CN108806250B - Regional traffic jam evaluation method based on speed sampling data - Google Patents
Regional traffic jam evaluation method based on speed sampling data Download PDFInfo
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- G08G1/00—Traffic control systems for road vehicles
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- 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
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Abstract
The invention provides a regional traffic jam evaluation method based on speed sampling data, which is characterized in that vehicle running data comprising instantaneous speed and corresponding geographical position is acquired by means of a vehicle-mounted GPS or other devices; dividing the geographic space of the corresponding city into different areas, and projecting the acquired data to the corresponding area of the map according to time periods; calculating the average traffic speed of the area in each time period for all the data falling in the area in each time period; for each effective area, the minimum value, the maximum value and the average value of the traffic speed of the effective area in all time periods are taken as references, the traffic speed of the effective area in any time period is converted into an index representing the congestion severity, and the congestion degree of the effective area is measured and calculated, so that the effective area is convenient for comparative analysis among different time and areas.
Description
Technical Field
The invention relates to an evaluation method, in particular to a regional traffic jam evaluation method based on speed sampling data.
Background
In recent years, with the expansion of urban population and the increase of automobile holding amount, the traffic jam problem of many big cities is becoming more serious. Many governments actively seek countermeasures for congestion management, and a series of social problems caused by the enlargement of urban scale also become research hotspots. With the continuous development of information technology and intelligent equipment, the technical upgrading and comprehensive coverage of vehicle-mounted equipment, intelligent sensing equipment and monitoring facilities provides more massive, more direct and more diversified data support. Various urban traffic data and data mining technologies are expected to provide a new idea for solving the problem of urban congestion. The traffic jam index is constructed based on the urban speed space-time distribution data, so that the urban traffic condition can be better mastered, the jam characteristics can be analyzed, the jam cause can be discussed, and corresponding countermeasures can be made. However, in the prior art, a universal quantitative index evaluation method capable of performing space-time fine-grained analysis is lacked.
Disclosure of Invention
The invention provides a regional traffic jam evaluation method based on speed sampling data, which can not only consider the difference of roads at all levels in grade and capacity, but also unify the traffic jam degrees at different time and in different space into a unified interval, and is convenient for carrying out comparative analysis at different time intervals and in different regions. The method specifically comprises the following steps:
step 1, vehicle road driving data containing information such as instantaneous speed and corresponding time geographic position are collected by means of a vehicle-mounted GPS device, a smart phone sensor or other road information collection devices, so that a city speed space-time distribution data basis is provided;
step 2, aiming at the acquired urban speed space-time distribution data, dividing all data into different time periods according to a certain time granularity (the granularity can be defined by a user), and aiming at the urban geographic range corresponding to the data, dividing the urban geographic range into a plurality of small areas according to a certain space granularity (the granularity can be defined by a user); projecting all road traffic data of different time periods to a certain specific area according to geographical position information;
step 3, taking an average value of all data falling in the effective area at each time interval to approximately obtain the traffic speed vi of the area at the time interval;
step 4, calculating the minimum value, the maximum value and the average value of the traffic speed of any effective area in all time periods, wherein the maximum traffic speed of a certain area in all time periods of a day is vmaxMinimum traffic speed vminAverage traffic speed vavg；
Step 5, for the speed v of the selected area in a certain period of timeiAnd reflecting the traffic congestion degree by calculating a traffic congestion index v':
the index has a value ranging from [ -1,1], wherein a value of 0 indicates that the traffic speed of the area at the moment is in an average state, a value greater than 0 indicates that the area is congested compared with the average traffic state throughout the day, a larger index indicates that the area is more congested at the moment, a value less than 0 indicates that the area is clear compared with the average traffic state throughout the day, and a smaller value indicates that the area is more clear at the moment.
And 6, in order to better accord with the daily cognitive habit, carrying out linear change on v ', mapping the v' to a range from 0 to 100, wherein the final congestion index theta is as follows:
θ＝(v′+1)*50
θ is proportional to the degree of congestion, θ being 0 means complete clear, θ being 50 means speed on average all day, and θ being 100 means extreme congestion.
The invention has the beneficial effects that:
1. because the evaluation method is of a data driving type, under the background of rapid development of intelligent equipment, data collection is convenient, flexible and timely, the congestion index collected based on the vehicle speed can be calculated in real time, and the space-time granularity is adjustable.
