CN109300304B - Method and device for determining historical road conditions - Google Patents
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- CN109300304B CN109300304B CN201710607863.9A CN201710607863A CN109300304B CN 109300304 B CN109300304 B CN 109300304B CN 201710607863 A CN201710607863 A CN 201710607863A CN 109300304 B CN109300304 B CN 109300304B
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Abstract
Embodiments of the present disclosure relate to a method and apparatus for determining historical road conditions. The method comprises the following steps: obtaining a sequence of positions of a vehicle from a historical database, the sequence of positions including a plurality of positions and timestamps for the plurality of positions; determining a starting road corresponding to the position sequence based on the position sequence; determining road complexity of roads in the vicinity of the starting road; setting the length of a subsequence in the sequence of positions based on the road complexity; determining a sequence of links matching the subsequence based on the length of the starting link and the subsequence; and determining a speed of travel of the vehicle on the sequence of roads based on the time stamps. The embodiment of the disclosure can accurately and efficiently match the position sequence to the road sequence by determining the starting road of the vehicle and setting the length of the basic position sequence for matching the road according to the road complexity near the starting road, thereby determining the historical road condition of the road.
Description
Technical Field
Embodiments of the present disclosure relate generally to the field of information technology, and more particularly, to a method and apparatus for determining historical road conditions based on a historical sequence of locations.
Background
The road condition refers to the condition of the road surface on the road, which is mainly expressed as the driving speed on the road. Traditional speed measurement methods include radar speed measurement, laser speed measurement, inductive coil speed measurement, video speed measurement, and the like. The traditional speed measurement mode needs special instruments or equipment, so the cost is high. In addition, the traditional speed measurement has a narrow monitoring range, and cannot measure all or most roads in a city.
With the popularization of navigation software, a large amount of vehicle historical position data (e.g., Global Positioning System (GPS) data) can be obtained, which brings an advantage in measuring speed by vehicle travel position data. Compared with traditional modes such as laser or coil speed measurement, the method obtains road conditions through vehicle-mounted historical positions, and has the characteristics of low cost, easiness in expansion, wide monitoring range and the like. Therefore, it becomes possible to obtain the urban road condition from the vehicle historical position data.
However, the navigation software may obtain the vehicle position data in various sources, so that the quality of the position points is different, and the vehicle position data (e.g. GPS data from teaching vehicles in driving schools) may not reflect real road conditions, so that the measured road conditions cannot be guaranteed. In addition, the traditional speed calculation method based on the position data usually adopts single-point road matching, so that the error rate of the matching is high, and the actual historical road condition of the road cannot be truly reflected.
Disclosure of Invention
In view of this, embodiments of the present disclosure propose a method and apparatus for determining historical road conditions. The embodiment of the disclosure can accurately and efficiently match the position sequence to the road sequence by determining the starting road of the vehicle and setting the length of the basic position sequence for matching the road according to the road complexity near the starting road, thereby determining the historical road condition of the road.
According to one aspect of the present disclosure, a method for determining historical road conditions is provided. The method comprises the following steps: obtaining a sequence of positions of a vehicle from a historical database, the sequence of positions including a plurality of positions and timestamps for the plurality of positions; determining a starting road corresponding to the position sequence based on the position sequence; determining road complexity of roads in the vicinity of the starting road; setting the length of a subsequence in the sequence of positions based on the road complexity; determining a sequence of links matching the subsequence based on the length of the starting link and the subsequence; and determining a speed of travel of the vehicle on the sequence of roads based on the time stamps.
According to another aspect of the present disclosure, an apparatus for determining historical road conditions. The apparatus includes a processor and a memory coupled to the processor and storing instructions that, when executed by the processor, perform the acts of: obtaining a sequence of positions of a vehicle from a historical database, the sequence of positions including a plurality of positions and timestamps for the plurality of positions; determining a starting road corresponding to the position sequence based on the position sequence; determining road complexity of roads in the vicinity of the starting road; setting the length of a subsequence in the sequence of positions based on the road complexity; determining a sequence of links matching the subsequence based on the length of the starting link and the subsequence; and determining a speed of travel of the vehicle on the sequence of roads based on the time stamps.
According to yet another aspect of the present disclosure, a computer-readable storage medium is provided. The computer readable storage medium has computer readable program instructions stored thereon. These computer-readable program instructions may be used to perform methods described in accordance with various embodiments of the present disclosure.
