CN112668892A - Method, device, electronic equipment and storage medium for determining parking risk - Google Patents
Method, device, electronic equipment and storage medium for determining parking risk Download PDFInfo
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Abstract
The present disclosure relates to a method, an apparatus, an electronic device and a storage medium for determining parking risk. In one method, a plurality of historical tickets respectively associated with a plurality of road segments in a road are obtained, a historical ticket of the plurality of historical tickets being a ticket generated by a vehicle parking within a road segment of the plurality of road segments. Based on the plurality of historical tickets, a first parking risk of a first road segment and a second parking risk of a second road segment in the plurality of road segments are respectively determined, and the first parking risk and the second parking risk respectively represent parking risks of the vehicle parking in the first road segment and the second road segment. And updating the second parking risk based on the position relation between the first road section and the second road section and the first parking risk. Further, a corresponding apparatus, electronic device and storage medium are provided. In this way, the risk of parking may be determined in a more accurate and efficient manner.
Description
Technical Field
Implementations of the present disclosure relate to data processing, and more particularly, to a method, apparatus, electronic device, and storage medium for determining a parking risk of a vehicle.
Background
With the development of navigation technology and on-line vehicle scheduling technology, more and more driving assistance-related applications have been provided. These applications may provide navigation services to the driver and guide the vehicle to a desired location. However, in a real road environment, there may be areas where parking is prohibited and restricted. If the vehicle is parked in these areas, it may interfere with normal traffic order and create a ticket. At this time, how to determine the parking risk at each position in the road in a more accurate manner becomes a research focus.
Disclosure of Invention
It is desirable to be able to develop and implement a solution that determines the risk of parking of a vehicle in a more efficient manner. It is desirable that the solution is compatible with existing applications and guides the driver's parking behaviour in a more efficient way and maintains normal traffic order.
According to a first aspect of the present disclosure, a method for determining a parking risk of a vehicle is provided. In the method, a plurality of historical tickets respectively associated with a plurality of road segments in a road are obtained, wherein the historical tickets in the plurality of historical tickets are tickets generated by a vehicle stopping in the road segments in the plurality of road segments. Based on the plurality of historical tickets, a first parking risk of a first road segment and a second parking risk of a second road segment in the plurality of road segments are respectively determined, and the first parking risk and the second parking risk respectively represent parking risks of the vehicle parking in the first road segment and the second road segment. And updating the second parking risk based on the position relation between the first road section and the second road section and the first parking risk.
According to a second aspect of the present disclosure, an apparatus for determining a parking risk of a vehicle is provided. The device includes: an acquisition module configured to acquire a plurality of historical tickets respectively associated with a plurality of road segments in a road, a historical ticket in the plurality of historical tickets being a ticket generated by a vehicle parking within a road segment in the plurality of road segments; a determining module configured to determine a first parking risk of a first road segment and a second parking risk of a second road segment in the plurality of road segments, respectively, based on the plurality of historical tickets, the first parking risk and the second parking risk representing parking risks of a vehicle parking in the first road segment and the second road segment, respectively; and an updating module configured to update the second parking risk based on the first parking risk and a positional relationship between the first road segment and the second road segment.
According to a third aspect of the present disclosure, there is provided an electronic device comprising: a memory and a processor; wherein the memory is for storing one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method according to the first aspect of the disclosure.
According to a fourth aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon one or more computer instructions, wherein the one or more computer instructions are executed by a processor to implement a method according to the first aspect of the present disclosure.
Drawings
The features, advantages and other aspects of various implementations of the present disclosure will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings, which illustrate, by way of example and not by way of limitation, several implementations of the present disclosure. In the drawings:
FIG. 1 schematically illustrates a block diagram of a road environment in which a technical solution according to one exemplary implementation of the present disclosure may be used;
FIG. 2 schematically shows a block diagram of a process for determining a parking risk of a vehicle according to an exemplary implementation of the present disclosure;
FIG. 3 schematically illustrates a flow chart of a method for determining parking risk of a vehicle according to an exemplary implementation of the present disclosure;
FIG. 4 schematically shows a block diagram of a process for determining a parking risk for determining a road segment associated with an exemplary implementation of the present disclosure;
FIG. 5 schematically illustrates a block diagram of time-varying parking risk according to an exemplary implementation of the present disclosure;
FIG. 6 schematically illustrates a block diagram of parking risk for multiple road segments in a road according to an exemplary implementation of the present disclosure; and
fig. 7 schematically illustrates a block diagram of a computing device/server for determining a parking risk of a vehicle according to an exemplary implementation of the present disclosure.
