Expressway drainage condition analysis method, device and equipment and storage medium

Abstract

The invention discloses an expressway drainage condition analysis method and device, equipment and a storage medium. Comprising the following steps: dividing vehicle journeys based on Internet of Vehicles mobile position data to obtain a journey sequence; matching the travel sequence with a road network, restoring a ground driving path of the vehicle on the road network, and planning a high-speed priority path; judging the acceptability of the high-speed priority path; based on the ground driving path and the corresponding high-speed priority path, establishing relational data representing the vehicle, the ground driving path, the high-speed priority path, the acceptability of the high-speed priority path and service demand supply, and based on the relational data, constructing a high-speed drainage analysis unit; and carrying out merging calculation on the high-speed drainage analysis unit to obtain a high-speed section with a drainage value and a target vehicle. According to the method, the drainage conditions of the high-speed and parallel ground roads are quantitatively analyzed, the target sections are identified, and operation decisions such as section priority ranking, target vehicle delineation, service supplementation and differentiated charging are supported.

Description

Technical Field

[0001] This application relates to the field of traffic data processing technology, and more specifically, to a method, apparatus, equipment, and storage medium for analyzing highway traffic diversion conditions. Background Technology

[0002] Currently, it is common for trunk highways and parallel surface roads to coexist within the same corridor. In this context, freight and other commercial vehicles switch routes between the two during their journeys, reflecting a multi-objective trade-off between price, time, reliability, and service accessibility. Surface roads generally suffer from safety risks due to intersection conflicts and mixed traffic, reduced efficiency and reliability caused by signal control and queuing, and energy consumption and carbon emission externalities due to frequent starts and stops. Therefore, rationally diverting surface trucks to highways can help improve traffic efficiency and safety, reduce energy consumption and emissions, optimize resource allocation, and also ensure the profitability of highway operations.

[0003] Within the same corridor, drivers' choice between highways and surface roads depends not only on toll fees and expected travel time, but also significantly on factors such as the ease of accessing / exiting highways, roadside services (gas stations, restaurants, overnight parking / showers, etc.), traffic restrictions / overloading control / construction, and individual habits and the asymmetry between outbound and inbound journeys. However, existing technologies mostly rely on descriptive assessments of single data points or scattered indicators, making it difficult to analyze the reasons for highway "diversion"; they lack a unified quantitative framework covering the entire corridor and oriented towards operational decision-making, making it difficult to reliably identify potential diversion targets at the "segment × time period" scale and form clear policy justifications. Summary of the Invention

[0004] This application provides a method, apparatus, equipment, and storage medium for analyzing highway diversion conditions, in order to at least solve the technical problems in related technologies that make it difficult to objectively quantify diversion conditions and identify diversionable sections and their causes.

[0005] According to one aspect of the embodiments of this application, a method for analyzing highway diversion conditions is provided, comprising: Vehicle trips are segmented based on vehicle network mobile location data to obtain trip sequences that include the trip start point, intermediate stops and the trip end point; Match the trip sequence with the road network to reconstruct the vehicle's ground driving path on the road network, and plan the highway priority path based on the same trip start and end point as the ground driving path; Determine the acceptability of the high-speed priority path, and determine that the high-speed priority path is acceptable when the acceptability meets the preset conditions; Based on ground driving paths and corresponding highway priority paths, establish relational data representing vehicles, ground driving paths, highway priority paths, highway priority path acceptability, and service demand and supply; and construct a highway traffic diversion analysis unit based on the relational data. Based on the high-speed traffic diversion analysis unit, the high-speed sections and target vehicles with traffic diversion value are obtained through merging calculations.

[0006] According to another aspect of the embodiments of this application, an analysis device for highway diversion conditions is also provided, comprising: The trip analysis module is used to divide vehicle trips based on vehicle network mobile location data to obtain a trip sequence that includes the trip start point, intermediate stops and the trip end point; The route planning module is used to match the trip sequence with the road network, reconstruct the ground driving path of the vehicle on the road network, and plan the highway priority path based on the same trip start and end point as the ground driving path; The acceptability analysis module is used to calculate and determine the acceptability of the high-speed priority path. When the acceptability meets the preset conditions, the high-speed priority path is determined to be acceptable. The traffic diversion analysis module is used to establish relational data representing vehicles, ground driving paths, highway priority paths, highway priority path acceptability, and service demand and supply based on ground driving paths and corresponding highway priority paths, and to construct highway traffic diversion analysis units based on relational data. The merge calculation module is used to perform merge calculations based on the high-speed traffic diversion analysis unit to obtain high-speed sections and target vehicles with traffic diversion value.

[0007] According to another aspect of the embodiments of this application, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the above-mentioned method for analyzing highway diversion conditions through the computer program.

[0008] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, wherein a computer program is stored in the computer program, which is configured to execute the above-described method for analyzing highway diversion conditions when running.

[0009] The technical solutions provided in this application embodiment may include the following beneficial effects: This application provides a method for analyzing highway traffic diversion conditions. Based on vehicle network mobile location data, vehicle journeys are segmented to obtain a journey sequence including the journey start point, intermediate stops, and journey end point. This journey sequence is then matched with the road network to reconstruct the vehicle's ground travel path on the road network. Fine-grained reconstruction of vehicle behavior on the ground is then performed.

[0010] Furthermore, a high-speed priority route is planned, and a high-speed traffic diversion analysis model is established based on the ground driving route and the corresponding high-speed priority route to obtain an integrated expression framework of "vehicle-road-demand / supply-assessment" to realize the quantitative analysis of high-speed traffic diversion conditions.