Drawings
The features, advantages and other aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description in conjunction with the accompanying drawings, in which several embodiments of the present disclosure are shown by way of illustration and not limitation, wherein:
FIG. 1 illustrates an architecture diagram of a computing system for determining historical road conditions in accordance with an embodiment of the present disclosure;
FIG. 2 illustrates a schematic diagram of road network information and location sequences in accordance with an embodiment of the present disclosure;
FIG. 3 illustrates a flow chart of a method for determining historical road conditions in accordance with an embodiment of the present disclosure;
FIG. 4 illustrates a flow diagram of another method for determining historical road conditions in accordance with an embodiment of the present disclosure;
FIG. 5 illustrates a flow chart of a method for determining a starting road to which a sequence of locations corresponds in accordance with an embodiment of the present disclosure;
FIG. 6 illustrates a flow diagram of a method for selecting a best matching road sequence from a set of candidate road sequences in accordance with an embodiment of the present disclosure;
FIG. 7A illustrates a frequency profile of the distance of a location point from a road;
FIG. 7B illustrates a probability distribution plot of the distance of a location point from a road;
8A-8B illustrate diagrams of a visualization presentation according to an embodiment of the present disclosure; and
FIG. 9 illustrates a schematic block diagram of a device that may be used to implement embodiments of the present disclosure.
Detailed Description
Various exemplary embodiments of the present disclosure are described in detail below with reference to the accompanying drawings. The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods and systems according to various embodiments of the present disclosure. It should be noted that each block in the flowchart or block diagrams may represent a module, a segment, or a portion of code, which may comprise one or more executable instructions for implementing the logical function specified in the respective embodiment. It should also be noted that, 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 flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, 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.
As used herein, the terms "include," "include," and similar terms are to be construed as open-ended terms, i.e., "including/including but not limited to," meaning that additional content can be included as well. In the present disclosure, the term "based on" is "based at least in part on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment".
It should be understood that these exemplary embodiments are given solely for the purpose of enabling those skilled in the art to better understand and thereby implement the embodiments of the present disclosure, and are not intended to limit the scope of the invention in any way.
Fig. 1 illustrates an architecture diagram of a computing system 100 for determining historical road conditions in accordance with an embodiment of the present disclosure. As shown in fig. 1, the system 100 includes a plurality of user devices 110, 112, 114, 116, a server 120, an electronic device 130, and a visual display unit 140. These devices may be interconnected by a network, which may optionally include, but is not limited to, the internet, a wide area network, a metropolitan area network, a local area network, a Virtual Private Network (VPN), a wireless communication network, and the like.
In some embodiments, the user device 110 and 116 may be a mobile device, wherein the mobile device refers to various terminal devices having internet access capability, having a satellite positioning system (such as GPS, "beidou" satellite navigation system), carrying various operating systems, and being capable of customizing various functions according to user requirements, including but not limited to smart phones, tablet computers, portable computers, and the like. In other embodiments, the user device 110 and 116 may also be a network-enabled in-vehicle navigation device.
In some embodiments, server 120 may be a server capable of receiving and storing location data from user devices. As shown in FIG. 1, a plurality of databases, such as a historical location database 122 and a historical speed database 124, may be included in the server 120. The historical location database 122 is used to record historical location data collected from user devices and may include a location field, a timestamp field for a location, a user device ID field, a user ID field, and the like. The historical speed database 124 is used to record the historical speed of the road, and may include a road segment ID field, a road speed field, a timestamp field, a user ID field, and an order ID field, among others. In some embodiments, the electronic device 130 may be any device capable of data processing, including a desktop computer, a laptop computer, a server, and so forth. In some embodiments, the visual display unit 140 may be any display unit, such as a display, a large matrix display screen, or the like.
In some embodiments, in the system 100, the server 120 receives location data from a plurality of user devices and then stores the received location data in the historical location database 122. Next, the electronic device 130 may retrieve and process historical location data from the historical location database 122 in the server 120 to generate a historical speed for the associated road segment, and then send the historical speed to the server 120 for storage in the historical speed database 124. In some embodiments, the server 120 may send the historical speed database 124 to the visual display unit 140 for visual presentation.
