CN105390000A - Traffic signal control system and method based on road condition traffic big data - Google Patents
Traffic signal control system and method based on road condition traffic big data Download PDFInfo
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Abstract
The invention provides a traffic signal control system and method based on road condition traffic big data. The method comprises the following steps: a client of a vehicle sending client information to a cloud platform; carrying out massive traffic data information cloud storage; dividing the data into real-time data and historical data according to time sequence; establishing a corresponding static database and a dynamic database according to the type of the data; generating a corresponding optimization control scheme and a coordination control scheme; determining road conditions of a control area according to the schemes, and setting corresponding configuration schemes; selecting the corresponding optimization control scheme; adjusting signal period, signal phase sequence and relative phase difference; and driving a signal lamp to execute an instruction transmitted based on the client. By utilizing the cloud storage and based on big data processing, trend is obtained, and an optimization scheme is made according to change rules; the optimal operation phase of the traffic signals is controlled, so that people are allowed to go out conveniently; and by comparing the collected real-time data with the historical data, the coordination control scheme is generated, so that the operation phase sequence, time (period) and phase sequence of the signal lamps can be adjusted in time, and emergency traffic conditions can be coped with.
Description
Technical field
The present invention relates to technical field of traffic signal control, especially relate to a kind of traffic signal control system based on the large data of road conditions traffic and method.
Background technology
Along with national economic development development with carry out, the quantity of transportation and communication day by day increases, and the pressure caused urban transportation is also more and more heavier.For alleviating traffic pressure, effectively dredging the vehicle on each road, by installing the mode of traffic lights at the parting of the ways, the traveling of vehicle being dredged and control; The shortcoming of traditional signal control method is exactly need to arrange a large amount of detecting devices at crossing or section, and cost compare is high, and the destruction of the road pavement such as coil checker is also more serious simultaneously.And coil, once deploy and cannot adjust, cannot accurately detect the queue length at crossing.Again one, by crossing or section detecting device to the control of signal, also can only be induction or the adaptive control of single intersection, cannot feasible region cooperation control.
Adopt the large data of Internet traffic, except real-time dynamic road condition information is provided, can also realize road condition predicting, history road condition query, bottleneck analysis of blocking up, can provide the data of shorter time, be all have a very large raising for improving the precision of traffic control.Along with new-energy automobile, smart city are built and the propelling of car networking, the putting space to good use and also will expand further of the large data of traffic, the precision of data and accuracy also can improve thereupon.
Therefore, provide a kind of traffic signal control system based on the large data of road conditions traffic of design and method, to utilize large data processing technique precisely to carry out road traffic control efficiently, and then improve intellectuality and the efficiency of point duty.
Summary of the invention
The problem to be solved in the present invention adopts large Data Management Analysis technology, carries out real-time monitoring control to transport information, and then alleviate traffic pressure, the problem of the solving road dredging wasting of resources.
For solving the problems of the technologies described above, the technical solution used in the present invention is: a kind of traffic signal control method based on the large data of road conditions traffic, is characterized in that, comprising:
Client on vehicle sends client-side information to cloud platform;
High in the clouds stores huge traffic data information;
Data are divided into real time data and historical data according to time series;
Corresponding static database and dynamic data base is built according to data type; Generate corresponding optimization control scheme and Coordinated Control Scheme;
Determine control area road conditions according to scheme, corresponding allocation plan is set; Select corresponding optimization control scheme, determine that the signal period of teleseme, signal phase sequence and relative phase difference generate configuration optimization scheme; Select Coordinated Control Scheme, determine that the signal period of teleseme, signal phase sequence and relative phase difference generate configuration coordinate scheme;
According to the allocation plan received, adjust the signal period of each signal controlling machine, signal phase sequence and relative phase difference;
Drive singal lamp performs.
Further, client-side information comprises client id, vehicle latitude and longitude coordinates, direct of travel, gait of march, time and request instruction; Described request instruction comprises craspedodrome request instruction, left-hand rotation request instruction, right-hand rotation request instruction and tune request instruction.
Further, described static database, by the method revised time series predicting model historical data weighted sum, set up the short-time traffic flow forecast algorithm under Weekly similarity characteristic, calculate average hourage between road network crossing according to historical data, each crossing red light be the spacing of average stop frequency and average latency and adjacent intersection according to road load degree allocative efficiency green time principle, for optimization control scheme provides parameter; According to the average overall travel speed of vehicle, and in the unit interval, the vehicle saturation degree of distance divides congestion level; The optimization control scheme that a congestion level is corresponding is respectively determined according to congestion level and period.