Detailed Description
Preferred implementations of the present disclosure will be described in more detail below with reference to the accompanying drawings. While a preferred implementation of the present disclosure is shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited by the implementations set forth herein. Rather, these implementations are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The term "include" and variations thereof as used herein is meant to be inclusive in an open-ended manner, i.e., "including but not limited to". Unless specifically stated otherwise, the term "or" means "and/or". The term "based on" means "based at least in part on". The terms "one example implementation" and "one implementation" mean "at least one example implementation". The term "another implementation" means "at least one additional implementation". The terms "first," "second," and the like may refer to different or the same object. Other explicit and implicit definitions are also possible below.
Hereinafter, an application environment according to an exemplary implementation of the present disclosure is described first with reference to fig. 1. Fig. 1 schematically shows a block diagram of a road environment 100 in which a technical solution according to an exemplary implementation of the present disclosure may be used. A plurality of roads may be included in the real road environment, and different roads may have different regulations as to whether or not parking is possible on both sides of the road. For example, a plurality of traffic signs may be disposed on both sides of the road 110. Specifically, the traffic sign 120 may indicate that the vehicle is prohibited from parking for a long time, and the traffic sign 122 may indicate that the vehicle is prohibited from parking. Meanwhile, a camera 130 may be further provided near the road, and different patterns may be drawn on the road surface of the road 110 for indicating various regulations regarding prohibition, restriction of parking. These traffic signs may sometimes be ignored while the driver drives the vehicle 140 over the roadway 110, resulting in a parking violation ticket.
Technical solutions have been proposed to alert the driver of an entry into a risky road segment (e.g. a no-parking road segment and/or a restricted parking road segment). However, these solutions determine the risk road segments based on traffic rules, traffic facilities, etc., with unsatisfactory accuracy. Thus, it is desirable that parking risk can be determined in a more convenient and efficient manner.
To at least partially address the deficiencies in the above-described solutions, according to an exemplary implementation of the present disclosure, parking risks may be determined for respective road segments in a road, respectively. It will be appreciated that when a vehicle is parked in a no-parking and/or restricted parking area, traffic may be blocked or even cause a traffic accident. Thus, the driver should be alerted to the relevant parking risk and guided to the area with lower risk. Specifically, a concept of parking risk propagation is proposed, and the parking risks of two road segments of a plurality of road segments may be updated based on the positional relationship between the two road segments. An overview of the technical solution according to an exemplary implementation of the present disclosure is provided first with reference to fig. 2.
In the context of the present disclosure, further details for determining parking risk will be described with an online vehicle allocation application as an example. In particular, the passenger may call the vehicle using an online vehicle distribution application, designate an entry point for embarking the passenger, and may designate an exit point. For convenience of description, the boarding point and the alighting point may be collectively referred to as a boarding point. If the point of ride is located on a prohibited or restricted parking road, it may disturb normal traffic order, induce road hazards, and generate a ticket. According to one exemplary implementation of the present disclosure, parking risks at various locations in a road may be determined, thereby guiding a driver to park in a zero risk (or low risk) location.
Fig. 2 schematically shows a block diagram of a process 200 for determining a parking risk of a vehicle according to an exemplary implementation of the present disclosure. As shown in fig. 2, the road 110 may include a plurality of road segments 210, …, and 220. The first parking risk 212 for the first road segment 210 and the second parking risk 222 for the second road segment 220 in the plurality of road segments may be determined based on a plurality of historical tickets respectively associated with the plurality of road segments. Here, the first parking risk 212 and the second parking risk 222 represent parking risks for a vehicle parking in the first road segment 210 and the second road segment 220, respectively. It will be appreciated that since two road segments may be directly or indirectly adjacent, the parking risk of adjacent road segments may be propagated to other road segments (as indicated by arrow 230). At this time, the parking risk of each road segment may be updated based on the positional relationship between the first road segment 210 and the second road segment 220.
With example implementations of the present disclosure, parking risks for a given road segment may be updated based on parking risks for other road segments in the vicinity of the given road segment. In this way, the risk distribution along the road 110 can be comprehensively considered, and thus the parking risk of each road segment can be determined in a more accurate manner and with finer granularity. In the following, further details of an exemplary implementation according to the present disclosure will be described with reference to fig. 3.