It should be understood that although only four user devices are shown in fig. 1, there may be more user devices; although only one server 120 is shown, system 100 may include multiple servers in a distributed arrangement, and the scope of embodiments of the present disclosure is not limited in this respect. Further, while the electronic device 130 and the visual display unit 140 are shown as separate from the server 120, it should be understood that the electronic device 130 and the visual display unit 140 may also be integrated in the server 120.
Fig. 2 illustrates a schematic diagram 200 of road network information and location sequences in accordance with an embodiment of the present disclosure. As shown in fig. 2, line segments 201, 202, 203, 204, 205, 206, 207, 208, and 209 represent roads, and the directions of the line segments shown in the drawing represent directions in the roads in which the vehicle is allowed to travel. For example, roads 207 with unidirectional arrows indicate unidirectional lanes (only driving in the direction of the arrows is allowed), while other roads with bidirectional arrows, such as road 202, indicate that these roads have bidirectional lanes. The position points 251, 252, 253, 254, 255, 256, 257, 258, and 259 shown in fig. 2 represent a sequence of positions of the vehicle. As shown in fig. 2, the position sequence of the vehicle deviates from the actual road due to the error.
If the conventional relationship between the position points in the position sequence and the road is used to determine the current road on which the device is located, an error may occur. For example, as shown in fig. 2, the position point 251 is closest to the road 207 based on the position relationship by the conventional method. However, this road 207 is a one-way lane, and if it is determined that the position point 251 is located on the road 207 based on only such a positional relationship, an error will occur in that the vehicle carrying the apparatus cannot travel from the one-way lane 207 to the road 202 matching the subsequent position points 252, 253.
Therefore, the traditional method cannot ensure the accuracy of matching the position point with the road. The embodiment of the disclosure can accurately and efficiently match the position sequence to the road sequence by determining the starting road of the vehicle and setting the length of the basic position sequence for matching the road according to the road complexity near the starting road, thereby determining the historical road condition of the road.
In addition, the embodiment of the present disclosure adopts the local road network complexity as the basis for the tracking length of the variable length position points. For example, the topology of the road network near the intersection is relatively complex, thus lengthening the length of the tracking subsequence; the topological structure of the road network on the straight road is relatively simple, so that the matching efficiency is ensured, and the matching accuracy at the road network intersection and under the condition of a complex road network is improved by adopting a shorter tracking subsequence length.
Fig. 3 illustrates a flowchart 300 of a method for determining historical road conditions in accordance with an embodiment of the present disclosure. For example, the method 300 described in fig. 3 may be performed by the electronic device 130 described above with reference to fig. 1.
At 302, a sequence of locations of a vehicle is obtained from a historical database, where the sequence of locations includes a plurality of locations (e.g., geographic coordinates of the locations) and a timestamp of the plurality of locations (acquisition time of the locations). For example, the historical location database 122 in fig. 1 includes location data from a plurality of user devices, each of which may record location data for a vehicle. In some embodiments, the location data in the historical location database 122 may be from an internet taxi service company, which typically operates a large number of vehicles whose GPS tracks are subject to a standardized specification, particularly when the vehicles are in an order status, where the speed of the vehicles is affected almost primarily by road conditions. Therefore, the historical position data obtained by the internet taxi-taking company has higher credibility and accuracy.
At 304, based on the sequence of locations, a starting link to which the sequence of locations corresponds is determined. For the matching method based on the position sequence, the matching accuracy of the starting road is very high, because if the starting road is matched incorrectly, the subsequent position points will seek to the wrong direction. As shown in fig. 2, if the vertical distance is simply used as the criterion, the start position of the position sequence will be matched to the road 207, which deviates from the real driving direction. Determining a starting road to which the position sequence corresponds based on the position sequence is described in detail below with reference to fig. 5.
At 306, the road complexity of the nearby roads of the starting road is determined. For example, in urban road networks, the complexity of roads varies. In some embodiments, road complexity may be quantified based on some predetermined criteria. In this way, the complexity can be divided into various types, for example into three levels of complexity, medium and non-complexity. At 308, the length of the sub-sequence in the sequence of positions is set based on the road complexity. For less complex areas (such as on a highway), fewer sequences of positions are typically used to calculate travel speed in order to improve computational efficiency. For areas with higher complexity (such as crossroads and turning points of parallel roads), more position sequences can be used to calculate the driving speed in order to ensure the accuracy of matching. For example, in the example of FIG. 2, after determining the start link 202, the length of the sub-sequence may be set to 7, i.e., the position points 251-.