Further, described dynamic data base, as real time data processing database.According to the data of Real-time Collection, calculate according to the average overall travel speed of vehicle and in the unit interval vehicle saturation degree of distance and historical data contrast, according to difference range determination Coordinated Control Scheme.
Further, drive singal lamp performs.
Further again, described jam level be divided into unimpeded, walk or drive slowly, block up, heavy congestion four grades.
Based on a traffic signal control system for the large data of road conditions traffic, by: client, cloud virtual machine, data storage server, data analytics server, Signal control Center, regional traffic signal controlling machine and signal lamp form;
Described client, the client on vehicle sends client-side information to cloud platform;
Described cloud virtual machine, the magnanimity crossing vehicle in each LINK section and the traffic information of traffic lights on the road obtained from high moral map;
Described data storage server, for obtaining data, data are divided into real time data and historical data according to time series, utilize known road network structure parameter and intersection signal timing parameter in conjunction with relevant traffic stream parameter Forecasting Approach for Short-term, generate traffic data information and store, for reading;
Data analytics server, for judging road junction jam state, building corresponding static database and dynamic data base according to data type, generating corresponding optimization control scheme and Coordinated Control Scheme;
Described Signal control Center, determines control area road conditions according to scheme, arranges corresponding allocation plan; Select corresponding optimization control scheme, determine that the signal period of teleseme, signal phase sequence and relative phase difference generate configuration optimization scheme; Select Coordinated Control Scheme, determine that the signal period of teleseme, signal phase sequence and relative phase difference generate configuration coordinate scheme;
Described regional traffic signal controlling machine, according to the allocation plan received, adjust relative phase differential, generate quick and smooth signal timing plan, according to coordination allocation plan and configuration scheme, be each machine signalization cycle, signal phase sequence and relative phase difference;
Described teleseme, makes the adjustment of signal period, signal phase sequence for drive singal lamp.
Preferably, described client, is arranged on vehicle, by data-interface, sends client-side information to cloud platform; Client-side information comprises client id, vehicle latitude and longitude coordinates, direct of travel, gait of march, time and request instruction; Described request instruction comprises craspedodrome request instruction, left-hand rotation request instruction, turn right request instruction and the request instruction that turns around.
Preferably, described static database, by the method revised time series predicting model historical data weighted sum, set up the short-time traffic flow forecast algorithm under Weekly similarity characteristic, calculate average hourage between road network crossing according to historical data, each crossing red light be the spacing of average stop frequency and average latency and adjacent intersection according to road load degree allocative efficiency green time principle, for optimization control scheme provides parameter; According to the average overall travel speed of vehicle, and in the unit interval, the vehicle saturation degree of distance divides congestion level; The optimization control scheme that a congestion level is corresponding is respectively determined according to congestion level and period.
Preferably, described dynamic data base, as real time data processing database.According to the data of Real-time Collection, calculate according to the average overall travel speed of vehicle and in the unit interval vehicle saturation degree of distance and historical data contrast, according to difference range determination Coordinated Control Scheme.
Preferably, described jam level be divided into unimpeded, walk or drive slowly, block up, heavy congestion four grades.
The advantage that the present invention has and beneficial effect are: based on the instruction of client transmissions, utilize high in the clouds to store, and grasp trend, make prioritization scheme according to Changing Pattern based on large data processing; Control the optimized operation phase place of traffic signals, be convenient for people to trip, according to real-time data capture, contrast with historical data, the Coordinated Control Scheme of generation, the operation of signal lamp can be adjusted in time, reply burst traffic.
Accompanying drawing explanation
Fig. 1 is the main flow chart of a kind of traffic signal control method based on the large data of road conditions traffic of the present invention;
Fig. 2 is the structural representation of a kind of traffic signal control system based on the large data of road conditions traffic of the present invention;
Fig. 3 is the detail flowchart of a kind of traffic signal control system based on the large data of road conditions traffic shown in Fig. 1;
In figure: 1, client, 2, cloud virtual machine, 3, data storage server, 4, data analytics server, 5, Signal control Center, 6, regional traffic signal controlling machine, 7, signal lamp, 31, data memory module, 301, history data store module, 302 real-time data memory modules.
Embodiment
Be clearly and completely described technical scheme of the present invention below in conjunction with accompanying drawing, obviously, described embodiment is the present invention's part embodiment, instead of whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art, not making the every other embodiment obtained under creative work prerequisite, belong to the scope of protection of the invention.
