Artificial intelligent systems and methods for predicting traffic accident locations

Systems and methods for predicting traffic accident location. A method includes: obtaining a plurality of accident records, each accident record is associated with a target user terminal and includes a plurality of locations; determining a plurality of refined accident locations by, for each of the plurality of accident records, operating a first clustering procedure with the corresponding plurality of locations of the target user terminal as inputs of the first clustering procedure and assigning a first result of the first clustering procedure as a refined accident location of the plurality of locations of the target user terminal; and determining at least one accident-prone road section by operating a second clustering procedure with the plurality of refined accident locations corresponding to the plurality of accident records as the inputs of the second clustering procedure.

TECHNICAL FIELD

The present disclosure generally relates to systems and methods for using artificial intelligence to determine traffic accident locations and accident-prone road sections, and display accident-prone road sections to a user mobile device.

BACKGROUND

Vehicles are widely used in public transportation and have been increasingly popular in people's daily commuting. When an accident happens, a driver of a vehicle, a passenger of the vehicle, a witness of the accident, or a police officer needs to report an accident time and an accident location (e.g., when and where does the accident happens) to seek roadside assistant, ambulant service, firetruck assistant, or insurance services. However, the reported accident time and accident location are often inaccurate, thus causing difficulties to locate the actual accident and delays to provide the necessary services. Accordingly, it is desirable to provide systems and methods for remotely determining an accurate accident time and an accurate accident location such that an efficient locating of an accident can be achieved. Furthermore, it is desirable to provide systems and methods for predicting a plurality of accident-prone areas to provide safety-driving assistant to the driver.

SUMMARY

An aspect of the present disclosure introduces a system of one or more electronic devices for predicting traffic accident locations, the system may include at least one storage medium including a first operation system and a set of instructions compatible with the first operation system for providing an accident-prone road section to at least one information receiving terminal; and at least one processor in communication with the storage medium, wherein when executing the first operation system and the set of instructions, the at least one processor is directed to: obtain a plurality of accident records of a plurality of traffic accidents, each of the plurality of accident records being associated with a corresponding target user terminal and including an on-record accident time of a traffic accident and a plurality of locations that the target user terminal appeared around the on-record accident time; determine a plurality of refined accident locations by, for each of the plurality of accident records, operating a first clustering procedure with the corresponding plurality of locations of the target user terminal as inputs of the first clustering procedure and assigning a first result of the first clustering procedure as a refined accident location of the plurality of locations of the target user terminal; in response to the determination of the plurality of refined accident locations, determine at least one accident-prone road section by operating a second clustering procedure with the plurality of refined accident locations corresponding to the plurality of accident records as the inputs of the second clustering procedure; in response to the determination of the at least one accident-prone road section, generate electronic signals including information of one of the at least one accident-prone road section; and direct the information receiving terminal to display an interface to present the at least one accident-prone road section by sending the electronic signals to the information receiving terminal.

In some embodiments, to operate the first clustering procedure, the at least one processor is directed to: identify a plurality of points corresponding to the inputs; determine a result cluster and a result point associated with the result cluster by a point-identification operation, including: selecting a candidate cluster of points from the plurality of points; selecting a candidate point from the candidate cluster of points; operating a first iterative operation until a first stop criteria is met, wherein the first iterative operation includes a plurality of first iterations, and each of the first iteration includes: using the candidate point as a center point and using the candidate cluster of points as a target cluster of points; identifying, from the target cluster of points, a cluster of points that is within a predetermined distance from the center point as the candidate cluster of points; and identifying a point from the candidate cluster of points as the candidate point.

In some embodiments, in each of the first iteration, the candidate point in the first iteration is associated with average coordinates of the corresponding candidate cluster of points in the first iteration.

In some embodiments, the at least one processor is further directed to: upon that the candidate cluster of points identified in a final first iteration of the first iterative operation meets the first stop criteria, obtain the candidate cluster of points corresponding to the final first iteration as a result cluster of the first iterative operation; and obtain the candidate point corresponding to the final first iteration as a result point of the first iterative operation.

In some embodiments, the at least one processor is further directed to output the result point of the first iterative operation as the first result of the first clustering procedure.

In some embodiments, the result point is one of a plurality of result points and the result cluster is one of a plurality of result clusters; and the at least one processor is further directed to obtain a plurality of subsequent result points and a plurality of subsequent result clusters based on the output of the first iterative operation by operating a second iterative operation until a second stop criteria is met, wherein the second iterative operation includes a plurality of second iterations, and each of the second iteration includes: updating the plurality of points by removing the result cluster of locations identified in the last iteration of the second iterative operation from the plurality of points; and determining a subsequent result cluster of points and a subsequent result point based on the updated plurality of points by performing the point-identification operation.

In some embodiments, the at least one processor is further directed to: for each of the plurality of result clusters, determine a stop time based on an amount of points in the result cluster; determine whether the stop time associated with the result cluster is greater than a time threshold; and in response to that the stop time is greater than the time threshold, assign the result point corresponding to the result cluster as the refined accident location.

In some embodiments, the second clustering procedure includes the first clustering procedure; and to determine the at least one accident-prone road section, the processor is further directed to: obtain the plurality of refined accident locations; determine a plurality of result clusters by operating the second clustering procedure with the plurality of refined accident locations as inputs of the second clustering procedure; and for each of the plurality of result clusters, assign a road section associated with the result cluster as an accident-prone road section.

In some embodiments, the at least one processor is further directed to: for each of the plurality of result clusters, determine an amount of points in the result cluster; determine whether the amount is greater than a density threshold; and in response that the amount is greater than the density threshold, determine a road section associated with the result cluster based on the points in the result cluster.

In some embodiments, to obtain the plurality of accident records, the at least one processor is further directed to: receive a plurality of accident reports, each of the plurality of accident reports being associated with a corresponding target user terminal and including an on-record accident time and an an-record accident location; obtain a plurality of historical locations of the target user terminals; for each of the plurality of accident reports, merge the corresponding on-record accident time and the an-record accident location with the historical locations of the corresponding target user terminal to obtain an accident record associated with the corresponding target user terminal.

In some embodiments, each of the plurality of accident reports includes at least one of: an accident report reported by a user of a target user terminal; an accident report reported by an insurance company; or an accident report reported by a traffic police.

According to another aspect of the present disclosure, a method for predicting traffic accident locations, implemented on one or more electronic devices having at least one storage medium, and at least one processor in communication with the at least one storage medium, may include: obtaining a plurality of accident records of a plurality of traffic accidents, each of the plurality of accident records being associated with a corresponding target user terminal and including an on-record accident time of a traffic accident and a plurality of locations that the target user terminal appeared around the on-record accident time; determining a plurality of refined accident locations by, for each of the plurality of accident records, operating a first clustering procedure with the corresponding plurality of locations of the target user terminal as inputs of the first clustering procedure and assigning a first result of the first clustering procedure as a refined accident location of the plurality of locations of the target user terminal; in response to the determination of the plurality of refined accident locations, determining at least one accident-prone road section by operating a second clustering procedure with the plurality of refined accident locations corresponding to the plurality of accident records as the inputs of the second clustering procedure; in response to the determination of the at least one accident-prone road section, generating electronic signals including information of one of the at least one accident-prone road section; and directing an information receiving terminal to display an interface to present the at least one accident-prone road section by sending the electronic signals to the information receiving terminal.