At 310, a sequence of links matching the subsequence is determined based on the length of the starting link and the subsequence. For example, based on sub-sequence 251-257, the road sequence 202, 203, 204 matching the sub-sequence may be determined. At 312, based on the time stamps, a travel speed of the vehicle on the sequence of roads is determined. For example, from the distances and time differences between the position points in the sub-sequence, the travel speed of the vehicle in the sub-sequence can be calculated as the travel speed on the matching road sequence. Optionally, at 314, historical road conditions for the sequence of roads may be visually presented on the map.
Fig. 4 illustrates a flow chart of another method 400 for determining historical road conditions in accordance with an embodiment of the present disclosure. It should be understood that the method 400 may be performed by the electronic device 130 described above with reference to fig. 1. As shown in fig. 4, at 402, a start position of a sub-sequence in a sequence of positions is determined, such as may be obtained by step 304 described with reference to fig. 3. At 404, the length of the sub-sequence in the sequence of positions is determined, which may be obtained, for example, by step 308 described with reference to fig. 3.
Generally, the larger the length value is, the more abundant the shape information contained in the position sequence is, and the more a road sequence which can be accurately matched with the position sequence is found. And if the length value is smaller, the shape information contained in the position sequence is less, and in a straight line section, because the topological structure of the road is simple, the position sequence can be accurately matched without being too long, but in a complicated road section, the position point sequence which is too short cannot sufficiently contain the shape and direction information of the position point, and therefore misjudgment is easy to occur.
In some embodiments, the base subsequence length k may be obtained by the following formula (2):
k is L × 2/v (p + q) (formula 1) where L denotes a length of a start link, v denotes a historical speed on the start link, p denotes the number of links following the start link, and q denotes the number of links parallel to the start link within a predetermined range (e.g., 20 meters).
At 406, it is determined whether the length of the subsequence is equal to or greater than the remaining number of positions. For example, in the position sequence 251-259 described in fig. 2, if the initial position after several updates is set as the position 258 and the length of the sub-sequence is set to 3, at least 3 position points are required to determine the traveling speed of the vehicle. Since the number of positions remaining in the sequence of positions 251-. If the length of the sub-sequence is greater than or equal to the remaining number of positions, this means that a plurality of positions can be selected successively to determine the travel speed of the matching road.
At 408, a plurality of candidate road sequences is determined. For example, the set of all possible road sequences is determined according to some predetermined algorithm. At 410, a best matching sequence of roads is selected from the plurality of candidate roads according to some predetermined criteria (described in detail below with reference to FIG. 6). At 412, historical travel speeds on the road sequence are determined. For example, a travel speed between a start position in the subsequence and an intermediate position in the subsequence may be determined.
Next, at 414, the starting position is updated to determine the next sequence in the sequence of positions. For example, a middle position or a position preceding the middle position in the sub-sequence may be set as the start position of the next sub-sequence in the position sequence. For example, the first subsequence 251-. Then, the intermediate position 254 or a position 253 preceding the intermediate position 254 may be taken as a starting position of the second subsequence. Thus, by the method 400 of the present disclosure, historical travel speeds on a road may be determined in segments.
For example, in some embodiments, the travel speed of the road may be calculated by the following equation (2)
V(1-floor(k/2))＝L(1-ceil(k/2))/T(1-ceil(k/2))(formula 2)
Wherein V(1-floor(k/2))Indicating the speed, L, of the road matching the 1 st position to the road preceding the road matching the first floor (k/2) position(1-ceil(k/2))Represents the distance, T, traveled from the 1 st position to the ceil (k/2) position(1-ceil(k/2))Represents the time from the 1 st position to the ceil (k/2) position, wherein floor (k/2) represents rounding down on k/2 and ceil (k/2) represents rounding up on k/2.
Fig. 5 illustrates a flow chart of a method for determining a starting road to which a sequence of positions corresponds in accordance with an embodiment of the present disclosure. It should be understood that method 500 may be performed by electronic device 130 described above with reference to fig. 1 and is a sub-step of step 304 described with reference to fig. 3 or step 402 described with reference to fig. 4. At 502, map data is encoded. At 504, the ith position in the sequence of positions is obtained, and the initial value of i may be set to 1. For example, the first location 251 in the sequence of locations 251-259 described in fig. 2 is obtained. At 506, the ith road near the ith location and the (i + 1) th road near the (i + 1) th location are determined. For example, a first road near the first location 251 and a second road near the second location 252 may be determined.