In describing the invention, it should be noted that, unless otherwise clearly defined and limited, term " installation ", " being connected ", " connection " should be interpreted broadly, and such as, can be fixedly connected with, also can be removably connect, or connect integratedly; Can be mechanical connection, also can be electrical connection; Can be directly be connected, also indirectly can be connected by intermediary, can be the connection of two element internals.For the ordinary skill in the art, concrete condition above-mentioned term concrete meaning in the present invention can be understood.
As shown in Figure 1, a kind of traffic signal control method based on the large data of road conditions traffic, comprising:
Step 101, the client on vehicle sends client-side information to cloud platform;
Step 102, high in the clouds stores huge traffic data information;
Data are divided into real time data and historical data according to time series by step 103;
Step 104, builds corresponding static database and dynamic data base according to data type; Generate corresponding optimization control scheme and Coordinated Control Scheme;
Step 105, determines control area road conditions according to scheme, arranges corresponding allocation plan; Select corresponding optimization control scheme, determine that the signal period of teleseme, signal phase sequence and relative phase difference generate configuration optimization scheme; Select Coordinated Control Scheme, determine that the signal period of teleseme, signal phase sequence and relative phase difference generate configuration coordinate scheme;
Step 106, according to the allocation plan received, adjusts the signal period of each signal controlling machine, signal phase sequence and relative phase difference;
Step 107, drive singal lamp performs.
Wherein, in step 101, client-side information comprises client id, vehicle latitude and longitude coordinates, direct of travel, gait of march, time and request instruction; Described request instruction comprises craspedodrome request instruction, left-hand rotation request instruction, right-hand rotation request instruction and tune request instruction.
Wherein, in step 104, static database is the method by revising time series predicting model historical data weighted sum, set up the short-time traffic flow forecast algorithm under Weekly similarity characteristic, calculate average hourage between road network crossing according to historical data, each crossing red light be the spacing of average stop frequency and average latency and adjacent intersection according to road load degree allocative efficiency green time principle, for optimization control scheme provides parameter; According to the average overall travel speed of vehicle, and in the unit interval, the vehicle saturation degree of distance divides congestion level; The optimization control scheme that each congestion level is corresponding is respectively determined according to congestion level and period.
The calculating of the average hourage between road network crossing should meet with following formula, T
i=t
i2-t
i1t
i2and t
i1be respectively vehicle i successively by the time of former and later two bayonet sockets; Normal distribution law is met and sample meets due to journey time
wherein,
after getting rid of minimum probability data according to above-mentioned formula, try to achieve the average travel time of arithmetic mean as this cycle.According to Velocity Time formula
average travel speed can be tried to achieve;
The magnitude of traffic flow original data series that structure forecast model is added up by certain time t as certain crossing in road network forms structure forecast model after accumulated method, is expressed as follows
wherein,
represent w week, the traffic flow forecasting value in being interrupted for the n after current time t hour; w
krepresent the weight of week k; η
krepresent correction factor;
represent w week, the historical traffic flows after t in the n period.
Calculate the magnitude of traffic flow in t period every day according to above formula algorithm, determine this moment vehicle saturation degree, and by this moment vehicle saturation degree divided rank.
Wherein, dynamic data base in step 104, as real time data processing database.According to the data of Real-time Collection, calculate according to the average overall travel speed of vehicle and in the unit interval vehicle saturation degree of distance and historical data contrast, according to difference range determination Coordinated Control Scheme.
Wherein, jam level described in step 104 be divided into unimpeded, walk or drive slowly, block up, heavy congestion four grades.
Based on a traffic signal control system for the large data of road conditions traffic, by: client 1, cloud virtual machine 2, data storage server 3, data analytics server 4, Signal control Center 5, regional traffic signal controlling machine 6 and signal lamp 7 form;
Described client 1, the client on vehicle sends client-side information to cloud platform;
Described cloud virtual machine 2, the magnanimity crossing vehicle in each LINK section and the traffic information of traffic lights on the road obtained from high moral map;
Described data storage server 3, comprise data memory module 31, be divided into history data store module 301 and real-time data memory module 302 for obtaining data, data are divided into real time data and historical data according to time series, utilize known road network structure parameter and intersection signal timing parameter in conjunction with relevant traffic stream parameter Forecasting Approach for Short-term, generate traffic data information and store, for reading;
Data analytics server 4, for judging road junction jam state, building corresponding static database and dynamic data base according to data type, generating corresponding optimization control scheme and Coordinated Control Scheme;
Described Signal control Center 5, determines control area road conditions according to scheme, arranges corresponding allocation plan; Select corresponding optimization control scheme, determine that the signal period of teleseme, signal phase sequence and relative phase difference generate configuration optimization scheme; Select Coordinated Control Scheme, determine that the signal period of teleseme, signal phase sequence and relative phase difference generate configuration coordinate scheme;
Described regional traffic signal controlling machine 6, according to the allocation plan received, adjust relative phase differential, generate quick and smooth signal timing plan, according to coordination allocation plan and configuration scheme, be each machine signalization cycle, signal phase sequence and relative phase difference;
Described teleseme 7, makes the adjustment of signal period, signal phase sequence for drive singal lamp.
