CN102663887B - Implementation system and method for cloud calculation and cloud service of road traffic information based on technology of internet of things - Google Patents

Abstract

The invention relates to an implementation system for cloud calculation and cloud service of road traffic information based on a technology of internet of things. The system comprises a vehicular GPS (global positioning system) arranged on a mobile vehicle, a GPS satellite, a GPS base station, a relay station and an information center, wherein the information center comprises a data storage server used for receiving and storing the data of the GPS on the mobile vehicle, a cloud calculation server used for sensing the traffic state, a road traffic information platform server used for providing the cloud service of the traffic information and a GIS (geographic information system) server; and the invention provides an implementation method for cloud calculation and cloud service of the road traffic information based on the technology of the internet of things. According to the invention, the road information acquisition and the road information service are integrated, the traffic detection for existence of any vehicle is realized, the detection and sense of a road traffic system in large scale are realized, and the traffic state is evaluated, guided and controlled based on the data, thereby providing the real-time road condition information and navigation service to a traveler.

Description

Traffic Information cloud computing and cloud service based on technology of Internet of things realize system and method

Technical field

The invention belongs to the application in road traffic state context of detection of GPS location and velocity measuring technique, GIS technology, cloud computing technology, urban road digital coding and the network communications technology, especially a kind of Intelligent road traffic-information service based on technology of Internet of things.

Background technology

Current traffic problems have become global " city common fault ", and traffic congestion is the main manifestations of city " traffic illness "." cause of disease " of urban traffic blocking comes from many factors, and traffic congestion directly affects people's trip quality, particularly utilizes the people of vehicular traffic.Road vehicle is crowded, and traffic hazard takes place frequently, and traffic environment worsens, energy shortage, environmental pollution constantly increases the weight of, the basic theory of these day by day serious traffic problems and modern transportation, i.e. and the requirements such as sensible, orderly, safe, comfortable, low energy consumption, low pollution are completely contrary.

The evaluation criterion of modern transportation system is safe, unimpeded, energy-conservation.Therefore how hold in urban highway traffic operation conditions service level, need to set up a kind of science, objective appraisal method.But owing to lacking at present a kind of system that road traffic service level is evaluated of relatively science and effective road traffic state detection means, thereby make citizen be difficult to understand accurately and hold in the change in time and space situation to urban highway traffic before travel; Relevant urban construction department drops into road infrastructure and the Expected Results of the traffic management measure taked is difficult to evaluate accurately; The comparison of city manager to city self historical development and the standard of passing judgment on other intercity lateral comparison shortage; Roading department carries out quantitatively scientific analysis to urban highway traffic development trend and the measure that need take and lacks necessary means.

Traffic information collection technology is considered to the gordian technique of a most important thing in intelligent transportation, and conventional traffic information collection technology has ground induction coil, magneto-dependent sensor, ultrasonic sensor, microwave, GPS and vision sensor at present; Because the transport information detecting sensors such as ground induction coil, magnetosensitive, ultrasound wave, microwave need to be embedded in underground face, when I&M, must destroy original road surface, affected road traffic, the pavement damage that simultaneously road of China causes due to reasons such as the overload of vehicle must be often safeguarded the sensor being embedded in below road; In addition these detection meanss can only perception go out on certain point on road or certain line the vehicle of process, therefore can only indirectly infer congestion in the speed of passing through vehicle of the set-up site of sensor; Therefore above-mentioned detection means exist that installation and maintenance inconvenience, cost of investment are high, poor anti jamming capability and the defect such as sensing range is limited.

Chinese invention patent application number is 200810090474.4 to disclose traffic situation determination system, this system provides a kind of traffic situation determination system, utilize GPS to carry out the congestion of the road that the driving trace of definite vehicle is corresponding, in the correct judgement of carrying out congestion, number of communications and amount of communication data that the signal post between vehicle and information center relates to can be reduced, alleviating and the low volume of communication cost of communication process burden can be realized.This road traffic state detection means exists certain defect, owing to not using cloud computing technology, infers that road traffic state exists the problems such as one-sidedness, locality and subjectivity simply by Vehicular behavior; Chinese invention patent application number is 200510026478.2 to disclose one and can be used for the traffic method for measuring of surface road net and system, this system adopts three layers of crossings, arterial street, urban main road network successively to measure to urban road, for arterial street, " the equivalent traffic capacity " concept and definite method are proposed; Adopt " density ratio " index, the service level scale value of the service level scale value curve calculation arterial highway providing according to the present invention, measures; Adopt " weighting density ratio " index to measure mains service level based on arterial highway measurement result; Carrying out congested area, crowded arterial highway and crowded crossing according to measurement result successively identifies.This traffic method for measuring not yet relates to most crucial road traffic state data acquisition problem.Chinese invention patent application number is 200810132938.3 to disclose a kind of Intellective traffic information system and disposal route thereof, comprises GPS module, for global positioning information is provided; With the mobile terminal that GPS module communicates, it is connected with cordless communication network; ITS Information server, it is connected with cordless communication network and provides Real-time Traffic Information according to mobile terminal request.This Intellective traffic information system and disposal route thereof do not relate to most crucial road traffic state data acquisition problem yet.Chinese invention patent application number is 200810034716.8 to disclose road traffic state determination methods and system, this system is using multiple traffic parameters as basis for estimation, set up funtcional relationship for different sections of highway, given weight, has improved the accuracy of traffic behavior judgement simultaneously.The method comprises: (1) chooses multiple traffic parameters; (2), by the sampling analysis to this road section traffic volume parameter, set the funtcional relationship between above-mentioned multiple traffic parameters and its corresponding crowding coefficient in this section and set the plurality of traffic parameter shared weighted value in this section degree of crowding judges; (3) for each state judgement end of term in week, the function that above-mentioned multiple traffic parameters in this section of Real-time Collection basis set, calculates the corresponding crowding coefficient of each traffic parameter; (4) corresponding with it weighted value of each traffic parameter crowding coefficient is done to weighted mean computing, obtain mean crowding coefficient; (5) compare mean crowding coefficient and crowding coefficient threshold value, thereby judge road traffic state.This judgment mode need to have multiple traffic parameter supports, and operand is large, and will on all main roads in city, obtain these traffic parameters is also an easy thing simultaneously, needs very large input and maintenance.

Cloud computing is that the one that Internet of Things era development is got up is calculated form, has embodied a kind of new Information Service Mode.Transport information cloud is the mode of operation of a kind of traffic information collection, processing and application.The key element that forms transport information cloud is mainly: (1) is stored the transport information of magnanimity on network into by communication; (2) storage of the transport information of magnanimity can not be restricted, and the computing power required for depth perception traffic can not be restricted; (3) transport information cloud computing can provide personalized, diversified information service for different users such as country, city, industry and travelers; (4) transport information cloud can provide computing basic facility, computing platform, traffic software and traffic basic data for user, and the service providing, without specific software is installed, is convenient to traveler and is used on the mobile device such as mobile phone, navigating instrument; (5) any transport information is all valuable, tradable, and the value of transport information has been amplified in cloud computing by the service of transport information.Traffic Information cloud is the transport information overall process being made up of cloud computing and information cloud service, and cloud computing is method or means, and transport information cloud service is object.

Taxi and bus dispose the vehicles of GPS, as can be used for gathering Real-time Traffic Information.These vehicles are distributed in system-wide net, along with the wagon flow effect that can serve as moving detector of moving.Vehicle GPS is automatically with data such as the position of certain sample frequency continuous recording vehicle, speed, moment, due to track of vehicle has been carried out to tracking control of full process, can obtain easily the required Back ground Informations of traffic-information service such as section transit time, average velocity and crossing mean delay.From technical standpoint, it has round-the-clock continuous working, the advantages such as system-wide net transport information can be provided in real time; From economic angle, equipment is relative with operating cost cheap, can make full use of the vehicle GPS configuring on the taxi in city and bus.The a lot of cities of China require taxi must configure vehicle GPS at present, are mainly used in the location of vehicle, are not also applied to collection Real-time Traffic Information.