Fig. 3 schematically shows a flow chart of a method 300 for determining a parking risk of a vehicle according to an exemplary implementation of the present disclosure. At block 310, a plurality of historical tickets respectively associated with a plurality of road segments in the road 110 are obtained, a historical ticket of the plurality of historical tickets being a ticket generated by a vehicle parking within a road segment of the plurality of road segments. It will be appreciated that a ticket is a direct proof that can measure whether parking is allowed at a certain location. For providers of online vehicle distribution applications, a large number of vehicles may generate multiple tickets during operation. The risk of parking is thus determined based on the collected plurality of historical tickets, which can be quantified in a simple and efficient manner.
It will be appreciated that the plurality of road segments herein may be divided by a predetermined length. According to an exemplary implementation manner of the present disclosure, the predetermined link length may be set by comprehensively considering both the link granularity and the calculation amount. It will be appreciated that there may be an offset in the positioning data (e.g., on the order of about 10 meters), and thus the predetermined road segment length may be set to 20 meters or other values greater than the positioning offset.
It will be appreciated that for too short road segments it is difficult to collect input data from such road segments for determining risk, and that such road segments are difficult to accommodate multiple vehicles parking, and thus too short road segments do not make much sense in determining parking risk. According to an exemplary implementation of the present disclosure, the predetermined link length may be adjusted in order to avoid generating too short links when dividing the links (i.e., a "remainder" when dividing the road 110). For example, the link length may be adjusted within plus or minus 4 meters (or other values).
Specifically, the road length of the road 110 may be determined, and the link length may be adjusted within a given range based on a ratio between the road length and a predetermined link length. Further, the road may be divided into a plurality of road segments based on the predetermined road segment length and the adjusted road segment length, thereby avoiding a "remainder" condition during the division. Assuming that the road length is 50 meters, the road may be divided into three segments of 16 meters, 16 meters and 18 meters. By means of the method and the device for dividing the road, the situation that the road section is too short when the road is divided can be avoided as much as possible. In this way, it is possible to improve the efficiency of dividing the road segments and to ensure that the length of each road segment is suitable for determining the parking risk.
It will be appreciated that the risk of parking on both sides of the road may differ due to the direction of travel of the road. According to one exemplary implementation of the present disclosure, a road may be divided into a plurality of segments based on a driving direction of the road. Specifically, each of the above-obtained road segments may be divided into two road segments according to the driving direction of the vehicle. With the exemplary implementation of the present disclosure, road segments may be divided at a finer granularity, thereby making it possible to determine parking risks of parking on both sides of a road in a more accurate manner.
At block 320, a first parking risk 212 for the first road segment 210 and a second parking risk 222 for the second road segment 220 in the plurality of road segments are determined, respectively, based on the plurality of historical tickets. Here, the first parking risk 212 and the second parking risk 222 represent parking risks for a vehicle parking in the first road segment 210 and the second road segment 220, respectively. Hereinafter, how to determine the parking risk for each road segment will be described with reference to fig. 4.
Fig. 4 schematically shows a block diagram of a process 400 of determining a parking risk for determining a road segment associated with an exemplary implementation of the present disclosure. As shown in fig. 4, the first parking risk 212 may be determined based on a ticket 410 occurring within the first road segment 210. According to an exemplary implementation of the present disclosure, the initial value of first parking risk 212 may be set to zero and first parking risk 212 may be updated based on the number of associated tickets. The first parking risk 212 may be increased if the location of occurrence of a historical ticket 410 of the plurality of historical tickets is determined to be within the first segment 210. An increase step (e.g., increase step of 1) may be set for updating first parking risk 212, at which point each ticket occurring within first segment 210 may increase first parking risk 212 by 1 unit. With exemplary implementations of the present disclosure, risk levels may be quantified based on the number of tickets to determine parking risks for individual road segments in a convenient and efficient manner.
It will be appreciated that the number of taxi orders associated with a road segment will also affect the risk assessment. In one example, 1 ticket is generated within a road segment and the number of orders associated with the road segment is 100. In another example, 1 ticket is generated within a certain road segment and the number of orders associated with that road segment is 2. It can be seen that the parking risk is not the same in both examples. Thus, first parking risk 210 may be determined based on the quantity of both the ticket 410 and the order 420.
