CN108806250A - A kind of area traffic jamming evaluation method based on speed sampling data - Google Patents
A kind of area traffic jamming evaluation method based on speed sampling data Download PDFInfo
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- CN108806250A CN108806250A CN201810585179.XA CN201810585179A CN108806250A CN 108806250 A CN108806250 A CN 108806250A CN 201810585179 A CN201810585179 A CN 201810585179A CN 108806250 A CN108806250 A CN 108806250A
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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
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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
- G08G1/0125—Traffic data processing
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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
- G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
Abstract
The present invention proposes a kind of area traffic jamming evaluation method based on speed sampling data, and the vehicle operation data comprising instantaneous velocity and corresponding geographical location is obtained by vehicle GPS or other devices；It will be divided into different regions to the geographical space of Yingcheng City, and collected data are projected to the corresponding region of map according to the time period；For falling all data in each region on each period, calculate the average traffic speed in this region period；For each effective coverage, by it on the basis of the minimum value, maximum value of traffic speed on all periods and average value, convert the traffic speed in the arbitrary period region to an index for representing congestion severity, region congestion level is calculated, convenient for the comparative analysis between different time and region.
Description
Technical field
The present invention relates to a kind of evaluation method more particularly to a kind of area traffic jamming evaluations based on speed sampling data
Method.
Background technology
In recent years, with the increase of the expansion of urban population and car ownership, many metropolitan traffic jam issues
It is increasingly serious.More ground government actively seeks to administer the countermeasure of congestion, expands a series of social concerns caused by city size
As research hotspot.With the continuous development of information technology and smart machine, mobile unit, intelligent sensing equipment and monitor and control facility
Technology upgrading and comprehensively covering provide it is a greater amount of, more directly, more polynary data supporting.All kinds of urban transportation data sum numbers
It is expected to according to digging technology to solve the problems, such as that urban congestion provides new thinking.Speed spatial and temporal distributions data structure based on city is handed over
Logical congestion index is conducive to preferably grasp urban traffic conditions, analysis congestion feature, inquires into the congestion origin cause of formation and formulate corresponding
Countermeasure.However in the prior art, lack quantizating index evaluation method general, that space-time fine granularity analysis can be carried out.
Invention content
The present invention proposes a kind of area traffic jamming evaluation method based on speed sampling data, can consider roads at different levels
Difference in grade and capacity, in traffic congestion degree unification to unification section that also can by different time and spatially, side
Just the comparative analysis between different periods and different zones is carried out.Specifically include following steps：
Step 1 includes wink by the acquisition of vehicle-mounted GPS apparatus, intelligent mobile phone sensor or other road information harvesters
The road vehicle running data of the information such as Shi Sudu and geographical location of corresponding moment, to provide the speed spatial and temporal distributions data in city
Basis；
Step 2, the city speed spatial and temporal distributions data for acquisition, will by certain time granularity (granularity can customize)
All data are divided into the different periods, for the corresponding urban geography range of the data, by it according to certain space granularity
(granularity can customize) is divided into several zonules；For all highway traffic datas in different time periods, by it according to ground
Reason location information projects to a certain specific region；
Step 3, for falling all data in effective coverage on each period, desirable mean value is come approximate when obtaining this
The traffic speed vi in the section region；
Step 4, for arbitrary effective coverage, calculate its traffic speed minimum value, maximum value on all periods, and
Average value, maximum traffic speed of certain region on one day all period are vmax, minimum traffic speed is vmin, average traffic speed
Degree is vavg；
Step 5, for the speed v on the selection area a certain periodi, reflect traffic by calculating traffic congestion index v '
Congestion level：
Between [- 1,1], which is 0 and illustrates at the traffic speed of the region at this moment the exponential number range
In average state, which is more than 0 and illustrates moment this area congestion compared with whole day average traffic state, the bigger theory of the index
Congestion is got in the bright region at this moment, which is less than 0 and illustrates that the moment this area is more unimpeded compared with whole day average traffic state,
The smaller explanation region of numerical value is more unimpeded at this moment.
