WO2020207291A1 - 一种轨迹的处理方法、装置、及电子设备 - Google Patents

一种轨迹的处理方法、装置、及电子设备 Download PDF

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Publication number
WO2020207291A1
WO2020207291A1 PCT/CN2020/082311 CN2020082311W WO2020207291A1 WO 2020207291 A1 WO2020207291 A1 WO 2020207291A1 CN 2020082311 W CN2020082311 W CN 2020082311W WO 2020207291 A1 WO2020207291 A1 WO 2020207291A1
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WIPO (PCT)
Prior art keywords
trajectory
sampling
sensing device
flow sensing
road flow
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PCT/CN2020/082311
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English (en)
French (fr)
Inventor
张欣
王悦文
李屾
刘跃虎
王函
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Alibaba Group Holding Ltd
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Alibaba Group Holding Ltd
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
    • G08G1/0145—Measuring and analyzing of parameters relative to traffic conditions for specific applications for active traffic flow control
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/065—Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/07—Controlling traffic signals
    • G08G1/08—Controlling traffic signals according to detected number or speed of vehicles

Definitions

  • the present invention relates to the field of traffic control technology, and more specifically, to a track processing method, a track processing device, an electronic device, and a computer readable medium.
  • GPS Global Positioning System
  • the existing trajectory can be acquired through a GPS device installed on the vehicle, or through a navigation application. Extracting trajectories from navigation applications has become an important data source to grasp the characteristics of urban travel. However, not all vehicles travel using a designated navigation application. Therefore, the trajectory extracted through a designated navigation application is only a sample of all travel data, not full data. This greatly affects the application value based on trajectory. Therefore, it is necessary to derive the full trajectory based on the known sampling trajectory.
  • road flow sensing devices such as coils, geomagnetism, radar, cameras, etc.
  • These road flow sensing devices can monitor the full amount of traffic passing through the deployed location.
  • these road flow sensing devices monitor only the cross-sectional flow and do not know the specific vehicle travel trajectory.
  • full coverage of the road network cannot be achieved. Therefore, it is necessary to use the traffic flow monitored by limited road flow sensing equipment to assist in calculating the full trajectory.
  • An object of the present invention is to provide a new technical solution for incrementally updating the trajectory using the traffic flow monitored by the road flow sensing device.
  • a trajectory processing method including:
  • the sampling trajectory includes the first traffic passing the road flow sensing device Sampling trace
  • the sampling trajectory further includes a second sampling trajectory that does not pass through a road flow sensing device, and the step of obtaining a full trajectory in the preset area according to the first full trajectory includes:
  • the step of incrementally updating the first sampling trajectory according to the traffic flow to obtain the first full trajectory passing through the road flow sensing device includes:
  • the first sampling trajectory after incremental update is used as the first full trajectory.
  • the step of determining the sampling saturation of each road flow sensing device according to the traffic flow and the first sampling trajectory includes:
  • the sampling saturation of each road flow sensing device is determined according to the number of first trajectories passing through each road flow sensing device and the traffic flow monitored by the corresponding road flow sensing device.
  • the step of incrementally updating the first sampling trajectory according to the sampling saturation includes:
  • the first sample is incrementally updated according to the candidate trajectory.
  • the step of incrementally updating the first sample according to the candidate trajectory based on the sampling saturation of each road flow sensing device passed by the candidate trajectory includes:
  • the first sample is incrementally updated according to the candidate trajectory.
  • the step of obtaining the acceptance probability of the candidate trajectory according to the sampling saturation of each road flow sensing device passed by the candidate trajectory includes:
  • the unsaturation of each road flow sensing device passed by the candidate trajectory is determined respectively; wherein the unsaturation is used for Reflect the gap between corresponding road flow sensing equipment and sampling saturation;
  • the geometric mean of the degree of unsaturation of each road flow sensing device passed by the candidate trajectory is calculated as the acceptance probability of the candidate trajectory.
  • the step of incrementally updating the first sample according to the candidate trajectory based on the acceptance probability includes:
  • the first sample is incrementally updated according to the candidate trajectory.
  • processing method further includes:
  • the probability threshold is adjusted according to the sampling saturation of each road flow sensing device obtained from the incrementally updated first sampling trajectory.
  • processing method further includes:
  • the incrementally updated first sampling trajectory determine the number of the first sampling trajectory after the incremental update of each of the road flow sensing devices
  • the step of incrementally updating the second sampling trajectory according to the first full trajectory and the first sampling trajectory to obtain a second full trajectory that has not passed through the road flow sensing device includes :
  • the second full track is incrementally updated according to the ratio to obtain the second full track.
  • processing method further includes:
  • a track processing device including:
  • the trajectory flow acquisition module is used to acquire the sampling trajectory in the preset area and the traffic flow monitored by the road flow sensing device set in the preset area within the preset statistical time period; wherein, the sampling trajectory includes passing The first sampling trace of the road flow sensing device;
  • a trajectory incremental update module configured to incrementally update the first sampled trajectory according to the traffic flow to obtain the first full trajectory passing through the road flow sensing device;
  • the full trajectory obtaining module is configured to obtain the full trajectory in the preset area according to the first full trajectory.
  • an electronic device including the processing device according to the second aspect of the present invention; or, including a processor and a memory, the memory is used to store executable instructions, the instructions For controlling the processor to execute the processing method according to the first aspect of the present invention.
  • a computer-readable storage medium having a computer program stored thereon, which when executed by a processor implements the processing method according to the first aspect of the present invention.
  • the traffic flow monitored by the road flow sensing device is taken as a constraint, and the first sampled trajectory passing by the road flow sensing device is incrementally updated, so that the final total trajectory in the preset area can be obtained More precise.
  • this embodiment can also be applied to a preset area where road flow sensing devices are sparse.
  • Fig. 1 is a block diagram of an example of a hardware configuration of an electronic device that can be used to implement an embodiment of the present invention.
  • FIG. 2 is a block diagram of another example of the hardware configuration of an electronic device that can be used to implement an embodiment of the present invention
  • FIG. 3 is a schematic flowchart of a method for processing a track according to an embodiment of the present invention.
  • FIG. 4 is a schematic diagram of an example of a preset area according to an embodiment of the present invention.
  • FIG. 5 is a schematic flowchart of an example of a method for processing a trajectory according to an embodiment of the present invention
  • Figure 6 is a functional block diagram of a track processing device according to an embodiment of the present invention.
  • Figure 7 is a functional block diagram of an electronic device according to a first embodiment of the present invention.
  • Fig. 8 is a schematic diagram of the hardware structure of an electronic device according to a second embodiment of the present invention.
  • FIG. 1 and 2 are block diagrams of the hardware configuration of an electronic device that can be used to implement the trajectory processing method of any embodiment of the present invention.
  • the electronic device may be a server 1100.
  • the server 1100 provides service points for processing, database, and communication facilities.
  • the server 1100 may be an integrated server or a distributed server that spans multiple computers or computer data centers.
  • the server can be of various types, such as, but not limited to, a web server, a news server, a mail server, a message server, an advertisement server, a file server, an application server, an interactive server, a database server, or a proxy server.
  • each server may include hardware, software, or embedded logic components or a combination of two or more such components for performing appropriate functions supported or implemented by the server.
  • the server may be a blade server, a cloud server, etc., or may be a server group composed of multiple servers, and may include one or more of the foregoing types of servers, and so on.
  • the server 1100 may be as shown in FIG. 1 and includes a processor 1110, a memory 1120, an interface device 1130, a communication device 1140, a display device 1150, and an input device 1160.
  • the server 1100 may also include a speaker, a microphone, etc., which are not limited herein.
  • the processor 1110 may be a dedicated server processor, or may be a desktop processor or a mobile processor that meets performance requirements, and is not limited herein.
  • the memory 1120 includes, for example, ROM (Read Only Memory), RAM (Random Access Memory), nonvolatile memory such as a hard disk, and the like.
  • the interface device 1130 includes, for example, various bus interfaces, such as a serial bus interface (including a USB interface), a parallel bus interface, and the like.
  • the communication device 1140 can perform wired or wireless communication, for example.
  • the display device 1150 is, for example, a liquid crystal display, an LED display touch screen, or the like.
  • the input device 1160 may include, for example, a touch screen, a keyboard, and the like.
  • the memory 1120 of the server 1100 is used to store instructions, which are used to control the processor 1110 to operate to at least execute the trajectory processing method according to any embodiment of the present invention.
  • Technical personnel can design instructions according to the disclosed scheme of the present invention. How the instruction controls the processor to operate is well known in the art, so it will not be described in detail here.
  • the present invention may only involve some of the devices.
  • the server 1100 only involves the storage 1120 and the processor 1110.
  • the electronic device may be a terminal device 1200 such as a PC or a notebook computer used by an operator, which is not limited herein.
  • the electronic device may be a terminal device 1200, and the terminal device 1200 may include a processor 1210, a memory 1220, an interface device 1230, a communication device 1240, a display device 1250, an input device 1260, a speaker 1270, Microphone 1280 and so on.
  • the terminal device 1200 may include a processor 1210, a memory 1220, an interface device 1230, a communication device 1240, a display device 1250, an input device 1260, a speaker 1270, Microphone 1280 and so on.
  • the processor 1210 may be a mobile version processor.
  • the memory 1220 includes, for example, ROM (Read Only Memory), RAM (Random Access Memory), nonvolatile memory such as a hard disk, and the like.
  • the interface device 1230 includes, for example, a USB interface, a headphone interface, and the like.
  • the communication device 1240 can, for example, perform wired or wireless communication.
  • the communication device 1240 may include short-range communication devices, such as based on Hilink protocol, WiFi (IEEE 802.11 protocol), Mesh, Bluetooth, ZigBee, Thread, Z-Wave, NFC, UWB, Any device that performs short-range wireless communication with a short-range wireless communication protocol such as LiFi.
  • the communication device 1240 may also include a remote communication device, for example, any device that performs WLAN, GPRS, 2G/3G/4G/5G remote communication.
  • the display device 1250 is, for example, a liquid crystal display, a touch display, or the like.
  • the input device 1260 may include, for example, a touch screen, a keyboard, and the like. The user can input/output voice information through the speaker 1270 and the microphone 1280.
  • the memory 1220 of the terminal device 1200 is used to store instructions, and the instructions are used to control the processor 1210 to operate to at least execute the trajectory processing method according to any embodiment of the present invention.
  • Technical personnel can design instructions according to the disclosed scheme of the present invention. How the instruction controls the processor to operate is well known in the art, so it will not be described in detail here.
  • the present invention may only involve some of the devices.
  • the terminal device 1200 only involves the memory 1220, the processor 1210, and the display device 1250.
  • a trajectory processing method is provided.
  • the processing method may be implemented by electronic equipment.
  • the electronic device may be the server 1100 as shown in FIG. 1 or the terminal device 1200 as shown in FIG. 2.
  • the method for processing the trajectory of this embodiment may include the following steps S1000 to S3000:
  • Step S1000 Obtain the sampling trajectory passing through the preset area within the preset statistical time period and the traffic flow monitored by the road flow sensing device set in the preset area.
