CN102663887A - 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 that GPS location and velocity measuring technique, GIS technology, cloud computing are technological, urban road digital coding and the network communications technology be in the application of road traffic state context of detection, especially a kind of intelligent 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 performance of city " traffic illness "." cause of disease " of urban traffic blocking comes from multiple factor, and the trip quality of the direct affect people's of traffic congestion, 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 serious day by day traffic problems and modern transportation, and promptly sensible, orderly, safe, comfortable, low energy consumption, to hang down requirement such as pollution be contrary fully.

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

The traffic information collection technology is considered to the gordian technique of a most important thing in the intelligent transportation, and traffic information collection technology commonly used at present has ground induction coil, magneto-dependent sensor, ultrasonic sensor, microwave, GPS and vision sensor; Because transport information detecting sensors such as ground induction coil, magnetosensitive, ultrasound wave, microwave need be embedded in below the road ground; Must destroy original road surface during I&M; Influenced road traffic, simultaneously the road of China road surface of causing owing to the reasons such as overload of vehicle is damaged and must be often the sensor that is embedded in below the road be safeguarded; In addition these detection meanss can only perception go out on certain point or certain the bar line on the road the vehicle of process, therefore can only infer congestion indirectly in the speed of passing through vehicle that the place is set of sensor; Defectives such as therefore above-mentioned detection means exists that installation and maintenance inconvenience, cost of investment are high, poor anti jamming capability and sensing range are limited.

The 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 the congestion of the driving trace road corresponding of the vehicle that GPS confirms; In the correct judgement of carrying out congestion, can reduce number of communications and amount of communication data that the signal post between vehicle and the information center relates to, can realize the low volumeization with communication cost that alleviates of communication process burden.This road traffic state detection means exists certain defective, owing to do not use the cloud computing technology, infers that through Vehicular behavior road traffic state exists problems such as one-sidedness, locality and subjectivity simply; The Chinese invention patent application number is 200510026478.2 to disclose a kind of traffic method for measuring of surface road net and system of can be used for; This system adopts crossing, arterial street, urban main road network successively to measure for three layers to urban road; To the arterial street, " the equivalent traffic capacity " notion and definite method are proposed; Adopt " density ratio " index, the service level scale value of the service level scale value curve calculation arterial highway that provides according to the present invention is measured; Measuring the result based on the arterial highway adopts " weighting density ratio " index that the mains service level is measured; Carrying out congested area, crowded arterial highway and crowded crossing according to the mensuration result successively discerns.This traffic method for measuring does not relate to most crucial road traffic state data as yet and obtains problem.The Chinese invention patent application number is 200810132938.3 to disclose a kind of Intellective traffic information system and disposal route thereof, comprises the GPS module, is used to provide global positioning information; With the portable terminal that the GPS module communicates, it is connected with cordless communication network; The intelligent transportation information server, it is connected with cordless communication network and according to mobile terminal request Real-time Traffic Information is provided.This Intellective traffic information system and disposal route thereof do not relate to most crucial road traffic state data yet and obtain problem.The Chinese invention patent application number is 200810034716.8 to disclose road traffic state determination methods and system; This system with a plurality of traffic parameters as basis for estimation; Set up funtcional relationship to different highway sections simultaneously, given weight has improved the accuracy that traffic behavior is judged.This method comprises: a plurality of traffic parameters are chosen in (1); (2), set the above-mentioned a plurality of traffic parameters and the funtcional relationship between its pairing crowding coefficient in this highway section and set these a plurality of traffic parameters shared weighted value in this highway section degree of crowding is judged through sampling analysis to this road section traffic volume parameter; (3) be used for each state and judge the end of term in week, gather above-mentioned a plurality of traffic parameters in this highway section in real time and, calculate the pairing crowding coefficient of each traffic parameter according to the function that sets; (4) weighted value of each traffic parameter crowding coefficient pairing with it done the weighted mean computing, obtain the mean crowding coefficient; (5) compare mean crowding coefficient and crowding coefficient threshold value, thereby judge road traffic state.This judgment mode needs a plurality of traffic parameter supports, and operand is big, and will on all main roads of city, obtain these traffic parameters simultaneously also is an easy thing, needs very big input and maintenance.

