CN110047277B - Urban road traffic jam ranking method and system based on signaling data - Google Patents
Urban road traffic jam ranking method and system based on signaling data Download PDFInfo
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Abstract
The invention discloses a method and a system for ranking urban road traffic jam based on signaling data, belonging to the field of urban traffic state monitoring and comprising the following steps: extracting the motion trail of each user in the base station network according to the signaling data; obtaining a target route which is most similar to each section of motion track in a road network; obtaining a plurality of road sections obtained by dividing each target route by the base station switching point, and calculating the movement speed of all users on each road section according to the length of the road section and the switching time of the base station; screening out the vehicle speed of the motor vehicle according to the vehicle speed threshold value of each road section in the target time period and calculating the average vehicle speed of the motor vehicle; obtaining the free flow speed of each road section to calculate the traffic jam index of each road section in a target time period; carrying out weighted average on the traffic congestion indexes of all road sections belonging to the same road according to the length of the road sections to obtain a road traffic congestion index; and ranking the road traffic jam according to the road traffic jam index. The invention can realize the urban traffic state detection with large range and low cost.
Description
Technical Field
The invention belongs to the field of urban traffic state monitoring, and particularly relates to an urban road traffic jam ranking method and system based on signaling data.
Background
Road traffic congestion ranking is an effective way to alleviate traffic congestion. Road traffic congestion ranking relies on monitoring of road traffic operating conditions, and urban traffic condition monitoring can be divided into two types, the first type is monitoring of traffic conditions using traditional roadside stationary sensors, and the other type is big data driven traffic monitoring. The roadside fixed sensor equipment comprises a road induction coil, Bluetooth, RFID, a speed measurement camera and the like, and can directly obtain accurate information about road section traffic parameters; however, the increase in the coverage of these sensors can bring about a significant increase in equipment costs, including equipment manufacturing costs, equipment installation costs, and equipment maintenance costs, so traffic monitoring based on roadside sensors is generally limited to only critical sections of urban roads. In the traffic monitoring driven by big data, the data source can be self-floating vehicle track data, public traffic intelligent card swiping data, mobile phone GPS positioning data and the like; the data are naturally generated and accumulated in daily activities of urban people, so that additional equipment does not need to be installed, and special data acquisition does not need to be additionally carried out; although the data imply a large amount of traffic-related information, the data cannot directly reflect the road traffic condition.
Of all the data generated in daily activities, the signaling data between mobile phones and their communication infrastructure has a good application prospect in city-wide traffic monitoring and road congestion ranking due to their high coverage. According to the statistical data of China industry and informatization department, the number of Chinese mobile phone users reaches 15.6 hundred million by 11 months in 2018, and other technologies cannot provide the coverage rate equivalent to the number of Chinese mobile phone users at present.
The location information of the signaling data is a simple mobile phone location method originated from a cellular cell location technology, and the location information is determined based on the cell ID of the base station where the mobile phone user is located. This method of location is advantageous over other precision location techniques (e.g., GPS) in terms of sample size, coverage, and cost and cycle of implementation. The radius of the coverage area of the base station cell is about 100-500 m in an urban area and about 400-1000 m in a suburban area, so that the positioning accuracy according to the position of the registered cell in the signaling data is rough and cannot be used for traffic monitoring.
Disclosure of Invention
Aiming at the defects and the improvement requirements of the prior art, the invention provides a method and a system for ranking urban road traffic jam based on signaling data, and aims to realize large-range and low-cost urban traffic state monitoring.