  • the preset area may be an area in the city where at least one road flow sensing device is selected according to application scenarios or specific needs.
  • the preset area may be as shown in FIG. 4.
  • road flow sensing devices 1 to 5 may be preset.
  • the sampled trajectory in this embodiment may be a trajectory extracted from a designated navigation application, including a spatial position sequence that records the travel process of the vehicle.
  • the spatial position sequence contains multiple points, and each point can contain latitude and longitude position information. Therefore, according to the latitude and longitude position information of each point in the spatial position sequence, each road segment in the preset area that the sampled trajectory passes through can be determined.
  • the road section in this embodiment may refer to a traffic line in one driving direction between two adjacent nodes on the traffic network.
  • each sampled trajectory can be matched to a specific road.
  • the road flow sensing device in this embodiment may be a coil, geomagnetism, radar, camera, etc. Each road flow sensing device can monitor the flow of vehicles passing through the section where it is deployed. The traffic flow monitored by the road flow sensing equipment is full data.
  • both the sampled trajectory and the traffic volume reflect the travel information of the vehicle in the same statistical time period in the preset area.
  • the statistical period can be set in advance according to application scenarios or specific requirements.
  • the statistical period may be 9:00:00 on March 14, 2019 to 10:00:00 on March 14, 2019.
  • the sampled trajectory acquired in this embodiment may be a trajectory extracted from a designated navigation application and generated from 9:00:00 on March 14, 2019 to 10:00:00 on March 14, 2019.
  • the traffic volume acquired in this embodiment may be the traffic detected by the corresponding road traffic sensing device in the preset area from 9:00:00 on March 14, 2019 to 10:00:00 on March 14, 2019. flow.
  • the sampling trajectory may include the first sampling trajectory passing the road flow sensing device, that is, at least one road flow sensing device is provided on the road section where the first sampling trajectory passes.
  • the sampling trajectory also includes a second sampling trajectory that does not pass the road flow sensing device, that is, no road flow sensing device is installed on all road sections passed by the second sampling trajectory.
  • no road flow sensing device is installed on all the road sections that the sampling trajectory A passes, that is, the sampling trajectory A does not pass through any road flow sensing device, and the road flow that the sampling trajectory B passes is set.
  • the sampling trajectory A may be used as the second sampling trajectory
  • the sampling trajectories B, C, and D may be used as the first sampling trajectory.
  • step S2000 the first sampled trajectory is incrementally updated according to the traffic flow to obtain the first full-volume trajectory passing the road flow sensing device.
  • the number of the first full track passing through each road flow sensing device may be equal to the traffic flow monitored by the corresponding road flow sensing device.
  • the step of obtaining the first full trajectory passing the road flow sensing device according to the traffic flow and the first sampling trajectory may include steps S2100 to S2300 as shown below:
  • Step S2100 Determine the sampling saturation of each road flow sensing device according to the traffic flow and the first sampling trajectory.
  • the sampling saturation can be used to reflect the sampling rate of the first sampling trajectory passing through the corresponding road flow sensing device.
  • the step of determining the sampling saturation of each road flow sensing device may include the following steps S2110 to S2120:
  • Step S2110 according to the first sampling trajectory, determine the number of first sampling trajectories passing through each road flow sensing device.
  • the number of first sampling trajectories passing each road flow sensing device can be determined.
  • the first sampling trajectory includes sampling trajectories B, C, and D, where the first sampling trajectory passing through the road flow sensing device 1 is sampling trajectories B and C, and the first sampling trajectory passing through the road flow sensing device 2
  • the first sampling trajectory is sampling trajectory D
  • the first sampling trajectory passing through the road flow sensing device 3 is sampling trajectory D
  • the first sampling trajectory passing through the road flow sensing device 4 is sampling trajectory D
  • the sampling track is the sampling track B.
  • the number of first sampling trajectories passing through the road flow sensing device 1 is 2
  • the number of first sampling trajectories passing through the road flow sensing device 2 is 1, and the first sampling trajectory passing through the road flow sensing device 3
  • the number of first sampling trajectories passing through the road flow sensing device 4 is 1, and the number of first sampling trajectories passing through the road flow sensing device 5 is 1.
  • Step S2120 Determine the sampling saturation of each road flow sensing device according to the number of first trajectories passing through each road flow sensing device and the traffic flow monitored by the corresponding road flow sensing device.
  • the number of first trajectories passing through each road flow sensing device and the ratio of the traffic flow monitored by the corresponding road flow sensing device may be determined separately as the sampling saturation of each road flow sensing device.
  • the sampling saturation ⁇ of the road flow sensing device 1 may be P1/Q1.
  • the sampling saturation ⁇ of the road flow sensing device can be greater than or equal to 0 and less than or equal to 1.
  • step S2200 the first sampling trajectory is incrementally updated according to the sampling saturation, so that the sampling saturation of each road flow sensing device obtained according to the incrementally updated first sampling trajectory reaches a set value.
  • the sampling saturation of the road flow sensing device is 1, which can indicate that the number of first sampled trajectories after the incremental update of the road flow sensing device is equal to the traffic flow monitored by the road flow sensing device. This shows that when the sampling saturation of the road flow sensing device is 1, the first sampling trajectory after the incremental update of the road flow sensing device is full data. Therefore, the setting value can be set to 1 in advance.
  • the step of incrementally updating the first sampling trajectory according to the sampling saturation may include the following steps S2210 to S2220:
  • Step S2210 Select at least one trajectory from the first sampled trajectory as a candidate trajectory.
  • a sampling trajectory may be randomly selected from the first sampling trajectory obtained in step S1000 as a candidate trajectory.
  • Step S2220 based on the sampling saturation of each road flow sensing device passed by the candidate trajectory, incrementally update the first sampling trajectory according to the candidate trajectory.
  • step S2210 may be continued, and a sampling trajectory is randomly selected from the first sampling trajectory obtained in step S1000 as a candidate trajectory.
  • the first sampled trajectory includes sampled trajectories B, C, D, and the number of sampled trajectories C is N. If the sampled trajectory C is randomly selected as a candidate trajectory, then if the sampled trajectory C is based For the sampling saturation of each road flow sensing device that passes, it is determined that the first sampling trajectory is incrementally updated according to the sampling trajectory C, then the sampling trajectory C is added to the first sampling trajectory, and the obtained incrementally updated first A sampled track will contain N+1 sampled tracks C.
  • the step of incrementally updating the first sampling trajectory according to the candidate trajectory may include steps S2221 to S2222 as shown below:
  • Step S2221 Obtain the acceptance probability of the candidate trajectory according to the sampling saturation of each road flow sensing device passed by the candidate trajectory.
  • the acceptance probability of the candidate trajectory may be the probability of incrementally updating the first sampling trajectory according to the candidate trajectory.
  • the sampling saturation of each road flow sensing device passed by the candidate trajectory may be based on the sampling saturation of each road flow sensing device passed by the candidate trajectory to determine the unsaturation of each road flow sensing device passed by the candidate trajectory;
  • the geometric mean of the degree of unsaturation of each road flow sensing device is used as the acceptance probability of the candidate trajectory.
  • the degree of unsaturation is used to reflect the gap between the corresponding road flow sensing device and sampling saturation. Specifically, when the sampling saturation of the road flow sensing device is saturated, the corresponding sampling saturation is 1, then the unsaturation may be the difference between 1 and the sampling saturation of the corresponding road flow sensing device.
  • the sampling saturation of each road flow sensing device passed by the candidate trajectory is ⁇ 1 , ⁇ 2 , ..., ⁇ k
  • the difference between 1 and the sampling saturation of each road flow sensing device passed by the candidate trajectory is 1- ⁇ 1 , 1- ⁇ 2 , ..., 1- ⁇ k, respectively .
  • the geometric mean of each difference The geometric mean ⁇ can be used as the acceptance probability of the candidate trajectory.
  • Step S2222 based on the acceptance probability, incrementally update the first sampling trajectory according to the candidate trajectory.
  • the positive energy update of the first sampling trajectory according to the candidate trajectory may specifically be: adding the candidate trajectory to the first sampling trajectory to obtain an incrementally updated first sampling trajectory.
  • the first sampling trajectory may be incrementally updated according to the candidate trajectory randomly according to the acceptance probability of the candidate trajectory.
  • the first sampling trajectory is incrementally updated according to the candidate trajectory.
  • the probability threshold may be preset according to application scenarios or specific requirements. For example, the probability threshold may be 90%. Then, when the acceptance probability of the candidate trajectory is greater than or equal to 90%, the first sampling trajectory is incrementally updated according to the candidate trajectory.
  • the probability threshold can also be adjusted according to the sampling saturation of each road flow sensing device obtained from the incrementally updated first sampling trajectory.
  • it may be a comparison table that is preset to reflect the correspondence between the probability threshold and the geometric mean (or arithmetic mean) of the sampling saturation. Then, it can be the sampling saturation of each road flow sensing device obtained according to the incrementally updated first sampling trajectory, and the geometric mean (or arithmetic average) of the incrementally updated sampling saturation is calculated. Look up the comparison table according to the geometric mean (or arithmetic mean) of the incrementally updated sampling saturation, and determine the probability threshold corresponding to the geometric mean (or arithmetic mean) of the incrementally updated sampling saturation. As the new probability threshold. And replace the old probability threshold with the new probability threshold to realize real-time adjustment of the probability threshold.
  • it may also be a calculation formula for the correspondence relationship between the geometric mean (or arithmetic mean) that characterizes the probability threshold and the sampling saturation in advance. Then, it can be the sampling saturation of each road flow sensing device obtained according to the incrementally updated first sampling trajectory, and the geometric mean (or arithmetic average) of the incrementally updated sampling saturation is calculated. The geometric mean (or arithmetic mean) of the sample saturation after the incremental update is searched and substituted into the calculation formula to obtain a new probability threshold. And replace the old probability threshold with the new probability threshold to realize real-time adjustment of the probability threshold.
  • adjusting the probability threshold according to the sampling saturation of each road flow sensing device obtained from the incrementally updated first sampling trajectory can improve the accuracy of incrementally updating the first sampling trajectory.
  • the step of obtaining the sampling saturation of each road flow sensing device according to the incrementally updated first sampling trajectory may include the following steps S2230 to S2250:
  • Step S2230 According to the incrementally updated first sampling trajectory, determine the number of incrementally updated first sampling trajectories of each road flow sensing device.
  • the method of determining the number of the incrementally updated first sampling trajectories of each road flow sensing device can be determined by referring to the aforementioned pair according to the first sampling trajectory. The description of the number of first sampling traces of the flow sensing device will not be repeated here.
  • Step S2240 Determine the sampling of each road flow sensing device after the incremental update according to the number of first sampling trajectories after the incremental update of each road flow sensing device and the traffic flow monitored by the corresponding road flow sensing device. saturation.
  • the method can refer to the foregoing description of determining the sampling saturation of each road flow sensing device based on the number of first sampling trajectories passing through each road flow sensing device and the traffic flow monitored by the corresponding road flow sensing device. I will not repeat them here.