At 508, it is determined whether the ith road and the (i + 1) th road are continuous, e.g., whether the first road and the second road are connected and the direction of the first road and the direction of the second road are continuous. If the ith road is contiguous with the (i + 1) th road, e.g., both the first and second roads are connected and the directions are contiguous, then at 510, the starting road of the sequence of locations may be determined, e.g., the device may be determined to be moving along the first and second roads, at which time the first road may be taken as the starting road.
If the ith link is not consecutive with the (i + 1) th link, then the value of i may be incremented by 1 at 512. For example, in response to a first road not being connected to a second road or a direction of the first road not being continuous with a direction of the second road, a second location is taken as a first location and a next location after the second location in the sequence of locations is taken as the second location.
For example, in the example depicted in fig. 2, where the first location 251 is matched to the road 207 and the second location 252 is matched to the road 202, the direction of the road 207 is not continuous with the direction of the road 202, and the road 207 cannot be the starting road. Next, the second location 252 is matched to the road 202, and the third location 253 is also matched to the road 202, and since both are matched to the road 202, the start road cannot be determined. Next, the third position 253 is matched to the road 202, the fourth position 254 is matched to the section 203, and since the road 202 and the road 203 are connected and the direction is the same, the road 202 is finally determined as the starting road of the position sequence 251-.
FIG. 6 illustrates a flow diagram of a method for selecting a best matching road sequence from a set of candidate road sequences in accordance with an embodiment of the present disclosure. It should be understood that method 600 may be performed by electronic device 130 described above with reference to fig. 1 and may be a sub-step of step 410 described with reference to fig. 4.
At 602, one or more (e.g., N, where N ≧ 1) candidate road sequences are determined. In some embodiments, a direction of travel of the vehicle may be determined, and one or more candidate road sequences may be determined based on the starting road, the direction of travel, and the length. For example, based on the starting position, the driving direction and the length of the sub-sequence, all possible candidate road sets corresponding to the sub-sequence may be determined, as in the example depicted in fig. 2, the candidate road set corresponding to the sub-sequence 251 and 257 may be the road sequence (202, 203, 204) or the road sequence (202, 203, 206).
At 604, a jth candidate road sequence of the N candidate road sequences is obtained, and then at 606, the shortest vertical distance of each position in the sub-sequence to all roads in the jth candidate road sequence is determined. In some embodiments, the vertical distance may be solved by, for example, the Helen's formula, however, it should be understood that any other vertical distance solving method may be applied to embodiments of the present disclosure.
At 608, based on the shortest vertical distance, a weighted probability for the shortest vertical distance is determined. For example, the probability distributions of different shortest vertical distances may be counted from the historical positioning data, and then the probability values of the weighted candidates may be determined. In some embodiments, the weighted probability for the shortest vertical distance is set to a zero value if the shortest vertical distance is greater than the threshold distance. At 610, the sum of all locations in the sub-sequence and the weighted probabilities of the jth candidate link sequence is determined. For example, in the example of FIG. 2, the sum of the weighted probabilities of each position in the subsequence 251 and 257 and the shortest vertical distance of the links in the sequence of links (202, 203, 204) is calculated.
At 612, it is determined whether j is less than N, and if j is less than N, indicating that there is still a set of candidate roads to be calculated, for example, step 604 can be repeated along with step 610 to calculate the sum of weighted probabilities of the shortest vertical distances of each position in the sub-sequence 251 along with 257 and the roads in the road sequence (202, 203, 206). If j is greater than or equal to N, indicating that all of the candidate road sets have been computed, the method 600 proceeds to 616, where the candidate road sequence with the greatest sum of weighted probabilities is selected as the best matching road sequence.
Fig. 7A illustrates a frequency profile 700 of the distance of a location point from a road. According to the vertical distance distribution of the position and the road, the GPS positioning error distance of the current system can be determined. As shown in fig. 7A, the vertical distance between the location point and the road with the confidence of 95% is 0.01 kilometer (km), that is, the distance between the location point and the road with the confidence of less than 0.01km is 95% of the total distance, so that 0.01km can be used as the variance of the prior probability function of the positioning error.
