CN103021176B - Discriminating method based on section detector for urban traffic state - Google Patents

Discriminating method based on section detector for urban traffic state Download PDF

Info

Publication number
CN103021176B
CN103021176B CN201210507080.0A CN201210507080A CN103021176B CN 103021176 B CN103021176 B CN 103021176B CN 201210507080 A CN201210507080 A CN 201210507080A CN 103021176 B CN103021176 B CN 103021176B
Authority
CN
China
Prior art keywords
traffic
section
interval
index
speed
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Expired - Fee Related
Application number
CN201210507080.0A
Other languages
Chinese (zh)
Other versions
CN103021176A (en
Inventor
王殿海
付凤杰
金盛
马东方
汤月华
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Zhejiang University ZJU
Original Assignee
Zhejiang University ZJU
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Zhejiang University ZJU filed Critical Zhejiang University ZJU
Priority to CN201210507080.0A priority Critical patent/CN103021176B/en
Publication of CN103021176A publication Critical patent/CN103021176A/en
Application granted granted Critical
Publication of CN103021176B publication Critical patent/CN103021176B/en
Expired - Fee Related legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Abstract

The invention discloses a discriminating method based on a section detector for urban traffic state. The existing traffic state discriminating method has low accuracy and less reliability. Based on the three traffic flow parameters of traffic flow, speed and time occupancy rate, the invention constructs a comprehensive congestion evaluation index, namely a traffic congestion index which is used for discriminating the road traffic state. The traffic state discriminating method provided by the invention comprises the steps of acquiring section traffic flow data at information publishing intervals, smoothing traffic flow parameters, calculating a speed congestion index and a occupancy congestion index, calculating a critical speed congestion index, calculating the traffic congestion index and discriminating the road traffic state. Based on the traffic information of a certain detecting section of a road and the overall consideration of various traffic flow parameters, the discriminating method based on the section detector for urban traffic state can automatically discriminate the traffic state of the road, and meanwhile utilize discriminating threshold values as few as possible and make full use of available resources, thereby facilitating the realization of a project.