In order to achieve the above object, according to one aspect of the present invention, there is provided a method for ranking urban road traffic congestion based on signaling data, comprising:
(1) extracting one or more sections of motion tracks of each user in a base station network according to signaling data acquired in a target time period;
(2) obtaining a route which is most similar to each section of motion track in a road network, and respectively taking the route as a target route corresponding to each section of motion track;
(3) obtaining a plurality of road sections obtained by dividing each target route by the base station switching point, and calculating the movement speed of all users on each road section according to the length of the road section and the switching time of the base station;
(4) screening out the speed of the motor vehicle from the movement speeds of all users on the corresponding road section according to the speed threshold of the motor vehicle on each road section in the target time period so as to calculate the average speed of the motor vehicle on each road section in the target time period;
(5) obtaining the free flow speed of each road section to calculate the road section traffic jam index of each road section in a target time period according to the free flow speed and the average motor vehicle speed;
(6) carrying out weighted average on the traffic congestion indexes of all road sections belonging to the same road according to the length of the road sections to obtain the road traffic congestion index of each road in a target time period;
(7) and carrying out traffic jam ranking on the roads in the target range according to the road traffic jam index.
Further, the method for establishing the base station network comprises the following steps: determining the coverage area of each base station according to the positioning information of the base station, and respectively using the coverage area as a base station cell corresponding to each base station; the base station network is composed of all base station cells.
Further, the step (1) further comprises: before the motion track of each user in the base station network is extracted, ping-pong switching data in the collected signaling data are deleted and drift data are corrected, so that the accuracy of road traffic jam ranking is improved.
Further, in step (1), extracting one or more motion trajectories of each user in the base station network according to the signaling data collected in the target time period, including:
if the residence time of the user in the same base station cell is larger than the corresponding residence time threshold value, the base station cell is judged to be a residence point;
the resident point divides the motion of each user into one or more sections, thereby extracting one or more sections of motion tracks of each user in the base station network.
Preferably, the method for acquiring the residence time threshold corresponding to any one base station cell includes:
obtaining all road sections in the road network in the base station cell, and obtaining the minimum movement speed on each road section according to historical movement data;
and calculating the base station switching time for switching the user from the base station cell to another base station cell along each road section according to the length of the road section and the corresponding minimum movement speed, and taking the longest base station switching time as the residence time threshold corresponding to the base station cell.
Further, the step (2) comprises:
(21) for each motion track c, obtaining all feasible routes between the starting point and the end point of the motion track in the road network;
(22) respectively obtaining base station ID sequences corresponding to all routes according to the movement track and the condition that all routes pass through a base station cell;
(23) calculating the similarity between the motion trail c and the base station ID sequence corresponding to each route as the similarity between the motion trail c and the corresponding route;
(24) and taking the route with the highest similarity degree with the motion trail c as the target route.
Further, the method for acquiring the vehicle speed threshold of the motor vehicle at any road section in the target time period comprises the following steps:
obtaining historical movement speed on the road section in a target time period according to historical movement data;
and carrying out cluster analysis on the acquired historical movement speed to obtain the vehicle speed threshold value of the motor vehicle on the road section in the target time period.
Further, the method for acquiring the free flow vehicle speed of any road section comprises the following steps:
obtaining the maximum movement speed v on the road section according to the historical movement datamaxAnd obtain the path provided by the big data platformFree stream vehicle speed v on a segmentdAnd the vehicle speed v of the road to which the road section belongsr；
Will maximum speed of movement vmaxFree stream vehicle speed vdAnd limiting the vehicle speed vrIs taken as the free-stream vehicle speed v on the road sectionf。
Further, the road section traffic congestion index of any road section is as follows:
wherein, V and VfThe average motor vehicle speed and the free flow vehicle speed of the road section are respectively, N is an amplification factor, and N is more than or equal to 1.