Cloud computing is a kind of calculating form that the Internet of Things era development is got up, and has embodied a kind of new Information Service Mode.The transport information cloud is the mode of operation of a kind of traffic information collection, processing and application.The key element that constitutes the transport information cloud is mainly: (1) is stored the transport information of magnanimity on the network into through communication; (2) storage of the transport information of magnanimity can not be restricted, and is used for the required computing power of depth perception traffic and can be restricted; (3) the transport information cloud computing can provide personalized, diversified information service for different users such as country, city, industry and travelers; (4) the transport information cloud can provide computing basic facility, computing platform, traffic software and traffic basic data for the user, and the service that is provided need not to install specific software, is convenient to traveler and on mobile devices such as mobile phone, navigating instrument, uses; (5) any transport information all is valuable, tradable, and the value of transport information has been amplified in cloud computing through the service of transport information.The Traffic Information cloud is the transport information overall process that is made up of cloud computing and information cloud service, and cloud computing is method or means, and the transport information cloud service is a purpose.

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

Cheu, Xie and Lee have studied the reliability of estimating the major trunk roads average speed with the vehicle speed data of moving vehicle; Result of study shows that absolute error needs 4%～5% locomotive or has 10 locomotives in the sampling period at least through this highway section less than 5km/h in 95% time as satisfying.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 returned according to vehicle GPS (longitude, latitude, speed, constantly) are calculated road-section average speed.

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

In the horizontal appraisement system of evaluation path transport services; Most crucial problem is the detection of vehicle flowrate, congestion status and the average speed of road, and therefore optimal detection means is to measure to real time direct vehicle flowrate, the average speed on the road and the length of blocking up simultaneously.

Commercial at present obtaining mainly contains following three kinds of modes on the road traffic real time data means: 1) annular coil induction type checkout equipment, detect 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 be embedded in annular coil on the road surface, and need destroy the road surface at 1 year about half when maintenance and installation serviceable life, belongs to contact and measure; 2) long-range traffic microwave detector is collected the data such as vehicle flowrate, roadway occupancy and average velocity in each track; This pick-up unit cost is high; 3) based on car plate identification detector and queue length detecting device, extend the car plate identification detector and the queue length detecting device at the stop line place in highway section through being installed in the crossing, utilize the queue length detecting device to obtain queue length L; The vehicle number N of moment t when utilizing the car plate identification detector to obtain vehicle and process detecting device through detecting device; The video detection system that possesses license plate identification, the identity through the identification vehicle detects hourage and the travel speed of motor vehicle on certain road, is thisly existing some problems aspect limitation and the real-time as the road traffic state detection means.These detection meanss belong to objectivity and detect, and are significant aspect the road traffic investigation.But the common problem of this detection means is to obtain indirectly vehicle flowrate and average speed through statistics then through the ruuning situation of each vehicle on the measurement road; Aspect implementation and operation, exist some defective, particularly existing deficiency aspect the evaluation indexes such as real-time, enforcement maintenance cost, calculating pressure and sensitivity index to the road service level.

At present the live transport information of the XML form that obtains from relevant traffic information service providers of some area of China was upgraded once in per 5 minutes, and content comprises that road section ID, highway section are initial, average overall travel speed (km/h) and time (yyyy-mm-dd hh:mm:ss).Yet this live telecast transport information can not reflect road conditions truly then for traveler provides the optimal path with Link Travel Time, and concrete shortcoming is following: (1) road information is difficult to and the map of navigation electronic Data Matching.We the live transport information road ID that obtains is that traffic provider is self-defining at present, can't realize mating through road ID: (2) Link Travel Time does not have direction to describe.Traffic network is directive, and for example in highway section, a certain north and south, being blocked up owing to traffic hazard shows as to northern possibility by south then maybe be unblocked to south by north.And should the live telecast transport information not have sake of clarity; (3) live transport information limited coverage area.Do not set up the road traffic flow real-time dynamic information system as yet fully, the per transport information that was provided in 5 minutes of relevant departments can only be static, quasi real time.Simultaneously, because Link Travel Time is relevant through the moment in highway section with vehicle, but the moment of searching for certain highway section of vehicle arrival when beginning is unknown.Original unimpeded highway section possibly stopped up before user's no show during the search beginning.

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

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

Summary of the invention

Transport information big for the limitation of the detection that overcomes existing Traffic Information, that transport information road ID is difficult to does not have direction to describe, provided with map of navigation electronic Data Matching, Link Travel Time can only be static, quasi real time, be difficult to from macroscopic view, sight, three angles of microcosmic, from people's deficiencies such as subjective feeling Real-Time Evaluation road traffic service level state; The present invention provide a kind of and have that sensing range is wide, accuracy of detection is high, detect real-time good, implement convenient, testing result is simple and clear; Promptly there is subjective feeling property achievement data that objective evaluation property achievement data is arranged again, and is convenient to city road networks at different levels and on time, space, realizes system and method based on the Traffic Information cloud computing of technology of Internet of things and cloud service what road traffic state carried out comprehensive evaluation.