According to still another aspect of the present disclosure, a non-transitory computer readable medium, comprising an operation system and at least one set of instructions compatible with the operation system for predicting traffic accident locations, wherein when executed by at least one processor of one or more electronic device, the at least one set of instructions directs the at least one processor to: obtain a plurality of accident records of a plurality of traffic accidents, each of the plurality of accident records being associated with a corresponding target user terminal and including an on-record accident time of a traffic accident and a plurality of locations that the target user terminal appeared around the on-record accident time; determine a plurality of refined accident locations by, for each of the plurality of accident records, operating a first clustering procedure with the corresponding plurality of locations of the target user terminal as inputs of the first clustering procedure and assigning a first result of the first clustering procedure as a refined accident location of the plurality of locations of the target user terminal; in response to the determination of the plurality of refined accident locations, determine at least one accident-prone road section by operating a second clustering procedure with the plurality of refined accident locations corresponding to the plurality of accident records as the inputs of the second clustering procedure; in response to the determination of the at least one accident-prone road section, generate electronic signals including information of one of the at least one accident-prone road section; and direct the information receiving terminal to display an interface to present the at least one accident-prone road section by sending the electronic signals to the information receiving terminal.

According to still another aspect of the present disclosure, a system for predicting traffic accident locations may include: a record obtaining module configured to obtain a plurality of accident records of a plurality of traffic accidents, each of the plurality of accident records being associated with a corresponding target user terminal and including an on-record accident time of a traffic accident and a plurality of locations that the target user terminal appeared around the on-record accident time; an accident location determination module configured to determine a plurality of refined accident locations by, for each of the plurality of accident records, operating a first clustering procedure with the corresponding plurality of locations of the target user terminal as inputs of the first clustering procedure and assigning a first result of the first clustering procedure as a refined accident location of the plurality of locations of the target user terminal; a road section determination module configured to, in response to the determination of the plurality of refined accident locations, determine at least one accident-prone road section by operating a second clustering procedure with the plurality of refined accident locations corresponding to the plurality of accident records as the inputs of the second clustering procedure; and an information sending module configured to send information of the at least one accident-prone road section to an information receiving terminal.

DETAILED DESCRIPTION

The positioning technology used in the present disclosure may include a global positioning system (GPS), a global navigation satellite system (GLONASS), a compass navigation system (COMPASS), a Galileo positioning system, a quasi-zenith satellite system (QZSS), a wireless fidelity (WiFi) positioning technology, or the like, or any combination thereof. One or more of the above positioning technologies may be used interchangeably in the present disclosure.

An aspect of the present disclosure relates to systems and methods for predicting accurate traffic accident locations and accident-prone road sections, and presenting the accident-prone road sections to a user of a user mobile device. To this end, the systems and methods may analyze the driving trajectories of an accident vehicle, and match an accident time and an accident location that are reported by an accident information provider (e.g., a driver of the accident vehicle, an insurance company of the accident vehicle, etc.) with the data of the driving trajectories. The systems may further obtain location data of the accident vehicle to determine an accurate traffic accident location. The systems may further cluster the location data according to durations of stop of the vehicle in an area to obtain the accurate traffic accident location of the accident vehicle. Furthermore, the systems may obtain a plurality of accurate traffic accident locations of a plurality of accident to determine accident-prone road sections. The systems may cluster the plurality of accurate traffic accident locations according to the frequencies of traffic accidents to obtain the accident-prone road sections, and present the accident-prone road sections to alert an information receiver (e.g., a driver, a passenger, etc.) to be cautious when passing by the accident-prone road sections.

FIG. 1is a schematic diagram of an exemplary AI system100according to some embodiments of the present disclosure. In some embodiments, the AI system100may be an online to offline service AI system. For example, the AI system100may be an online to offline service platform for transportation services such as taxi hailing, chauffeur services, delivery vehicles, carpool, bus service, driver hiring, shuttle services, and online navigation services. The AI system100may be an online platform including a server110, a user terminal120, an information source130, a storage140, a network150, and an information receiving terminal160. The server110may include a processing engine112.

The server110may be configured to process information and/or data relating to an accident record. For example, the server110may determine a refined accident location for each accident record. As another example, the server110may determine a plurality of accident-prone road section based on a plurality of refined accident locations associated with a plurality of accident record. The refined accident location may be an accurate location where an actual traffic accident included in the accident record happens. The accident-prone road section may be a place or an area where traffic accidents are more likely to happen than other places or areas.

In some embodiments, the server110may be a single server, or a server group. The server group may be centralized, or distributed (e.g., server110may be a distributed system). In some embodiments, the server110may be local or remote. For example, the server110may access information and/or data stored in the information proving system130, and/or the storage130via the network150. As another example, the server110may connect to the storage130to access stored information and/or data. In some embodiments, the server110may be implemented on a cloud platform. Merely by way of example, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an inter-cloud, a multi-cloud, or the like, or any combination thereof. In some embodiments, the server110may be implemented on a computing device200having one or more components illustrated inFIG. 2in the present disclosure.

In some embodiments, the server110may include a processing engine112. The processing engine112may process information and/or data relating to the accident record to perform one or more functions described in the present disclosure. For example, the processing engine112may determine a refined accident location for each accident record. As another example, the processing engine112may determine a plurality of accident-prone road section based on a plurality of refined accident locations associated with a plurality of accident record. In some embodiments, the processing engine112may include one or more processing engines (e.g., single-core processing engine(s) or multi-core processor(s)). Merely by way of example, the processing engine112may include one or more hardware processors, such as a central processing unit (CPU), an application-specific integrated circuit (ASIC), an application-specific instruction-set processor (ASIP), a graphics processing unit (GPU), a physics processing unit (PPU), a digital signal processor (DSP), a field programmable gate array (FPGA), a programmable logic device (PLD), a controller, a microcontroller unit, a reduced instruction-set computer (RISC), a microprocessor, or the like, or any combination thereof.

The user terminal120may be any type of devices that used by a user to report a traffic accident. For example, the user terminal120may send an accident report including a record accident time and a record accident location for reporting a traffic accident to the server110. The user of the user terminal120may be any organization or individual that reports the traffic accident, for example, a driver (or a passenger) of the traffic accident, a traffic police who handles the traffic accident, a staff of an insurance company of the traffic accident, a witness of the traffic accident, or the like, or any combination thereof. The record accident time and the record accident location may be different from the corresponding actual accident time and actual accident location.

In some embodiments, the user terminal120may include any type of devices, for example, a mobile device, an electronic device, an automobile, or the like, or any combination thereof. For example, the user terminal120may include a desktop computer120-1, a laptop computer120-2, a built-in device in a motor vehicle120-3, a mobile device120-4, or the like, or any combination thereof. The built-in device120-3may include an onboard computer, an onboard television, an onboard positioning system, etc. The mobile device120-4may include a wearable device, a smart mobile device, a virtual reality device, an augmented reality device, or the like, or any combination thereof. In some embodiments, the wearable device may include a smart bracelet, a smart footgear, a smart glass, a smart helmet, a smart watch, a smart clothing, a smart backpack, a smart accessory, or the like, or any combination thereof. In some embodiments, the smart mobile device may include a smartphone, a personal digital assistance (PDA), a gaming device, a navigation device, a point of sale (POS) device, or the like, or any combination thereof. In some embodiments, the virtual reality device and/or the augmented reality device may include a virtual reality helmet, a virtual reality glass, a virtual reality patch, an augmented reality helmet, an augmented reality glass, an augmented reality patch, or the like, or any combination thereof. For example, the virtual reality device and/or the augmented reality device may include a Google Glass™, a RiftCon™, a Fragments™, a Gear VR™, etc. In some embodiments, the user terminal120may be a device with positioning technology for locating the position of the user terminal120and/or the user thereof. In some embodiments, the user terminal120may be implemented on a computing device200having one or more components illustrated inFIG. 2, or a mobile device300having one or more components illustrated inFIG. 3in the present disclosure.