2. The road speed data are directly projected to different areas of the map according to the longitude and latitude, and compared with a road matching algorithm, the operation method is simpler and more direct. Under the support of sufficient data, a reasonable threshold is set according to the time and space division granularity to carry out regional filtering, and then the road network distribution of the city can be drawn.
3. Compared with the traffic jam evaluation method designed based on road traffic density, traffic flow, queuing length, travel time and the like, the method can directly measure and calculate the traffic jam conditions of different areas in each time period and can also calculate by combining the respective actual traffic conditions of the different areas, namely, the difference between the road grade and the carrying capacity of each area is considered
4. Different from other common methods aiming at describing road congestion conditions, the method measures and calculates the relative congestion condition of a specific urban area, and the relative congestion condition is calculated based on the historical conditions of the area, so that the evaluation method can have smooth evolution capability along with the continuous change of the historical data, so that the current congestion condition of the area can be measured and calculated more stably and truly.
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FIG. 1 is a flow chart of the method of the present invention
FIG. 2 shows an analysis 6 according to the invention: traffic jam condition of 30 Beijing city
FIG. 3 shows an analysis 8 according to the invention: 00 Beijing city traffic jam condition
FIG. 4 is a time chart (h) of congestion in all regions of Beijing City
FIG. 5 shows the congestion density variation of each ring of early peak in Beijing City
FIG. 6 shows the congestion density variation of each ring of late peak in Beijing City
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.
The invention provides a regional traffic jam evaluation method based on speed sampling data, as shown in fig. 1, fig. 1 is a flow chart of the method of the invention, and a specific implementation mode is explained below by combining with an application example in Beijing City.
In the embodiment, floating car data provided by a taxi company in Beijing city is used, and the data set is from vehicle-mounted equipment of the taxi in Beijing and provides relevant information such as speed, time, position, passenger carrying condition and the like of the vehicle. After the relevant data is filtered and combined as necessary, the data is divided into groups of 5 minutes, and the data can be divided into 6: 00 to 24: all data for 00 are divided into 216 data groups. In fact, the miniaturization, the low cost and the popularization of equipment such as a GPS and the like enable the data to be collected very easily, and a shared automobile, even a private car and the like have the capacity of collecting the data, so that the feasibility and the usability of the method are further ensured;
selecting a square area of about 50km by 50km corresponding to the longitude and latitude range of the data set on a Beijing city map, dividing the square area according to the accuracy of 100m by 100m, dividing the Beijing city into 500 by 500 grid areas, and calculating the longitude and latitude initial range of each area;
for a set of road speed data over each time segment, all speed information therein may be projected ontoAnd (3) filtering the corresponding areas on the map according to the data acquisition amount of each area, and deleting the corresponding areas if the data acquisition amount of a certain area does not exceed 3 in more than half of the time periods. For all data falling in the effective area in a certain period of time, the traffic speed v of the area in the certain period of time is approximated by taking the average valuei；
For each effective area, traffic speed information of the area from 6 am to 12 pm every 5 minutes can be obtained, namely for each effective area, an all-day speed sequence with the length of 216 can be obtained;
for each effective area, setting the maximum speed of the whole day as vmaxMinimum velocity vminCalculating the average velocity, set as vavg；
Velocity v of each region in any time period all dayiThe calculation method of the congestion index in the period is as follows:
θ＝(v′+1)*50
in one embodiment, the minimum speed of a certain area in the whole day is 0km/h, the maximum speed is 40km/h, the average speed is 25km/h, and if the traffic speed of the certain area in a certain time period is 35km/h, the value of theta is
The area is smooth compared with the average traffic state all day at the moment; if the traffic speed of the area in a certain period of time is 10km/h, the value of theta is
I.e. the area is congested at that moment in time compared to the average traffic conditions throughout the day.
FIG. 2 is a graph of 6: 30 Beijing City traffic congestion, fig. 3 is 8: 00 Beijing city traffic jam, the darker the color indicates the jam, and the lighter the color indicates the smoothness. Through the index, the traffic conditions of different areas of the city in each time period can be clearly reflected.
