CN103761430B - A kind of road network peak period recognition methods based on Floating Car - Google Patents

Abstract

The invention relates to a road network peak time identification method based on a floating car, which solves the defect that there is no road network peak time identification method based on the floating car technology compared with the prior art. The invention comprises the following steps: calculating the speed of a single vehicle sample on a road section by using the GPS data of the floating car; extracting the average travel speed of the road section; calculating the periodic traffic congestion index TCI; and extracting the start and end time points of morning and evening peak hours. The present invention can analyze the time-varying law of road network traffic through the existing floating car technology and urban road congestion analysis system, and extract the road network peak hours.

Description

A method for identifying road network peak hours based on floating cars

technical field

The invention relates to the technical field of road traffic planning, in particular to a method for identifying road network peak hours based on floating vehicles.

Background technique

Floating car technology is a technology to obtain road traffic conditions based on the dynamic position information of vehicles running on the road surface. The floating car (taxi or bus) with GPS information can collect the displacement information of the vehicle in real time, and the time series of vehicle position The coordinates are matched with the map, and the speed data of the floating vehicle can be obtained. The floating car technology can store the data collected for one year in the database, and use the speed information of the periodic road section to obtain the flow information of the periodic road section.

AADT (annual average daily traffic) is a very important parameter for traffic models and management decisions, and plays a key role in research fields such as traffic planning, road design, traffic safety, traffic demand analysis, and traffic control. . The existing AADT calculation no longer uses the traditional manual survey method, but also uses the average speed of the road section obtained by the floating car technology, and realizes the intelligent and accurate estimation of the road AADT through a series of model calculations. It meets the data requirements of traffic planning, traffic design, and traffic management, and improves work efficiency.

Peak hours refer to the morning and evening peak hours of road traffic caused by commuter traffic. The morning peak hours are usually 7:00-9:00, and the evening peak hours are usually 17:00-19:00. The specific time breakpoints vary with regions, road grades, and road sections. The peak hour estimation algorithm can realize the calculation of urban road network and regional peak hour, provide the basic data source for the road condition release service, and play an important role in traffic management and traffic information service. In the process of calculating AADT using floating car technology, when calculating hourly traffic flow, the determination of peak hours is based on the morning and evening peak hours of road traffic described in the "Urban Road Traffic Congestion Evaluation Index System" (draft for comments). However, the peak hours of each city are different. If a unified standard is used, it is obviously different from the actual peak hours of each city, which will affect the accuracy of AADT's intelligent estimation. How to develop a method for identifying road network peak hours based on floating car technology for different cities has become a technical problem that needs to be solved urgently.

Contents of the invention

The purpose of the present invention is to solve the defect that there is no road network peak hour identification method based on floating car technology in the prior art, and provide a road network peak hour identification method based on floating car to solve the above problems.

In order to achieve the above object, the technical scheme of the present invention is as follows:

A method for identifying road network peak hours based on floating cars, comprising the following steps:

Use the GPS data of the floating car to calculate the speed of the bicycle sample on the road section;

Extract the average travel speed of the road section;

Calculate the periodic traffic congestion index TCI;

Extract the start and end time points of morning and evening peak hours.

The described utilization of floating car GPS data to calculate road section bicycle sample speed comprises the following steps:

Obtain the path information {P _i , i=1, 2, L, n} of the front and rear adjacent points passed by the sample vehicle j through the GPS data of the floating car;

The average travel speed of this section of the path is obtained by the path length Δd _j and the time difference Δt _j

If there is only one or km/h, the Assign it to road section P ₁ ; otherwise, according to the principles of the four traffic states, combine the instantaneous speed v ₁ of the starting point and the instantaneous speed v ₂ of the ending point, and assign a value to the speed of each road section along the way.