The information source130may be a source configured to provide information for the system100. For example, the information source130may provide the system100with traffic accident reports. The information source130may be a traffic system of traffic police or an insurance system of an insurance company. As another example, the information source130may provide the system100with information and/or data relating to traffic accidents, such as news, real-time traffic conditions, road monitoring image or video, etc. The information source130may be a TV station, a radio station, a real-time media, a road network, a road monitoring device, or the like, or any combination thereof. In some embodiments, the information source130may include a network port to send and/or receive information to one or more components in the AI system100(e.g., the server110, the storage140, etc.). For example, the information130may send a plurality of accident reports to the server110via the network port.

In some embodiments, the storage140may include at least one network port to communicate with other devices in the AI system100. For example, the storage140may be connected to the network150to communicate with one or more components of the AI system100(e.g., the server110, the user terminal120) via the at least one network port. One or more components in the AI system100may access the data or instructions stored in the storage140via the network150. In some embodiments, the storage140may be directly connected to or communicate with one or more components in the AI system100(e.g., the server110, the user terminal120). In some embodiments, the storage140may be part of the server110.

The network150may facilitate exchange of information and/or data. In some embodiments, one or more components of the online to offline service AI system100(e.g., the server110, the user terminal120, and the storage140) may transmit information and/or data to other component(s) in the online to offline service AI system100via the network150. For example, the server110may obtain a plurality of accident records from an information receiving port of the information source130via the network150. In some embodiments, the network150may be any type of wired or wireless network, or combination thereof. Merely by way of example, the network150may include a cable network, a wireline network, an optical fiber network, a tele communications network, an intranet, an Internet, a local area network (LAN), a wide area network (WAN), a wireless local area network (WLAN), a metropolitan area network (MAN), a wide area network (WAN), a public telephone switched network (PSTN), a Bluetooth network, a ZigBee network, a near field communication (NFC) network, or the like, or any combination thereof. In some embodiments, the network150may include one or more network access points. For example, the network150may include wired or wireless network access points such as base stations and/or internet exchange points150-1,150-2, . . . , through which one or more components of the online to offline service AI system100may be connected to the network150to exchange data and/or information between them.

The information receiving terminal160may be any type of devices that used by a user to receive information relating to accident-prone road sections. For example, when the information receiving terminal160appears around an accident-prone road section, the information receiving terminal160may be a mobile device that receive an alert voice or an alert display of the accident-prone road section from the server110. The user of the information receiving terminal160may be a driver, a passenger, a pedestrian, or the like, or any combination thereof. In some embodiments, the information receiving terminal160may be a device similar to, or same as the user terminal120. In some embodiments, the information receiving terminal160may be a device with positioning technology for locating the position of the information receiving terminal160and/or the user thereof. In some embodiments, the information receiving terminal160may be implemented on a computing device200having one or more components illustrated inFIG. 2, or a mobile device300having one or more components illustrated inFIG. 3in the present disclosure.

FIG. 2is a schematic diagram illustrating exemplary hardware and software components of a computing device200on which the server110, and/or the user terminal130may be implemented according to some embodiments of the present disclosure. For example, the processing engine112may be implemented on the computing device200and configured to perform functions of the processing engine112disclosed in this disclosure.

The computing device200may be used to implement any component of AI system100that perform one or more functions disclosed in the present disclosure. For example, the processing engine112may be implemented on the computing device200, via its hardware, software program, firmware, or a combination thereof. Although only one such computer is shown, for convenience, the computer functions relating to the online to offline service as described herein may be implemented in a distributed fashion on a number of similar platforms, to distribute the processing load.

The computing device200, for example, may include COM ports250connected to and from a network connected thereto to facilitate data communications. The COM port250may refer to any network port, information exchange port, or any information transmitting port to facilitate data communications. The computing device200may also include a processor (e.g., the processor220), in the form of one or more processors (e.g., logic circuits), for executing program instructions. For example, the processor may include interface circuits and processing circuits therein. The interface circuits may be configured to receive electronic signals from a bus210, wherein the electronic signals encode structured data and/or instructions for the processing circuits to process. The processing circuits may conduct logic calculations, and then determine a conclusion, a result, and/or an instruction encoded as electronic signals. The processing circuits may also generate electronic signals including the conclusion or the result (e.g., the accident-prone road section) and a triggering code. In some embodiments, the trigger code may be in a format recognizable by an operation system (or an application installed therein) of an electronic device (e.g., the user terminal120) in the AI system100. For example, the trigger code may include an instruction, a code, a mark, a symbol, or the like, or any combination thereof, that can activate certain functions and/or operations of a mobile phone or let the mobile phone execute a predetermined program(s). In some embodiments, the trigger code may be configured to rend the operation system (or the application) of the electronic device to generate a presentation of the conclusion or the result (e.g., accident-prone road section) on an interface of the electronic device. Then the interface circuits may send out the electronic signals from the processing circuits via the bus210.

The exemplary computing device may include the internal communication bus210, program storage and data storage of different forms including, for example, a disk270, and a read only memory (ROM)230, or a random access memory (RAM)240, for various data files to be processed and/or transmitted by the computing device. The exemplary computing device may also include program instructions stored in the ROM230, RAM240, and/or other type of non-transitory storage medium to be executed by the processor220. The methods and/or processes of the present disclosure may be implemented as the program instructions. The exemplary computing device may also include operation systems stored in the ROM230, RAM240, and/or other type of non-transitory storage medium to be executed by the processor220. The program instructions may be compatible with the operation systems for providing the online to offline service. The computing device200also includes an I/O component260, supporting input/output between the computer and other components. The computing device200may also receive programming and data via network communications.

Merely for illustration, only one processor is illustrated inFIG. 2. Multiple processors are also contemplated; thus, operations and/or method steps performed by one processor as described in the present disclosure may also be jointly or separately performed by the multiple processors. For example, if in the present disclosure the processor of the computing device200executes both step A and step B, it should be understood that step A and step B may also be performed by two different processors jointly or separately in the computing device200(e.g., the first processor executes step A and the second processor executes step B, or the first and second processors jointly execute steps A and B).

FIG. 3is a schematic diagram illustrating exemplary hardware and/or software components of an exemplary mobile device300on which the user terminal130may be implemented according to some embodiments of the present disclosure.

As illustrated inFIG. 3, the mobile device300may include a communication unit310, a display320, a graphic processing unit (GPU)330, a central processing unit (CPU)340, an I/O350, a memory360, and a storage390. The CPU may include interface circuits and processing circuits similar to the processor220. In some embodiments, any other suitable component, including but not limited to a system bus or a controller (not shown), may also be included in the mobile device300. In some embodiments, a mobile operating system370(e.g., iOS™, Android™, Windows Phone™, etc.) and one or more applications380may be loaded into the memory360from the storage390in order to be executed by the CPU340. The applications380may include a browser or any other suitable mobile apps for receiving and rendering information relating to a voice request for a service. User interactions with the information stream may be achieved via the I/O350and provided to the processing engine112and/or other components of the AI system100via the network120. The communication unit310may be any information exchange port, information transmitting port, or network port to facilitate data communications.