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 installed on the moving vehicle, comprise data storage server, the cloud computing server that is used for the perception traffic behavior that is used to accept and store the moving vehicle gps data, Traffic Information Platform Server and the GIS server that is used to provide the transport information cloud service in the described information center; 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 through the image data in the transport information cloud computing database is carried out deep processing, calculates the road service level situation that obtains certain point, certain bar line, certain zone and entire city on the urban road; Comprise road-section average speed calculation module, highway section velocity variable computing module, all road service level output modules in highway section road service level determination module and the city;

Described Traffic Information Platform Server and GIS server are used to provide the various cloud services of urban highway traffic: the cloud service pattern of 24 hours situation of the traffic behavior in highway section issue, the path planning cloud service pattern with human-computer interaction function, dynamic route planning cloud service pattern, based on the road service level cloud service pattern in the city of GIS.

Further; In the road service level determination module of described highway section; Output format is made up of following five parameter values, is respectively 14 moment parameter value, 23 detection space position parameter value, 1 travel direction property parameters value, 1 road service level parameter value and 2 vehicle average overall travel speed; Parameter is used to represent to detect temporal information constantly constantly; Parameter Time representes with 14 bit data forms constantly; Be YYYYMMDDHHMMSS, wherein 1～4 YYYY representes 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 that branch, 13～14 SS represent second; Detect the central point spatial positional information that space position parameter is used to represent certain highway section, detect space position parameter SegmentID and represent with 23 bit data forms; The driveway travel directions property parameters is used to represent the travel direction information in track; Driveway travel directions property parameters Direction representes 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; Road service level parameter is used to represent the congestion status of road; Road service level parameter S erviceLevel uses 1 to represent as alpha format; Road service level grade is divided into 6 grades such as A, B, C, D, E, F; Wherein A representes that road service level grade is best, and F representes that the poorest grade of road service level; 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; Highway 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; Be used to calculate the vehicle average velocity of SegmentID, at first with the SegmentID on certain bar road be on a certain travel direction at center with ± 0.4Km is the volume coordinate (x that radius is confirmed two frontier points _J-n, y _J-n), (x _J+m, y _J+m); Then through sampling time, volume coordinate scope and vehicle operating direction retrieval qualified record in described data storage server; Calculate all that have identical moving direction in each road section scope with formula (4) and dispose the average velocity of the vehicle of vehicle GPS equipment

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

In the formula, v _{K, g}Be illustrated on the g highway section of k bar road in the SI, i.e. the average translational speed of the vehicle of SegmentID, n be in the SI on the g highway section of k bar road, promptly dispose the sum of the vehicle of vehicle GPS equipment in the SegmentID scope, v _iFor in the SI in the SegmentID scope the average translational speed of i vehicle.

Further, described highway section velocity variable computing module is used to calculate the changes in vehicle speed rate in the SegmentID scope, computing method 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 the formula, cov _{K, g}Be illustrated in the changes in vehicle speed rate on the g highway section of k bar road in the SI, i.e. changes in vehicle speed rate in the SegmentID scope; v _{K, g}Be illustrated in the average translational speed of vehicle on the g highway section of k bar road in the SI, i.e. the average translational speed of vehicle in the SegmentID scope; N is for disposing the sum of the vehicle of vehicle GPS equipment, the i.e. sum of the vehicle that disposes vehicle GPS equipment in the SegmentID scope on the g highway section of k bar road in the SI; v _iBe the average translational speed of i vehicle in the SI, i.e. the average translational speed of i vehicle in the SI in the SegmentID scope.

All road service level output modules in the described city; Be used to process, calculate and export the road service level on each highway section of every road; The Time value is set by the sampling instant value; Data format is YYYYMMDDHHMMSS; The SegmentID value is worth by the locus of the central point in each highway section to be set; The Direction value is that 0 expression deviates from the travel direction of road starting end, and 1 expression is towards the travel direction of road starting end; Time value, SegmentID ± 0.4Km value and Direction value retrieval qualified record in described data storage server calculate the value that average speed is set Speed with formula (4), set the value of ServiceLevel with the judged result of table 1;

Table 1

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

The cloud service pattern of 24 hours situation of the traffic behavior in described highway section issue is used to investigate the traffic behavior in certain highway section; The user through man-machine interface fixed time section obtain this time period the traffic behavior change curve, specify certain highway section, certain travel direction and certain time period to obtain the traffic behavior change curve of certain highway section and certain time period.

