CN104778839A - Urban road downstream directional traffic state judgment method based on video detector - Google Patents

Abstract

The invention provides an urban road downstream directional traffic state judgment method based on a video detector. At present, judgment of the urban road traffic state is mainly based on floating vehicle data, and differences of the passenger loading rate and the vehicle operation rate of a floating vehicle at different time periods are large, so that the data sample number is insufficient, and distribution is uneven. According to the urban road downstream directional traffic state judgment method, the road section traveling time of different traffic streams in a road section is obtained through the video detector, and the road section traffic jam indexes of the different traffic streams in the road section are then constructed for conducting traffic state judgment on the different traffic streams. By means of the urban road downstream directional traffic state judgment method, sufficient data supports are obtained by sufficiently using existing video resources, and the shortcomings that in a traditional inspection method, the sample number is small, and distribution is uneven are overcome; a calculating method for the traveling time and the traveling speed is simple and rapid, and engineering realization is easy; traffic information of various cross sections and various regions are synthesized, the road traffic jam judgment method is provided, and data supports and decision basis are provided for distributing the road resources in a balanced mode and inducing the traffic streams to reasonably run.

Description

Direction traffic state judging method is divided in urban road downstream based on video detector

Technical field

The present invention relates to a kind of urban road downstream based on video detector point direction traffic state judging method, for urban traffic control and management, belong to intelligent transportation research field.

Background technology

Scientific and reasonable estimation is carried out to road traffic state, dynamic decision foundation can be provided for traffic administration person and traffic participant, the benign development of induction urban transportation.

The differentiation of current urban road traffic state is mainly foundation with floating car data.As the current only taxi can supporting large-scale application floating vehicle data acquisition source, it itself is also a kind of commerial vehicle, the cabin factor of Different periods and bus dispatching rate widely different, and often concentrate on the concentrated region of public activity and important passenger corridor, this ride characteristic can cause floating car data sample size deficiency and skewness, and then affects accuracy and the accuracy of relevant traffic parameter.Although section detector uses extensively in the collection of traffic data, the exception of individual detector often causes the unreliability of data, therefore still not too accurate to the differentiation of traffic behavior.Section often differentiates by current traffic state judging as a whole simultaneously, have ignored the difference of section different directions traffic flow, downstream left turn lane, Through Lane, right-turn lane often present different traffic behavior, therefore by road traffic delay various flows to taking into account that the differentiation of traffic behavior is very necessary.Along with day by day improving of video detecting device installation is also further extensive, day by day urgent to transport information demand of traffic trip person and supvr, and the traffic data feature of video detector can better meet at present for the pressing needs of traffic state judging, it is very necessary and urgent for therefore setting up a urban road downstream based on a video detector point direction traffic state judging method.

Summary of the invention

The object of the present invention is to provide a kind of urban road downstream based on video detector point direction traffic state judging method.

Basic thought of the present invention is the Link Travel Time being obtained per share different directions traffic flow in section by video detector, then construct the road section traffic volume congestion index of different directions traffic flow in section, respectively the traffic flow of different directions traffic flow is carried out to the differentiation of traffic behavior.For achieving the above object, the traffic state judging method that the present invention proposes comprises issues interval journey time, vehicle travel speed, road section traffic volume congestion index, section desirable travel speed, traffic state judging method five steps by video data calculating different directions telecommunication flow information.

Basic step of the present invention is as follows:

Average travel time in the Information issued interval of c1, the traffic flow of calculating different directions.

Average travel speed in the Information issued interval of c2, calculating downstream different directions traffic flow.

C3, according to average travel speed in the Information issued interval of downstream different directions traffic flow, set up road section traffic volume congestion index.

The desirable travel speed of c4, calculating downstream different directions traffic flow.

C5, according to road section traffic volume congestion index, traffic state judging is carried out to downstream different directions traffic flow.

In the Information issued interval calculating different directions traffic flow in step c1, the process of average travel time comprises:

C11, get and required differentiate that the sampling interval of section l is the signal period duration c of downstream intersection.