According to another aspect of the present invention, there is provided an urban road traffic congestion ranking system based on signaling data, comprising: the device comprises a motion track extraction module, a road network matching module, a motion speed acquisition module, a motor vehicle speed acquisition module, a first calculation module, a second calculation module and a ranking module;
the motion track extraction module is used for extracting one or more sections of motion tracks of each user in the base station network according to the signaling data acquired in the target time period;
the road network matching module is used for obtaining a route which is most similar to the motion trail of each section in the road network and respectively used as a target route corresponding to the motion trail of each section;
the movement speed acquisition module is used for acquiring a plurality of road sections obtained by dividing each target route by the base station switching point and calculating the movement speed of all users on each road section according to the length of the road section and the switching time of the base station;
the motor vehicle speed acquisition module is used for screening out the motor vehicle speed from the movement speeds of all users on the corresponding road section according to the motor vehicle speed threshold value of each road section in the target time period so as to calculate the average motor vehicle speed on each road section in the target time period;
the first calculation module is used for obtaining the free flow speed of each road section so as to calculate the road section traffic jam index of each road section in a target time period according to the free flow speed and the average motor vehicle speed;
the second calculation module is used for carrying out weighted average on the traffic congestion indexes of all road sections belonging to the same road according to the length of the road sections so as to obtain the road traffic congestion indexes of all roads in the target time period;
the ranking module is used for ranking the traffic jam of the roads in the target range according to the road traffic jam index;
the base station switching point is the intersection point of the motion track or route and the boundary of the base station cell, and the base station switching time is the time from the time when the user enters the base station cell to the time when the user moves to the adjacent base station cell.
Generally, by the above technical solution conceived by the present invention, the following beneficial effects can be obtained:
(1) according to the urban road traffic jam ranking method and system based on the signaling data, the signaling data are utilized to calculate the traffic jam index of the road and complete the road traffic jam ranking, the signaling data are naturally generated and accumulated in daily life of users, extra equipment installation and maintenance cost is not required to be invested, and the coverage rate of the signaling data is high, so that the urban traffic state monitoring with large range and low cost can be realized.
(2) According to the urban road traffic jam ranking method and system based on the signaling data, the base station switching data are used for carrying out relevant calculation, the calculation result is bound with the specific road, and the junction between the base station switching data and two base station cells contains more accurate positioning information, so that the method and system can accurately acquire the traffic jam condition of the road, and the accuracy of the road traffic jam ranking is improved.
(3) According to the urban road traffic jam ranking method and system based on the signaling data, the free flow speed of each road section in the target time period is determined by integrating the multidimensional information, the accuracy of the road section traffic jam index can be improved, and the accuracy of the road traffic jam index is further improved.
(4) According to the urban road traffic congestion ranking method and system based on the signaling data, after the average motor vehicle speed and the free flow vehicle speed of the road section are obtained, the calculated road section congestion index is limited in a more specific range, so that the finally calculated road traffic congestion index can reflect the traffic congestion condition of the road, and the visualization of the result is facilitated, thereby providing convenience for the real-time monitoring of the urban traffic state.
Drawings
Fig. 1 is a flowchart of a method for ranking urban road traffic congestion based on signaling data according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an application example provided by an embodiment of the present invention;
fig. 3 is a flowchart of an average vehicle speed obtaining method on any road section according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
Before explaining the technical scheme of the invention in detail, signaling data is briefly introduced.
The signaling data positioning is a simple mobile phone positioning method originated from a cellular cell positioning technology, and position information is determined based on the cell ID of a base station where a mobile phone user is located; the realization principle is as follows: GSM (Global System for Mobile Communication, Global System for Mobile communications) has a "cellular" network structure, and a Mobile phone user needs to perform location registration in a located base station cell if performing Communication service, and can locate the user to an area covered by a signal of the base station cell by extracting an ID number of the registered base station cell; compared with other precise positioning technologies (such as GPS), the positioning method has advantages in sample size, coverage and implementation cost and period;
when a mobile phone user who is in a conversation moves from a base station cell to another base station cell, switching of a location area occurs; in order to ensure the quality and continuity of user communication, the base station platform switches the mobile platform from one call channel to another call channel, that is, the base station cell is switched.
The radius of the coverage area of the base station cell is about 100-500 m in an urban area and about 400-1000 m in a suburban area, so that the positioning accuracy according to the position of the registered cell in the signaling data is rough and cannot be used for traffic monitoring. However, the base station handover data occurs at the junction of two base station cells, and includes more accurate location information, and the location information is relatively hidden and cannot be directly acquired, and in order to obtain the road traffic condition, more detailed road network binding and behavior determination work needs to be performed.