According to one exemplary implementation of the present disclosure, the number of tickets for a historical ticket within the first road segment 210 generated during a predetermined time period (e.g., 1 month or other length of time) may be determined, and the number of orders for a historical order within the first road segment 210 generated during the predetermined time period may be determined. First parking risk 212 may then be set based on the number of tickets and the number of orders. The order here refers to an order for getting on or off the vehicle in the first route section 210.
According to one exemplary implementation of the present disclosure, the order is the same type as the ticket. For example, if the ticket is a ticket due to boarding action, the order therein is an order for boarding within the first road segment 210; if the ticket is due to an alighting activity, the order therein is an order to alight in the first leg 210. For example, first parking risk 212 may be set based on a ratio of the number of tickets and the number of orders. By means of the method and the device for determining the parking risk of the road sections, the parking risk of each road section can be determined in a normalization mode, and therefore accuracy of a risk determination process is improved. It will be appreciated that how first parking risk 212 is determined is described above with only first segment 210 as an example. Similar processing may be performed for other road segments in order to determine parking risks for each road segment separately.
It will be appreciated that at different points in the day, there may be differences in parking risk for the same road segment. For example, a certain road may be manually managed, and thus the risk of parking during the day may be high, and the risk of parking during the night may be low. At this time, the attribute of the parking risk with respect to time may be further set based on the penalty time of the historical penalty ticket. According to an exemplary implementation of the present disclosure, the historical ticket may include a penalty time, at which point the portion of the first parking risk 212 associated with the penalty time may be increased based on the penalty time.
According to one exemplary implementation of the present disclosure, a day may be divided into a plurality of time periods, and a parking risk associated with a certain penalty time is determined based on a ticket at the penalty time. Fig. 5 schematically illustrates a block diagram of a time-varying parking risk 500 according to an exemplary implementation of the present disclosure. As shown in FIG. 5, assume that the majority of the tickets generated in the first segment 210 are centered at 7: 00 to 19: 00, then the risk of parking is higher in the above time period (as shown by risk curve 510), and at 19: 00 to the next day 7: the risk of parking between 00 is low. With the exemplary implementations of the present disclosure, parking risks for a given road segment at various points in time of the day may be determined with a finer granularity. In this way, the accuracy of the risk determination process can be further improved, thereby guiding the driver to stop at a zero risk or low risk road segment.
According to one exemplary implementation of the present disclosure, orders and tickets over a longer period of time may be collected and parking risks for individual road segments determined based on the time difference between the penalty date and the current time. In other words, the attenuation factor of the parking risk may be set based on the time difference. Specifically, orders and penalties for the past 3 months may be collected. For orders and penalties within the last 1 month, the decay factor may be set to "1" (indicating no decay is performed). For orders and tickets within the past 2 months, the decay factor may be set to "0.7" to indicate that the parking risk due to these orders and tickets only has a 70% impact on determining the parking risk. Further, for orders and tickets within the past 3 rd month, the decay factor may be set to "0.3" to indicate a lower impact.
Specifically, assume that the order and penalty ticket quantities in the past 1 month are 100 and 1, respectively, the order and penalty ticket quantities in the past 2 month content are 100 and 2, respectively, and the order and penalty ticket quantities in the past 3 month are 100 and 5, respectively. The parking risk of the first segment 210 may be determined based on the following equation 1:
parking risk 1/100 +2/100 0.7+5/100 0.3 ═ 0.039%
Equation 1
With the exemplary implementation of the present disclosure, data related to parking risks may be collected more comprehensively and efficiently, thereby improving the accuracy of the risk determination process.
The process of determining a parking risk for each of a plurality of road segments has been described above. After the parking risk for each road segment has been determined, the parking risk for a certain road segment(s) of the plurality of road segments may be updated based on risk propagation rules. In the following, more details are described returning to fig. 3. At block 330 of fig. 3, second parking risk 222 is updated based on the positional relationship between first road segment 210 and second road segment 220 and first parking risk 212.
It will be appreciated that parking risks herein may be propagated from high risk road segments to low risk road segments. Thus, the risk of parking for other road segments in the vicinity of a given road segment may be updated when it is determined that the risk for the given road segment is high. It will be understood that risk propagation herein refers to the propagation of risk from high risk road segments to low risk road segments. Thus, when it is determined that first parking risk 212 is higher than the second parking risk, the second parking risk is updated.