Fig. 7B illustrates a probability distribution graph 750 of the distance of a location point from a road. Because the GPS has positioning errors, the GPS can be considered as credible within a certain distance range, and if the judgment is carried out through a simple vertical distance, the matching probability is linearly reduced according to the distance. The ideal prior probability function should be within a certain distance range, the matching probability of the ideal prior probability function does not change too much, and the matching probability after a certain distance is exceeded changes greatly with the distance. Embodiments of the present disclosure approximate the prior probability model using a probability function like a normal function whose variance is the magnitude of the GPS positioning error of the system, i.e., 0.01 km. That is, at a vertical distance within 0.01km, a probability value for the vertical distance may be calculated, e.g., the greater the distance, the greater the probability value may be; over a vertical distance of 0.01km, the vertical distance may be ignored, e.g. its probability value may be set to zero.
Fig. 8A illustrates a diagram 800 of a visualization presentation in accordance with an embodiment of the present disclosure. As shown in fig. 8A, the historical road conditions of each road sequence can be visually displayed on a map, wherein different driving speed intervals can correspond to different colors or lines. In some embodiments, the speed of a road segment may be calculated for a particular time period, or the speed of a road segment may be calculated for the same time period of each day. Fig. 8A illustrates a diagram 850 of a visualization presentation, in accordance with an embodiment of the present disclosure. As shown in fig. 8B, the number of vehicles in each speed section can be further shown to restore the overall speed of the city, so that the user can visually obtain the historical road conditions of the road.
In addition, the device for determining the historical road condition is also provided. The apparatus includes a processor and a memory coupled to the processor and storing instructions that, when executed by the processor, perform the acts of: obtaining a sequence of positions of a vehicle from a historical database, the sequence of positions including a plurality of positions and timestamps for the plurality of positions; determining a starting road corresponding to the position sequence based on the position sequence; determining road complexity of roads in the vicinity of the starting road; setting the length of a subsequence in the sequence of positions based on the road complexity; determining a sequence of links matching the subsequence based on the length of the starting link and the subsequence; and determining a speed of travel of the vehicle on the sequence of roads based on the time stamps.
It should be understood that the apparatus may be implemented in a variety of ways. For example, in certain embodiments, the device may be implemented in hardware, software, or a combination of software and hardware. Wherein the hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory for execution by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the methods and systems described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided, for example, on a magnetic disk, an optical disk carrier medium, a programmable memory such as a read-only memory, or a data carrier such as an optical or electronic signal carrier. The devices and apparatuses of the embodiments of the present disclosure may be implemented not only by hardware circuits such as a very large scale integrated circuit or a gate array, a semiconductor such as a logic chip, a transistor, or the like, or a programmable hardware device such as a field programmable gate array, a programmable logic device, or the like, but also by software executed by various types of processors, for example, and by a combination of the above hardware circuits and software.
FIG. 9 illustrates a schematic block diagram of an electronic device 900 that may be used to implement embodiments of the present disclosure. It should be understood that electronic device 900 may be implemented as electronic device 130 described in fig. 1. As shown in fig. 9, device 900 includes a Central Processing Unit (CPU)901 that can perform various appropriate actions and processes in accordance with computer program instructions stored in a Read Only Memory (ROM)902 or loaded from a storage unit 908 into a Random Access Memory (RAM) 903. In the RAM 903, various programs and data required for the operation of the device 900 can also be stored. The CPU 901, ROM 902, and RAM 903 are connected to each other via a bus 904. An input/output (I/O) interface 905 is also connected to bus 904.
A number of components in the device 900 are connected to the I/O interface 905, including: an input unit 906 such as a keyboard, a mouse, and the like; an output unit 907 such as various types of displays, speakers, and the like; a storage unit 908 such as a magnetic disk, optical disk, or the like; and a communication unit 909 such as a network card, a modem, a wireless communication transceiver, and the like. The communication unit 909 allows the device 900 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.
The various processes and processes described above, such as methods 300, 400, 500, and 600, may be performed by processing unit 901. For example, in some embodiments, methods 300, 400, 500, and 600 may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 908. In some embodiments, part or all of the computer program may be loaded and/or installed onto device 900 via ROM 902 and/or communications unit 909. When the computer program is loaded into RAM 903 and executed by CPU 901, one or more of the steps of methods 300, 400, 500, and 600 described above may be performed.