Described path planning cloud service pattern with human-computer interaction function is used for letting the user specify departure place and destination to obtain optimum path planning and navigation through man-machine interface; Specific practice is: described cloud computing server is the path of retrieval from the departure place to the destination automatically; Go out from one of the origin-to-destination close together or some paths as traffic route at electronic map marker; The running time that is spent according to the every paths of these path computing then; The user has just accomplished path planning through man-machine interface after according to a certain traffic routes of Information Selection such as path that is shown and running time, and navigating instrument gets into navigational state;

Performing step is following,

S1; Step 1: the user specifies departure place and destination through man-machine interface; Described cloud computing server parses service content and starting point, endpoint information, generates one or some alternative paths automatically according to starting point, endpoint information, and a paths of every generation all is the set Route of several SegmentID; The chained list of representing several SegmentID annexations, the algorithm use A* algorithm of generation pass have also been generated simultaneously;

S2, step 2: from some alternative paths, confirm to select the path;

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

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

S5; Step 5: from the estimation of the vehicle average running speed of this SegmentID through this SegmentID needed time of distance the last SegmentID to the chained list; Owing in each SegmentID, all comprised the spatial positional information of this SegmentID; Therefore, distance can directly calculate between two adjacent SegmentID;

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

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

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

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

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

S11, step 11: calculating path is from the prediction cumulative time that starting point is breasted the tape, and preserves these data and be used to export to the user;

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

Described dynamic route planning cloud service pattern is used for the continuous variation according to user designated destination and real-time traffic, cooks up optimal path; Specific practice is: the GPS navigation device obtains the position and the travel direction of present vehicle automatically; Described cloud computing server automatically retrieval from now to the path of destination; The running time that is spent according to the every paths of these path computing then; This service mode is mainly considered the dynamic change of road condition, as situation such as traffic hazard has taken place, and therefore need be used for dynamically adjustment driving path according to present road traffic state; The information of vehicle present position is to be brought in constant renewal in by the location of GPS navigation appearance; This cloud service pattern is to belong to a kind of minimum running time dynamic route planning, thereby helps the continuous variation of traveler according to real-time traffic, cooks up optimal path.

The road service level cloud service pattern in described city based on GIS is used to make a general survey of the Assessment of Service Level for Urban Roads state of browsing; Specific practice is: the user browses the road service level situation of certain point, certain bar line, certain zone and entire city on the urban road through generalized information system; At first be to confirm its corresponding SegmentID according to selected zone; Read the state-of-the-art record in the described cloud computing server according to these SegmentID then; With the road service state in the corresponding SegmentID record; Be about to ServiceLevel value and be indicated on the highway section of the pairing generalized information system of these SegmentID with color, make the user from urban road CIS very clear obtain the urban road macroscopic view, the road service level visual information of sight and microcosmic; The service level grade and the marker color correspondence table of road section are as shown in table 2,

The service level grade	The color of on GIS, representing
		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 said road traffic cloud service implementation method is following: the user sets terminal through 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 the user; Generate a URL, and this URL is sent to described Traffic Information Platform Server and GIS server, wait for the result that described Traffic Information Platform Server and GIS server are beamed back then.Described Traffic Information Platform Server and GIS server are after receiving the URL that the user sends; This URL is resolved; Obtain traffic cloud service type; Like path computing, map overview, location, information inquiry, CMMB service etc., the cloud service type can obtain through the value of type among the URL; Then according to which type of operation of COS decision carrying out; Then according among the URL { start} is with { value of end} is judged the SegmentID value of given start-stop node; After obtaining SegmentID; Judge whether given start-stop SegmentID is effective; Confirming it is under the situation of effective SegmentID, server calculates two the shortest internodal and some second shortest paths, provides the running time confession user selection in different paths then according to the path; Navigating instrument just got into navigational state after the user had selected certain paths, 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; Realized that vehicle just has traffic to detect; Realized large scale road traffic system detection senses, and traffic behavior estimated, induced and controls to have important significance for theories and actual application value for traveler provides traffic information based on these data.

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

Beneficial effect of the present invention mainly shows: 1, the transport information cloud computing is the basis that the realization of traffic behavior control provides information calculations; 2, comprised information such as time, space, travel direction, road service level and average velocity in the recorded information, 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 is gathered through effective information trading mechanism, make the GPS information obtained can all the period of time, gamut reflection road traffic; 4, through cloud computing, can traffic control property be induced with the GPS dynamic navigation to merge, realize the cloud service of traffic GPS information, equilibrium road network flow peak period is effectively alleviated the part and is blocked up.

Description of drawings

Fig. 1 is the structured flowchart of vehicle GPS;

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

Fig. 3 is for being processed into temporal information, spatial positional information, road driving directional information, road track direction information, road service level class information and the length information that blocks up the composition synoptic diagram of 43 characters;

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

Fig. 5 for from the angle of information organization with the 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 synoptic diagram with spatial positional information, temporal information and road service level grade with three dimensional space coordinate;

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

Fig. 8 is the road service level of certain highway section, city direction day and the change curve of average overall travel speed;

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

Figure 10 is a kind of dynamic route planning cloud service software processes process flow diagram.

