Method and device for determining traffic volume data based on multi-source data, equipment and medium

Abstract

The invention provides a method and device for determining traffic volume data based on multi-source data, equipment and a medium. The specific implementation scheme is as follows: obtaining original traffic volume data of a target road section collected by each traffic data source; wherein the acquisition range of each traffic data source covers a target road section, and the type of the traffic data source comprises at least one of an electronic toll station, a common highway toll station, an over-limit detection station and a bridge health monitoring station; converting the original traffic volume data of the target road section acquired by each traffic data source into standard traffic volume data with a uniform format; and determining first target traffic volume data of the target road section based on the standard traffic volume data of the target road section corresponding to each traffic data source. According to the scheme provided by the invention, the traffic volume of the specific road section in the target time period can be determined through multi-source data complementation, so that the quality and integrity of traffic data acquired by the cross dispatching station are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent transportation, and in particular to a method and device for determining traffic volume data based on multi-source data, equipment and a medium. BACKGROUND

[0002] Traffic volume statistical investigation stations (hereinafter referred to as "traffic investigation stations") are stations for collecting and monitoring road traffic data in the field of transportation. They collect, transmit and store traffic data such as road network traffic volume and vehicle axle load in real time through professional equipment such as sensors, monitoring devices and communication devices, and can provide decision-making basis for highway planning and construction, traffic operation management, emergency disposal, etc. During the process of traffic condition statistical investigation, due to factors such as equipment failure, network failure, mechanical and electrical failure, and weather, the collected data of the traffic investigation stations of some road sections may be missing or incorrect, so that the collected data of the traffic investigation stations cannot effectively meet the needs of planning and construction, traffic scheduling, reducing congestion, etc., and the operation efficiency and safety of the transportation system are reduced. SUMMARY

[0003] The embodiments of the present application provide a method, device, equipment and medium for determining traffic volume data based on multi-source data, to alleviate or solve one or more technical problems existing in the prior art.

[0004] In a first aspect, the embodiments of the present application provide a method for determining traffic volume data based on multi-source data, comprising:

[0005] obtaining original traffic volume data of a target road section collected by each traffic data source; wherein the collection range of each traffic data source covers the target road section, and the type of the traffic data source includes at least one of an electronic non-stop toll station, a general highway toll station, an over-limit detection station, and a bridge health monitoring station;

[0006] converting the original traffic volume data of the target road section collected by the traffic data source into standard traffic volume data with unified format;

[0007] determining first target traffic volume data of the target road section based on the standard traffic volume data of the target road section corresponding to each traffic data source.

[0008] In a second aspect, the embodiments of the present application provide a device for determining traffic volume data based on multi-source data, comprising:

[0009] an original traffic volume determination module configured to obtain original traffic volume data of a target road section collected by each traffic data source; wherein the collection range of each traffic data source covers the target road section, and the type of the traffic data source includes at least one of an electronic non-stop toll station, a general highway toll station, an over-limit detection station, and a bridge health monitoring station;

[0010] The standard traffic volume conversion module is configured to convert original traffic volume data of a target road section collected by a traffic data source into standard traffic volume data in a unified format.

[0011] The target traffic volume determination module is configured to determine first target traffic volume data of the target road section based on the standard traffic volume data of the target road section corresponding to each traffic data source.

[0012] In a third aspect, an electronic device is provided, including a processor and a memory, the memory storing instructions, the instructions being loaded and executed by the processor to implement the method of any of the embodiments.

[0013] In a fourth aspect, a computer readable storage medium is provided, the computer readable storage medium storing a computer program, the computer program being executed by a processor to implement the method of any of the embodiments.

[0014] In a fifth aspect, a computer program product is provided, including a computer program, the computer program being executed by a processor to implement the method of any of the embodiments.

[0015] The traffic data processing method of the embodiments can determine the traffic volume of a specific road section in a target time period through multi-source data complementation, improve the quality and integrity of the traffic data collected by the traffic management station, and thus meet the traffic management requirements and improve the overall operation efficiency of the traffic system.

[0016] The above summary is merely intended to illustrate the present description and is not intended to limit in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present application will be readily apparent to those skilled in the art by reference to the drawings and the following detailed description. BRIEF DESCRIPTION OF DRAWINGS

[0017] In the drawings, like numerals refer to like elements throughout the various drawings. The drawings are not necessarily to scale, the emphasis instead being placed on the relations between various elements. It should be understood that the drawings only depict some embodiments in accordance with the present disclosure and should not be considered limiting of the scope of the disclosure.

[0018] Figure 1 A flowchart of a method for determining traffic volume data based on multi-source data according to an embodiment of the present disclosure is shown.

[0019] Figure 2 A flowchart of a method for calculating the traffic volume of a target road section based on upstream and downstream traffic data sources according to an embodiment of the present disclosure is shown.