Aiming at the problem that the existing road traffic jam ranking method can not realize the real-time monitoring of the urban traffic state with large range and low cost, the urban road traffic jam ranking method based on the signaling data provided by the invention is shown in figure 1 and comprises the following steps:
(1) extracting one or more sections of motion tracks of each user in a base station network according to signaling data acquired in a target time period;
the length of the target time interval can be set according to specific requirements so as to realize traffic state monitoring with different granularities;
in an optional embodiment, in step (1), extracting one or more motion trajectories of each user in the base station network according to signaling data collected in a target time period specifically includes:
if the residence time of the user in the same base station cell is larger than the corresponding residence time threshold value, the base station cell is judged to be a residence point;
dividing the motion of each user into one or more sections by the resident point, thereby extracting one or more sections of motion tracks of each user in the base station network;
the residence time threshold corresponding to each base station cell can be set to be a fixed time length, such as half an hour, according to the traffic state characteristics, so as to ensure that whether a user is in a motion state or a residence state can be accurately identified, and then a residence point is determined; in this embodiment, the method for obtaining the residence time threshold corresponding to any one base station cell specifically includes:
obtaining all road sections in the road network in the base station cell, and obtaining the minimum movement speed on each road section according to historical movement data;
calculating the base station switching time of a user for switching from the base station cell to another base station cell along each road section according to the length of the road section and the corresponding minimum movement speed, and taking the longest base station switching time as a residence time threshold corresponding to the base station cell;
(2) obtaining a route which is most similar to each section of motion track in a road network, and respectively taking the route as a target route corresponding to each section of motion track;
in an optional embodiment, step (2) specifically includes:
(21) for each motion track c, obtaining all feasible routes between the starting point and the end point of the motion track in the road network;
specifically, open source maps such as OpenStreetMap and the like can be adopted to obtain relevant feasible routes;
(22) respectively obtaining a motion track c and a base station ID sequence corresponding to each route according to the motion track and the condition that each route passes through a base station cell;
specifically, a base station ID sequence corresponding to the motion trail c can be obtained according to the collected signaling data, and the base station ID sequence corresponding to each route can be obtained by means of an AreGIS platform;
(23) calculating the similarity between the motion trail c and the base station ID sequence corresponding to each route as the similarity between the motion trail c and the corresponding route;
calculating the similarity between the motion track c and the base station ID sequence corresponding to one of the routes, specifically adopting a sequence comparison algorithm based on an Edit Distance (ED), or a sequence comparison algorithm based on a Longest Common Subsequence (LCS), or adopting other sequence comparison algorithms; the ED and LCS based algorithms have the following advantages: firstly, no requirement is made on the sampling rate; the lengths of the tracks which are not required to be compared are equal;
in this embodiment, specifically, the Needleman-Wunsch algorithm is used, which is a sequence comparison algorithm based on the longest common subsequence, and the basic idea is to find out the longest identical subsequence existing in both sequences when matching the two sequences; the longest common subsequence does not require elements to appear consecutively, but requires that the order of appearance be consistent, e.g., sequence X ═ { P1, P2, P3, P4}, and sequence Y ═ Pl, P3, P2}, then the most common subsequence for sequences X and Y is { P1, P3 }; compared with an algorithm based on an edit distance, the sequence comparison algorithm based on the LCS adopted by the embodiment only focuses on the matching points, and does not need to match each track point, so that the robustness is better for the noise points and the data defect condition in the track;
(24) taking the route with the highest similarity degree with the motion track c as a target route;
(3) obtaining a plurality of road sections obtained by dividing each target route by the base station switching point, and calculating the movement speed of all users on each road section according to the length of the road section and the switching time of the base station;
the road section length is provided by road network information, and the base station switching time is provided by base station switching data;
as shown in fig. 2, the determined target route is a route L through road network matching, and when the user moves on the route L, the acquired base station ID sequence is: A-B-C-D-E-F-G-H, the left side is a base station cell entry point, the base station switching point corresponds to L-2-3-4-5-6-7-8, the route L is divided into 7 road sections by the base station switching point, and the road sections are S in length respectivelyA-SB-SC-SD-SE-SF-SGThe speed of the user' S movement over 7 links, V, can thus be determined from the speed formula V ═ S/t1-2，V2-3，V3-4，V4-5，V5-6，V6-7，V7-8；