By applying the index, various analyses on traffic jam can be carried out, for example, if the jam index is greater than 85, the congestion index is regarded as congestion, the time length of the index of greater than 85 in each area is counted and is used as the time length of the congestion time in one day, and the analysis result is shown in fig. 4, so that under the congestion judgment standard, the congestion time in most areas in Beijing city is less than 1.5 hours all day. Areas with relatively long congestion times are distributed near CBD business areas of the Dongbei ring, the Dongbei ring and the middle CBD business areas, near financial streets, lotus pool east roads near Beijing West stations and the like, and the areas are consistent with other research conclusions.
The congestion conditions of different areas among Beijing loops in peak periods can be further analyzed by utilizing the result of the evaluation method, and the change condition of the congestion point density among the loops along with the time can be counted. Each loop is approximately represented at a different distance from the city center, and the congestion density is the total number of congestion areas in the area that occupy all of the active areas. Fig. 5 and 6 show the change in the congestion density in each area at the peak in the morning and at the peak in the evening. It can be seen that the congestion density in different areas increases significantly in the early and late peak periods compared to the average peak period, wherein the congestion density in the late peak period is higher than that in the early peak period, that is, the congestion area in the late peak period is more. Secondly, the congestion density of different areas is also different, and the congestion density of the areas closer to the city center is larger, and the congestion density is gradually reduced outwards, which is consistent with the actual situation of the most congestion of the city center.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (4)
1. A regional traffic jam evaluation method based on speed sampling data is characterized by comprising the following steps: step 1, collecting vehicle road driving data by means of a vehicle-mounted GPS or other devices; step 2, dividing the acquired vehicle driving data according to time granularity, dividing corresponding cities according to geographic space granularity, projecting all data of each time period into corresponding areas on a map according to longitude and latitude, and selecting effective areas according to projection data volume of each area; step 3, calculating the average traffic speed of the area in each time period for all the data falling into any area in each time period, and taking the average value to approximate the traffic speed v of the area in the time periodi(ii) a Step 4, calculating the minimum value, the maximum value and the average value of the road speed of any effective area in all time periods of the whole day, wherein the maximum traffic speed of a certain area in all time periods of the day is vmaxMinimum traffic speed vminAverage traffic speed vavg(ii) a And 5, converting the road traffic speed in any time period of the area into [0, 100 ] representing the congestion severity degree by taking the minimum value, the maximum value and the average value of the all-day road speed in any area in the step 4 as references]An index over the interval, the greater the index indicating a greater congestion of the zone over a selected period of time, for a speed v over a period of time of the selected zoneiAnd reflecting the traffic congestion degree by calculating a traffic congestion index v':
and carrying out linear processing on v ', and mapping the v' to a range from 0 to 100, wherein the final congestion index theta is as follows:
θ＝(v′+1)*50。
2. the method of claim 1, wherein the collected vehicle road travel data includes an instantaneous speed of the vehicle, a corresponding time, and a corresponding geographic location information, wherein the time is at least as accurate as a minute, and the geographic location information is expressed in terms of latitude and longitude.
3. The regional traffic congestion evaluation method based on speed sampling data according to claim 2, characterized in that, for the collected vehicle driving data, all data need to be divided into several groups according to different time periods; aiming at the geographic space range corresponding to the collected vehicle driving data, the collected vehicle driving data needs to be divided into a plurality of small areas according to space granularity; for vehicle acquisition data of different time periods, the data need to be projected to corresponding specific areas according to geographic position information, and areas with too small data projection amount are filtered out.
4. The method as claimed in claim 3, wherein θ is in the range of 0-100, θ is equal to 50, which indicates that the traffic speed of the area is in an average state in the time period, θ is greater than 50, which indicates that the area is congested in the time period compared with the average traffic speed of the whole day, θ is greater than 50, which indicates that the area is congested in the time period, θ is less than 50, which indicates that the area is clear in the time period compared with the average traffic speed of the whole day, and θ is smaller, which indicates that the area is clear in the time period.
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| CN109410586B (en) * | 2018-12-13 | 2020-06-05 | 中南大学 | Traffic state detection method based on multi-metadata fusion |
| CN109712394A (en) * | 2019-01-15 | 2019-05-03 | 青岛大学 | A kind of congestion regions discovery method |
| CN112750299B (en) * | 2019-10-29 | 2022-05-06 | 华为云计算技术有限公司 | 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 |
| 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 |
| 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 |
| 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 |
| CN117315934A (en) * | 2023-09-25 | 2023-12-29 | 阜阳交通能源投资有限公司 | Expressway traffic flow real-time monitoring and congestion prediction system based on unmanned aerial vehicle |
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