The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for carrying out various aspects of the present disclosure. The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device over 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, fiber optic 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 a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present disclosure may be assembly instructions, Instruction Set Architecture (ISA) instructions, machine related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry can execute computer-readable program instructions to implement aspects of the present disclosure by utilizing state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
It should be noted that although in the above detailed description several means or sub-means of the device are mentioned, this division is only exemplary and not mandatory. Indeed, the features and functions of two or more of the devices described above may be embodied in one device in accordance with embodiments of the present disclosure. Conversely, the features and functions of one apparatus described above may be further divided into embodiments by a plurality of apparatuses.
The above description is only an alternative embodiment of the present disclosure and is not intended to limit the embodiments of the present disclosure, and various modifications and changes may be made to the embodiments of the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiments of the present disclosure should be included in the scope of protection of the embodiments of the present disclosure.
While embodiments of the present disclosure have been described with reference to several particular embodiments, it should be understood that embodiments of the present disclosure are not limited to the particular embodiments disclosed. The embodiments of the disclosure are intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims. The scope of the following claims is to be accorded the broadest interpretation so as to encompass all such modifications and equivalent structures and functions.
Claims (14)
1. A method for determining historical road conditions, comprising:
obtaining a sequence of positions of a vehicle from a historical database, the sequence of positions including a plurality of positions and timestamps for the plurality of positions;
determining a starting road corresponding to the position sequence based on the position sequence;
determining road complexity of roads in the vicinity of the starting road;
setting the length of a subsequence in the sequence of positions based on the road complexity;
determining a sequence of roads matching the subsequence based on the starting road and the length of the subsequence; and
determining a speed of travel of the vehicle on the sequence of roads based on the time stamps,
wherein determining the starting road corresponding to the position sequence comprises:
obtaining a first position and a second position in the position sequence, wherein the second position is the position of the first position at the next moment;
determining a first road near the first location and a second road near the second location;
identifying the first road as a starting road for the vehicle in response to the first road being connected with the second road and the direction of the first road being continuous with the direction of the second road.
2. The method of claim 1, wherein determining a sequence of links that matches the subsequence comprises:
determining a direction of travel of the vehicle;
determining one or more candidate road sequences based on the starting road, the direction of travel, and the length; and
selecting the sequence of roads from the one or more candidate sequences of roads.
3. The method of claim 2, wherein selecting the sequence of roads from the one or more candidate sequences of roads comprises:
for each of the one or more candidate sequences of roads:
determining the shortest vertical distance between each position in the subsequence and all roads in each candidate road sequence;
determining the sum of the shortest vertical distances of all positions in the sub-sequence and each candidate road sequence; and
selecting, as the road sequence, a candidate road sequence with a smallest sum of the shortest vertical distances from the one or more candidate road sequences.
4. The method of claim 2, wherein selecting the sequence of roads from the one or more candidate sequences of roads comprises:
for each of the one or more candidate sequences of roads:
determining the shortest vertical distance between each position in the subsequence and all roads in each candidate road sequence;
determining a weighted probability for the shortest vertical distance based on the shortest vertical distance;
determining a sum of all locations in the subsequence and the weighted probability of each candidate road sequence; and
selecting the candidate road sequence with the largest sum of the weighted probabilities as the road sequence from the one or more candidate road sequences.
5. The method of claim 4, wherein determining a weighted probability for the shortest vertical distance comprises:
in response to the shortest vertical distance being greater than a threshold distance, setting the weighted probability for the shortest vertical distance to a zero value.
6. The method of claim 1, wherein determining a travel speed of the vehicle on the sequence of roads comprises:
determining a travel speed between a starting position in the subsequence and an intermediate position in the subsequence; and
setting the intermediate position or a position preceding the intermediate position as a starting position of a next subsequence in the sequence of positions.
7. The method of any of claims 1-6, further comprising:
and visually displaying the historical road conditions of the road sequence on a map, wherein different driving speed intervals correspond to different colors or lines.
