CN101799985B - Highway tunnel traffic identification method - Google Patents
Highway tunnel traffic identification method Download PDFInfo
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- CN101799985B CN101799985B CN201010126864XA CN201010126864A CN101799985B CN 101799985 B CN101799985 B CN 101799985B CN 201010126864X A CN201010126864X A CN 201010126864XA CN 201010126864 A CN201010126864 A CN 201010126864A CN 101799985 B CN101799985 B CN 101799985B
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Abstract
The invention discloses a highway tunnel traffic identification method, which is characterized in that 1. an inlet vehicle identification device and an inlet vehicle speed detection device, an outlet vehicle identification device and an outlet vehicle speed detection device are arranged; 2. a central processing unit obtains the information and speed of the inlet vehicles and the information and speed of the outlet vehicles, and the entering and exiting times of the vehicles are recorded; 3. practical traveling time is calculated; 4. the threshold value range of the traveling time of the vehicles normally and safely passing through the tunnel is determined; 5. abnormal vehicles are determined; 6. abnormal conditions occurring in the tunnel are determined; and 7. the number of the vehicles besieged in the tunnel and average queuing length are calculated and a traffic abnormal event alarming is sent. The invention has the obvious effect that the line laying is simple, implement is easy, cost is low, and real-time monitoring is carried out aiming to the highway tunnel traffic conditions so as to facilitate statistic data; and whether the abnormal conditions occur or not can be obtained in time, and the conditions in the tunnel after the abnormal conditions occur can be predicted.
Description
Technical field
The present invention mainly belongs to Road Tunnel Safety and detects the monitoring technique field, is a kind of highway tunnel traffic identification method specifically.
Background technology
China's traffic abnormity detects and mainly is divided into direct detection and indirect detection two classes at present.Directly detect and comprise artificial on-the-spot inspection and Video Detection dual mode, because the highway that China builds has at present generally been installed the TV monitoring and controlling video camera, Video Detection has replaced artificial cruising substantially, makes that managerial personnel can be in the traffic circulation of Surveillance center's monitor road.Along with the development of pattern-recognition, Digital Image Processing and computer vision technique, the video traffic abnormality detection system will be widely used in field of traffic control, become intelligent transportation system Data Source accurately and reliably.
Indirect detection is mainly used inductor checkout equipments such as toroid winding, detects and gathers traffic flow parameter, the generation of analysis and judgement traffic abnormity incident.
The shortcoming of prior art is: but the traffic in the vcehicular tunnel often is difficult to statistics, if the tunnel is long, just many rig cameras need be installed in the tunnel, the monitoring of realization non-blind area, could obtain the vehicle condition in the tunnel, its installation cost is too high, and the tunnel is long more, and installation cost is just high more.
Summary of the invention
The purpose of this invention is to provide a kind of highway tunnel traffic identification method,, monitor in real time, and cost is low at the highway tunnel traffic situation, easy to implement, be easy to data statistics.
For achieving the above object, the invention provides a kind of highway tunnel traffic identification method, its key is: carry out as follows:
Step 1: arrange inlet vehicle identifier and inlet vehicle speed detector device in the vcehicular tunnel porch, arrange outlet vehicle identifier and outlet vehicle speed detector device in the vcehicular tunnel exit;
The output terminal of described entry and exit vehicle identifier and entry and exit vehicle speed detector device all is connected on the central processing unit;
Step 2: the collection of inlet vehicle identifier enters the inlet information of vehicles a1 in tunnel, and the inlet vehicle speed detector device obtains the inlet vehicle speed V that enters the tunnel
A1, described central processing unit obtains described inlet information of vehicles a1 and inlet vehicle speed V
A1, and registration of vehicle sails the t1 constantly that enters in tunnel into;
The outlet information of vehicles a2 in tunnel is rolled in the collection of outlet vehicle identifier away from, and the outlet vehicle speed detector device obtains the outlet vehicle speed V that rolls the tunnel away from
A2, described central processing unit obtains described outlet information of vehicles a2 and outlet vehicle speed V
A2, and registration of vehicle rolls the t2 constantly that rolls away from tunnel away from;
If pass through time T through minimum speed per hour
0, still not obtaining the outlet information of vehicles a2 consistent with described inlet information of vehicles a1, described central processing unit judges that then this car occurs unusually in the tunnel;
If obtain inlet information of vehicles a1 and inlet vehicle speed V
A1After, pass through time T through minimum speed per hour
0After, do not obtain its outlet information of vehicles a2 and outlet vehicle speed V yet
A2, can judge directly that then this car is unusual.