In particular, assuming that the first parking risk 212 of the first road segment 210 is above a predetermined threshold, an update procedure is triggered. According to an exemplary implementation of the present disclosure, it may be specified, for example, that an update process is triggered when first parking risk 212 is found to be non-zero. Alternatively and/or additionally, the predetermined threshold may be set to other values (e.g., 1%). According to an example implementation of the present disclosure, a distance between the first and second segments 210 and 220 may be determined. For example, the distance may be defined in terms of the number of road segments between two road segments. At this time, the distance between two directly adjacent links is 0.
According to one exemplary implementation of the present disclosure, the smaller the distance between two road segments, the greater the influence of a high-risk road segment on a low-risk road segment. In other words, risk propagation is inversely proportional to distance. It will be appreciated that the higher the risk, the greater the impact of risk propagation on other road segments in the vicinity. At this point, risk propagation is proportional to first parking risk 212. According to an example implementation of the present disclosure, second parking risk 222 may be updated based on the distance and first parking risk 212. For example, the updated second parking risk may be determined based on the following equation 2:
wherein Risk'2Indicating an updated second parking Risk, Risk1And Risk2Representing a first risk of parking and a second risk of parking, respectively, and Disc represents the distance between the first road segment and the second road segment.
It will be appreciated that equation 2 only schematically illustrates one specific example for updating parking risk. According to one exemplary implementation of the present disclosure, the updated parking risk may be determined based on other formulas. An upper limit of the influence range may be set, for example, the influence range may be specified as 2 (or other numerical value). In other words, the first parking risk can be propagated only within a range of a distance of not more than 2, and if the distance between the first road segment and the second road segment is more than 2, the first parking risk is not propagated to the second road segment. According to one exemplary implementation of the present disclosure, the influence range may be set based on the magnitude of the first parking risk. In other words, the greater the risk of parking, the greater the range of influence.
Continuing with the example above, first parking risk 212 is 3.9% (non-zero), and thus an update process may be triggered. Assuming that the second parking risk 222 determined based on the ticket and the order is 0 and the distance between the first and second road segments 210 and 220 is 0, the updated second parking risk may be expressed as formula 3:
in the following, further details regarding risk propagation are described with reference to fig. 6. Fig. 6 schematically shows a block diagram of parking risk 600 for a plurality of road segments in a road according to an exemplary implementation of the present disclosure. As shown, the road 110 is divided into a plurality of road segments 610, 620, 630, 640, 650, 660, and 670. Assuming that the parking risk of the section 640 is 3.9% and the influence range is 2, the updated parking risks of 3 sections (distances of 0 to 2, respectively) near the section 640 are represented as 1.95%, 1.3%, and 0.975%, respectively, in order of the distance from near to far.
Fig. 6 shows the different risks in different shades of gray, with legends 680, 682, 684 and 686 representing micro-, low-, medium-and high-risk, respectively. In fig. 6, it is initially determined based on the ticket and the order that the parking risk for only the road segment 640 is non-zero. After updating based on the risk propagation process described above, segments 610 and 670 belong to micro-risk segments, segments 620 and 660 belong to low-risk segments, and segments 630 and 650 belong to medium-risk segments. With the exemplary implementation of the present disclosure, parking risks of a plurality of other road segments around the roadside may be comprehensively considered, and then the risk of each road segment may be determined in a more accurate manner.
It will be appreciated that the risk of parking on individual road sections may be influenced by the traffic regulations in the area. For example, a new no-parking area may be set along the road 110, or a no-parking area along the road 110 may be cancelled. At this time, a traffic rule associated with the road may also be acquired. Assuming that traffic regulations dictate that no-parking areas be set along a certain road, the method 300 described above may be performed for that road. For another example, assuming that traffic regulations dictate that all no-parking areas along road 110 be cancelled, method 300 may no longer be performed for road 110. According to an exemplary implementation manner of the disclosure, related news trends can also be acquired, and assuming that traffic news emphasizes that the management of prohibiting parking needs to be strengthened, the parking risk of each road section can be correspondingly increased. For example, the step size increase described above may be increased from the original 1 to 1.5.
With the exemplary implementation of the present disclosure, the parking risk of each road segment can be adjusted accordingly with the latest traffic rules and traffic news dynamics, so that the obtained parking risk can more truly and accurately reflect the probability that parking in the road segment may cause a violation ticket.