The judging methods of the four traffic state principles are as follows:

deceleration state, satisfied When , the speed value of the initial section is assigned as The speed value of other road sections is the total travel time Δt _j minus the travel time of the initial road section, and then the speed is obtained by dividing the distance by the time;

accelerated state, satisfied When , the speed value of the end road section is assigned as The speed value of other road sections is the total travel time Δt _j minus the travel time of the initial road section, and then the speed is obtained by dividing the distance by the time;

Decelerate first and then accelerate, the speed value of the initial section is assigned to v ₁ , the speed value of the end section is assigned to v ₂ , the speed value of the middle section is the total travel time Δt _j minus the travel time of the initial section, and then the passing distance is divided by the time gets speed;

Accelerate first and then decelerate, and the speed value of the route section is assigned as

The formula for calculating the average travel speed of the extracted section is

Among them, V _i is the average speed of the arc segment P _i , l _i is the length of the arc segment P _i , t _ij is the travel time of the jth vehicle on the arc segment P _i in the path, and n _i is the travel time of the arc segment P _i . The number of vehicles involved in the calculation.

The described calculation period traffic congestion index TCI comprises the following steps:

Congestion state identification is performed based on the average travel speed V _i of the road section, and the congested road section is judged;

Calculating the road segment congestion mileage ratio RCR, respectively calculating the expressway congestion mileage ratio RCRf, the trunk road congestion mileage ratio RCRa, the secondary trunk road congestion mileage ratio RCRm and the branch road congestion mileage ratio RCRl, the calculation formula is as follows:

RCR=RCRf*ω ₁ +RCRa*ω ₂ +RCRm*ω ₃ +RCRl*ω ₄ ;

in,

L(i) is the length of road segment i, Lc(i) is the length of road segment i where congestion occurs,

n _f : total number of expressway sections,

n _a : the total number of arterial road sections,

n _m : the total number of secondary trunk road sections,

n _l : total number of branch road sections,

w1, w2, w3, w4 respectively represent the weight of roads of each level,

To calculate the road network traffic congestion index TCI, the calculation formula is as follows:

Where: a=RCR*100.

The extraction of the start and end time points of morning and evening peak hours includes the following steps:

Determine whether the TCI curve obeys the normal distribution within 24 hours a day. If it obeys the normal distribution, enter the next step of calculation. If it does not obey the normal distribution, it means that the traffic is abnormal on that day. Delete the data and re-select the data;

Set the confidence value c, c is the allowable error range between the estimated value and the overall parameters;

According to the 24-hour TCI change value, take the maximum and minimum values of TCI,

Divided from 0:00 to 12:00, the maximum value of TCI is max_a, where a is the number of cycles from 1 to 288, and the cycle is 5 minutes; the minimum value of the front part of TCI is min_t1, where t1 is the cycle number corresponding to the minimum value; TCI The rear minimum value min_t2, where t2 is the number of cycles corresponding to the minimum value;

Divided from 12:00 to 24:00, the maximum value of TCI is max_p, where p is the number of cycles from 1 to 288, and the cycle is 5 minutes; the minimum value of the front part of TCI is min_t3, where t3 is the number of cycles corresponding to the minimum value; the rear part of TCI The minimum value min_t4, where t4 is the number of cycles corresponding to the minimum value;

Calculate the total area of the area S1, S2, S3, S4,

Calculate the variance areas S ₁ ', S ₂ ', S ₃ ', S ₄ ',

Substitute S1, S2, S3, S4 and S ₁ ', S ₂ ', S ₃ ', S ₄ ' into the formula c=S _i '/S _i to solve, and obtain j1, j2, j3, j4 respectively by solving , where, i=1,2,3,4, S _i 'is the variance area, S _i is the interval area;

Determine the morning peak period as T1 to T2, and determine the evening peak period as T3 to T4, where T1, T2, T3, and T4 correspond to the cycle start times of j1, j2, j3, and j4 respectively.

The formula for judging whether the TCI curve obeys a normal distribution is:

and

in: is the arithmetic mean, M is the median, and s is the standard deviation.