Step 4: described central processing unit is tried to achieve the actual travel time T=t2-t1 of target vehicle n by the tunnel;
Described central processing unit is tried to achieve the average overall travel speed V=(V of target vehicle n by the tunnel
A1+ V
A2)/2;
Step 5: described central processing unit is according to described average overall travel speed
Determine the normal safety of vehicle n by tunnel running time threshold range,
Wherein L is the distance of tunnel portal to tunnel exit;
Step 6: whether the actual travel time T of the more described vehicle n of described central processing unit passes through in the tunnel running time threshold range Ts in the normal safety of described vehicle n, if pass through outside the tunnel running time threshold range Ts in the normal safety of vehicle n, it is unusual then to be judged as this vehicle n, and determine first vehicle abnormality, and send information;
Whether the actual travel time T that described central processing unit is relatively stated vehicle n+1 again passes through in the tunnel running time threshold range Ts in the normal safety of described vehicle n+1, if pass through outside the tunnel running time threshold range Ts in the normal safety of vehicle n+1, it is unusual then to be judged as this vehicle n+1, and definite second car is unusual;
Step 7: described central processing unit is in unit interval S, whether determine the vehicle abnormality number greater than m,, then regard as indivedual vehicles and break down if the vehicle abnormality number is less than or equal to m, and other vehicles are not impacted, return step 3 and monitor next vehicle n+1; If the vehicle abnormality number greater than m, then is defined as occurring in the tunnel a large amount of vehicle abnormalities;
Step 8: according to the vehicle data of described inlet vehicle identifier collection sign, described central processing unit calculates stranded vehicle number and average queue length in the hole, and sends the traffic abnormity event alarms.
In the step 1, described entry and exit vehicle identifier is a video camera, and wherein the porch video camera is located at 100m～350m place outside the vcehicular tunnel inlet, and the exit video camera is located at 100m～350m place outside the vcehicular tunnel outlet.
If two-tube tunnel, and the hole in two tunnels is not far from one another, and physical features is smooth therebetween, then video camera and vehicle speed detector device can be arranged on the intermediate zone at two holes, monitors two hole traffic flow situations simultaneously.
In the step 6, whether described actual travel time T in the normal safety of described vehicle n by the decision method in the tunnel running time threshold range Ts is: judge whether Ts 〉=T, if T＞Ts then is judged as vehicle abnormality.
In the step 7, definite mode of described unit interval S is, is the initial moment when described central processing unit is determined first vehicle abnormality, advanced unit interval S after, whether the vehicle that described central processing unit statistics is rolled away from unit interval S unusual.
In the step 3, described minimum speed per hour is passed through time T
0=L/V
0, V wherein
0Be the minimum speed limit in tunnel, L is the distance of tunnel portal to tunnel exit.
Remarkable result of the present invention is: the highway tunnel traffic identification method that a kind of track laying is easy, easy to implement, cost is low is provided, at the highway tunnel traffic situation, has monitored in real time, be easy to data statistics.Whether can in time obtain has abnormal conditions to take place in the tunnel, and after in time extrapolating the abnormal conditions generation, the situation in the tunnel.
Description of drawings
Fig. 1: process flow diagram of the present invention;
Fig. 2: the layout of equipment of the present invention.
Embodiment
Below in conjunction with the drawings and specific embodiments the present invention is described in further detail.
As shown in Figure 1, 2: a kind of highway tunnel traffic identification method is to carry out as follows:
Step 1: arrange inlet vehicle identifier 1 and inlet vehicle speed detector device 2 in the vcehicular tunnel porch, arrange outlet vehicle identifier 3 and outlet vehicle speed detector device 4 in the vcehicular tunnel exit;
The output terminal of described entry and exit vehicle identifier and entry and exit vehicle speed detector device all is connected on the central processing unit 5;
Described entry and exit vehicle identifier 1 is a video camera, and wherein the porch video camera is located at 100m～350m place outside the vcehicular tunnel inlet, and the exit video camera is located at 100m～350m place outside the vcehicular tunnel outlet.
If two-tube tunnel, and the hole in two tunnels is not far from one another, and physical features is smooth therebetween, then video camera and vehicle speed detector device can be arranged on the intermediate zone at two holes, monitors two hole traffic flow situations simultaneously.
