CN111091720A - Congestion road section identification method and device based on signaling data and floating car data - Google Patents
Congestion road section identification method and device based on signaling data and floating car data Download PDFInfo
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- CN111091720A CN111091720A CN202010205213.3A CN202010205213A CN111091720A CN 111091720 A CN111091720 A CN 111091720A CN 202010205213 A CN202010205213 A CN 202010205213A CN 111091720 A CN111091720 A CN 111091720A
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/065—Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
- G08G1/0112—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
Abstract
The embodiment of the invention provides a method and a device for identifying congested road sections based on signaling data and floating car data, wherein the method comprises the following steps: acquiring signaling data of user equipment on a target road; determining traffic volume on the target road according to the signaling data; acquiring multi-source floating car data on a target road; determining the road speed of a target road according to the multi-source floating car data; and determining the road section congestion condition of the target road according to the traffic traveling volume and the road section speed. In the embodiment of the invention, the signaling data is used as the identification basis of the travel mode, the travel demand is matched into the road network according to the stop point and the track of the signaling, the service level of each time section of the road section is researched and judged by combining the road capacity, and the multisource floating car data is combined with the service level of the road section, so that the accurate identification of the congested road section in the road network is realized.
Description
Technical Field
The embodiment of the invention relates to the technical field of data processing, in particular to a method and a device for identifying a congested road section based on signaling data and floating car data.
Background
At present, the traffic jam problem in urban construction is getting more serious, and the urban traffic state and the economic development level are seriously influenced by dense traffic layout, disordered road planning, unreasonable traffic equipment arrangement and the like. For urban planning and road traffic construction departments, the method has the advantages of large workload, low efficiency and accuracy, and can be used for establishing urban infrastructure, managing complicated large-scale road networks, planning and correcting road facilities and the like. Therefore, the method has great significance in analyzing the frequently congested road sections in the urban road, reasonably allocating urban resources and planning the urban road.
In recent years, floating car data are widely applied to road condition research, the floating car can acquire and return driving data of the vehicle in real time, and the floating car has a certain guiding effect on the running condition of a road network. The method comprises the steps of obtaining position data of the floating car based on a congestion identification algorithm technology of floating car data, and calculating track speed of the floating car according to position information of different time points, so that congestion conditions of road sections are predicted. However, the method has the disadvantages of small sample size and single data source, and the estimation of the actual condition of the road is insufficient, so that the accurate discrimination of the congestion condition cannot be realized.
Disclosure of Invention
The embodiment of the invention provides a method and a device for identifying congested road sections based on signaling data and floating car data, and solves the problem that the existing identification method cannot accurately discriminate congestion conditions.
In a first aspect, an embodiment of the present invention provides a method for identifying a congested road segment based on signaling data and floating car data, where the method includes:
acquiring signaling data of user equipment on a target road;
determining traffic volume on the target road according to the signaling data;
acquiring multi-source floating car data on a target road;
determining the road speed of the target road according to the multi-source floating car data;
and determining the road section congestion condition of the target road according to the traffic traveling volume and the road section speed.
Optionally, the determining the traffic volume on the target road according to the signaling data includes:
clustering a plurality of base stations associated with the target road;
determining a staying track of the user equipment according to the signaling data recorded by the base station after the clustering;
determining a dwell point with the dwell time not less than the preset time in the dwell track as an effective dwell point;
traversing all the effective stop points, and determining the trip starting and ending point of the user equipment;
and (4) counting the traffic traveling quantity between all the traveling starting and ending points in the research range.
Optionally, before the traffic volume between all travel starting and ending points in the statistical research range, the method further comprises:
acquiring travel time and travel speed of travel based on the signaling data;
and determining a travel mode according to the travel time and the travel speed.
Optionally, the determining a section speed of the target road according to the multi-source floating car data includes:
dividing the target road into a plurality of road segments;
a home road segment that matches data points of the multi-source floating car data in a plurality of the road segments;
calculating the average speed of each type of floating car on the attribution road section;
and calculating the speed of the fusion road condition of the attributive road section according to the average speed.
Optionally, the home road segment matching data points of the multi-source floating car data in a plurality of the road segments comprises:
determining a distance between the data point to each of the road segments;
determining the road segment with the minimum distance to the data point as a home road segment of the data point;
and the distance from the data point to the two ends of the road section is not more than the length of the road section.
Optionally, the calculating the average speed of each category of floating cars on the home road segment includes:
calculating the average speed of each type of floating car on the attribution road section through a first formula and a second formula;
the first formula is:
the second formula is:
wherein v ismRepresenting the average speed, v, over the road section at time mjIs the average speed of the jth vehicle on the road section, sjThe sum of the distances between two adjacent signal points of the jth vehicle is used for representing the running distance of the jth vehicle on the road section; t is tjThe time difference between two adjacent signal points of the jth vehicle is used for representing the total residence time of the jth vehicle on the road section; and n is the total number of vehicles.