Description

Urban road traffic state method of discrimination based on section detecting device
Technical field
The present invention relates to a kind of urban road traffic state method of discrimination based on section detecting device, for urban traffic control and management, belong to intelligent transportation research field.
Background technology
Road traffic state is carried out to scientific and reasonable estimation, can provide dynamic decision foundation for traffic administration person and traffic participant, the benign development of induction urban transportation.
The at present differentiation of urban road traffic state is mainly taking floating car data as foundation, taking video monitoring and artificial observation for supplementary.As the current only taxi that can support large-scale application floating vehicle data acquisition source, it itself is also a kind of commerial vehicle, cabin factor and bus dispatching rate in the different periods are widely different, and often concentrate on the concentrated region of public activity and important passenger traffic passage, this ride characteristic can have influence on Floating Car sample size and the counting accuracy for calculating section travel speed; Because video monitoring resource is very limited, human factor is larger on the impact of artificial observation and video monitoring result, and therefore the precision of existing traffic state judging method and reliability are lower.Existing traffic state judging method is unique foundation of differentiating mainly with speed simultaneously, and stability and reliability requirement to speed data are higher, and time occupancy is also a key factor weighing traffic behavior.Therefore setting up one is very urgent based on urban road traffic state method of discrimination section detecting device and time of fusion occupation rate.
Summary of the invention
The object of the present invention is to provide a kind of urban road traffic state method of discrimination based on section detecting device.The basic thought of the method is with the magnitude of traffic flow, speed, these three the traffic flow parameter structure evaluation index of comprehensively blocking up---traffic congestion indexes of time occupancy, utilizes traffic congestion index to differentiate section traffic behavior.For achieving the above object, the traffic state judging method that the present invention proposes comprises that information issues section traffic flow data obtains in interval level and smooth step, the speed of step traffic flow parameter block up exponential sum occupation rate block up step, critical velocity that index calculates the block up step that index calculates, the step of traffic congestion index calculating, the step of road section traffic volume condition discrimination.
The traffic state judging method that the present invention proposes, the laying situation that has comprised three kinds of section detecting devices: there is one group of section detecting device traffic state judging, have many group detecting device traffic state judgings, sensorless traffic state judging.
Having one group of section detecting device traffic state judging is a kind of urban road traffic state method of discrimination, differentiate section and only have one group of detecting device, each issue obtains traffic flow data by detecting device in interval, determine traffic congestion index, according to predefined traffic congestion index ranking interval, judge traffic behavior.
Having many group section detecting device traffic state judgings is to differentiate section to have many group detecting devices, according to the each group of detector location COMPREHENSIVE CALCULATING road section traffic volume index that blocks up, according to predefined traffic congestion index ranking interval, carries out traffic behavior judgement.
Sensorless traffic state judging is the one that urban road traffic state is differentiated, and differentiating section does not have detecting device, according to the traffic behavior of upstream and downstream line, realizes the traffic state judging in this section by setting upstream and downstream traffic behavior weight.
The basic step of this method is as follows:
C1, from each track, in each section detecting device, obtain these three traffic flow parameters of the magnitude of traffic flow, speed and time occupancy in this this track of detection section according to the pre-determined sampling interval time; Then obtain the magnitude of traffic flow, speed and time occupancy that information issue interval characterizes this this track traffic stream characteristics of detection section; Traffic flow parameter is carried out to pre-service, and the information that obtains is issued interval and is characterized the magnitude of traffic flow, speed and the time occupancy of this detection section traffic stream characteristics.
C2, traffic flow parameter level and smooth.
C3, according to the pretreated traffic flow parameter computing velocity exponential sum occupation rate index that blocks up that blocks up.
C4, according to dividing the critical velocity corresponding to the critical speed calculation of the category of roads index that blocks up.
C5, calculate according to the speed exponential sum occupation rate index that blocks up that blocks up index---the traffic congestion index that comprehensively blocks up.
C6, the critical velocity obtaining according to the step c4 traffic congestion index that exponential sum c5 obtains that blocks up judges section traffic behavior.
The process of obtaining arithmetic for real-time traffic flow parameter in step c1 comprises:
C11, determine the section of required detection and section section Loop detector layout situation.
C12, specified data sampling interval: choosing sampling interval is 1 minute.
C13, by the magnitude of traffic flow, speed and time occupancy data on every track in each sampling interval of ring section detector acquisition.
C14, the magnitude of traffic flow, speed and time occupancy data by every track in each sampling interval calculate each information and issue the magnitude of traffic flow, speed and the time occupancy data on Nei Meitiao track, interval.
C15, the each track arithmetic for real-time traffic flow parameter obtaining in step c14 is carried out to pre-service, obtain characterizing the traffic flow parameter of this detection section.
In step c13, for the each section detecting device on every track, obtain the magnitude of traffic flow and the time occupancy in corresponding track, and the speed of each car;
From section detecting device, obtaining traffic flow parameter specifically comprises:
C131, obtain traffic flow parameter;
In each sampling interval, the magnitude of traffic flow is passed through the vehicle number of this section detecting device in one minute, and unit is veh;
Figure 2012105070800100002DEST_PATH_IMAGE001
In formula: i---of this section ibar track;
k---the kindividual sampling interval;
n---in sampling interval by the of this section detecting device ncar;
---the kindividual sampling interval ion bar track, pass through the vehicle number of this section detecting device;
Figure 2012105070800100002DEST_PATH_IMAGE003
---the kindividual sampling interval ithe magnitude of traffic flow in bar track;
C132, acquisition speed parameter;
In each sampling interval, average velocity passes through the average velocity of all vehicles of this section detecting device in one minute, and unit is km/h;
Figure 761295DEST_PATH_IMAGE004
In formula:
Figure 2012105070800100002DEST_PATH_IMAGE005
---the kindividual sampling interval ithe average velocity of bar track vehicle.
Figure 685258DEST_PATH_IMAGE006
---the nthe car speed during by section detecting device;
C133, acquisition time occupation rate parameter;
In each sampling interval, time occupancy takies the T.T. of detecting device and the number percent of 1 minute by all vehicles of this section detecting device in one minute.
Figure 2012105070800100002DEST_PATH_IMAGE007
In formula:
Figure 333277DEST_PATH_IMAGE008
---the ncar occupies the time of detecting device during by section detecting device;
Figure 2012105070800100002DEST_PATH_IMAGE009
---the kindividual sampling interval ithe occupation rate in bar track;
The abnormal data that needs to reject each track section detecting device in step c14, can adopt threshold value screening method, rejects the magnitude of traffic flow, speed and the time occupancy data that exceed certain threshold value; Then also need that the qualified data information of carrying out is issued to interval data synthetic, obtain each information and issue the magnitude of traffic flow, speed and the time occupancy data on Nei Meitiao track, interval, detailed step is as follows:
C141, each information are issued the traffic flow parameter processing on Nei Meitiao track, interval;
In issue interval, the magnitude of traffic flow is issued in interval and is passed through the vehicle number of this section detecting device, and unit is veh, that is:
Figure 890684DEST_PATH_IMAGE010
In formula: k---the kindividual sampling interval,
Figure 2012105070800100002DEST_PATH_IMAGE011
;
m---information is issued interval, m=1,2,3,4,5,10,15 ... Deng;
---the ibar lane information is issued the flow in interval;