Beneficial effect

Compared with the prior art, a method for identifying road network peak hours based on floating vehicles in the present invention can analyze the time-varying law of road network traffic and extract road network peaks through the existing floating vehicle technology and urban road congestion analysis system. time period. Get the time period with the most severe traffic load on the road network, provide data support for traffic managers and traffic planners, and improve the accuracy of AADT's intelligent estimation.

Description of drawings

Fig. 1 is method flowchart of the present invention

Figure 2 is the TCI 24-hour curve change diagram and corresponding parameter annotation diagram

detailed description

In order to have a further understanding and understanding of the structural features of the present invention and the achieved effects, the preferred embodiments and accompanying drawings are used for a detailed description, as follows:

As shown in Figure 1, a kind of road network peak hour identification method based on floating car of the present invention, comprises the following steps:

The first step is to use the GPS data of the floating car to calculate the sample speed of the bicycle on the road section.

Use the GPS data of the floating car to calculate the average travel speed of the single vehicle sample in a statistical period of the road section. First, obtain the path information {P _i , i=1, 2, L, n}. Secondly, based on the GPS data, the average travel speed of this section of the path can be obtained through the path length Δd _j and the time difference Δt _j Again, when there is only one road section, it means not to cross the intersection or When the km/h is in the unblocked state, the Assign it to the road section P ₁ ; otherwise, according to the combination of the instantaneous speed v ₁ of the starting point and the instantaneous speed v ₂ of the end point, assign the speed of each road section of the route in four traffic states.

When the floating car is in the deceleration state, it satisfies When , the speed value of the initial section is assigned as The speed value of other road sections is the total travel time Δt _j minus the travel time of the initial road section, and then the speed is obtained by dividing the distance by the time, and then the speed is assigned to the road section P ₁ .

When the floating car is accelerating, satisfy When , the speed value of the end road section is assigned as The speed value of other road sections is the total travel time Δt _j minus the travel time of the initial road section, and then the speed is obtained by dividing the distance by the time, and then the speed is assigned to the road section P ₁ .

When the floating car is decelerating first and then accelerating, the speed value of the initial section is assigned as v ₁ , the speed value of the ending section is assigned as v ₂ , the speed value of the middle section is the total travel time Δt _j minus the travel time of the initial section, and then pass Divide the distance by the time to get the speed, and assign the speed to the link P ₁ .

When the floating car is accelerating first and then decelerating, the speed value of the passing road section is assigned as then Assigned to road segment P ₁ .

The second step is to extract the average travel speed of the road segment.

The formula for calculating the average travel speed of the extracted road section is

Among them, V _i is the average speed of the arc segment P _i , l _i is the length of the arc segment P _i , t _ij is the travel time of the jth vehicle on the arc segment P _i in the path, and n _i is the travel time of the arc segment P _i . The number of vehicles involved in the calculation. Here, when n _i is equal to 0, that is, when there is no data coverage on this road segment, we supplement it with the historical average speed of different time periods of a week accumulated in history; when n _i is not equal to 0, the average travel speed of the road segment is multiple samples The harmonic mean speed of .

The third step is to calculate the periodic traffic congestion index TCI.

Periodic traffic congestion index refers to a statistical period (usually 5 minutes), using a value of 0 to 10 to describe the congestion level of the current regional road network, which is a quantitative index to describe the degree of congestion. The calculation steps are as follows:

First, the congestion state is identified based on the average travel speed V _i of the road section, and the congested road section is judged.

According to the speed interval table in Table 1, it is judged whether the current road section belongs to the congested state, and the data of the road section in the congested state will be proposed for the next step of processing. The data source of the threshold table in Table 1 is based on the corresponding threshold value of the average travel speed of motor vehicles on the urban main road according to the "Urban Road Traffic Management Evaluation Index System" published in 2010, and fine-tuned according to the actual traffic characteristics of Hefei. of. When used in different cities, it can be adapted and fine-tuned according to the actual local traffic characteristics.