  • Step S2250 when the sampling saturation of the road flow sensing device after the incremental update reaches the set value, execute the subsequent step S2300; when the sampling saturation of the road flow sensing device after the incremental update does not reach the set value, then Step S2210 is continued, and a sampling trajectory is randomly selected from the first sampling trajectories obtained in step S1000 as a candidate trajectory.
  • Step S2300 Use the incrementally updated first sampling trajectory as the first full trajectory.
  • Step S3000 Obtain the full trajectory in the preset area according to the first full trajectory.
  • the sampling trajectory also includes a second sampling trajectory that does not pass the road flow sensing device, that is, no road flow sensing device is installed on all road sections that the second sampling trajectory passes.
  • the step of obtaining the full trajectory in the preset area according to the first full trajectory may include steps S3100 to S3200 as shown below:
  • Step S3100 According to the first full trajectory and the first sampling trajectory, the second sampling trajectory is incrementally updated to obtain the second full trajectory without passing through the road flow sensing device.
  • the second trajectory that has not passed the road flow sensing device is subjected to equal-proportional oversampling processing to obtain the second full trajectory that has not passed the road flow sensing device.
  • incrementally updating the second sampling trajectory according to the first full trajectory and the first sampling trajectory to obtain the second full trajectory without passing through the road flow sensing device may include steps S3110 to S3120 as shown below:
  • Step S3110 Determine the ratio of the number of the first full track to the number of the first sampled track.
  • the number of first full trajectories obtained in step S2000 is N1'
  • the number of first sampled trajectories obtained in step S1000 is N1
  • the number of first full trajectories obtained in step S3110 and the number of first sampled trajectories The ratio of the quantity is N1'/N1.
  • step S3120 the second sampling trajectory is incrementally updated according to the ratio to obtain the second full trajectory.
  • the second sampling trajectory may include M sampling trajectories A, then by incrementally updating the second sampling trajectory according to the ratio N1′/N1, it is obtained that the second sampling trajectory contains M*N1′/N1
  • the second sampling trajectory after the incremental update of the sampling trajectory A is the second full trajectory.
  • Step S3200 Combine the first full trajectory and the second full trajectory to obtain the full trajectory in the preset area.
  • the full trajectory in the preset area can be obtained.
  • the traffic flow is first determined according to the traffic flow.
  • the first sampling trajectory of the flow sensing device is incrementally updated to obtain the first full trajectory, and then the full trajectory in the preset area is obtained according to the first full trajectory.
  • this embodiment can also be applied to a preset area where road flow sensing devices are sparse.
  • traffic control can be performed on the preset area according to the full trajectory.
  • the traffic control mode of the preset area can be controlled according to the obtained full trajectory, the traffic lights in the preset area can be controlled; the travel demand in the preset area can be determined; the traffic simulation can be performed for the preset area; You can also do traffic guidance for preset areas.
  • FIG. 5 is an example of a processing method of a trajectory.
  • the example uses the preset area shown in FIG. 4 as an example to describe the processing method of the trajectory.
  • the processing method may include the following steps S5001 to S5010:
  • Step S5001 Obtain the sampling trajectory passing through the preset area within the preset statistical time period and the traffic flow monitored by the road flow sensing device set in the preset area.
  • road flow sensing devices 1 to 5 are preset.
  • the traffic flow monitored by the road flow sensing device 1 can be Q1
  • the traffic flow monitored by the road flow sensing device 2 can be Q2
  • the traffic flow monitored by the road flow sensing device 3 can be Q3
  • the traffic flow monitored by the road flow sensing device 4 The traffic flow can be Q4, and the traffic flow monitored by the road flow sensing device 5 can be Q5.
  • the sampling trajectory includes the first sampling trajectory passing the road flow sensing device and the second sampling trajectory not passing the road flow sensing device.
  • sampling trajectory A does not pass through any road flow sensing equipment
  • sampling trajectory B passes through road flow sensing equipment 1
  • sampling trajectory C passes through road flow sensing equipment 1
  • sampling trajectory D passed the road flow sensing equipment 2, 3, 4.
  • the sampling trajectory A may be used as the second sampling trajectory
  • the sampling trajectories B, C, and D may be used as the first sampling trajectory.
  • Step S5002 Determine the sampling saturation of each road flow sensing device according to the traffic flow and the first sampling trajectory.
  • the ratio of the number of first trajectories passing through each road flow sensing device and the traffic flow monitored by the corresponding road flow sensing device may be determined separately as the sampling saturation of each road flow sensing device.
  • the sampling saturation ⁇ of the road flow sensing device 1 may be P1/Q1.
  • Step S5003 Select at least one trajectory from the first sampled trajectory as a candidate trajectory.
  • Step S5004 Obtain the acceptance probability of the candidate trajectory according to the sampling saturation of each road flow sensing device passed by the candidate trajectory.
  • it may be to determine the difference between 1 and the sampling saturation of each road flow sensing device passed by the candidate trajectory; and then determine the geometric mean of each difference as the acceptance probability of the candidate trajectory.
  • the sampling saturation of each road flow sensing device passed by the candidate trajectory is ⁇ 1 , ⁇ 2 , ..., ⁇ k
  • the difference between 1 and the sampling saturation of each road flow sensing device passed by the candidate trajectory is 1- ⁇ 1 , 1- ⁇ 2 , ..., 1- ⁇ k, respectively .
  • the geometric mean of each difference The geometric mean ⁇ can be used as the acceptance probability of the candidate trajectory.
  • Step S5005 Based on the acceptance probability, the first sampling trajectory is incrementally updated according to the candidate trajectory.
  • the first sampling trajectory is randomly updated according to the candidate trajectory.
  • the first sampled trajectory includes sampled trajectories B, C, D, and the number of sampled trajectories C is N. If the sampled trajectory C is randomly selected as a candidate trajectory, then if the sampled trajectory C is based For the sampling saturation of each road flow sensing device that passes, it is determined that the first sampling trajectory is incrementally updated according to the sampling trajectory C, then the sampling trajectory C is added to the first sampling trajectory, and the obtained incrementally updated first A sampled track will contain N+1 sampled tracks C.
  • Step S5006 Determine whether the sampling saturation of each road flow sensing device obtained according to the incrementally updated first sampling trajectory reaches the set value, if yes, perform the following step S5007; if not, return to perform the foregoing step S5003 .
  • Step S5007 Use the incrementally updated first sampling trajectory as the first full trajectory passing through the road flow sensing device.
  • Step S5008 Determine the ratio of the number of the first full track to the number of the first sampled track.
  • the number of first full trajectories obtained in step 5007 is N1'
  • the number of first sampling trajectories obtained in step S5001 is N1
  • the number of first full trajectories obtained in step S5008 and the number of first sampling trajectories is N1'/N1.
  • step S5009 the second sampling trajectory is incrementally updated according to the ratio to obtain the second full trajectory that has not passed the road flow sensing device.
  • the second sampling trajectory may include M sampling trajectories A, then by incrementally updating the second sampling trajectory according to the ratio N1′/N1, it is obtained that the second sampling trajectory contains M*N1′/N1
  • the second sampling trajectory after the incremental update of the sampling trajectory A is the second full trajectory.
  • Step S5010 Combine the first full trajectory and the second full trajectory to obtain the full trajectory in the preset area.
  • a trajectory processing device 6000 is provided, as shown in FIG. 6, including a trajectory flow acquisition module 6100, a trajectory increment update module 6200, and a full trajectory acquisition module 6300.
  • the trajectory flow acquisition module 6100 is used to acquire the traffic flow monitored by the sampling trajectory in the preset area and the road flow sensing device set in the preset area within the preset statistical time period; wherein, the sampling trajectory may include passing road flow
  • the first sampling trajectory of the sensing device the trajectory incremental update module 6200 is used to incrementally update the first sampling trajectory according to the traffic flow to obtain the first full-volume trajectory passing the road flow sensing device.
  • the full trajectory obtaining module 6300 is used to obtain the full trajectory in the preset area according to the first full trajectory.
  • the sampling trajectory may further include a second sampling trajectory that does not pass through the road flow sensing device, and the full trajectory obtaining module 6300 may also be used for:
  • the second sampling trajectory is incrementally updated to obtain the second full trajectory without passing through the road flow sensing device
  • incrementally updating the first sampled trajectory according to the traffic volume to obtain the first full volume trajectory passing through the road volume sensing device may include:
  • the sampling saturation is used to reflect the sampling rate of the first sampling trajectory passing through the corresponding road flow sensing device;
  • the first sampling trajectory after incremental update is taken as the first full trajectory.
  • determining the sampling saturation of each road flow sensing device according to the traffic flow and the first sampling trajectory may include:
  • the first sampling trajectory determine the number of first trajectories passing through each road flow sensing device
  • the incremental update of the first sampling trajectory according to the sampling saturation may include:
  • the first sample is incrementally updated according to the candidate trajectory.
  • incrementally updating the first sample according to the candidate trajectory may include:
  • the first sample is incrementally updated according to the candidate trajectory.
  • obtaining the acceptance probability of the candidate trajectory according to the sampling saturation of each road flow sensing device passed by the candidate trajectory may include:
  • the unsaturation of each road flow sensing device passed by the candidate trajectory is determined respectively; where the unsaturation is used to reflect the corresponding road flow sensing device The gap to sampling saturation;
  • incrementally updating the first sample according to the candidate trajectory may include:
  • the first sample is incrementally updated according to the candidate trajectory.
  • the processing device 6000 may further include:
  • the processing device 6000 may further include:
  • incrementally updating the second sampling trajectory according to the first full trajectory and the first sampling trajectory to obtain the second full trajectory without passing through the road flow sensing device may include:
  • the second full track is incrementally updated according to the ratio to obtain the second full track.
  • the processing device 6000 may further include:
  • a module for traffic control in a preset area according to the full trajectory is a module for traffic control in a preset area according to the full trajectory.
  • the trajectory processing device 6000 can be implemented in various ways.
  • the trajectory processing device 6000 can be implemented by configuring the processor through instructions.
  • the instructions can be stored in the ROM, and when the device is started, the instructions are read from the ROM into the programmable device to implement the track processing device 6000.
  • the track processing device 6000 can be solidified into a dedicated device (for example, ASIC).
  • the trajectory processing device 6000 can be divided into mutually independent units, or they can be combined together for implementation.
  • the trajectory processing device 6000 may be implemented by one of the foregoing various implementation manners, or may be implemented by a combination of two or more of the foregoing various implementation manners.
  • the trajectory processing device 6000 can have multiple implementation forms.
  • the trajectory processing device 6000 can be any software product or functional module running in an application program that provides trajectory processing services, or these software products Or the peripheral embedded parts, plug-ins, patches, etc. of the application, or these software products or the application itself.
  • an electronic device 7000 is also provided.
  • the electronic device 7000 may be the server 1100 shown in FIG. 1 or the terminal device 1200 shown in FIG. 2.
  • the electronic device 7000 may include the aforementioned trajectory processing device 6000 for implementing the trajectory processing method of any embodiment of the present invention.