[0020] Figure 3A schematic diagram showing calculation of traffic volume of a target road section based on navigation sample data according to an embodiment of the present application is shown.

[0021] Figure 4 A structural block diagram of an apparatus for determining traffic volume data based on multi-source data according to an embodiment of the present application is shown.

[0022] Figure 5 A block diagram of an electronic device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0023] Embodiments of the present application will be described in more detail by referring to the attached drawings. While certain embodiments of the present application are shown in the drawings, it is understood that the present application can be embodied in various forms and should not be construed as being limited to the embodiments set forth herein, but rather, these embodiments are provided so that the present application will be more thoroughly and completely understood. It should be understood that the drawings and embodiments of the present application are for illustrative purposes only and are not intended to limit the scope of the present application.

[0024] The following terms will be used hereinafter. It should be noted that the following is a simple explanation of the basic concepts involved in the embodiments of the present application, and it should be understood that the basic concepts introduced below do not limit the embodiments of the present application.

[0025] Highway traffic condition survey: referred to as highway traffic survey, refers to the process of using professional equipment and scientific methods to systematically collect and analyze key traffic characteristics such as traffic flow, vehicle speed, axle load, etc. The data obtained by highway traffic survey is an important basis for decision-making in highway planning, construction, operation management, and emergency disposal.

[0026] Traffic flow: referred to as traffic volume, refers to the number of vehicles or pedestrians passing through a road lane, a location or a cross section per unit of time, generally counted in hours, days or years. Among them, cross section traffic volume is a commonly used index to measure the traffic conditions of highways in the field of highway transportation, which is directly obtained by the traffic volume survey station set on the highway.

[0027] Highway ETC (Electronic Toll Collection): Highway ETC can collect vehicle information (license plate number, vehicle type, color, brand), vehicle pass data (passing time, passing location, driving direction), vehicle speed and driving state, etc. Data, providing accurate toll services and comprehensive traffic management support, improving the intelligent level of traffic management.

[0028] The interchange area collection road network: referred to as the regional road network, is an area traffic volume collection system composed of automatic interworking stations and ETCs of expressways. The interchange area collection road network can cover key nodes such as city boundaries or provincial boundaries, and can connect areas with dense traffic flow such as city entrances and exits, tourist attractions, and logistics parks to reflect the traffic volume distribution characteristics of national, provincial, or county road networks.

[0029] In the process of traffic condition statistical investigation, due to factors such as equipment failure, network failure, mechanical and electrical failure, and weather, the traffic volume collection data of some road sections collected by ordinary interworking stations may be missing, so that the interchange data collected by ordinary interworking stations cannot meet the needs of road planning and construction, traffic scheduling, and congestion reduction, thereby reducing the operation efficiency and safety of the traffic system.

[0030] The method for determining traffic volume data based on multi-source data provided in the embodiments of the present application can complement multi-source data to determine the traffic volume of a specific road section in a target time period, improve the quality and integrity of the traffic data collected by interchange stations, and thus meet the needs of traffic management and improve the overall operation efficiency of the traffic system.

[0031] Figure 1 A flowchart of the method for determining traffic volume data based on multi-source data according to the embodiments of the present application is shown. Referring to Figure 1 The method for determining traffic volume data based on multi-source data provided in the embodiments of the present application specifically includes the following steps:

[0032] Step S101: Obtain the original traffic volume data of a target road section collected by each traffic data source; wherein the collection range of each traffic data source covers the target road section; and the type of the traffic data source includes at least one of an electronic non-stop toll station, an ordinary highway toll station, an over-limit detection station, and a bridge health monitoring station.

[0033] Step S102: Convert the original traffic volume data of the target road section collected by each traffic data source into standard traffic volume data with unified format.

[0034] Step S103: Determine the first target traffic volume data of the target road section based on the standard traffic volume data of the target road section corresponding to each traffic data source.

[0035] Exemplarily, the target road section can be a road section for which the traffic volume data corresponding to a target time period is missing, and the traffic volume corresponding to the target time period needs to be estimated, such as a highway road section or an urban trunk road section for which the traffic volume data is missing. In other examples, the target road section can also be a road section for which the traffic volume data corresponding to the target time period actually exists, but the traffic volume corresponding to the target time period needs to be verified or calibrated.

[0036] Exemplarily, the traffic data source can be various monitoring or management stations capable of collecting traffic data of the target road section, and types of the traffic data source can include at least one of an electronic non-stop toll station, a general highway toll station, an overload detection station, a bridge health monitoring station, a license plate recognition station, and an off-site law enforcement station.