Table 1 Speed interval table of road state division based on the average travel speed of the road section

category Level 1: Free Flow Level 2: smooth Level 3: Delay Level 4: Crowded Level 5: Congestion highway Above 65km/h 65-40km/h 40-30km/h 30-15km/h Below 15km/h main road Above 50km/h 50-30km/h 30-22km/h 22-12km/h Below 12km/h secondary road Above 40km/h 40-25km/h 25-18km/h 18-10km/h Below 10km/h branch road Above 35km/h 35-23km/h 23-16km/h 16-8km/h Below 8km/h

Second, calculate the road segment congestion mileage ratio RCR.

Calculate the expressway congestion mileage ratio RCRf, the main road congestion mileage ratio RCRa, the secondary trunk road congestion mileage ratio RCRm and the branch road congestion mileage ratio RCRl respectively, and the calculation formula is as follows:

RCR=RCRf*ω ₁ +RCRa*ω ₂ +RCRm*ω ₃ +RCRl*ω ₄ ;

in,

L(i) is the length of road segment i, Lc(i) is the length of road segment i where congestion occurs,

n _f : total number of expressway sections,

n _a : the total number of arterial road sections,

n _m : the total number of secondary trunk road sections,

n _l : total number of branch road sections,

w1, w2, w3, w4 respectively represent the weight of roads of each level,

w1, w2, w3, and w4 respectively represent the weights of roads of each grade, which are obtained from the statistical analysis of the historical data of the total mileage of roads of each grade of road network vehicles. In Table 2, there are weekday weight recommendation tables and holiday weight recommendation tables. In practice, w1, w2, w3, and w4 can also be specified and calculated with reference to local standards.

Table 2 Beijing Local Standards of "Urban Road Traffic Congestion Evaluation Index System"

working day highway main road secondary road branch road total Peak hours 0.19 0.43 0.15 0.23 1.00 Full day average 0.20 0.41 0.16 0.23 1.00 holidays highway main road secondary road branch road total Full day average 0.20 0.41 0.16 0.23 1.00

Finally, calculate the road network traffic congestion index TCI.

Calculated as follows:

Among them: a=RCR*100, according to the value of a, select the corresponding calculation formula.

The fourth step is to extract the start and end time points of morning and evening peak hours.

Including the following steps:

(1) As shown in Figure 2, first judge whether the TCI curve obeys the normal distribution within 24 hours a day. If it obeys the normal distribution, enter the next step of calculation. If it does not obey the normal distribution, it means that the traffic on the day is abnormal and there is an emergency occur. The data is not easy to use as a reference to calculate the data, so the data is discarded and the data is reselected. The method used to judge whether the TCI curve obeys the normal distribution is one of the normal distribution test methods in existing statistics. It is carried out by using the ratio of the sample median M to the arithmetic mean and the relationship between the arithmetic mean and the standard deviation. Judgment, reflecting peak shape and kurtosis, the formula is as follows:

and

in: is the arithmetic mean, M is the median, and s is the standard deviation.

(2) Set the confidence value c, which is the allowable error range between the estimated value and the overall parameters. The confidence value is an estimated value of the judgment, which can be specified according to the actual needs of the city and the decision-maker. Generally speaking, in order to ensure greater credibility, the confidence value is generally greater than 90.

(3) According to the 24-hour TCI change value, take the maximum and minimum values of TCI.

Divided from 0:00 to 12:00, the maximum value of TCI is max_a, where a is the number of cycles from 1 to 288, and the cycle is 5 minutes. 288 is divided according to 24 hours with a cycle of 5 minutes. The minimum value at the front of the TCI is min_t1, where t1 is the number of cycles corresponding to the minimum value. The minimum value min_t2 at the back of the TCI, where t2 is the number of cycles corresponding to the minimum value.

Divided from 12:00 to 24:00, the maximum value of TCI is max_p, where p is the number of cycles from 1 to 288, and the cycle is 5 minutes. TCI front minimum value min_t3, where t3 is the number of cycles corresponding to the minimum value. TCI rear minimum value min_t4, where t4 is the number of cycles corresponding to the minimum value.