  • the electronic device 7000 may further include a processor 7100 and a memory 7200, where the memory 7200 is used to store executable instructions; the processor 7100 is used to execute the electronic device 7000 according to the control of the instructions.
  • a computer-readable storage medium on which a computer program is stored, and the computer program, when executed by a processor, implements the track processing method as in any embodiment of the present invention.
  • the present invention may be a system, a method and/or a computer program product.
  • the computer program product may include a computer-readable storage medium loaded with computer-readable program instructions for enabling a processor to implement various aspects of the present invention.
  • the computer-readable storage medium may be a tangible device that can hold and store instructions used by the instruction execution device.
  • the computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing.
  • Computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) Or flash memory), static random access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanical encoding device, such as a printer with instructions stored thereon
  • RAM random access memory
  • ROM read-only memory
  • EPROM erasable programmable read-only memory
  • flash memory flash memory
  • SRAM static random access memory
  • CD-ROM compact disk read-only memory
  • DVD digital versatile disk
  • memory stick floppy disk
  • mechanical encoding device such as a printer with instructions stored thereon
  • the computer-readable storage medium used here is not interpreted as a transient signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (for example, light pulses through fiber optic cables), or through wires Transmission of electrical signals.
  • the computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to various computing/processing devices, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and/or a wireless network.
  • the network may include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and/or edge servers.
  • the network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network, and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing/processing device .
  • the computer program instructions used to perform the operations of the present invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or in one or more programming languages.
  • Source code or object code written in any combination, the programming language includes object-oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as "C" language or similar programming languages.
  • Computer-readable program instructions can be executed entirely on the user's computer, partly on the user's computer, executed as a stand-alone software package, partly on the user's computer and partly executed on a remote computer, or entirely on the remote computer or server carried out.
  • the remote computer can be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, using an Internet service provider to access connection).
  • LAN local area network
  • WAN wide area network
  • an electronic circuit such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), can be customized by using the status information of the computer-readable program instructions.
  • the computer-readable program instructions are executed to implement various aspects of the present invention.
  • These computer-readable program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine such that when these instructions are executed by the processor of the computer or other programmable data processing device , A device that implements the functions/actions specified in one or more blocks in the flowchart and/or block diagram is produced. It is also possible to store these computer-readable program instructions in a computer-readable storage medium. These instructions make computers, programmable data processing apparatuses, and/or other devices work in a specific manner, so that the computer-readable medium storing instructions includes An article of manufacture, which includes instructions for implementing various aspects of the functions/actions specified in one or more blocks in the flowchart and/or block diagram.
  • each block in the flowchart or block diagram may represent a module, program segment, or part of an instruction, and the module, program segment, or part of an instruction contains one or more functions for implementing the specified logical function.
  • Executable instructions may also occur in a different order from the order marked in the drawings. For example, two consecutive blocks can actually be executed in parallel, or they can sometimes be executed in the reverse order, depending on the functions involved.
  • each block in the block diagram and/or flowchart, and the combination of the blocks in the block diagram and/or flowchart can be implemented by a dedicated hardware-based system that performs the specified functions or actions Or it can be realized by a combination of dedicated hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, implementation by software, and implementation by a combination of software and hardware are all equivalent.