[0037] In the road network collected in the highway traffic regulation area, in addition to the general traffic regulation station, the highway ETC gantry, the general highway toll station, the overload detection station, the vehicle detector, the license plate recognition station, and the bridge health monitoring station can also collect part of the traffic volume data. For example, the highway ETC gantry can collect vehicle entry information, vehicle passing time, vehicle type, gantry billing mileage, billing amount, and other information through the vehicle-mounted electronic tag or the vehicle electronic pass card. Among them, the vehicle passing time, the vehicle type, and the gantry billing mileage can be converted into standard traffic volume data of the target road section. The overload detection station can be used to detect whether the vehicle is overloaded, and by converting the traffic data such as vehicle passing time, license plate, vehicle type, and axle load data recorded by the overload detection station, the traffic volume data of the target road section can be supplemented. The bridge health monitoring station can monitor the health of the bridge through sensors, and by converting the traffic data such as the number of vehicle passing, the vehicle type, and the axle load data recorded by the bridge health monitoring station, the traffic volume data of the target road section can be supplemented. In this way, in the case that the traffic volume data collected by the general traffic regulation station is missing or has errors, the traffic volume of the target road section can be supplemented or verified by converting and fusing the data of multiple traffic volume data sources covering the target road section.

[0038] It should be noted that the specific types of the traffic data source described above are only for illustration and do not limit the present application. Those skilled in the art can flexibly set the traffic data source used according to the actual situation.

[0039] In an embodiment, the standard traffic volume data can include at least one of a data source number, a vehicle type, a vehicle license plate, a driving direction, a standard observation time, a vehicle speed, and axle load information.

[0040] Exemplarily, the original traffic volume data can be traffic data directly collected by each traffic data source. For example, the original traffic volume data of an electronic non-stop toll station can include ETC tag identification information, entry station code, exit station code, transaction time, vehicle type code, and the like, the original traffic volume data of a general highway toll station can include toll lane number, manually entered license plate, toll amount, release time, handwritten vehicle type notes, and the like, and the original traffic volume data of an oversize detection station can include detection equipment identification information, vehicle axle group information, weighing data, detection passing time, oversize percentage, and the like. The standard traffic volume data in a unified format can be traffic volume data obtained by converting the original traffic volume data, which is completely consistent in field structure, data type, value range, time granularity, and the like. In an example, the types of the standard traffic volume data can include at least one of data source number, vehicle type, vehicle license plate, driving direction, standard observation time, vehicle speed, and axle load information.

[0041] Exemplarily, the original traffic volume data of the target road section collected by each traffic data source can be converted into standard traffic volume data in a unified format. In an example, the original traffic volume data of the target road section collected by each traffic data source can be converted into standard traffic volume data in a unified format based on a preset data conversion model. The data conversion model can be a model for converting original traffic volume data of different data sources into standard traffic volume data. The data conversion model can read the original traffic volume data of each traffic data source, call corresponding analysis rules according to the data source type, extract effective information related to the standard traffic volume, map the extracted effective information to the corresponding field of the standard traffic volume data according to the corresponding mapping rules, and generate the standard traffic volume data of the target road section. After generating the standard traffic volume data in a unified format, the standard traffic volume data can be checked to remove errors, and finally determine the first target traffic volume data of the target road section.

[0042] The embodiments of the present application can convert original traffic volume data of different structures and different fields into standard traffic volume data in a unified format, so that the converted standard traffic volume data can be used for subsequent traffic volume statistical analysis, road section flow comparison, and the like. Through multi-source data complementation, the traffic volume of a specific road section in a target time period can be determined, and the quality and completeness of the traffic data collected by the interchange station can be improved.

[0043] In an embodiment, based on the standard traffic volume data of the target road section corresponding to each traffic data source, the first target traffic volume data of the target road section is determined, including: in the case that there are at least two types of traffic data sources, the types of the standard traffic volume data of the target road section corresponding to each traffic data source are integrated to determine the first target traffic volume data of the target road section.

[0044] Exemplarily, after the original traffic volume data of the target road section collected by each traffic data source is converted into the standard traffic volume data with unified format, in the case that there are at least two types of traffic data sources, the standard traffic volume data of the target road section corresponding to each traffic data source can be integrated based on the data types that the standard traffic volume data should include, that is, the converted standard traffic volume data of each traffic data source is fused to form the standard traffic volume data with comprehensive types. For example, the data types required by the standard traffic volume data can include the number of vehicles passing, the passing time of vehicles, and the axle load data of vehicles. The electronic non-stop toll station can only record the number of vehicles passing and the passing time of vehicles, and does not record the axle load data of vehicles, and the over-limit detection station records the axle load data of passing vehicles. At this time, the data collected by each traffic data source is fused based on the data types required by the standard traffic volume data according to the lack of data types of different traffic data sources to form the traffic volume data with comprehensive types. In this way, the embodiment of the present application solves the problem of incomplete and inconsistent data types under multiple data sources by supplementing the data types, and improves the integrity and consistency of the final calculated target traffic volume data.