One of ordinary skill in the art would understand that when an element of the AI system100performs, the element may perform through electrical signals and/or electromagnetic signals. For example, when a user terminal120processes a task, such as report a traffic accident, the user terminal120may operate logic circuits in its processor to process such task. When the user terminal120sends out the accident report, a processor of the user terminal120may generate electrical signals encoding the accident report. The processor of the user terminal120may then send the electrical signals to at least one information receiving port of an information providing system associated with the user terminal120. The information providing system may include the user terminal120, the network150, and the information receiving port between the network150and the server110. The user terminal120communicates with the information providing system via a wired network, the at least one information exchange port may be physically connected to a cable, which may further transmit the electrical signals to an input port (e.g., an information exchange port) of the server110. If the user terminal120communicates with the information providing system via a wireless network, the at least one information receiving port of the information providing system may be one or more antennas, which may convert the electrical signals to electromagnetic signals. Within an electronic device, such as the user terminal120, and/or the server110, when a processor thereof processes an instruction, sends out an instruction, and/or performs an action, the instruction and/or action is conducted via electrical signals. For example, when the processor retrieves or saves data from a storage medium (e.g., the storage140), it may send out electrical signals to a read/write device of the storage medium, which may read or write structured data in the storage medium. The structured data may be transmitted to the processor in the form of electrical signals via a bus of the electronic device. Here, an electrical signal may refer to one electrical signal, a series of electrical signals, and/or a plurality of discrete electrical signals. After the processor of the server110determining a result, the processor may generate electric signals encoding the result, and send the electric signals to at least one information transmitting port of an information receiving system. The information receiving system may include the information receiving terminal160, the network150, and the information transmitting port between the network150and the server110. The information receiving terminal160communicates with the information receiving system via a wired network, the at least one information transmitting port may be physically connected to a cable, which may further transmit the electrical signals to an input port (e.g., an information exchange port) of the server110. If the information receiving terminal160communicates with the information receiving system via a wireless network, the at least one information transmitting port of the information receiving system may be one or more antennas, which may convert the electrical signals to electromagnetic signals.

FIG. 4is a block diagram illustrating an exemplary processing engine112according to some embodiments of the present disclosure. The processing engine112may include a record obtaining module410, an accident location determination module420, a road section determination module430, an information sending module440, and a data merging module450.

The record obtaining module410may be configured to obtain a plurality of accident records. In some embodiments, each of the plurality of accident records is associated with a corresponding target user terminal and includes an on-record accident time and a plurality of locations that the target user terminal appeared around the on-record accident time. For example, an accident record of the plurality of accident records may record a traffic accident of a vehicle associated with a target user terminal. The on-record accident time may be a time reported by a user who witnessed the traffic accident. The plurality of historical locations that the target user terminal appeared around the on-record accident time may be obtained by matching all of historical locations of the target user terminal with the on-record accident time.

The accident location determination module420may be configured to determine a plurality of refined accident locations. A refined accident location may be an accurate location where the actual traffic accident happens. In some embodiments, the accident location determination module420may use a first clustering procedure based on the plurality of locations of a target user terminal. For example, the accident location determination module420may take the plurality of locations of the target user terminal as the input of the first clustering procedure. The accident location determination module420may assign a first result of the first clustering procedure as a refined accident location of the plurality of locations of the target user terminal. In some embodiments, the accident location determination module420may determine the plurality of refined accident locations for corresponding plurality of accident records. Detail descriptions of determining the plurality of refined accident locations may be found elsewhere in the present disclosure (e.g.,FIGS. 5-11and the descriptions thereof).

The road section determination module430may be configured to determine at least one accident-prone road section. An accident-prone road section may be an area where traffic accidents are more likely to happen than other areas. In some embodiments, the road section determination module430may determine the at least one accident-prone road section using a second clustering procedure with the plurality of refined accident locations corresponding to the plurality of accident records as an input. Detail descriptions of determining the plurality of refined accident locations may be found elsewhere in the present disclosure (e.g.,FIGS. 5-13and the descriptions thereof).

The information sending module440may be configured to send information relating to the at least one accident-prone road section to an information receiving terminal. In some embodiments, the information relating to the at least one accident-prone road section may be in a format of a plurality of electronic signals together with a triggering code. The triggering code may be configured to rend an application to generate a presentation of the at least one accident-prone road section on an interface of the information receiving terminal.

The data merging module450may be configured to obtain a plurality of accident reports and a plurality of historical locations of the target user terminals. The data merging module450may merge an on-record accident time and an on-record accident location in each accident report with the plurality of historical locations of the target user terminal to obtain an accident record associated with the corresponding target user terminal. Detail descriptions of merging data to obtain an accident record may be found elsewhere in the present disclosure (e.g.,FIG. 14and the descriptions thereof).

The modules in the processing engine112may be connected to or communicate with each other via a wired connection or a wireless connection. The wired connection may include a metal cable, an optical cable, a hybrid cable, or the like, or any combination thereof. The wireless connection may include a Local Area Network (LAN), a Wide Area Network (WAN), a Bluetooth, a ZigBee, a Near Field Communication (NFC), or the like, or any combination thereof. Two or more of the modules may be combined into a single module, and any one of the modules may be divided into two or more units. For example, the record obtaining module410and the data merging module450may be combined as a single module which may both merge the accident report and obtain the accident record. As another example, the processing engine112may include a storage module (not shown) used to store data and/or information for determining the refined accident locations and/or accident-prone road sections.

FIG. 5Ais a flowchart illustrating an exemplary process for determining at least one accident-prone road section according to some embodiments of the present disclosure. The process500may be executed by the on-demand service AI system100. For example, the process500may be implemented as a set of instructions (e.g., an application) stored in the storage ROM230or RAM240. The processor220may execute the set of instructions, and when executing the instructions, it may be configured to perform the process500. The operations of the illustrated process presented below are intended to be illustrative. In some embodiments, the process500may be accomplished with one or more additional operations not described and/or without one or more of the operations discussed. Additionally, the order in which the operations of the process as illustrated inFIG. 5Aand described below is not intended to be limiting.

In process510, the processing engine112(e.g., the processor220, the record obtaining module410) may obtain a plurality of accident records. In some embodiments, each of the plurality of accident records is associated with a corresponding target user terminal and includes an on-record accident time and a plurality of locations that the target user terminal appeared around the on-record accident time.

In some embodiments, an accident record of the plurality of accident records may record a traffic accident of a vehicle associated with a target user terminal. When the traffic accident happens, a user of the target user terminal (or a staff of an insurance company that deals with the traffic accident, a traffic police who handles the traffic accident) may report an on-record accident time and an on-record accident location. The on-record accident time may be a time reported by a user who witnessed the traffic accident. In some embodiments, the on-record accident time may be different from an actual accident time when the traffic accident happens. For example, the actual accident time is 15:00, and the on-record accident time reported by a user who witnessed the traffic accident is about 15:24. The on-record accident location may be a location reported by a user who witnessed the traffic accident. In some embodiments, the on-record accident location may be different from an actual accident location reported by a user who witnessed the traffic accident. For example, the actual accident location is an intersection of No. 1 road and No. 2 road, and the on-record accident location is around No. 1 road.