[0011] Finally, based on the high-speed traffic diversion analysis model, a merge calculation is performed to obtain the high-speed sections and target vehicles with traffic diversion value. This enables the quantifiable identification of high-speed sections with potential for traffic diversion, forming a unified, interpretable, and transferable traffic diversion evaluation index system, which directly supports the priority ranking of sections and the refined delineation of target vehicles for traffic diversion. Attached Figure Description

[0012] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a flowchart of an optional highway diversion condition analysis method according to an embodiment of this application; Figure 2 This is a schematic diagram of a highway traffic diversion scenario according to an embodiment of this application; Figure 3 This is a schematic diagram illustrating the dimensions of highway traffic diversion condition analysis according to an embodiment of this application; Figure 4 This is a schematic diagram of an analysis device for an optional highway traffic diversion condition according to an embodiment of this application; Figure 5 This is a schematic diagram of the structure of an optional electronic device according to an embodiment of this application. Detailed Implementation

[0013] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0014] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0015] Existing technologies propose analytical methods for inferring highway traffic diversion phenomena based on high-speed network toll data. However, such as... Figure 2 As shown in (a), if vehicles only travel on the surface roads of the corridor, their behavior is not visible in the highway network toll data, therefore existing technologies struggle to accurately perceive the scale of highway "diversion." Furthermore, as... Figure 2 As shown in (b), the existing technology only knows that the vehicle leaves the highway at point B and then returns to the highway via point C, but it is difficult to reconstruct the vehicle's behavior on the ground in a fine-grained manner. It lacks the ability to identify the vehicle's ground path selection, parking and other patterns, and cannot analyze the reasons for the "diversion" of the highway.

[0016] Freight and other commercial vehicles are not only a crucial source of revenue for highways but also a significant risk to traffic safety. Analyzing truck behavior data can reconstruct their driving and parking patterns along the highway-ground corridor, identify route selection motivations, and thus provide verifiable evidence for traffic diversion condition assessment, target section identification, and operational optimization. It should be noted that the analytical method used in this application is also applicable to traffic diversion analysis of other vehicle types, such as passenger cars.

[0017] This application introduces freight vehicle network data and integrates it with network toll collection, road network and POI and operation status data to reconstruct the details of ground driving and parking, and construct an integrated expression framework of "vehicle-road-demand / supply-assessment" to objectively quantify the diversion conditions at the section and time scale, and identify diversion sections and their causes.

[0018] First, the characters in this application are defined as follows: Assume the road network is ,in It is a set of road network nodes. It is the set of edges in the road network.

[0019] Let the data of the highway toll collection network gantry (including gantries on the main line of the highway and gantries at toll station entrances and exits) be as follows:

[0020] in, A gantry is uniquely identified. The latitude and longitude coordinates of the gantry were defined. It is the side where the gantry is located in the road network. For the gantry in Offset on.

[0021] Let the highway network toll record be as follows:

[0022] in, A vehicle is uniquely identified; It refers to the type of vehicle, such as passenger cars and freight trucks; This refers to the vehicle's weight, obtained from the weighing equipment at the highway entrance station; A gantry that uniquely identifies a highway; The moment the vehicle passes through the gantry.

[0023] Assume the freight vehicle network location data is recorded as follows:

[0024] in, It is the truck's location device ID, which can uniquely identify a vehicle; The moment of data collection; Define the vehicle in The latitude and longitude coordinates of the moment; For vehicles in Instantaneous velocity at a given moment For vehicles in The instantaneous direction angle at a given moment.

[0025] Let the road network POI record be:

[0026] in, A POI is uniquely identified. It refers to the type of POI, such as highway service areas, toll booths, restaurants, gas stations, repair shops, factories, etc. on surface roads; It is the name of the POI; The coordinates of the POI are defined; The edges connecting the entrances / exits of this POI to the road network are defined. For entrances and exits at Offset on.

[0027] The analysis method for highway traffic diversion conditions according to embodiments of this application will be described in detail below with reference to the accompanying drawings. For example... Figure 1As shown, the method mainly includes the following steps: S101 divides vehicle trips based on vehicle network mobile location data, obtaining a trip sequence that includes the trip start point, intermediate stops, and trip end point.

[0028] First, perform basic data preprocessing, based on... Construct location references and perform spatiotemporal alignment of various types of data, including but not limited to: Map the high-speed network toll collection gantry data table to the high-speed sections of the road network, confirm the directed position of each gantry in the high-speed network (associated with the edges of the high-speed network and the offset on the edges), and backfill the high-speed network toll collection gantry data records. and Field.

[0029] POI records are mapped to the road network. The directed position of each POI within the road network (associated with an edge in the road network and its offset on that edge) is then used to populate the POI data record. and Field.

[0030] Unify the recording of freight vehicle network location data to The coordinate system.

[0031] Based on historical traffic information, obtain the information for each edge of the road network. On each characteristic day Feature time slice (Let's assume a 5-minute time slice) speed information and the expected speed .

[0032] Furthermore, trips are segmented based on vehicle-to-everything (V2X) mobile location data.

[0033] In practice, truck behavior is organized according to origin and destination (OD), such as commuting and traveling. If the OD of trucks is not identified, subsequent analysis of truck behavior, especially the analysis of high-frequency behavior, will lack a basis.

