Bus route mining method and device

Abstract

The invention provides a bus route mining method and device, and relates to the technical field of artificial intelligence, in particular to the technical field of intelligent traffic and deep learning. A specific embodiment of the method comprises the steps of matching a user track of a user in a bus running period with a bus route to obtain a matched track point, an unmatched track point and a matched bus route; determining a maximum matching track segment based on the matching track points and the mismatching track points; performing space-time matching on the maximum matching track segment and the real-time bus track of the matched bus route to obtain a space-time matching degree; determining a confidence coefficient based on the real-time bus track with the highest space-time matching degree; and determining the bus route corresponding to the real-time bus track with the highest time-space matching degree when the user takes the bus in response to the condition that the confidence meets a preset condition.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, and in particular to the field of smart transportation and deep learning technology. Background Art

[0002] Smart transportation integrates advanced internet technologies such as the Internet of Things, cloud computing, big data, and mobile internet, building upon intelligent transportation. This technology aggregates traffic information and provides real-time traffic data-based services. Extensive use of data processing technologies such as data modeling and data mining ensures the systematic, real-time nature of smart transportation, interactive information exchange, and widespread service provision. For public transportation users, smart transportation significantly optimizes travel routes and facilitates efficient travel planning.

[0003] Currently, bus routes for public transportation users are usually mined based on their click-through behavior on bus plans. Some users search for bus plans through map apps before traveling, and for the bus plans they are interested in, they may click to view the details. Summary of the Invention

[0004] The embodiments of the present disclosure provide a bus route mining method, apparatus, device, storage medium, and program product.

[0005] In a first aspect, an embodiment of the present disclosure proposes a bus route mining method, comprising: matching a user trajectory with a bus route to obtain matching trajectory points, unmatched trajectory points, and a matching bus route; determining a maximum matching trajectory segment based on the matching trajectory points and the unmatched trajectory points; performing spatiotemporal matching between the maximum matching trajectory segment and a real-time bus trajectory of the matching bus route to obtain a spatiotemporal matching degree; determining a confidence level based on the real-time bus trajectory with the highest spatiotemporal matching degree; and, in response to the confidence level satisfying a preset condition, determining a bus route corresponding to the real-time bus trajectory with the highest spatiotemporal matching degree that the user took.

[0006] In a second aspect, an embodiment of the present disclosure proposes a bus route mining device, comprising: a first matching module, configured to match a user trajectory with a bus route to obtain matching trajectory points, unmatched trajectory points, and a matching bus route; a first determination module, configured to determine a maximum matching trajectory segment based on the matching trajectory points and the unmatched trajectory points; a second matching module, configured to perform spatiotemporal matching between the maximum matching trajectory segment and a real-time bus trajectory of the matching bus route to obtain a spatiotemporal matching degree; a second determination module, configured to determine a confidence level based on the real-time bus trajectory with the highest spatiotemporal matching degree; and a third determination module, configured to determine, in response to the confidence level satisfying a preset condition, the bus route corresponding to the real-time bus trajectory with the highest spatiotemporal matching degree that the user took.

[0007] In a third aspect, an embodiment of the present disclosure proposes an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method described in the first aspect.

[0008] In a fourth aspect, an embodiment of the present disclosure proposes a non-transitory computer-readable storage medium storing computer instructions, where the computer instructions are used to enable a computer to execute the method described in the first aspect.

[0009] In a fifth aspect, an embodiment of the present disclosure proposes a computer program product, including a computer program, which implements the method described in the first aspect when executed by a processor.

[0010] The key or important features of the embodiments of the present disclosure are not intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Other features, objects, and advantages of the present disclosure will become more apparent upon reading the detailed description of the non-limiting embodiments made with reference to the following drawings. The drawings are provided for a better understanding of the present disclosure and do not constitute a limitation of the present disclosure. Among them: Figure 1 is a flow chart of an embodiment of a bus route mining method according to the present disclosure; Figure 2 is a flowchart of another embodiment of the bus route mining method according to the present disclosure; Figure 3 This is a diagram of preferred routes to work; Figure 4 This is a diagram of the preferred route for leaving get off work; Figure 5 It is a schematic diagram for searching bus routes; Figure 6 It is a real-time bus route diagram; Figure 7 is a structural schematic diagram of an embodiment of a bus route excavation device according to the present disclosure; Figure 8 It is a block diagram of an electronic device used to implement the bus route mining method according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0012] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0013] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in the present disclosure may be combined with each other. The present disclosure will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0014] Figure 1 A process 100 of an embodiment of a bus route mining method according to the present disclosure is shown. The bus route mining method includes the following steps: Step 101: Match the user trajectory with the bus route to obtain matching trajectory points, unmatched trajectory points, and matching bus routes.

