technology, in order to increase the probability of assigning the first taxi order remaining after the current iteration of order distribution to a real vehicle in the next iteration of order distribution, the server 250 is configured to, in addition to including the first taxi order in the next batch of taxi orders, increase a predetermined threshold estimated arrival time of the model candidate vehicle 502. According to some non-limiting embodiments of the present technology, the server 250 is configured to increase the predetermined threshold estimated arrival time based on a buffering period before including the first taxi order in the next batch of taxi orders. For example, in some non-limiting embodiments of the present technology, the server 250 is configured to increase the predetermined threshold estimated arrival time of the model candidate vehicle 502 in each successive iteration of order distribution by a predetermined increment, such as 1, 2, or 5 minutes. In another example, server 250 is configured to increase a predetermined threshold estimated arrival time of model candidate vehicle 502 at each successive iteration of order distribution by a predetermined number of times, such as by 1.5, 2.0, or 3.0 times.

[0168] In this case, in accordance with some non-limiting embodiments of the present technology, the server 250 is capable of exponentially increasing the specified threshold estimated arrival time depending on the buffering period before including the first taxi order in the next batch of taxi orders received at the next iteration of order distribution, according to the above formula (2).

[0169] Obviously, if the server 250 does not assign any order from the batch 240 of taxi orders to the candidate model vehicle, step 810 in the method 800 is skipped and a transition is made directly to step 812.

[0170] The next step in method 800 is step 812.

Step 812: when assigning a taxi order to a candidate vehicle from the set of candidate vehicles that is not a model candidate vehicle, initiating the processor to dispatch the candidate vehicle according to the taxi order.

[0171] When assigning a taxi order to a candidate vehicle that is not a model candidate vehicle, such as (1) when assigning a second taxi order to the first candidate vehicle 302 in the current iteration of order distribution, as shown in the example of Fig. 5, or (2) when assigning the first taxi order to the third candidate vehicle 702 in the next iteration of order distribution, as shown in the example of Fig. 7, the server 250 is capable of dispatching the candidate vehicle in step 812 according to the taxi order, transmitting to the driver of this vehicle via the graphical user interface of the application for drivers of the Internet transportation platform 260 a corresponding notification and data on the route to the pick-up location associated with this taxi order, as indicated above.

[0172] Thus, by suspending the execution of a taxi order that remains unassigned after the current iteration of order distribution until the next iteration of order distribution, some non-restrictive embodiments of the method 800 make it possible to select a vehicle that is capable of arriving at the pick-up location associated with this taxi order faster than the vehicle available at the current iteration of order distribution, due to a change in the coverage of a given area by available vehicles. Accordingly, some non-restrictive embodiments of the present technology make it possible to reduce the waiting time for vehicles by passengers while their taxi orders are being executed.

[0173] This completes the implementation of method 800.

[0174] Possible changes and improvements to the above-described embodiments of the present technology may be obvious to a person skilled in the art. The preceding description is provided for illustrative purposes only and is not intended to limit the scope of the invention. The scope of protection of the present technology is determined solely by the scope of the appended claims.

[0175] Although embodiments of the invention have been described above with specific steps performed in a particular order, it should be understood that these steps may be combined, divided into parts, or reordered without departing from the spirit and scope of the present technology. Accordingly, the order and grouping of steps are not limiting for the present technology.

Claims (40)