[0034] In an exemplary scenario, for The identified truck is defined as follows: Let the time-series-compliant set of movement location records extracted from the freight vehicle networking system be... ,have , This application integrates road condition information, POI information, and other data to divide the continuous point sequence, clearly defining the starting point (O), travel segment, and arrival point (D) for each vehicle journey.

[0035] Furthermore, define the stopping point: set up yes A continuous subsequence, if:

[0036] Then it is called As a stopping segment, its center This is a stopping point.

[0037] in, The function calculates the spherical distance between two points; The radius threshold of the stopping point; The duration threshold for the stopping point.

[0038] Furthermore, eliminate false stops during driving: First, based on traffic information, if the stop is located in an extremely congested area, the stop will be deleted. If a high-frequency "stop-go-stop" pattern occurs within the stopping section and the net displacement span is large, it is marked as "queueing" and is not considered a valid stop; this situation often occurs in scenarios such as traffic lights and toll station entrances and exits.

[0039] This application categorizes stops into two types: origin / end points (O / D), defined as the first / last valid stop of a trip; and intermediate stops, valid stops located between O and D, representing activities such as refueling, dining, rest, inspection, and maintenance. This application argues that these two types of stops exhibit significant differences in individual and group behavior, as shown in the table below:

[0040] This difference can serve as prior knowledge to assist in identifying stopping point categories and dividing OD (Original Design Location).

[0041] Furthermore, this application introduces spatial entropy to quantify and analyze the aforementioned differences: For all the stopping points obtained by each vehicle, calculate the radius as... DBSCAN clustering yields clusters .

[0042] Define normalized individual spatial entropy:

[0043] in, For bicycles in the first The number of times a cluster stops.

[0044] Define normalized population spatial entropy:

[0045] in, For all vehicles in the first The number of times a cluster stops.

[0046] The table above can then be expressed based on spatial entropy as follows:

[0047] Among them, revisit refers to the scale of repeatedly visiting a certain stop point.

[0048] The itinerary division method in this application includes: First, based on the mobile location data of freight vehicles connected to the Internet, the stopping sections are identified using preset conditions, the center of which is determined as the stopping point, and false stopping during the driving process is eliminated by combining road condition information.

[0049] Furthermore, stops are categorized into two types: origin / destination (O / D) stops and intermediate stops. Differences in individual and group behavior between these two types of stops are identified.

[0050] Furthermore, spatial entropy is introduced to quantify the differences in stopping points. DBSCAN clustering is performed on the stopping points, and after obtaining the clusters, normalized individual spatial entropy and normalized group spatial entropy are defined respectively. Individual spatial entropy is low and group spatial entropy is high at the origin and destination points, while individual spatial entropy is high and group spatial entropy is low at intermediate activity stopping points. This difference can be used as prior knowledge to assist in stopping point category identification and OD (Original Demand) division.

[0051] By dividing the journey, we can obtain the journey (Trip) represented by a sequence of stops from O to intermediate stop point D in freight transportation, and obtain the corresponding continuous subsequence of movement position records ordered by time. .

[0052] S102 matches the travel sequence with the road network to reconstruct the vehicle's ground travel path on the road network, and plans a high-speed priority path based on the same travel start and end point as the ground travel path.

[0053] In one implementation, the travel sequence is matched with the road network to reconstruct the vehicle's ground travel path on the road network.

[0054] right In the road network Perform a map-matching operation. Map-matching is a technique that aligns trajectory data with map data. Its purpose is to map the original location points onto the most probable road network, reconstructing the vehicle's travel path on the road network and obtaining the edge order, denoted as:

[0055] And provide the entry / exit time pairs for each edge:

[0056] These entry / exit times are derived from matched interpolation (obtained from observation data) and satisfy the following:

[0057]

[0058] in, To obtain the vehicle based on the matching interpolation Travel time on the road.

[0059] Furthermore, the demand vector for vehicles is obtained based on intermediate activity stopping points.

[0060] First, based on each intermediate activity stop in this trip, a feature vector containing multiple dimensions is constructed, including arrival time, stop duration, spatial location and point of interest information, availability, convenience, and economy.

[0061] Next, for each type of demand, the probability of satisfying the demand during this stop is calculated based on the feature vector of the stopping point. Simultaneously, the interval between reaching each stopping point and the last time the demand was satisfied is recorded; this interval is related to the urgency of the demand.

[0062] Then, by combining the probability of meeting the demand at the stopping point with a weighting function set according to the urgency of the demand, the demand intensity for each demand in this trip is calculated.

[0063] The demand intensity is normalized. The normalized demand intensity is then combined into a vector, which is the demand vector for this trip.

[0064] Specifically, let the set of intermediate activity stops for this trip be . Let the possible demand set of intermediate activity stopping points be . .

[0065] For each stop Construct feature vectors (Including dimensions such as arrival time, length of stay, space / POI, availability, convenience, and cost).

[0066] For each type of demand , can be based on This stopover is to meet the demand. The probability of:

[0067] In addition, record To reach Time since the last time the demand was met The intervals (such as the interval between the last meal, continuous driving time, mileage / duration since the last refueling, etc.) are related to demand. It is related to the urgency.

[0068] This application defines this trip as... The intensity of demand (scalar) is:

[0069] A monotonically non-decreasing weighted function based on urgency is defined. To facilitate cross-trip comparisons, [the following is used]: Perform the normalization operation, and record the result as... The final requirement vector for this trip is defined as follows:

[0070] Furthermore, high-speed priority paths are planned on the same OD.