[0045] In an embodiment, before determining the first target traffic volume data of the target road section, the method further includes: comparing the data values of the standard traffic volume data of the target road section collected by each traffic data source in the target time period, and correcting the abnormal values in the standard traffic volume data of the target road section.

[0046] Exemplarily, when the traffic data sources are two or more, the standard traffic volume data values of each traffic data source obtained after data type fusion can be subjected to numerical verification. Through certain verification rules, it is determined whether the data is reasonable, the abnormal values are identified and removed, the standard traffic volume data values of each data source after verification and removal of abnormal values are compared, and the existing deviation is corrected to finally obtain the first target traffic volume data of the target road section. For example, assuming that the target road section has two traffic data sources of ETC toll station and ordinary highway toll station in an hour, and the number of large trucks passing collected by the two traffic data sources is 30 and 280 respectively. Assuming that the verification rule is that the data value deviation of the same vehicle type exceeding 30% is determined as abnormal, and combined with the historical data of the road section (the number of large trucks passing per hour is more than 20 to 40), it can be determined that the 280 traffic volume data collected by the ordinary highway toll station is an abnormal value and is removed. In this way, through numerical verification and abnormal value removal, the interference of false data on the traffic volume calculation of the target road section can be reduced, the reliability of the data is improved, and thus the finally determined target traffic volume data can more truly and accurately reflect the traffic volume situation of the target road section.

[0047] In an embodiment, the original traffic data of each traffic data source on the target road section is converted into standard traffic volume data in a unified format, including: converting the original traffic data of each traffic data source on the target road section into standard traffic volume data in a standard time granularity; wherein the standard traffic volume data in the standard time granularity includes real-time traffic volume data or period traffic volume data.

[0048] For example, the time granularity can be the time interval unit for the traffic data source to count traffic volume data, such as counting traffic volume data in minutes, hours or days. Different traffic data sources can use different time granularities. Table 1 below shows the time granularities commonly used by some traffic data sources.

[0049] Table 1 Traffic volume data source and corresponding time granularity

[0050]

[0051] As shown in the above table, the traffic data sources such as highway ETC gantry, over-limit detection station, and bridge health monitoring station aggregate traffic volume based on single vehicle data, and their time granularity can be set to count traffic volume data in minutes, such as 1 minute or 5 minutes, or set to count traffic volume data in hours, days or years. The traffic volume data obtained from ordinary highway toll data usually has a time granularity of 1 day.

[0052] For example, the original traffic data of each traffic data source on the target road section can be converted into standard traffic volume data in a standard time granularity; wherein the standard traffic volume data in the standard time granularity includes real-time traffic volume data or period traffic volume data. For example, a preset standard time granularity (such as 5 minutes or 1 hour) can be determined, and for the original traffic data of each traffic data source on the target road section, the data can be aggregated or split according to the standard time granularity based on its own time record. If the time granularity of the original data is smaller than the standard time granularity, multiple original data in the same standard time granularity can be accumulated to obtain traffic volume data in the standard time granularity. If the time granularity of the original data is larger than the standard time granularity, the original data can be split according to its distribution characteristics in the time period to obtain traffic volume data in each standard time granularity. Finally, the processed data can be arranged into standard traffic volume data containing standard time granularity identifier, target road section identifier, vehicle type, traffic volume value, etc. If the standard traffic volume data uses a shorter standard time granularity (such as 1 minute or 5 minutes), it is real-time traffic volume data, and if it uses a longer standard time granularity (such as 1 day or 1 week), it is period traffic volume data.

[0053] The embodiment of the application can solve the problem of time dimension mismatch caused by different original data recording frequencies of different traffic data sources by unifying the time granularity of target traffic volume data, so that the converted standard traffic volume data can be directly compared and integrated at the time level, thereby improving the applicability and application value of traffic data.

[0054] In one embodiment, the traffic data sources can include electronic non-stop toll stations, and converting the original traffic data of each traffic data source corresponding to the target road section into standard traffic volume data in a unified format includes at least one of the following:

[0055] Converting the gantry number of the electronic non-stop toll station into a data source number;

[0056] Converting the observation period of the electronic non-stop toll station into a standard observation time;

[0057] Converting the charging vehicle type code collected by the electronic non-stop toll station into a vehicle type.