Embodiment

Below in conjunction with accompanying drawing the present invention is 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 installed on the moving vehicle, comprise data storage server, the cloud computing server that is used for the perception traffic behavior that is used to accept and store the moving vehicle gps data, Traffic Information Platform Server and the GIS server that is used to provide the transport information cloud service in the described information center; Communication is as shown in Figure 2 between vehicle GPS, gps satellite, GPS base station, relay station and the information center;

Described vehicle GPS comprises antenna element, receiving element, computing unit and three parts of mobile comm unit, and is as shown in Figure 1; The main effect of antenna element is: when gps satellite rises time-out from the local horizon, can catch, tracking satellite, receive and amplify gps signal; The main effect of receiving element is: write down gps signal and signal is separated the mediation Filtering Processing, restore the navigation message that gps satellite sends, separate and ask travel-time and the carrier phase difference of signal between the star of station; The main effect of computing unit is: obtain navigation positioning data in real time or adopt to survey the mode of aftertreatment, calculate then obtain locate, test the speed, data such as timing and travel direction; Be a sampling period to send information such as this vehicle location, speed and traffic direction through mobile comm unit with per 2 seconds at last;

Longitude that is based on the World Geodesic Coordinate System 1984 WGS-84 of U.S. Department of Defense and latitude data that receiving element in vehicle GPS equipment obtains are from longitude and latitude data directly computed range and road-section average speed; Need the longitude and latitude in the WGS-84 coordinate system be converted into planimetric rectangular coordinates through Gauss projection, the distance of passing through within a certain period of time according to vehicle is then calculated average velocity; Therefore need in the computing unit of vehicle GPS equipment, calculate the speed and the traffic direction of vehicle, WGS-84 coordinate system longitude and latitude (L, B) convert into the Gaussian plane rectangular coordinate (x, computing method y) shown in formula (1),

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

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

Vehicle GPS obtains longitude, latitude and the moment data point p+1 group, i.e. (L altogether in chronological order 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 through 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, with the average translational speed of formula (2) calculating a certain car in 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 the formula, v _iBe the average translational speed of a certain vehicle in the SI, d is the displacement of a certain vehicle in the SI, t _dBe the SI;

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

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

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

Described data storage server comprises data acquisition module and data processing memory module; Described data acquisition module receives the information such as vehicle location, speed and traffic direction that send over from each vehicle GPS equipment through mobile communications network, and described then data processing memory module is with ID number of this vehicle GPS equipment and write in the transport information cloud computing database with information such as its position, speed and traffic directions;

Described cloud computing server comprises certain road-section average speed calculation module, certain highway section velocity variable computing module; All road service level output modules in certain highway section road service level determination module, the city, through the image data in the transport information cloud computing database is carried out deep processing, calculate the road service level situation that obtains certain point, certain bar line, certain zone and entire city on the urban road;

The output format of described certain highway section road service level determination module is made up of following five parameter values, is respectively 14 moment parameter value, 23 detection space position parameter value, 1 travel direction property parameters value, 1 road service level parameter value and 2 vehicle average overall travel speed; Parameter is used to represent to detect temporal information constantly constantly; Parameter Time representes with 14 bit data forms constantly; Be YYYYMMDDHHMMSS, wherein 1～4 YYYY representes 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 that branch, 13～14 SS represent second; Detect the central point spatial positional information that space position parameter is used to represent certain highway section, detect space position parameter SegmentID and represent with 23 bit data forms; The driveway travel directions property parameters is used to represent the travel direction information in track; Driveway travel directions property parameters Direction representes 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; Road service level parameter is used to represent the congestion status of road; Road service level parameter S erviceLevel uses 1 to represent as alpha format; Road service level grade is divided into 6 grades such as A, B, C, D, E, F; Wherein A representes that road service level grade is best, and F representes that the poorest grade of road service level; 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; Highway 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, be used to represent from the relative position of setting the coordinate central point logic mark code, be used for natural number coding, branch road information coding from the origin-to-destination of road; Totally 23 codings are as shown in Figure 4; Wherein absolute position encoder is the most preceding 6, and the 1st to the 3rd bit representation longitude, and the 4th to the 6th bit representation latitude; Such as the data that obtain is 120030; 120 expression east longitudes, 120 degree, 030 expression north latitude, 30 degree are the Hangzhous through leaving the city that can obtain correspondence in the infosystem in; Logic mark code is the 7th to 17; Central point with the city is divided into A, B, C, a D4 quadrant district with the zone; The quadrant district at the starting point of the 7th bit representation road place representes x, the y coordinate at two ends, street, the 8th the x coordinate to the 9th bit representation street starting point with 4 bit digital; The 10th y coordinate to the 11st bit representation street starting point; The quadrant district at the terminal point place of the 12nd bit representation road, the 13rd x coordinate, the 15th y coordinate to the 16th bit representation street terminal point to the 14th bit representation street terminal point; Be aided with lowercase for two parallel and two ends x, street that the y coordinate is identical and distinguish in proper order, represent with 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, layout is carried out at single right two ends that extend to, a left side; For have only one-sided street crossing if adopt the odd number layout, in the right-hand employing even numbers layout of road at the left of road; Branch road information is encoded to the 23rd, and branch road information coding N representes that the place ahead is obstructed, and L representes to turn right and forbids, R representes to turn left to forbid;