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Abstract

一种轨迹的处理方法、装置（6000）、及电子设备（7000），处理方法包括：获取在预设统计时段内经过预设区域内采样轨迹（A-D）、及设置在预设区域内的道路流量感知设备（1-5）监测到的车流量；其中，采样轨迹（A-D）包括经过道路流量感知设备（1-5）的第一采样轨迹（B,C,D）（S1000）；根据车流量对第一采样轨迹（B,C,D）进行增量更新，得到经过道路流量感知设备（1-5）的第一全量轨迹（S2000）；根据第一全量轨迹得到预设区域内的全量轨迹（S3000）。

Description

一种轨迹的处理方法、装置、及电子设备
本申请要求2019年04月10日递交的申请号为201910286594.X、发明名称为“一种轨迹的处理方法、装置、及电子设备”中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本发明涉及交通控制技术领域，更具体地，涉及一种轨迹的处理方法、一种轨迹的处理装置、一种电子设备、及一种计算机可读介质。
背景技术
随着GPS(Global Positioning System，全球定位系统)设备的普及，城市中越来越多车辆在出行时会通过GPS记录轨迹。轨迹是用于估算城市出行信息的大数据。掌握城市完整的交通出行信息对城市交通管理至关重要。
现有的轨迹可以是通过设置在车辆上的GPS设备来获取，也可以是通过导航应用来获取。从导航应用中提取轨迹已经成为掌握城市出行特征的重要数据源。但是并不是所有车辆出行都会使用指定的导航应用，因此，通过指定的导航应用提取的轨迹只是全部出行数据的一个采样，而不是全量数据。这大大影响了基于轨迹的应用价值。因此，需要基于已知的采样轨迹来推导全量的轨迹。
当前各城市都有部署各种道路流量感知设备(如线圈、地磁、雷达、相机等)，这些道路流量感知设备可以监测到经过所部署位置的全量车流量。但是，这些道路流量感知设备监测到的只是断面流量，不知道具体的车辆出行轨迹。而且，由于道路流量感知设备的部署成本和维护成本都较高，无法做到道路网络的全覆盖。因此，需要利用有限的道路流量感知设备监测到的车流量来辅助推算出全量轨迹。
发明内容
本发明的一个目的是提供一种利用道路流量感知设备监测到的车流量对轨迹进行增量更新的新技术方案。
根据本发明的第一方面，提供了一种轨迹的处理方法，包括：
获取在预设统计时段内经过预设区域内采样轨迹、及设置在所述预设区域内的道路流量感知设备监测到的车流量；其中，所述采样轨迹包括经过道路流量感知设备的第一 采样轨迹；
根据所述车流量对所述第一采样轨迹进行增量更新，得到经过所述道路流量感知设备的第一全量轨迹；
根据所述第一全量轨迹得到所述预设区域内的全量轨迹。
可选的，所述采样轨迹还包括未经过道路流量感知设备的第二采样轨迹，以及，所述根据所述第一全量轨迹得到所述预设区域内的全量轨迹的步骤包括：
根据所述第一全量轨迹和所述第一采样轨迹，对所述第二采样轨迹进行增量更新，得到未经过所述道路流量感知设备的第二全量轨迹；
合并所述第一全量轨迹和所述第二全量轨迹，得到所述预设区域内的全量轨迹。
可选的，所述根据所述车流量对所述第一采样轨迹进行增量更新，得到经过所述道路流量感知设备的第一全量轨迹的步骤包括：
根据所述车流量和所述第一采样轨迹，确定每个道路流量感知设备的采样饱和度；其中，所述采样饱和度用于反映经过对应道路流量感知设备的第一采样轨迹的采样率；
根据所述采样饱和度对所述第一采样轨迹进行增量更新，使得根据增量更新后的第一采样轨迹得到的每个道路流量感知设备的采样饱和度均达到设定值；
将增量更新后的第一采样轨迹作为所述第一全量轨迹。
可选的，所述根据所述车流量和所述第一采样轨迹，确定每个道路流量感知设备的采样饱和度的步骤包括：
根据所述第一采样轨迹，确定经过每个所述道路流量感知设备的第一轨迹的数量；
分别根据经过每个所述道路流量感知设备的第一轨迹的数量、及对应道路流量感知设备监测到的车流量，确定每个道路流量感知设备的采样饱和度。
可选的，所述根据所述采样饱和度对所述第一采样轨迹进行增量更新的步骤包括：
从所述第一采样轨迹中选取至少一条轨迹，作为备选轨迹；
基于所述备选轨迹所经过的每个道路流量感知设备的采样饱和度，根据所述备选轨迹对所述第一采样进行增量更新。
可选的，所述基于所述备选轨迹所经过的每个道路流量感知设备的采样饱和度，根据所述备选轨迹对所述第一采样进行增量更新的步骤包括：
根据所述备选轨迹所经过的每个道路流量感知设备的采样饱和度，得到所述备选轨迹的接受概率；
基于所述接受概率，根据所述备选轨迹对所述第一采样进行增量更新。
可选的，所述根据所述备选轨迹所经过的每个道路流量感知设备的采样饱和度，得到所述备选轨迹的接受概率的步骤包括：
根据所述备选轨迹所经过的每个道路流量感知设备的采样饱和度，分别确定所述备选轨迹所经过的每个道路流量感知设备的不饱和度；其中，所述不饱和度用于反映对应道路流量感知设备至采样饱和的差距；
计算所述备选轨迹所经过的每个道路流量感知设备的不饱和度的几何平均数，作为所述备选轨迹的接受概率。
可选的，所述基于所述接受概率，根据所述备选轨迹对所述第一采样进行增量更新的步骤包括：
在所述接受概率大于或等于预设的概率阈值的情况下，根据所述备选轨迹对所述第一采样进行增量更新。
可选的，所述处理方法还包括：
根据增量更新后的第一采样轨迹得到的每个道路流量感知设备的采样饱和度调整所述概率阈值。
可选的，所述处理方法还包括：
根据增量更新后的第一采样轨迹，确定经过每个所述道路流量感知设备的增量更新后的第一采样轨迹数量；
分别根据经过每个道路流量感知设备的增量更新后的第一采样轨迹数量、及对应道路流量感知设备监测到的车流量，确定增量更新后每个道路流量感知设备的采样饱和度；
在增量更新后每个道路流量感知设备的采样饱和度均达到设定值时，执行将增量更新后的第一采样轨迹作为所述第一全量轨迹的步骤。
可选的，所述根据所述第一全量轨迹和所述第一采样轨迹，对所述第二采样轨迹进行增量更新，得到未经过所述道路流量感知设备的第二全量轨迹的步骤包括：
确定所述第一全量轨迹的数量与所述第一采样轨迹的数量之间的比值；
根据所述比值对所述第二全量轨迹进行增量更新，得到所述第二全量轨迹。
可选的，所述处理方法还包括：
根据所述全量轨迹，对所述预设区域进行交通控制。
根据本发明的第二方面，提供了一种轨迹的处理装置，包括：
轨迹流量获取模块，用于获取在预设统计时段内经过预设区域内采样轨迹、及设置在所述预设区域内的道路流量感知设备监测到的车流量；其中，所述采样轨迹包括经过 道路流量感知设备的第一采样轨迹；
轨迹增量更新模块，用于根据所述车流量对所述第一采样轨迹进行增量更新，得到经过所述道路流量感知设备的第一全量轨迹；
全量轨迹获得模块，用于根据所述第一全量轨迹得到所述预设区域内的全量轨迹。
根据本发明的第三方面，提供了一种电子设备，包括根据本发明第二方面所述的处理装置；或者，包括处理器和存储器，所述存储器用于存储可执行的指令，所述指令用于控制所述处理器执行根据本发明第一方面所述的处理方法。
根据本发明的第四方面，提供了一种计算机可读存储介质，其上存储有计算机程序，所述计算机程序在被处理器执行时实现根据本发明第一方面所述的处理方法。
在本发明的实施例中，将道路流量感知设备监测到的车流量作为约束，对经过道路流量感知设备的第一采样轨迹进行增量更新后，可以使得最终得到的预设区域内的全量轨迹更加精确。而且，本实施例也可以适用于道路流量感知设备稀疏的预设区域。
通过以下参照附图对本发明的示例性实施例的详细描述，本发明的其它特征及其优点将会变得清楚。
附图说明
被结合在说明书中并构成说明书的一部分的附图示出了本发明的实施例，并且连同其说明一起用于解释本发明的原理。
图1是可用于实现本发明的实施例的电子设备的硬件配置的一个例子的框图。
图2是可用于实现本发明的实施例的电子设备的硬件配置的另一个例子的框图；
图3是根据本发明实施例的轨迹的处理方法的流程示意图；
图4是根据本发明实施例的预设区域的一个例子的示意图；
图5是根据本发明实施例的轨迹的处理方法的一个例子的流程示意图；
图6是根据本发明实施例的轨迹的处理装置的原理框图；
图7是根据本发明第一个实施例提供的电子设备的原理框图；
图8是根据本发明第二个实施例提供的电子设备的硬件结构示意图。
具体实施方式
现在将参照附图来详细描述本发明的各种示例性实施例。应注意到：除非另外具体说明，否则在这些实施例中阐述的部件和步骤的相对布置、数字表达式和数值不限制本 发明的范围。
以下对至少一个示例性实施例的描述实际上仅仅是说明性的，决不作为对本发明及其应用或使用的任何限制。
对于相关领域普通技术人员已知的技术、方法和设备可能不作详细讨论，但在适当情况下，所述技术、方法和设备应当被视为说明书的一部分。
在这里示出和讨论的所有例子中，任何具体值应被解释为仅仅是示例性的，而不是作为限制。因此，示例性实施例的其它例子可以具有不同的值。
应注意到：相似的标号和字母在下面的附图中表示类似项，因此，一旦某一项在一个附图中被定义，则在随后的附图中不需要对其进行进一步讨论。
<硬件配置>
图1和图2是可用于实现本发明任意实施例的轨迹的处理方法的电子设备的硬件配置的框图。
在一个实施例中，如图1所示，电子设备可以是服务器1100。
服务器1100提供处理、数据库、通讯设施的业务点。服务器1100可以是整体式服务器或是跨多计算机或计算机数据中心的分散式服务器。服务器可以是各种类型的，例如但不限于，网络服务器，新闻服务器，邮件服务器，消息服务器，广告服务器，文件服务器，应用服务器，交互服务器，数据库服务器，或代理服务器。在一些实施例中，每个服务器可以包括硬件，软件，或用于执行服务器所支持或实现的合适功能的内嵌逻辑组件或两个或多个此类组件的组合。例如，服务器例如刀片服务器、云端服务器等，或者可以是由多台服务器组成的服务器群组，可以包括上述类型的服务器中的一种或多种等等。
本实施例中，服务器1100可以如图1所示，包括处理器1110、存储器1120、接口装置1130、通信装置1140、显示装置1150、输入装置1160。
在该实施例中，服务器1100还可以包括扬声器、麦克风等等，在此不做限定。
处理器1110可以是专用的服务器处理器，也可以是满足性能要求的台式机处理器、移动版处理器等，在此不做限定。存储器1120例如包括ROM(只读存储器)、RAM(随机存取存储器)、诸如硬盘的非易失性存储器等。接口装置1130例如包括各种总线接口，例如串行总线接口(包括USB接口)、并行总线接口等。通信装置1140例如能够进行有线或无线通信。显示装置1150例如是液晶显示屏、LED显示屏触摸显示屏等。输入装置1160例如可以包括触摸屏、键盘等。
在该实施例中，服务器1100的存储器1120用于存储指令，该指令用于控制处理器1110进行操作以至少执行根据本发明任意实施例的轨迹的处理方法。技术人员可以根据本发明所公开方案设计指令。指令如何控制处理器进行操作，这是本领域公知，故在此不再详细描述。
尽管在图1中示出了服务器1100的多个装置，但是，本发明可以仅涉及其中的部分装置，例如，服务器1100只涉及存储器1120和处理器1110。
在一个实施例中，电子设备可以是操作人员使用的PC机、笔记本电脑等终端设备1200，在此不做限定。
本实施例中，参照图2所示，电子设备可以是终端设备1200,终端设备1200可以包括处理器1210、存储器1220、接口装置1230、通信装置1240、显示装置1250、输入装置1260、扬声器1270、麦克风1280等等。