[0071] In one implementation, a set departure time is obtained, and a time-varying road network with traffic information for the same departure and arrival points is loaded. Based on a preset path planning algorithm, the minimum time-consuming high-speed path is solved according to the high-speed priority principle to obtain the high-speed priority path.

[0072] This path consists of a series of edges. The entry and exit times of each edge are recursively calculated based on time-varying traffic information. Starting from the departure time, the exit time of each edge is equal to the entry time plus the travel time determined by the historical traffic information corresponding to the entry time of that edge. The entry time of the next edge is the exit time of the previous edge.

[0073] Specifically, in setting the departure time Under the premise of the same starting point / destination point and In the road network Time-varying road network generated after loading traffic information for the same period The minimum time-consuming path for "high-speed priority" is denoted as:

[0074] The predicted value for each edge's entry / exit time is:

[0075] These entry / departure times are calculated recursively based on time-varying traffic information:

[0076]

[0077] in, Based on the vehicle's departure time Then, historical road condition information for each side during the corresponding time period is obtained.

[0078] Furthermore, a high-speed supply vector is obtained based on the high-speed priority planning path.

[0079] In one implementation, POIs reachable via drive testing are searched along a high-speed priority path. For each type of demand, a set of POIs that meet the demand and are close to the path are selected. A feature vector is constructed for each POI, including dimensions such as arrival time, dwell time, space / POI, availability, convenience, and economy, and the probability of meeting the demand is calculated. Then, combined with weighting factors, the supply intensity of the high-speed priority path for that demand is calculated using a formula, and finally, the supply intensity of each demand category is combined into a high-speed service supply vector.

[0080] Specifically, along the highway priority path Search and road testing can reach Points of Interest (POIs) for each type of demand. The resulting POI set is:

[0081] set up For vehicles along the path After POI The moment of the closest point.

[0082] For each POI Construct feature vectors (Including dimensions such as arrival time, permitted stay duration, space / POI, availability, convenience, and cost-effectiveness).

[0083] The POI facility meets the category requirements. The probability of demand:

[0084] This high-speed priority path is defined for The supply intensity (scalar) is:

[0085] in These are optional weights, representing factors such as the urgency of meeting needs. If... ,but Furthermore, the supply vector for this high-speed priority path is defined as:

[0086] According to the scheme in this application, the vehicle's driving trajectory is reconstructed based on the truck's vehicle-to-everything (V2X) trajectory, and a high-speed priority path is generated according to the high-speed priority principle. Fine-grained reconstruction of the truck's behavior on the ground is performed, analyzing vehicle demand and high-speed supply to reconstruct details of ground driving and parking.

[0087] S103 determines the acceptability of the high-speed priority path, and if the acceptability meets the preset conditions, the high-speed priority path is determined to be acceptable.

[0088] Specifically, the time rationality score, access convenience score, high-speed ratio score, and service demand satisfaction score of the high-speed priority path are calculated; based on the time rationality score, access convenience score, high-speed ratio score, and service demand satisfaction score, a weighted sum is performed using preset weights to calculate the acceptability of the high-speed priority path; when the acceptability is greater than or equal to the preset acceptability threshold, the high-speed priority path is determined to be acceptable.

[0089] Specifically, the service demand satisfaction score is used to characterize the level of service demand satisfaction of vehicles, where service demand can be determined based on ground travel routes, and service supply is determined by the services available from high-speed priority candidate routes.

[0090] The time reasonableness score is calculated based on the total time of the ground travel route and the total time of the highway priority route. Time reasonableness score:

[0091] in, This represents the total time for the ground travel route. This represents the total time for the high-speed priority path. For a tolerable slowdown ratio (e.g., 0.05–0.10). For extremely small positive numbers, avoid dividing by zero, and the same applies below.

[0092]

[0093] Will Cut off to Within, this formula ensures (The same applies below.)

[0094] Furthermore, based on the total mileage of the ground driving route, the total mileage of the highway priority route, and the baseline mileage between trips, the access convenience score is calculated.

[0095] Accessibility score: Consider combining the ease of detours and access to / from highways into a cost, and then converting it into a score.

[0096]

[0097]

[0098] in, This represents the total distance of the ground travel route. This represents the total mileage of the high-speed priority path. The baseline mileage between O and D is the smaller of the ground driving path mileage and the shortest path mileage between O and D, serving as a more objective baseline. The purpose is to prevent It is inherently unreasonable.

[0099] The parameter is used to measure the inconvenience of getting on and off highways. The higher the value, the higher the cost of convenience to take a detour via the highway. This measures the inconvenience caused by factors such as road conditions and the number of traffic lights on highway connecting roads, in addition to the detour mileage. The penalty coefficient for inconvenience; The acceptable overall detour threshold (e.g., 0.20).

[0100] The high-speed ratio score is calculated based on the total mileage of the high-speed priority path and the high-speed mileage within the total mileage of the high-speed priority path.

[0101] High-speed percentage score: If the percentage of high-speed routes is too low in the high-speed priority path, then the value of this path is not significant.

[0102]

[0103] in, This represents the total mileage of the high-speed priority path. This represents the mileage of high-speed routes within the total mileage of the high-speed priority path.

[0104] This represents the minimum expected percentage of high-speed travel.

[0105] The demand vector for vehicles is determined based on intermediate stops along the journey, the supply vector for highways is determined based on highway priority paths, and the service demand satisfaction score is calculated based on the demand vector and supply vector.