Described client 1, is arranged on vehicle, by data-interface, sends client-side information to cloud platform; Client-side information comprises client id, vehicle latitude and longitude coordinates, direct of travel, gait of march, time and request instruction; Described request instruction comprises craspedodrome request instruction, left-hand rotation request instruction, turn right request instruction and the request instruction that turns around.
Described static database, by the method revised time series predicting model historical data weighted sum, set up the short-time traffic flow forecast algorithm under periodical similarity characteristic, calculate average hourage between road network crossing according to historical data, each crossing red light be the spacing of average stop frequency and average latency and adjacent intersection according to road load degree allocative efficiency green time principle, for optimization control scheme provides parameter; According to the average overall travel speed of vehicle, and in the unit interval, the vehicle saturation degree of distance divides congestion level; The optimization control scheme that a congestion level is corresponding is respectively determined according to congestion level and period.
Described dynamic data base, as real time data processing database.According to the data of Real-time Collection, calculate according to the average overall travel speed of vehicle and in the unit interval vehicle saturation degree of distance and historical data contrast, according to difference range determination Coordinated Control Scheme.
Described jam level be divided into unimpeded, walk or drive slowly, block up, heavy congestion four grades.
Specific embodiment is as follows: client 1 is mounted on a vehicle, sends customer instruction information, and provide more specific location information by client, upload to cloud server 2, cloud server 2 carries out real time data renewal, utilizes data storage server 3, historical data and real time data classification is stored; And being sent to data analytics server 4, data analytics server 4 pairs of historical datas are made data analysis and are divided congestion level, and according to real time data determination cur-rent congestion rank; The congestion level determination Coordinated Control Scheme that the optimization control scheme normal root that data analytics server 4 makes correspondence according to congestion level is determined according to real time data; Synchronous signal control center 5 determines control area road conditions according to scheme, arranges corresponding allocation plan; Select corresponding optimization control scheme, determine that the signal period of teleseme, signal phase sequence and relative phase difference generate configuration optimization scheme; Select Coordinated Control Scheme, determine that the signal period of teleseme, signal phase sequence and relative phase difference generate configuration coordinate scheme Signal control Center 5 and adopt serial ports to control with regional traffic signal controlling machine 6, between two serial ports, pass through Fiber connection.Regional traffic signal controlling machine 6, according to the allocation plan received, adjusts relative phase differential, generates quick and smooth signal timing plan, according to coordination allocation plan and configuration scheme, is each machine signalization cycle, signal phase sequence and relative phase difference.
Last it is noted that above each embodiment is only in order to illustrate technical scheme of the present invention, be not intended to limit; Although with reference to foregoing embodiments to invention has been detailed description, those of ordinary skill in the art is to be understood that: it still can be modified to the technical scheme described in foregoing embodiments, or carries out equivalent replacement to wherein some or all of technical characteristic; And these amendments or replacement, do not make the essence of appropriate technical solution depart from the scope of various embodiments of the present invention technical scheme.
Claims (10)
1. based on a traffic signal control method for the large data of road conditions traffic, it is characterized in that, comprising:
Client on vehicle sends client-side information to cloud platform;
High in the clouds stores huge traffic data information;
Data are divided into real time data and historical data according to time series;
Corresponding static database and dynamic data base is built according to data type; Generate corresponding optimization control scheme and Coordinated Control Scheme;
Determine control area road conditions according to scheme, corresponding allocation plan is set; Select corresponding optimization control scheme, determine that the signal period of teleseme, signal phase sequence and relative phase difference generate configuration optimization scheme; Select Coordinated Control Scheme, determine that the signal period of teleseme, signal phase sequence and relative phase difference generate configuration coordinate scheme;
According to the allocation plan received, adjust the signal period of each signal controlling machine, signal phase sequence and relative phase difference;
Drive singal lamp performs.