C12, determine the traffic flow D that sails into from crossing, upstream u direction, downstream intersection d direction is rolled away from _udthe Link Travel Time of middle single unit vehicle:

$T_{\mathrm{ud}}^{\mathrm{lki}}=t_d^{\mathrm{lki}}-t_u^{\mathrm{lki}}$

In formula, ---l section is sailed into from crossing, upstream u direction in a kth sampling interval, the Link Travel Time of vehicle i that downstream intersection d direction is rolled away from, u, d comprise left straight right, get 1,2,3, k=1 respectively, 2,3 ..., K;

---the moment that on l section, in a kth sampling interval, vehicle i rolls away from from downstream intersection d direction;

---the moment that on l section, in a kth sampling interval, vehicle i sails into from crossing, upstream u direction.

C13, determine traffic flow D in sampling interval _udaverage travel time:

$T_{\mathrm{ud}}^{\mathrm{lk}}=\frac{\underset{i=1}{\overset{N_{\mathrm{ud}}^{\mathrm{lk}}}{\mathrm{\Σ}}}{T}_{\mathrm{ud}}^{\mathrm{lki}}}{N_{\mathrm{ud}}^{\mathrm{lk}}}$

In formula, ---l section to be sailed into from crossing, upstream u direction, traffic flow D that downstream intersection d direction is rolled away from _udaverage travel time in a kth sampling interval.

---traffic flow D in a kth sampling interval on l section _udvehicle number.

C14, comformed information issue traffic flow D in interval _udaverage travel time:

In c141, data integrity situation, determine traffic flow D _ud`information issued interval journey time, wherein, the K of sampling interval is got doubly in Information issued interval, and K is integer.

$T_{\mathrm{ud}}^{\mathrm{lm}}=\frac{\underset{k=1}{\overset{K}{\mathrm{\Σ}}}{T}_{\mathrm{ud}}^{\mathrm{lk}}{N}_{\mathrm{ud}}^{\mathrm{lk}}}{\underset{k=1}{\overset{K}{\mathrm{\Σ}}}{N}_{\mathrm{ud}}^{\mathrm{lk}}}=\frac{\underset{k=1}{\overset{K}{\mathrm{\Σ}}}{T}_{\mathrm{ud}}^{\mathrm{lk}}{N}_{\mathrm{ud}}^{\mathrm{lk}}}{N_{\mathrm{ud}}^{\mathrm{lm}}}$

In formula, ---l road traffic delay D _udaverage travel time in m Information issued interval;

In c142, shortage of data situation, Different Traffic Flows Information issued interval journey time:

$T_{\mathrm{ud}}^{\mathrm{lm}}=T_{\mathrm{ud}}^{{\mathrm{lmh}}_1}+(1-\mathrm{\α})(T_{\mathrm{ud}}^{l(m-1)h_1}-T_{\mathrm{ud}}^{l(m-1)})+\mathrm{\α}(T_{\mathrm{ud}}^{{\mathrm{lmh}}_1}-T_{\mathrm{ud}}^{{\mathrm{lmh}}_2})$

In formula, ---l road traffic delay D in current date proxima luce (prox. luc) _udaverage travel time in m Information issued interval;

---l road traffic delay D in first two days of current date _udaverage travel time in m Information issued interval;

---l road traffic delay D in current date proxima luce (prox. luc) _udaverage travel time in m-1 Information issued interval;

---l road traffic delay D _udaverage travel time in m-1 Information issued interval;

α---current information issues the historical data variable quantity of interval m and the data variation amount of last Information issued interval m-1 weight coefficient, α ∈ [0,1], when m interval and m-1 interval historical data change greatly, α value is comparatively large, otherwise α value is less, generally desirable 0.5.

The process calculating downstream different directions traffic flow average travel speed in Information issued interval in step c2 comprises:

C21, determine downstream different directions traffic flow D _dstroke distances L _ud.

C22, the travel speed determined in the Information issued interval of left-hand rotation direction, downstream (d=1) traffic flow:

$v_1^{\mathrm{lm}}=\frac{\underset{u=1}{\overset{3}{\mathrm{\Σ}}}{N}_{u1}^{\mathrm{lm}}{L}_{u1}}{\underset{u=1}{\overset{3}{\mathrm{\Σ}}}{T}_{u1}^{\mathrm{lm}}{N}_{u1}^{\mathrm{lm}}}$

C23, the travel speed determined in the Information issued interval of craspedodrome direction, downstream (d=2) traffic flow:

$v_2^{\mathrm{lm}}=\frac{\underset{u=1}{\overset{3}{\mathrm{\Σ}}}{N}_{u2}^{\mathrm{lm}}{L}_{u2}}{\underset{u=1}{\overset{3}{\mathrm{\Σ}}}{T}_{u2}^{\mathrm{lm}}{N}_{u2}^{\mathrm{lm}}}$

C24, the travel speed determined in the Information issued interval of right-hand rotation direction, downstream (d=3) traffic flow:

$v_3^{\mathrm{lm}}=\frac{\underset{u=1}{\overset{3}{\mathrm{\Σ}}}{N}_{u3}^{\mathrm{lm}}{L}_{u3}}{\underset{u=1}{\overset{3}{\mathrm{\Σ}}}{T}_{u3}^{\mathrm{lm}}{N}_{u3}^{\mathrm{lm}}}$

The process that the travel speed issued in interval according to downstream different directions telecommunication flow information in step c3 sets up road section traffic volume congestion index comprises:

Under different road traffic states, driver is different by the journey time of section l, then travel speed is different, therefore by the degree that the traveled distance speed and desirable travel speed that compare driver depart from, can judge the congested in traffic degree in section:

$M_d^{\mathrm{lm}}=(v_{\mathrm{d\; expected}}^l-v_d^{\mathrm{lm}})/v_{\mathrm{d\; expected}}^l$

In formula, ---l section is traffic flow D in m Information issued interval _droad section traffic volume congestion index;

---l road traffic delay D _ddesirable travel speed.The calculating of desirable travel speed will be introduced in step c4.

The process calculating the desirable travel speed of downstream different directions traffic flow in step c4 comprises:

Highway coideal travel speed is free stream velocity, and urban road free stream velocity is more difficult records, and simultaneously because the desirable travel speed in section is relevant to section condition, intersection signal etc., different sections has different desirable travel speeds.Therefore, based on the history range speed of section, calculate the desirable travel speed of Different Traffic Flows on different sections of highway.

$v_{\mathrm{d\; expected}}^l=\max \left(v_{\mathrm{d\; his}}^l\right)/\mathrm{\η}$

In formula, the reduction coefficient of the desirable travel speed in η---section;

---l road traffic delay D _dhistory travel speed.

According to road section traffic volume congestion index, the process that traffic state judging is carried out in downstream different directions traffic flow is comprised in step c5:

C51, the fiducial interval adopting traffic behavior to change, process the critical localisation of traffic behavior change.Definition ± Δ M is that the normal fluctuation of state change is interval, and ± Δ M is relevant to the matching rate of video detection data:

$\mathrm{\Δ}{M}_d^{\mathrm{lm}}=\frac{\mathrm{\β}(1-\underset{u=1}{\overset{3}{\mathrm{\Σ}}}{q}_{\mathrm{ud}}^{\mathrm{lm}}/\underset{u=1}{\overset{3}{\mathrm{\Σ}}}{q}_{\mathrm{ud}\max }^{\mathrm{lm}}){\mathrm{\σ}}_{v_d^{\mathrm{lm}}}}{v_{\mathrm{d\; expected}}^l}$

In formula, ---l section is traffic flow D in m Information issued interval _dthe normal fluctuation of road section traffic volume congestion index interval;

---l section is traffic flow D in m Information issued interval _udactual vehicle number;

---l section is traffic flow D in m Information issued interval _udcoupling vehicle number;

---l section is traffic flow D in m Information issued interval _dthe standard deviation of vehicle travel speed;

β---related coefficient.

C52, traffic state judging is carried out to downstream different directions traffic flow comprise the following steps according to road section traffic volume congestion index and its normal fluctuation interval.

C521 m issues in interval, traffic flow D _dtraffic behavior when being red, issue traffic flow D in interval for m+1 _dthe distinguishing rule of traffic behavior be:

1. M is worked as _d∈ (M _r-Δ M _d, 1] time, traffic behavior is red;

2. M is worked as _d∈ (M _g-Δ M _d, M _r-Δ M _d] time, traffic behavior is yellow;

3. M is worked as _d∈ [0, M _g-Δ M _d] time, traffic behavior is green.

C522 m issues in interval, traffic flow D _dtraffic behavior when being yellow, traffic flow D in m+1 issue interval _dthe distinguishing rule of traffic behavior be:

1. M is worked as _d∈ (M _r+ Δ M _d, 1] time, traffic behavior is red;

2. M is worked as _d∈ (M _g-Δ M _d, M _r+ Δ M _d] time, traffic behavior is yellow;

3. M is worked as _d∈ [0, M _g-Δ M _d] time, traffic behavior is green.

C523 m issues in interval, traffic flow D _dtraffic behavior when being green, judge that m+1 is issued traffic flow D in interval _dthe distinguishing rule of traffic behavior be:

1. M is worked as _d∈ (M _r+ Δ M _d, 1] time, traffic behavior is red;

2. M is worked as _d∈ (M _g+ Δ M _d, M _r+ Δ M _d] time, traffic behavior is yellow;

3. M is worked as _d∈ [0, M _g+ Δ M _d] time, traffic behavior is green.

Beneficial effect of the present invention: the travel time data of the section different directions traffic flow that the present invention utilizes video detecting device to acquire, calculate the travel speed that respective quadrature is through-flow, then traffic behavior residing for this section different directions traffic flow of automatic discrimination, the method adopts and tries one's best few discrimination threshold and make full use of existing resource simultaneously, is easy to Project Realization.

Accompanying drawing explanation

Fig. 1 is traffic state judging method flow diagram;

Fig. 2 is traffic flow travel direction schematic diagram;

Fig. 3 is traffic flow operating range schematic diagram;

Fig. 4 is link travel velocity profile;

Fig. 5 is traffic state judging.

Embodiment

Below in conjunction with accompanying drawing, the present invention will be described in detail, and as shown in Figure 1, concrete steps of the present invention are as follows.

Step one, computing information issues traffic flow D in interval _udaverage travel time, the direction of traffic flow as shown in Figure 2:

The signal period duration 120s of downstream intersection is got at data sampling interval.Traffic flow D _udthe Link Travel Time of middle single unit vehicle is:

$T_{\mathrm{ud}}^{\mathrm{lki}}=t_d^{\mathrm{lki}}-t_u^{\mathrm{lki}}$

Traffic flow D in a kth sampling interval on section _udvehicle number be the then average travel time of Different Traffic Flows in sampling interval:

$T_{\mathrm{ud}}^{\mathrm{lk}}=\frac{\underset{i=1}{\overset{N_{\mathrm{ud}}^{\mathrm{lk}}}{\mathrm{\Σ}}}{T}_{\mathrm{ud}}^{\mathrm{lki}}}{N_{\mathrm{ud}}^{\mathrm{lk}}}$

Computing information issues traffic flow D in interval _udaverage travel time.

Between low peak period, K=10, peak period K=5.Then in data integrity situation, traffic flow D in Information issued interval _udaverage travel time be:

$T_{\mathrm{ud}}^{\mathrm{lm}}=\frac{\underset{k=1}{\overset{K}{\mathrm{\Σ}}}{T}_{\mathrm{ud}}^{\mathrm{lk}}{N}_{\mathrm{ud}}^{\mathrm{lk}}}{\underset{k=1}{\overset{K}{\mathrm{\Σ}}}{N}_{\mathrm{ud}}^{\mathrm{lk}}}=\frac{\underset{k=1}{\overset{K}{\mathrm{\Σ}}}{T}_{\mathrm{ud}}^{\mathrm{lk}}{N}_{\mathrm{ud}}^{\mathrm{lk}}}{N_{\mathrm{ud}}^{\mathrm{lm}}}$

In shortage of data situation, traffic flow D in Information issued interval _udaverage travel time be:

$T_{\mathrm{ud}}^{\mathrm{lm}}=T_{\mathrm{ud}}^{{\mathrm{lmh}}_1}+(1-\mathrm{\α})(T_{\mathrm{ud}}^{l(m-1)h_1}-T_{\mathrm{ud}}^{l(m-1)})+\mathrm{\α}(T_{\mathrm{ud}}^{{\mathrm{lmh}}_1}-T_{\mathrm{ud}}^{{\mathrm{lmh}}_2})$

Wherein α gets 0.5.