Step 6 is accustomed to more to meet daily cognition, to v ' carry out linear changes, is mapped between 0-100, finally
Congestion index θ it is as follows：
θ=(v '+1) * 50
θ is directly proportional to congestion level, and θ=0 indicates completely unimpeded, and θ=50 indicates that speed is in whole day average level, and θ=
100 indicate extreme congestion.
The beneficial effects of the present invention are：
1. since the evaluation method of the present invention is data driven type, under the background of smart machine fast development, data are received
Collection is convenient, flexible, timely, and the congestion index based on car speed acquisition can accomplish to calculate in real time, and space-time granularity is adjustable.
2. road speeds data directly to be projected to the different zones of map according to longitude and latitude, this operating method and road
Matching algorithm is compared, simpler direct.Under the support of adequate data, reasonable threshold is set according to time, space granularity of division
Value carries out area filter, you can depicts the road network dispatch in city.
3. being commented with the traffic congestion being designed based on road traffic density, the magnitude of traffic flow, queue length, travel time etc.
Valence method is compared, and this method can go out traffic congestion situation of the different zones in each period with direct measuring, also can be in conjunction with difference
The respective actual traffic situation in region is calculated, that is, considers the difference of each region category of roads and carrying capacity
4. being different from the common other methods for being intended to description congestion in road situation, this method has calculated town region
Opposite congestion status, and the relative status is the account of the history based on the region to calculate, therefore with historical data
It constantly alternates, which can have smooth differentiation ability, be allowed to more stable, more realistically calculate the current of the region
Congestion status.
Description of the drawings
Fig. 1 is flow chart of the method for the present invention
Fig. 2 is with present invention analysis 6：30 Beijing Communication jam situations
Fig. 3 is with present invention analysis 8：00 Beijing Communication jam situation
Fig. 4 is Beijing's each department whole day congestion time diagram (h)
Fig. 5 is each ring congestion variable density situation of Beijing's morning peak
Fig. 6 is each ring congestion variable density situation of Beijing's evening peak
Specific implementation mode
In order to make the purpose , technical scheme and advantage of the present invention be clearer, with reference to the accompanying drawings and embodiments, right
The present invention is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, and
It is not used in the restriction present invention.As long as in addition, technical characteristic involved in the various embodiments of the present invention described below
It does not constitute a conflict with each other and can be combined with each other.
The present invention proposes a kind of area traffic jamming evaluation method based on speed sampling data, as shown in FIG. 1, FIG. 1 is
Flow chart of the method for the present invention illustrates specific implementation mode with reference to the application example of Beijing.
In embodiment, the floating car data provided using Beijing's taxi company, the data set comes from Beijing Taxi Industry
Mobile unit, provide the relevant informations such as car speed, time, position, carrying situation.It is necessary having been done to related data
Filtering with merge after, according to 5 minutes be one group divided, can by one day from 6：00 to 24：00 all data are divided into
216 data groups.In fact, the minimizing of the equipment such as GPS, cheaper and universalness make ten partial volume of acquisition of this kind of data
Easily, shared automobile, even private car etc. have had the ability for acquiring this kind of data, further ensure the feasibility of this method
With availability；
The square region that about 50km*50km corresponding with data set longitude and latitude range is chosen on Beijing's map, according to
The precision of 100m*100m divides, and Beijing can be divided into the net region of 500*500, for each region, can calculate
Go out its longitude and latitude initial range；
For the road speeds data set on each period, wherein all velocity informations can be projected to map
On corresponding region, can be filtered at this time according to the collection capacity of data on each region, if a certain region is more than half
Several periods, upper data collection capacity was less than 3, then was left out.For falling the institute in effective coverage on a certain period
There are data, by taking mean value computation come the approximate traffic speed v for obtaining the region in this periodi；
To each effective coverage, can obtain the region from early 6 up to evening 12 when every 5 minutes one traffic speeds letter
Breath, i.e., to each effective coverage, can obtain one a length of 216 whole day velocity series；
To each effective coverage, if whole day maximum speed is vmax, minimum speed vmin, average speed is calculated, is set as
vavg；
To the speed v of each region whole day arbitrary periodi, the computational methods of the period congestion index are as follows：
θ=(v '+1) * 50
In one embodiment, a certain region whole day minimum speed is 0km/h, maximum speed 40km/h, and average speed is
25km/h, if then the regional traffic speed of a certain period is 35km/h, θ values are
Illustrate that the moment this area is more unimpeded compared with whole day average traffic state；If the regional traffic speed of a certain period
For 10km/h, then its θ value is
That is moment this area congestion compared with whole day average traffic state.