C142, each information are issued the speed parameter processing on Nei Meitiao track, interval;
Issuing average velocity in interval is, the weighted mean value of each sampling interval average velocity, and taking the flow of each sampling interval as weight, unit is km/h, that is:
In formula:
Figure 469750DEST_PATH_IMAGE014
---the ibar lane information is issued the average velocity in interval.
C143, each information are issued the time occupancy parameter processing on Nei Meitiao track, interval
In issue interval, time occupancy is got the mean value of each sampling interval time occupancy, that is:
Figure 2012105070800100002DEST_PATH_IMAGE015
In formula:
Figure 237855DEST_PATH_IMAGE016
---the ibar lane information is issued the occupation rate in interval;
5. the traffic flow parameter of in step c15, each information being issued on Nei Meitiao track, interval synthesizes, and obtains the corresponding section magnitude of traffic flow, time occupancy and speed;
C151, the processing of section traffic flow parameter;
Section flow is to issue in interval to pass through the vehicle number of this section, i.e. each track flow sum, and unit is veh, that is:
Figure 2012105070800100002DEST_PATH_IMAGE017
In formula: j---the jindividual information is issued interval;
Figure 697655DEST_PATH_IMAGE018
---the jindividual information is issued the section flow at interval;
C152, the processing of section speed parameter
Section speed is the weighted mean value of track average velocity, and taking track flow as weight, unit is km/h, that is:
Figure DEST_PATH_IMAGE019
In formula:
Figure 616457DEST_PATH_IMAGE020
---the jindividual information is issued the velocity amplitude at interval.
C152, the processing of section time occupancy parameter;
Section time occupancy is the mean value of Ratio of driveway occupancy time, that is:
Figure 2012105070800100002DEST_PATH_IMAGE021
In formula: ---the jindividual information is issued interlude occupation rate.
Step c2 detects to reality the profile data obtaining and carries out smoothing processing, and the stability of guarantee system operation, reduces the interference of enchancement factor, and the continuity and the stability that keep traffic behavior to issue, be calculated as follows in detail:
Figure 2012105070800100002DEST_PATH_IMAGE023
Figure 255566DEST_PATH_IMAGE024
In formula: ---the after level and smooth jindividual information is issued time occupancy in interval;
Figure 214164DEST_PATH_IMAGE026
---the after level and smooth jindividual information is issued velocity amplitude in interval;
Figure 102485DEST_PATH_IMAGE018
---the jindividual information is issued the section flow in interval;
Figure 171941DEST_PATH_IMAGE022
---the jindividual information is issued the time occupancy in interval;
Figure 524425DEST_PATH_IMAGE020
---the jindividual information is issued the speed in interval;
β---smoothing factor, has β 1 , β 2 , β 3 .
Step c3 is normalized traffic flow data, the speed of the obtaining exponential sum time occupancy index that blocks up that blocks up, and detailed step is as follows.
The c31 speed index that blocks up;
Suppose that the block up relation of index of speed and speed is linear, and the minimum value of speed (0) and maximal value (
Figure DEST_PATH_IMAGE027
) the corresponding speed index that blocks up is respectively 1,0.The block up computing formula of index of speed is:
Figure 594537DEST_PATH_IMAGE028
In formula: j v ---the speed index that blocks up;
---free stream velocity;
Figure DEST_PATH_IMAGE029
---the section speed after level and smooth.
The c32 time occupancy index that blocks up;
Time occupancy and the time occupancy index that blocks up is linear, the minimum value (0) of time occupancy and maximal value (
Figure 577722DEST_PATH_IMAGE030
) the corresponding speed index that blocks up is respectively 0,1.The block up computing formula of index of time occupancy is as follows:
Figure DEST_PATH_IMAGE031
In formula: j o ---the time occupancy index that blocks up;
Figure 174926DEST_PATH_IMAGE030
---the historical maximal value of time occupancy;
---the section time occupancy after level and smooth.
Step c4 is according to the critical velocity value of urban road grade classification
Figure DEST_PATH_IMAGE033
,
Figure 338240DEST_PATH_IMAGE034
urban road traffic state is divided into block up, slow and unimpeded three grades, according to critical velocity value
Figure 500231DEST_PATH_IMAGE033
, calculate the corresponding critical velocity index that blocks up:
Figure 273508DEST_PATH_IMAGE036
In formula: j 1, j 2---the critical value that traffic behavior changes;
Figure 810668DEST_PATH_IMAGE033
,
Figure 966231DEST_PATH_IMAGE034
---the critical velocity that traffic behavior is divided;
v f ---free stream velocity.
The step c5 fusion speed exponential sum time occupancy index that blocks up that blocks up, sets up the comprehensive index of blocking up---traffic congestion index, and detailed step is as follows:
Mono-group of detecting device section of c51
Figure DEST_PATH_IMAGE037
In formula, j---the composite target of traffic state judging, is called traffic congestion index;
j v ---the speed index that blocks up;
j o ---the time occupancy index that blocks up;
η---the weight coefficient of speed index and time occupancy index, value is 0-1, system can be defaulted as 0.5, and adjusts according to actual conditions.
C52 organizes detecting device section more
While there is many group section detecting devices, need to carry out comprehensive distinguishing and calculate according to the position of section detecting device the traffic congestion index of line.
Figure 210130DEST_PATH_IMAGE038
In formula: j di ---detecting device itraffic congestion index;
l di ---detecting device ito the distance of its downstream detector;
l d1 ---the distance apart from the nearest detecting device of downstream stop line apart from stop line.
C53 is without detecting section
When upstream and downstream section equal sensorless, this road section traffic volume state is grey, unknown.
Upstream or downstream are provided with detecting device, and this road section traffic volume state above downstream road section traffic behavior is foundation, utilize upstream and downstream traffic congestion index to calculate the traffic congestion index in this section.
Figure DEST_PATH_IMAGE039
(18)
In formula: j up ---the traffic congestion index of upstream detector;
j down ---the traffic congestion index of downstream detector;
Figure 711388DEST_PATH_IMAGE040
---the weight coefficient of upstream and downstream detecting device traffic congestion index, in the time of downstream road section sensorless,
Figure DEST_PATH_IMAGE041
; In the time of the sensorless of section, upstream,
Figure 676457DEST_PATH_IMAGE042
; When upstream and downstream all has detecting device, .
Step c6 has considered the problem of processing at the critical localisation of traffic behavior variation, the credibility interval that adopts traffic behavior to change.Definition ± Δ jfor the normal fluctuation interval of state variation, the detailed step of traffic state judging is as follows.
If c61 jin individual issue interval, when traffic behavior is red, judge j+1issue interval traffic state judging according to being:
1. work as
Figure 554152DEST_PATH_IMAGE044
time, traffic behavior is red;
2. work as
Figure DEST_PATH_IMAGE045
time, traffic behavior is yellow;
3. work as time, traffic behavior is green.
If c62 jin individual issue interval, when traffic behavior is yellow, judge the j+1issue interval traffic state judging according to being:
1. work as
Figure DEST_PATH_IMAGE047
time, traffic behavior is red;
2. at that time, traffic behavior was yellow;
3. work as time, traffic behavior is green.
If c63 jin individual issue interval, when traffic behavior is green, judge j+1issue interval traffic state judging according to being:
1. work as
Figure DEST_PATH_IMAGE051
time, traffic behavior is red;
2. work as
Figure DEST_PATH_IMAGE053
time, traffic behavior is yellow;
3. work as
Figure DEST_PATH_IMAGE055
time, traffic behavior is green.
In above-mentioned rule, Δ j 1 with Δ j 2 value need to be according to actual conditions setting.
Beneficial effect of the present invention: the transport information that the present invention is based on some detection sections on section, consider multiple traffic flow parameter, this section of automatic discrimination traffic behavior of living in, the method adopts and tries one's best few discrimination threshold and make full use of existing resource simultaneously, is easy to Project Realization.
Brief description of the drawings
Fig. 1 is traffic state judging method flow diagram;
Fig. 2 is speed and the speed number mark relation curves that block up;
Fig. 3 is time occupancy and the time occupancy exponential relationship curve that blocks up;
Fig. 4 is line Loop detector layout schematic diagram;
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:
Step 1 is obtained track sampling interval data:
Certain section iin the sampling interval of bar track, the computing formula of flow, average velocity and time occupancy is as follows:
Figure DEST_PATH_IMAGE057
(1)
(2)
(3)
In formula: i---of this section ibar track;
k---the kindividual sampling interval;
n---in sampling interval by the of this section detecting device ncar;
Figure DEST_PATH_IMAGE063