According to one exemplary implementation of the present disclosure, a parking behavior of a driver may be guided based on a determined parking risk. Assuming that the point of ride selected by the passenger is located on a high risk road segment, it may be recommended to stop within a road segment with a lower risk in the vicinity of the point of ride. Specifically, if it is determined that the vehicle is to be parked within a target road segment (e.g., the second road segment 220 described above) of the plurality of road segments, a target time associated with the parking action may be acquired. Further, a risk prediction for parking within the target road segment may be determined based on the target time (e.g., daytime) and the target parking risk for the target road segment. The risk of parking at the target time may be determined based on a risk profile of the target road segment. If the risk of parking is high, the driver and passengers may be prompted to park in other less risky locations. If the risk of parking is low, parking may be performed at the desired home position.
It will be appreciated that the above description describes, by way of example only, the process of determining parking risk in a vehicle allocation application. According to an exemplary implementation of the present disclosure, the technical solution described above may also be used in other applications. For example, the above-described solution may be used in a vehicle navigation application. Assuming that the destination input by the driver is located on a high-risk road segment, the driver may be prompted for a parking risk and recommended candidate low-risk parking spots (e.g., a permitted parking road segment or a parking lot, etc.) to the driver.
The procedure of the method for determining the parking risk has been described above with reference to fig. 2 to 6. According to an exemplary implementation of the present disclosure, an apparatus for determining a parking risk of a vehicle is provided. The device includes: an acquisition module configured to acquire a plurality of historical tickets respectively associated with a plurality of road segments in a road, a historical ticket in the plurality of historical tickets being a ticket generated by a vehicle parking within a road segment in the plurality of road segments; a determining module configured to determine a first parking risk of a first road segment and a second parking risk of a second road segment in the plurality of road segments, respectively, based on the plurality of historical tickets, the first parking risk and the second parking risk representing parking risks of a vehicle parking in the first road segment and the second road segment, respectively; and an updating module configured to update the second parking risk based on the first parking risk and a positional relationship between the first road segment and the second road segment.
According to one exemplary implementation of the present disclosure, the update module includes: a distance determination module configured to determine a distance between the first road segment and the second road segment in response to determining that the first parking risk for the first road segment is above a predetermined threshold; and a risk update module configured to update the second parking risk based on the distance and the first parking risk.
According to an exemplary implementation of the present disclosure, the risk update module is further configured to update the second parking risk to be proportional to the first parking risk and inversely proportional to the distance.
According to one exemplary implementation of the present disclosure, the determining module includes: an increase module configured to increase the first risk of parking in response to determining that a location of occurrence of a historical ticket of the plurality of historical tickets is within the first road segment.
According to an exemplary implementation of the disclosure, the historical ticket includes a penalty time, and the boost module includes: a time module configured to increase a portion of the first parking risk associated with the penalty time.
According to an exemplary implementation of the present disclosure, the increasing module includes: a ticket determination module configured to determine a number of tickets for a historical ticket within a first road segment generated during a predetermined time period; an order determination module configured to determine an order quantity for a historical order within a first road segment generated during a predetermined time period; a setting module configured to set a first parking risk based on the ticket quantity and the order quantity.
According to an exemplary implementation of the disclosure, the historical ticket includes a penalty date, and the boost module includes: a difference determination module configured to determine a time difference between the penalty date and the current date time; an attenuation module configured to set a first parking risk based on the time difference.
According to one exemplary implementation of the present disclosure, a plurality of road segments in a road are determined by: determining the road length of a road; adjusting a predetermined link length based on the road length; and dividing the road into a plurality of road segments based on the predetermined road segment length and the adjusted road segment length.
According to an exemplary implementation of the present disclosure, the plurality of road segments in the road are determined by: the road is divided into a plurality of segments based on a driving direction of the road.
According to an exemplary implementation of the present disclosure, the apparatus further includes a media acquisition module configured to acquire traffic rules and news associated with the road; and the update module is further configured to update the plurality of parking risks based on the traffic rules and news.
According to an exemplary implementation of the disclosure, the update module is further configured to: in response to determining that the first parking risk is higher than the second parking risk, updating the second parking risk.
According to an exemplary implementation of the present disclosure, further comprising: a time acquisition module configured to acquire a target time associated with the parking action in response to determining that the vehicle is to park within a second road segment of the plurality of road segments; and a prediction module configured to determine a risk prediction for parking within the target road segment based on the target time and the second risk.