Cheu, Xie and Lee have studied the reliability of estimating major trunk roads average speed with the vehicle speed data of moving vehicle, result of study shows to be less than 5km/h as met absolute error within 95% time, needs 4%～5% locomotive or in the sampling period, has 10 locomotives at least by this section.Quiroga and Bullock think that road section length should be divided into 0.32～0.8 kilometer, and the sampling time will reach 1～2 second.The key of this technology is that the data (longitude, latitude, speed, moment) of returning according to vehicle GPS are calculated road-section average speed.

Realize accuracy of detection high, detect real-time key good, that testing result is simple and clear be will by direct, simple and clear, calculate simple, visual road traffic detection means and whether directly obtain certain road traffic in following 6 kinds of status informations, road traffic state is in service level A: unimpeded; Service level B: substantially unimpeded; Service level C: tentatively block up; Service level D: block up: service level E: seriously block up; Service level F: localized road and large area paralysis.

In evaluation path level of service appraisement system, most crucial problem is the detection of vehicle flowrate, congestion status and the average speed of road, and therefore optimal detection means is directly to measure in real time vehicle flowrate, the average speed on road and the length of blocking up simultaneously.

At present commercial obtaining mainly contains following three kinds of modes in road traffic real time data means: 1) annular coil induction type checkout equipment, detects data such as road traffic flow, the flow direction, the speed of a motor vehicle, lane occupancy ratio and vehicle commander, queue lengths; This detection means need to be embedded in annular coil on road surface, and 1 year half left and right, need to destroy road surface when safeguarding and installing serviceable life, belongs to contact and measure; 2) long-range traffic microwave detector, collects the data such as vehicle flowrate, roadway occupancy and the average velocity in each track; This pick-up unit cost is high; 3), based on car plate identification detector and queue length detecting device, by being arranged on car plate identification detector and the queue length detecting device at stop line place in extension section, crossing, utilize queue length detecting device to obtain queue length L; The vehicle number N of the moment t while utilizing car plate identification detector to obtain vehicle through detecting device and process detecting device; Possess the video detection system of license plate identification, detect hourage and the travel speed of motor vehicle on certain road by the identity of identification vehicle, thisly aspect limitation and real-time, existing some problems as road traffic state detection means.These detection meanss belong to objectivity and detect, significant aspect road traffic investigation.But the common problem of this detection means is then to come by statistics indirectly to obtain vehicle flowrate and average speed by the ruuning situation of each vehicle in measurement road, aspect implementation and operation, exist some defect, particularly aspect the evaluation indexes such as real-time, putting maintenance into practice cost, calculating pressure and sensitivity index, existing deficiency for Assessment of Serviceability of Roads.

The live transport information of the XML form that at present some area of China obtains from relevant traffic information service providers is upgraded once for every 5 minutes, and content comprises that road section ID, section are initial, average overall travel speed (km/h) and time (yyyy-mm-dd hh:mm:ss).But this fact transport information can not reflect that road conditions are then for traveler provides the optimal path with Link Travel Time truly, concrete shortcoming is as follows: (1) road information is difficult to mate with data in navigation electronic map.The live transport information road ID that we obtain is at present that traffic provider is self-defining, cannot realize by road ID and mating: (2) Link Travel Time does not have direction to describe.Traffic network is directive, for example, in section, a certain north and south, and may because traffic hazard shows as, block up may be unblocked to south by north to north by south.And this fact transport information does not have sake of clarity; (3) live transport information limited coverage area.Set up road traffic flow real-time dynamic information system not yet completely, the every transport information providing for 5 minutes of relevant departments can only be static, quasi real time.Meanwhile, because Link Travel Time is relevant through the moment in section with vehicle, but the moment that when search starts, vehicle arrives certain section is unknown.When search starts, original unimpeded section may have been stopped up before user does not arrive.

The urban transportation of China will be in mixed traffic state within a very long time.Under mixed traffic condition, service level achievement data has following characteristics: the diversity of (1) data acquisition object: not only need to gather road section traffic volume data but also need to gather crossing internal transportation data, often need to observe multiple behavior and the parameter thereof of traffic unit simultaneously in once observing simultaneously; (2) space-time of data leap property is strong: in order to obtain the achievement data of varying service level grade under different transportation conditions, detection need to be captured in the data in certain hour and spatial extent, and need to be online data.For above demand, Traffic Information cloud computing and cloud service based on technology of Internet of things can realize this demand.

Realizing and implementing key is easily to adopt friendly type, contactless, the large-area road traffic state detection means of a kind of road of not destroying road surface or not relating to pavement construction, utilizes as far as possible existing equipment and investment simultaneously; The service state of road is the comprehensive embodiment of the many factors such as condition of road surface, operation conditions, means of transportation situation and traffic safety status, although wait by statistics by detecting these many status datas that to calculate be the service level status information that can obtain road, preferably can be straightforward, simple and convenient, service status information that economy obtains road in real time.

Summary of the invention

Large in order to overcome the limitation of detection of existing Traffic Information, transport information road ID is difficult to mate with data in navigation electronic map, Link Travel Time does not have direction to describe, the transport information providing can only be static, quasi real time, be difficult to from macroscopic view, middle sight, three angles of microcosmic, from deficiencies such as people's subjective feeling Real-Time Evaluation road traffic service level states, the invention provides one, to have sensing range wide, accuracy of detection is high, detection real-time is good, it is convenient to implement, testing result is simple and clear, there is subjective feeling achievement data to have again objective evaluation achievement data, and be convenient to city road networks at different levels in the time, Traffic Information cloud computing and cloud service based on technology of Internet of things that road traffic state is carried out to comprehensive evaluation on space realize system and method.

The technical solution adopted for the present invention to solve the technical problems is:

A kind of Traffic Information cloud computing and cloud service based on technology of Internet of things realizes system, comprise the vehicle GPS, GPS base station, relay station and the information center that are arranged on moving vehicle, described information center comprises for accepting with the data storage server of storing mobile vehicle GPS data, for the cloud computing server of perception traffic behavior, for Traffic Information Platform Server and the GIS server of transport information cloud service are provided; Described vehicle GPS comprises antenna element, receiving element, computing unit and mobile comm unit;

Described data storage server comprises data acquisition module and data processing memory module;

Described cloud computing server, in order to by the image data in transport information cloud computing database is carried out to deep processing, calculates the Assessment of Serviceability of Roads situation that obtains certain point, certain line, certain region and whole city on urban road; Comprise road-section average speed calculation module, section velocity variable computing module, all Assessment of Serviceability of Roads output modules in section Assessment of Serviceability of Roads determination module and city;

Described Traffic Information Platform Server and GIS server, for providing the various cloud services of urban highway traffic: the Assessment of Serviceability of Roads cloud service pattern in the cloud service pattern that 24 hours situations of the traffic behavior in section are issued, the path planning cloud service pattern with human-computer interaction function, dynamic route planning cloud service pattern, city based on GIS.