Optionally, the categories of floating cars include: taxis, passenger-freight vehicles, private cars and buses;
calculating the speed of the fusion road condition of the attribution road section according to the average speed, wherein the calculation comprises the following steps:
calculating the speed of the fused road condition of the attributive road section through a third formula;
the third formula is:
wherein, wtFor taxis on road sectionsThe sample size of (a); w is arThe sample size of the passenger and freight vehicles in the road section; w is acThe sample size of the private car on the road section; w is apThe sample size of the bus on the road section; deltapIs used for marking whether a bus lane exists in a road section, has 0 and does not have 1, vtFor the speed, v, of the taxi on the road sectionrFor the speed, v, of the passenger-cargo vehicle on the road sectioncSpeed, v, of a private car on a road sectionpThe speed of the bus in the road section.
Optionally, after the calculating the speed of the merged road condition of the home road segment, the method further includes:
storing the speed of the fused road condition into a historical speed library of the fused road condition;
if the currently calculated speed of the fused road condition is not 0, determining the currently calculated speed of the fused road condition as the speed of the target road;
if the currently calculated fusion road condition speed is 0, calling the historical fusion road condition speed in the same time period from the historical fusion road condition speed library, and determining the historical fusion road condition speed as the road section speed of the target road.
In a second aspect, an embodiment of the present invention provides a congested road segment identifying device, including:
the first acquisition module is used for acquiring signaling data of user equipment on a target road;
the first determining module is used for determining the traffic running amount on the target road according to the signaling data;
the second acquisition module is used for acquiring multi-source floating car data on the target road;
the second determining module is used for determining the road section speed of the target road according to the multi-source floating car data;
and the third determining module is used for determining the road section congestion condition of the target road according to the traffic travel amount and the road section speed.
Optionally, the first determining module includes:
a first processing unit, configured to perform clustering processing on a plurality of base stations associated with the target road;
a first determining unit, configured to determine a staying track of the user equipment according to the signaling data recorded by the base station after the clustering process;
a second determining unit, configured to determine a dwell point in the dwell trajectory, where the dwell time is not less than a preset time, as an effective dwell point;
a third determining unit, configured to traverse all the valid stop points, and determine a trip start and end point of the user equipment;
and the statistical unit is used for counting the traffic travel amount between all travel starting and ending points in the research range.
Optionally, the first determining module further includes:
a first obtaining unit, configured to obtain travel time and travel speed of a trip based on the signaling data;
and the fourth determining unit is used for determining a travel mode according to the travel time and the travel speed.
Optionally, the second determining module includes:
a dividing unit configured to divide the target road into a plurality of segments;
the matching unit is used for matching the attribution road sections of the data points of the multi-source floating car data in the plurality of road sections;
the first calculation unit is used for calculating the average speed of each type of floating car on the attributive road section;
and the second calculating unit is used for calculating the speed of the fused road condition of the attributive road section according to the average speed.
Optionally, the matching unit includes:
a first determining subunit, configured to determine distances between the data points and the respective road segments;
a second determining subunit, configured to determine the road segment with the smallest distance to the data point as a home road segment of the data point;
and the distance from the data point to the two ends of the road section is not more than the length of the road section.
Optionally, the first calculating unit is further configured to calculate an average speed of each class of floating cars on the home road segment through a first formula and a second formula;
the first formula is:
the second formula is:
wherein v ismRepresenting the average speed, v, over the road section at time mjIs the average speed of the jth vehicle on the road section, sjThe sum of the distances between two adjacent signal points of the jth vehicle is used for representing the running distance of the jth vehicle on the road section; t is tjThe time difference between two adjacent signal points of the jth vehicle is used for representing the total residence time of the jth vehicle on the road section; and n is the total number of vehicles.
Optionally, the categories of floating cars include: taxis, passenger-freight vehicles, private cars and buses;
the second calculating unit is further configured to calculate a speed of the fused road condition of the home road segment according to a third formula;
the third formula is:
wherein, wtThe sample size of the taxi on the road section; w is arThe sample size of the passenger and freight vehicles in the road section; w is acThe sample size of the private car on the road section; w is apThe sample size of the bus on the road section; deltapIs used for marking whether a bus lane exists in a road section, has 0 and does not have 1, vtFor the speed, v, of the taxi on the road sectionrFor the speed, v, of the passenger-cargo vehicle on the road sectioncIn order to speed the private car on the road section,vpthe speed of the bus in the road section.