Fig. 2 is 6：30 Beijing Communication jam situations, Fig. 38：00 Beijing Communication jam situation, the deeper explanation of color
More congestion, color more elementary introduction are bright more unimpeded.By the index, it can clearly reflect upper city different zones of each period
Traffic.
Various analyses can be carried out to traffic congestion using this index, for example, this congestion index is considered as more than 85
Congestion, statistics each region index are more than 85 time span, and as the duration of congestion time in one day, analysis result is as schemed
Shown in 4, it can be seen that under such congestion criterion, the whole day congestion time in Beijing major part region is both less than 1.5
Hour.Congestion time long area distribution East 2nd Ring Road, East 3rd Ring Road and between the business districts CBD near, near financial Street,
And the ground such as lotus flower pool East Road near Beijing West Railway Station, this is also consistent with other research conclusions.
The result of this evaluation method can be utilized further to analyze the different zones between each loop in Beijing at peak
The jam situation of section counts the congestion dot density between each loop and changes with time situation.Approximation with away from it is intown not
Same distance represents each loop, and congestion density is the sum that the congestion regions in the region account for all effective coverages.Fig. 5, Fig. 6 are
Each region congestion variable density situation of early evening peak.Can be seen that peak time morning and evening, the congestion density of different zones all compared with
Flat peak period significantly rises, wherein the congestion density of evening peak is higher than morning peak, that is to say, that the region of evening peak congestion is more
It is more.Secondly, the congestion density of different zones is also variant, bigger closer to intown region congestion density, more outside, congestion
Density is sequentially reduced, this is also consistent with the reality that city center is most stifled.
Finally it should be noted that：The above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although
Present invention has been described in detail with reference to the aforementioned embodiments, it will be understood by those of ordinary skill in the art that：It still may be used
To modify to the technical solution recorded in previous embodiment or equivalent replacement of some of the technical features；And
These modifications or replacements, the spirit and model of various embodiments of the present invention technical solution that it does not separate the essence of the corresponding technical solution
It encloses.
Claims (7)
1. a kind of area traffic jamming evaluation method based on speed sampling data, it is characterised in that data-driven, in real time calculating,
Spatial dimensionality, granularity are adjustable etc..Specifically include following steps：Step 1, by vehicle GPS or other device collection vehicle roads
Running data；Step 2, the vehicle operation data of acquisition is divided according to time granularity, it will be empty by geography to Yingcheng City
Between granularity divided, for all data of each period, the corresponding region on map is projected into according to longitude and latitude
It is interior, and effective coverage is selected according to each region projection data volume；Step 3, for falling the institute in arbitrary region on each period
There are data, calculates the average traffic speed in this region period Nei；Step 4, for arbitrary effective coverage, it is calculated in whole day institute
There are road speeds minimum value, maximum value and the average value on the period；Step 5, most with arbitrary region whole day road speeds in step 4
On the basis of small value, maximum value and average value, converts the road traffic speed in the region any time period to a representative and gather around
Index on [0,100] section of stifled severity, the bigger explanation region more congestion on the selected period of the index.
2. the area traffic jamming evaluation method according to claim 1 based on speed sampling data, which is characterized in that institute
The road vehicle running data of acquisition need to include instantaneous velocity, corresponding time and the corresponding geographical location information of vehicle,
Wherein the time is at least accurate to minute, and geographical location information is indicated with longitude and latitude.
3. the area traffic jamming evaluation method according to claim 2 based on speed sampling data, which is characterized in that needle
To the vehicle operation data of acquisition, need all data being divided into several groups by different time sections；It is corresponded to for the data set
Geospatial area, need by its spatially granularity division be several zonules；Number is acquired for vehicle in different time periods
According to needing according to geographical location information to project to it on corresponding specific region, and filter out the very little region of data projection amount.