[0106] Service demand fulfillment score: As previously stated ,have:

[0107] in, For the weighting coefficients of various demands, there are ; The total threshold for tolerable gaps.

[0108] Calculate each type of requirement The gap is only penalized for the portion where "supply falls short of demand," and if there is no demand ( or supply exceeds demand ( No points will be deducted.

[0109] Considering the rationality and acceptability of prioritizing high-speed routes:

[0110] Among them, weight .

[0111] If acceptable If the value exceeds the set threshold, the high-speed priority path is considered acceptable; otherwise, it is considered unacceptable.

[0112] In one implementation, the acceptability of a high-speed priority path is determined. If preset conditions are met, such as saving preset time or saving preset transportation costs, the high-speed priority path is considered acceptable.

[0113] In one implementation, the method further includes: determining that the high-speed priority path is unacceptable when the time reasonableness score is less than a preset first threshold; or, determining that the high-speed priority path is unacceptable when the access convenience score is less than a preset second threshold; or, determining that the high-speed priority path is unacceptable when the high-speed proportion score is less than a preset third threshold; or, determining that the high-speed priority path is unacceptable when the service demand satisfaction score is less than a preset fourth threshold; or, determining that the high-speed priority path is acceptable when the time reasonableness score is greater than or equal to the preset first threshold, the access convenience score is greater than or equal to the preset second threshold, the high-speed proportion score is greater than or equal to the preset third threshold, and the service demand satisfaction score is greater than or equal to the preset fourth threshold; and recording the reason for unacceptability when the high-speed priority path is unacceptable.

[0114] Understandably, the implementation method of this application sets thresholds for the acceptability judgment parameters of each dimension, such as time reasonableness, access convenience, high-speed ratio, and POI satisfaction, and can define differentiated acceptability standards, which will not be elaborated here. Furthermore, if the high-speed priority path is determined to be unacceptable, the reason for unacceptability is recorded, such as unreasonable time, inconvenient access, or unmet needs (determined by each dimension parameter). The reason for unacceptability can help improve the competitiveness of high-speed routes. For example, if POI satisfaction is not met, it can instruct high-speed routes to build corresponding POIs and services. If the high-speed priority path has no absolute defects in any dimension, it indicates that the high-speed priority path is less competitive than ground-based routes, and can be optimized through measures such as differentiated pricing.

[0115] S104 establishes relational data representing vehicles, ground driving paths, highway priority paths, highway priority path acceptability, and service demand supply based on ground driving paths and corresponding highway priority paths, and constructs a highway diversion analysis unit based on the relational data.

[0116] Furthermore, based on ground driving paths and corresponding highway priority paths, relational data representing vehicles, ground driving paths, highway priority paths, highway priority path acceptability, and service demand and supply are established. A highway traffic diversion analysis unit is then constructed based on this relational data. This includes extracting vehicle-dimensional information, highway priority path-dimensional information, ground path-dimensional information, service demand and supply-dimensional information, and highway priority path acceptability-dimensional information to obtain relational data representing vehicles, ground driving paths, highway priority paths, highway priority path acceptability, and service demand and supply; and constructing a highway traffic diversion analysis unit based on this relational data.

[0117] In one implementation, the service demand-supply relationship refers to the relationship between the service demand corresponding to the vehicle's ground travel path and the services that the corresponding high-speed priority path can provide.

[0118] In one implementation, vehicle-dimensional information is obtained based on vehicle identification, trip start and end points, and departure time data; highway-priority path-dimensional information is obtained based on highway entrances, exits, highway segment edge order, highway path length, highway road resistance, and toll information; ground path-dimensional information is obtained based on the edge order, ground path length, and ground road resistance of the ground driving path; service demand and supply-dimensional information is obtained based on the highway supply vector and vehicle demand vector; and acceptability-dimensional information is obtained based on the acceptability of the highway-priority path, as well as its time rationality score, access convenience score, highway proportion score, and service demand satisfaction score.

[0119] Based on the obtained vehicle ground paths, highway priority paths, and their analysis results, this application can abstract the following: Figure 3 The four types of objects and their relationships are shown: vehicles, ground routes, highway routes, and demand / supply. For each ground route, a corresponding "highway priority" candidate route can be constructed that can be compared at the same departure time; the parking demand of vehicles is inferred from intermediate stopping points, and the supply of POIs along the route is represented by roadside facilities.

[0120] Under the constraint of "meeting demand," the costs (time, detour convenience, tolls, etc.) and benefits that vehicles incur when switching from ground to highway can be quantified. If a vehicle does not switch, the reasons can be attributed to gaps such as service deficiencies, inconvenient access, or insufficient time advantage. These dimensional relationships constitute the core analytical framework for highway traffic diversion conditions. This framework can summarize and evaluate the traffic diversion value of sections and vehicle groups, identify the types of services that need to be supplemented on the highway side, and provide a basis for optimizing operational strategies such as differentiated tolling. Furthermore, this structure can be transferred to diversion scenarios with only toll gantry data, supplementing the corresponding behavioral and service profiles on the ground side. In one implementation, the highway traffic diversion analysis unit stores comparable and traceable relationship data between feasible ground paths and priority highway paths at the same origin-destination (OD) and departure time.

[0121] In one implementation, the constructed high-speed drainage analysis unit is shown below: ; in, The corresponding vehicle dimension includes the vehicle's ID, OD (Original Design Location), and departure time.

[0122] The corresponding high-speed priority path dimension includes the high-speed entrance, exit, edge order of high-speed segments, high-speed path length, road resistance at high speeds (including expected travel time, road conditions such as speed limits, etc.), and tolls.