Optionally, the second determining module further includes:
the storage unit is used for storing the fused road condition speed into a historical fused road condition speed library;
the second determining module is further configured to determine the speed of the merged road condition as the speed of the target road section when the speed of the merged road condition is not 0;
the second determining module is further configured to, when the traffic fusion speed is 0, call a historical traffic fusion speed in the same time period from the historical traffic fusion speed library, and determine the historical traffic fusion speed as the speed of the target road.
In the embodiment of the invention, the signaling data is used as the identification basis of the travel mode, the travel demand is matched into the road network according to the stop point and the track of the signaling, the service level of each time interval of the road section is researched and judged by combining the road capacity, the multi-source floating car speed is combined with the service level of the road section, and the accurate identification of the congested road section in the road network is realized.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments of the present invention will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts;
fig. 1 is a schematic flowchart of a congested road segment identification based on signaling data and floating car data according to an embodiment of the present invention;
fig. 2 is a schematic flow chart illustrating a travel mode identification according to an embodiment of the present invention;
fig. 3 is a schematic flow chart illustrating calculation of the speed of the merged road condition according to the embodiment of the present invention;
fig. 4 is a schematic structural diagram of a congested road segment identification device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the embodiments of the present invention, words such as "exemplary" or "for example" are used to mean serving as examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "e.g.," an embodiment of the present invention is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the word "exemplary" or "such as" is intended to present concepts related in a concrete fashion.
Herein, relational terms such as "first" and "second", and the like, are used solely to distinguish one from another of like names, and do not imply a relationship or order between the names.
In order to facilitate understanding of the technical solutions of the present application, the following technical contents are first described:
(1) and judging the traffic congestion road sections based on the floating car data.
The method comprises the steps of analyzing and processing Global Positioning System (GPS) data uploaded by floating cars in real time in a traffic flow, enabling the floating car data to be correlated with urban road data in time and space, matching to obtain real-time speed data of road sections, and finally obtaining traffic condition information of cities. Compared with the traditional traffic information acquisition technology, the floating car technology has the advantages of low cost, accurate speed data, strong real-time performance and the like. However, the coverage section and the intensity of the floating car data sample car are not controlled, and the data quality can fluctuate to a certain extent.
(2) And identifying the congested road section based on the mobile phone signaling data.
The mobile phone actively contacts with the mobile communication network in the displacement and reports the current position by sending information. The average travel time and the travel speed value in the process of updating the position of the mobile phone can be obtained by utilizing the timestamps corresponding to the updating of the position of the mobile phone and the switching of the cell, combining the processes of electronic map and map matching feature identification, data calculation and the like. Compared with floating car data and mobile phone signaling data, the method has the advantages that the sample size is larger, but the estimation accuracy of the method for the speed is lower. More be fit for being applied to the judgement of trip mode.
(3) And identifying the congested road section based on the license plate.
And identifying the license plate information of the motor vehicle on the road section by the adjacent road section monitoring equipment, and calculating the travel speed of the motor vehicle according to the travel mileage and the travel time so as to judge the congestion state of the road section. The license plate recognition system has the characteristic of all-weather work within 7-24 hours, the precision of the collected data is high, the number of the monitored samples is large, and the application of the technology needs large-scale investment and construction to guarantee. If the road section is too long, the traffic state is judged according to the average travel speed, and the dynamic change of the traffic state in the road section along with the time cannot be reflected.
The technical scheme has the following defects:
(1) the congestion identification algorithm technology based on floating car data obtains the position data of the floating car through a GPS, and the track speed of the floating car is calculated through the position information of different time points, so that the congestion condition of a road section is inferred. The method has the advantages that the sample size is small, the data source is single, the actual condition of the road is insufficiently estimated, and therefore accurate discrimination of the congestion condition cannot be achieved.
(2) The mobile phone signaling data has large sample amount, contains various travel modes such as non-motor vehicles, tracks, buses and the like, has poor accuracy and low efficiency in estimating the speed of a road section, and is more suitable for judging the travel mode based on the speed.
(3) The license plate-based congested road segment identification application needs large-scale investment and construction to guarantee. If the road section is too long, the traffic state is judged according to the average travel speed, and the dynamic change of the traffic state in the road section along with the time cannot be reflected. The technology for identifying the congested road sections based on license plate identification is high in cost, long in time consumption and not easy to popularize and apply.
Therefore, at present, congestion road section identification based on signaling data and floating car data based on mobile phone signaling and multi-source floating car data fusion is needed.