Further, in described section Assessment of Serviceability of Roads determination module, output format is made up of following five parameter values, is respectively the moment parameter value of 14, the detection space location parameter value of 23, the travel direction property parameters value of 1, the Assessment of Serviceability of Roads parameter value of 1 and the vehicle average overall travel speed of 2; Moment parameter is for representing to detect the temporal information in moment, moment parameter Time represents with 14 bit data forms, for YYYYMMDDHHMMSS, wherein 1～4 YYYY represents that the year of the Gregorian calendar, 5～6 MM represent that the month of the Gregorian calendar, 7～8 DD represent that the day of the Gregorian calendar, 9～10 HH represent hour, 11～12 MM represent point, 13～14 SS represent second; Detection space location parameter is for representing the central point spatial positional information in certain section, and detection space location parameter SegmentID represents with 23 bit data forms; Driveway travel directions property parameters is for representing the travel direction information in track, driveway travel directions property parameters Direction represents with 1 bit data form, regulation deviates from the travel direction property parameters value i=0 of road starting end, towards the travel direction property parameters value i=1 of road starting end; Assessment of Serviceability of Roads parameter is for representing the congestion status of road, Assessment of Serviceability of Roads parameter S erviceLevel represents for alpha format with 1, Assessment of Serviceability of Roads grade is divided into 6 grades such as A, B, C, D, E, F, wherein A represents that Assessment of Serviceability of Roads grade is best, and F represents that the poorest grade of Assessment of Serviceability of Roads; A record of road section service level detection module output is to be made up of five parameter values of Time+SegmentID+Direction+ServiceLevel+Speed like this, section travel direction on the road of every record and detection becomes one-to-one relationship, 41 of the length of a record.

Further again, described road-section average speed calculation module, for calculating the vehicle average velocity of SegmentID, the first volume coordinate (x that determines two frontier points take ± 0.4Km as radius in a certain travel direction centered by the SegmentID on certain road _j-n, y _j-n), (x _j+m, y _j+m); Then by sampling time, volume coordinate scope and vehicle operating direction retrieval qualified record in described data storage server, calculate the average velocity in each road section scope with all vehicles that dispose vehicle-mounted GPS equipment of identical moving direction with formula (4)

$v_{k,g}=\frac{\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{v}_i}{n}---\left(4\right)$

In formula, v _{k, g}be illustrated on the g section of k article of road in sampling interval, i.e. the average translational speed of the vehicle of SegmentID, n be in sampling interval on the g section of k article of road, within the scope of SegmentID, dispose the sum of the vehicle of vehicle-mounted GPS equipment, v _ifor in sampling interval within the scope of SegmentID the average translational speed of i vehicle.

Further, described section velocity variable computing module, for calculating the changes in vehicle speed rate within the scope of SegmentID, computing method as shown in formula (5),

${\mathrm{cov}}_{k,g}=\frac{\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{(1-v_i/v_{k,g})}^2}{n}\×100---\left(5\right)$

In formula, cov _{k, g}be illustrated in the changes in vehicle speed rate on the g section of k article of road in sampling interval, i.e. changes in vehicle speed rate within the scope of SegmentID; v _{k, g}be illustrated in the average translational speed of vehicle on the g section of k article of road in sampling interval, i.e. the average translational speed of vehicle within the scope of SegmentID; N is the sum that disposes the vehicle of vehicle-mounted GPS equipment in sampling interval on the g section of k article of road, i.e. the sum of the vehicle that disposes vehicle-mounted GPS equipment within the scope of SegmentID; v _ifor the average translational speed of i vehicle in sampling interval, i.e. the average translational speed of i vehicle in sampling interval within the scope of SegmentID.

All Assessment of Serviceability of Roads output modules in described city, for processing, calculate and export the Assessment of Serviceability of Roads on each section of every road, Time value is set by sampling instant value, data layout is YYYYMMDDHHMMSS, SegmentID value is worth to set by the locus of the central point in each section, Direction value is the travel direction that 0 expression deviates from road starting end, and 1 represents the travel direction towards road starting end; Time value, SegmentID ± 0.4Km value and Direction value retrieval qualified record in described data storage server, set the value of Speed with formula (4) calculating average velocity, set the value of ServiceLevel by the judged result of table 1;

Table 1

In upper table, Max-S is just decided to be 95, Mid-S and is just decided to be 75, Low-S is just decided to be 35, Min-S and is just decided to be 1, Max-cov and is just decided to be 99, cov-75 is just decided to be 75, cov-70 is just decided to be 70, cov-55 and is just decided to be 55, cov-45 and is just decided to be 45, cov-35 is just decided to be 35, cov-25 is just decided to be 25, cov-15 and is just decided to be 15, cov-10 and is just decided to be 10.

The cloud service pattern of the traffic behavior situation issue in 24 hours in described section, for investigating the traffic behavior in certain section; User by man-machine interface fixed time section obtain this time period traffic behavior change curve, specify certain section, certain travel direction and certain time period to obtain the traffic behavior change curve of certain section and certain time period.

The described path planning cloud service pattern with human-computer interaction function, for allowing user specify departure place and destination to obtain optimum path planning and navigation by man-machine interface; Specific practice is: described cloud computing server is retrieval path from origin to destination automatically, go out from one of origin-to-destination close together or some paths as traffic route at electronic map marker, then the running time spending according to the every paths of these path computing, user is by man-machine interface according to just having completed path planning after a certain traffic route of the shown Information Selection such as path and running time, and navigating instrument enters navigational state;

Performing step is as follows,

S1, step 1: user specifies departure place and destination by man-machine interface, described cloud computing server parses service content and starting point, endpoint information, automatically generate one or some alternative paths according to starting point, endpoint information, one paths of every generation is all the set Route of several SegmentID, also generated the chained list that represents several SegmentID annexations, the algorithm of generation pass adopts A* algorithm simultaneously;

S2, step 2: determine selecting paths from some alternative paths;

S3, step 3 a: SegmentID who reads driving path in the chained list from selecting paths;

S4, step 4: directly obtain the vehicle average running speed of this up-to-date SegmentID from a SegmentID information from the record of described cloud computing server;

S5, step 5: from the vehicle average running speed estimation of this SegmentID by this SegmentID to the needed time of distance a upper SegmentID chained list, owing to all having comprised the spatial positional information of this SegmentID in each SegmentID, therefore, between two adjacent SegmentID, distance can directly calculate;

S6, step 6: read next SegmentID information from chained list;

S7, step 7: directly obtain the vehicle average running speed of this up-to-date SegmentID from this SegmentID information from the record of described cloud computing server;

S8, step 8: from the vehicle average running speed estimation of this SegmentID by this SegmentID to the needed time of distance a upper SegmentID chained list;

S9, step 9: calculating path is from the starting point prediction cumulative time of SegmentID up till now;

S10, step 10: judge whether to have arrived terminal, if words forward S11 to; Do not meet Rule of judgment, continue to calculate the required time of next SegmentID, forward S6 to;

S11, step 11: the prediction cumulative time of calculating path from starting point to terminal, and preserve these data and be used for exporting to user;

S12, step 12: judge whether to have traveled through all alternative paths, if words export the prediction running time of all alternative paths, termination routine; If do not meet Rule of judgment, continue to calculate the prediction running time of next alternative path, forward S3 to.

Described dynamic route planning cloud service pattern, for according to the continuous variation of user designated destination and real-time traffic, cooks up optimal path, specific practice is: position and the travel direction of the current vehicle of GPS navigation device automatic acquisition, described cloud computing server automatically retrieval from the path to destination now, then the running time spending according to the every paths of these path computing, this service mode is mainly considered the dynamic change of road condition, as the situations such as traffic hazard are there is, therefore need according to present road traffic state for dynamically adjusting driving path, the information of vehicle present position is to be constantly updated by the location of GPS navigation instrument, this cloud service pattern is to belong to a kind of minimum running time dynamic route planning, thereby help traveler according to the continuous variation of real-time traffic, cook up optimal path.

The Assessment of Serviceability of Roads cloud service pattern in the described city based on GIS, for making a general survey of the Assessment of Service Level for Urban Roads state of browsing; Specific practice is: user browses certain point on urban road, the Assessment of Serviceability of Roads situation in certain line, certain region and whole city by generalized information system; First be to determine its corresponding SegmentID according to selected region, then read the state-of-the-art record in described cloud computing server according to these SegmentID, by the road service state in corresponding SegmentID record, use color marking on the section of the corresponding generalized information system of these SegmentID by ServiceLevel value, make user's very clear Assessment of Serviceability of Roads visual information that obtains urban road macroscopic view, middle sight and microcosmic from urban road CIS; The service level grade table corresponding to marker color of road section is as shown in table 2,

Service level grade	The color representing on GIS
		A	Blue
B	Blue yellow
		C	Yellow
D	Yellowish orange
		E	Orange
F	Red

Table 2.