[0123] The corresponding ground path dimension refers to the ground path corresponding to the highway entrance and exit, including the edge order of the ground path, the length of the ground path, and the road resistance of the ground path (including travel time, road conditions such as speed limits, traffic lights, etc.).

[0124] Corresponding to the supply and demand dimensions, it covers the set of high-speed accessible POIs and the service characteristics they can provide, the set of ground accessible POIs and the service characteristics they can provide, as well as the gap between the two.

[0125] It records the competitive landscape between high-speed and ground routes, including the acceptability of high-speed priority routes and evaluation scores for various dimensions.

[0126] This application assesses the rationality of highway priority routes from dimensions such as time rationality, detour and access convenience rationality, highway proportion rationality, and POI demand satisfaction rationality, making the conclusions quantifiable, traceable, and auditable. By generating highway diversion analysis units, an integrated expression framework of "vehicle-road-demand / supply-assessment" is constructed to achieve quantitative assessment of diversion conditions.

[0127] S105 performs merge calculations based on the high-speed traffic diversion analysis unit to obtain high-speed sections and target vehicles with traffic diversion value.

[0128] Merging calculations for high-speed drainage analysis units can support (but are not limited to) the following typical applications: In one implementation, data is aggregated for the high-speed traffic diversion analysis unit based on the high-speed priority path dimension. The number of vehicles in each high-speed section corresponding to the high-speed priority path and the competition score of the high-speed priority path relative to the ground path are counted. Based on the number of vehicles and the competition score, a list of high-speed sections with traffic diversion value is determined.

[0129] Specifically, the number of vehicles in each highway section is counted, as well as the competition score of the highway priority path relative to the ground path. The competition score is used to quantitatively represent the competitive advantage of the highway priority path relative to the ground path. The competition score can be determined based on the acceptability of the highway priority path. The traffic attraction value of the highway section is quantified by the weighted sum of the number of vehicles and the competition score, and a list of highway sections with high traffic attraction value is selected and output.

[0130] Furthermore, data from the high-speed traffic diversion analysis unit is aggregated at the vehicle dimension to identify high-frequency vehicles, statistically analyze the correlation between high-frequency vehicle travel and each high-speed section, and output target vehicles with traffic diversion value and traffic diversion time periods. The correlation includes the potential frequency and time period of selecting high-speed sections.

[0131] High-frequency vehicles are selected as potential vehicles, and the correlation between the travel of potential vehicles and various highway sections is analyzed. This correlation includes selecting the potential frequency and time period of highway sections to help identify the target vehicles and reach windows for traffic diversion.

[0132] Identify vehicle travel patterns, such as typical travel times. For example, if a vehicle usually departs between 7:00 AM and 9:00 AM on weekdays, this period can be considered a potential window for attracting customers.

[0133] Furthermore, data from the high-speed traffic diversion analysis unit is aggregated based on the service demand and supply dimensions to obtain the service demand vector of ground routes and the service supply vector corresponding to high-speed priority routes. The service gaps in each high-speed section are statistically analyzed to obtain the types and priorities of services that need to be supplemented. Data from the high-speed traffic diversion analysis unit is also aggregated based on the acceptability dimension of high-speed priority routes. Based on the time rationality score, access convenience score, high-speed ratio score, service demand satisfaction score, and the types and priorities of services that need to be supplemented, traffic diversion strategies corresponding to high-speed sections with traffic diversion value and target vehicles are generated.

[0134] Specifically, by analyzing demand and supply data, service gaps for each type of service within each highway section are determined. Prioritization criteria for these service gaps are then established based on factors such as the type of gap. For example, services with larger gaps have higher priority; services related to basic needs such as safety and rest also have higher priority.

[0135] Furthermore, based on Based on the recorded scores across various dimensions, and considering the competitive landscape between highway sections and surface routes, targeted coupons are designed to compensate for and adjust this competitive situation, thereby attracting traffic. For example, gas station fees are higher on highways compared to surface roads; therefore, targeted gas coupons can be issued to adjust the competitive landscape and attract more customers.

[0136] In another embodiment of this application, the high-speed drainage analysis method includes: Vehicle trips are segmented based on vehicle network mobile location data to obtain trip sequences that include the trip start point, intermediate stops and the trip end point; Match the trip sequence with the road network to reconstruct the ground driving path of the vehicle on the road network; plan the highway priority path based on the same trip start and end point; Based on intermediate stop points and high-speed priority path POI data, determine the supply and demand relationship; Calculate the time rationality score, access convenience score, high-speed ratio score, and service demand satisfaction score of the high-speed priority path. Based on the time rationality score, access convenience score, high-speed ratio score, and service demand satisfaction score, calculate the acceptability of the high-speed priority path. When the acceptability is greater than or equal to the preset acceptability threshold, the high-speed priority path is determined to be acceptable.

[0137] Extract vehicle-level information, highway priority path-level information, ground path-level information, service demand and supply-level information, and highway priority path acceptability-level information to obtain relational data representing the relationship between vehicles, ground driving paths, highway priority paths, and highway service demand and supply; based on the relational data, generate highway traffic diversion analysis units.

[0138] Based on the high-speed traffic diversion analysis unit, the merged calculation is performed to obtain the high-speed sections with traffic diversion value, target vehicles, and corresponding traffic diversion strategies.