1. A computer method for determining and initiating the dispatch of at least one vehicle for at least one taxi order, implemented by a processor associated with the vehicles, which, at the current iteration of order distribution from a plurality of order distribution iterations, provides for: - receiving data by the processor about a set of taxi orders in a given zone; - determining by the processor a set of candidate vehicles, each of which corresponds to an estimated time of arrival (ETA) value at a location associated with a taxi order from a plurality of taxi orders, for distribution among the plurality of taxi orders; - formation by the processor in the set of candidate vehicles of an indication of a model candidate vehicle that corresponds to the threshold estimated time of arrival for ordering a taxi; - distribution by the processor of a set of candidate vehicles across a plurality of taxi orders with minimization of the total estimated arrival time, determined by the estimated arrival time values corresponding to each of the set of candidate vehicles, including the model candidate vehicle, at destinations for taxi orders from the plurality of taxi orders; - suspension by the processor of assigning a taxi order to one of the candidate vehicles included in the set of candidate vehicles at the current iteration of order distribution when assigning a taxi order from the set of taxi orders to a model candidate vehicle; - inclusion by the taxi order processor in the next set of taxi orders that must be received at the next iteration of order distribution after the current iteration of order distribution from the set of iterations of order distribution; and - initiation by the processor of dispatching a candidate vehicle in accordance with a taxi order when assigning a taxi order to a candidate vehicle from the set of candidate vehicles that is not a model candidate vehicle. 2. The method according to paragraph 1, which, before forming an indication of a model candidate vehicle, additionally provides for: - determining by the processor for the candidate vehicle an assessment characterizing the vehicle features associated with that candidate vehicle; and - applying by the processor this estimate to the estimated arrival time in order to obtain a modified estimated arrival time for the candidate vehicle, wherein the distribution of the set of candidate vehicles across the plurality of taxi orders is based on the modified estimated arrival times corresponding to each of the set of candidate vehicles. 3. The method of claim 2, wherein the vehicle features of the candidate vehicle include at least one of the following: (1) a safety parameter associated with the candidate vehicle, (2) a number of candidate vehicles in the set of candidate vehicles, and (3) properties of the candidate vehicle. 4. The method according to paragraph 3, wherein the safety parameter characterizes the probability of a traffic accident involving a candidate vehicle on the route to the location associated with the taxi order. 5. The method of claim 4, which further comprises determining by the processor a safety parameter associated with the candidate vehicle based on at least one of the following: (1) a safety rating of the driver of the candidate vehicle and (2) a route safety rating associated with a route to a location associated with the taxi order. 6. The method of claim 5, which further comprises determining a driver safety score based on at least one of the following: (1) driver characteristics, (2) driver experience, (3) driver driving style, (4) number of traffic accidents involving the driver, and (5) a driver-related traffic compliance rate. 7. The method of claim 3, wherein the properties of the candidate vehicle include at least one of the following: the make and model of the candidate vehicle, the year of manufacture of the candidate vehicle, the volume of the luggage compartment of the candidate vehicle, and the presence of a child seat in the candidate vehicle. 8. The method according to claim 1, which in addition to including the taxi order in the next set of taxi orders provides for the processor to increase the threshold estimated arrival time for this taxi order in the next iteration of order distribution. 9. The method according to claim 8, wherein the increase in the threshold estimated time of arrival provides for such an increase in the threshold estimated time of arrival, in which the longer the buffering period before including the taxi order in the next set of taxi orders, the longer the threshold estimated time of arrival for this taxi order in the next iteration of order distribution. 10. The method of claim 9, wherein increasing the threshold estimated time of arrival comprises an exponential increase in the threshold estimated time of arrival depending on the buffering period. 11. The method according to claim 10, wherein increasing the threshold estimated time of arrival is carried out according to the following formula: where d is the initial value of the threshold estimated arrival time before the increase, - the period of time before the inclusion of a taxi order in the next set of taxi orders, a , b and c are constant coefficients. 12. The method of claim 1, which further comprises determining by the processor a threshold estimated time of arrival based on environmental conditions in a given area and/or estimated arrival times for each of the set of candidate vehicles. 13. The method according to claim 12, wherein the threshold estimated time of arrival is the same for all orders from a plurality of taxi orders in a given zone until the threshold estimated time of arrival for a taxi order is increased. 14. The method according to paragraph 13, in which for taxi orders in another zone that differs in geographical location from the specified zone, a different threshold estimated arrival time value is used. 15. The method according to paragraph 13, in which the same threshold estimated time of arrival is used in zones that differ in geographical location. 16. A server for distributing vehicles for taxi orders, comprising a processor and physical machine-readable memory for storing commands, in which the processor, when executing commands at the current iteration of order distribution from a plurality of iterations of order distribution, is capable of: - obtaining data on a set of taxi orders in a given area; - determining a set of candidate vehicles, each of which corresponds to a calculated value of the arrival time at a location associated with a taxi order from a set of taxi orders, for distribution across the set of taxi orders; - formation in the set of candidate vehicles of an indication of a model candidate vehicle that corresponds to the threshold estimated time of arrival for ordering a taxi; - distribution of a set of candidate vehicles among a set of taxi orders with minimization of the total estimated arrival time, determined by the estimated arrival time values corresponding to each of the set of candidate vehicles, including the model candidate vehicle, at the destinations of taxi orders from the set of taxi orders; - suspension of assignment of a taxi order to one of the candidate vehicles included in the set of candidate vehicles at the current iteration of order distribution when assigning a taxi order from the set of taxi orders to a model candidate vehicle; - inclusion of the taxi order in the next set of taxi orders that should be received at the next iteration of order distribution after the current iteration of order distribution from the set of iterations of order distribution; and - initiating the dispatch of a candidate vehicle in accordance with a taxi order when assigning a taxi order to a candidate vehicle from the set of candidate vehicles that is not a model candidate vehicle. 17. The server according to item 16, wherein the processor, before generating an indication of a candidate model vehicle, is additionally capable of: - determine for a candidate vehicle an assessment that characterizes the vehicle attributes associated with that candidate vehicle; and - apply this estimate to the estimated arrival time to obtain a modified estimated arrival time for the candidate vehicle, wherein the distribution of the set of candidate vehicles across the set of taxi orders is based on the modified estimated arrival times corresponding to each of the set of candidate vehicles. 18. The server of claim 16, wherein the processor, in addition to including the taxi order in the next set of taxi orders, is capable of increasing the threshold estimated arrival time for this taxi order in the next iteration of distributing orders. 19. The server of claim 18, wherein the processor is capable of providing such an increase in the threshold estimated arrival time, in which the longer the buffering period before including a taxi order in the next set of taxi orders, the greater the threshold estimated arrival time for this taxi order in the next iteration of order distribution. 20. The server of claim 19, wherein the processor is capable of providing an exponential increase in the threshold estimated arrival time depending on the buffering period.