A kind of Traffic Information cloud computing and cloud service implementation method based on technology of Internet of things, the process of described road traffic cloud service implementation method is as follows: user sets terminal by network computing device, network computing device can be PC, mobile phone, navigating instrument etc., the terminal position that described cloud computing server end is set according to user, generate a URL, and this URL is sent to described Traffic Information Platform Server and GIS server, then wait for the result that described Traffic Information Platform Server and GIS server are beamed back.Described Traffic Information Platform Server and GIS server are receiving after the URL that user sends, this URL is resolved, obtain traffic cloud service type, as path computing, map overview, location, information inquiry, CMMB service etc., cloud service type can obtain by the value of type in URL; Then determine to carry out which type of operation according to COS; Then according to the { start} and { value of end}, judges the SegmentID value of given start-stop node in URL; Obtaining after SegmentID, judge that whether given start-stop SegmentID is effective, determining be effective SegmentID in the situation that, server calculates two the shortest internodal and some second shortest paths, then the running time that provides different paths according to path is for user's selection, after user has selected certain paths, navigating instrument just enters navigational state, and according to the real-time condition correction guidance path of road.

Technical conceive of the present invention is: therefore, developing the cloud computing of a kind of GPS information merges information acquisition and information service, having realized vehicle just has traffic to detect, realize large scale road traffic system detection senses, and based on these data, traffic behavior is evaluated, induced and controls, for providing traffic information, traveler there is important theory significance and actual application value.

Analyze road traffic circulation situation should from macroscopic view, sight, three angles of microcosmic choose corresponding evaluation index and carry out.Macroscopic perspective is that whole urban road network traffic circulation index is carried out to assay; Middle sight angle is according to aspects such as urban road grade, administrative region, passage, loop gateways, and road net traffic is carried out to assay; Microcosmic angle is that the traffic circulation of certain road, certain crossing is carried out to assay.How from macroscopic view, middle sight, three angles of microcosmic are carried out the A+E of urban road traffic state, need to obtain the point in urban road simultaneously, line, face, the spatial informations such as region and temporal information, and this spatial information can be convenient to participate in directly computing in the road networks at different levels of city, can calculate by the traffic circulation state of putting on road the traffic circulation state that road is reached the standard grade, the traffic circulation state of reaching the standard grade from road can calculate the traffic circulation state face, traffic circulation state from face can calculate the whole road grid traffic running status in certain region.

Beneficial effect of the present invention is mainly manifested in: the basis that 1, transport information cloud computing provides information to calculate for the realization of traffic behavior control; 2, recorded information has comprised the information such as time, space, travel direction, Assessment of Serviceability of Roads and average velocity, and these information can be participated in computing directly, greatly reduce the calculating pressure of transport services cloud, have improved the level of cloud service; 3, attract more vehicle to become the probe vehicles that telecommunication flow information gathers by effective information trading mechanism, make the GPS information obtained can all the period of time, gamut reflection road traffic; 4, by cloud computing, traffic control induction can be merged with GPS dynamic navigation, realize traffic GPS information cloud service, equilibrium road network flow peak period, effectively alleviates part and blocks up.

Accompanying drawing explanation

Fig. 1 is the structured flowchart of vehicle GPS;

Fig. 2 is communication scheme between vehicle GPS, gps satellite, GPS base station, relay station and information center;

Fig. 3 is the composition schematic diagram that temporal information, spatial positional information, road driving directional information, road track direction information, Assessment of Serviceability of Roads class information and the length information that blocks up is processed into 43 characters;

Fig. 4 is the formation schematic diagram of path space positional information;

Fig. 5 be from the angle of Information Organization by urban road state be divided into macroscopic aspect, the inforamtion tree structural drawing of sight aspect and microcosmic point;

Fig. 6 is for to represent schematic diagram by spatial positional information, temporal information and Assessment of Serviceability of Roads grade with three dimensional space coordinate;

Fig. 7 is a kind of schematic diagram that the formation of the transport information cloud based on traffic GPS and transport information cloud service realize system;

Fig. 8 is the Assessment of Serviceability of Roads of certain section, city direction day and the change curve of average overall travel speed;

Fig. 9 is a kind of path planning cloud service software processing flow chart with human-computer interaction function;

Figure 10 is a kind of dynamic route planning cloud service software processing flow chart.

Embodiment

Below in conjunction with accompanying drawing, the invention will be further described.

Embodiment 1

With reference to Fig. 1～Figure 10, a kind of Traffic Information cloud computing and cloud service based on technology of Internet of things realizes system, comprise the vehicle GPS, GPS base station, relay station and the information center that are arranged on moving vehicle, described information center comprises for accepting with the data storage server of storing mobile vehicle GPS data, for the cloud computing server of perception traffic behavior, for Traffic Information Platform Server and the GIS server of transport information cloud service are provided; Between vehicle GPS, gps satellite, GPS base station, relay station and information center, communication as shown in Figure 2;

Described vehicle GPS comprises antenna element, receiving element, computing unit and three parts of mobile comm unit, as shown in Figure 1; The Main Function of antenna element is: in the time that gps satellite is from horizon, can catch, tracking satellite, receive and amplify gps signal; The Main Function of receiving element is: record gps signal and signal is separated to mediation filtering processing, restore the navigation message that gps satellite sends, solution is asked travel-time and the carrier phase difference of signal between the star of station; The Main Function of computing unit is: obtain in real time navigation positioning data or adopt the mode of after logging process, then calculate to obtain and locate, test the speed, regularly and the data such as travel direction; Finally send the information such as this vehicle location, speed and traffic direction by mobile comm unit take every 2 seconds as one sampling period;

What the receiving element in vehicle-mounted GPS equipment obtained is longitude and the latitude data based on the World Geodesic Coordinate System 1984 WGS-84 of U.S. Department of Defense, cannot directly calculate distance and road-section average speed from longitude and latitude data; Need to the longitude and latitude in WGS-84 coordinate system be converted into planimetric rectangular coordinates by Gauss projection, the distance of then passing through within a certain period of time according to vehicle is calculated average velocity; Therefore need in the computing unit of vehicle-mounted GPS equipment, calculate speed and the traffic direction of vehicle, WGS-84 coordinate system longitude and latitude (L, B) computing method that are converted to Gaussian plane rectangular coordinate (x, y) are as shown in formula (1)

$\left\{\begin{array}{c}x=X+\frac{\mathrm{<figure-callout\; id="2"\; label="Nt"\; filenames="HDA0000152858260000051.png"\; state="\{\{state\}\}">Nt</figure-callout>}}{2}{\mathrm{<figure-callout\; id="2"\; label="cos"\; filenames="HDA0000152858260000051.png"\; state="\{\{state\}\}">cos</figure-callout>}}^2{\mathrm{<figure-callout\; id="2"\; label="Bl"\; filenames="HDA0000152858260000051.png"\; state="\{\{state\}\}">Bl</figure-callout>}}^2+\frac{\mathrm{Nt}}{24}(5-{\mathrm{<figure-callout\; id="2"\; label="t"\; filenames="HDA0000152858260000051.png"\; state="\{\{state\}\}">t</figure-callout>}}^2+{9\mathrm{\&eta;}}^2+{4\mathrm{\&eta;}}^2){\cos }^4{\mathrm{Bl}}^4+\frac{\mathrm{Nt}}{720}(61-{58\mathrm{<figure-callout\; id="2"\; label="t"\; filenames="HDA0000152858260000051.png"\; state="\{\{state\}\}">t</figure-callout>}}^2+t^4){\mathrm{<figure-callout\; id="6"\; label="cos"\; filenames="HDA0000152858260000042.png,HDA0000152858260000051.png"\; state="\{\{state\}\}">cos</figure-callout>}}^6{\mathrm{Bl}}^6\\ y=N\cos \mathrm{Bl}+\frac{N}{6}(1-{\mathrm{<figure-callout\; id="2"\; label="t"\; filenames="HDA0000152858260000051.png"\; state="\{\{state\}\}">t</figure-callout>}}^2+{\mathrm{\&eta;}}^2){\mathrm{<figure-callout\; id="3"\; label="cos"\; filenames="HDA0000152858260000051.png"\; state="\{\{state\}\}">cos</figure-callout>}}^3{\mathrm{<figure-callout\; id="3"\; label="Bl"\; filenames="HDA0000152858260000051.png"\; state="\{\{state\}\}">Bl</figure-callout>}}^3+\frac{N}{120}(5-{18\mathrm{<figure-callout\; id="2"\; label="t"\; filenames="HDA0000152858260000051.png"\; state="\{\{state\}\}">t</figure-callout>}}^2+t^4+{14\mathrm{\&eta;}}^2-{58\mathrm{\&eta;}}^2{t}^2){\cos }^5{\mathrm{Bl}}^5\end{array}\right.---\left(1\right)$