Fig. 7 schematically illustrates a block diagram of a computing device/server 700 for determining parking risk according to an exemplary implementation of the present disclosure. It should be understood that the computing device/server 700 illustrated in FIG. 7 is merely exemplary and should not be construed as limiting in any way the functionality and scope of the embodiments described herein.
As shown in fig. 7, computing device/server 700 is in the form of a general purpose computing device. Components of computing device/server 700 may include, but are not limited to, one or more processors or processing units 710, memory 720, storage 730, one or more communication units 740, one or more input devices 750, and one or more output devices 760. The processing unit 710 may be a real or virtual processor and may be capable of performing various processes according to programs stored in the memory 720. In a multi-processor system, multiple processing units execute computer-executable instructions in parallel to improve the parallel processing capabilities of computing device/server 700.
Computing device/server 700 typically includes a number of computer storage media. Such media may be any available media that is accessible by computing device/server 700 and includes, but is not limited to, volatile and non-volatile media, removable and non-removable media. Memory 720 may be volatile memory (e.g., registers, cache, Random Access Memory (RAM)), non-volatile memory (e.g., Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory), or some combination thereof. Storage 730 may be a removable or non-removable medium and may include a machine-readable medium, such as a flash drive, a magnetic disk, or any other medium, which may be capable of being used to store information and/or data (e.g., training data for training) and which may be accessed within computing device/server 700.
Computing device/server 700 may further include additional removable/non-removable, volatile/nonvolatile storage media. Although not shown in FIG. 7, a magnetic disk drive for reading from and writing to a removable, non-volatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, non-volatile optical disk may be provided. In these cases, each drive may be connected to a bus (not shown) by one or more data media interfaces. Memory 720 may include a computer program product 725 having one or more program modules configured to perform the various methods or acts of the various embodiments of the disclosure.
According to an exemplary implementation of the present disclosure, a computer-readable storage medium is provided, on which one or more computer instructions are stored, wherein the one or more computer instructions are executed by a processor to implement the above-described method.
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products implemented in accordance with the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processing unit of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various implementations of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The foregoing has described implementations of the present disclosure, and the above description is illustrative, not exhaustive, and not limited to the implementations disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described implementations. The terminology used herein was chosen in order to best explain the principles of implementations, the practical application, or improvements to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the implementations disclosed herein.
Claims (16)
1. A method for determining a parking risk of a vehicle, comprising:
obtaining a plurality of historical tickets respectively associated with a plurality of road segments in a road, wherein a historical ticket in the plurality of historical tickets is a ticket generated by a vehicle parking in a road segment in the plurality of road segments;
determining a first parking risk of a first road segment and a second parking risk of a second road segment in the plurality of road segments respectively based on the plurality of historical tickets, the first parking risk and the second parking risk representing parking risks of a vehicle parking in the first road segment and the second road segment respectively; and
updating the second parking risk based on the first parking risk and a positional relationship between the first road segment and the second road segment.
2. The method of claim 1, wherein updating the second parking risk based on the first parking risk and a positional relationship between the first road segment and the second road segment comprises:
in response to determining that the first risk of parking for the first road segment is above a predetermined threshold, determining a distance between the first road segment and the second road segment; and
updating the second parking risk based on the distance and the first parking risk.
3. The method of claim 2, wherein updating the second parking risk based on the distance and the first parking risk comprises:
updating the second parking risk to be proportional to the first parking risk and inversely proportional to the distance.
4. The method of claim 1, wherein determining the first parking risk comprises: in response to determining that a location of occurrence of a historical ticket of the plurality of historical tickets is within the first road segment, increasing the first parking risk.
5. The method of claim 4, wherein the historical ticket includes a penalty time, and increasing the first parking risk comprises: increasing a portion of the first parking risk associated with the penalty time.
6. The method of claim 4, wherein increasing the first parking risk comprises:
determining a ticket quantity for a historical ticket within the first road segment generated during a predetermined time period;
determining an order quantity for historical orders within the first road segment generated during a predetermined time period;
and setting the first parking risk based on the number of the penalty orders and the number of the orders.
7. The method of claim 4, wherein the historical ticket includes a penalty date, and increasing the first parking risk comprises:
determining a time difference between the penalty date and a current date-time;
setting the first parking risk based on the time difference.
8. The method of claim 1, wherein the plurality of segments in the road are determined using the steps of:
determining a road length of the road;
adjusting the predetermined link length based on the road length; and
dividing the road into the plurality of road segments based on the predetermined road segment length and the adjusted road segment length.