Step 2: inlet vehicle identifier 1 is gathered the inlet information of vehicles a1 that enters the tunnel, and inlet vehicle speed detector device 2 obtains the inlet vehicle speed V that enters the tunnel
A1, described central processing unit 5 obtains described inlet information of vehicles a1 and inlet vehicle speed V
A1, and registration of vehicle sails the t1 constantly that enters in tunnel into;
Step 3: described central processing unit 5 described inlet information of vehicles a1 of comparison and outlet information of vehicles a2, if the two unanimity then enters step 4;
If pass through time T through minimum speed per hour
0, still not obtaining the outlet information of vehicles a2 consistent with described inlet information of vehicles a1,5 of described central processing units judge that this car occurs unusually in the tunnel;
If obtain inlet information of vehicles a1 and inlet vehicle speed V
A1After, pass through time T through minimum speed per hour
0After, do not obtain its outlet information of vehicles a2 and outlet vehicle speed V yet
A2, can judge directly that then this car is unusual.
Described minimum speed per hour is passed through time T
0=L/V
0, V wherein
0Be the minimum speed limit in tunnel, L is the distance of tunnel portal to tunnel exit.
Step 4: described central processing unit 5 is tried to achieve the actual travel time T=t2-t1 of target vehicle n by the tunnel;
Described central processing unit 5 is tried to achieve the average overall travel speed of target vehicle n by the tunnel
Step 5: described central processing unit 5 is according to described average overall travel speed
, determine the normal safety of vehicle n by tunnel running time threshold range,
Wherein L is the distance of tunnel portal to tunnel exit;
Step 6: whether the actual travel time T of described central processing unit 5 more described vehicle n passes through in the tunnel running time threshold range Ts in the normal safety of described vehicle n, if pass through in the tunnel running time threshold range Ts in the normal safety of vehicle n, it is normal then to be judged as this vehicle n, returns step 3 and monitors next vehicle n+1; If by outside the tunnel running time threshold range Ts, it is unusual then to be judged as this vehicle n, and determines first vehicle abnormality, and sends information and give the Road Detection monitor staff in the normal safety of vehicle n; Remind the monitor staff to note.
Whether the actual travel time T that described central processing unit 5 is relatively stated vehicle n+1 again passes through in the tunnel running time threshold range Ts in the normal safety of described vehicle n+1, if pass through outside the tunnel running time threshold range Ts in the normal safety of vehicle n+1, it is unusual then to be judged as this vehicle n+1, and definite second car is unusual;
Whether described actual travel time T in the normal safety of described vehicle n by the decision method in the tunnel running time threshold range Ts is: judge whether Ts 〉=T, if T＞Ts then is judged as unusual.
Step 7: described central processing unit 5 is in unit interval S, whether determine the vehicle abnormality number greater than m,, then regard as indivedual vehicles and break down if the vehicle abnormality number is less than or equal to m, and other vehicles are not impacted, return step 3 and monitor next vehicle n+1; If the vehicle abnormality number greater than m, then is defined as occurring in the tunnel a large amount of vehicle abnormalities;
Definite mode of described unit interval S is, is the initial moment when described central processing unit 5 is determined first vehicle abnormality, advanced unit interval S after, whether the vehicle that described central processing unit 5 statistics are rolled away from unit interval S unusual.
Step 8: gather the vehicle data of sign according to described inlet vehicle identifier 1, described central processing unit 5 calculates stranded vehicle number and average queue length in the hole, and sends the traffic abnormity event alarms.
For example in the tunnel of a 1Km, setting the minimum speed of a motor vehicle in the tunnel is 30Km/ hour, then vehicle can be again passes through the tunnel in 2 minutes by minimum speed per hour, therefore, unit interval S can be set at 2 minutes, can establish m=2, if m＞2, if 3 and 3 above vehicle fees of appearance are normally by the tunnel in 2 minutes, just can judge to occur a large amount of vehicle abnormalities in the tunnel; Central processing unit 5 sends warning message.