Referring to fig. 1, an embodiment of the present invention provides a method for identifying a congested road segment based on signaling data and floating car data, where the method includes the following specific steps:
step 101: acquiring signaling data of user equipment on a target road, and then executing step 102;
in the embodiment of the present invention, the user equipment refers to user equipment capable of being acquiring signaling data in a target area, and the user equipment may be equipment that a user can carry with, for example: mobile phones, tablet computers, notebook computers, and the like; the user equipment may also be a non-mobile fixed device, such as: a monitor, a server, etc., and the type of the user equipment is not particularly limited in the embodiment of the present invention.
The travel track of the user equipment on the target road can be obtained through the signaling data of the user equipment, and the travel track of the user equipment is used for representing the travel track of the user, so that the travel demand of the user on the target road can be obtained.
Specifically, the wireless signals sent by different base stations cover the whole road, the coverage area of each base station divides the target road into a plurality of sections, and the user switches when moving on the road and crossing the range of the base station. Recording a series of base station IDs passed by the user in the switching data, and arranging according to time to obtain continuous track points in the moving process of the user. And calculating the travel track of each user switching road section by using the ID sequence in the path. And carrying out travel amount statistics according to each road based on the obtained single-user track, so that travel demands can be tracked and analyzed.
Further, after the signaling data of the user equipment is obtained, data cleaning is performed on the signaling data.
In the embodiment of the invention, switching information extraction and screening are carried out on the movement track of each user according to the time sequence, and no movement track and interference data are filtered. The method mainly comprises the following steps:
(1) filtering network basic parameters, namely filtering indoor base stations and base stations irrelevant to roads to ensure the validity of data;
(2) processing the user equipment sample to remove the user data of the user equipment which does not move on the road, wherein the user data can be regarded as noise data;
(3) and (4) performing statistical analysis, namely analyzing various statistical values of the sample, screening according to the rule of the result to achieve the purpose of removing noise, and finally obtaining a user switching information table for matching.
Step 102: determining the traffic volume on the target road according to the signaling data, and then executing step 105;
in the embodiment of the invention, the traffic volume (OD) analysis is carried out on the target road based on the signaling data, and the traffic volume on the target road is determined.
The embodiment of the invention provides a mode for determining traffic volume on a target road according to signaling data, which comprises the following specific processes:
(1) clustering a plurality of base stations associated with a target road;
in the embodiment of the invention, the base stations in a certain distance (which can be set according to the average service radius of the base stations) in the target area are clustered and combined according to the latitude and longitude information of the base stations.
(2) Determining a staying track of the user equipment according to the signaling data recorded by the base station after the clustering;
in the embodiment of the invention, the staying track of the user equipment is determined according to the base station after the aggregation treatment; although the user equipment sometimes does not shift, the recording position of the user equipment will jump between adjacent base stations, which is called ping-pong effect, in order to eliminate the ping-pong effect, the influence on the travel OD estimation is avoided, and at the same time, the short-distance travel which divides the long-distance travel into a plurality of times is avoided, and the base stations which are two or more times continuously in the user record are defined as the staying base stations.
(3) Determining a dwell point with the dwell time not less than the preset time in the dwell track as an effective dwell point;
in the embodiment of the invention, an effective stay point in the effective stay track of each mobile phone user, the stay time of which is not less than the set stay time threshold value, is selected, and optionally, the stay time threshold value is set to be 1 hour, which corresponds to one purposeful trip.
(4) Traversing all the effective stop points, and determining the trip starting and ending points of the user equipment;
in the embodiment of the invention, all the user effective stay tables are traversed, and the starting point and the ending point of the trip chain are judged.
(5) Counting the traffic travel amount between all travel starting and ending points in the research range;
further, based on the signaling data, travel time and travel speed of travel are obtained; and determining a travel mode according to the travel time and the travel speed.
Referring to fig. 2, the travel data of the service base station is matched to a road network, and a travel mode is identified according to a travel distance and an average speed:
1. based on the base station position and the time point data, the travel time and the travel speed of the signaling data are obtained, the travel mode is primarily judged, and the judgment result is that the motor vehicle travels or the non-motor vehicle travels;
2. matching the travel demand belonging to the motor vehicle travel with a Geographic Information System (GIS) line network, and judging the motor vehicle travel as a rail transit travel if the travel mode is matched with the GIS rail transit line network; if the travel mode is matched with the GIS bus net, judging the bus as the conventional bus travel, and converting the traffic demand according to the bus travel sharing; and if the network is not matched with the GIS rail transit network and the GIS public transit network, the taxi, the passenger and freight vehicle or the social car is judged to go out.
It should be noted that the above-mentioned method needs to have city mobile phone signaling data, multi-source floating car data, geographic information data and city road network information data; the travel mode identification based on the mobile phone signaling data is based on the whole road network and is checked by combining the daily traffic of the public transport and the track.