Step 2, as shown in Figure 3, computing information issues the average travel speed of downstream different directions traffic flow in interval to the operating range of downstream different directions traffic flow, as Fig. 4:

The average travel speed that left-hand rotation direction, downstream (d=1) telecommunication flow information is issued in interval is:

$v_1^{\mathrm{lm}}=\frac{\underset{u=1}{\overset{3}{\mathrm{\Σ}}}{N}_{u1}^{\mathrm{lm}}{L}_{u1}}{\underset{u=1}{\overset{3}{\mathrm{\Σ}}}{T}_{u1}^{\mathrm{lm}}{N}_{u1}^{\mathrm{lm}}}$

The average travel speed that craspedodrome direction, downstream (d=2) telecommunication flow information is issued in interval is:

$v_2^{\mathrm{lm}}=\frac{\underset{u=1}{\overset{3}{\mathrm{\Σ}}}{N}_{u2}^{\mathrm{lm}}{L}_{u2}}{\underset{u=1}{\overset{3}{\mathrm{\Σ}}}{T}_{u2}^{\mathrm{lm}}{N}_{u2}^{\mathrm{lm}}}$

The average travel speed that right-hand rotation direction, downstream (d=3) telecommunication flow information is issued in interval is:

$v_3^{\mathrm{lm}}=\frac{\underset{u=1}{\overset{3}{\mathrm{\Σ}}}{N}_{u3}^{\mathrm{lm}}{L}_{u3}}{\underset{u=1}{\overset{3}{\mathrm{\Σ}}}{T}_{u3}^{\mathrm{lm}}{N}_{u3}^{\mathrm{lm}}}$

Step 3, the travel speed issuing interval according to downstream different directions telecommunication flow information sets up road section traffic volume congestion index, is calculated as follows:

$M_d^{\mathrm{lm}}=(v_{\mathrm{d\; expected}}^l-v_d^{\mathrm{lm}})/v_{\mathrm{d\; expected}}^l$

Step 4, calculates the desirable travel speed of downstream different directions traffic flow:

$v_{\mathrm{d\; expected}}^l=\max \left(v_{\mathrm{d\; his}}^l\right)/0.9$

Step 5, according to road section traffic volume congestion index, traffic state judging is carried out to downstream different directions traffic flow:

Adopt the fiducial interval ± Δ M of traffic behavior change, process the critical localisation of traffic behavior change, ± Δ M is calculated as follows:

$\mathrm{\Δ}{M}_d^{\mathrm{lm}}=\frac{\mathrm{\β}(1-\underset{u=1}{\overset{3}{\mathrm{\Σ}}}{q}_{\mathrm{ud}}^{\mathrm{lm}}/\underset{u=1}{\overset{3}{\mathrm{\Σ}}}{q}_{\mathrm{ud}\max }^{\mathrm{lm}}){\mathrm{\σ}}_{v_d^{\mathrm{lm}}}}{v_{\mathrm{d\; expected}}^l}$

Wherein, β value is 0.5.

According to urban road speed divided rank table (table 1) setting traffic congestion index divided rank, as table 2.

Table 1 urban road speed divided rank

Table 2 traffic congestion index divided rank table

Then issue in interval for m, traffic flow D _dtraffic behavior when being red, issue traffic flow D in interval for m+1 _dthe distinguishing rule of traffic behavior be:

1. M is worked as _d∈ (0.5-Δ M _d, 1] time, traffic behavior is red;

2. M is worked as _d∈ (0.7-Δ M _d, 0.5-Δ M _d] time, traffic behavior is yellow;

3. M is worked as _d∈ [0,0.7-Δ M _d] time, traffic behavior is green.

Issue in interval for m, traffic flow D _dtraffic behavior when being yellow, traffic flow D in m+1 issue interval _dthe distinguishing rule of traffic behavior be:

1. M is worked as _d∈ (0.5+ Δ M _d, 1] time, traffic behavior is red;

2. M is worked as _d∈ (0.7-Δ M _d, 0.5+ Δ M _d] time, traffic behavior is yellow;

3. M is worked as _d∈ [0,0.7-Δ M _d] time, traffic behavior is green.

Issue in interval for m, traffic flow D _dtraffic behavior when being green, judge that m+1 is issued traffic flow D in interval _dthe distinguishing rule of traffic behavior be:

1. M is worked as _d∈ (0.5+ Δ M _d, 1] time, traffic behavior is red;

2. M is worked as _d∈ (0.7+ Δ M _d, 0.5+ Δ M _d] time, traffic behavior is yellow;

3. M is worked as _d∈ [0,0.7+ Δ M _d] time, traffic behavior is green.

Differentiate and the results are shown in Figure 5.