[0058] It should be noted that the traffic data collected by the ETC gantry and the traffic volume data collected by the ordinary traffic regulation station have a correlation. Specifically, from the perspectives of site layout and equipment operation, the ETC gantry system involves highway toll business, and its road network coverage range, equipment precision requirement, operation and maintenance level, and owner attention degree are superior to those of the physical highway traffic regulation station to a great extent. From the perspective of the matching relationship of specific data indicators of highway traffic regulation data and toll data, the part of the public fields of the two types of data are basically consistent, and the main indicator fields such as vehicle flow, vehicle type, location speed, total load and axle number of vehicles and goods, and driving direction completely meet the data conversion conditions. From the perspective of saving construction cost and avoiding repeated construction of information facilities, the highway traffic regulation station facilities can not be separately constructed in principle, and the automatic fusion of highway traffic regulation data and toll data can be realized through real-time data indicator automatic conversion function, which can complement multiple source data to determine the traffic volume of a specific road section in a target time period, thereby improving the quality and integrity of the collected traffic data of the traffic regulation station. Therefore, the ETC gantry resources can be used as the traffic regulation station in the highway traffic condition statistical survey to monitor and collect data, and the fusion application of ETC gantry data and traffic regulation data can be realized. Table 2 below shows the conversion relationship between the related data items of ETC gantry data and traffic regulation data.

[0059] Table 2 Conversion relationship between ETC gantry data and traffic regulation data

[0060]

[0061]

[0062]

[0063] Exemplarily, converting the original traffic data of each traffic data source corresponding to the target road section into the standard traffic volume data in the unified format can include at least one of the following: converting the gantry number of the electronic non-stop toll station into the data source number, converting the observation period of the electronic non-stop toll station into the standard observation time, and converting the charging vehicle type code collected by the electronic non-stop toll station into the vehicle type. By uniformly converting the original traffic data collected by the ETC gantry, the multi-source data complementarity is achieved, and the traffic volume of a specific road section in a target time period can be determined, thereby improving the quality and integrity of the traffic data collected by the interchange station.

[0064] In an embodiment, the traffic data source can include an overload detection station, and converting the original traffic data of each traffic data source corresponding to the target road section into the standard traffic volume data in the unified format can include at least one of the following:

[0065] converting the number of the overload detection station into the data source number;

[0066] converting the observation period of the overload detection station into the standard observation time;

[0067] converting the axle load detection data of the overload detection station into the axle load information.

[0068] It should be noted that the overload detection station is a special law enforcement station set up by the traffic and transportation management department to govern the vehicle overload behavior, and is mainly responsible for detecting, identifying and handling the overload of the running freight vehicles, so as to maintain the safety of highway facilities, ensure road traffic safety and normal transportation order. The overload detection station uses the method of sampling inspection record and statistics to obtain various vehicle type flow, total weight, axle number, etc. through data analysis. Therefore, the overload detection station can be used as an interchange station in the highway traffic condition statistical survey to monitor and collect data, and the fusion application of the overload detection station data and the interchange data can be realized. Table 3 below shows the data field name collected by the overload detection station. Table 4 below shows the traffic volume data indexes formed by the overload detection station data.

[0069] Table 3 Data field name collected by the overload detection station

[0070]

[0071]

[0072] Table 4 Traffic volume data indexes formed by the overload detection station data

[0073]

[0074] Exemplarily, the original traffic data of each traffic data source corresponding to the target road section can be converted into standard traffic volume data in a unified format, including at least one of the following: converting the number of the overload detection station into the data source number, converting the observation period of the overload detection station into the standard observation time, and converting the axle load detection data of the overload detection station into the axle load information. By converting the original traffic data collected by the overload detection station into a unified format, the multi-source data complementarity is realized, and the traffic volume of a specific road section in a target time period can be determined, thereby improving the quality and integrity of the traffic data collected by the traffic regulation station.

[0075] In an embodiment, the traffic data source includes a bridge health monitoring station, and converting the original traffic data of each traffic data source corresponding to the target road section into standard traffic volume data in a unified format includes at least one of the following:

[0076] converting the number of the bridge health monitoring station into the data source number;

[0077] converting the observation period of the bridge health monitoring station into the standard observation time;

[0078] converting the axle load detection data of the bridge health monitoring station into the axle load information.

[0079] It should be noted that the bridge health monitoring station is a professional monitoring and management system established for real-time mastering of the safety state of the bridge structure, and is usually composed of a sensor network, a data transmission system, a data processing and analysis platform, and a warning mechanism, and is widely used in the safety protection work of large bridges and important small and medium-sized bridges. The dynamic weighing system installed in the bridge monitoring system can efficiently and accurately measure the vehicle weight and axle load, and obtain the vehicle type, flow, total weight, and axle number of various vehicles through data analysis. Therefore, the bridge health monitoring station can be used as a traffic regulation station in the highway traffic condition statistical survey to perform data monitoring and collection, and the fusion application of the bridge health monitoring station data and the traffic regulation data is realized. Table 5 below shows the data field name collected by the bridge health monitoring station. Table 6 below shows the traffic volume data indexes formed by the bridge health monitoring station data.

