CN108765946B - Lane group traffic demand prediction method based on red light running automatic recording system data - Google Patents

Abstract

The invention discloses a lane group traffic demand prediction method based on red light running automatic recording system data. Firstly, acquiring the travel time of a road section through license plate matching; secondly, determining the travel time change rate of each period; then determining the virtual period duration corresponding to each period; and finally, measuring and calculating the number of the driven vehicles in each period and calculating the traffic demand of the lane group. The invention overcomes two defects based on coil detection in the prior art: the supersaturated traffic demand cannot be detected or the difference between different lane groups cannot be distinguished, and a technical basis is provided for the fine optimization of signal control.

Description

Traffic demand prediction method of lane group based on data of automatic red light running record system

technical field

The invention relates to a method for predicting traffic demand of a lane group, in particular to a method for predicting the traffic demand of a lane group based on data from an automatic recording system for running a red light, and belongs to the field of intelligent traffic research.

Background technique

Traffic demand is one of the basic parameters of traffic management and control. Accurate and reliable traffic demand is the premise and basis for signal optimization, which directly determines the implementation effect of the signal scheme. Existing control systems are all based on fixed detection equipment, such as coil detectors, microwave detectors, video detectors, etc., to count the number of vehicles passing through a specific detection section within a unit period of time, and treat the number of vehicles as traffic demand for traffic management. with control.

At present, there are two types of installation positions of the fixed detection equipment, one is the position of the exit road upstream of the road section, and the other is the position of the entrance road downstream of the road section. For the first type of layout location, the detector can actually detect the number of vehicles passing through the cross-section per unit time, which is the real traffic demand, but cannot identify the steering attributes of these vehicles at the downstream intersection, and cannot distinguish the traffic demand of the lanes; The detector can distinguish the genus of steering, but in the supersaturated state, some vehicles that actually arrive cannot pass through the downstream intersection normally, and the number of vehicles passing through per unit time is less than the actual traffic demand, and there is a serious underestimation of traffic demand. risk.

In recent years, major cities in my country have successively installed automatic red light recording systems. The system installs video detection equipment about 20 meters behind the parking line on the entrance road, which can record vehicle license plates and the time information when passing the parking line. The vehicle travel time information can be obtained through the corresponding matching of the data recorded at the two intersections upstream and downstream of the road segment. For a specific road section, travel time is closely related to traffic demand. The greater the demand, the higher the average travel time per unit period, and the smaller the change rate of travel time between the two vehicles. Therefore, the real-time traffic demand of the lane group can be estimated with the help of the travel time data of each basic unit with the signal cycle duration as the analysis unit.

The invention can be directly applied to the field of traffic control, and the traffic demand of lane groups is estimated by using the data of the currently increasingly popular automatic red light running recording system, which will provide a technical basis for the upgrading and reconstruction of the existing traffic control system. At the same time, the method realizes the information multiplexing of the data of the automatic recording system for running red lights, reduces the dependence of the urban traffic control system on traditional detection equipment such as ground sensing coils and microwaves, and reduces the operation and maintenance cost of the entire traffic control system.

SUMMARY OF THE INVENTION

Aiming at the deficiencies of the prior art, the present invention provides a traffic demand forecasting method for a lane group based on data from an automatic recording system for running a red light.

The basic idea of the present invention is: the increase in traffic demand is the direct cause of the deterioration of the traffic state, and for the downstream entrance of a specific road section, the greater the average travel time of the vehicles passing through the cycle and the smaller the change rate of the travel time, the lower the traffic state is. The worse, there is a fixed relationship between the average travel time and the rate of change of travel time among passing vehicles in the cycle and the traffic demand. At the same time, since many road sections are located at the entrance and exit of the unit, the vehicles entering and leaving will also affect the traffic demand, and the automatic red light recording system cannot detect the relevant information of these vehicles, and there may be a certain error in calculating the average travel time. In order to consider the influence of this part of the vehicle, the present invention uses the change rate of travel time in a unit period to estimate the traffic demand.

The departing vehicles of a certain lane group of the downstream entrance road in period i enter from the upstream intersection within a certain period of time, and the ratio of the number of departing vehicles to the size of this period of time is the traffic demand of period i. At the same time, there is a one-to-one correspondence between the driving-in period and the signal period, and the driving-in period can be regarded as a virtual period in which the departing vehicle enters the upstream section. Considering the phenomenon of missing detection in the automatic red light running recording system, the number of departing vehicles automatically detected by the system may be small, which can be corrected by the change rate of the travel time. Therefore, the traffic demand prediction method for lane groups based on the data of the automatic recording system for running red lights, the main tasks include: (1). Obtaining the travel time of the road segment through license plate matching; (2) Determining the change rate of travel time in each cycle; (3). Determine the virtual cycle duration corresponding to each cycle; (4). Measure the number of vehicles leaving each cycle and calculate the traffic demand of the lane group.