Calculating the movement speed of each road section corresponding to each section of movement track by the same method, so that the movement speed of all users on each road section can be obtained;
(4) screening out the speed of the motor vehicle from the movement speeds of all users on the corresponding road section according to the speed threshold of the motor vehicle on each road section in the target time period so as to calculate the average speed of the motor vehicle on each road section in the target time period;
in an alternative embodiment, as shown in fig. 3, the method for obtaining the vehicle speed threshold of any road segment in the target time period comprises the following steps:
obtaining historical movement speed on the road section in a target time period according to historical movement data;
performing cluster analysis on the acquired historical movement speed to obtain a motor vehicle speed threshold value on the road section in a target time period;
in this embodiment, a specific method for performing cluster analysis on historical movement speeds is kmeans clustering, and the method enables speeds in the same cluster to approach each other and speed differences among different clusters to be large; the main travel modes on the road comprise walking, bicycle travel and motor vehicle travel, and because the speed relations of the three travel modes of walking, bicycle and motor vehicle meet the characteristics of proximity in classes and large difference between classes, the algorithm can well classify the historical speed values into the three modes, and further can obtain speed thresholds for distinguishing the various travel modes, including motor vehicle speed thresholds;
(5) obtaining the free flow speed of each road section to calculate the road section traffic jam index of each road section in a target time period according to the free flow speed and the average motor vehicle speed;
in an urban road network, due to the restriction of limited road geometric forms, traffic control and other factors, the free flow speed cannot be achieved; for a certain road section in a target time period, under different application scenes, the free flow speed of the road section in the target time period can be obtained by adopting any one of reference speeds such as a road speed limit, a road section maximum movement speed, a free flow speed provided by an internet big data platform and the like;
in this embodiment, the method for acquiring the free flow vehicle speed of any road segment specifically includes:
obtaining the maximum movement speed v on the road section according to the historical movement datamaxAnd obtain big data planeFree-flow speed v provided by the table on the road sectiondAnd the vehicle speed v of the road to which the road section belongsr；
Will maximum speed of movement vmaxFree stream vehicle speed vdAnd limiting the vehicle speed vrIs taken as the free-stream vehicle speed v on the road sectionfI.e. vf＝{vmax,vd,vr}；
The Baidu and Goodand Internet big data platform mainly obtains average vehicle speed and calculates free flow vehicle speed v through mobile phone GPS signalsd(ii) a Limiting the vehicle speed vrRoad network data provided by electronic map navigation data generators of four-dimensional, high-grade, Kalimeride and the like can be obtained, for example, the speed limit of an intercity express way is 80 kilometers per hour, the speed limit of a main road in the city is 60 kilometers per hour, the speed limit of a high-speed ramp is 30 kilometers per hour and the like; in the embodiment, the multi-dimensional values are simultaneously integrated to determine the free flow speed of each road section in the target time period, and the adjustment and maintenance are continuously performed, so that the accuracy of the calculated traffic jam index can be improved;
in an alternative embodiment, the free-flow vehicle speed V of each road section in the target time period is obtainedfAnd after averaging the vehicle speed V of the motor vehicle, calculating the road section traffic jam index of the corresponding road section as follows:
where N is an amplification factor, in this embodiment, N is 10; compared with other methods for calculating the traffic congestion index, the method for calculating the road section traffic congestion index through the formula can limit the calculated road section congestion index in a more specific range, so that the finally calculated road traffic congestion index can reflect the traffic congestion condition of the road and is convenient for visualizing the result, thereby providing convenience for real-time monitoring of the urban traffic state;
(6) carrying out weighted average on the traffic congestion indexes of all road sections belonging to the same road according to the length of the road sections to obtain the road traffic congestion index of each road in a target time period;
the road sections among the base station switching points are only one part of the urban road network roads, all the road sections forming each road can be obtained according to the road ID, and the weighted average value of the TPI of the road sections belonging to the same road is calculated according to the length of the road sections, so that the road traffic congestion index of the road can be obtained;
(7) carrying out traffic jam ranking on the roads in the target range according to the road traffic jam index;
specifically, the roads in the target range (e.g., a specific city) may be ranked in ascending or descending order of the road traffic congestion indexes, thereby implementing the road traffic congestion ranking.