In formula, l=L-L ₀, t=tanB, η ²=e ' ²cos ²b,

L, B are respectively calculation level geodetic longitude and latitude, and x, y are respectively Gaussian plane rectangular coordinate, and l is calculation level geodetic longitude L and the central meridian longitude L of projection zone ₀poor, N is prime vertical radius, the major semi-axis that a is reference ellipsoid, a=6378137m, the short radius that b is reference ellipsoid, b=6356752.3142m, the first excentricity that e is reference ellipsoid, e ²=0.00669437999013, e ²=0.00673949674227, X is the meridian arc length of equator to the latitude parallel circle that is B;

Vehicle GPS obtains in chronological order longitude, latitude and moment data point p+1 group, i.e. (L altogether in sampling time interval ₀, B ₀, t ₀, (L ₁, B ₁, t ₁), (L ₂, B ₂, t ₂) ... (L _p, B _p, t _p), the planimetric coordinates that calculates these points by formula (1) is (x ₀, y ₀), (x ₁, y ₁), (x ₂, y ₂) ... (x _p, y _p), corresponding sampling time interval is t _d=t _p-t ₀, by the average translational speed of formula (2) calculating a certain car within the sampling time,

$v_i=\frac{d}{t_d}=\frac{\underset{k=0}{\overset{p-1}{\mathrm{\&Sigma;}}}{[{(x_{k+1}-x_k)}^2+{(y_{k+1}-y_k)}^2]}^{1/2}}{t_p-t_0}---\left(2\right)$

In formula, v _ifor the average translational speed of a certain vehicle in sampling interval, d is the displacement of a certain vehicle in sampling interval, t _dfor sampling interval;

The formula for moving direction (3) of vehicle calculates,

$K_d={\tan }^{-1}\left(\frac{y_p-y_0}{x_p-x_0}\right)---\left(3\right)$

At present commercially available very big part vehicle-mounted GPS equipment can realize vehicle continuous location, test the speed, and can obtain the data such as its direction of motion, its positioning error in 15m, velocity estimation error in ± 1km/h, these precision indexs have met the computational accuracy requirement of Traffic Information cloud computing; In the present invention, road section length is divided into 0.8 kilometer, within the sampling time of 2 seconds, have 10 locomotives at least by this section, this is all available locomotive more than city for 2～3 grades, China, and taxi and bus also can meet the sampling precision requirement of Traffic Information cloud computing substantially;

Described data storage server comprises data acquisition module and data processing memory module, described data acquisition module receives the information such as the vehicle location, speed and the traffic direction that send over from each vehicle-mounted GPS equipment by mobile communications network, then described data processing memory module writes the information such as ID Bing Tongqi position, speed and the traffic direction of this vehicle-mounted GPS equipment in transport information cloud computing database;

Described cloud computing server comprises certain road-section average speed calculation module, certain section velocity variable computing module, all Assessment of Serviceability of Roads output modules in certain section Assessment of Serviceability of Roads determination module, city, by the image data in transport information cloud computing database is carried out to deep processing, calculate the Assessment of Serviceability of Roads situation that obtains certain point, certain line, certain region and whole city on urban road;

The output format of certain described section Assessment of Serviceability of Roads determination module is made up of following five parameter values, is respectively the moment parameter value of 14, the detection space location parameter value of 23, the travel direction property parameters value of 1, the Assessment of Serviceability of Roads parameter value of 1 and the vehicle average overall travel speed of 2; Moment parameter is for representing to detect the temporal information in moment, moment parameter Time represents with 14 bit data forms, for YYYYMMDDHHMMSS, wherein 1～4 YYYY represents that the year of the Gregorian calendar, 5～6 MM represent that the month of the Gregorian calendar, 7～8 DD represent that the day of the Gregorian calendar, 9～10 HH represent hour, 11～12 MM represent point, 13～14 SS represent second; Detection space location parameter is for representing the central point spatial positional information in certain section, and detection space location parameter SegmentID represents with 23 bit data forms; Driveway travel directions property parameters is for representing the travel direction information in track, driveway travel directions property parameters Direction represents with 1 bit data form, regulation deviates from the travel direction property parameters value i=0 of road starting end, towards the travel direction property parameters value i=1 of road starting end; Assessment of Serviceability of Roads parameter is for representing the congestion status of road, Assessment of Serviceability of Roads parameter S erviceLevel represents for alpha format with 1, Assessment of Serviceability of Roads grade is divided into 6 grades such as A, B, C, D, E, F, wherein A represents that Assessment of Serviceability of Roads grade is best, and F represents that the poorest grade of Assessment of Serviceability of Roads; A record of road section service level detection module output is to be made up of five parameter values such as Time+SegmentID+Direction+ServiceLevel+Speed like this, section travel direction on the road of every record and detection becomes one-to-one relationship,, 41 of the length of record, coded format is as shown in Figure 3;

Described space position parameter SegmentID comprises absolute position encoder, marks code, the natural number coding for the origin-to-destination to from road, branch road information coding for representing from the logic of the relative position of setting coordinate central point; Totally 23 codings as shown in Figure 4, wherein absolute position encoder is the most first 6, the 1st to the 3rd bit representation longitude, the 4th to the 6th bit representation latitude, such as the data that obtain are 120030,120 represent that east longitudes 120 spend, and 030 represents that north latitude 30 spends, and by leaving in, in infosystem, can to obtain corresponding city be Hangzhou; Logic mark code is the 7th to 17, region is divided into A, B, C, a D4 quadrant district by central point with city, the quadrant district at the starting point place of the 7th bit representation road, represent x, the y coordinate at two ends, street by 4 bit digital, the 8th the x coordinate to the 9th bit representation street starting point, the 10th the y coordinate to the 11st bit representation street starting point, the quadrant district at the terminal place of the 12nd bit representation road, the 13rd the x coordinate to the 14th bit representation street terminal, the 15th the y coordinate to the 16th bit representation street terminal; Be aided with the difference of lowercase order for two parallel and two ends x, street that y coordinate is identical, represent by the 17th figure place; Natural number coding is the 18th to the 22nd, according to from south to north, ascending numbering from the east to the west, least unit is 1cm, and layout is carried out at left single right two ends that extend to, for only have one-sided street crossing if adopt odd number layout at the left of road, in the right-hand employing even numbers layout of road; Branch road information coding is the 23rd, and branch road information coding N represents that front is obstructed, and L represents right turn ban, and R represents left turn ban;

Described logic mark code is 11, from the 7th to the 17th, for representing from intown relative position, its naming rule is: take center mark position, city as initial point, East and West direction is x axle, north-south is y axle, city is divided into A, B, C, a D4 quadrant district, consider that megalopolis is in district radius 100km, represent x, the y coordinate at two ends, street by 4 bit digital, for two parallel at a distance of within the scope of 1km and two ends x, street that y coordinate is identical can be aided with a, b, the difference of c....... order;