(4) Calculate the total area of the area S1, S2, S3, S4, the calculation formula is as follows:

(5) Calculate the variance area S ₁ ', S ₂ ', S ₃ ', S ₄ ', the calculation formula is as follows:

(6) Substitute S1, S2, S3, S4 and S ₁ ', S ₂ ', S ₃ ', S ₄ ' into the formula c=S _i '/S _i to solve, and obtain j1, j2, j3, j4, where i=1,2,3,4, S _i ' is the variance area, S _i is the interval area.

(7) Determine the morning peak period as T1 to T2, and determine the evening peak period as T3 to T4, where T1, T2, T3, and T4 correspond to the cycle start times of j1, j2, j3, and j4 respectively. Since j1, j2, j3, j4 represent here, through 24 hours and 5 minutes as the period of 288 cycles, through the specific time points T1, T2, T3, T4 represented by j1, j2, j3, j4, Thus it can be judged that the morning peak hours are T1-T2 and the evening peak hours are T3-T4.

The basic principles, main features and advantages of the present invention have been shown and described above. Those skilled in the art should understand that the present invention is not limited by the above-mentioned embodiments. What are described in the above-mentioned embodiments and the description are only the principles of the present invention. Variations and improvements, which fall within the scope of the claimed invention. The scope of protection required by the present invention is defined by the appended claims and their equivalents.

Claims (5)