Beneficial effects of the present invention: The present invention overcomes two types of defects in existing coil-based detection: inability to detect oversaturated traffic demand or inability to distinguish differences between different lane groups, and provides a technical basis for refined optimization of signal control. At the same time, this method does not need to add any detection equipment. While realizing the multiplexing of system data such as automatic recording of red light running, it also reduces the dependence on traditional ground sensing coils, microwaves and other cross-section detection equipment. It is the development direction of traffic signal control in the future. The upgrading of the signal control system provides certain technical support.

Description of drawings

Figure 1 is a flow chart of the algorithm implementation process;

Figure 2. License plate matching traffic flow diagram;

Figure 3 Calculation principle of the rate of change of travel time

Fig. 4 The relationship between the virtual period and the entry period of blocked vehicles and the entry period of free passing vehicles.

Detailed ways

The present invention will be further described below with reference to the accompanying drawings.

The basic steps of the present invention are as follows:

c1. For a lane group of an entrance road, extract the data of the automatic recording system for red light running at the upstream and downstream intersections through the network topology structure, and use the license plate matching technology to calculate the travel time of the road section passing through this lane group.

c2. Calculate the change rate of the travel time of the vehicle affected by the block according to the travel time data of the vehicle leaving the vehicle in the cycle.

c3. Extract the maximum travel time and the minimum travel time of the departing vehicle in the cycle, and calculate the virtual cycle duration.

c4. According to the change rate of travel time, calculate the number of vehicles leaving in a period, and then calculate the arrival rate of vehicles, that is, traffic demand, according to the number of vehicles entering the virtual period and the duration of the virtual period.

The process of step c1 includes:

c11. For a specific lane group p, determine its corresponding upstream and downstream intersection according to the network topology.

c12. Using the automatic recording system for running red lights, extract the license plate information and the departure time information of the vehicles leaving the downstream intersection through the lane group p, and record them as database 1.

c13. Use the automatic recording system for running red lights to extract the license plate information of the vehicles entering the lane group p and the departure time information at the upstream intersection, which is recorded as database 2, including three traffic flows: straight-in, left-turn in and right-in ,as shown in picture 2.

c14. Through the license plate matching of databases 1 and 2, calculate the travel time of the road segment leaving the vehicle; assuming that a is any vehicle leaving the lane group p, t _a and t' _a are vehicle a in databases 1 and 2 The corresponding time information, the travel time of vehicle a is:

T _a =t _{a -t'a} (1-a)

In the formula, T _a is the travel time of the vehicle on the road segment to which the lane group p belongs, and the unit is seconds.

Step c2 includes:

c21. Assuming that the current cycle is i, and there are m samples in the i cycle, the sample set of travel time is:

T _i =[T _i,1 ,T _i,2 ,...,T _i,m ] (1-b)

In the formula, T _i is the travel time sample of cycle i, and T _i,m is the travel time of the mth vehicle leaving the vehicle in i cycle, and the unit is seconds.

In the sample travel time, if the individual travel time is not greater than the free flow travel time, it means that the vehicle passes freely in cycle i and is not blocked by it; furthermore, if there is a sample in cycle i, the travel time is not greater than the free flow time. The cycle is under-saturated if the flow travel time is in an undersaturated state, otherwise the cycle is over-saturated. The free-passing vehicle travel time is equal, and its rate of change is 0, independent of traffic demand. Therefore, the effect of free-passing vehicles should be excluded when determining the rate of change of travel time.

c22. Assuming that there are a total of n blocked travel time samples in period i, that is, among the m sample data, the first n are blocked vehicles, and the last (m-n) are free passing vehicles, then the least squares method can be used to obtain the travel time change rate :

In the formula, S _i is the change rate of travel time in period i, dimensionless; t _i,j is the moment when the jth vehicle in period i enters the upstream of the road section.

In particular, when period i is in saturation, the value of n is 0. The principle of step c22 is shown in FIG. 3 .