[0139] The method provided in this application, based on the vehicle network trajectory of trucks, reconstructs the vehicle's driving trajectory and generates a high-speed priority path for the vehicle according to the high-speed priority principle; it performs fine-grained reconstruction of the truck's behavior on the ground to accurately analyze the relationship between vehicle demand and supply. The rationality of the high-speed priority path is quantitatively evaluated from multiple dimensions, including time rationality, detour and access convenience rationality, high-speed proportion rationality, and POI service satisfaction rationality. Based on the rationality conclusion of the high-speed priority path, an integrated expression framework of "vehicle-road-demand / supply-evaluation" is constructed. This framework objectively quantifies diversion conditions at the segment × time scale, identifies diversionable segments and their causes, and migrates the model to high-speed operation toll data for diversion diagnosis, providing feedback and reinforcing the model's effectiveness.

[0140] According to another aspect of the embodiments of this application, an apparatus for analyzing highway diversion conditions for implementing the above-described method for analyzing highway diversion conditions is also provided. For example... Figure 4 As shown, the device includes: The trip analysis module 401 is used to divide the vehicle trip based on the vehicle network mobile location data to obtain a trip sequence including the trip start point, intermediate stops and the trip end point; The route planning module 402 is used to match the trip sequence with the road network, reconstruct the ground driving path of the vehicle on the road network, and plan the high-speed priority path based on the same trip start and end point as the ground driving path; The acceptability analysis module 403 is used to determine the acceptability of the high-speed priority path. When the acceptability meets the preset conditions, the high-speed priority path is determined to be acceptable. Traffic diversion analysis module 404 is used to establish relational data representing vehicles, ground driving paths, highway priority paths, highway priority path acceptability, and service demand supply based on ground driving paths and corresponding highway priority paths, and to construct a highway traffic diversion analysis unit based on the relational data. The merging calculation module 405 is used to perform merging calculations based on the high-speed diversion analysis unit to obtain high-speed sections and target vehicles with diversion value.

[0141] It should be noted that the highway diversion condition analysis device provided in the above embodiments is only illustrated by the division of the above functional modules when executing the highway diversion condition analysis method. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the highway diversion condition analysis device and the highway diversion condition analysis method embodiments provided in the above embodiments belong to the same concept, and the implementation process is detailed in the method embodiments, which will not be repeated here.

[0142] According to another aspect of the embodiments of this application, an electronic device corresponding to the highway diversion condition analysis method provided in the foregoing embodiments is also provided to execute the highway diversion condition analysis method described above.

[0143] Please refer to Figure 5 This illustrates a schematic diagram of an electronic device provided by some embodiments of this application. For example... Figure 5 As shown, the electronic device includes: a processor 500, a memory 501, a bus 502, and a communication interface 503. The processor 500, the communication interface 503, and the memory 501 are connected via the bus 502. The memory 501 stores a computer program that can run on the processor 500. When the processor 500 runs the computer program, it executes the highway traffic diversion condition analysis method provided in any of the foregoing embodiments of this application.

[0144] The memory 501 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 503 (which can be wired or wireless), such as the Internet, wide area network, local area network, or metropolitan area network.

[0145] Bus 502 can be an ISA bus, PCI bus, or EISA bus, etc. Buses can be divided into address buses, data buses, control buses, etc. Memory 501 is used to store programs. After receiving execution instructions, processor 500 executes the program. The highway traffic diversion condition analysis method disclosed in any of the aforementioned embodiments of this application can be applied to processor 500, or implemented by processor 500.

[0146] The processor 500 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 500 or by instructions in software form. The processor 500 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules may reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 501. The processor 500 reads the information in memory 501 and, in conjunction with its hardware, completes the steps of the above method.

[0147] The electronic device provided in this application embodiment and the method for analyzing highway traffic diversion conditions provided in this application embodiment are based on the same inventive concept and have the same beneficial effects as the methods they adopt, operate or implement.

[0148] According to another aspect of the embodiments of this application, a computer-readable storage medium corresponding to the highway diversion condition analysis method provided in the foregoing embodiments is also provided, wherein a computer program (i.e., a program product) is stored thereon, and when the computer program is run by a processor, it executes the highway diversion condition analysis method provided in any of the foregoing embodiments.

[0149] It should be noted that examples of computer-readable storage media may also include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media, which will not be elaborated here.

[0150] The computer-readable storage medium provided in the above embodiments of this application and the method for analyzing highway diversion conditions provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the applications stored therein.

[0151] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0152] The above embodiments merely illustrate several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this patent should be determined by the appended claims.

Claims

1. A method for analyzing traffic diversion conditions on highways, characterized in that, include: Vehicle trips are segmented based on vehicle network mobile location data to obtain trip sequences that include the trip start point, intermediate stops and the trip end point; The travel sequence is matched with the road network to reconstruct the vehicle's ground travel path on the road network. Based on the same travel start and end point as the ground travel path, a high-speed priority path is planned. Determine the acceptability of the high-speed priority path, and determine that the high-speed priority path is acceptable when the acceptability meets a preset condition; Based on the ground driving path and the corresponding highway priority path, establish relational data representing vehicles, ground driving paths, highway priority paths, highway priority path acceptability, and service demand supply; and construct a highway traffic diversion analysis unit based on the relational data. Based on the high-speed traffic diversion analysis unit, the high-speed sections and target vehicles with traffic diversion value are obtained through merging calculations.