1. a road network peak hour identification method based on floating cars, is characterized in that, comprises the following steps: 1) Use the GPS data of the floating car to calculate the sample speed of the bicycle on the road section; 2) Extract the average travel speed of the road section; 3) Calculate the periodic traffic congestion index TCI; 4) Extract the morning and evening peak hour start and end time points; the described extraction morning and evening peak hour start and end time points includes the following steps: 141) Determine whether the TCI curve obeys the normal distribution within 24 hours a day. If it obeys the normal distribution, enter the next step of calculation; 142) Set the confidence value c, c is the error range allowed by the estimated value and the overall parameter; 143) According to the 24-hour TCI change value, take the maximum and minimum values of TCI, Divided from 0:00 to 12:00, the maximum value of TCI is max_a, where a is the number of cycles from 1 to 288, and the cycle is 5 minutes; the minimum value of the front part of TCI is min_t1, where t1 is the cycle number corresponding to the minimum value; TCI The rear minimum value min_t2, where t2 is the number of cycles corresponding to the minimum value; Divided from 12:00 to 24:00, the maximum value of TCI is max_p, where p is the number of cycles from 1 to 288, and the cycle is 5 minutes; the minimum value of the front part of TCI is min_t3, where t3 is the number of cycles corresponding to the minimum value; the rear part of TCI The minimum value min_t4, where t4 is the number of cycles corresponding to the minimum value; 144) Calculate the total area of the area S1, S2, S3, S4, ${\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; t</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; a</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; T</span>}\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; I</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; min</span>}<span\; class="notranslate">\; \_</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; \&Element;</span><span\; class="notranslate">\; \&lsqb;</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; a</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; ,</span>$ ${\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; a</span>}}{\overset{\mathrm{<span\; class="notranslate">\; t</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; T</span>}\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; I</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; min</span>}<span\; class="notranslate">\; \_</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; \&Element;</span><span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; a</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; ,</span>$ ${\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; t</span>}\mathrm{<span\; class="notranslate">\; 3</span>}}{\overset{\mathrm{<span\; class="notranslate">\; p</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; T</span>}\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; I</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; min</span>}<span\; class="notranslate">\; \_</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; \&Element;</span><span\; class="notranslate">\; \&lsqb;</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; p</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; ,</span>$ ${\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; 4</span>}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; p</span>}}{\overset{\mathrm{<span\; class="notranslate">\; t</span>}\mathrm{<span\; class="notranslate">\; 4</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; T</span>}\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; I</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; min</span>}<span\; class="notranslate">\; \_</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 4</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; \&Element;</span><span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; p</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 4</span>}}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; ;</span>$ 145) Calculate the variance area S ₁ ′, S ₂ ′, S ₃ ′, S ₄ ′, ${{\mathrm{<span\; class="notranslate">\; S</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>\underset{{\mathrm{<span\; class="notranslate">\; j</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}{\overset{\mathrm{<span\; class="notranslate">\; a</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; T</span>}\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; I</span>}<span\; class="notranslate">\; \_</span>{\mathrm{<span\; class="notranslate">\; j</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; min</span>}<span\; class="notranslate">\; \_</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; \&Element;</span><span\; class="notranslate">\; \&lsqb;</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; a</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; ,</span>$ ${\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; a</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; j</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; T</span>}\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; I</span>}<span\; class="notranslate">\; \_</span>{\mathrm{<span\; class="notranslate">\; j</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; min</span>}<span\; class="notranslate">\; \_</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; \&Element;</span><span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; a</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; ,</span>$ ${\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; =</span>\underset{{\mathrm{<span\; class="notranslate">\; j</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}}{\overset{\mathrm{<span\; class="notranslate">\; p</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; T</span>}\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; I</span>}<span\; class="notranslate">\; \_</span>{\mathrm{<span\; class="notranslate">\; j</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; min</span>}<span\; class="notranslate">\; \_</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; \&Element;</span><span\; class="notranslate">\; \&lsqb;</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; p</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; ,</span>$ ${\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; 4</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; p</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; j</span>}}_{\mathrm{<span\; class="notranslate">\; 4</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; T</span>}\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; I</span>}<span\; class="notranslate">\; \_</span>{\mathrm{<span\; class="notranslate">\; j</span>}}_{\mathrm{<span\; class="notranslate">\; 4</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; min</span>}<span\; class="notranslate">\; \_</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 4</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; \&Element;</span><span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; p</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 4</span>}}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; ;</span>$ 146) Substitute S1, S2, S3, S4 and S ₁ ′, S ₂ ′, S ₃ ′, and S ₄ ′ into the formula c=S _i ′/S _i to solve, and obtain j1, j2, j3 respectively by solving , j4, wherein, i=1,2,3,4, S _i 'is the variance area, S _i is the interval area; 147) Determine the morning peak period as T1 to T2, and determine the evening peak period as T3 to T4, where T1, T2, T3, and T4 correspond to the cycle start times of j1, j2, j3, and j4 respectively. 