For the ease of carrying out transport information cloud computing, in the present invention, each city road is divided into several sections, the length in each section is 0.8Km, and the volume coordinate of the central point in the rear each section of definition division is as detection space location parameter; Because the positional information of storing at described data storage server is Gaussian plane rectangular coordinate, therefore need to set up the mapping relations of Gaussian plane rectangular coordinate and detection space location parameter, set up the volume coordinate (x of the central point in each section _j, y _j) with the mapping table of detection space location parameter SegmentID;

Certain described road-section average speed calculation module, for calculating the vehicle average velocity of SegmentID; First volume coordinate (the x that determines two frontier points take ± 0.4Km as radius in a certain travel direction centered by the SegmentID on certain road _j-n, y _j-n), (x _j+m, y _j+m); Then by sampling time, volume coordinate scope and vehicle operating direction retrieval qualified record in described data storage server, calculate the average velocity in each road section scope with all vehicles that dispose vehicle-mounted GPS equipment of identical moving direction with formula (4)

$v_{k,g}=\frac{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{v}_i}{n}---\left(4\right)$

In formula, v _{k, g}be illustrated on the g section of k article of road in sampling interval, i.e. the average translational speed of the vehicle of SegmentID, n is the sum that disposes the vehicle of vehicle-mounted GPS equipment in sampling interval on the g section of k article of road, v _ifor the average translational speed of i vehicle in sampling interval;

Certain described section velocity variable computing module, for calculating the changes in vehicle speed rate within the scope of SegmentID, computing method as shown in formula (5),

${\mathrm{cov}}_{k,g}=\frac{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{(1-v_i/v_{k,g})}^2}{n}\&times;100---\left(5\right)$

In formula, cov _{k, g}be illustrated in the changes in vehicle speed rate on the g section of k article of road in sampling interval, i.e. changes in vehicle speed rate within the scope of SegmentID; v _{k, g}be illustrated in the average translational speed of vehicle on the g section of k article of road in sampling interval, i.e. the average translational speed of vehicle within the scope of SegmentID; N is the sum that disposes the vehicle of vehicle-mounted GPS equipment in sampling interval on the g section of k article of road, i.e. the sum of the vehicle that disposes vehicle-mounted GPS equipment within the scope of SegmentID; v _ifor the average translational speed of i vehicle in sampling interval, i.e. the average translational speed of i vehicle in sampling interval within the scope of SegmentID;

Described road section service level determination module, for judging the service level of current road section, is divided into 6 grades such as A, B, C, D, E, F by road section service level grade, and the step of decision process is as follows:

The service level grade of road section comprehensively judges that table is as shown in table 1;

Table 1

In upper table, Max-S is just decided to be 95, Mid-S and is just decided to be 75, Low-S and is just decided to be 35, Min-S is just decided to be 1, Max-cov is just decided to be 99, cov-75 and is just decided to be 75, cov-70 and is just decided to be 70, cov-55 is just decided to be 55, cov-45 is just decided to be 45, cov-35 and is just decided to be 35, cov-25 and is just decided to be 25, cov-15 is just decided to be 15, cov-10 and is just decided to be 10;

All Assessment of Serviceability of Roads output modules in described city, for processing, calculate and export the Assessment of Serviceability of Roads on each section of every road, output data layout is as shown in Figure 3; Time value is set by sampling instant value, data layout is YYYYMMDDHHMMSS, SegmentID value is worth to set by the locus of the central point in each section, and Direction value is the travel direction that 0 expression deviates from road starting end, and 1 represents the travel direction towards road starting end; Time value, SegmentID ± 0.4Km value and Direction value retrieval qualified record in described data storage server, set the value of Speed with formula (4) calculating average velocity, set the value of ServiceLevel by the judged result of table 1;

Described Traffic Information Platform Server and GIS server, for providing the various cloud services of urban highway traffic; For the cloud service of urban transportation, comprising: the Assessment of Serviceability of Roads cloud service pattern in the cloud service pattern that 24 hours situations of the traffic behavior in certain section are issued, the path planning cloud service pattern with human-computer interaction function, dynamic route planning cloud service pattern, city based on GIS; The present invention from the angle of Information Organization by urban road state be divided into macroscopic aspect, the inforamtion tree structural drawing of sight aspect and microcosmic point, as shown in Figure 5;

The cloud service pattern of the traffic behavior situation issue in 24 hours in certain described section, for investigating the traffic behavior in certain section; Because the present invention is 2 seconds to the detection sampling interval of urban road traffic state, so for the central point in each section, will in described cloud computing server, produce a length every 2 seconds be the record of 41, utilize these data can be processed into easily various statistical forms, for section central point be the upper Youth League school of Hangzhou Wen Erlu, eastwards travel direction, on April 20th, 2012 from 1 o'clock to 23: 59: 58, can obtain traffic behavior change curve in as shown in Figure 8 one day by the statistics processing to 43200 record data; User by man-machine interface fixed time section obtain this time period traffic behavior change curve, specify certain section, certain travel direction and certain time period to obtain the traffic behavior change curve of certain section and certain time period; Can obtain by the statistics processing to 43200 record data the road traffic service change situation that spatial positional information, temporal information and Assessment of Serviceability of Roads grade represent with three dimensional space coordinate as shown in Figure 6;

The described path planning cloud service pattern with human-computer interaction function, for allowing user specify departure place and destination to obtain optimum path planning and navigation by man-machine interface; Specific practice is: described cloud computing server is retrieval path from origin to destination automatically, go out from one of origin-to-destination close together or some paths as traffic route at electronic map marker, then the running time spending according to the every paths of these path computing, calculation process as shown in Figure 9, user is by man-machine interface according to just having completed path planning after a certain traffic route of the shown Information Selection such as path and running time, and navigating instrument enters navigational state;

Illustrate that with accompanying drawing 9 one of path planning realizes system below,

S1, step 1: user specifies departure place and destination by man-machine interface, described cloud computing server parses service content and starting point, endpoint information, according to starting point, endpoint information generates one or some alternative paths automatically, one paths of every generation is all the set Route of several SegmentID, also generated the chained list that represents several SegmentID annexations simultaneously, the algorithm of generation pass adopts A* algorithm, the list of references that A* algorithm is realized is: T.A.J.Nicholson.Finding the Shortest Route Between Two Points in a Network.The Computer Journal, 1966, 9 (3): 275-280,

S2, step 2: determine selecting paths from some alternative paths;

S3, step 3 a: SegmentID who reads driving path in the chained list from selecting paths;

S4, step 4: directly obtain the vehicle average running speed of this up-to-date SegmentID from a SegmentID information from the record of described cloud computing server;

S5, step 5: from the vehicle average running speed estimation of this SegmentID by this SegmentID to the needed time of distance a upper SegmentID chained list, owing to all having comprised the spatial positional information of this SegmentID in each SegmentID, therefore, between two adjacent SegmentID, distance can directly calculate;

S6, step 6: read next SegmentID information from chained list;

S7, step 7: directly obtain the vehicle average running speed of this up-to-date SegmentID from this SegmentID information from the record of described cloud computing server;

S8, step 8: from the vehicle average running speed estimation of this SegmentID by this SegmentID to the needed time of distance a upper SegmentID chained list;

S9, step 9: calculating path is from the starting point prediction cumulative time of SegmentID up till now;

S10, step 10: judge whether to have arrived terminal, if words forward S11 to; Do not meet Rule of judgment, continue to calculate the required time of next SegmentID, forward S6 to;

S11, step 11: the prediction cumulative time of calculating path from starting point to terminal, and preserve these data and be used for exporting to user;

S12, step 12: judge whether to have traveled through all alternative paths, if words export the prediction running time of all alternative paths, termination routine; If do not meet Rule of judgment, continue to calculate the prediction running time of next alternative path, forward S3 to;