Step 103: acquiring multi-source floating car data on a target road, and then executing step 104;
step 104: determining the road section speed of the target road according to the multi-source floating car data, and then executing step 105;
in the embodiment of the invention, one of key technologies in the process of acquiring and processing the traffic information of the floating car is map matching of the floating car data, namely, a vehicle positioning track obtained by a positioning device is compared with road information in an electronic map database, and a most possible driving road section of the vehicle and a most possible position of the vehicle on the road section are determined through a specific algorithm so as to calculate the passing speed of the road section.
The embodiment of the invention provides a way for determining the speed of a section of a target road, which comprises the following specific processes:
(1) dividing a target road into a plurality of road sections;
in the embodiment of the invention, in the road network graph, the whole road is broken into a plurality of road segments according to intersections and the like, and each road segment can be approximately regarded as a straight line segment.
(2) Matching the attribution road sections of the data points of the multi-source floating car data in the plurality of road sections;
specifically, the distance between the data point and each road section is determined; determining the road section with the minimum distance to the data point as the attribution road section of the data point; and the distances from the data points to the two ends of the road section are not more than the length of the road section.
In the embodiment of the present invention, distances from a Floating Car Data (FCD) point to each link are compared, and a link corresponding to the minimum value is a home link of the point. Since the point-to-line distance may be out of the range of the line segment, a constraint must be added to the calculation that the distances from the FCD point to the two end points are not greater than the length of the line segment itself.
(3) Calculating the average speed of each type of floating car on the home road section;
in the embodiment of the invention, according to the matched FCD points, the average passing speed of the road section is simulated by the average speed of each vehicle on each road section.
The embodiment of the invention provides a mode for calculating the average speed of various types of floating cars on a home road section, which comprises the following specific processes:
calculating the average speed of each type of floating car on the attribution road section through a first formula and a second formula;
the first formula is:
the second formula is:
wherein v ismRepresenting the average speed, v, over the road section at time mjIs the average speed of the jth vehicle on the road section, sjThe sum of the distances between two adjacent signal points of the jth vehicle is used for representing the running distance of the jth vehicle on the road section; t is tjThe time difference between two adjacent signal points of the jth vehicle is used for representing the total residence time of the jth vehicle on the road section; and n is the total number of vehicles.
(4) Calculating the speed of the fusion road condition of the attributive road section according to the average speed;
in the embodiment of the invention, after the calculation results of the speed data of the floating vehicles from various types on the road section are obtained, the fusion calculation of the multi-source speed data is carried out according to the road section condition.
In some embodiments, the categories of floating cars include: the method comprises the following steps that taxis, passenger and freight vehicles, private cars and buses are judged, whether a bus lane exists in a current road section or not is judged, if the bus lane exists, bus speed data need to be removed, and weighted fusion calculation is conducted on the speed data of the social cars, the taxis and the passenger and freight vehicles; and if no bus lane exists, performing weighted fusion calculation on all speed data including bus data.
Specifically, the process of calculating the speed of the fused road condition of the home road segment is as follows:
calculating the speed of the fused road conditions of the attributive road sections through a third formula;
the third formula is:
wherein, wtThe sample size of the taxi on the road section; w is arThe sample size of the passenger and freight vehicles in the road section; w is acThe sample size of the private car on the road section; w is apThe sample size of the bus on the road section; deltapIs used for marking whether a bus lane exists in a road section, has 0 and does not have 1, vtFor the speed, v, of the taxi on the road sectionrFor the speed, v, of the passenger-cargo vehicle on the road sectioncSpeed, v, of a private car on a road sectionpThe speed of the bus in the road section.
It should be noted that the problems to be noticed during the data fusion calculation of the multi-source floating car include: i) removing suspicious data by a Grabas criterion method before calculation, and performing weighted fusion calculation on the residual effective data; ii) for the small sample data with N less than 10, carrying out normal distribution judgment on the speed values by a Charpy-Wilkeley method, and removing suspicious values.
Further, storing the calculated fusion road condition speed into a historical fusion road condition speed library;
further, referring to fig. 3, it is determined whether a certain road segment has real-time speed data, and if the speed of the integrated road condition is directly used, and if the current real-time speed does not exist, the historical road condition speed of the road segment at the same time is called.
If the current calculated speed of the fused road condition is not 0, determining the current calculated speed of the fused road condition as the speed of the target road section;
if the current calculated fusion road condition speed is 0, calling the historical fusion road condition speed in the same time period from the historical fusion road condition speed library, and determining the historical fusion road condition speed as the road section speed of the target road.
It should be noted that the flow from step 101 to step 102 and the flow from step 103 to step 104 may be executed simultaneously or sequentially.