The process of step c3 includes:

c31. Screen the maximum travel time T _i,max in the period i, and determine the entry time of the vehicle with the maximum travel time in the period i at the upstream intersection, which is recorded as t _i,max .

c32. For the mn free passing vehicles in the period i, record the times when the first and last free passing vehicles enter the upstream of the road section as t _i,n+1 and t _i,m respectively.

c33. Use the maximum travel time T _i,max , the times t _i,n+1 and t _i,m of the first and last freely passing vehicles to enter the upstream of the road section in the period i to filter, and calculate the departure vehicles in the period i The length of the entry period for the part affected by the signal blocking and the length of the driving period for the part not affected by the blocking:

c _i,1 =t _i,n+1 -t _i,max (1-d)

c _i,2 =t _i,m -t _i,n+1 (1-e)

In the formula, c _i,1 and c _i,2 are the length of the entry period of the vehicle leaving the part affected by the signal light block and the length of the entry period of the part not affected by the signal light block in the period i, respectively, in seconds.

In step c33, the relationship among c _i,1 , c _i,2 and c _i is shown in FIG. 4 .

c34. Use c _i,1 and c _i,2 in step c33 to calculate the virtual period duration, and the calculation formula is:

c _i = _ci,1 + _ci,2 (1-f)

In the formula, ci is the virtual period duration corresponding to period _i , in seconds.

The process of step c4 includes:

c41. According to the traffic wave theory, the number of departing vehicles in the period c _i,1 can be calculated by the travel time change rate S _i :

In the formula, Q _i,1 is the number of vehicles leaving in the period of c _i,1 , the unit is vehicle; k _jam is the congestion density, the unit is vehicle/m; u _f is the free flow speed, the unit is m/s; u _w is the starting wave velocity, in m/s; in application, k _jam , u _f and u _w are all fixed values, and the fluctuations in different periods are small, which can be determined by actual observation.

c42. Assuming that the vehicle arrival rate in the same virtual period, that is, the traffic demand is uniform, that is, the vehicle arrival rate in periods c _i,1 and c _i,2 are equal, the total number of vehicles leaving in period i can be estimated, and the expression is:

In the formula, Q _i is the number of vehicles leaving the downstream stop line in period i, and the unit is vehicle.

c43. According to the concept of traffic demand, the ratio of the total number of vehicles leaving in period i to the virtual period is the traffic demand corresponding to this period, namely:

In the formula, q _i is the traffic demand of period i, and its unit is vehicles/second.

Example:

Take the license plate data of the two intersections of a certain road section in a city and the timestamp data of the vehicle passing the stop line as an example. The data is from 16:10:00 to 17:10:00, and the time interval is 5 minutes. The specific implementation process is shown in Figure 1.

1. Calculate the travel time of the road segment passing through this lane group

Extract the license plate information and departure time information of the vehicle leaving the downstream intersection through the lane group p, and record it as database 1; extract the vehicle license plate information entering the lane group p and the departure time information at the upstream intersection, and record it as database 2 , the specific distribution is shown in Figure 2; through the license plate matching of databases 1 and 2, the travel time of the road segment of the departing vehicle is calculated.

2. Calculate the rate of change of vehicle travel time affected by the blockage

There are m samples and n blocked travel time samples in the current i cycle, then the sample set of travel time is: T _i =[T _i,1 ,T _i,2 ,...,T _i,m ]. The rate of change of travel time can be obtained by the following formula:

When determining the rate of change of travel time, the influence of free passing vehicles should be excluded. The principle is shown in Figure 3.

3. Calculate the virtual cycle duration

Calculate the entry period length of the part affected by the signal light blocking and the entry period length of the part not affected by the signal light blocking in the period i by the following formula:

c _i,1 =t _i,m+1 -t _i,max

c _i,2 =t _i,n -t _i,m+1

Figure 4 shows the relationship between the length of the entry period of the vehicle leaving the part affected by the signal light block and the length of the entry period _ci,1 and _ci,2 of the part not affected by the block of the signal light in the period i. Calculate the dummy period duration _ci .

4. Calculate the vehicle arrival rate, that is, the traffic demand

Calculate the number of vehicles leaving within the period c _i,1 by the following formula:

The traffic demand is uniform, and the total number of vehicles leaving in period i is estimated. And calculate the traffic demand corresponding to this cycle, the formula is as follows:

The final result is shown in the table below.

It can be seen from the table results that the relative error of the present invention is 16.19%, which meets the precision requirements of traffic control, indicating that this aspect has practical value.