处理器1210可以是移动版处理器。存储器1220例如包括ROM(只读存储器)、RAM(随机存取存储器)、诸如硬盘的非易失性存储器等。接口装置1230例如包括USB接口、耳机接口等。通信装置1240例如能够进行有线或无线通信，通信装置1240可以包括短距离通信装置，例如是基于Hilink协议、WiFi(IEEE 802.11协议)、Mesh、蓝牙、ZigBee、Thread、Z-Wave、NFC、UWB、LiFi等短距离无线通信协议进行短距离无线通信的任意装置，通信装置1240也可以包括远程通信装置，例如是进行WLAN、GPRS、2G/3G/4G/5G远程通信的任意装置。显示装置1250例如是液晶显示屏、触摸显示屏等。输入装置1260例如可以包括触摸屏、键盘等。用户可以通过扬声器1270和麦克风1280输入/输出语音信息。
在该实施例中，终端设备1200的存储器1220用于存储指令，该指令用于控制处理器1210进行操作以至少执行根据本发明任意实施例的轨迹的处理方法。技术人员可以根据本发明所公开方案设计指令。指令如何控制处理器进行操作，这是本领域公知，故在此不再详细描述。
尽管在图2中示出了终端设备1200的多个装置，但是，本发明可以仅涉及其中的部分装置，例如，终端设备1200只涉及存储器1220和处理器1210和显示装置1250。
<方法实施例>
在本实施例中，提供一种轨迹的处理方法。该处理方法可以是由电子设备实施。该电子设备可以是如图1所示的服务器1100，或者是如图2所示终端设备1200。
根据图3所示，本实施例的轨迹的处理方法可以包括如下步骤S1000～S3000：
步骤S1000，获取在预设统计时段内经过预设区域内的采样轨迹、及设置在该预设区域内的道路流量感知设备监测到的车流量。
在一个实施例中，预设区域可以是根据应用场景或具体需求在城市中选定设置有至少一个道路流量感知设备的区域。例如，该预设区域可以是如图4所示。在如图4所示的预设区域中指定的路段上，可以是预先设置有道路流量感知设备1～5。
本实施例中的采样轨迹可以是从指定的导航应用中提取的轨迹，包括记录车辆出行过程的空间位置序列。空间位置序列中包含多个点，每个点可以包含经纬度位置信息。因此，根据空间位置序列中每个点的经纬度位置信息，就可以确定采样轨迹经过的预设区域中的每个路段。其中，本实施例中的路段可以是指交通网络上相邻两个节点之间在一个行驶方向上的交通线路。
根据采样轨迹的空间位置序列中每个点所包含的经纬度信息，就可以将每个采样轨迹匹配到具体的道路上，具体可以参考如图4所示的采样轨迹A、B、C、D。
本实施例中的道路流量感知设备可以是线圈、地磁、雷达、相机等设备。每个道路流量感知设备可以监测经过自身所部署路段的车流量。道路流量感知设备监测到的车流量为全量数据。
在本实施例中，采样轨迹和车流量均是反映预设区域在同一统计时段内的车辆出行信息。统计时段可以是预先根据应用场景或具体需求设定的。例如，统计时段可以为2019年3月14日9:00:00～2019年3月14日10:00:00。那么，本实施例中获取的采样轨迹可以是从指定的导航应用中提取的、在2019年3月14日9:00:00～2019年3月14日10:00:00内产生的轨迹，本实施例中获取的车流量可以是预设区域内对应的道路流量感知设备在在2019年3月14日9:00:00～2019年3月14日10:00:00内监测到的车流量。
本实施例中，采样轨迹可以包括经过道路流量感知设备的第一采样轨迹，即第一采样轨迹经过的路段上设置有至少一个道路流量感知设备。采样轨迹还包括未经过道路流量感知设备的第二采样轨迹，即第二采样轨迹经过的所有路段上均未设置有道路流量感知设备。
在如图4所示的例子中，采样轨迹A经过的所有路段上均未设置有道路流量感知设备，即采样轨迹A未经过任一个道路流量感知设备，采样轨迹B经过的路段中设置了流量感知设备1、2、5，即采样轨迹B经过了道路流量感知设备1、2、5；采样轨迹C经过的路段中设置了流量感知设备1，采样轨迹C经过了道路流量感知设备1；采样轨迹D经过的路段中设置了流量感知设备2、3、4，采样轨迹D经过了道路流量感知设备2、3、4。 那么，可以是将采样轨迹A作为第二采样轨迹，将采样轨迹B、C、D作为第一采样轨迹。
步骤S2000，根据车流量对第一采样轨迹进行增量更新，得到经过道路流量感知设备的第一全量轨迹。
具体的，经过每个道路流量感知设备的第一全量轨迹的数量，可以是与对应道路流量感知设备监测到的车流量相等。
在一个实施例中，根据车流量和第一采样轨迹，得到经过道路流量感知设备的第一全量轨迹的步骤可以包括如下所示的步骤S2100～S2300：
步骤S2100，根据车流量和第一采样轨迹，确定每个道路流量感知设备的采样饱和度。
其中，采样饱和度可以用于反映经过对应道路流量感知设备的第一采样轨迹的采样率。
在一个实施例中，根据车流量和第一采样轨迹，确定每个道路流量感知设备的采样饱和度的步骤可以包括如下所示的步骤S2110～S2120：
步骤S2110，根据第一采样轨迹，确定经过每个道路流量感知设备的第一采样轨迹的数量。
具体的，根据每个第一采样轨迹所经过的道路流量感知设备，就可以确定经过每个道路流量感知设备的第一采样轨迹的数量。
在如图4所示的例子中，第一采样轨迹包括采样轨迹B、C、D，其中，经过道路流量感知设备1的第一采样轨迹为采样轨迹B、C，经过道路流量感知设备2的第一采样轨迹为采样轨迹D，经过道路流量感知设备3的第一采样轨迹为采样轨迹D，经过道路流量感知设备4的第一采样轨迹为采样轨迹D，经过道路流量感知设备5的第一采样轨迹为采样轨迹B。那么，可以确定经过道路流量感知设备1的第一采样轨迹为采样轨迹的数量为2，经过道路流量感知设备2的第一采样轨迹的数量为1，经过道路流量感知设备3的第一采样轨迹的数量为1，经过道路流量感知设备4的第一采样轨迹的数量为1，经过道路流量感知设备5的第一采样轨迹的数量为1。
步骤S2120，分别根据经过每个道路流量感知设备的第一轨迹的数量、及对应道路流量感知设备监测到的车流量，确定每个道路流量感知设备的采样饱和度。
具体的，可以是分别确定经过每个道路流量感知设备的第一轨迹的数量、及对应道路流量感知设备监测到的车流量的比值，作为每个道路流量感知设备的采样饱和度。
例如，在经过道路流量感知设备1的第一轨迹的数量为P1，道路流量感知设备1监 测到的车流量为Q1的情况下，道路流量感知设备1的采样饱和度ρ可以是P1/Q1。
由于道路流量感知设备监测到的车流量是全量数据，因此在正常情况下，P1≤Q1，因此，道路流量感知设备的采样饱和度ρ可以大于等于0、且小于等于1。
步骤S2200，根据该采样饱和度对第一采样轨迹进行增量更新，使得根据增量更新后的第一采样轨迹得到的每个道路流量感知设备的采样饱和度均达到设定值。
在正常情况下，道路流量感知设备的采样饱和度为1，则可以表示经过该道路流量感知设备的增量更新后的第一采样轨迹的数量、与该道路流量感知设备监测的车流量相等。这说明，道路流量感知设备的采样饱和度为1时，经过该道路流量感知设备的增量更新后的第一采样轨迹为全量数据。因此，可以预先设定该设定值为1。
在一个实施例中，根据采样饱和度对第一采样轨迹进行增量更新的步骤可以包括如下所示的步骤S2210～S2220：
步骤S2210，从第一采样轨迹中选取至少一条轨迹，作为备选轨迹。
在一个例子中，可以是从根据步骤S1000中得到的第一采样轨迹中随机选取一条采样轨迹，作为备选轨迹。
步骤S2220，基于备选轨迹所经过的每个道路流量感知设备的采样饱和度，根据备选轨迹对第一采样轨迹进行增量更新。
基于备选轨迹所经过的每个道路流量感知设备的采样饱和度，可以确定是否根据备选轨迹对第一采样轨迹进行增量更新。如果确定根据备选轨迹对第一采样轨迹进行增量更新，则将备选轨迹增加至第一采样轨迹中，得到增量更新后的第一采样轨迹。如果确定不根据备选轨迹对第一采样轨迹进行增量更新，则可以是继续执行步骤S2210，重新从根据步骤S1000中得到的第一采样轨迹中随机选取一条采样轨迹，作为备选轨迹。
在如图4所示的例子中，第一采样轨迹包括采样轨迹B、C、D，且采样轨迹C的数量为N，如果随机选取到采样轨迹C作为候选轨迹，那么，如果基于采样轨迹C所经过的每个道路流量感知设备的采样饱和度，确定根据采样轨迹C对第一采样轨迹进行增量更新，则将采样轨迹C增加至第一采样轨迹中，得到的增量更新后的第一采样轨迹中将包含N+1条采样轨迹C。
在一个实施例中，基于备选轨迹所经过的每个道路流量感知设备的采样饱和度，根据备选轨迹对第一采样轨迹进行增量更新的步骤可以包括如下所示的步骤S2221～S2222：
步骤S2221，根据备选轨迹所经过的每个道路流量感知设备的采样饱和度，得到备 选轨迹的接受概率。
其中，备选轨迹的接受概率可以是根据备选轨迹对第一采样轨迹进行增量更新的概率。
具体的，可以是根据备选轨迹所经过的每个道路流量感知设备的采样饱和度，分别确定备选轨迹所经过的每个道路流量感知设备的不饱和度；再确定备选轨迹所经过的每个道路流量感知设备的不饱和度的几何平均数，作为备选轨迹的接受概率。其中，不饱和度用于反映对应道路流量感知设备至采样饱和的差距。具体的，在道路流量感知设备采样饱和的情况下，其对应的采样饱和度为1，那么，不饱和度可以是1与对应道路流量感知设备的采样饱和度之间的差值。
例如，在备选轨迹共经过k个道路流量感知设备，且该备选轨迹所经过的每个道路流量感知设备的采样饱和度分别为ρ 1、ρ 2、……、ρ k的情况下，可以是确定1与备选轨迹所经过的每个道路流量感知设备的采样饱和度之间的差值分别为1-ρ 1、1-ρ 2、……、1-ρ k。再确定每个差值的几何平均数
Figure PCTCN2020082311-appb-000001
该几何平均数θ则可以作为备选轨迹的接受概率。
备选轨迹经过的道路流量感知设备的采样饱和度越小，该备选轨迹的接受概率越大；备选轨迹经过的道路流量感知设备的采样饱和度越大，该备选轨迹的接受概率越小。
步骤S2222，基于该接受概率，根据备选轨迹对第一采样轨迹进行增量更新。
根据备选轨迹对第一采样轨迹进行正能量更新具体可以是：将备选轨迹增加至第一采样轨迹中，得到增量更新后的第一采样轨迹。
在一个实施例中，可以是按照备选轨迹的接受概率，随机根据备选轨迹对第一采样轨迹进行增量更新。
在另一个实施例中，还可以是在接受概率大于或等于预设的概率阈值的情况下，根据备选轨迹对第一采样轨迹进行增量更新。
该概率阈值可以是预先根据应用场景或具体需求设定好的。例如，该概率阈值可以是90％。那么，在备选轨迹的接受概率大于或等于90％的情况下，根据备选轨迹对第一采样轨迹进行增量更新。
在此基础上，还可以根据增量更新后的第一采样轨迹得到的每个道路流量感知设备的采样饱和度调整该概率阈值。
在一个例子中，可以是预先设定有反映概率阈值和采样饱和度的几何平均数(或算数平均数)之间对应关系的对照表。那么，可以是根据增量更新后的第一采样轨迹得到的每个道路流量感知设备的采样饱和度，并计算增量更新后的采样饱和度的几何平均数(或算数平均数)。根据增量更新后的采样饱和度的几何平均数(或算数平均数)查找该对照表，可以确定与增量更新后的采样饱和度的几何平均数(或算数平均数)对应的概率阈值，作为新的概率阈值。并用该新的概率阈值替换旧的概率阈值，实现概率阈值的实时调整。
在另一个例子中，还可以是预先设定表征概率阈值和采样饱和度的几何平均数(或算数平均数)之间对应关系计算公式。那么，可以是根据增量更新后的第一采样轨迹得到的每个道路流量感知设备的采样饱和度，并计算增量更新后的采样饱和度的几何平均数(或算数平均数)。将增量更新后的采样饱和度的几何平均数(或算数平均数)查找代入该计算公式，可以得到新的概率阈值。并用该新的概率阈值替换旧的概率阈值，实现概率阈值的实时调整。
在本实施例中，根据增量更新后的第一采样轨迹得到的每个道路流量感知设备的采样饱和度调整该概率阈值，可以提高对第一采样轨迹进行增量更新的准确率。
根据增量更新后的第一采样轨迹得到的每个道路流量感知设备的采样饱和度的步骤可以包括如下所示的步骤S2230～S2250：
步骤S2230，根据增量更新后的第一采样轨迹，确定经过每个道路流量感知设备的增量更新后的第一采样轨迹的数量。
根据增量更新后的第一采样轨迹，确定经过每个道路流量感知设备的增量更新后的第一采样轨迹的数量的方式，可以参照前述的对根据第一采样轨迹，确定经过每个道路流量感知设备的第一采样轨迹的数量的描述，在此不再赘述。
步骤S2240，分别根据经过每个道路流量感知设备的增量更新后的第一采样轨迹的数量、及对应道路流量感知设备监测到的车流量，确定增量更新后每个道路流量感知设备的采样饱和度。