[0015] In this embodiment, the execution subject of the bus route mining method may match the user trajectory with the bus route to obtain matching trajectory points, unmatched trajectory points, and matching bus routes.

[0016] The bus route mining method is typically executed by a server. The server can be either hardware or software. If the server is hardware, it can be implemented as a distributed server cluster consisting of multiple servers or as a single server. If the server is software, it can be implemented as multiple software programs or software modules (for example, to provide distributed services) or as a single software program or software module. This is not specifically limited here.

[0017] Among them, user trajectories are usually trajectories generated by users during the bus operation period. Obtaining the user trajectories of users during the bus operation period for matching can filter out trajectories generated by users who do not take bus routes, thereby reducing the matching workload. These user trajectories are matched with all bus routes. If the distance between a trajectory point of the user trajectory and at least one bus route is less than a preset distance threshold (such as 5 meters), then the trajectory point is a matching trajectory point, which can be marked as 1 and the point sequence is recorded; if the distance between a trajectory point of the user trajectory and all bus routes is greater than or equal to the preset distance threshold, then the trajectory point is an unmatched trajectory point and can be marked as -1. Based on the matching trajectory points, the matching bus route can be determined. For example, if the number and proportion of matching trajectory points are both greater than the preset threshold, and the matching trajectory points are in order, then it can be determined that the bus route is a matching bus route.

[0018] It should be noted that in order to reuse stations, the bus group has a very high degree of local overlap between different bus routes. Therefore, a user trajectory can usually be matched to multiple (e.g., 8-9) bus routes.

[0019] Step 102: Determine the maximum matching trajectory segment based on the matching trajectory points and the non-matching trajectory points.

[0020] In this embodiment, the execution entity may determine the maximum matching trajectory segment based on the matching trajectory points and the non-matching trajectory points.

[0021] In the real world, user trajectories typically only match bus routes for a limited period of time, and trajectory positioning is prone to drift. Therefore, it is necessary to mine the maximum segment of a user's trajectory that matches a bus route, while also considering a certain degree of error tolerance. Based on the matching and non-matching points in a user's trajectory, the matching rate can be calculated, and the maximum matching segment can be mined.

[0022] In some embodiments, for each matching trajectory point, the subsequent trajectory points of the matching trajectory point can be traversed backwards to perform a trajectory point matching step: the number of matching points and the number of non-matching points of the subsequent trajectory points are counted, and the matching rate is calculated based on the matching and non-matching numbers of the subsequent trajectory points. The traversal is stopped until the matching rate falls below a preset matching rate threshold, and a sub-maximum matching trajectory segment is obtained. The subsequent trajectory points of the sub-maximum matching trajectory segment are then traversed, and the above trajectory point matching steps are repeated to obtain additional sub-maximum matching trajectory segments. Finally, based on all sub-maximum matching trajectory segments, the maximum matching trajectory segment is determined. The matching rate is equal to the quotient of the number of matching points of the subsequent trajectory point and the total number of points of the subsequent trajectory point.

[0023] The preset match rate threshold can be determined based on the road scenario, and different road scenarios may correspond to different preset match rate thresholds. Generally, road scenarios with complex traffic conditions will cause positioning drift, while road scenarios with simple traffic conditions will enable accurate positioning. Therefore, the preset match rate threshold corresponding to a road scenario with complex traffic conditions will be higher than the preset match rate threshold corresponding to a road scenario with simple traffic conditions.

[0024] For example, in common highway scenarios, the road conditions near the road are simple and there is usually no interference from other roads. When locating a track point, it is usually possible to locate the track point on the correct road. Therefore, the preset matching rate threshold for common highway scenarios is set higher, such as 70%-80%.

[0025] For example, in scenarios such as tunnels, elevated roads, and parallel roads, there are usually other roads near these roads, and the nearby road conditions are complex, which can easily lead to positioning drift. The road in the tunnel is relatively close to the road above it. When locating a trajectory point, a trajectory point that was originally on the road in the tunnel may be mistakenly located on the road above the tunnel. The road on the elevated road is relatively close to the road below it. When locating a trajectory point, a trajectory point that was originally on the road on the elevated road may be mistakenly located on the road below the elevated road. The left and right roads of a parallel road are relatively close. When locating a trajectory point, a trajectory point that was originally on the left road may be mistakenly located on the right road. Therefore, the preset matching rate threshold corresponding to scenarios such as tunnels, elevated roads, and parallel roads will be set lower, such as 30-50%.