---the kindividual sampling interval ion bar track, pass through the vehicle number of this section detecting device;
Figure DEST_PATH_IMAGE065
---the ncar occupies the time of detecting device during by section detecting device;
Figure DEST_PATH_IMAGE067
---the nthe car speed during by section detecting device;
Figure DEST_PATH_IMAGE069
---the kindividual sampling interval ithe flow in bar track;
---the kindividual sampling interval ithe occupation rate in bar track;
Figure DEST_PATH_IMAGE073
---the kindividual sampling interval ithe average velocity of bar track vehicle.
Step 2 is calculated lane information and is issued interval data:
Suppose that the time interval that information is issued is mminute, m=1,2,3,4,5,10,15 ... Deng, ibar lane information is issued the flow in interval
Figure DEST_PATH_IMAGE075
for mthe algebraic sum of individual sampling interval, time occupancy
Figure DEST_PATH_IMAGE077
for mthe mean value of individual sampling interval, speed for mthe weighted mean value of individual sampling interval, computing formula is as follows.
Figure DEST_PATH_IMAGE081
(4)
Figure DEST_PATH_IMAGE083
(5)
Figure DEST_PATH_IMAGE085
(6)
In formula: k---the kindividual sampling interval,
Figure DEST_PATH_IMAGE087
;
m---information is issued interval, m=1,2,3,4,5,10,15 ... Deng;
---the ibar lane information is issued the flow in interval;
Figure DEST_PATH_IMAGE089
---the ibar lane information is issued the occupation rate in interval;
Figure 558225DEST_PATH_IMAGE090
---the ibar lane information is issued the average velocity in interval.
Step 3 is synthesized section traffic flow data:
Suppose that this detection section has lbar track, the computing formula of section traffic flow data is as follows:
Figure 865710DEST_PATH_IMAGE092
(7)
Figure 756306DEST_PATH_IMAGE094
(8)
Figure 743241DEST_PATH_IMAGE096
(9)
In formula: j---the jindividual information is issued interval;
Figure 289760DEST_PATH_IMAGE098
---the jindividual information is issued the section flow at interval;
Figure 650203DEST_PATH_IMAGE100
---the jindividual information is issued interlude occupation rate;
Figure 598567DEST_PATH_IMAGE102
---the jindividual information is issued the velocity amplitude at interval.
Step 4 is carried out the level and smooth of traffic flow modes parameter:
Section time occupancy and speed adopt following methods to carry out smoothing processing:
Figure 753474DEST_PATH_IMAGE104
(10)
Figure 787289DEST_PATH_IMAGE106
(11)
In formula:
Figure 685844DEST_PATH_IMAGE108
---the after level and smooth jindividual information is issued time occupancy in interval;
---the after level and smooth jindividual information is issued velocity amplitude in interval;
---the jindividual information is issued the section flow in interval;
Figure 743417DEST_PATH_IMAGE112
---the jindividual information is issued the time occupancy in interval;
Figure DEST_PATH_IMAGE113
---the jindividual information is issued the speed in interval;
β---smoothing factor, has β 1 , β 2 , β 3 .
Step 5 is determined block up index and interval:
(1) speed index
Suppose that the block up relation of index of speed and speed is linear, as Fig. 2 speed and speed are blocked up as shown in several relation curves.The block up computing formula of index of speed is:
Figure DEST_PATH_IMAGE115
(12)
In formula: j v ---the speed index that blocks up;
Figure DEST_PATH_IMAGE117
---free stream velocity;
Figure DEST_PATH_IMAGE119
---the section speed after level and smooth.
(2) the time occupancy index that blocks up
The block up relation curve of index of time occupancy and time occupancy blocks up as shown in exponential relationship curve as Fig. 3 time occupancy and time occupancy, and the block up computing formula of index of time occupancy is as follows:
(13)
In formula: j o ---the time occupancy index that blocks up;
Figure DEST_PATH_IMAGE123
---the historical maximal value of time occupancy;
---the section time occupancy after level and smooth.
(3) speed block up index ranking divide
According to practical experience both domestic and external, the division of urban road traffic state should be the travel speed in section according to major parameter.Based on this consideration, the critical velocity value of dividing according to urban road grade and traffic behavior is divided into urban road traffic state and blocks up, slow and unimpeded three grades, as shown in the table.
Table 1 urban road speed divided rank
Figure DEST_PATH_IMAGE127
Can obtain the block up divided rank of index of speed according to the block up normalization formula of index and table 1 of speed, in table 2.
Figure DEST_PATH_IMAGE129
(14)
Figure DEST_PATH_IMAGE131
(15)
In formula: j 1, j 2---the critical value that traffic behavior changes;
Figure DEST_PATH_IMAGE133
,
Figure DEST_PATH_IMAGE135
---the critical velocity that traffic behavior is divided;
v f ---free stream velocity.
The table 2 urban road speed index divided rank of blocking up
Figure DEST_PATH_IMAGE137
Step 6 is set up the comprehensive index of blocking up:
(1) calculate single group detecting device road section traffic volume index that blocks up
Consider the impact of speed and time occupancy, the composite target of setting up traffic state judging is as follows:
Figure DEST_PATH_IMAGE139
(16)
In formula, j---the composite target of traffic state judging, is called traffic congestion index;
j v ---the speed index that blocks up;
j o ---the time occupancy index that blocks up;
η---the weight coefficient of speed index and time occupancy index, value is 0-1, system can be defaulted as 0.5, and adjusts according to actual conditions.
(2) calculate many group detecting device road section traffic volumes index that blocks up
While there is many group section detecting devices, need to carry out comprehensive distinguishing and calculate according to the position of section detecting device the traffic congestion index of line j.Line Loop detector layout schematic diagram is as shown in Fig. 4 line Loop detector layout schematic diagram.
In figure, x up with x down represent respectively the coordinate position (with the coordinate replacement of intersection parking line) of upstream and downstream crossing; x d1 , x d2 , x di ..., x dn represent respectively from 1 to nthe coordinate position of individual section detecting device.
The traffic congestion index of line can obtain with the weighting processing of all detecting device traffic congestion indexes in this line, and its computing formula is as follows:
Figure DEST_PATH_IMAGE141
(17)
In formula: j di ---detecting device itraffic congestion index;
l di ---detecting device ito the distance of its downstream detector;
l d1 ---the distance apart from the nearest detecting device of downstream stop line apart from stop line.
(3) determine without detecting the road section traffic volume index that blocks up
When upstream and downstream section equal sensorless, this road section traffic volume state is grey, unknown.
Upstream or downstream are provided with detecting device, and this road section traffic volume state above downstream road section traffic behavior is foundation, and traffic congestion formula of index is as follows:
Figure DEST_PATH_IMAGE143
(18)
In formula: j up ---the traffic congestion index of upstream detector;
j down ---the traffic congestion index of downstream detector;
Figure DEST_PATH_IMAGE145
---the weight coefficient of upstream and downstream detecting device traffic congestion index, in the time of downstream road section sensorless,
Figure DEST_PATH_IMAGE147
; In the time of the sensorless of section, upstream, ; When upstream and downstream all has detecting device,
Figure DEST_PATH_IMAGE151
.
Step 7 is carried out traffic state judging:
In the time carrying out traffic state judging, the problem that the critical localisation that needs consideration to change at traffic behavior is especially processed, with the continuous and stable that ensures that traffic behavior changes.Therefore,, in the time carrying out traffic state judging, need to consider the credibility interval of state variation.Definition ± Δ jfor the normal fluctuation interval of state variation, jdiscriminate interval can be expressed as traffic state judging figure (Fig. 5).
(1) if jin individual issue interval, when traffic behavior is red, judge j+1issue interval traffic state judging according to being:
1. work as
Figure DEST_PATH_IMAGE153
time, traffic behavior is red;
2. work as
Figure DEST_PATH_IMAGE155
time, traffic behavior is yellow;
3. work as
Figure 2012105070800100002DEST_PATH_IMAGE156
time, traffic behavior is green.
(2) if jin individual issue interval, when traffic behavior is yellow, judge the j+1issue interval traffic state judging according to being:
1. work as
Figure 703676DEST_PATH_IMAGE157
time, traffic behavior is red;
2. work as time, traffic behavior is yellow;
3. work as
Figure 66229DEST_PATH_IMAGE156
time, traffic behavior is green.
(3) if jin individual issue interval, when traffic behavior is green, judge j+1issue interval traffic state judging according to being:
1. work as
Figure 562938DEST_PATH_IMAGE157
time, traffic behavior is red;
2. work as
Figure 2012105070800100002DEST_PATH_IMAGE160
time, traffic behavior is yellow;
3. work as time, traffic behavior is green.
In above-mentioned rule, Δ j 1 with Δ j 2 value need to be according to actual conditions setting.