Claims (5)
1. highway tunnel traffic identification method is characterized in that: carry out as follows:
Step 1: arrange inlet vehicle identifier (1) and inlet vehicle speed detector device (2) in the vcehicular tunnel porch, arrange outlet vehicle identifier (3) and outlet vehicle speed detector device (4) in the vcehicular tunnel exit;
The output terminal of described entry and exit vehicle identifier and entry and exit vehicle speed detector device all is connected on the central processing unit (5);
Step 2: inlet vehicle identifier (1) collection enters the inlet information of vehicles a1 in tunnel, and inlet vehicle speed detector device (2) obtains the inlet vehicle speed V that enters the tunnel
A1, described central processing unit (5) obtains described inlet information of vehicles a1 and inlet vehicle speed V
A1, and registration of vehicle sails the t1 constantly that enters in tunnel into;
Outlet vehicle identifier (3) collection is rolled the outlet information of vehicles a2 in tunnel away from, and outlet vehicle speed detector device (4) obtains the outlet vehicle speed V that rolls the tunnel away from
A2, described central processing unit (5) obtains described outlet information of vehicles a2 and outlet vehicle speed V
A2, and registration of vehicle rolls the t2 constantly that rolls away from tunnel away from;
Step 3: described central processing unit (5) described inlet information of vehicles a1 of comparison and outlet information of vehicles a2, if the two unanimity then enters step 4;
If pass through time T through minimum speed per hour
0, still not obtaining the outlet information of vehicles a2 consistent with described inlet information of vehicles a1, described central processing unit (5) judges that then this car occurs unusually in the tunnel;
Step 4: described central processing unit (5) is tried to achieve the actual travel time T=t2-t1 of target vehicle n by the tunnel;
Described central processing unit (5) is tried to achieve the average overall travel speed of target vehicle n by the tunnel
Step 5: described central processing unit (5) is according to described average overall travel speed
Determine the normal safety of vehicle n by tunnel running time threshold range,
Wherein L is the distance of tunnel portal to tunnel exit;
Step 6: whether the actual travel time T of the more described vehicle n of described central processing unit (5) passes through in the tunnel running time threshold range Ts in the normal safety of described vehicle n, if pass through in the tunnel running time threshold range Ts in the normal safety of vehicle n, it is normal then to be judged as this vehicle n, returns step 3 and monitors next vehicle n+1; If by outside the tunnel running time threshold range Ts, it is unusual then to be judged as this vehicle n, and determines first vehicle abnormality, and sends information in the normal safety of vehicle n;
Whether the actual travel time T that described central processing unit (5) is relatively stated vehicle n+1 again passes through in the tunnel running time threshold range Ts in the normal safety of described vehicle n+1, if pass through outside the tunnel running time threshold range Ts in the normal safety of vehicle n+1, it is unusual then to be judged as this vehicle n+1, and definite second car is unusual;
Step 7: described central processing unit (5) is in unit interval S, whether determine the vehicle abnormality number greater than m,, then regard as indivedual vehicles and break down if the vehicle abnormality number is less than or equal to m, and other vehicles are not impacted, return step 3 and monitor next vehicle n+1; If the vehicle abnormality number greater than m, then is defined as occurring in the tunnel a large amount of vehicle abnormalities;
Step 8: gather the vehicle data of sign according to described inlet vehicle identifier (1), described central processing unit (5) calculates stranded vehicle number and average queue length in the hole, and sends the traffic abnormity event alarms.
2. a kind of highway tunnel traffic identification method according to claim 1, it is characterized in that: in the step 1, described entry and exit vehicle identifiers (1) are video camera, wherein the porch video camera is located at 100m～350m place outside the vcehicular tunnel inlet, and the exit video camera is located at 100m～350m place outside the vcehicular tunnel outlet.
3. a kind of highway tunnel traffic identification method according to claim 1, it is characterized in that: in the step 6, whether described actual travel time T in the normal safety of described vehicle n by the decision method in the tunnel running time threshold range Ts is: judge whether Ts 〉=T, if T＞Ts then is judged as vehicle abnormality.
4. a kind of highway tunnel traffic identification method according to claim 1, it is characterized in that: in the step 7, definite mode of described unit interval S is, when described central processing unit (5) is determined first vehicle abnormality is the initial moment, after advancing unit interval S, whether the vehicle that described central processing unit (5) statistics is rolled away from unit interval S is unusual.