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

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

Described certain road-section average speed calculation module is used to calculate the vehicle average velocity of SegmentID; At first with the SegmentID on certain bar road be on a certain travel direction at center with ± 0.4Km is the volume coordinate (x that radius is confirmed two frontier points _J-n, y _J-n), (x _J+m, y _J+m); Then through sampling time, volume coordinate scope and vehicle operating direction retrieval qualified record in described data storage server; Calculate all that have identical moving direction in each road section scope with formula (4) and dispose the average velocity of the vehicle of vehicle GPS equipment

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

In the formula, v _{K, g}Be illustrated on the g highway section of k bar road in the SI, i.e. the average translational speed of the vehicle of SegmentID, n is for disposing the sum of the vehicle of vehicle GPS equipment, v on the g highway section of k bar road in the SI _iAverage translational speed for i vehicle in the SI;

Described certain highway section velocity variable computing module is used to calculate the changes in vehicle speed rate in the SegmentID scope, computing method 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 the formula, cov _{K, g}Be illustrated in the changes in vehicle speed rate on the g highway section of k bar road in the SI, i.e. changes in vehicle speed rate in the SegmentID scope; v _{K, g}Be illustrated in the average translational speed of vehicle on the g highway section of k bar road in the SI, i.e. the average translational speed of vehicle in the SegmentID scope; N is for disposing the sum of the vehicle of vehicle GPS equipment, the i.e. sum of the vehicle that disposes vehicle GPS equipment in the SegmentID scope on the g highway section of k bar road in the SI; v _iBe the average translational speed of i vehicle in the SI, i.e. the average translational speed of i vehicle in the SI in the SegmentID scope;

Described road section service level determination module is used to judge the service level of current road section road section service level grade is divided into 6 grades such as A, B, C, D, E, F, and the step of decision process is following:

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

Table 1

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

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

Described Traffic Information Platform Server and GIS server are used to provide the various cloud services of urban highway traffic; Cloud service for urban transportation comprises: the cloud service pattern of 24 hours situation of the traffic behavior in certain highway section issue, the path planning cloud service pattern with human-computer interaction function, dynamic route planning cloud service pattern, based on the road service level cloud service pattern in the city of GIS; The present invention from the angle of information organization with the 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 24 hours situation of the traffic behavior in described certain highway section issue is used to investigate the traffic behavior in certain highway section; Because the present invention is 2 seconds to the detection SI of urban road traffic state; So for the central point in each highway section, it is every that will in described cloud computing server, produce a length at a distance from 2 seconds be 41 record; Utilize these data can be processed into various statistical forms easily; For the highway section central point for Hangzhou Wen Erlu attend the Youth League school, eastwards travel direction, 59 minutes and 58 seconds on April 20th, 2012 from zero time to 23, can obtain traffic behavior change curve in a day shown in accompanying drawing 8 through statistics processing to 43200 record data; The user through man-machine interface fixed time section obtain this time period the traffic behavior change curve, specify certain highway section, certain travel direction and certain time period to obtain the traffic behavior change curve of certain highway section and certain time period; Statistics processing through to 43200 record data can obtain the road traffic service change situation that spatial positional information, temporal information and road service level grade are represented with three dimensional space coordinate shown in accompanying drawing 6;

Described path planning cloud service pattern with human-computer interaction function is used for letting the user specify departure place and destination to obtain optimum path planning and navigation through man-machine interface; Specific practice is: described cloud computing server is the path of retrieval from the departure place to the destination automatically; Go out from one of the origin-to-destination close together or some paths as traffic route at electronic map marker; The running time that is spent according to the every paths of these path computing then; Calculation process is shown in accompanying drawing 9; The user has just accomplished path planning through man-machine interface after according to a certain traffic routes of Information Selection such as path that is shown and running time, and navigating instrument gets into navigational state;