4. the area traffic jamming evaluation method according to claim 3 based on speed sampling data, which is characterized in that right
In falling all data in arbitrary effective coverage on each period, mean value is taken approximate to obtain the traffic in this region period
Speed vi。
5. the area traffic jamming evaluation method according to claim 4 based on speed sampling data, which is characterized in that right
In arbitrary effective coverage, its traffic speed minimum value, maximum value and average value on all periods is calculated, if certain region
Maximum traffic speed on one day all period is vmax, minimum traffic speed is vmin, average traffic speed is vavg。
6. the area traffic jamming evaluation method according to claim 5 based on speed sampling data, which is characterized in that right
Speed v on the selection area a certain periodi, by calculating traffic congestion index v ' reflection traffic congestion degree：
And to v ' carry out linear process, mapped between 0-100, final congestion index θ is as follows：
θ=(v '+1) * 50.
7. the area traffic jamming evaluation method according to claim 6 based on speed sampling data, which is characterized in that institute
The range of θ is stated between 0-100, θ is equal to traffic speed of the 50 explanation regions in this period and is in average state, and θ is more than 50
The region is indicated in the congestion compared with whole day average traffic speed of this period, the bigger explanation regions θ in the more congestion of this period,
θ is less than 50 and indicates that the region is more unimpeded compared with whole day average traffic speed in this period, and the smaller explanation regions θ are in this period
It is more unimpeded.
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| CN109712394A (en) * | 2019-01-15 | 2019-05-03 | 青岛大学 | A kind of congestion regions discovery method |
| CN111462498A (en) * | 2020-05-29 | 2020-07-28 | 青岛大学 | Frequent congestion area identification method and equipment |
| CN112435472A (en) * | 2020-11-12 | 2021-03-02 | 北京嘀嘀无限科技发展有限公司 | Congestion analysis method, device, equipment and storage medium |
| CN112533140A (en) * | 2020-11-24 | 2021-03-19 | 天津市市政工程设计研究院 | Shared bicycle distribution condition evaluation method based on index |
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| CN109410586A (en) * | 2018-12-13 | 2019-03-01 | 中南大学 | A kind of Traffic State Detection Method based on multivariate data fusion |
| CN109712394A (en) * | 2019-01-15 | 2019-05-03 | 青岛大学 | A kind of congestion regions discovery method |
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| CN112750299A (en) * | 2019-10-29 | 2021-05-04 | 华为技术有限公司 | Traffic flow analysis method and device |
| CN111462498B (en) * | 2020-05-29 | 2021-08-20 | 青岛大学 | Frequent congestion area identification method and equipment |
| CN111462498A (en) * | 2020-05-29 | 2020-07-28 | 青岛大学 | Frequent congestion area identification method and equipment |
| CN112435472A (en) * | 2020-11-12 | 2021-03-02 | 北京嘀嘀无限科技发展有限公司 | Congestion analysis method, device, equipment and storage medium |
| CN112533140B (en) * | 2020-11-24 | 2021-10-12 | 天津市赛英工程建设咨询管理有限公司 | Shared bicycle distribution condition evaluation method based on index |
| CN112533140A (en) * | 2020-11-24 | 2021-03-19 | 天津市市政工程设计研究院 | Shared bicycle distribution condition evaluation method based on index |
| CN114999155A (en) * | 2022-05-26 | 2022-09-02 | 南斗六星系统集成有限公司 | Congestion evaluation method, device, equipment and storage medium for vehicle track |
| CN114999155B (en) * | 2022-05-26 | 2024-03-19 | 南斗六星系统集成有限公司 | Congestion evaluation method, device and equipment for vehicle track and storage medium |
| CN115472008A (en) * | 2022-08-30 | 2022-12-13 | 东南大学 | Network appointment travel time-space characteristic analysis method based on k-means clustering |
| CN115472008B (en) * | 2022-08-30 | 2023-09-19 | 东南大学 | Network vehicle travel space-time characteristic analysis method based on k-means clustering |
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