Described dynamic route planning cloud service pattern, for according to the continuous variation of user designated destination and real-time traffic, cooks up optimal path, specific practice is: position and the travel direction of the current vehicle of GPS navigation device automatic acquisition, described cloud computing server automatically retrieval from the path to destination now, then the running time spending according to the every paths of these path computing, this service mode is mainly considered the dynamic change of road condition, as the situations such as traffic hazard are there is, therefore need according to present road traffic state for dynamically adjusting driving path, calculation process as shown in Figure 10, calculation process and accompanying drawing 9 are similar, difference is starting point and vehicle present position, the information of vehicle present position is to be constantly updated by the location of GPS navigation instrument, this cloud service pattern is to belong to a kind of minimum running time dynamic route planning, thereby help traveler according to the continuous variation of real-time traffic, cook up optimal path,

Certainly also have some other traffic cloud service pattern, such as bee-line path planning, optimal path planning and minimum cost path planning, these paths planning methods all belong to prior art;

The Assessment of Serviceability of Roads cloud service pattern in the described city based on GIS, for making a general survey of the Assessment of Service Level for Urban Roads state of browsing; Specific practice is: user browses certain point on urban road, the Assessment of Serviceability of Roads situation in certain line, certain region and whole city by generalized information system; First be to determine its corresponding SegmentID according to selected region, then read the state-of-the-art record in described cloud computing server according to these SegmentID, by the road service state in corresponding SegmentID record, use color marking on the section of the corresponding generalized information system of these SegmentID by ServiceLevel value, make user's very clear Assessment of Serviceability of Roads visual information that obtains urban road macroscopic view, middle sight and microcosmic from urban road CIS; The service level grade table corresponding to marker color of road section is as shown in table 2,

Service level grade	The color representing on GIS
		A	Blue
B	Blue yellow
		C	Yellow
D	Yellowish orange
		E	Orange
F	Red

The service level grade table corresponding to marker color of table 2 road section.

Embodiment 2

A kind of Traffic Information cloud computing and cloud service implementation method based on technology of Internet of things, the process of road traffic cloud service implementation method is as follows: user sets terminal by network computing device, network computing device can be PC, mobile phone, navigating instrument etc., the terminal position that described cloud computing server end is set according to user, generate a URL, and this URL is sent to described Traffic Information Platform Server and GIS server, then wait for the result that described Traffic Information Platform Server and GIS server are beamed back.Described Traffic Information Platform Server and GIS server are receiving after the URL that user sends, this URL is resolved, obtain traffic cloud service type, as path computing, map overview, location, information inquiry, CMMB service etc., cloud service type can obtain by the value of type in URL; Then determine to carry out which type of operation according to COS; Then according to the { start} and { value of end}, judges the SegmentID value of given start-stop node in URL; Obtaining after SegmentID, judge that whether given start-stop SegmentID is effective, determining be effective SegmentID in the situation that, server calculates two the shortest internodal and some second shortest paths, then the running time that provides different paths according to path is for user's selection, after user has selected certain paths, navigating instrument just enters navigational state, and according to the real-time condition correction guidance path of road.

The cloud computing of road traffic and the cloud service of road traffic are a kind of Information ecology circulation theories of obtaining, transmit, calculating and apply about transport information, as shown in Figure 7.This theory is paid attention to the value of information, emphasize that Traffic Information should flow and accumulate traffic cloud, and traffic cloud is carried out to deep processing, finally pursue the road traffic cloud service of high-quality, information by effective is carried out traffic behavior control, the operation of final road improvement traffic.

Claims (9)

1. Traffic Information cloud computing and the cloud service based on technology of Internet of things realizes system, it is characterized in that: comprise the vehicle GPS, GPS base station, relay station and the information center that are arranged on moving vehicle, described information center comprises for accepting with the data storage server of storing mobile vehicle GPS data, for the cloud computing server of perception traffic behavior, for Traffic Information Platform Server and the GIS server of transport information cloud service are provided;

Described vehicle GPS comprises antenna element, receiving element, computing unit and mobile comm unit;

Described data storage server comprises data acquisition module and data processing memory module;

Described cloud computing server, in order to by the image data in transport information cloud computing database is carried out to deep processing, calculates the Assessment of Serviceability of Roads situation that obtains certain point, certain line, certain region and whole city on urban road; Comprise road-section average speed calculation module, section velocity variable computing module, all Assessment of Serviceability of Roads output modules in section Assessment of Serviceability of Roads determination module and city;

Described Traffic Information Platform Server and GIS server, for providing the various cloud services of urban highway traffic: the Assessment of Serviceability of Roads cloud service pattern in the cloud service pattern that 24 hours situations of the traffic behavior in section are issued, the path planning cloud service pattern with human-computer interaction function, dynamic route planning cloud service pattern, city based on GIS; In described section Assessment of Serviceability of Roads determination module, output format is made up of following five parameter values, is respectively the moment parameter value of 14, the detection space location parameter value of 23, the travel direction property parameters value of 1, the Assessment of Serviceability of Roads parameter value of 1 and the vehicle average overall travel speed of 2; Moment parameter is for representing to detect the temporal information in moment, moment parameter Time represents with 14 bit data forms, for YYYYMMDDHHMMSS, wherein 1～4 YYYY represents that the year of the Gregorian calendar, 5～6 MM represent that the month of the Gregorian calendar, 7～8 DD represent that the day of the Gregorian calendar, 9～10 HH represent hour, 11～12 MM represent point, 13～14 SS represent second; Detection space location parameter is for representing the central point spatial positional information in certain section, and detection space location parameter SegmentID represents with 23 bit data forms; Driveway travel directions property parameters is for representing the travel direction information in track, driveway travel directions property parameters Direction represents with 1 bit data form, regulation deviates from the travel direction property parameters value i=0 of road starting end, towards the travel direction property parameters value i=1 of road starting end; Assessment of Serviceability of Roads parameter is for representing the congestion status of road, Assessment of Serviceability of Roads parameter S erviceLevel represents for alpha format with 1, Assessment of Serviceability of Roads grade is divided into A, B, C, D, E, a F6 grade, wherein A represents that Assessment of Serviceability of Roads grade is best, and F represents that the poorest grade of Assessment of Serviceability of Roads; A record of road section service level detection module output is to be made up of five parameter values of Time+SegmentID+Direction+ServiceLevel+Speed like this, section travel direction on the road of every record and detection becomes one-to-one relationship, 41 of the length of a record.

2. Traffic Information cloud computing and the cloud service based on technology of Internet of things as claimed in claim 1 realizes system, it is characterized in that: described road-section average speed calculation module, for calculating the vehicle average velocity of SegmentID, the first volume coordinate (x that determines two frontier points take ± 0.4Km as radius in a certain travel direction centered by the SegmentID on certain road _j-n, y _j-n), (x _j+m, y _j+m); Then by sampling time, volume coordinate scope and vehicle operating direction retrieval qualified record in described data storage server, calculate the average velocity in each road section scope with all vehicles that dispose vehicle-mounted GPS equipment of identical moving direction with formula (4)

$v_{k,g}=\frac{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{v}_i}{n}---\left(4\right)$

In formula, v _k,gbe illustrated on the g section of k article of road in sampling interval, i.e. the average translational speed of the vehicle of SegmentID, n be in sampling interval on the g section of k article of road, within the scope of SegmentID, dispose the sum of the vehicle of vehicle-mounted GPS equipment, v _ifor in sampling interval within the scope of SegmentID the average translational speed of i vehicle.