In the method for ranking road traffic jam based on signaling data, the method for establishing the base station network comprises the following steps: determining the coverage area of each base station according to the positioning information of the base station, and respectively using the coverage area as a base station cell corresponding to each base station; specifically, the coverage area of each base station can be determined by establishing a Thiessen polygon, and can also be determined by adopting other modes;
after each base station cell is determined, all the base station cells form the base station network; fig. 2 shows a base station network in which each base station cell is a regular hexagon.
In an optional embodiment, in the above method for ranking road traffic congestion based on signaling data, in order to wash the data to improve the accuracy of ranking the road traffic congestion, step (1) may further include: before extracting the motion track of each user in the base station network, deleting ping-pong switching data in the collected signaling data and correcting the drift data;
in a GSM communication system, if a mobile terminal just falls in an overlapping area of adjacent cells, the mobile terminal may be switched back and forth between two base stations, which causes a ping-pong effect; in order to overcome the adverse effect of ping-pong switching on motion trajectory analysis, a switching time threshold method of adjacent base stations can be adopted for judgment, and the method specifically comprises the following steps:
stepl, if the starting switching position of a certain switching is the same as the ending switching position of the subsequent switching, and such switching occurs two or more times in succession, go to Step 2;
step 2: calculating the same switching time interval delta T if delta T is smaller than the preset ping-pong switching judgment time threshold TpIf so, determining that a ping-pong switching phenomenon exists in the positioning data of the user, and turning to Step 3;
step 3: the switching starting position of the first switching is called ping-pong switching starting position, the switching ending position of the last switching is called ping-pong switching ending position, and the ping-pong switching data processing rule is to delete the coordinate data between the ping-pong switching starting position and the ping-pong switching ending position.
As can be seen from the above rules, in the base station handover behaviors shown in table 1, a ping-pong handover phenomenon exists among the handover 2, the handover 3, and the handover 4, and after the operation of deleting ping-pong handover data, the handover data of the handover 2 and the handover 3 will be deleted; in table 1, Cell denotes a base station;
table 1 base station handover example
| Switch number | Switching start position | End of handover position | |
| 1 | CellA | CellB | |
| 2 | | CellC | |
| 3 | | CellB | |
| 4 | CellB | CellC |
Considering the influence of different actual urban road network environments and base station erection modes, the phenomenon that a mobile terminal suddenly receives signals of a cell far away from the mobile terminal to communicate, the signals are changed into a logically adjacent cell, and false switching occurs; the data is characterized in that the data is unlikely to move rapidly to another position in a short time at a moving speed exceeding a reasonable range in one position; for abnormal drift data existing in original data, the following method can be adopted to correct the abnormal drift data:
three consecutive position points a (lng) in the position trajectory data processed by the Step1 Step above are giveni,lati,ti)，B(lngj,latj,tj) And C (lng)k,latk,tk) Respectively calculating the minimum moving speed V between the position points A and BijMinimum moving speed V between position points B and Cjk:
If Vij＞Vth，Vjk＞VthIf the point B is a data abnormal point, deleting the data of the point; wherein, lngi、lngjAnd lngkLongitude, lat, representing location points A, B and C, respectivelyi、latjAnd latkIndicates the latitude, t, of location points A, B and C, respectivelyi、tjAnd tkIndividual watchTime, V, of user arriving at location points A, B and CthFor travel limit speed, the highest speed limit for urban road traffic may be set as the speed limit in an urban environment.