Claims (10)

1. the urban road traffic state method of discrimination based on section detecting device, is characterized in that the method comprises the following steps:
C1, from each track, in each section detecting device, obtain and detect these three traffic flow parameters of the magnitude of traffic flow, speed and time occupancy in this track of section according to the pre-determined sampling interval time; Then obtain the magnitude of traffic flow, speed and time occupancy that information issue interval characterizes this this track traffic stream characteristics of detection section; Traffic flow parameter is carried out to pre-service, and the information that obtains is issued interval and is characterized the magnitude of traffic flow, speed and the time occupancy of this detection section traffic stream characteristics;
C2, traffic flow parameter level and smooth;
C3, according to the pretreated traffic flow parameter computing velocity exponential sum occupation rate index that blocks up that blocks up;
C4, according to dividing the critical velocity corresponding to the critical speed calculation of the category of roads index that blocks up;
C5, calculate according to the speed exponential sum occupation rate index that blocks up that blocks up index---the traffic congestion index that comprehensively blocks up;
C6, the critical velocity obtaining according to the step c4 traffic congestion index that exponential sum c5 obtains that blocks up judges section traffic behavior.
2. the urban road traffic state method of discrimination based on section detecting device according to claim 1, is characterized in that: the process of obtaining arithmetic for real-time traffic flow parameter in step c1 comprises:
C11, determine the section of required detection and section section Loop detector layout situation;
C12, specified data sampling interval: choosing sampling interval is 1 minute;
C13, by the magnitude of traffic flow, speed and time occupancy data on every track in each sampling interval of ring section detector acquisition;
C14, the magnitude of traffic flow, speed and time occupancy data by every track in each sampling interval calculate each information and issue the magnitude of traffic flow, speed and the time occupancy data on Nei Meitiao track, interval;
C15, the each track arithmetic for real-time traffic flow parameter obtaining in step c14 is carried out to pre-service, obtain characterizing the traffic flow parameter of this detection section.
3. the urban road traffic state method of discrimination based on section detecting device according to claim 2, it is characterized in that: in step c13 for the each section detecting device on every track, obtain the magnitude of traffic flow and the time occupancy in corresponding track, and the speed of each car;
From section detecting device, obtaining traffic flow parameter specifically comprises:
C131, obtain traffic flow parameter;
In each sampling interval, the magnitude of traffic flow is passed through the vehicle number of this section detecting device in one minute, and unit is veh;
q i(k)=N i(k)
In formula: i---the i article of track in this section;
K---k sampling interval;
In n---sampling interval, pass through n car of this section detecting device;
N i(k)---on i article of track of k sampling interval, pass through the vehicle number of this section detecting device;
Q i(k)---the magnitude of traffic flow in i article of track of k sampling interval;
C132, acquisition speed parameter;
In each sampling interval, average velocity passes through the average velocity of all vehicles of this section detecting device in one minute, and unit is km/h;
v i ( k ) = Σ n = 1 N i ( k ) v in ( k ) q i ( k )
In formula: v i(k)---the average velocity of i article of track vehicle of k sampling interval;
V in(k)---speed when n car passes through section detecting device;
C133, acquisition time occupation rate parameter;
In each sampling interval, time occupancy takies the T.T. of detecting device and the number percent of 1 minute by all vehicles of this section detecting device in one minute;
o i ( k ) = Σ n = 1 N i ( k ) t in ( k ) 60
In formula: t in(k)---n car occupies the time of detecting device during by section detecting device;
O i(k)---the occupation rate in i article of track of k sampling interval.
4. the urban road traffic state method of discrimination based on section detecting device according to claim 2, it is characterized in that: the abnormal data that needs to reject each track section detecting device in step c14, adopt threshold value screening method, reject the magnitude of traffic flow, speed and the time occupancy data that exceed certain threshold value; Then the qualified data information of carrying out is issued to interval data synthetic, obtain each information and issue the magnitude of traffic flow, speed and the time occupancy data on Nei Meitiao track, interval, detailed step is as follows:
C141, each information are issued the traffic flow parameter processing on Nei Meitiao track, interval
In issue interval, the magnitude of traffic flow is issued in interval and is passed through the vehicle number of this section detecting device, and unit is veh, that is:
q i = Σ k = 1 m q i ( k )
In formula: k---k sampling interval, k ∈ (1, m];
M---information is issued interval,
Q i---i article of lane information issued the flow in interval;
C142, each information are issued the speed parameter processing on Nei Meitiao track, interval
Issuing average velocity in interval is, the weighted mean value of each sampling interval average velocity, and taking the flow of each sampling interval as weight, unit is km/h, that is:
v i = Σ k = 1 m v i ( k ) q i ( k ) Σ i = 1 m q i ( k )
In formula: v i---i article of lane information issued the average velocity in interval;
C143, each information are issued the time occupancy parameter processing on Nei Meitiao track, interval
In issue interval, time occupancy is got the mean value of each sampling interval time occupancy, that is:
o i = 1 m Σ k = 1 m o i ( k )
In formula: o i---i article of lane information issued the occupation rate in interval.
5. the urban road traffic state method of discrimination based on section detecting device according to claim 2, it is characterized in that: the traffic flow parameter of in step c15, each information being issued on Nei Meitiao track, interval synthesizes, and obtains the corresponding section magnitude of traffic flow, time occupancy and speed;
C151, the processing of section traffic flow parameter
Section flow is to issue in interval to pass through the vehicle number of this section, i.e. each track flow sum, and unit is veh, that is:
q ‾ ( j ) = Σ i = 1 l q i
In formula: j---j information is issued interval;
Figure FDA0000457907460000044
---j information is issued the section flow at interval;
C152, the processing of section speed parameter
Section speed is the weighted mean value of track average velocity, and taking track flow as weight, unit is km/h, that is:
v ‾ ( j ) = Σ i = 1 l v i q i Σ i = 1 l q i
In formula:
Figure FDA0000457907460000052
---j information is issued the velocity amplitude at interval;
C152, the processing of section time occupancy parameter