[0026] Splitting user trajectories into maximum matching segments effectively preserves the most likely real-time bus trajectories and reduces the trajectory size. Compared to traditional methods that split trajectories by fixed time, this approach offers higher performance and matching results.

[0027] Step 103 : Perform spatiotemporal matching on the maximum matching trajectory segment and the real-time bus trajectory of the matching bus route to obtain a spatiotemporal matching degree.

[0028] In this embodiment, the execution entity may perform spatiotemporal matching on the maximum matching trajectory segment and the real-time bus trajectory of the matching bus route to obtain a spatiotemporal matching degree.

[0029] In some embodiments, for each maximum matching trajectory segment, temporal and spatial matching is performed between the maximum matching trajectory segment and the real-time bus trajectory of each bus on the matching bus route to obtain a spatiotemporal matching degree. For each maximum matching trajectory segment and the real-time bus trajectory of each bus on the matching bus route, the trajectory points of the maximum matching trajectory segment are temporally matched with the trajectory points of the real-time bus trajectory to obtain a temporal matching trajectory point pair; the temporal matching trajectory point pairs are then spatially matched to obtain a spatiotemporal matching degree.

[0030] Each track point in the maximum matching trajectory segment and each track point in the real-time bus trajectory contain a timestamp and coordinates. Temporal matching can be performed by matching the timestamps of the track points in the maximum matching trajectory segment with those of the real-time bus trajectory. The track point pair with the smallest timestamp difference is identified as the temporally matching track point pair. Spatial matching can be performed by matching the coordinates of the found temporally matching track point pair. If the distance between the two track points is less than a preset distance threshold (e.g., 50 meters), the two track points are spatially matched, i.e., a spatiotemporally matching track point pair. If the distance between the two track points is greater than or equal to the preset distance threshold, the two track points are spatially mismatched. After the spatiotemporal matching of all track points in the maximum matching trajectory segment and all track points in the real-time bus trajectory is completed, the number of spatiotemporally matching track point pairs can be counted. Based on this number of spatiotemporally matching track point pairs, the spatiotemporal matching degree can be determined. The spatiotemporal matching degree can be equal to the quotient of the number of spatiotemporally matching track point pairs and the total number of track points in the maximum matching trajectory segment.

[0031] In some embodiments, the probability that a maximum matching trajectory segment is a real-time bus trajectory that matches a bus route can be scored using preset strategy rules or classification models. The higher the probability that a maximum matching trajectory segment is a real-time bus trajectory that matches a bus route, the higher the spatiotemporal match between the maximum matching trajectory segment and the real-time bus trajectory that matches the bus route. The lower the probability that a maximum matching trajectory segment is a real-time bus trajectory that matches a bus route, the lower the spatiotemporal match between the maximum matching trajectory segment and the real-time bus trajectory that matches the bus route.

[0032] Based on the user's historical trajectory, the user's historical bus routes can be mined, and the frequency of matching bus routes in the mined historical bus routes can be determined. Based on the user's real-time trajectory, key features such as trajectory speed, whether the trajectory stops at a station, and similarity with bus trajectories can be extracted, and the probability that the user is a bus user can be determined based on these key features. By taking a weighted sum of the frequency of matching bus routes and the probability that the user is a bus user, the probability that the largest matching trajectory segment is a real-time bus trajectory that matches the bus route can be obtained. For another example, by inputting the frequency of matching bus routes and the frequency of the user being a bus user into a classification model, the probability that the largest matching trajectory segment is a real-time bus trajectory that matches the bus route can be obtained. The classification model can be a binary classification model or a multi-classification model. Step 104: Determine the confidence level based on the real-time bus trajectory with the highest spatiotemporal matching degree.

[0033] In this embodiment, the execution entity may determine the confidence level based on the real-time bus trajectory with the highest spatiotemporal matching level.

[0034] In some embodiments, the real-time bus trajectory with the highest spatiotemporal matching degree is selected, and a confidence level can be calculated based on the number of matching points and the matching point ratio of the real-time bus trajectory with the highest spatiotemporal matching degree. Generally, the confidence level is positively correlated with the number of matching points and the matching point ratio. The greater the number of matching points and the greater the matching point ratio, the higher the confidence level; the fewer the number of matching points and the smaller the matching point ratio, the lower the confidence level.