[0080] Table 5 Data field name collected by the bridge health monitoring station

[0081]

[0082]

[0083] Table 6 Traffic volume data indexes formed by the bridge health monitoring station data

[0084]

[0085] Exemplarily, each traffic data source can convert the original traffic data corresponding to the target road section into standard traffic volume data in a unified format, including at least one of: converting the bridge health monitoring station number into a data source number, converting the observation period of the bridge health monitoring station into a standard observation time, and converting the axle load detection data of the bridge health monitoring station into axle load information. In this way, by converting the original traffic data collected by the bridge health monitoring station into a unified format, the multi-source data complementarity is realized, and the traffic volume of the specific road section in the target time period can be determined, thereby improving the quality and integrity of the traffic data collected by the traffic regulation station.

[0086] It should be noted that, between adjacent geographical areas, due to the convergence of factors such as economic development level, population distribution scale, traffic facility construction situation, and vehicle inflow and outflow situation, there is a significant correlation between the traffic volume data of geographically adjacent road sections.

[0087] Exemplarily, the traffic volume data of the target road section in the target time period can be estimated by the traffic volume data of the traffic data sources corresponding to the adjacent road sections of the target road section. Since the traffic flow has spatial continuity in the road network, the traffic volume of the upstream traffic data source of the target road section can reflect the vehicle flow state entering the target road section, and the traffic volume of the downstream traffic data source of the target road section can reflect the vehicle flow state output by the target road section. Therefore, by solving the traffic volume of the target road section in the target time period from the traffic volumes of the upstream traffic data source and the downstream traffic data source, the spatial correlation can be fully utilized to ensure the accuracy of the traffic volume estimation of the target road section.

[0088] Figure 2 A flowchart showing a method for calculating the traffic volume of a target road section based on upstream and downstream traffic data sources according to an embodiment of the present application is shown. In one embodiment, the method for determining traffic volume data based on multi-source data provided by the embodiment of the present application further comprises:

[0089] Step S201: determining the upstream traffic data source and the downstream traffic data source associated with the target road section;

[0090] Step S202: determining the second target traffic volume corresponding to the target road section in the target time period according to the original traffic volume data corresponding to the target time period of the upstream traffic data source and the downstream traffic data source.

[0091] According to the original traffic volume data corresponding to the upstream traffic data sources and the downstream traffic data sources respectively at the target time period, the second target traffic volume of the target road section at the target time period can be determined, which can include: converting the original traffic volume data of the target road section collected by each upstream traffic data source and downstream traffic data source into standard traffic volume data with unified format, calculating the weighted average value of the standard traffic volume data corresponding to the upstream traffic data sources and the downstream traffic data sources at the target time period, and taking the weighted average value as the second target traffic volume of the target road section at the target time period.

[0092] In the embodiments of the present application, the average value of the detection data of the traffic data sources of the two adjacent road sections before and after the target road section can be used to supplement the missing traffic volume of the target road section at the target time period.

[0093] According to the original traffic volume data corresponding to the upstream traffic data sources and the downstream traffic data sources respectively at the target time period, the second target traffic volume of the target road section at the target time period can be determined, which can include: converting the original traffic volume data of the target road section collected by each upstream traffic data source and downstream traffic data source into standard traffic volume data with unified format, calculating the weighted average value of the standard traffic volume data corresponding to the upstream traffic data sources and the downstream traffic data sources at the target time period, and taking the weighted average value as the second target traffic volume of the target road section at the target time period.

[0094] It should be noted that the specific determination method of the weight in the above weighted calculation is only for illustration and does not constitute a limitation to the present application, and those skilled in the art can flexibly set it according to the actual situation.

[0095] In this way, by calculating the weighted average value of the traffic volume data corresponding to the upstream traffic data sources and the downstream traffic data sources at the target time period through weighted calculation, and taking the weighted average value as the second target traffic volume of the target road section at the target time period, the missing traffic volume of the target road section can be more accurately estimated, the integrity and reliability of the traffic data can be improved, accurate data support can be provided for road planning, traffic scheduling and other applications, and the overall operation efficiency of the traffic system can be improved.

[0096] Figure 3 A schematic diagram for calculating the traffic volume of a target road section based on navigation sample data according to an embodiment of the present application is shown. According to the schematic diagram, the traffic volume of the target road section at the target time period can be calculated based on the traffic data sources of the two adjacent road sections before and after the target road section. Figure 3As shown, modern navigation application vendors have been able to indirectly collect a large amount of data about highway traffic conditions through their large user base and positioning technology. These map applications can record the travel paths and times of users and also track the movement trajectories of vehicles through GPS signals, thereby generating detailed reports about traffic flow, speed, and congestion. Navigation sample data can reflect the distribution characteristics of overall road network flow to some extent, and thus can be combined with navigation sample data to estimate traffic volume on road segments without highway traffic station coverage.