Step 105: determining the road section congestion condition of the target road according to the traffic traveling volume and the road section speed;
in the embodiment of the invention, the traffic travel reflects the traffic demand, the road section speed reflects the road section service level, and the road section congestion condition of the target road is determined based on the traffic travel and the road section speed.
In order to have an intuitive feeling, it is necessary to analyze the speed data and the road network data in combination. Because the road grades are different, the definitions of the corresponding road congestion are also different, and optionally, the road condition determination criteria for the roads with different grades are defined as the following table:
| road grade | Severe congestion | Moderate congestion | Light congestion | Is basically unblocked | Is unblocked |
| Express way | v≤20 | 20＜v≤35 | 35＜v≤50 | 50＜v≤65 | v＞65 |
| Main road | v≤15 | 15＜v≤20 | 20＜v≤30 | 30＜v≤40 | v＞40 |
| Secondary trunk and branch | v≤10 | 10＜v≤15 | 15＜v≤25 | 25＜v≤35 | v＞35 |
And carrying out grade mapping on the road section service level and the road condition grade based on the travel demand according to the speed index, and analyzing congestion causes of the road sections under different conditions.
(1) The road section service level is high, and the road section speed is low. The low traffic demand but the low road segment speed may cause such congestion: a) the distance of the intersection is short, and the signal timing is unreasonable; b) traffic events and road occupation construction. The congestion cause can be analyzed by combining with historical data, for example, the sporadic congestion of the road section can be related to external interference factors such as traffic accidents and road occupation construction, and the problem in the aspect of traffic organization needs to be considered when the congestion occurs with certain regularity.
(2) The road section service level is low, and the road section speed is low. The road congestion is caused by large traffic demand and high road saturation. The traffic planning department should pay more attention to the traffic organization optimization and reasonable diversion problem of such road sections.
(3) The road section service level is high, and the road section speed is high. The traffic demand is less, and highway section traffic conditions are good.
(4) The service water of the road section is low, and the speed of the road section is high. The road section saturation degree is higher but the traffic efficiency is better, and the bus/track sharing proportion is high.
Accurate identification and verification of road conditions of road sections are achieved through floating car data and mobile phone signaling data, and accurate identification of congested road sections of urban road networks is achieved.
In the embodiment of the invention, the signaling data is used as the identification basis of the travel mode, the travel demand is matched into the road network according to the stop point and the track of the signaling, the service level of each time interval of the road section is researched and judged by combining the road capacity, the multi-source floating car speed is combined with the service level of the road section, and the accurate identification of the congested road section in the road network is realized.
Referring to fig. 4, an embodiment of the present invention provides a congested road segment identification device 400, including:
a first obtaining module 401, configured to obtain signaling data of a user equipment on a target road;
a first determining module 402, configured to determine a traffic traveling volume on the target road according to the signaling data;
a second obtaining module 403, configured to obtain multi-source floating car data on a target road;
a second determining module 404, configured to determine a road speed of the target road according to the multi-source floating car data;
and a third determining module 405, configured to determine a road section congestion condition of the target road according to the traffic travel amount and the road section speed.
Optionally, the first determining module 402 includes:
a first processing unit, configured to perform clustering processing on a plurality of base stations associated with the target road;
a first determining unit, configured to determine a staying track of the user equipment according to the signaling data recorded by the base station after the clustering process;
a second determining unit, configured to determine a dwell point in the dwell trajectory, where the dwell time is not less than a preset time, as an effective dwell point;
a third determining unit, configured to traverse all the valid stop points, and determine a trip start and end point of the user equipment;
and the statistical unit is used for counting the traffic travel amount between all travel starting and ending points in the research range.
Optionally, the first determining module 402 further includes:
a first obtaining unit, configured to obtain travel time and travel speed of a trip based on the signaling data;
and the fourth determining unit is used for determining a travel mode according to the travel time and the travel speed.
Optionally, the second determining module 404 includes:
a dividing unit configured to divide the target road into a plurality of segments;
the matching unit is used for matching the attribution road sections of the data points of the multi-source floating car data in the plurality of road sections;
the first calculation unit is used for calculating the average speed of each type of floating car on the attributive road section;
and the second calculating unit is used for calculating the speed of the fused road condition of the attributive road section according to the average speed.
Optionally, the matching unit includes:
a first determining subunit, configured to determine distances between the data points and the respective road segments;
a second determining subunit, configured to determine the road segment with the smallest distance to the data point as a home road segment of the data point;
and the distance from the data point to the two ends of the road section is not more than the length of the road section.