[0035] In some embodiments, when calculating the confidence level, the road scene can also be combined. The confidence level is calculated based on the road scene, as well as the number of matching points and the ratio of matching points of the real-time bus trajectory with the highest spatiotemporal matching. Among them, the preset matching point number threshold and the preset matching point ratio threshold can be determined according to the road scene, and different road scenes can correspond to different preset matching point number thresholds and different preset matching point ratio thresholds. By comparing the number of matching points with the preset matching point number threshold corresponding to the road scene, a matching point comparison result can be obtained. Similarly, by comparing the matching point ratio with the preset matching point ratio threshold corresponding to the road scene, a matching point ratio comparison result can be obtained. Then, based on the matching point number comparison result and the matching point ratio comparison result, the confidence level can be calculated.

[0036] In some embodiments, the preset matching point count threshold and the preset matching point ratio threshold can be determined based on the yaw degree of the road scene. Typically, the preset matching point count threshold and the preset matching point ratio threshold corresponding to a road scene with a high yaw degree are higher than the preset matching point count threshold and the preset matching point ratio threshold corresponding to a road scene with a low yaw degree.

[0037] For example, in tunnel scenarios, the yaw rate is usually very high. Therefore, the preset matching point threshold and preset matching point ratio threshold for tunnels are set lower, such as 50 points and 30%.

[0038] For another example, in a highway scenario, the yaw degree of a highway section with an overpass is higher than that of an ordinary highway section. Therefore, the preset matching point count threshold and the preset matching point ratio threshold for a highway section with an overpass are higher than those for an ordinary highway section. For example, the preset matching point count threshold for a highway section with an overpass is set to 80 points, and the preset matching point ratio threshold is set to 50%; while the preset matching point count threshold for an ordinary highway section is set to 100 points, and the preset matching point ratio threshold is set to 70%.

[0039] For another example, for ordinary road scenarios, the yaw degree of ordinary roads in complex terrain (such as roads surrounded by tall buildings) is higher than that of ordinary roads in simple terrain. Therefore, the preset matching point count threshold and the preset matching point ratio threshold for ordinary roads in complex terrain are higher than those for ordinary roads in simple terrain. For example, the preset matching point count threshold for ordinary roads in complex terrain is set to 80 points, and the preset matching point ratio threshold is set to 50%; while the preset matching point count threshold for ordinary roads in simple terrain is set to 100 points, and the preset matching point ratio threshold is set to 70%. Step 105: In response to the confidence level meeting the preset conditions, determine the bus route corresponding to the real-time bus trajectory of the user with the highest spatiotemporal matching degree.

[0040] In this embodiment, the execution entity may determine whether the confidence level satisfies a preset condition. If the preset condition is satisfied, the bus route corresponding to the real-time bus trajectory with the highest spatiotemporal matching degree of the user may be determined.

[0041] The preset conditions may be conditions pre-set based on the road scene. Different road scenes may correspond to different preset conditions. For example, the number of matching points is compared with a preset matching point threshold, and the matching point ratio is compared with a preset matching point ratio threshold; if the number of matching points is greater than the preset matching point threshold, and the matching point ratio is greater than the preset matching point ratio threshold, then it is determined that the confidence level meets the preset conditions; if the number of matching points is less than or equal to the preset matching point threshold, or the matching point ratio is less than or equal to the preset matching point ratio threshold, then it is determined that the confidence level does not meet the preset conditions. Different road scenes may correspond to different preset matching point thresholds and preset matching point ratio thresholds. For ordinary highway scenes, the preset matching point threshold and the preset matching point ratio threshold will be higher; for scenes prone to positioning drift, such as tunnels, elevated roads, and parallel roads, the preset matching point threshold and the preset matching point ratio threshold will be lower.

[0042] Only when the confidence level meets the preset conditions will the bus route corresponding to the user's real-time bus trajectory with the highest spatiotemporal match be determined, and the user's trajectory will be mined to match the bus route once. The user's trajectory timestamp and station information are also recorded, providing information on the time the user's trajectory was generated and the spatial location of the stations passed. Compared to traditional mining solutions that rely on spatial matching of trajectory matching routes, this significantly improves the accuracy of preference mining.

[0043] The disclosed embodiments provide a bus route mining method that accurately identifies whether a user's single trajectory matches a bus trajectory of a certain route by temporally and spatially matching user trajectories with real-time bus trajectories, thereby improving the accuracy of bus route mining.

[0044] Continue to refer Figure 2, which shows a process 200 of another embodiment of the bus route mining method according to the present disclosure. The bus route mining method includes the following steps: Step 201 : Match the user trajectory with the bus route to obtain matching trajectory points, unmatched trajectory points, and matching bus routes.