Claims (6)

1. direction traffic state judging method is divided in the urban road downstream based on video detector, it is characterized in that the method comprises the following steps:

Average travel time in the Information issued interval of c1, the traffic flow of calculating different directions;

Average travel speed in the Information issued interval of c2, calculating downstream different directions traffic flow;

C3, according to average travel speed in the Information issued interval of downstream different directions traffic flow, set up road section traffic volume congestion index;

The desirable travel speed of c4, calculating downstream different directions traffic flow;

C5, according to road section traffic volume congestion index, traffic state judging is carried out to downstream different directions traffic flow.

2. direction traffic state judging method is divided in the urban road downstream based on video detector according to claim 1, it is characterized in that: the process calculating average travel time in Information issued interval in c1 comprises:

C11, get and required differentiate that the sampling interval of section l is the signal period duration c of downstream intersection; Information issued is spaced apart the integral multiple of the signal period duration c of downstream intersection;

In c12, data integrity situation, determine traffic flow D _ud`information issued interval journey time, wherein, the K of sampling interval is got doubly in Information issued interval, and K is integer;

$T_{\mathrm{ud}}^{\mathrm{lm}}=\frac{\underset{k=1}{\overset{K}{\mathrm{\Σ}}}{T}_{\mathrm{ud}}^{\mathrm{lk}}{N}_{\mathrm{ud}}^{\mathrm{lk}}}{\underset{k=1}{\overset{K}{\mathrm{\Σ}}}{N}_{\mathrm{ud}}^{\mathrm{lk}}}=\frac{\underset{k=1}{\overset{K}{\mathrm{\Σ}}}{T}_{\mathrm{ud}}^{\mathrm{lk}}{N}_{\mathrm{ud}}^{\mathrm{lk}}}{N_{\mathrm{ud}}^{\mathrm{lm}}}$

In formula, represent l road traffic delay D _udaverage travel time in m Information issued interval;

In c13, shortage of data situation, Different Traffic Flows Information issued interval journey time:

$T_{\mathrm{ud}}^{\mathrm{lm}}=T_{\mathrm{ud}}^{{\mathrm{lmh}}_1}+(1-\mathrm{\α})(T_{\mathrm{ud}}^{{l(m-1)h}_1}-T_{\mathrm{ud}}^{l(m-1)})+\mathrm{\α}(T_{\mathrm{ud}}^{{\mathrm{lmh}}_1}-T_{\mathrm{ud}}^{{\mathrm{lmh}}_2})$

In formula, represent l road traffic delay D in current date proxima luce (prox. luc) _udaverage travel time in m Information issued interval;

represent l road traffic delay D in first two days of current date _udaverage travel time in m Information issued interval;

represent l road traffic delay D in current date proxima luce (prox. luc) _udaverage travel time in m-1 Information issued interval;

represent l road traffic delay D _udaverage travel time in m-1 Information issued interval;

α represents that current information issues the historical data variable quantity of interval m and the data variation amount of last Information issued interval m-1 weight coefficient, α ∈ [0,1].

3. direction traffic state judging method is divided in the urban road downstream based on video detector according to claim 1, it is characterized in that: the process calculating downstream different directions traffic flow average travel speed in Information issued interval in c2 comprises:

C21, determine the stroke distances L of different directions traffic flow _ud;

C22, the travel speed determined in the Information issued interval of downstream left-hand rotation direction traffic flow, now d=1:

$v_1^{\mathrm{lm}}=\frac{\underset{u=1}{\overset{3}{\mathrm{\Σ}}}{N}_{u1}^{\mathrm{lm}}{L}_{u1}}{\underset{u=1}{\overset{3}{\mathrm{\Σ}}}{T}_{u1}^{\mathrm{lm}}{N}_{u1}^{\mathrm{lm}}}$

C23, the travel speed determined in the Information issued interval of downstream craspedodrome direction traffic flow, now d=2:

$v_2^{\mathrm{lm}}=\frac{\underset{u=1}{\overset{3}{\mathrm{\Σ}}}{N}_{u2}^{\mathrm{lm}}{L}_{u2}}{\underset{u=1}{\overset{3}{\mathrm{\Σ}}}{T}_{u2}^{\mathrm{lm}}{N}_{u2}^{\mathrm{lm}}}$

C24, the travel speed determined in the Information issued interval of downstream right-hand rotation direction traffic flow, now d=3:

$v_3^{\mathrm{lm}}=\frac{\underset{u=1}{\overset{3}{\mathrm{\Σ}}}{N}_{u3}^{\mathrm{lm}}{L}_{u3}}{\underset{u=1}{\overset{3}{\mathrm{\Σ}}}{T}_{u3}^{\mathrm{lm}}{N}_{u3}^{\mathrm{lm}}}.$

4. direction traffic state judging method is divided in the urban road downstream based on video detector according to claim 1, it is characterized in that: set up road section traffic volume congestion index process according to the travel speed at downstream different directions telecommunication flow information issue interval in c3 as follows:

$M_d^{\mathrm{lm}}=(v_{\mathrm{dexpected}}^l-v_d^{\mathrm{lm}})/v_{\mathrm{dexpected}}^l$

In formula, represent l section traffic flow D in m Information issued interval _droad section traffic volume congestion index;

represent l road traffic delay D _ddesirable travel speed.

5. direction traffic state judging method is divided in the urban road downstream based on video detector according to claim 1, it is characterized in that: the process calculating the desirable travel speed of downstream different directions traffic flow in c4 is as follows:

$v_{\mathrm{d\; exp\; ected}}^l=\max \left(v_{\mathrm{dhis}}^l\right)/\mathrm{\η}$

In formula, η represents the reduction coefficient of the desirable travel speed in section; represent l road traffic delay D _dhistory travel speed.

6. direction traffic state judging method is divided in the urban road downstream based on video detector according to claim 1, it is characterized in that: comprise the process that traffic state judging is carried out in downstream different directions traffic flow according to road section traffic volume congestion index in c5:

The calculating of the fiducial interval of c51, traffic behavior change:

Definition ± Δ M is that the normal fluctuation of state change is interval, and ± Δ M is relevant to the matching rate of video detection data:

${\mathrm{\ΔM}}_d^{\mathrm{lm}}=\frac{\mathrm{\β}(1-\underset{u=1}{\overset{3}{\mathrm{\Σ}}}{q}_{\mathrm{ud}}^{\mathrm{lm}}/\underset{u=1}{\overset{3}{\mathrm{\Σ}}}{q}_{\mathrm{ud}\max }^{\mathrm{lm}}){\mathrm{\σ}}_{v_d^{\mathrm{lm}}}}{v_{\mathrm{d\; exp\; ected}}^l}$

In formula, represent l section traffic flow D in m Information issued interval _dthe normal fluctuation of road section traffic volume congestion index interval;

represent l section traffic flow D in m Information issued interval _udactual vehicle number;

represent l section traffic flow D in m Information issued interval _udcoupling vehicle number;

represent l section traffic flow D in m Information issued interval _dthe standard deviation of vehicle travel speed;

β represents related coefficient;

C52, traffic state judging carried out to downstream different directions traffic flow comprise the following steps according to road section traffic volume congestion index and its normal fluctuation interval:

Issue in interval for c521, m, traffic flow D _dtraffic behavior when being red, issue traffic flow D in interval for m+1 _dthe distinguishing rule of traffic behavior be:

1. M is worked as _d∈ (M _r-Δ M _d, 1] time, traffic behavior is red;

2. M is worked as _d∈ (M _g-Δ M _d, M _r-Δ M _d] time, traffic behavior is yellow;

3. M is worked as _d∈ [0, M _g-Δ M _d] time, traffic behavior is green;

Issue in interval for c522, m, traffic flow D _dtraffic behavior when being yellow, traffic flow D in m+1 issue interval _dthe distinguishing rule of traffic behavior be:

1. M is worked as _d∈ (M _r+ Δ M _d, 1] time, traffic behavior is red;

2. M is worked as _d∈ (M _g-Δ M _d, M _r+ Δ M _d] time, traffic behavior is yellow;

3. M is worked as _d∈ [0, M _g-Δ M _d] time, traffic behavior is green;

Issue in interval for c523, m, traffic flow D _dtraffic behavior when being green, judge that m+1 is issued traffic flow D in interval _dthe distinguishing rule of traffic behavior be:

1. M is worked as _d∈ (M _r+ Δ M _d, 1] time, traffic behavior is red;

2. M is worked as _d∈ (M _g+ Δ M _d, M _r+ Δ M _d] time, traffic behavior is yellow;

3. M is worked as _d∈ [0, M _g+ Δ M _d] time, traffic behavior is green.