2. a kind of road network peak hour identification method based on floating car according to claim 1, is characterized in that: described utilization floating car GPS data calculation section bicycle sample speed comprises the following steps: 21) Obtain the path information {P _i ,i=1,2,...,n} of the two adjacent points passed by the sample vehicle j through the GPS data of the floating car, where n is the road section included in the path of the vehicle number; 22) Obtain the average travel speed of this path through the path length Δd _j and the total travel time Δt _j 23) If there is only one or km/h, the Assign it to road section P ₁ ; otherwise, according to the principles of the four traffic states, combine the instantaneous speed v ₁ of the starting point and the instantaneous speed v ₂ of the end point, and assign a value to the speed of each road section of the route; The judging methods of the four traffic state principles are as follows: 231) Deceleration state, satisfy When , the speed value of the initial section is assigned as The speed value of other road sections is the total travel time Δt _j minus the travel time of the initial road section, and then the speed is obtained by dividing the distance by the time; 232) Acceleration state, satisfy When , the speed value of the end road section is assigned as The speed value of other road sections is the total travel time Δt _j minus the travel time of the initial road section, and then the speed is obtained by dividing the distance by the time; 233) Decelerate first and then accelerate. The speed value of the initial road section is assigned to v ₁ , the speed value of the end road section is assigned to v ₂ , the speed value of the middle road section is the total travel time Δt _j minus the travel time of the initial road section, and then divided by the distance get velocity at that time; 234) Accelerate first and then decelerate, and the speed value of the route section is assigned as 3. a kind of road network peak hour identification method based on floating car according to claim 1, it is characterized in that: described extraction section average travel speed computing formula is ${\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}\frac{{\mathrm{<span\; class="notranslate">\; l</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; l</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{<span\; class="notranslate">\; (</span>\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\frac{{\mathrm{<span\; class="notranslate">\; l</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; no</span>}}{\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}}}<span\; class="notranslate">\; ,</span>& \begin{array}{cc}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; f</span>}& {\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&NotEqual;</span>\mathrm{<span\; class="notranslate">\; 0</span>}\end{array}\\ \stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; ,</span>& \begin{array}{cc}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; f</span>}& {\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}\end{array}\end{array}\right.<span\; class="notranslate">\; .</span>$ 4. a kind of road network peak hour identification method based on floating car according to claim 1, is characterized in that, described calculation cycle traffic congestion index TCI comprises the following steps: 41) Carry out congestion state identification based on the average travel speed V _i of the road section, and determine the congested road section; 42) Calculate road section congestion mileage ratio RCR, respectively calculate expressway congestion mileage ratio RCRf, trunk road congestion mileage ratio RCRa, secondary trunk road congestion mileage ratio RCRm and branch road congestion mileage ratio RCRl, the calculation formula is as follows: If it is an expressway, If it is a main road, If it is a secondary trunk road, If it is a branch, RCR=RCRf*ω ₁ +RCRa*ω ₂ +RCRm*ω ₃ +RCRl*ω ₄ ; in, L(i) is the length of road segment i, Lc(i) is the length of road segment i where congestion occurs, n _f : total number of expressway sections, n _a : the total number of arterial road sections, n _m : the total number of secondary trunk road sections, n _l : total number of branch road sections, w1, w2, w3, w4 respectively represent the weight of roads of each level, 43) Calculate the road network traffic congestion index TCI, the calculation formula is as follows: $\mathrm{<span\; class="notranslate">\; T</span>}\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; I</span>}<span\; class="notranslate">\; =</span>\left\{\begin{array}{c}\frac{\mathrm{<span\; class="notranslate">\; a</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; a</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; 4</span>}<span\; class="notranslate">\; )</span>\\ \mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; a</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 4</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 4</span>}<span\; class="notranslate">\; <</span>\mathrm{<span\; class="notranslate">\; a</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; 8</span>}<span\; class="notranslate">\; )</span>\\ \mathrm{<span\; class="notranslate">\; 4</span>}<span\; class="notranslate">\; +</span>\frac{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; a</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 8</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 8</span>}<span\; class="notranslate">\; <</span>\mathrm{<span\; class="notranslate">\; a</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; 11</span>}<span\; class="notranslate">\; )</span>\\ \mathrm{<span\; class="notranslate">\; 6</span>}<span\; class="notranslate">\; +</span>\frac{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; a</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 11</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 11</span>}<span\; class="notranslate">\; <</span>\mathrm{<span\; class="notranslate">\; a</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; 14</span>}<span\; class="notranslate">\; )</span>\\ \mathrm{<span\; class="notranslate">\; 8</span>}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; a</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 14</span>}}{\mathrm{<span\; class="notranslate">\; 5</span>}}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 14</span>}<span\; class="notranslate">\; <</span>\mathrm{<span\; class="notranslate">\; a</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; twenty\; four</span>}<span\; class="notranslate">\; )</span>\\ \mathrm{<span\; class="notranslate">\; 10</span>}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; a</span>}<span\; class="notranslate">\; \&Greater\; Equal;</span>\mathrm{<span\; class="notranslate">\; twenty\; four</span>}<span\; class="notranslate">\; )</span>\end{array}\right.$ Where: a=RCR*100. 5. a kind of road network peak hour identification method based on floating car according to claim 1, is characterized in that, the formula of described judgment TCI curve obeys normal distribution is: and