[0045] Step 202: Determine the maximum matching trajectory segment based on the matching trajectory points and the non-matching trajectory points.

[0046] Step 203 : Perform spatiotemporal matching on the maximum matching trajectory segment and the real-time bus trajectory of the matching bus route to obtain a spatiotemporal matching degree.

[0047] Step 204 : Determine the confidence level based on the real-time bus trajectory with the highest spatiotemporal matching degree.

[0048] Step 205 : In response to the confidence level meeting the preset condition, determining the bus route corresponding to the real-time bus trajectory of the user with the highest spatiotemporal matching level.

[0049] In this embodiment, the specific operations of steps 201-205 are already described in Figure 1 In the illustrated embodiment, steps 101-105 are described in detail and will not be repeated here.

[0050] Step 206: Record the timestamp and site information of the user's trajectory.

[0051] In this embodiment, the execution subject of the bus route mining method can record the timestamp and station information of the user trajectory, thereby obtaining the time when the user trajectory was generated and the spatial location information of the passed stations.

[0052] Step 207 : mining bus routes based on the user's multi-day user trajectory to determine the bus routes taken by the user on multiple days.

[0053] In this embodiment, the execution entity may mine bus routes based on the user's multi-day user trajectory to determine the bus routes taken by the user on multiple days.

[0054] The user's multi-day user trajectory is obtained, and steps 201-206 are executed for each day's user trajectory to mine bus routes, so as to determine the bus routes taken by the user on multiple days.

[0055] Step 208: Determine whether the user is a public transportation user based on the public transportation routes that the user has taken on multiple days.

[0056] In this embodiment, the execution entity may determine whether the user is a public transportation user based on the public transportation routes that the user has taken on multiple days.

[0057] By mining bus routes based on user trajectories over multiple days and counting the frequency of users taking bus routes, we can determine whether a user is a bus user, thereby accurately mining bus users. For example, if a user takes a bus route 10 times or more within 7 days, the user is considered a bus user; if the user takes a bus route less than 10 times within 7 days, the user is not considered a bus user.

[0058] Step 209 : In response to the user being a public transportation user, the user's preferred public transportation routes are determined based on the public transportation routes taken by the user on multiple days.

[0059] In this embodiment, when the user is a public transportation user, the execution entity may determine the user's preferred public transportation route based on the public transportation routes that the user has taken over multiple days.

[0060] By mining bus routes based on user trajectories over multiple days and counting the frequency of each bus route, we can accurately identify the user's preferred bus routes. For example, if a user took bus route A five times, bus route B four times, and bus route C once within seven days, we can determine that the user prefers bus routes A and B.

[0061] Step 210: Record the timestamps and site information of the user's trajectory over multiple days.

[0062] In this embodiment, the execution entity may record the timestamps and site information of the user's trajectory over multiple days, thereby obtaining the time when the user's trajectory was generated and the spatial location information of the sites passed through.

[0063] Step 211 : Determine the user's preferred time and preferred site based on the timestamps and site information of the user's trajectory over multiple days.

[0064] In this embodiment, the execution entity may determine the user's preferred time and preferred site based on the timestamps and site information of the user's trajectory over multiple days.

[0065] By mining bus routes based on user trajectories over multiple days and counting the time periods and stops at which users take their preferred bus routes, we can accurately identify users' preferred times and stops. For example, if a user takes bus route A at stop a seven times between 7 and 9 a.m. and once at stop b, and takes bus route B at stop c seven times and once at stop d between 4 and 9 p.m., then the user prefers taking bus route A at stop a between 7 and 9 a.m. and bus route B at stop c between 4 and 9 p.m.

[0066] Step 212: Push bus information to the user based on the preferred bus route, preferred time, and preferred station.

[0067] In this embodiment, the execution entity may push bus information to the user based on the user preference mining results such as preferred bus routes, preferred times, and preferred stops.

[0068] The results of user preference mining can be widely used in map applications, public transportation applications, and travel applications, including but not limited to: optimizing the sorting effect of public transportation plans based on user preferences when users search for public transportation routes, optimizing the sorting results of real-time public transportation recommendations based on user preferences when users view real-time public transportation routes, and actively pushing real-time public transportation routes preferred by users at preferred times.