In some embodiments, the target user terminal may be a device including a positioning technology for obtaining real-time locations of the vehicle. For example, the target user terminal may be a mobile terminal of a driver or a mobile terminal of a passenger of the vehicle, a vehicle navigation system, an onboard positioning system, or the like, or any combination thereof. The real-time locations may be stored in a storage medium (e.g., the storage140, the ROM230, the RAM240, etc.) of the AI system100as a plurality of historical locations. For example, the target user terminal may obtain a location every predetermined time period (e.g., every 5 second, every 10 seconds, every 30 seconds, etc.) and send the location and a time corresponding to the location to the storage medium. When the processing engine112obtains a traffic accident report of the vehicle, the processing engine112may access the storage medium to obtain a plurality of locations that the target user terminal appeared around the on-record accident time and/or around the on-record accident location by matching the on-record accident time and/or the on-record accident location with a plurality of historical locations. The plurality of locations that the target user terminal appeared around the on-record accident time may include the locations that the user terminal appeared during a period of time ranging from a first predetermined time period before the on-record accident time to a second predetermined time period after the on-record accident time. The first and/or second predetermined time period may be determined by the processing engine112and a user thereof. For example, when the on-record accident time is 15:00, the processing engine112may obtain a plurality of locations of the target user terminal from 14:00-16:00. The plurality of locations that the target user terminal appeared around the on-record accident location may include locations that the user terminal appeared in an area ranging from a predetermined distance from the on-record accident location. The predetermined distance may be determined by the processing engine112and a user thereof. For example, the on-record accident location is Crossroad 1, the processing engine112may obtain a plurality of locations of the target user terminal in an area ranging from 5 km from the Crossroad 1.

In process520, the processing engine112(or the accident location determination module420) may determine a plurality of refined accident locations. In some embodiments, the plurality of refined accident locations are determined using a first clustering procedure based on the plurality of locations of the target user terminal. For example, the processing engine112may take the plurality of locations of the target user terminal as the input of the first clustering procedure. The processing engine112may assign a first result of the first clustering procedure as a refined accident location of the plurality of locations of the target user terminal. In some embodiments, the refined accident location may be an accurate location where the actual traffic accident happens.

In some embodiments, the plurality of locations of the target user terminal may be mapped to a plurality of points on a map. The density of the plurality of points during a predetermined period of time may reflect a stop time of the vehicle associated with the target user terminal in the plurality of locations. For example, an area including higher-density points on the map during one hour indicates that the vehicle stops at the area longer than an area including lower-density points on the map during the same one hour. In some embodiments, the processing engine112may operate the first clustering procedure based on a stop time of the corresponding vehicle. The first clustering procedure may be a mothed and/or algorithm to cluster the plurality of points corresponding to the plurality of locations of the target user terminal. For example, the processing engine112may input the plurality of points into the first clustering procedure. The first clustering procedure may cluster the plurality of points to obtain a cluster that includes the densest points, and select a point associated with average coordinates of the points in the cluster as the first result of the first clustering procedure. The processing engine112may determine a location corresponding to the selected point as the refined accident location of the target user terminal. In some embodiments, the first clustering procedure may be a density-based clustering method, such as a DBSCAN algorithm, an OPTICS algorithm, a Mean-shift algorithm, a DENCLUE algorithm, or the like, or any combination thereof. In some embodiments, an exemplary method of the first clustering procedure may be found elsewhere in the present disclosure (e.g.,FIGS. 6-10and the descriptions thereof).

In process530, based on the determination of the plurality of refined accident locations, the processing engine112(or the road section determine module430) may determine at least one accident-prone road section. In some embodiments, the at least one accident-prone road section may be determined using a second clustering procedure with the plurality of refined accident locations as an input.

In some embodiments, an accident-prone road section may be an area where traffic accidents are more likely to happen than other areas. The accident-prone road section may be an exact location (e.g., a street intersection), a road segment (e.g., from 100 meters to 300 meters of a service road prior to an expressway entrance), or the like, or any combination thereof.

In some embodiments, the plurality of refined accident locations may be mapped to a plurality of points on a map. The density of the plurality of points may reflect an amount of accidents that happened in the past. An area with a higher density of points is likely to have more traffic accidents than the areas with a lower density of points. In some embodiments, the processing engine112may operate the second clustering procedure using the plurality of points as an input. The second clustering procedure may be a mothed and/or algorithm to cluster the plurality of points corresponding to the plurality of refined accident locations. For example, the processing engine112may input the plurality of points into the second clustering procedure. The second clustering procedure may cluster the plurality of points to obtain at least one cluster that includes denser points than other clusters, and identify an area (e.g., an exact location, a road segment) for each of the at least one cluster as an accident-prone road section. In some embodiments, the processing engine112may identify the area based on the corresponding cluster. For example, the processing engine112may determine an average coordinate of the plurality of points in the at least one cluster, and assign a road segment within a predetermined distance from the average coordinate as a corresponding accident-prone road section. In some embodiments, the second clustering procedure may be a density-based clustering method, such as a DBSCAN algorithm, an OPTICS algorithm, a Mean-shift algorithm, a DENCLUE algorithm, or the like, or any combination thereof. In some embodiments, an exemplary method of the second clustering procedure may be found elsewhere in the present disclosure (e.g.,FIGS. 6-11and the descriptions thereof). The second clustering procedure may be the same method as the first clustering procedure. For example, the second clustering procedure and the first clustering procedure may be the exemplary method described inFIGS. 6-11and the descriptions thereof. Alternatively, the second clustering procedure may be a different method as the first clustering procedure. For example, the second clustering procedure is the DBSCAN algorithm, and the first clustering method may be the exemplary method described inFIGS. 6-11and the descriptions thereof.

In some embodiments, after determining the at least one accident-prone road section, the processing engine112(e.g., the processor220, the information sending module440) may generate electronic signals including the at least one accident-prone road section and a triggering code. In some embodiments, the triggering code may be in a format recognizable by an application (e.g., a transportation service application, a taxi hailing service application, a navigation service application, etc.) installed in an information receiving terminal. For example, the trigger code may be an instruction, a code, a mark, a symbol, or the like, or any combination thereof, which can activate information receiving terminal or cause the information receiving terminal to execute a computer-readable program. The triggering code may be configured to rend the application to generate a presentation of the at least one accident-prone road section on an interface of the information receiving terminal. For example, when the information receiving terminal enters an area within a predetermined distance from one of the at least one accident-prone road section, the processing engine112may direct the information receiving terminal to display the corresponding accident-prone road section to remind the user to drive or walk carefully. In some embodiments, the processing engine112(e.g., the processor220, the information sending module440) may send the electronic signals to the information receiving terminal via the information transmitting port of the information receiving system. In response to receiving the electronic signals, the information receiving terminal may present the accident-prone road section on the interface thereof to remind the user to be careful.FIG. 5Bis an exemplary user interface of the information receiving terminal for presenting an accident-prone road section according to some embodiments of the present disclosure.

FIG. 6is a flowchart illustrating an exemplary process for operating a first clustering procedure according to some embodiments of the present disclosure. The process600may be executed by the AI system100. For example, the process600may be implemented as a set of instructions (e.g., an application) stored in the storage ROM230or RAM240. The processor220may execute the set of instructions, and when executing the instructions, it may be configured to perform the process600. The operations of the illustrated process presented below are intended to be illustrative. In some embodiments, the process600may be accomplished with one or more additional operations not described and/or without one or more of the operations discussed. Additionally, the order in which the operations of the process as illustrated inFIG. 6and described below is not intended to be limiting.

In process610, the processing engine112(e.g., the processor220) may identify a plurality of points corresponding to the input of the first clustering procedure. In some embodiments, each of plurality of locations of the target user terminal may be mapped to a point on a map according to a coordinate of the location.

In process620, the processing engine112(e.g., the processor220) may determine a result cluster and a result point associated with the result cluster by a point-identification operation.

In some embodiments, the processing engine112may divide the plurality of identified points into a plurality of clusters according to some predetermined rules, and select the result cluster from the plurality of clusters. For example, the processing engine112may select a cluster that includes the densest points as the result cluster. In some embodiments, the processing engine112may determine the result point based on the points in the result cluster. For example, the processing engine112may determine an average coordinate or a weighted average coordinate of the points in the result cluster, and assign a point having the average coordinate or the weighted average coordinate as the result point. As another example, the processing engine112may determine a point in the result cluster that is nearest to the average coordinate or the weighted average coordinate as the result point. The point-identification operation may be a method or algorithm for determining the result cluster and the result points. Detail description of the point-identification operation may be found inFIG. 7and the descriptions thereof.