5. a kind of highway tunnel traffic identification method according to claim 1 is characterized in that: in the step 3, described minimum speed per hour is passed through time T
0=L/V
0, V wherein
0Be the minimum speed limit in tunnel, L is the distance of tunnel portal to tunnel exit.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201010126864XA CN101799985B (en) | 2010-03-18 | 2010-03-18 | Highway tunnel traffic identification method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201010126864XA CN101799985B (en) | 2010-03-18 | 2010-03-18 | Highway tunnel traffic identification method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN101799985A CN101799985A (en) | 2010-08-11 |
| CN101799985B true CN101799985B (en) | 2011-10-26 |
Family
ID=42595654
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| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201010126864XA Expired - Fee Related CN101799985B (en) | 2010-03-18 | 2010-03-18 | Highway tunnel traffic identification method |
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| Country | Link |
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| CN (1) | CN101799985B (en) |
Families Citing this family (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102831786B (en) * | 2011-06-16 | 2016-05-18 | 深圳市安华信科技发展有限公司 | A kind of method for supervising of vehicle driving state in tunnel and supervising device thereof |
| CN103077609A (en) * | 2012-12-26 | 2013-05-01 | 上海市城市建设设计研究总院 | Tunnel traffic accident monitoring method and system based on sensing of multiple sensors |
| CN104867412B (en) * | 2015-05-14 | 2017-03-08 | 成都依路达海科技有限公司 | A kind of induction escape system for tunnel |
| CN105931462B (en) * | 2016-06-27 | 2018-09-04 | 安徽四创电子股份有限公司 | Tunnel accident judgment method based on Car license recognition |
| RU2699583C1 (en) | 2016-06-30 | 2019-09-06 | Ниссан Мотор Ко., Лтд. | Method of tracking objects and object tracking device |
| CN106228813A (en) * | 2016-09-19 | 2016-12-14 | 四川隧唐科技股份有限公司 | A kind of vehicle management system based on image recognition technology |
| CN107067716A (en) * | 2016-10-14 | 2017-08-18 | 招商局重庆交通科研设计院有限公司 | Tunnel traffic accident evacuation method |
| CN106448180B (en) * | 2016-10-24 | 2019-09-10 | 东南大学 | A kind of major long tunnel traffic events real-time detection method and detection system |
| CN107180533A (en) * | 2017-06-22 | 2017-09-19 | 北京中交华安科技有限公司 | Road method for early warning and device |
| CN107895479A (en) * | 2017-11-06 | 2018-04-10 | 胡晓华 | A kind of freeway tunnel accident judges system and method |
| CN107978156A (en) * | 2017-12-22 | 2018-05-01 | 武汉万集信息技术有限公司 | Vehicle checking method and system in a kind of tunnel |
| CN109523791B (en) * | 2018-10-31 | 2021-10-26 | 武汉烽火众智数字技术有限责任公司 | Early warning analysis method and device for retained vehicle |
| 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 |
| CN109979242A (en) * | 2019-05-08 | 2019-07-05 | 司文 | A kind of freeway tunnel lane change, overtake other vehicles prior-warning device and method |
| CN110610610B (en) * | 2019-09-20 | 2022-04-15 | 腾讯科技（深圳）有限公司 | Vehicle access management method and device and storage medium |
| CN113345218A (en) * | 2020-03-03 | 2021-09-03 | 杭州海康威视数字技术股份有限公司 | Vehicle driving abnormity detection method and device, electronic equipment and readable storage medium |
| CN114582137B (en) * | 2020-11-30 | 2024-09-06 | 阿里巴巴集团控股有限公司 | Road condition determining method and device, cloud service and storage medium |
| CN113393705B (en) * | 2021-05-31 | 2022-07-15 | 云南思码蔻科技有限公司 | Road condition management system based on reserved quantity of vehicles in tunnel or road |
| CN113965901B (en) * | 2021-10-22 | 2024-02-13 | 山东高速股份有限公司 | Expressway tunnel personnel monitoring method based on wireless probe |
| CN114708737B (en) * | 2022-03-30 | 2023-06-06 | 招商局重庆交通科研设计院有限公司 | Emergency linkage control method and system for electromechanical facilities of highway tunnel |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000298793A (en) * | 1999-04-13 | 2000-10-24 | Matsushita Electric Ind Co Ltd | In-tunnel accident detecting device |
| ATE301861T1 (en) * | 2000-12-30 | 2005-08-15 | Goddert Peters | TUNNEL MONITORING SYSTEM IN A TUNNEL |
| CN1303576C (en) * | 2002-11-26 | 2007-03-07 | 朱肃生 | Intelligent highway early warning system |
| CN1607558A (en) * | 2003-10-14 | 2005-04-20 | 宋现力 | Wayside signaling management system for freeway |
| CN100459704C (en) * | 2006-05-25 | 2009-02-04 | 浙江工业大学 | Intelligent tunnel safety monitoring apparatus based on omnibearing computer vision |
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| CN101799985A (en) | 2010-08-11 |
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