The invention also provides a system for realizing the urban road traffic jam ranking method based on the signaling data, which comprises the following steps: the device comprises a motion track extraction module, a road network matching module, a motion speed acquisition module, a motor vehicle speed acquisition module, a first calculation module, a second calculation module and a ranking module;
the motion track extraction module is used for extracting one or more sections of motion tracks of each user in the base station network according to the signaling data acquired in the target time period;
the road network matching module is used for obtaining a route which is most similar to the motion trail of each section in the road network and respectively used as a target route corresponding to the motion trail of each section;
the movement speed acquisition module is used for acquiring a plurality of road sections obtained by dividing each target route by the base station switching point and calculating the movement speed of all users on each road section according to the length of the road section and the switching time of the base station;
the motor vehicle speed acquisition module is used for screening out the motor vehicle speed from the movement speeds of all users on the corresponding road section according to the motor vehicle speed threshold value of each road section in the target time period so as to calculate the average motor vehicle speed on each road section in the target time period;
the first calculation module is used for obtaining the free flow speed of each road section so as to calculate the road section traffic jam index of each road section in a target time period according to the free flow speed and the average motor vehicle speed;
the second calculation module is used for carrying out weighted average on the traffic congestion indexes of all road sections belonging to the same road according to the length of the road sections so as to obtain the road traffic congestion indexes of all roads in the target time period;
the ranking module is used for ranking the traffic jam of the roads in the target range according to the road traffic jam index;
the base station switching point is an intersection point of a motion track or a route and a base station cell boundary, and the base station switching time is the time from entering a base station cell to moving to an adjacent base station cell;
in this embodiment, the detailed implementation of each module may refer to the description of the method embodiment described above, and will not be repeated here.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (8)
1. A method for ranking urban road traffic jam based on signaling data is characterized by comprising the following steps:
(1) extracting one or more sections of motion tracks of each user in a base station network according to signaling data acquired in a target time period;
(2) obtaining a route which is most similar to each section of motion track in a road network, and respectively taking the route as a target route corresponding to each section of motion track;
(3) obtaining a plurality of road sections obtained by dividing each target route by the base station switching point, and calculating the movement speed of all users on each road section according to the length of the road section and the switching time of the base station;
(4) screening out the speed of the motor vehicle from the movement speeds of all users on the corresponding road section according to the speed threshold of the motor vehicle on each road section in the target time period so as to calculate the average speed of the motor vehicle on each road section in the target time period;
(5) obtaining the free flow speed of each road section to calculate the road section traffic jam index of each road section in the target time period according to the free flow speed and the average motor vehicle speed;
(6) carrying out weighted average on the traffic congestion indexes of all road sections belonging to the same road according to the length of the road sections to obtain the road traffic congestion index of each road in the target time period;
(7) carrying out traffic jam ranking on the roads in the target range according to the road traffic jam index;
the base station switching point is an intersection point of a motion track or a route and a base station cell boundary, and the base station switching time is the time from entering a base station cell to moving to an adjacent base station cell; in the step (1), extracting one or more motion trajectories of each user in the base station network according to the signaling data acquired in the target time period includes:
if the residence time of the user in the same base station cell is larger than the corresponding residence time threshold value, the base station cell is judged to be a residence point;
dividing the motion of each user into one or more sections by the resident point, thereby extracting one or more sections of motion tracks of each user in the base station network;
the method for establishing the base station network comprises the following steps: determining the coverage area of each base station according to the positioning information of the base station, and respectively using the coverage area as a base station cell corresponding to each base station; the base station network is composed of all base station cells.
2. The method for ranking urban road traffic congestion based on signaling data according to claim 1, wherein said step (1) further comprises: before the motion track of each user in the base station network is extracted, ping-pong switching data in the collected signaling data are deleted and the drift data are rectified.
3. The method for ranking the urban road traffic jam based on the signaling data as recited in claim 1, wherein the method for acquiring the residence time threshold corresponding to any one base station cell comprises the following steps:
obtaining all road sections in the road network in the base station cell, and obtaining the minimum movement speed on each road section according to historical movement data;
and calculating the base station switching time for switching the user from the base station cell to another base station cell along each road section according to the length of the road section and the corresponding minimum movement speed, and taking the longest base station switching time as the residence time threshold corresponding to the base station cell.