[0097] Exemplarily, with reference to the traffic volume of road segments with both mobile phone navigation sample data and highway traffic station data, the flow ratio between different road segments can be obtained through navigation sample data, the traffic volume of road segments with navigation sample data but without highway traffic station collected data can be estimated, and finally the traffic volume of road segments with both types of data can be estimated. The accuracy of the algorithm can be evaluated through the error rate of the estimated value and the actual value, and the calculation formula is as follows:

[0098]

[0099] Wherein, Q can be the traffic volume of the blank road segment to be estimated, Q l may be the traffic volume of the reference highway traffic station covered road segment, Qs can be the navigation sample data of the road segment to be estimated, and Qs l may be the navigation sample data of the reference highway traffic station covered road segment.

[0100] In this way, by calculating the traffic volume of road segments without highway traffic station collected data through navigation sample data, multi-source data complementarity can be achieved, the traffic volume of a specific road segment in a target time period can be determined, and the quality and completeness of traffic data collected by traffic stations can be improved.

[0101] Embodiments of the present application also provide a device for determining traffic volume data based on multi-source data. Figure 4 A structural block diagram of a device for determining traffic volume data based on multi-source data according to an embodiment of the present application is shown. As shown, Figure 4 the device provided by the embodiments of the present application can include:

[0102] An original traffic volume determination module 401 is configured to obtain original traffic volume data of a target road segment collected by each traffic data source; the collection range of each traffic data source covers the target road segment; the type of the traffic data source includes at least one of an electronic non-stop toll station, a general highway toll station, an oversize detection station, and a bridge health monitoring station;

[0103] A standard traffic volume conversion module 402 is configured to convert the original traffic volume data of the target road segment collected by each traffic data source into standard traffic volume data in a unified format;

[0104] The target traffic volume determination module 403 is used to determine the first target traffic volume data of the target road segment based on the standard traffic volume data of the target road segment corresponding to each traffic data source.

[0105] This application also provides an electronic device. Figure 5 A structural block diagram of an electronic device according to an embodiment of this application is shown. Figure 5 As shown, the electronic device includes a processor 510 and a memory 520. The memory 520 stores instructions, which are loaded and executed by the processor 510 to implement the method of any of the embodiments of this application. The number of memories 520 and processors 510 can be one or more.

[0106] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method of any one of the embodiments of this application.

[0107] This application also provides a computer program product, including a computer program that, when executed by a processor, implements any of the methods described in this application.

[0108] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. General-purpose processors can be microprocessors or any conventional processor. It is worth noting that the processor can be a processor supporting the Advanced Reduced Instruction Set Computing (RISC) machine (ARM) architecture.

[0109] Further, the aforementioned memory can include a read-only memory, and a random access memory, and can further include a non-volatile random access memory. The memory can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memory. The non-volatile memory can include a read-only memory (ROM), a programmable ROM (PROM), an erasable PROM (EPROM), an electrically EPROM (EEPROM), or a flash memory. The volatile memory can include a random access memory (RAM) that is used as an external cache. Many forms of RAM are available. For example, a static RAM (SRAM), a dynamic RAM (DRAM), a synchronous DRAM (SDRAM), a double data rate SDRAM (DDR SDRAM), an enhanced SDRAM (ESDRAM), a Synchlink DRAM (SLDRAM), and a direct Rambus RAM (DR RAM) are available.

[0110] In the above-described embodiments, all or a part can be implemented by software, hardware, firmware, or any combination thereof. When implemented as software, it can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed by a computer, all or a part of the procedures or functions according to the present disclosure are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable apparatus. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another computer-readable storage medium.

[0111] In the description of the application, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the application. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in one or more embodiments or examples. In addition, different embodiments or examples described in the specification and characteristics of different embodiments or examples can be combined and combined by those skilled in the art without contradiction.

[0112] In addition, the terms "first", "second" are only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include at least one of the features. In the description of the application, the meaning of "multiple" is two or more, unless otherwise specifically limited.

[0113] Any process or method descriptions in flow charts or described elsewhere herein can be understood as representing code modules, segments, or portions of code that include one or more executable instructions for implementing specific logic functions or other processes. And the various embodiments of the application can include additional or fewer steps or processes in comparison to those shown in the figures.

[0114] The logic and / or steps represented in flow charts or otherwise described herein, for example, can be embodied in computer-readable instructions, which can be used to cause one or more processors to perform the actions indicated in the steps. The computer-readable instructions can be stored on one or more storage media or memory devices associated with the one or more processors.