Section time occupancy is the mean value of Ratio of driveway occupancy time, that is:
o ‾ ( j ) = 1 l Σ i = 1 l o i
In formula:
Figure FDA0000457907460000054
---j information is issued interlude occupation rate.
6. the urban road traffic state method of discrimination based on section detecting device according to claim 1, it is characterized in that: step c2 detects to reality the profile data obtaining and carries out smoothing processing, the stability of guarantee system operation, reduce the interference of enchancement factor, the continuity and the stability that keep traffic behavior to issue, be calculated as follows in detail:
o ^ ( j ) = β 1 o ‾ ( j ) + β 2 o ‾ ( j - 1 ) + β 3 o ‾ ( j - 2 ) β 1 + β 2 + β 3
v ^ ( j ) = β 1 q ‾ ( j ) v ‾ ( j ) + β 2 q ‾ ( j - 1 ) v ‾ ( j - 1 ) + β 3 q ‾ ( j - 2 ) v ‾ ( j - 2 ) β 1 q ‾ ( j ) + β 2 q ‾ ( j - 1 ) + β 3 q ‾ ( j - 2 )
In formula:
Figure FDA0000457907460000057
---time occupancy in j information issue interval after level and smooth;
Figure FDA0000457907460000058
---velocity amplitude in j information issue interval after level and smooth;
Figure FDA0000457907460000059
---j information is issued the section flow in interval;
Figure FDA00004579074600000510
---j information is issued the time occupancy in interval;
Figure FDA00004579074600000511
---j information is issued the speed in interval;
β---smoothing factor, has β 1, β 2, β 3.
7. the urban road traffic state method of discrimination based on section detecting device according to claim 1, is characterized in that: step c3 is normalized traffic flow data, the speed of the obtaining exponential sum time occupancy index that blocks up that blocks up, and detailed step is as follows;
The c31 speed index that blocks up
Suppose that the block up relation of index of speed and speed is linear, and the minimum value of speed (0) and maximal value (v f) the corresponding speed index that blocks up is respectively 1,0; The block up computing formula of index of speed is:
J v = 1 - v ^ v f
In formula: J v---the speed index that blocks up;
V f---free stream velocity;
Figure FDA0000457907460000062
---the section speed after level and smooth;
The c32 time occupancy index that blocks up
Time occupancy and the time occupancy index that blocks up is linear, minimum value (0) and the maximal value (o of time occupancy max) the corresponding speed index that blocks up is respectively 0,1; The block up computing formula of index of time occupancy is as follows:
J o = o ^ o max
In formula: J o---the time occupancy index that blocks up;
O max---the historical maximal value of time occupancy;
Figure FDA0000457907460000064
---the section time occupancy after level and smooth.
8. the urban road traffic state method of discrimination based on section detecting device according to claim 1, is characterized in that: step c4 is according to the critical velocity value v of urban road grade classification 1, v 2urban road traffic state is divided into block up, slow and unimpeded three grades, according to critical velocity value v 1, v 2calculate the corresponding critical velocity index that blocks up:
J 1 = 1 - v 1 v f
J 2 = 1 - v 2 v f
In formula: J 1, J 2---the critical value that traffic behavior changes;
V 1, v 2---the critical velocity that traffic behavior is divided;
V f---free stream velocity.
9. the urban road traffic state method of discrimination based on section detecting device according to claim 1, it is characterized in that: the step c5 fusion speed exponential sum time occupancy index that blocks up that blocks up, set up the comprehensive index of blocking up---traffic congestion index, detailed step is as follows:
Mono-group of detecting device section of c51
J=ηJ v+(1-η)J o
In formula, the composite target of J---traffic state judging, is called traffic congestion index;
J v---the speed index that blocks up;
J o---the time occupancy index that blocks up;
The weight coefficient of η---speed index and time occupancy index, value is 0-1, and adjusts according to actual conditions;
C52 organizes detecting device section more
While there is many group section detecting devices, need to carry out comprehensive distinguishing and calculate according to the position of section detecting device the traffic congestion index of line;
J = Σ i = 1 n l di J di Σ i = 1 n l di
In formula: J di---the traffic congestion index of detecting device i;
L di---detecting device i is to the distance of its downstream detector;
L d1---the distance apart from the nearest detecting device of downstream stop line apart from stop line;
C53 is without detecting section
When upstream and downstream section equal sensorless, this road section traffic volume state is grey, unknown;
Upstream or downstream are provided with detecting device, and this road section traffic volume state above downstream road section traffic behavior is foundation, utilize upstream and downstream traffic congestion index to calculate the traffic congestion index in this section;
J=αJ up+(1-α)J down (18)
In formula: J up---the traffic congestion index of upstream detector;
J down---the traffic congestion index of downstream detector;
α---the weight coefficient of upstream and downstream detecting device traffic congestion index, in the time of downstream road section sensorless, α=1; In the time of the sensorless of section, upstream, α=0; When upstream and downstream all has detecting device, α=0.5.
10. the urban road traffic state method of discrimination based on section detecting device according to claim 1, is characterized in that: step c6 has considered the problem of processing at the critical localisation of traffic behavior variation, the credibility interval that adopts traffic behavior to change; Definition ± Δ J is the normal fluctuation interval of state variation, and the detailed step of traffic state judging is as follows;
If j of c61 issues in interval, when traffic behavior is red, judge that j+1 issues interval traffic state judging according to being:
1. as J ∈ (J 1-Δ J 1, 1] time, traffic behavior is red;
2. as J ∈ (J 2-Δ J 2, J 1-Δ J 1] time, traffic behavior is yellow;
3. as J ∈ [0, J 2-Δ J 2] time, traffic behavior is green;
If j of c62 issues in interval, when traffic behavior is yellow, judge that j+1 issues interval traffic state judging according to being:
1. as J ∈ (J 1+ Δ J 1, 1] time, traffic behavior is red;
2. as J ∈ (J 2-Δ J 2, J 1+ Δ J 1] time, traffic behavior is yellow;
3. as J ∈ [0, J 2-Δ J 2] time, traffic behavior is green;
If j of c63 issues in interval, when traffic behavior is green, judge that j+1 issues interval traffic state judging according to being:
1. as J ∈ (J 1+ Δ J 1, 1] time, traffic behavior is red;
2. as J ∈ (J 2+ Δ J 2, J 1+ Δ J 1] time, traffic behavior is yellow;
3. as J ∈ [0, J 2+ Δ J 2] time, traffic behavior is green;
In above-mentioned distinguishing rule, Δ J 1with Δ J 2value need to be according to actual conditions setting.
CN201210507080.0A 2012-11-29 2012-11-29 Discriminating method based on section detector for urban traffic state Expired - Fee Related CN103021176B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201210507080.0A CN103021176B (en) 2012-11-29 2012-11-29 Discriminating method based on section detector for urban traffic state