Below with a kind of realization system of accompanying drawing 9 explanation path plannings,

S1; Step 1: the user specifies departure place and destination through man-machine interface, and described cloud computing server parses service content and starting point, endpoint information, generates one or some alternative paths automatically according to starting point, endpoint information; One paths of every generation all is the set Route of several SegmentID; Simultaneously also generated the chained list of representing several SegmentID annexations, the algorithm use A* algorithm of generation pass, the list of references that the 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: from some alternative paths, confirm to select the path;

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

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

S5; Step 5: from the estimation of the vehicle average running speed of this SegmentID through this SegmentID needed time of distance the last SegmentID to the chained list; Owing in each SegmentID, all comprised the spatial positional information of this SegmentID; Therefore, distance can directly calculate between two adjacent SegmentID;

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

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

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

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

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

S11, step 11: calculating path is from the prediction cumulative time that starting point is breasted the tape, and preserves these data and be used to export to the user;

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

Described dynamic route planning cloud service pattern is used for the continuous variation according to user designated destination and real-time traffic, cooks up optimal path; Specific practice is: the GPS navigation device obtains the position and the travel direction of present vehicle automatically, described cloud computing server automatically retrieval from now to the path of destination, the running time that is spent according to the every paths of these path computing then; This service mode is mainly considered the dynamic change of road condition; As situation such as traffic hazard has taken place, and therefore need be used for dynamically adjustment driving path according to present road traffic state, calculation process is shown in accompanying drawing 10; Calculation process and accompanying drawing 9 are similar; Different is starting point and vehicle present position, and the information of vehicle present position is to be brought in constant renewal in by the location of GPS navigation appearance, and this cloud service pattern is to belong to a kind of minimum running time dynamic route planning; Thereby help the continuous variation of traveler, cook up optimal path according to real-time traffic;

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

The road service level cloud service pattern in described city based on GIS is used to make a general survey of the Assessment of Service Level for Urban Roads state of browsing; Specific practice is: the user browses the road service level situation of certain point, certain bar line, certain zone and entire city on the urban road through generalized information system; At first be to confirm its corresponding SegmentID according to selected zone; Read the state-of-the-art record in the described cloud computing server according to these SegmentID then; With the road service state in the corresponding SegmentID record; Be about to ServiceLevel value and be indicated on the highway section of the pairing generalized information system of these SegmentID with color, make the user from urban road CIS very clear obtain the urban road macroscopic view, the road service level visual information of sight and microcosmic; The service level grade and the marker color correspondence table of road section are as shown in table 2,

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

The service level grade of table 2 road section and marker color correspondence table.

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 following: the user sets terminal through 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 the user; Generate a URL, and this URL is sent to described Traffic Information Platform Server and GIS server, wait for the result that described Traffic Information Platform Server and GIS server are beamed back then.Described Traffic Information Platform Server and GIS server are after receiving the URL that the user sends; This URL is resolved; Obtain traffic cloud service type; Like path computing, map overview, location, information inquiry, CMMB service etc., the cloud service type can obtain through the value of type among the URL; Then according to which type of operation of COS decision carrying out; Then according among the URL { start} is with { value of end} is judged the SegmentID value of given start-stop node; After obtaining SegmentID; Judge whether given start-stop SegmentID is effective; Confirming it is under the situation of effective SegmentID, server calculates two the shortest internodal and some second shortest paths, provides the running time confession user selection in different paths then according to the path; Navigating instrument just got into navigational state after the user had selected certain paths, 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 use about transport information, shown in accompanying drawing 7.This theory is paid attention to the value of information; Emphasical Traffic Information should flow and accumulate the traffic cloud, and the traffic cloud is carried out deep processing, finally pursues the road traffic cloud service of high-quality; Carry out traffic behavior control, the operation of final road improvement traffic through the information of effective.

Claims (10)

1. a 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 installed on the moving vehicle, comprise data storage server, the cloud computing server that is used for the perception traffic behavior that is used to accept and store the moving vehicle gps data, Traffic Information Platform Server and the GIS server that is used to provide the transport information cloud service in the described information center;

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 through the image data in the transport information cloud computing database is carried out deep processing, calculates the road service level situation that obtains certain point, certain bar line, certain zone and entire city on the urban road; Comprise road-section average speed calculation module, highway section velocity variable computing module, all road service level output modules in highway section road service level determination module and the city;

Described Traffic Information Platform Server and GIS server are used to provide the various cloud services of urban highway traffic: the cloud service pattern of 24 hours situation of the traffic behavior in highway section issue, the path planning cloud service pattern with human-computer interaction function, dynamic route planning cloud service pattern, based on the road service level cloud service pattern in the city of GIS.