Optionally, the first calculating unit is further configured to calculate an average speed of each class of floating cars on the home road segment through a first formula and a second formula;
the first formula is:
the second formula is:
wherein v ismRepresenting the average speed, v, over the road section at time mjIs the average speed of the jth vehicle on the road section, sjThe sum of the distances between two adjacent signal points of the jth vehicle is used for representing the running distance of the jth vehicle on the road section; t is tjThe time difference between two adjacent signal points of the jth vehicle is used for representing the total residence time of the jth vehicle on the road section; and n is the total number of vehicles.
Optionally, the categories of floating cars include: taxis, passenger-freight vehicles, private cars and buses;
the second calculating unit is further configured to calculate a speed of the fused road condition of the home road segment according to a third formula;
the third formula is:
wherein, wtThe sample size of the taxi on the road section; w is arThe sample size of the passenger and freight vehicles in the road section; w is acThe sample size of the private car on the road section; w is apThe sample size of the bus on the road section; deltapIs used for marking whether a bus lane exists in a road section, has 0 and does not have 1, vtFor the speed, v, of the taxi on the road sectionrFor the speed, v, of the passenger-cargo vehicle on the road sectioncSpeed, v, of a private car on a road sectionpThe speed of the bus in the road section.
Optionally, the second determining module 404 further includes:
the storage unit is used for storing the fused road condition speed into a historical fused road condition speed library;
the second determining module 404 is further configured to determine the fused road condition speed as a road segment speed of the target road when the fused road condition speed is not 0;
the second determining module 404 is further configured to, when the traffic fusion speed is 0, call a historical traffic fusion speed in the same time period from the historical traffic fusion speed library, and determine the historical traffic fusion speed as the speed of the target road.
In the embodiment of the invention, the signaling data is used as the identification basis of the travel mode, the travel demand is matched into the road network according to the stop point and the track of the signaling, the service level of each time interval of the road section is researched and judged by combining the road capacity, the multi-source floating car speed is combined with the service level of the road section, and the accurate identification of the congested road section in the road network is realized.
The above description is only an embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions within the technical scope of the present disclosure should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (14)
1. A method for identifying a congested road section based on signaling data and floating car data is characterized in that the method comprises the following steps:
acquiring signaling data of user equipment on a target road;
determining traffic volume on the target road according to the signaling data;
acquiring multi-source floating car data on a target road;
determining the road speed of the target road according to the multi-source floating car data;
determining the road section congestion condition of the target road according to the traffic running amount and the road section speed;
the determining the road speed of the target road according to the multi-source floating car data comprises the following steps:
dividing the target road into a plurality of road segments;
a home road segment that matches data points of the multi-source floating car data in a plurality of the road segments;
calculating the average speed of each type of floating car on the attribution road section;
and calculating the speed of the fusion road condition of the attributive road section according to the average speed.
2. The method of claim 1, wherein determining the traffic volume on the target link based on the signaling data comprises:
clustering a plurality of base stations associated with the target road;
determining a staying track of the user equipment according to the signaling data recorded by the base station after the clustering;
determining a dwell point with the dwell time not less than the preset time in the dwell track as an effective dwell point;
traversing all the effective stop points, and determining the trip starting and ending point of the user equipment;
and (4) counting the traffic traveling quantity between all the traveling starting and ending points in the research range.
3. The method of claim 2, wherein prior to the amount of travel traffic between all travel starting and ending points within the statistical study, the method further comprises:
acquiring travel time and travel speed of travel based on the signaling data;
and determining a travel mode according to the travel time and the travel speed.
4. The method of claim 1, wherein said matching a home segment of data points of said multi-source floating car data among a plurality of said segments comprises:
determining a distance between the data point to each of the road segments;
determining the road segment with the minimum distance to the data point as a home road segment of the data point;
and the distance from the data point to the two ends of the road section is not more than the length of the road section.
5. The method of claim 1, wherein said calculating an average speed of each category of floating car on the home road segment comprises:
calculating the average speed of each type of floating car on the attribution road section through a first formula and a second formula;
the first formula is:
the second formula is:
wherein v ismRepresenting the average speed, v, over the road section at time mjIs the average speed of the jth vehicle on the road section, sjThe sum of the distances between two adjacent signal points of the jth vehicle is used for representing the running distance of the jth vehicle on the road section; t is tjThe time difference between two adjacent signal points of the jth vehicle is used for representing the total residence time of the jth vehicle on the road section; and n is the total number of vehicles.