9. The method of claim 1, wherein the plurality of segments in the road are determined using the steps of: dividing the road into the plurality of segments based on a driving direction of the road.
10. The method of claim 1, further comprising:
acquiring traffic rules and news associated with the road; and
updating the plurality of parking risks based on the traffic rules and news.
11. The method of claim 1, wherein updating the second parking risk comprises: updating the second parking risk in response to determining that the first parking risk is higher than the second parking risk.
12. The method of claim 1, further comprising:
in response to determining that the vehicle is to be parked within the second road segment, obtaining a target time associated with the parking action; and
determining a risk prediction for parking within the target road segment based on the target time and the second parking risk.
13. An apparatus for determining parking risk of a vehicle, comprising:
an acquisition module configured to acquire a plurality of historical tickets respectively associated with a plurality of road segments in a road, a historical ticket in the plurality of historical tickets being a ticket generated by a vehicle parking within a road segment in the plurality of road segments;
a determination module configured to determine, based on the plurality of historical tickets, a first parking risk for a first road segment and a second parking risk for a second road segment of the plurality of road segments, respectively, the first parking risk and the second parking risk representing parking risks for a vehicle parking within the first road segment and the second road segment, respectively; and
an updating module configured to update the second parking risk based on a position relationship between the first road segment and the second road segment and the first parking risk.
14. An electronic device, comprising:
a memory and a processor;
wherein the memory is to store one or more computer instructions, wherein the one or more computer instructions are to be executed by the processor to implement the method of any one of claims 1 to 12.
15. A computer readable storage medium having one or more computer instructions stored thereon, wherein the one or more computer instructions are executed by a processor to implement the method of any one of claims 1 to 12.
16. A computer program product comprising computer programs/instructions, characterized in that the computer programs/instructions, when executed by a processor, implement the steps of the method according to claim 1.
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| CN202011612563.8A CN112668892A (en) | 2020-12-30 | 2020-12-30 | Method, device, electronic equipment and storage medium for determining parking risk |
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| CN202011612563.8A CN112668892A (en) | 2020-12-30 | 2020-12-30 | Method, device, electronic equipment and storage medium for determining parking risk |
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140074402A1 (en) * | 2012-09-12 | 2014-03-13 | Lexisnexis Risk Solutions Fl Inc. | Systems and methods for determining risks associated with driving routes |
| CN105590461A (en) * | 2016-03-09 | 2016-05-18 | 余水平 | Method for prompting and guiding roadside parking by means of role-breaking big data |
| CN111179578A (en) * | 2018-11-09 | 2020-05-19 | 北京嘀嘀无限科技发展有限公司 | Method and system for determining parking place limitation |
| CN111739325A (en) * | 2020-06-15 | 2020-10-02 | 腾讯科技（深圳）有限公司 | Method, apparatus, device and medium for determining abnormal parking probability and navigation control |
| CN111814071A (en) * | 2019-12-25 | 2020-10-23 | 北京嘀嘀无限科技发展有限公司 | Boarding point recommendation method and device, storage medium and electronic equipment |
| CN111859173A (en) * | 2019-10-23 | 2020-10-30 | 北京嘀嘀无限科技发展有限公司 | Boarding point recommendation method and system |
-
2020
- 2020-12-30 CN CN202011612563.8A patent/CN112668892A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140074402A1 (en) * | 2012-09-12 | 2014-03-13 | Lexisnexis Risk Solutions Fl Inc. | Systems and methods for determining risks associated with driving routes |
| CN105590461A (en) * | 2016-03-09 | 2016-05-18 | 余水平 | Method for prompting and guiding roadside parking by means of role-breaking big data |
| CN111179578A (en) * | 2018-11-09 | 2020-05-19 | 北京嘀嘀无限科技发展有限公司 | Method and system for determining parking place limitation |
| CN111859173A (en) * | 2019-10-23 | 2020-10-30 | 北京嘀嘀无限科技发展有限公司 | Boarding point recommendation method and system |
| CN111814071A (en) * | 2019-12-25 | 2020-10-23 | 北京嘀嘀无限科技发展有限公司 | Boarding point recommendation method and device, storage medium and electronic equipment |
| CN111739325A (en) * | 2020-06-15 | 2020-10-02 | 腾讯科技（深圳）有限公司 | Method, apparatus, device and medium for determining abnormal parking probability and navigation control |
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