FIG. 7is a flowchart illustrating an exemplary process for operating a point-identification operation according to some embodiments of the present disclosure. The process700may be executed by the AI system100. For example, the process700may be implemented as a set of instructions (e.g., an application) stored in the storage ROM230or RAM240. The processor220may execute the set of instructions, and when executing the instructions, it may be configured to perform the process700. The operations of the illustrated process presented below are intended to be illustrative. In some embodiments, the process700may be accomplished with one or more additional operations not described and/or without one or more of the operations discussed. Additionally, the order in which the operations of the process as illustrated inFIG. 7and described below is not intended to be limiting.

In process710, the processing engine112(e.g., the processor220) may select a candidate cluster of points from the plurality of points. In some embodiments, the candidate cluster of points may include all of the plurality of points corresponding to the plurality of locations of the target user terminal. Alternatively, the candidate cluster of points may include part of the plurality of points corresponding to the plurality of locations of the target user terminal.

In process720, the processing engine112(e.g., the processor220) may select a candidate point from the candidate cluster of points. In some embodiments, the candidate point may be an average coordinate of the candidate cluster of points, a weighted average coordinate of the candidate cluster of points, or any calculated value of the candidate cluster of points. In some embodiments, the candidate point may be a point in the candidate cluster of points that is nearest to the average coordinate of the candidate cluster of points, the weighted average coordinate of the candidate cluster of points, or any calculated value of the candidate cluster of points.

In process730, the processing engine112(e.g., the processor220) may operate a first iterative operation with respect to the candidate cluster and the candidate point. In some embodiments, the first iterative operation may include a plurality of iterations.

FIG. 8is a flowchart illustrating an exemplary process for operating a first iterative operation according to some embodiments of the present disclosure. Each of the plurality of iterations may include three processes of810-830in sequence.

In process810, the processing engine112(e.g., the processor220) may use the candidate point as a center point and use the candidate cluster of points as a target cluster of points. The center point may be used as a midpoint to obtain a cluster of points that is within a predetermined distance from the center point.

In process820, the processing engine112(e.g., the processor220) may identify, from the target cluster of points, a cluster of points that are within a predetermined distance from the center point as the candidate cluster of points. For example, the processing engine112may update the candidate cluster of points by selecting a plurality of points from the target cluster of points that are within the predetermined distance from the center point. As another example, the processing engine112may determine a distance of each point of the target cluster of points from the center point and select a plurality of points that the corresponding distances are within the predetermined distance to obtain the candidate cluster of points.

In some embodiments, the predetermined distance may be determined based on a vehicle type associated with the target user terminal, an average driving speed of the vehicle associated with the target user terminal, a traffic accident type of the corresponding vehicle associated with the target user terminal, or the like, or any combination thereof. For example, the predetermined distance corresponding to a car is different from the predetermined distance corresponding to an electric bicycle. The predetermined distance corresponding to the car is 15 meters, and the predetermined distance corresponding to the electric bicycle is 8 meters.

In process830, the processing engine112(e.g., the processor220) may identify a point from the candidate cluster of points as the candidate point. In some embodiments, the processing engine112may calculate an average coordinate of the candidate cluster of points, a weighted average coordinate of the candidate cluster of points, or any calculated value of the candidate cluster of points. The candidate point may be a point in the candidate cluster of points that is nearest to the average coordinate of the candidate cluster of points, the weighted average coordinate of the candidate cluster of points, or any calculated value of the candidate cluster of points. In some embodiments, the candidate point may be update as a point in the candidate cluster of points that is nearest to the average coordinate of the candidate cluster of points, the weighted average coordinate of the candidate cluster of points, or any calculated value of the candidate cluster of points.

After each iteration of the first iterative operation, in process740, the processing engine112(e.g., the processor220) may determine that whether the candidate cluster (or the candidate point) meets a first stop criteria. If the candidate cluster (or the candidate point) meets the first stop criteria, the processing engine112may proceed to the process750. If the candidate cluster (or the candidate point) does not meet the first stop criteria, the processing engine112may proceed to a next iteration of the first iterative operation in the process800as shown inFIG. 8. For example, the processing engine112may proceed back to the process810to using the candidate point identified from the process830in the last iteration as a center point and using the candidate cluster of points identified from the process820in the last iteration as a target cluster of points to continue the iterations in the first iterative operation until the candidate cluster (or the candidate point) meets the first stop criteria.

In some embodiments, the first stop criteria may be that during the plurality of iterations in the first iterative operation, a candidate cluster (or a candidate point) generated in an iteration is the same as the candidate cluster (or the candidate point) generated in the last iteration. For example, if the candidate clusters (or the candidate points) of two continuous iterations are the same, the processing engine112may stop the first iterative operation to obtain a result cluster and/or a result point. In some embodiments, the first stop criteria may be that an amount of iterations in the first iterative operation is greater than a first iteration threshold. The first iteration threshold may be any value that is predetermined by the processing engine112or a user thereof according to different application scenes. For example, if the amount of the iterations in the first iterative operation is greater than 20, the processing engine112may stop the first iterative operation to obtain a result cluster and/or a result point of the first iterative operation.

In process750, the processing engine112(e.g., the processor220) may obtain the candidate cluster of the points corresponding to the final iteration as a result cluster of the first iterative operation. The final iteration may be the last iteration before the candidate cluster (or the candidate point) meets the first stop criteria. The processing engine112may assign the candidate cluster of the points in the final iteration as the result cluster of the first iterative operation. In some embodiments, the result cluster may be a densest cluster among the plurality of candidate clusters after the first iterative operation.

In process760, the processing engine112(or the processor220) may obtain the candidate point corresponding to the final iteration as a result point of the first iterative operation. The processing engine112may assign the candidate point in the final iteration as the result point of the first iterative operation. In some embodiments, the result point may be a point nearest to an average coordinate of the densest cluster.

In some embodiments, the processing engine112(or the processor220) may output the result point of the first iterative operation as the first result of the first clustering procedure. The result point of the first iterative operation may be assigned as the refined accident location associated with the target user terminal. For example, the result point is a point nearest to an average coordinate of the densest cluster among the plurality of points corresponding to the plurality of locations of the user terminal. As the result cluster is the densest cluster among the plurality of candidate clusters, which indicates that traffic accident is most likely to happen in the area corresponding to the result cluster, the result point may indicate a most possible accident location where the target user terminal stops for a longest time. Therefore, the processing engine112may assign the result point as the refined accident location.

FIG. 9is a flowchart illustrating an exemplary process for operating a first clustering procedure according to some embodiments of the present disclosure. The process900may be executed by the AI system100. For example, the process900may be implemented as a set of instructions (e.g., an application) stored in the storage ROM230or RAM240. The processor220may execute the set of instructions, and when executing the instructions, it may be configured to perform the process900. The operations of the illustrated process presented below are intended to be illustrative. In some embodiments, the process900may be accomplished with one or more additional operations not described and/or without one or more of the operations discussed. Additionally, the order in which the operations of the process as illustrated inFIG. 9and described below is not intended to be limiting.

In some embodiments, the processing engine112may implement the process900inFIG. 9after the process600inFIG. 6to further operate the first clustering procedure. The processing engine112may determine a plurality of result points and a plurality of result clusters by operating the first clustering procedure. In some embodiments, the result point determined in the process620inFIG. 6may be one of the plurality of result points and the result cluster determined in the process620inFIG. 6may be one of the plurality of result clusters.