分别根据经过每个道路流量感知设备的增量更新后的第一采样轨迹的数量、及对应道路流量感知设备监测到的车流量，确定增量更新后每个道路流量感知设备的采样饱和度的方式，可以参照前述的对分别根据经过每个道路流量感知设备的第一采样轨迹的数量、及对应道路流量感知设备监测到的车流量，确定每个道路流量感知设备的采样饱和度的描述，在此不再赘述。
步骤S2250，在增量更新后的道路流量感知设备的采样饱和度达到设定值时，执行后续步骤S2300；在增量更新后的道路流量感知设备的采样饱和度未达到设定值时，则继续执行步骤S2210，重新从根据步骤S1000中得到的第一采样轨迹中随机选取一条采样轨迹，作为备选轨迹。
步骤S2300，将增量更新后的第一采样轨迹作为第一全量轨迹。
步骤S3000，根据第一全量轨迹得到预设区域内的全量轨迹。
在一个例子中，采样轨迹还包括未经过道路流量感知设备的第二采样轨迹，即第二采样轨迹经过的所有路段上均未设置有道路流量感知设备。那么，根据第一全量轨迹得到预设区域内的全量轨迹的步骤可以包括如下所示的步骤S3100～S3200：
步骤S3100，根据第一全量轨迹和第一采样轨迹，对第二采样轨迹进行增量更新，得到未经过道路流量感知设备的第二全量轨迹。
具体的，可以是按照经过道路流量感知设备的第一轨迹的增量比例，对未经过道路流量感知设备的第二轨迹进行等比例过采样处理，得到未经过道路流量感知设备的第二全量轨迹。
在一个实施例中，根据第一全量轨迹和第一采样轨迹，对第二采样轨迹进行增量更新，得到未经过道路流量感知设备的第二全量轨迹可以包括如下所示的步骤S3110～S3120：
步骤S3110，确定第一全量轨迹的数量和第一采样轨迹的数量的比值。
在根据步骤S2000得到的第一全量轨迹的数量为N1′，步骤S1000得到的第一采样轨迹的数量为N1的情况下，通过本步骤S3110得到的第一全量轨迹的数量和第一采样轨迹的数量的比值为N1′/N1。
步骤S3120，根据该比值对第二采样轨迹，进行增量更新，得到第二全量轨迹。
在如图4所示的例子中，第二采样轨迹可以包括M条采样轨迹A，那么通过根据该比值N1′/N1对第二采样轨迹进行增量更新，得到包含M*N1′/N1条采样轨迹A的增量更新后的第二采样轨迹，即得到第二全量轨迹。
步骤S3200，合并第一全量轨迹和第二全量轨迹，得到该预设区域内的全量轨迹。
通过合并经过道路流量感知设备的第一全量轨迹和未经过道路流量感知设备的第二全量轨迹，就可以得到该预设区域内的全量轨迹。
在本发明的实施例中，通过获取预设统计时段内经过预设区域内的采样轨迹、及设 置在该预设区域内的道路流量感知设备监测到的车流量，先根据车流量对经过道路流量感知设备的第一采样轨迹进行增量更新得到第一全量轨迹，再根据第一全量轨迹得到该预设区域内的全量轨迹。这样，将道路流量感知设备监测到的车流量作为约束，对经过道路流量感知设备的第一采样轨迹进行增量更新，可以使得最终得到的预设区域内的全量轨迹更加精确。而且，本实施例也可以适用于道路流量感知设备稀疏的预设区域。
在本发明的一个实施例中，在得到预设区域内的全量轨迹之后，可以根据该全量轨迹，对该预设区域进行交通控制。
具体的，根据得到的全量轨迹对该预设区域进行交通控制的方式，可以对预设区域内的交通信号灯进行控制；可以确定预设区域内的出行需求；可以针对预设区域做交通仿真；还可以针对预设区域做交通诱导。
<例子>
图5为一个例子的轨迹的处理方法，该例子以图4所示的预设区域为例，对轨迹的处理方法进行描述。该处理方法可以包括如下步骤S5001～S5010：
步骤S5001，获取在预设统计时段内经过预设区域内的采样轨迹、及设置在该预设区域内的道路流量感知设备监测到的车流量。
在如图4所示的预设区域中，预先设置有道路流量感知设备1～5。获取的采样轨迹A、B、C、D。道路流量感知设备1监测到的车流量可以是Q1，道路流量感知设备2监测到的车流量可以是Q2，道路流量感知设备3监测到的车流量可以是Q3，道路流量感知设备4监测到的车流量可以是Q4，道路流量感知设备5监测到的车流量可以是Q5。
其中，采样轨迹中包括经过道路流量感知设备的第一采样轨迹、及未经过道路流量感知设备的第二采样轨迹。
在如图4所示的例子中，采样轨迹A未经过任一个道路流量感知设备，采样轨迹B经过了道路流量感知设备1、2、5，采样轨迹C经过了道路流量感知设备1，采样轨迹D经过了道路流量感知设备2、3、4。那么，可以是将采样轨迹A作为第二采样轨迹，将采样轨迹B、C、D作为第一采样轨迹。
步骤S5002，根据车流量和第一采样轨迹，确定每个道路流量感知设备的采样饱和度。
可以是分别确定经过每个道路流量感知设备的第一轨迹的数量、及对应道路流量感知设备监测到的车流量的比值，作为每个道路流量感知设备的采样饱和度。
例如，在经过道路流量感知设备1的第一轨迹的数量为P1，道路流量感知设备1监 测到的车流量为Q1的情况下，道路流量感知设备1的采样饱和度ρ可以是P1/Q1。
步骤S5003，从第一采样轨迹中选取至少一条轨迹，作为备选轨迹。
步骤S5004，根据备选轨迹所经过的每个道路流量感知设备的采样饱和度，得到备选轨迹的接受概率。
具体的，可以是确定1与备选轨迹所经过的每个道路流量感知设备的采样饱和度之间的差值；再确定每个差值的几何平均数，作为备选轨迹的接受概率。
例如，在备选轨迹共经过k个道路流量感知设备，且该备选轨迹所经过的每个道路流量感知设备的采样饱和度分别为ρ 1、ρ 2、……、ρ k的情况下，可以是确定1与备选轨迹所经过的每个道路流量感知设备的采样饱和度之间的差值分别为1-ρ 1、1-ρ 2、……、1-ρ k。再确定每个差值的几何平均数
Figure PCTCN2020082311-appb-000002
该几何平均数θ则可以作为备选轨迹的接受概率。
步骤S5005，基于该接受概率，根据备选轨迹对第一采样轨迹进行增量更新。
在本例中，可以是按照备选轨迹的接受概率，随机根据备选轨迹对第一采样轨迹进行增量更新。
在如图4所示的例子中，第一采样轨迹包括采样轨迹B、C、D，且采样轨迹C的数量为N，如果随机选取到采样轨迹C作为候选轨迹，那么，如果基于采样轨迹C所经过的每个道路流量感知设备的采样饱和度，确定根据采样轨迹C对第一采样轨迹进行增量更新，则将采样轨迹C增加至第一采样轨迹中，得到的增量更新后的第一采样轨迹中将包含N+1条采样轨迹C。
步骤S5006，确定根据增量更新后的第一采样轨迹得到的每个道路流量感知设备的采样饱和度是否达到设定值，如是，则执行下述步骤S5007；如否，则返回执行上述步骤S5003。
步骤S5007，将增量更新后的第一采样轨迹作为经过道路流量感知设备的第一全量轨迹。
步骤S5008，确定第一全量轨迹的数量和第一采样轨迹的数量的比值。
在根据步骤5007得到的第一全量轨迹的数量为N1′，步骤S5001得到的第一采样轨迹的数量为N1的情况下，通过本步骤S5008得到的第一全量轨迹的数量和第一采样轨迹的数量的比值为N1′/N1。
步骤S5009，根据该比值对第二采样轨迹，进行增量更新，得到未经过道路流量感知设备的第二全量轨迹。
在如图4所示的例子中，第二采样轨迹可以包括M条采样轨迹A，那么通过根据该比值N1′/N1对第二采样轨迹进行增量更新，得到包含M*N1′/N1条采样轨迹A的增量更新后的第二采样轨迹，即得到第二全量轨迹。
步骤S5010，合并第一全量轨迹和第二全量轨迹，得到该预设区域内的全量轨迹。
<装置实施例>
在本实施例中，提供一种轨迹的处理装置6000，如图6所示，包括轨迹流量获取模块6100、轨迹增量更新模块6200、及全量轨迹获得模块6300。该轨迹流量获取模块6100用于获取在预设统计时段内经过预设区域内采样轨迹、及设置在预设区域内的道路流量感知设备监测到的车流量；其中，采样轨迹可以包括经过道路流量感知设备的第一采样轨迹；该轨迹增量更新模块6200用于根据车流量对第一采样轨迹进行增量更新，得到经过道路流量感知设备的第一全量轨迹。该全量轨迹获得模块6300用于根据第一全量轨迹得到预设区域内的全量轨迹。
在一个实施例中，采样轨迹还可以包括未经过道路流量感知设备的第二采样轨迹，该全量轨迹获得模块6300还可以用于：
根据第一全量轨迹和第一采样轨迹，对第二采样轨迹进行增量更新，得到未经过道路流量感知设备的第二全量轨迹；
合并第一全量轨迹和第二全量轨迹，得到预设区域内的全量轨迹。
在一个实施例中，根据车流量对第一采样轨迹进行增量更新，得到经过道路流量感知设备的第一全量轨迹可以包括：
根据车流量和第一采样轨迹，确定每个道路流量感知设备的采样饱和度；其中，采样饱和度用于反映经过对应道路流量感知设备的第一采样轨迹的采样率；
根据采样饱和度对第一采样轨迹进行增量更新，使得根据增量更新后的第一采样轨迹得到的每个道路流量感知设备的采样饱和度均达到设定值；
将增量更新后的第一采样轨迹作为第一全量轨迹。
在一个实施例中，根据车流量和第一采样轨迹，确定每个道路流量感知设备的采样饱和度可以包括：
根据第一采样轨迹，确定经过每个道路流量感知设备的第一轨迹的数量；
分别根据经过每个道路流量感知设备的第一轨迹的数量、及对应道路流量感知设备 监测到的车流量，确定每个道路流量感知设备的采样饱和度。
在一个实施例中，根据采样饱和度对第一采样轨迹进行增量更新可以包括：
从第一采样轨迹中选取至少一条轨迹，作为备选轨迹；
基于备选轨迹所经过的每个道路流量感知设备的采样饱和度，根据备选轨迹对第一采样进行增量更新。
在一个实施例中，基于备选轨迹所经过的每个道路流量感知设备的采样饱和度，根据备选轨迹对第一采样进行增量更新可以包括：
根据备选轨迹所经过的每个道路流量感知设备的采样饱和度，得到备选轨迹的接受概率；
基于接受概率，根据备选轨迹对第一采样进行增量更新。
在一个实施例中，根据备选轨迹所经过的每个道路流量感知设备的采样饱和度，得到备选轨迹的接受概率可以包括：
根据备选轨迹所经过的每个道路流量感知设备的采样饱和度，分别确定备选轨迹所经过的每个道路流量感知设备的不饱和度；其中，不饱和度用于反映对应道路流量感知设备至采样饱和的差距；
计算备选轨迹所经过的每个道路流量感知设备的不饱和度的几何平均数，作为备选轨迹的接受概率。
在一个实施例中，基于所述接受概率，根据所述备选轨迹对所述第一采样进行增量更新可以包括：
在接受概率大于或等于预设的概率阈值的情况下，根据备选轨迹对第一采样进行增量更新。
在一个实施例中，该处理装置6000还可以包括：
用于根据增量更新后的第一采样轨迹得到的每个道路流量感知设备的采样饱和度调整概率阈值的模块。
在一个实施例中，该处理装置6000还可以包括：
用于根据增量更新后的第一采样轨迹，确定经过每个道路流量感知设备的增量更新后的第一采样轨迹数量的模块；
用于分别根据经过每个道路流量感知设备的增量更新后的第一采样轨迹数量、及对应道路流量感知设备监测到的车流量，确定增量更新后每个道路流量感知设备的采样饱和度的模块；
用于在增量更新后每个道路流量感知设备的采样饱和度均达到设定值时，将增量更新后的第一采样轨迹作为第一全量轨迹的模块。
在一个实施例中，根据第一全量轨迹和第一采样轨迹，对第二采样轨迹进行增量更新，得到未经过道路流量感知设备的第二全量轨迹可以包括：
确定第一全量轨迹的数量与第一采样轨迹的数量之间的比值；
根据比值对第二全量轨迹进行增量更新，得到第二全量轨迹。
在一个实施例中，该处理装置6000还可以包括：
用于根据全量轨迹，对预设区域进行交通控制的模块。
本领域技术人员应当明白，可以通过各种方式来实现轨迹的处理装置6000。例如，可以通过指令配置处理器来实现轨迹的处理装置6000。例如，可以将指令存储在ROM中，并且当启动设备时，将指令从ROM读取到可编程器件中来实现轨迹的处理装置6000。例如，可以将轨迹的处理装置6000固化到专用器件(例如ASIC)中。可以将轨迹的处理装置6000分成相互独立的单元，或者可以将它们合并在一起实现。轨迹的处理装置6000可以通过上述各种实现方式中的一种来实现，或者可以通过上述各种实现方式中的两种或更多种方式的组合来实现。