8. An apparatus for determining historical road conditions, comprising:
a processor;
a memory coupled to the processor and storing instructions that, when executed by the processor, perform the following:
obtaining a sequence of positions of a vehicle from a historical database, the sequence of positions including a plurality of positions and timestamps for the plurality of positions;
determining a starting road corresponding to the position sequence based on the position sequence;
determining road complexity of roads in the vicinity of the starting road;
setting the length of a subsequence in the sequence of positions based on the road complexity;
determining a sequence of roads matching the subsequence based on the starting road and the length of the subsequence; and
determining a speed of travel of the vehicle on the sequence of roads based on the time stamps,
wherein determining the starting road corresponding to the position sequence comprises:
obtaining a first position and a second position in the position sequence, wherein the second position is the position of the first position at the next moment;
determining a first road near the first location and a second road near the second location;
identifying the first road as a starting road for the vehicle in response to the first road being connected with the second road and the direction of the first road being continuous with the direction of the second road.
9. The apparatus of claim 8, wherein determining a sequence of roads that matches the subsequence comprises:
determining a direction of travel of the vehicle;
determining one or more candidate road sequences based on the starting road, the direction of travel, and the length; and
selecting the sequence of roads from the one or more candidate sequences of roads.
10. The apparatus of claim 9, wherein selecting the sequence of roads from the one or more candidate sequences of roads comprises:
for each of the one or more candidate sequences of roads:
determining the shortest vertical distance between each position in the subsequence and all roads in each candidate road sequence;
determining the sum of the shortest vertical distances of all positions in the sub-sequence and each candidate road sequence; and
selecting, as the road sequence, a candidate road sequence with a smallest sum of the shortest vertical distances from the one or more candidate road sequences.
11. The apparatus of claim 9, wherein selecting the sequence of roads from the one or more candidate sequences of roads comprises:
for each of the one or more candidate sequences of roads:
determining the shortest vertical distance between each position in the subsequence and all roads in each candidate road sequence;
determining a weighted probability for the shortest vertical distance based on the shortest vertical distance;
determining a sum of all locations in the subsequence and the weighted probability of each candidate road sequence; and
selecting the candidate road sequence with the largest sum of the weighted probabilities as the road sequence from the one or more candidate road sequences.
12. The apparatus of claim 11, wherein determining a weighted probability for the shortest vertical distance comprises:
in response to the shortest vertical distance being greater than a threshold distance, setting the weighted probability for the shortest vertical distance to a zero value.
13. The apparatus of claim 8, wherein determining a travel speed of the vehicle on the sequence of roads comprises:
determining a travel speed between a starting position in the subsequence and an intermediate position in the subsequence; and
setting the intermediate position or a position preceding the intermediate position as a starting position of a next subsequence in the sequence of positions.
14. The apparatus of any of claims 8-13, the acts further comprising:
and visually displaying the historical road conditions of the road sequence on a map, wherein different driving speed intervals correspond to different colors or lines.
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| JP2007178126A (en) * | 2005-12-26 | 2007-07-12 | Aisin Aw Co Ltd | Travel link specification system |
| CN101424535B (en) * | 2007-10-30 | 2012-05-30 | 武汉大学 | Vehicle positioning method and device |
| JP2010060489A (en) * | 2008-09-05 | 2010-03-18 | Seiko Epson Corp | Satellite orbital modeling propriety determination method, long term prediction orbital data supply method, and satellite orbital modeling propriety determination device |
| CN103680138A (en) * | 2012-09-25 | 2014-03-26 | 西安思丹德信息技术有限公司 | A method for monitoring vehicle road conditions |
| CN103177585B (en) * | 2013-02-27 | 2015-07-08 | 上海美慧软件有限公司 | Road turning average travel speed calculating method based on floating car data |
| CN104034337B (en) * | 2014-05-20 | 2017-01-18 | 清华大学深圳研究生院 | Map matching method and device for geographic position point of floating vehicle |
| CN105225510B (en) * | 2014-06-27 | 2018-04-17 | 国际商业机器公司 | Method and system for the road network for verifying map |
| CN104200042B (en) * | 2014-09-17 | 2017-09-01 | 王伟 | A kind of location data trace tracking method |
| CN104504898A (en) * | 2014-12-11 | 2015-04-08 | 王东宇 | Online map matching method based on floating car data on tunnel road section |
| CN104573116B (en) * | 2015-02-05 | 2017-11-03 | 哈尔滨工业大学 | The traffic abnormity recognition methods excavated based on GPS data from taxi |
| CN106156531B (en) * | 2016-08-04 | 2020-09-01 | 复旦大学 | Travel time estimation method based on low-sampling historical track |
| CN106840175B (en) * | 2016-12-06 | 2020-01-10 | 北京中交兴路信息科技有限公司 | Method and device for matching vehicle running track with road network |
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