2. a kind of traffic signal control method based on the large data of road conditions traffic according to claim 1, is characterized in that: client-side information comprises client id, vehicle latitude and longitude coordinates, direct of travel, gait of march, time and request instruction; Described request instruction comprises craspedodrome request instruction, left-hand rotation request instruction, right-hand rotation request instruction and tune request instruction.
3. a kind of traffic signal control method based on the large data of road conditions traffic according to claim 1, it is characterized in that: described static database, by the method revised time series predicting model historical data weighted sum, set up the short-time traffic flow forecast algorithm under periodical similarity characteristic, the average hourage between road network crossing is calculated according to historical data, each crossing red light is that the spacing of average stop frequency and average latency and adjacent intersection is according to road load degree allocative efficiency green time principle, for optimization control scheme provides parameter, according to the average overall travel speed of vehicle, and in the unit interval, the vehicle saturation degree of distance divides congestion level, the optimization control scheme that a congestion level is corresponding is respectively determined according to congestion level and period.
4. a kind of traffic signal control method based on the large data of road conditions traffic according to claim 1, is characterized in that: described dynamic data base, as real time data processing database.According to the data of Real-time Collection, calculate according to the average overall travel speed of vehicle and in the unit interval vehicle saturation degree of distance and historical data contrast, according to difference range determination Coordinated Control Scheme.
5. a kind of traffic signal control method based on the large data of road conditions traffic according to claim 3, is characterized in that: described jam level be divided into unimpeded, walk or drive slowly, block up, heavy congestion four grades.
6., based on a traffic signal control system for the large data of road conditions traffic, it is characterized in that, by: client, cloud virtual machine, data storage server, data analytics server, Signal control Center, regional traffic signal controlling machine and signal lamp form;
Described client, the client on vehicle sends client-side information to cloud platform;
Described cloud virtual machine, the magnanimity crossing vehicle in each LINK section and the traffic information of traffic lights on the road obtained from high moral map;
Described data storage server, for obtaining data, data are divided into real time data and historical data according to time series, utilize known road network structure parameter and intersection signal timing parameter in conjunction with relevant traffic stream parameter Forecasting Approach for Short-term, generate traffic data information and store, for reading;
Data analytics server, for judging road junction jam state, building corresponding static database and dynamic data base according to data type, generating corresponding optimization control scheme and Coordinated Control Scheme;
Described Signal control Center, determines control area road conditions according to scheme, arranges corresponding allocation plan; Select corresponding optimization control scheme, determine that the signal period of teleseme, signal phase sequence and relative phase difference generate configuration optimization scheme; Select Coordinated Control Scheme, determine that the signal period of teleseme, signal phase sequence and relative phase difference generate configuration coordinate scheme;
Described regional traffic signal controlling machine, according to the allocation plan received, adjust relative phase differential, generate quick and smooth signal timing plan, according to coordination allocation plan and configuration scheme, be teleseme signalization cycle, signal phase sequence and relative phase difference;
Described teleseme, makes the adjustment of signal period, signal phase sequence for drive singal lamp.
7. a kind of traffic signal control system based on the large data of road conditions traffic according to claim 6, is characterized in that: described client, is arranged on vehicle, by data-interface, sends client-side information to cloud platform; Client-side information comprises client id, vehicle latitude and longitude coordinates, direct of travel, gait of march, time and request instruction; Described request instruction comprises craspedodrome request instruction, left-hand rotation request instruction, turn right request instruction and the request instruction that turns around.
8. a kind of traffic signal control system based on the large data of road conditions traffic according to claim 6, it is characterized in that: described static database, by the method revised time series predicting model historical data weighted sum, set up the short-time traffic flow forecast algorithm under periodical similarity characteristic, the average hourage between road network crossing is calculated according to historical data, each crossing red light is that the spacing of average stop frequency and average latency and adjacent intersection is according to road load degree allocative efficiency green time principle, for optimization control scheme provides parameter, according to the average overall travel speed of vehicle, and in the unit interval, the vehicle saturation degree of distance divides congestion level, the optimization control scheme that a congestion level is corresponding is respectively determined according to congestion level and period.
9. a kind of traffic signal control system based on the large data of road conditions traffic according to claim 6, is characterized in that: described dynamic data base, as real time data processing database.According to the data of Real-time Collection, calculate according to the average overall travel speed of vehicle and in the unit interval vehicle saturation degree of distance and historical data contrast, according to difference range determination Coordinated Control Scheme.
10. a kind of traffic signal control system based on the large data of road conditions traffic according to claim 8, is characterized in that: described jam level be divided into unimpeded, walk or drive slowly, block up, heavy congestion four grades.
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