In process910, the processing engine112(or the processor220) may obtain a plurality of subsequent result points and a plurality of subsequent result clusters based on the output of the first iterative operation by operating a second iterative operation. In some embodiments, the second iterative operation may include a plurality of iterations.

FIG. 10is a flowchart illustrating an exemplary process for operating a second iterative operation according to some embodiments of the present disclosure. The second iterative operation may include a plurality of iterations. Each of the plurality of iterations may include three processes of1010-1030in sequence.

In process1010, the processing engine112(or the processor220) may update the plurality of points by removing the result cluster of points identified in the last iteration of the second iterative operation from the plurality of points. At the initial step, the plurality of points may be updated to remove the result cluster of points identified in the first iterative operation (as shown in the process760inFIG. 7). Further, the plurality of points may be updated to remove the result cluster of points identified in the last iteration of the second iterative operation.

In process1020, the processing engine112(or the processor220) may determine a subsequent result cluster of points and a subsequent result point based on the updated plurality of points by performing the point-identification operation as illustrated inFIGS. 7-8. The subsequent result cluster of points identified in an iteration of the second iterative operation may have less density than the result cluster of points identified in the prior iteration of the second iterative operation. Therefore, by performing the second iterative operation, i.e., by iteratively updating the plurality of points to remove the result cluster of points identified from a prior iteration, one or more subsequent result clusters of points and one or more subsequent result points may be obtained. Further, the one or more subsequent result clusters sequentially identified during the iterations of the second iterative operation may be in a descending order with respect to the density.

In process920, the processing engine112(or the processor220) may determine whether the second iterative operation meets a second stop criteria. If the second iterative operation meets the second stop criteria, the processing engine112may stop the second iterative operation. If the second iterative operation does not meet the second stop criteria, the processing engine112may continue the iterations (e.g., the processes1010-1020) until the second iterative operation meets the second stop criteria.

In some embodiments, the second stop criteria may be that during the plurality of iterations in the second iterative operation, a candidate cluster (or a candidate point) generated in an iteration is the same as the candidate cluster (or the candidate point) generated in the last iteration. For example, if the candidate clusters (or the candidate points) of two continuous iterations are the same, the processing engine112may stop the second iterative operation. In some embodiments, the second stop criteria may be that an amount of iterations in the second iterative operation is greater than a second iteration threshold. The second iteration threshold may be any value that is predetermined by the processing engine112or a user thereof according to different application scenes. For example, if the amount of the iterations in the second iterative operation is greater than 10, the processing engine112may stop the second iterative operation to obtain a result point of the remainder of the plurality of result points and a result cluster of the remainder of the plurality of result clusters.

FIG. 11is a flowchart illustrating an exemplary process for determining a refined accident location according to some embodiments of the present disclosure. The process1100may be executed by the AI system100. For example, the process1100may be implemented as a set of instructions (e.g., an application) stored in the storage ROM230or RAM240. The processor220may execute the set of instructions, and when executing the instructions, it may be configured to perform the process1100. The operations of the illustrated process presented below are intended to be illustrative. In some embodiments, the process1100may be accomplished with one or more additional operations not described and/or without one or more of the operations discussed. Additionally, the order in which the operations of the process as illustrated inFIG. 11and described below is not intended to be limiting.

In process1110, for each of the plurality of result clusters, the processing engine112(or the processor220) may determine a stop time based on an amount of points in the result cluster. The stop time may be a time duration that the target user terminal stops at an area associated with the result cluster. For example, when the target user terminal obtains one point of a location at the area corresponding to the result cluster every 5 seconds, and the amount of points in the result cluster corresponding to the locations of the target user terminal is 200. The processing engine may calculate the stop time associated with the result cluster is 1000 seconds (200*5).

In process1120, the processing engine112(or the processor220) may determine whether the stop time associated with the result cluster is greater than a time threshold. In some embodiments, the time threshold may be predetermined by the processing engine112or a user thereof according to different application scenes. For example, the time threshold may be determined based on a traffic condition of a road where the vehicle is. As another example, the time threshold may be determined based on a longest waiting time of a traffic light. In some embodiments, the determination that the stop time is greater than the time threshold may cause the processing engine112to remove the result clusters with vehicles having a short stay or only waiting for the traffic light.

In some embodiments, if the stop time associated with the result cluster is greater than the time threshold, the processing engine112may proceed to process1130. If the stop time associated with the result cluster is not greater than the time threshold, the processing engine112may proceed to process1140.

In process1130, the processing engine112(or the processor220) may assign the result point corresponding to the result cluster as the refined accident location. For example, when the vehicle stops for an enough long time in an area, the processing engine112may predict that there is a traffic accident in the area. Therefore, the processing engine112may assign the result point corresponding to the result cluster as the refined accident location.

In process1140, the processing engine112(or the processor220) may abandon the corresponding accident record to proceed to another accident record of another target user terminal. For example, when the vehicle stops only a short time in an area, the processing engine112may predict that there is not a traffic accident in the area. In some embodiments, the processing engine112may proceed to another accident record of another target user terminal according to the method described inFIGS. 6-11in the present disclosure.

FIG. 12is a flowchart illustrating an exemplary process for determining at least one accident-prone road section according to some embodiments of the present disclosure. The process1200may be executed by the AI system100. For example, the process1200may be implemented as a set of instructions (e.g., an application) stored in the storage ROM230or RAM240. The processor220may execute the set of instructions, and when executing the instructions, it may be configured to perform the process1200. The operations of the illustrated process presented below are intended to be illustrative. In some embodiments, the process1200may be accomplished with one or more additional operations not described and/or without one or more of the operations discussed. Additionally, the order in which the operations of the process as illustrated inFIG. 12and described below is not intended to be limiting.

In process1210, the processing engine112(or the processor220) may obtain the plurality of refined accident locations. In some embodiments, the plurality of refined accident locations may be determined by operating the first cluster procedure as described inFIGS. 6-11in the present disclosure. In some embodiments, the plurality of refined accident locations may be predetermined and stored in a storage medium (e.g., the storage140, the ROM230, the RAM240, etc.) of the AI system100.

In process1220, the processing engine112(or the processor220) may determine a plurality of result clusters by operating the second clustering procedure with the plurality of refined accident locations as inputs of the second clustering procedure.

In some embodiments, the second clustering procedure may be the same as the first clustering procedure. For example, the processing engine112may identify a plurality of points corresponding to the plurality of refined accident locations on the map. The processing engine112may determine a result cluster by the point-identification operation. For example, the processing engine112may select a candidate cluster of points from the plurality of points corresponding to the plurality of refined accident locations. The candidate cluster of points may include all of the plurality of points or part of the plurality of points. The processing engine112may select a candidate point from the candidate cluster of points. The candidate point may be any value calculated based on the candidate cluster of points, such as an average coordinate, a weighted average coordinate, or the like, or any combination thereof. The processing engine112may operate the first iterative operation including a plurality of iterations until the first stop criteria is met. During each iteration of the first iterative operation, the processing engine112may use the candidate point generated in the last iteration as a center point and use the candidate cluster generated in the last iteration as a target cluster of points, identify from the target cluster of points, a cluster of points that are within a predetermined distance from the center point as the candidate cluster of points, and identify a point from the candidate cluster of points as the candidate point. The candidate cluster of points may be updated to a new cluster of points that are within the predetermined distance from the center point. In some embodiments, the predetermined distance may be predetermined by the processing engine112or a user thereof. For example, the predetermined distance may be determined based on an area corresponding to the refined accident locations, a road type of a road corresponding to the refined accident locations, or the like, or any combination thereof. Merely by way of example, the predetermined distance may be 300 meters, 500 meters, 1000 meters, etc. If the candidate cluster of points identified in a final iteration of the first iterative operation meets the first stop criteria, the processing engine112may obtain the candidate cluster of points corresponding to the final iteration as a result cluster of the first iterative operation. In addition, the processing engine112may operate a second iterative operation including a plurality of iterations until a second stop criteria is met to obtain remainder of the plurality of result clusters. During each iteration of the second iterative operation, the processing engine112may remove the result cluster of points identified in the first iterative operation from the plurality of points to update the plurality of points, and determine another cluster of a plurality of result clusters by the point-identification operation described above. After a plurality of iterations to determine the plurality of result clusters, the processing engine112may obtain the clusters of refined accident locations in a descending order of density of the refined accident locations. The first result cluster may be a densest cluster of refined accident locations, which may indicate that traffic accidents are more likely to happen at a road section associated with the first result cluster.