3. Traffic Information cloud computing and the cloud service based on technology of Internet of things as claimed in claim 2 realizes system, it is characterized in that: described section velocity variable computing module, for calculating the changes in vehicle speed rate within the scope of SegmentID, computing method as shown in Equation (5)

${\mathrm{cov}}_{k,g}=\frac{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{(1-v_i/v_{k,g})}^2}{n}\&times;100---\left(5\right)$

In formula, cov _k,gbe illustrated in the changes in vehicle speed rate on the g section of k article of road in sampling interval, i.e. changes in vehicle speed rate within the scope of SegmentID; v _k,gbe illustrated in the average translational speed of vehicle on the g section of k article of road in sampling interval, i.e. the average translational speed of vehicle within the scope of SegmentID; N is the sum that disposes the vehicle of vehicle-mounted GPS equipment in sampling interval on the g section of k article of road, i.e. the sum of the vehicle that disposes vehicle-mounted GPS equipment within the scope of SegmentID; v _ifor the average translational speed of i vehicle in sampling interval, i.e. the average translational speed of i vehicle in sampling interval within the scope of SegmentID.

4. Traffic Information cloud computing and the cloud service based on technology of Internet of things as claimed in claim 1 realizes system, it is characterized in that: all Assessment of Serviceability of Roads output modules in described city, for processing, calculate and export the Assessment of Serviceability of Roads on each section of every road, Time value is set by sampling instant value, data layout is YYYYMMDDHHMMSS, SegmentID value is worth to set by the locus of the central point in each section, Direction value is the travel direction that 0 expression deviates from road starting end, 1 represents the travel direction towards road starting end, Time value, SegmentID ± 0.4Km value and Direction value retrieval qualified record in described data storage server, set the value of Speed with formula (4) calculating average velocity, set the value of ServiceLevel by the judged result of table 1,

Table 1

In upper table, Max-S is just decided to be 95, Mid-S and is just decided to be 75, Low-S is just decided to be 35, Min-S and is just decided to be 1, Max-cov and is just decided to be 99, cov-75 is just decided to be 75, cov-70 is just decided to be 70, cov-55 and is just decided to be 55, cov-45 and is just decided to be 45, cov-35 is just decided to be 35, cov-25 is just decided to be 25, cov-15 and is just decided to be 15, cov-10 and is just decided to be 10.

5. Traffic Information cloud computing and the cloud service based on technology of Internet of things as claimed in claim 1 realizes system, it is characterized in that: the cloud service pattern of the traffic behavior situation issue in 24 hours in described section, for investigating the traffic behavior in certain section; User by man-machine interface fixed time section obtain this time period traffic behavior change curve, specify certain section, certain travel direction and certain time period to obtain the traffic behavior change curve of certain section and certain time period.

6. Traffic Information cloud computing and the cloud service based on technology of Internet of things as claimed in claim 1 realizes system, it is characterized in that: the described path planning cloud service pattern with human-computer interaction function, for allowing user specify departure place and destination to obtain optimum path planning and navigation by man-machine interface; Specific practice is: described cloud computing server is retrieval path from origin to destination automatically, go out from one of origin-to-destination close together or some paths as traffic route at electronic map marker, then the running time spending according to the every paths of these path computing, user is by man-machine interface according to just having completed path planning after a certain traffic route of the Information Selection of shown path and running time, and navigating instrument enters navigational state;

Performing step is as follows,

S1, step 1: user specifies departure place and destination by man-machine interface, described cloud computing server parses service content and starting point, endpoint information, automatically generate one or some alternative paths according to starting point, endpoint information, one paths of every generation is all the set Route of several SegmentID, also generated the chained list that represents several SegmentID annexations, the algorithm of generation pass adopts A* algorithm simultaneously;

S2, step 2: determine selecting paths from some alternative paths;

S3, step 3 a: SegmentID who reads driving path in the chained list from selecting paths;

S4, step 4: directly obtain the vehicle average running speed of this up-to-date SegmentID from a SegmentID information from the record of described cloud computing server;

S5, step 5: from the vehicle average running speed estimation of this SegmentID by this SegmentID to the needed time of distance a upper SegmentID chained list, owing to all having comprised the spatial positional information of this SegmentID in each SegmentID, therefore, between two adjacent SegmentID, distance can directly calculate;

S6, step 6: read next SegmentID information from chained list;

S7, step 7: directly obtain the vehicle average running speed of this up-to-date SegmentID from this SegmentID information from the record of described cloud computing server;

S8, step 8: from the vehicle average running speed estimation of this SegmentID by this SegmentID to the needed time of distance a upper SegmentID chained list;

S9, step 9: calculating path is from the starting point prediction cumulative time of SegmentID up till now;

S10, step 10: judge whether to have arrived terminal, if words forward S11 to; Do not meet Rule of judgment, continue to calculate the required time of next SegmentID, forward S6 to;

S11, step 11: the prediction cumulative time of calculating path from starting point to terminal, and preserve these data and be used for exporting to user;

S12, step 12: judge whether to have traveled through all alternative paths, if words export the prediction running time of all alternative paths, termination routine; If do not meet Rule of judgment, continue to calculate the prediction running time of next alternative path, forward S3 to.

7. Traffic Information cloud computing and the cloud service based on technology of Internet of things as claimed in claim 1 realizes system, it is characterized in that: described dynamic route planning cloud service pattern, for according to the continuous variation of user designated destination and real-time traffic, cook up optimal path, specific practice is: position and the travel direction of the current vehicle of GPS navigation device automatic acquisition, described cloud computing server automatically retrieval from the path to destination now, then the running time spending according to the every paths of these path computing, this service mode is mainly considered the dynamic change of road condition, in the situation that having there is traffic hazard, need to be according to present road traffic state for dynamically adjusting driving path, the information of vehicle present position is to be constantly updated by the location of GPS navigation instrument, this cloud service pattern is to belong to a kind of minimum running time dynamic route planning, thereby help traveler according to the continuous variation of real-time traffic, cook up optimal path.

8. Traffic Information cloud computing and the cloud service based on technology of Internet of things as claimed in claim 1 realizes system, it is characterized in that: the Assessment of Serviceability of Roads cloud service pattern in the described city based on GIS, for making a general survey of the Assessment of Service Level for Urban Roads state of browsing; Specific practice is: user browses certain point on urban road, the Assessment of Serviceability of Roads situation in certain line, certain region and whole city by generalized information system; First be to determine its corresponding SegmentID according to selected region, then read the state-of-the-art record in described cloud computing server according to these SegmentID, by the road service state in corresponding SegmentID record, use color marking on the section of the corresponding generalized information system of these SegmentID by ServiceLevel value, make user's very clear Assessment of Serviceability of Roads visual information that obtains urban road macroscopic view, middle sight and microcosmic from urban road CIS; The service level grade table corresponding to marker color of road section is as shown in table 2,

Service level grade The color representing on GIS A Blue B Blue yellow C Yellow D Yellowish orange E Orange F Red

Table 2.

9. a Traffic Information cloud computing based on technology of Internet of things as claimed in claim 1 and cloud service realize the road traffic cloud service implementation method of system, it is characterized in that: the process of described road traffic cloud service implementation method is as follows: user sets terminal by network computing device, network computing device is PC, mobile phone or navigating instrument, the terminal position that described cloud computing server end is set according to user, generate a URL, and this URL is sent to described Traffic Information Platform Server and GIS server, then wait for the result that described Traffic Information Platform Server and GIS server are beamed back, described Traffic Information Platform Server and GIS server are receiving after the URL that user sends, this URL is resolved, obtain traffic cloud service type, as path computing, map overview, location, information inquiry, CMMB service, cloud service type can obtain by the value of type in URL, then determine to carry out which type of operation according to COS, then according to the { start} and { value of end}, judges the SegmentID value of given start-stop node in URL, obtaining after SegmentID, judge that whether given start-stop SegmentID is effective, determining be effective SegmentID in the situation that, server calculates two the shortest internodal and some second shortest paths, then the running time that provides different paths according to path is for user's selection, after user has selected certain paths, navigating instrument just enters navigational state, and according to the real-time condition correction guidance path of road.

Images