2. The method according to claim 1, characterized in that, Determining the acceptability of the high-speed priority path, and determining that the high-speed priority path is acceptable when the acceptability meets preset conditions, includes: Calculate the time rationality score, access convenience score, high-speed ratio score, and service demand satisfaction score of the high-speed priority path; Based on the time rationality score, access convenience score, high speed ratio score, and service demand satisfaction score, the acceptability of the high speed priority path is calculated by weighted summation using preset weights. When the acceptability is greater than or equal to a preset acceptability threshold, the high-speed priority path is determined to be acceptable.

3. The method according to claim 2, characterized in that, The time rationality score is calculated based on the total time of the ground driving route and the total time of the highway priority route. The access convenience score is calculated based on the total mileage of the ground driving route, the total mileage of the highway priority route, and the baseline mileage between trips. The high-speed ratio score is calculated based on the total mileage of the high-speed priority path and the high-speed mileage in the total mileage of the high-speed priority path. The service demand vector of the vehicle is determined based on the intermediate stops in the trip, the highway service supply vector is determined based on the highway priority path, and the service demand satisfaction score is calculated based on the service demand vector and the service supply vector.

4. The method according to claim 2, characterized in that, Also includes: When the time reasonableness score is less than a preset first threshold, the high-speed priority path is determined to be unacceptable. or, When the accessibility score is less than a preset second threshold, the high-speed priority path is determined to be unacceptable; or, When the high-speed ratio score is less than a preset third threshold, the high-speed priority path is determined to be unacceptable; or, When the service demand satisfaction score is less than a preset fourth threshold, the high-speed priority path is determined to be unacceptable. or, When the time rationality score is greater than or equal to a preset first threshold, the access convenience score is greater than or equal to a preset second threshold, the high-speed ratio score is greater than or equal to a preset third threshold, and the service demand satisfaction score is greater than or equal to a preset fourth threshold, the high-speed priority path is determined to be acceptable. If the high-speed priority path is unacceptable, record the reason why it is unacceptable.

5. The method according to claim 1, characterized in that, Based on the ground driving paths and corresponding highway priority paths, establish relational data representing vehicles, ground driving paths, highway priority paths, highway priority path acceptability, and service demand supply. Based on this relational data, construct a highway traffic diversion analysis unit, including: Extract vehicle-level information, highway priority path-level information, ground path-level information, service demand and supply-level information, and highway priority path acceptability-level information to obtain data representing the relationship between vehicles, ground driving paths, highway priority paths, highway priority path acceptability, and service demand and supply. Based on the aforementioned relationship data, a high-speed traffic diversion analysis unit is constructed.

6. The method according to claim 1, characterized in that, Based on the high-speed traffic diversion analysis unit, a merge calculation is performed to obtain high-speed sections and target vehicles with traffic diversion value, including: Data is aggregated for the high-speed traffic diversion analysis unit based on the high-speed priority path dimension. The number of vehicles in each high-speed section corresponding to the high-speed priority path and the competition score of the high-speed priority path relative to the ground path are counted. Based on the number of vehicles and the competition score, a list of high-speed sections with traffic diversion value is determined. Data is aggregated from the perspective of vehicles in the high-speed traffic diversion analysis unit to identify high-frequency vehicles. The correlation between the travel of these high-frequency vehicles and each high-speed section is statistically analyzed, and target vehicles with traffic diversion value and traffic diversion time periods are output. The correlation includes the potential frequency and time period of the selected high-speed section.

7. The method according to claim 1 or 6 further includes performing a merging calculation based on the high-speed diversion analysis unit to obtain high-speed sections with diversion value and diversion strategies corresponding to target vehicles, characterized in that, Data is aggregated from the perspectives of service demand and supply in the high-speed traffic diversion analysis unit to obtain the service demand vector of the ground path and the service supply vector corresponding to the high-speed priority path. The service gap of each high-speed section is statistically analyzed to obtain the types and priorities of services that need to be supplemented. Data from the high-speed traffic diversion analysis unit is aggregated based on the acceptability dimension of high-speed priority paths. Based on the time rationality score, access convenience score, high-speed ratio score, service demand satisfaction score, and the types and priorities of services that need to be supplemented, traffic diversion strategies are generated for high-speed sections and target vehicles with traffic diversion value.

8. An analysis device for highway traffic diversion conditions, characterized in that, include: The trip analysis module is used to divide vehicle trips based on vehicle network mobile location data to obtain a trip sequence that includes the trip start point, intermediate stops and the trip end point; The route planning module is used to match the trip sequence with the road network, reconstruct the ground driving path of the vehicle on the road network, and plan a high-speed priority path based on the same trip start and end point as the ground driving path; The acceptability analysis module is used to determine the acceptability of the high-speed priority path, and when the acceptability meets the preset conditions, the high-speed priority path is determined to be acceptable. The traffic diversion analysis module is used to establish relational data representing vehicles, ground driving paths, highway priority paths, highway priority path acceptability, and service demand supply based on the ground driving paths and corresponding highway priority paths, and to construct a highway traffic diversion analysis unit based on the relational data. The merging calculation module is used to perform merging calculations based on the high-speed traffic diversion analysis unit to obtain high-speed sections and target vehicles with traffic diversion value.

9. An electronic device, characterized in that, It includes a processor and a memory storing program instructions, the processor being configured to, when executing the program instructions, perform the method for analyzing highway diversion conditions as described in any one of claims 1 to 7.

10. A computer-readable medium, characterized in that, It stores computer-readable instructions that are executed by a processor to implement a method for analyzing highway diversion conditions as described in any one of claims 1 to 7.

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