[0069] Figure 3 A schematic diagram showing preferred routes to work. Figure 4 This diagram shows a user's preferred route after get off work. A user takes bus route A to and from get off work every day. The mined information includes: between 7:00 and 9:00 AM, the user prefers route A (from stop a to stop b), with stops a and c being preferred; between 4:00 PM and 9:00 PM, the user prefers route A (from stop b to stop a), with stops b and d being preferred. This, combined with the current time and user location, allows the user's bus route and direction preferences to be accurately inferred. If the user is near home, route A (from stop a to stop b) is determined; if the user is near work, route A (from stop b to stop a) is determined. If the user's location makes it difficult to accurately determine their direction preference, the time of day can be considered. If the time is between 7:00 and 9:00 AM, route A (from stop a to stop b) is preferred; if the time is between 4:00 PM and 9:00 PM, route A (from stop b to stop a) is preferred.

[0070] Figure 5 A schematic diagram of searching for bus routes is shown. Figure 6 A diagram shows real-time bus routes. A user takes bus route A to and from get off work every day. Mined data includes: between 7:00 and 9:00 AM, the user prefers route A (from stop a to stop b), with stops a and c being preferred; between 4:00 PM and 9:00 PM, the user prefers route A (from stop b to stop a), with stops b and d being preferred. When the user views bus routes home, the map identifies their preferences and prioritizes route A (from stop b to stop a). Similarly, when the user views real-time bus routes, the map intelligently prioritizes route A (from stop b to stop a), providing a more efficient and enjoyable user experience.

[0071] This disclosed embodiment provides a bus route mining method that accurately mines bus users and their preferences. By matching user trajectories with real-time bus trajectories in time and space, it accurately identifies whether a user's single trajectory matches a bus trajectory for a specific route. By accumulating matching data over multiple days, it can accurately identify whether the user is a bus user and the routes they frequently take.

[0072] The results of user preference mining can be applied to map, public transportation, and travel apps. Based on a user's frequently used routes, real-time bus arrival information (such as bus location and congestion) can be accurately delivered to users through push notifications and intelligent recommendations. This increases user reliance on these apps, thereby enabling personalized travel recommendations. Furthermore, when users search for public transportation routes, prioritizing their frequently used routes will generally result in a better user experience and increased efficiency, thus enabling personalized route sorting.

[0073] Further references Figure 7 As an implementation of the methods shown in the above figures, the present disclosure provides an embodiment of a bus route excavation device. Figure 1 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0074] like Figure 7 As shown, the bus route mining device 700 of this embodiment may include: a first matching module 701, a first determination module 702, a second matching module 703, a second determination module 704, and a third determination module 705. The first matching module 701 is configured to match a user trajectory with a bus route to obtain matching trajectory points, unmatched trajectory points, and a matching bus route; the first determination module 702 is configured to determine a maximum matching trajectory segment based on the matching trajectory points and the unmatched trajectory points; the second matching module 703 is configured to perform spatiotemporal matching between the maximum matching trajectory segment and the real-time bus trajectory of the matching bus route to obtain a spatiotemporal matching degree; the second determination module 704 is configured to determine a confidence level based on the real-time bus trajectory with the highest spatiotemporal matching degree; and the third determination module 705 is configured to determine, in response to the confidence level satisfying a preset condition, the bus route corresponding to the real-time bus trajectory with the highest spatiotemporal matching degree that the user took.

[0075] In this embodiment, the bus route mining device 700 includes the first matching module 701, the first determination module 702, the second matching module 703, the second determination module 704, and the third determination module 705. The specific processing and technical effects thereof can be referred to in the respective Figure 1 The relevant descriptions of steps 101-105 in the corresponding embodiment are not repeated here.

[0076] In some optional implementations of this embodiment, the bus route mining device 700 further includes: a first recording module configured to record the timestamp and station information of the user trajectory.

[0077] In some optional implementations of this embodiment, the first determination module 702 is further configured to: traverse subsequent trajectory points of the matching trajectory point, perform a trajectory point matching step: count the number of matching points and the number of non-matching points of the subsequent trajectory points, and calculate the matching rate based on the number of matching points and the number of non-matching points of the subsequent trajectory points until the matching rate is less than a preset matching rate threshold, stop traversal, and obtain a sub-first maximum matching trajectory segment; continue traversing subsequent trajectory points of the sub-maximum matching trajectory segment, repeat the trajectory point matching step, and obtain other sub-maximum matching trajectory segments; and determine the maximum matching trajectory segment based on all sub-maximum matching trajectory segments.

[0078] In some optional implementations of this embodiment, the preset matching rate threshold is determined according to the road scene.

[0079] In some optional implementations of this embodiment, the second matching module 703 is further configured to: perform time matching on the trajectory points of the maximum matching trajectory segment and the trajectory points of the real-time bus trajectory of the matching bus route to obtain a time matching trajectory point pair; and perform spatial matching on the time matching trajectory point pair to obtain a spatiotemporal matching degree.