[0115] It should be understood that parts of the application can be implemented in hardware, software, firmware or a combination thereof. In the above-described embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. All or part of the steps of the above-described embodiment method can be instructed by a program to complete the relevant hardware, which can be stored in a computer readable storage medium, and the program includes one or a combination of the steps of the method embodiment when executed.

[0116] In addition, each of the function units in each embodiment of the present application can be integrated in one processing module, or each unit can be physically present separately, or two or more units can be integrated in one module. The integrated module can be realized in the form of hardware or in the form of a software function module. When the integrated module is realized in the form of a software function module and sold or used as an independent product, it can also be stored in a computer readable storage medium. The storage medium can be a read-only memory, a magnetic disk or an optical disk, etc.

[0117] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of various changes or replacements within the technical scope disclosed in the present application, and these should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method of determining traffic volume data based on multi-source data, characterized in that, The method comprises: acquiring original traffic volume data of a target road section collected by each traffic data source, wherein the collection range of each traffic data source covers the target road section, and the type of the traffic data source includes at least one of an electronic non-stop toll station, a general highway toll station, an overload detection station, and a bridge health monitoring station; converting the original traffic volume data of the target road section collected by the traffic data source into standard traffic volume data in a unified format; determining first target traffic volume data of the target road section based on the standard traffic volume data of the target road section corresponding to each traffic data source.

2. The method of claim 1, wherein, The determination of the first target traffic volume data of the target road section based on the standard traffic volume data of the target road section corresponding to each traffic data source includes: In the case where there are at least two types of traffic data sources, the first target traffic volume data of the target road section is determined by comprehensively considering the types of the standard traffic volume data of the target road section corresponding to each traffic data source.

3. The method of claim 2, wherein, Before determining the first target traffic volume data of the target road section, the method further comprises: comparing the data values of the standard traffic volume data of the target road section collected by each traffic data source in the target time period, and correcting abnormal values in the standard traffic volume data of the target road section.

4. The method of claim 1, wherein, The conversion of the original traffic data of the target road section corresponding to each traffic data source into standard traffic volume data in a unified format includes: converting the original traffic data of the target road section corresponding to each traffic data source into standard traffic volume data in a standard time granularity; wherein the standard traffic volume data in the standard time granularity includes real-time traffic volume data or time period traffic volume data.

5. The method of claim 1, wherein, The standard traffic volume data includes at least one of data source number, vehicle type, vehicle license plate, driving direction, standard observation time, vehicle speed, and axle load information.

6. The method of claim 1, wherein, The traffic data source includes an electronic non-stop toll station; the conversion of the original traffic data of the target road section corresponding to each traffic data source into standard traffic volume data in a unified format includes at least one of: converting the gantry number of the electronic non-stop toll station into the data source number; converting the observation period of the electronic non-stop toll station into the standard observation time; converting the charging vehicle type code collected by the electronic non-stop toll station into the vehicle type.

7. The method of claim 1, wherein, The traffic data source includes an overload detection station; the conversion of the original traffic data of the target road section corresponding to each traffic data source into standard traffic volume data in a unified format includes at least one of: converting the number of the overload detection station into the data source number; converting the observation period of the overload detection station into the standard observation time; converting the axle load detection data of the overload detection station into the axle load information.

8. The method of claim 1, wherein, The traffic data source includes a bridge health monitoring station; the conversion of the original traffic data of the target road section corresponding to each traffic data source into standard traffic volume data in a unified format includes at least one of: converting the number of the bridge health monitoring station into the data source number; convert an observation period of the bridge health monitoring station into the standard observation time; convert axle load detection data of the bridge health monitoring station into the axle load information.

9. The method of claim 1, wherein, The method further comprises: determining an upstream traffic data source and a downstream traffic data source associated with the target road section; determining second target traffic volume data of the target road section in a target time period according to original traffic volume data respectively corresponding to the upstream traffic data source and the downstream traffic data source in the target time period.

10. An apparatus for determining traffic volume data based on multi-source data, the apparatus comprising: comprise: an original traffic volume determination module, configured to acquire original traffic volume data of a target road section collected by each traffic data source; wherein a collection range of each traffic data source covers the target road section, and a type of the traffic data source comprises at least one of an electronic non-stop toll station, a general highway toll station, an over-limit detection station, and a bridge health monitoring station; a standard traffic volume conversion module, configured to convert original traffic volume data of the target road section collected by the traffic data source into standard traffic volume data in a unified format; a target traffic volume determination module, configured to determine first target traffic volume data of the target road section based on standard traffic volume data of the target road section corresponding to each traffic data source.

11. An electronic device, comprising: comprise: a processor and a memory, wherein the memory stores instructions loaded and executed by the processor to implement the method according to any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the method according to any one of claims 1 to 9.

13. A computer program product, characterised in that, comprise a computer program, and the computer program is executed by the processor to implement the method according to any one of claims 1 to 9.

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