Claims (2)

1. The lane group traffic demand prediction method based on the red light running automatic recording system data is characterized by comprising the following steps of:

c1, extracting data of the automatic red light running recording system of the upstream and downstream intersections by a network topological structure aiming at a certain entrance lane group, and calculating the road section travel time passing through the lane group by using a license plate matching technology;

c2, estimating the change rate of the vehicle travel time influenced by the retardation according to the travel time data of the vehicle in the cycle;

c3, extracting the maximum travel time and the minimum travel time of the vehicle driving away from the upstream intersection in the period, namely the time information of the vehicle driving into the upstream road section, and calculating the virtual period duration;

c4, calculating the number of vehicles driving away in a period according to the travel time change rate, and further calculating the arrival rate of the vehicles according to the number of vehicles driving into the virtual period and the virtual period duration;

step c1 specifically includes:

c11, determining the corresponding upstream and downstream intersections of the lane group p according to the network topology structure;

c12, extracting the license plate information and the driving-away time information of the vehicles driving away from the downstream intersection through the lane group p by using the red light running automatic recording system, and recording the information as a first database;

c13, extracting the license plate information of the vehicle entering the lane group p and the driving-away time information of the upstream intersection by using a red light running automatic recording system, recording the information as a second database, and containing three streams of straight driving, left-turning driving and right-turning driving;

c14, calculating the road section travel time of the driven vehicle through the matching of the license plates of the two databases; let a be any vehicle leaving the lane group p, t_aAnd t'_aThe time information corresponding to the vehicle a in the first database and the second database is the following, and the travel time of the vehicle a is as follows:

T_a＝t_a-t′_a(1-a)

in the formula, T_aThe travel time of the vehicle on the road section to which the lane group p belongs is second;

step c2 specifically includes:

c21, assuming that the current cycle is i, and m samples are in total in the i cycle, the sample set of the travel time is:

T_i＝[T_i,1,T_i,2,…,T_i,m](1-b)

in the formula, T_iRun-time samples of period i, T_i,mThe travel time of the mth vehicle driving away from the vehicle in the i period is second;

c22, assuming that n total obstructed travel time samples in the period i are provided, namely m sample data, the front n are obstructed vehicles, and the rear (m-n) is a free passing vehicle, obtaining the travel time change rate by using a least square method:

in the formula, S_iThe travel time change rate in the period i is dimensionless; t is t_i,jThe time when the jth vehicle drives into the upstream of the road section in the period i;

step c3 specifically includes:

c31 screening the maximum travel time T in the period i_i,maxDetermining the maximum travel time vehicle crossing upstream in the period iThe entry time of the fork entry is denoted t_i,max；

c32, recording the time when the first free passing vehicle and the last free passing vehicle drive into the upstream of the road section as t respectively for m-n free passing vehicles in the period i_i,n+1And t_i,m；

c33 screening the maximum travel time T in the period i_i,maxThe time t at which the first and last free passing vehicles pass upstream of the route section_i,n+1And t_i,mCalculating the length of the entering time period of the part of the vehicle which is influenced by the signal lamp retardation and the length of the entering time period of the part which is not influenced by the signal lamp retardation within the period i:

c_i,1＝t_i,n+1-t_i,max(1-d)

c_i,2＝t_i,m-t_i,n+1(1-e)

in the formula, c_i,1And c_i,2The length of the entering time interval of the part of the vehicle which is driven away in the period i and influenced by the signal lamp retardation and the length of the entering time interval of the part which is not influenced by the signal lamp retardation are respectively, and the unit is second;

c34, using c in step c33_i,1And c_i,2Calculating the virtual period duration by the following calculation formula:

c_i＝c_i,1+c_i,2(1-f)

in the formula, c_iThe unit is the virtual period duration corresponding to the period i, and the unit is second;

step c4 specifically includes:

c41 determining the travel time change rate S according to the traffic wave theory_iMeasurement and calculation time period c_i,1Number of vehicles driven inside:

in the formula, Q_i,1Is c_i,1The number of the vehicles driven away in a time period is in units of vehicles; k is a radical of_jamIs the congestion density in units of vehicles/meter; u. of_fIs the free flow velocity in meters per second; u. of_wThe unit is the wave velocity of the starting wave in meters/second;

c42. assume vehicle arrival rate in the same virtual period, i.e., segment c_i,1And c_i,2If the arrival rates of the vehicles in the period i are equal, estimating the total number of the vehicles driven away in the period i, wherein the expression is as follows:

in the formula, Q_iThe number of vehicles driving away from the downstream stop line in the period i is the unit of the vehicle;

c43, according to the concept of traffic demand, the ratio of the total number of vehicles driven away in the period i to the virtual period is the traffic demand corresponding to the period, namely:

in the formula, q_iThe traffic demand for cycle i is given in units of vehicles/second.

2. The method for predicting the traffic demand of the lane group based on the data of the automatic red light running recording system as claimed in claim 1, wherein: in step c2, the effect of free passage through the vehicle is rejected in determining the rate of change of travel time.

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