[0080] In some optional implementations of this embodiment, the second matching module 703 is further configured to: perform control matching on the time matching trajectory point pairs to obtain the number of time-space matching trajectory point pairs; and determine the time-space matching degree based on the number of time-space matching trajectory point pairs.

[0081] In some optional implementations of this embodiment, the second determination module 704 is further configured to calculate the confidence level based on the number of matching points and the proportion of matching points of the real-time bus trajectory with the highest spatiotemporal matching level.

[0082] In some optional implementations of this embodiment, the second determination module 704 is further configured to calculate the confidence level based on the road scene and the number and proportion of matching points of the real-time bus trajectory with the highest spatiotemporal matching level.

[0083] In some optional implementations of this embodiment, the second determination module 704 is further configured to: compare the number of matching points of the real-time bus trajectory with the highest spatiotemporal matching degree with a preset matching point threshold corresponding to the road scene to obtain a matching point comparison result; compare the matching point ratio of the real-time bus trajectory with the highest spatiotemporal matching degree with a preset matching point ratio threshold corresponding to the road scene to obtain a matching point ratio comparison result; and calculate the confidence level based on the matching point number comparison result and the matching point ratio comparison result.

[0084] In some optional implementations of this embodiment, the preset matching point count threshold and the preset matching point ratio threshold are determined based on the yaw degree of the road scene. In some optional implementations of this embodiment, the bus route mining apparatus 700 further includes: a fourth determination module configured to perform bus route mining on the user's multi-day user trajectory to determine the bus routes the user took on those multiple days; and a fifth determination module configured to determine whether the user is a bus user based on the bus routes the user took on those multiple days.

[0085] In some optional implementations of this embodiment, the bus route mining device 700 further includes: a sixth determination module configured to determine the user's preferred bus routes based on the bus routes that the user has taken on multiple days in response to the user being a bus user.

[0086] In some optional implementations of this embodiment, the bus route mining device 700 also includes: a second recording module, configured to record the timestamps and station information of the user's trajectory over multiple days; and a sixth determination module, configured to determine the user's preferred time and preferred station based on the timestamps and station information of the user's trajectory over multiple days.

[0087] In some optional implementations of this embodiment, the bus route mining device 700 further includes: a push module configured to push bus information to the user based on the preferred bus route, preferred time and preferred station.

[0088] In the technical solutions disclosed herein, the acquisition, storage, and application of user personal information involved comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0089] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0090] Figure 8 A schematic block diagram of an example electronic device 800 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are provided as examples only and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0091] like Figure 8As shown, device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. RAM 803 may also store various programs and data required for the operation of device 800. Computing unit 801, ROM 802, and RAM 803 are interconnected via a bus 804. An input / output (I / O) interface 805 is also connected to bus 804.

[0092] Various components in device 800 are connected to I / O interface 805, including an input unit 806, such as a keyboard, mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a magnetic disk, optical disk, etc.; and a communication unit 809, such as a network card, modem, wireless communication transceiver, etc. The communication unit 809 allows device 800 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0093] The computing unit 801 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 performs the various methods and processes described above, such as the bus route mining method. For example, in some embodiments, the bus route mining method may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 808. In some embodiments, part or all of the computer program may be loaded and / or installed onto the device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the bus route mining method described above may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to perform the bus route mining method via any other suitable means (e.g., via firmware).

[0094] Various embodiments of the systems and techniques described above can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0095] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0096] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fibers, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0097] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0098] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0099] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0100] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions provided by this disclosure can be achieved. This is not limited herein.

[0101] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A bus route mining method, comprising: Match the user trajectory with the bus route to obtain matching trajectory points, unmatched trajectory points, and matching bus routes; determining a maximum matching trajectory segment based on the matching trajectory points and the non-matching trajectory points; Performing spatiotemporal matching on the maximum matching trajectory segment and the real-time bus trajectory of the matching bus route to obtain a spatiotemporal matching degree; Determine the confidence level based on the real-time bus trajectory with the highest spatiotemporal matching degree; In response to the confidence level satisfying a preset condition, it is determined that the user takes a bus route corresponding to the real-time bus trajectory with the highest spatiotemporal matching level.

2. The method according to claim 1, wherein After determining that the user takes the bus route corresponding to the real-time bus trajectory with the highest spatiotemporal matching degree, the method further includes: The timestamp and site information of the user's trajectory are recorded.