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201210507080.0A CN103021176B (en) 2012-11-29 2012-11-29 Discriminating method based on section detector for urban traffic state

Publications (2)

Publication Number Publication Date
CN103021176A CN103021176A (en) 2013-04-03
CN103021176B true CN103021176B (en) 2014-06-11

Family

ID=47969731

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201210507080.0A Expired - Fee Related CN103021176B (en) 2012-11-29 2012-11-29 Discriminating method based on section detector for urban traffic state

Country Status (1)

Country Link
CN (1) CN103021176B (en)

Families Citing this family (36)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103226666B (en) * 2013-05-10 2016-12-28 天津市市政工程设计研究院 Sophisticated functions intercommunication road crossing system efficacy assessments method
CN103280098B (en) * 2013-05-23 2015-06-10 北京交通发展研究中心 Traffic congestion index calculation method
CN103593976B (en) * 2013-11-28 2016-01-06 青岛海信网络科技股份有限公司 Based on the method and system of detecting device determination road traffic state
CN104050803B (en) * 2014-06-23 2016-10-26 北京航空航天大学 A kind of region highway network evaluation of running status method
CN104408913B (en) * 2014-11-03 2016-03-16 东南大学 A kind of traffic flow three parameter real-time predicting method considering temporal correlation
CN104484994B (en) * 2014-12-17 2016-08-17 合肥革绿信息科技有限公司 Urban road network traffic based on array radar runs index number evaluation method
CN105788289A (en) * 2014-12-17 2016-07-20 上海宝康电子控制工程有限公司 Method and system for realizing traffic condition assessment and analysis based on computer software system
CN104464294B (en) * 2014-12-17 2016-08-31 合肥革绿信息科技有限公司 A kind of road section traffic volume method for evaluating state based on array radar
CN104766475B (en) * 2015-04-09 2017-01-11 银江股份有限公司 Urban traffic bottleneck mining method
CN104809869A (en) * 2015-04-10 2015-07-29 安徽四创电子股份有限公司 Viaduct entrance ramp traffic state identifying method based on lifting height limiting rack
CN104851293B (en) * 2015-05-18 2017-03-29 安徽科力信息产业有限责任公司 A kind of road section traffic volume congestion index evaluation methodology based on spot spe J
CN105389987B (en) * 2015-12-03 2018-03-23 青岛海信网络科技股份有限公司 A kind of road traffic condition Forecasting Methodology and device
CN105427600B (en) * 2015-12-09 2017-11-28 中兴软创科技股份有限公司 A kind of congestion in road real-time identification method and device based on FCD
CN105551241B (en) * 2015-12-09 2018-02-02 中兴软创科技股份有限公司 A kind of real-time jamming analysis method based on FCD and EP multi-source datas
CN105405294A (en) * 2015-12-30 2016-03-16 杭州中奥科技有限公司 Early warning method of traffic congestion roads
CN105702031B (en) * 2016-03-08 2018-02-23 北京航空航天大学 Road network key road segment recognition methods based on macroscopical parent map
CN106781569B (en) * 2016-11-18 2017-10-13 姜正 The method that intelligent transportation instruction device based on radio communication monitors road traffic state
CN106530700A (en) * 2016-11-22 2017-03-22 安徽科力信息产业有限责任公司 Method for judging traffic congestion based on fixed detector
CN106960582B (en) * 2017-03-12 2019-05-07 浙江大学 A method of the region bottleneck control based on macroscopical parent map
CN107331163A (en) * 2017-06-30 2017-11-07 贵阳海信网络科技有限公司 A kind of queue length computational methods and device
CN107491420B (en) * 2017-07-06 2020-10-30 重庆大学 Automatic reference searching method of McMaster event detection algorithm
CN107507421B (en) * 2017-08-22 2019-11-01 重庆交通大学 Method for rapidly judging traffic state and device
CN107610470A (en) * 2017-10-31 2018-01-19 迈锐数据（北京）有限公司 A kind of traffic congestion evaluation method and device
CN108765939B (en) * 2018-05-11 2021-02-02 贵阳信息技术研究院（中科院软件所贵阳分部） Dynamic traffic congestion index calculation method based on clustering algorithm
CN108777068B (en) * 2018-06-13 2021-03-02 西华大学 Traffic flow bottleneck identification method based on multi-dimensional detection coil acquisition cycle
CN108922209B (en) * 2018-07-20 2021-06-04 江苏永诚交通集团有限公司 Cloud intelligent traffic signal lamp system
CN109035775B (en) * 2018-08-22 2020-12-08 青岛海信网络科技股份有限公司 Method and device for identifying emergency
CN109410597B (en) * 2018-11-09 2021-07-13 南京讯飞智慧空间信息科技有限公司 Method, device and system for detecting traffic flow at park entrance and exit
CN109255956A (en) * 2018-11-12 2019-01-22 长安大学 A kind of charge station's magnitude of traffic flow method for detecting abnormality
CN109712394A (en) * 2019-01-15 2019-05-03 青岛大学 A kind of congestion regions discovery method
CN111489545B (en) * 2019-01-28 2023-03-31 阿里巴巴集团控股有限公司 Road monitoring method, device and equipment and storage medium
CN111009128A (en) * 2020-01-07 2020-04-14 上海宝康电子控制工程有限公司 Method for realizing real-time studying and judging treatment of intersection traffic state based on arrival-departure model
CN112590886B (en) * 2020-12-30 2022-08-26 卡斯柯信号有限公司 Method for judging and alarming running jam of train section
CN114005275B (en) * 2021-10-25 2023-03-14 浙江交投高速公路运营管理有限公司 Highway vehicle congestion judging method based on multi-data source fusion
CN114862011B (en) * 2022-04-29 2023-04-07 上海理工大学 Road section time-interval traffic demand estimation method considering congestion state
CN115862335A (en) * 2023-02-27 2023-03-28 北京理工大学前沿技术研究院 Congestion early warning method, system and storage medium based on vehicle-road information fusion