In process1230, for each of the plurality of result clusters, the processing engine112(or the processor220) may assign a road section associated with the result cluster as an accident-prone road section.

In some embodiments, the road section may be a place or an area on a road. In some embodiments, the processing engine112may determine the road section based on the result cluster. For example, the processing engine112may calculate a center point of the result cluster of points, and assign a place around the center point as the accident-prone road section. As another example, the processing engine112may assign an area that is within a predetermined distance from the center point as the accident-prone road section. The predetermined distance may be determined according to different application scenes. For example, the area that is within 500 meters from the center point may be assigned as the accident-prone road section.

FIG. 13is a flowchart illustrating an exemplary process for determining at least one accident-prone road section according to some embodiments of the present disclosure. The process1300may be executed by the AI system100. For example, the process1300may be implemented as a set of instructions (e.g., an application) stored in the storage ROM230or RAM240. The processor220may execute the set of instructions, and when executing the instructions, it may be configured to perform the process1300. The operations of the illustrated process presented below are intended to be illustrative. In some embodiments, the process1300may be accomplished with one or more additional operations not described and/or without one or more of the operations discussed. Additionally, the order in which the operations of the process as illustrated inFIG. 13and described below is not intended to be limiting.

In process1310, for each of the plurality of result clusters, the processing engine112(or the processor220) may determine an amount of points in the result cluster. In some embodiments, the amount of points may indicate a frequency of traffic accidents. The greater the amount of points is, the higher the frequency of traffic accidents is.

In process1320, the processing engine112(or the processor220) may determine whether the amount is greater than a density threshold. In some embodiments, the density threshold may be predetermined by the processing engine112or a user thereof according to different application scenes. For example, the density threshold may be determined based on a road type of a road corresponding to the result cluster of points. As another example, the density threshold may be a value that is not less than 2. That is to say, if there are two or more refined traffic accident locations in the result cluster, the area corresponding to the result cluster may be an accident-prone area.

In some embodiments, if the amount is greater than the density threshold, the processing engine112may proceed to process1330. If the amount is not greater than the density threshold, the processing engine112may proceed to process1340.

In process1330, the processing engine112(or the processor220) may determine a road section corresponding to the result cluster based on the points in the result cluster. For example, if the amount of points in the result cluster is high enough, which indicates that the frequency of traffic accidents happening at the road section corresponding to the result cluster of points is high, the processing engine112may predict the corresponding road section is an accident-prone road section.

In process1340, the processing engine112(or the processor220) may skip the result cluster. For example, if the amount of points in the result cluster is low, which indicates that the frequency of traffic accidents happening at the road section corresponding to the result cluster of points is low, the processing engine112may predict the corresponding road section is not an accident-prone road section, and proceed to another result cluster to determine whether there is an accident-prone road section.

FIG. 14is a flowchart illustrating an exemplary process for obtaining a plurality of accident records according to some embodiments of the present disclosure. The process1400may be executed by the AI system100. For example, the process1400may be implemented as a set of instructions (e.g., an application) stored in the storage ROM230or RAM240. The processor220may execute the set of instructions, and when executing the instructions, it may be configured to perform the process1400. The operations of the illustrated process presented below are intended to be illustrative. In some embodiments, the process1400may be accomplished with one or more additional operations not described and/or without one or more of the operations discussed. Additionally, the order in which the operations of the process as illustrated inFIG. 14and described below is not intended to be limiting.

In process1410, the processing engine112(or the processor220, the data merging module450) may receive a plurality of accident reports. In some embodiments, each of the plurality of accident reports is associated with a corresponding target user terminal and includes an on-record accident time and an on-record accident location.

In some embodiments, when an traffic accident happens, a user of the target user terminal (e.g., a driver of a vehicle that happens the traffic accident, a passenger of the vehicle, a staff of an insurance company that deals with the traffic accident, a traffic police who handles the traffic accident, or a witness of the accident) may report the accident to seek an insurance services, roadside assistance, ambulant service, firetruck assistant or the like, or any combination thereof. The accident report may include an on-record accident time and an on-record accident location. The on-record accident time may be a time reported by the user of the user terminal. In some embodiments, the on-record accident time may be different from an actual accident time when the traffic accident happens. For example, the actual accident time is 15:00, and the on-record accident time reported by the user is about 15:24. The on-record accident location may be a location reported by the user of the user terminal. In some embodiments, the on-record accident location may be different from an actual accident location reported by the user of the user terminal. For example, the actual accident location is an intersection of No. 1 road and No. 2 road, and the on-record accident location is around No. 1 road.

In process1420, the processing engine112(or the processor220, the data merging module450) may obtain a plurality of historical locations of the target user terminals.

In some embodiments, the target user terminal may be a device including a positioning technology for obtaining real-time locations of the vehicle. For example, the target user terminal may be a mobile terminal of a driver or a mobile terminal of a passenger of the vehicle, a vehicle navigation system, an onboard positioning system, or the like, or any combination thereof. The real-time locations may be stored in a storage medium (e.g., the storage140, the ROM230, the RAM240, etc.) of the AI system100as a plurality of historical locations. For example, the target user terminal may obtain a location every predetermined time period (e.g., every 5 second, every 10 seconds, every 30 seconds, etc.) and send the location and a time when corresponding to the location to the storage medium.

In process1430, for each of the plurality of accident reports, the processing engine112(or the processor220, the data merging module450) may merge the corresponding on-record accident time and the an-record accident location with the historical locations of the corresponding target user terminal to obtain an accident record associated with the corresponding target user terminal.

When the processing engine112obtains a traffic accident report of the vehicle, the processing engine112may access the storage medium to obtain a plurality of locations that the target user terminal appeared around the on-record accident time and/or around the on-record accident location by matching the on-record accident location to a plurality of historical locations. The plurality of locations that the target user terminal appeared around the on-record accident time may include the locations that the user terminal appeared during a period of time ranging from a first predetermined time period before the on-record accident time to a second predetermined time period after the on-record accident time. The first and/or second predetermined time period may be determined by the processing engine112and a user thereof. For example, when the on-record accident time is 15:00, the processing engine112may obtain a plurality of locations of the target user terminal from 14:00-16:00. The plurality of locations that the target user terminal appeared around the on-record accident location may include locations that the user terminal appeared in an area ranging from a predetermined distance from the on-record accident location. The predetermined distance may be determined by the processing engine112and a user thereof. For example, the on-record accident location is Crossroad 1, the processing engine112may obtain a plurality of locations of the target user terminal in an area ranging from 5 km from the Crossroad 1.