在本实施例中，轨迹的处理装置6000可以具有多种实现形式，例如，轨迹的处理装置6000可以是任何的提供轨迹处理服务的软件产品或者应用程序中运行的功能模块，或者是这些软件产品或者应用程序的外设嵌入件、插件、补丁件等，还可以是这些软件产品或者应用程序本身。
<电子设备>
在本实施例中，还提供一种电子设备7000。该电子设备7000可以是图1所示的服务器1100，也可以是如图2所示的终端设备1200。
在一方面，如图7所示，该电子设备7000可以包括前述的轨迹的处理装置6000，用于实施本发明任意实施例的轨迹的处理方法。
在另一方面，如图8所示，电子设备7000还可以包括处理器7100和存储器7200，该存储器7200用于存储可执行的指令；该处理器7100用于根据指令的控制运行电子设备7000执行根据本发明任意实施例的轨迹的处理方法。
<计算机可读存储介质>
在本实施例中，还提供一种计算机可读存储介质，其上存储有计算机程序，计算机程序在被处理器执行时实现如本发明任意实施例的轨迹的处理方法。
本发明可以是系统、方法和/或计算机程序产品。计算机程序产品可以包括计算机可读存储介质，其上载有用于使处理器实现本发明的各个方面的计算机可读程序指令。
计算机可读存储介质可以是可以保持和存储由指令执行设备使用的指令的有形设备。计算机可读存储介质例如可以是――但不限于――电存储设备、磁存储设备、光存储设备、电磁存储设备、半导体存储设备或者上述的任意合适的组合。计算机可读存储介质的更具体的例子(非穷举的列表)包括：便携式计算机盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM或闪存)、静态随机存取存储器(SRAM)、便携式压缩盘只读存储器(CD-ROM)、数字多功能盘(DVD)、记忆棒、软盘、机械编码设备、例如其上存储有指令的打孔卡或凹槽内凸起结构、以及上述的任意合适的组合。这里所使用的计算机可读存储介质不被解释为瞬时信号本身，诸如无线电波或者其他自由传播的电磁波、通过波导或其他传输媒介传播的电磁波(例如，通过光纤电缆的光脉冲)、或者通过电线传输的电信号。
这里所描述的计算机可读程序指令可以从计算机可读存储介质下载到各个计算/处理设备，或者通过网络、例如因特网、局域网、广域网和/或无线网下载到外部计算机或外部存储设备。网络可以包括铜传输电缆、光纤传输、无线传输、路由器、防火墙、交换机、网关计算机和/或边缘服务器。每个计算/处理设备中的网络适配卡或者网络接口从网络接收计算机可读程序指令，并转发该计算机可读程序指令，以供存储在各个计算/处理设备中的计算机可读存储介质中。
用于执行本发明操作的计算机程序指令可以是汇编指令、指令集架构(ISA)指令、机器指令、机器相关指令、微代码、固件指令、状态设置数据、或者以一种或多种编程语言的任意组合编写的源代码或目标代码，所述编程语言包括面向对象的编程语言—诸如Smalltalk、C++等，以及常规的过程式编程语言—诸如“C”语言或类似的编程语言。计算机可读程序指令可以完全地在用户计算机上执行、部分地在用户计算机上执行、作为一个独立的软件包执行、部分在用户计算机上部分在远程计算机上执行、或者完全在远程计算机或服务器上执行。在涉及远程计算机的情形中，远程计算机可以通过任意种类的网络—包括局域网(LAN)或广域网(WAN)—连接到用户计算机，或者，可以连接到外部计算机(例如利用因特网服务提供商来通过因特网连接)。在一些实施例中，通过利用计算机可读程序指令的状态信息来个性化定制电子电路，例如可编程逻辑电路、现场可编程门阵列(FPGA)或可编程逻辑阵列(PLA)，该电子电路可以执行计算机可读程序指令，从而实现本发明的各个方面。
这里参照根据本发明实施例的方法、装置(系统)和计算机程序产品的流程图和/或框图描述了本发明的各个方面。应当理解，流程图和/或框图的每个方框以及流程图和/或框图中各方框的组合，都可以由计算机可读程序指令实现。
这些计算机可读程序指令可以提供给通用计算机、专用计算机或其它可编程数据处理装置的处理器，从而生产出一种机器，使得这些指令在通过计算机或其它可编程数据处理装置的处理器执行时，产生了实现流程图和/或框图中的一个或多个方框中规定的功能/动作的装置。也可以把这些计算机可读程序指令存储在计算机可读存储介质中，这些指令使得计算机、可编程数据处理装置和/或其他设备以特定方式工作，从而，存储有指令的计算机可读介质则包括一个制造品，其包括实现流程图和/或框图中的一个或多个方框中规定的功能/动作的各个方面的指令。
也可以把计算机可读程序指令加载到计算机、其它可编程数据处理装置、或其它设备上，使得在计算机、其它可编程数据处理装置或其它设备上执行一系列操作步骤，以产生计算机实现的过程，从而使得在计算机、其它可编程数据处理装置、或其它设备上执行的指令实现流程图和/或框图中的一个或多个方框中规定的功能/动作。
附图中的流程图和框图显示了根据本发明的多个实施例的系统、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上，流程图或框图中的每个方框可以代表一个模块、程序段或指令的一部分，所述模块、程序段或指令的一部分包含一个或多个用于实现规定的逻辑功能的可执行指令。在有些作为替换的实现中，方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如，两个连续的方框实际上可以基本并行地执行，它们有时也可以按相反的顺序执行，这依所涉及的功能而定。也要注意的是，框图和/或流程图中的每个方框、以及框图和/或流程图中的方框的组合，可以用执行规定的功能或动作的专用的基于硬件的系统来实现，或者可以用专用硬件与计算机指令的组合来实现。对于本领域技术人员来说公知的是，通过硬件方式实现、通过软件方式实现以及通过软件和硬件结合的方式实现都是等价的。
以上已经描述了本发明的各实施例，上述说明是示例性的，并非穷尽性的，并且也不限于所披露的各实施例。在不偏离所说明的各实施例的范围和精神的情况下，对于本技术领域的普通技术人员来说许多修改和变更都是显而易见的。本文中所用术语的选择，旨在最好地解释各实施例的原理、实际应用或对市场中的技术改进，或者使本技术领域的其它普通技术人员能理解本文披露的各实施例。本发明的范围由所附权利要求来限定。

Claims (15)

  1. 一种轨迹的处理方法，其中，包括：
    获取在预设统计时段内经过预设区域内采样轨迹、及设置在所述预设区域内的道路流量感知设备监测到的车流量；其中，所述采样轨迹包括经过道路流量感知设备的第一采样轨迹；
    根据所述车流量对所述第一采样轨迹进行增量更新，得到经过所述道路流量感知设备的第一全量轨迹；
    根据所述第一全量轨迹得到所述预设区域内的全量轨迹。
  2. 根据权利要求1所述的处理方法，其中，所述采样轨迹还包括未经过道路流量感知设备的第二采样轨迹，以及，所述根据所述第一全量轨迹得到所述预设区域内的全量轨迹的步骤包括：
    根据所述第一全量轨迹和所述第一采样轨迹，对所述第二采样轨迹进行增量更新，得到未经过所述道路流量感知设备的第二全量轨迹；
    合并所述第一全量轨迹和所述第二全量轨迹，得到所述预设区域内的全量轨迹。
  3. 根据权利要求2所述的处理方法，其中，所述根据所述车流量对所述第一采样轨迹进行增量更新，得到经过所述道路流量感知设备的第一全量轨迹的步骤包括：
    根据所述车流量和所述第一采样轨迹，确定每个道路流量感知设备的采样饱和度；其中，所述采样饱和度用于反映经过对应道路流量感知设备的第一采样轨迹的采样率；
    根据所述采样饱和度对所述第一采样轨迹进行增量更新，使得根据增量更新后的第一采样轨迹得到的每个道路流量感知设备的采样饱和度均达到设定值；
    将增量更新后的第一采样轨迹作为所述第一全量轨迹。
  4. 根据权利要求3所述的处理方法，其中，所述根据所述车流量和所述第一采样轨迹，确定每个道路流量感知设备的采样饱和度的步骤包括：
    根据所述第一采样轨迹，确定经过每个所述道路流量感知设备的第一轨迹的数量；
    分别根据经过每个所述道路流量感知设备的第一轨迹的数量、及对应道路流量感知设备监测到的车流量，确定每个道路流量感知设备的采样饱和度。
  5. 根据权利要求3所述的处理方法，其中，所述根据所述采样饱和度对所述第一采样轨迹进行增量更新的步骤包括：
    从所述第一采样轨迹中选取至少一条轨迹，作为备选轨迹；
    基于所述备选轨迹所经过的每个道路流量感知设备的采样饱和度，根据所述备选轨迹对所述第一采样进行增量更新。
  6. 根据权利要求5所述的处理方法，其中，所述基于所述备选轨迹所经过的每个道路流量感知设备的采样饱和度，根据所述备选轨迹对所述第一采样进行增量更新的步骤包括：
    根据所述备选轨迹所经过的每个道路流量感知设备的采样饱和度，得到所述备选轨迹的接受概率；
    基于所述接受概率，根据所述备选轨迹对所述第一采样进行增量更新。
  7. 根据权利要求6所述的处理方法，其中，所述根据所述备选轨迹所经过的每个道路流量感知设备的采样饱和度，得到所述备选轨迹的接受概率的步骤包括：
    根据所述备选轨迹所经过的每个道路流量感知设备的采样饱和度，分别确定所述备选轨迹所经过的每个道路流量感知设备的不饱和度；其中，所述不饱和度用于反映对应道路流量感知设备至采样饱和的差距；
    计算所述备选轨迹所经过的每个道路流量感知设备的不饱和度的几何平均数，作为所述备选轨迹的接受概率。
  8. 根据权利要求6所述的处理方法，其中，所述基于所述接受概率，根据所述备选轨迹对所述第一采样进行增量更新的步骤包括：
    在所述接受概率大于或等于预设的概率阈值的情况下，根据所述备选轨迹对所述第一采样进行增量更新。
  9. 根据权利要求8所述的处理方法，其中，所述处理方法还包括：
    根据增量更新后的第一采样轨迹得到的每个道路流量感知设备的采样饱和度调整所述概率阈值。
  10. 根据权利要求3所述的处理方法，其中，所述处理方法还包括：
    根据增量更新后的第一采样轨迹，确定经过每个所述道路流量感知设备的增量更新后的第一采样轨迹的数量；
    分别根据经过每个道路流量感知设备的增量更新后的第一采样轨迹的数量、及对应道路流量感知设备监测到的车流量，确定增量更新后每个道路流量感知设备的采样饱和度；
    在增量更新后每个道路流量感知设备的采样饱和度均达到设定值时，执行将增量更新后的第一采样轨迹作为所述第一全量轨迹的步骤。
  11. 根据权利要求2所述的处理方法，其中，所述根据所述第一全量轨迹和所述第一采样轨迹，对所述第二采样轨迹进行增量更新，得到未经过所述道路流量感知设备的第二全量轨迹的步骤包括：
    确定所述第一全量轨迹的数量与所述第一采样轨迹的数量之间的比值；
    根据所述比值对所述第二全量轨迹进行增量更新，得到所述第二全量轨迹。
  12. 根据权利要求1-11中任一项所述的处理方法，其中，所述处理方法还包括：
    根据所述全量轨迹，对所述预设区域进行交通控制。
  13. 一种轨迹的处理装置，其中，包括：
    轨迹流量获取模块，用于获取在预设统计时段内经过预设区域内采样轨迹、及设置在所述预设区域内的道路流量感知设备监测到的车流量；其中，所述采样轨迹包括经过道路流量感知设备的第一采样轨迹；
    轨迹增量更新模块，用于根据所述车流量对所述第一采样轨迹进行增量更新，得到经过所述道路流量感知设备的第一全量轨迹；
    全量轨迹获得模块，用于根据所述第一全量轨迹得到所述预设区域内的全量轨迹。
  14. 一种电子设备，其中，包括根据权利要求13所述的处理装置；或者，包括处理器和存储器，所述存储器用于存储可执行的指令，所述指令用于控制所述处理器执行根据权利要求1至12中任一项所述的处理方法。
  15. 一种计算机可读存储介质，其上存储有计算机程序，所述计算机程序在被处理器执行时实现如权利要求1至12中任一项所述的处理方法。
PCT/CN2020/082311 2019-04-10 2020-03-31 一种轨迹的处理方法、装置、及电子设备 Ceased WO2020207291A1 (zh)

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