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5815093A (en) * 1996-07-26 1998-09-29 Lextron Systems, Inc. Computerized vehicle log
CN101833858A (en) * 2009-12-17 2010-09-15 南京城际在线信息技术有限公司 Method for judging road traffic state based on annular coil of signal lamp system
CN102013171A (en) * 2010-12-10 2011-04-13 隋亚刚 Traffic control system for improving road capacity
CN102063794A (en) * 2011-01-14 2011-05-18 隋亚刚 Urban expressway automatic even detecting and synergetic command dispatching system based on occupation ratio data
CN102592451A (en) * 2012-02-23 2012-07-18 浙江大学 Method for detecting road traffic incident based on double-section annular coil detector

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5815093A (en) * 1996-07-26 1998-09-29 Lextron Systems, Inc. Computerized vehicle log
CN101833858A (en) * 2009-12-17 2010-09-15 南京城际在线信息技术有限公司 Method for judging road traffic state based on annular coil of signal lamp system
CN102013171A (en) * 2010-12-10 2011-04-13 隋亚刚 Traffic control system for improving road capacity
CN102063794A (en) * 2011-01-14 2011-05-18 隋亚刚 Urban expressway automatic even detecting and synergetic command dispatching system based on occupation ratio data
CN102592451A (en) * 2012-02-23 2012-07-18 浙江大学 Method for detecting road traffic incident based on double-section annular coil detector

Also Published As

Publication number Publication date
CN103021176A (en) 2013-04-03

Similar Documents

Publication Publication Date Title
CN103021176B (en) Discriminating method based on section detector for urban traffic state
CN102968901B (en) Method for acquiring regional congestion information and regional congestion analyzing device
CN105825669B (en) A kind of system and method for identifying urban expressway traffic bottleneck
CN104157156B (en) A kind of highway Dangerous Area speed of a motor vehicle dynamic management method for early warning
CN102750824B (en) Urban road traffic condition detection method based on voting of network sorter
CN105913661B (en) A kind of express highway section traffic state judging method based on charge data
CN102737508B (en) Urban road traffic state detection method combined with support vector machine (SVM) and back propagation (BP) neural network
CN101599217B (en) Method for rapidly judging traffic state
CN102750825B (en) Urban road traffic condition detection method based on neural network classifier cascade fusion
CN104851287B (en) Method for urban road link travel time detection based on video detector
CN108629973A (en) Road section traffic volume congestion index computational methods based on fixed test equipment
CN104021671B (en) The determination methods of the road real-time road that a kind of svm combines with fuzzy Judgment
CN106781490A (en) Urban highway traffic analysis ＆ appraisement on operation system
CN106408943A (en) Road-network traffic jam discrimination method based on macroscopic fundamental diagram
CN108986509B (en) Urban area path real-time planning method based on vehicle-road cooperation
CN104778834A (en) Urban road traffic jam judging method based on vehicle GPS data
CN104484996A (en) Road segment traffic state distinguishing method based on multi-source data
CN101739828A (en) Urban traffic area jamming judgment method by combining road traffic and weather state
CN107170247B (en) Method and device for determining queuing length of intersection
CN105225500A (en) A kind of traffic control aid decision-making method and device
CN104575050B (en) A kind of fast road ramp intellectual inducing method and device based on Floating Car
CN102592451B (en) Method for detecting road traffic incident based on double-section annular coil detector
CN105654720B (en) Loop detector layout method based on urban road congestion identification
CN101783074A (en) Method and system for real-time distinguishing traffic flow state of urban road
CN103824450B (en) Based on the large-scale activity Special running layout of roads method of traffic behavior rule

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
C14 Grant of patent or utility model
GR01 Patent grant
CF01 Termination of patent right due to non-payment of annual fee
CF01 Termination of patent right due to non-payment of annual fee

Granted publication date: 20140611

Termination date: 20151129