3. The method according to claim 1, wherein The determining of a maximum matching trajectory segment based on the matching trajectory points and the non-matching trajectory points includes: Traversing subsequent trajectory points of the matching trajectory point, performing a trajectory point matching step: counting the number of matching points and the number of non-matching points of the subsequent trajectory points, and calculating a matching rate based on the number of matching points and the number of non-matching points of the subsequent trajectory points. When the matching rate is less than a preset matching rate threshold, traversal is stopped to obtain a sub-maximum matching trajectory segment; Continue traversing subsequent trajectory points of the sub-maximal matching trajectory segment, repeating the trajectory point matching step, and obtaining other sub-maximal matching trajectory segments; Based on all sub-maximum matching trajectory segments, the maximum matching trajectory segment is determined.

4. The method according to claim 3, wherein: The preset matching rate threshold is determined according to the road scene.

5. The method according to claim 1, wherein The performing spatiotemporal matching on the maximum matching trajectory segment and the real-time bus trajectory of the matching bus route to obtain a spatiotemporal matching degree includes: Time-matching the trajectory points of the maximum matching trajectory segment with the trajectory points of the real-time bus trajectory of the matching bus route to obtain a time-matched trajectory point pair; The time matching trajectory point pairs are spatially matched to obtain the time-space matching degree.

6. The method according to claim 5, wherein: The spatially matching the time-matching trajectory point pairs to obtain the spatiotemporal matching degree includes: Performing spatial matching on the time-matching trajectory point pairs to obtain the number of space-time matching trajectory point pairs; The spatiotemporal matching degree is determined based on the number of spatiotemporal matching trajectory point pairs.

7. The method according to claim 1, wherein The confidence level is determined based on the real-time bus trajectory with the highest spatiotemporal matching, including: The confidence level is calculated based on the number of matching points and the proportion of matching points of the real-time bus trajectory with the highest spatiotemporal matching level.

8. The method according to claim 7, wherein: The calculating of the confidence level based on the number of matching points and the proportion of matching points of the real-time bus trajectory with the highest spatiotemporal matching level includes: The confidence level is calculated based on the road scene and the number of matching points and the proportion of matching points of the real-time bus trajectory with the highest spatiotemporal matching level.

9. The method according to claim 8, wherein The confidence level is calculated based on the road scene and the number and ratio of matching points of the real-time bus trajectory with the highest spatiotemporal matching level, including: Comparing the matching points of the real-time bus trajectory with the highest spatiotemporal matching degree with a preset matching point threshold corresponding to the road scene to obtain a matching point comparison result; Comparing the matching point ratio of the real-time bus trajectory with the highest spatiotemporal matching degree with a preset matching point ratio threshold corresponding to the road scene to obtain a matching point ratio comparison result; The confidence level is calculated based on the comparison result of the number of matching points and the comparison result of the proportion of matching points.

10. The method according to claim 9, wherein: The preset matching point number threshold and the preset matching point ratio threshold are determined according to the yaw degree of the road scene.

11. The method according to any one of claims 1 to 10, wherein The method further comprises: Performing bus route mining on the user's multi-day user trajectory to determine the bus routes taken by the user on multiple days; Based on the bus routes taken by the user on multiple days, it is determined whether the user is a bus user.

12. The method according to claim 11, wherein The method further comprises: In response to the user being a public transportation user, the user's preferred public transportation routes are determined based on public transportation routes taken by the user on multiple days.

13. The method according to claim 11, wherein The method further comprises: Record the timestamps and site information of the user's trajectory over the multiple days; Based on the timestamps and site information of the user's trajectories over multiple days, the user's preferred time and preferred site are determined.

14. The method according to claim 13, wherein The method further comprises: Based on the preferred bus route, the preferred time and the preferred station, bus information is pushed to the user.

15. A bus route excavation device comprising: A first matching module is configured to match the user trajectory with the bus route to obtain matching trajectory points, unmatched trajectory points, and matching bus routes; a first determining module configured to determine a maximum matching trajectory segment based on the matching trajectory points and the non-matching trajectory points; A second matching module is configured to perform spatiotemporal matching on the maximum matching trajectory segment and the real-time bus trajectory of the matching bus route to obtain a spatiotemporal matching degree; A second determination module is configured to determine a confidence level based on the real-time bus trajectory with the highest spatiotemporal matching degree; The third determining module is configured to determine, in response to the confidence level satisfying a preset condition, that the user takes a bus route corresponding to the real-time bus trajectory with the highest spatiotemporal matching level.

16. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 14.

17. A non-transitory computer-readable storage medium storing computer instructions, the computer instructions being configured to cause the computer to execute the method of any one of claims 1 to 14.

18. A computer program product comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 14.

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