4. The method for ranking urban road traffic congestion based on signaling data according to claim 1, wherein said step (2) comprises:
(21) for each motion track c, obtaining all feasible routes between the starting point and the end point of the motion track in the road network;
(22) respectively obtaining the movement track c and a base station ID sequence corresponding to each route according to the movement track and the condition that each route passes through a base station cell;
(23) calculating the similarity between the motion trail c and the base station ID sequence corresponding to each route as the similarity between the motion trail c and the corresponding route;
(24) and taking the route with the highest similarity degree with the motion trail c as the target route.
5. The method for ranking the urban road traffic jam based on the signaling data as recited in claim 1, wherein the method for obtaining the vehicle speed threshold of any road section in the target time period is as follows:
obtaining historical movement speed on the road section in the target time period according to historical movement data;
and carrying out cluster analysis on the acquired historical movement speed to obtain the vehicle speed threshold value of the motor vehicle on the road section in the target time period.
6. The method for ranking the urban road traffic jam based on the signaling data as recited in claim 1, wherein the method for acquiring the free flow vehicle speed of any road section comprises the following steps:
obtaining the maximum movement speed v on the road section according to the historical movement datamaxAnd obtaining the free flow speed v on the road section provided by the big data platformdAnd the vehicle speed v of the road to which the road section belongsr；
Setting the maximum movement velocity vmaxSaid free stream vehicle speed vdAnd the vehicle speed limit vrIs taken as the free-stream vehicle speed v on the road sectionf。
7. As in claimThe method for ranking the urban road traffic congestion based on the signaling data, which is claimed in claim 1, is characterized in that the road section traffic congestion index of any road section is as follows:
wherein, V and VfThe average motor vehicle speed and the free flow vehicle speed of the road section are respectively, N is an amplification factor, and N is more than or equal to 1.
8. An urban road traffic congestion ranking system based on signaling data, comprising: the device comprises a motion track extraction module, a road network matching module, a motion speed acquisition module, a motor vehicle speed acquisition module, a first calculation module, a second calculation module and a ranking module;
the motion track extraction module is used for extracting one or more sections of motion tracks of each user in the base station network according to the signaling data acquired in the target time period;
the road network matching module is used for obtaining a route which is most similar to the motion trail of each section in the road network and respectively used as a target route corresponding to the motion trail of each section;
the movement speed acquisition module is used for acquiring a plurality of road sections obtained by dividing each target route by the base station switching point and calculating the movement speed of all users on each road section according to the length of the road section and the switching time of the base station;
the motor vehicle speed acquisition module is used for screening out the motor vehicle speed from the movement speeds of all users on the corresponding road section according to the motor vehicle speed threshold value of each road section in the target time period so as to calculate the average motor vehicle speed on each road section in the target time period;
the first calculation module is used for obtaining the free flow speed of each road section so as to calculate the road section traffic jam index of each road section in the target time period according to the free flow speed and the average motor vehicle speed;
the second calculation module is used for carrying out weighted average on the traffic congestion indexes of all road sections belonging to the same road according to the length of the road sections so as to obtain the road traffic congestion index of each road in the target time period;
the ranking module is used for ranking the traffic jam of the roads in the target range according to the road traffic jam index;
the base station switching point is an intersection point of a motion track or a route and a base station cell boundary, and the base station switching time is the time from entering a base station cell to moving to an adjacent base station cell; the motion trail extraction module extracts one or more motion trails of each user in the base station network according to the signaling data collected in the target time period, and comprises the following steps: if the residence time of the user in the same base station cell is larger than the corresponding residence time threshold value, the base station cell is judged to be a residence point; dividing the motion of each user into one or more sections by the resident point, thereby extracting one or more sections of motion tracks of each user in the base station network;
the method for establishing the base station network comprises the following steps: determining the coverage area of each base station according to the positioning information of the base station, and respectively using the coverage area as a base station cell corresponding to each base station; the base station network is composed of all base station cells.
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