6. The method of claim 5, wherein the categories of floating cars include: taxis, passenger-freight vehicles, private cars and buses;
calculating the speed of the fusion road condition of the attribution road section according to the average speed, wherein the calculation comprises the following steps:
calculating the speed of the fused road condition of the attributive road section through a third formula;
the third formula is:
wherein, wtThe sample size of the taxi on the road section; w is arThe sample size of the passenger and freight vehicles in the road section; w is acThe sample size of the private car on the road section; w is apThe sample size of the bus on the road section; deltapIs used for marking whether a bus lane exists in a road section, has 0 and does not have 1, vtFor the speed of taxi on road sectionDegree, vrFor the speed, v, of the passenger-cargo vehicle on the road sectioncSpeed, v, of a private car on a road sectionpThe speed of the bus in the road section.
7. The method of claim 6,
after the calculating the speed of the merged road condition of the home road segment, the method further comprises:
storing the speed of the fused road condition into a historical speed library of the fused road condition;
if the currently calculated speed of the fused road condition is not 0, determining the currently calculated speed of the fused road condition as the speed of the target road;
if the currently calculated fusion road condition speed is 0, calling the historical fusion road condition speed in the same time period from the historical fusion road condition speed library, and determining the historical fusion road condition speed as the road section speed of the target road.
8. A congested road segment identification device, comprising:
the first acquisition module is used for acquiring signaling data of user equipment on a target road;
the first determining module is used for determining the traffic running amount on the target road according to the signaling data;
the second acquisition module is used for acquiring multi-source floating car data on the target road;
the second determining module is used for determining the road section speed of the target road according to the multi-source floating car data;
the third determining module is used for determining the road section congestion condition of the target road according to the traffic running amount and the road section speed;
the second determining module includes:
a dividing unit configured to divide the target road into a plurality of segments;
the matching unit is used for matching the attribution road sections of the data points of the multi-source floating car data in the plurality of road sections;
the first calculation unit is used for calculating the average speed of each type of floating car on the attributive road section;
and the second calculating unit is used for calculating the speed of the fused road condition of the attributive road section according to the average speed.
9. The apparatus of claim 8, wherein the first determining module comprises:
a first processing unit, configured to perform clustering processing on a plurality of base stations associated with the target road;
a first determining unit, configured to determine a staying track of the user equipment according to the signaling data recorded by the base station after the clustering process;
a second determining unit, configured to determine a dwell point in the dwell trajectory, where the dwell time is not less than a preset time, as an effective dwell point;
a third determining unit, configured to traverse all the valid stop points, and determine a trip start and end point of the user equipment;
and the statistical unit is used for counting the traffic travel amount between all travel starting and ending points in the research range.
10. The apparatus of claim 9, wherein the first determining module further comprises:
a first obtaining unit, configured to obtain travel time and travel speed of a trip based on the signaling data;
and the fourth determining unit is used for determining a travel mode according to the travel time and the travel speed.
11. The apparatus of claim 8, wherein the matching unit comprises:
a first determining subunit, configured to determine distances between the data points and the respective road segments;
a second determining subunit, configured to determine the road segment with the smallest distance to the data point as a home road segment of the data point;
and the distance from the data point to the two ends of the road section is not more than the length of the road section.
12. The apparatus of claim 8,
the first calculation unit is further used for calculating the average speed of each type of floating car on the attribution road section through a first formula and a second formula;
the first formula is:
the second formula is:
wherein v ismRepresenting the average speed, v, over the road section at time mjIs the average speed of the jth vehicle on the road section, sjThe sum of the distances between two adjacent signal points of the jth vehicle is used for representing the running distance of the jth vehicle on the road section; t is tjThe time difference between two adjacent signal points of the jth vehicle is used for representing the total residence time of the jth vehicle on the road section; and n is the total number of vehicles.
13. The apparatus of claim 12, wherein the categories of floating cars include: taxis, passenger-freight vehicles, private cars and buses;
the second calculating unit is further configured to calculate a speed of the fused road condition of the home road segment according to a third formula;
the third formula is:
wherein, wtThe sample size of the taxi on the road section; w is arFor passenger and freight vehiclesA sample size at a road segment; w is acThe sample size of the private car on the road section; w is apThe sample size of the bus on the road section; deltapIs used for marking whether a bus lane exists in a road section, has 0 and does not have 1, vtFor the speed, v, of the taxi on the road sectionrFor the speed, v, of the passenger-cargo vehicle on the road sectioncSpeed, v, of a private car on a road sectionpThe speed of the bus in the road section.
14. The apparatus of claim 13, wherein the second determining module further comprises:
the storage unit is used for storing the fused road condition speed into a historical fused road condition speed library;
the second determining module is further configured to determine the speed of the merged road condition as the speed of the target road section when the speed of the merged road condition is not 0;
the second determining module is further configured to, when the traffic fusion speed is 0, call a historical traffic fusion speed in the same time period from the historical traffic fusion speed library, and determine the historical traffic fusion speed as the speed of the target road.
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