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: in the road service level determination module of described highway section; Output format is made up of following five parameter values, is respectively 14 moment parameter value, 23 detection space position parameter value, 1 travel direction property parameters value, 1 road service level parameter value and 2 vehicle average overall travel speed; Parameter is used to represent to detect temporal information constantly constantly; Parameter Time representes with 14 bit data forms constantly; Be YYYYMMDDHHMMSS, wherein 1～4 YYYY representes 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 that branch, 13～14 SS represent second; Detect the central point spatial positional information that space position parameter is used to represent certain highway section, detect space position parameter SegmentID and represent with 23 bit data forms; The driveway travel directions property parameters is used to represent the travel direction information in track; Driveway travel directions property parameters Direction representes 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; Road service level parameter is used to represent the congestion status of road; Road service level parameter S erviceLevel uses 1 to represent as alpha format; Road service level grade is divided into 6 grades such as A, B, C, D, E, F; Wherein A representes that road service level grade is best, and F representes that the poorest grade of road service level; 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; Highway section travel direction on the road of every record and detection becomes one-to-one relationship, 41 of the length of a record.

According to claim 1 or claim 2 realize system based on the Traffic Information cloud computing of technology of Internet of things and cloud service; It is characterized in that: described road-section average speed calculation module; Be used to calculate the vehicle average velocity of SegmentID, at first with the SegmentID on certain bar road be on a certain travel direction at center with ± 0.4Km is the volume coordinate (x that radius is confirmed two frontier points _J-n, y _J-n), (x _J+m, y _J+m); Then through sampling time, volume coordinate scope and vehicle operating direction retrieval qualified record in described data storage server; Calculate all that have identical moving direction in each road section scope with formula (4) and dispose the average velocity of the vehicle of vehicle GPS equipment

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

In the formula, v _{K, g}Be illustrated on the g highway section of k bar road in the SI, i.e. the average translational speed of the vehicle of SegmentID, n be in the SI on the g highway section of k bar road, promptly dispose the sum of the vehicle of vehicle GPS equipment in the SegmentID scope, v _iFor in the SI in the SegmentID scope the average translational speed of i vehicle.

According to claim 1 or claim 2 realize system based on the Traffic Information cloud computing of technology of Internet of things and cloud service; It is characterized in that: described highway section velocity variable computing module; Be used to calculate the changes in vehicle speed rate in the SegmentID scope, computing method are 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 the formula, cov _{K, g}Be illustrated in the changes in vehicle speed rate on the g highway section of k bar road in the SI, i.e. changes in vehicle speed rate in the SegmentID scope; v _{K, g}Be illustrated in the average translational speed of vehicle on the g highway section of k bar road in the SI, i.e. the average translational speed of vehicle in the SegmentID scope; N is for disposing the sum of the vehicle of vehicle GPS equipment, the i.e. sum of the vehicle that disposes vehicle GPS equipment in the SegmentID scope on the g highway section of k bar road in the SI; v _iBe the average translational speed of i vehicle in the SI, i.e. the average translational speed of i vehicle in the SI in the SegmentID scope.

5. 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: all road service level output modules in the described city; Be used to process, calculate and export the road service level on each highway section of every road; The Time value is set by the sampling instant value, and data layout is YYYYMMDDHHMMSS, and the SegmentID value is worth by the locus of the central point in each highway section to be set; The Direction value is that 0 expression deviates from the travel direction of road starting end, and 1 expression is towards the travel direction of road starting end; Time value, SegmentID ± 0.4Km value and Direction value retrieval qualified record in described data storage server calculate the value that average velocity is set Speed with formula (4), set the value of ServiceLevel with the judged result of table 1;

Table 1

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

According to claim 1 or claim 2 realize system based on the Traffic Information cloud computing of technology of Internet of things and cloud service, it is characterized in that: the cloud service pattern of the traffic behavior in described highway section situation issue in 24 hours is used to investigate the traffic behavior in certain highway section; The user through man-machine interface fixed time section obtain this time period the traffic behavior change curve, specify certain highway section, certain travel direction and certain time period to obtain the traffic behavior change curve of certain highway section and certain time period.

7. Traffic Information cloud computing and the cloud service based on technology of Internet of things as claimed in claim 1 or 2 realizes system; It is characterized in that: described path planning cloud service pattern with human-computer interaction function is used for allowing the user specify departure place and destination to obtain optimum path planning and navigation through man-machine interface; Specific practice is: described cloud computing server is the path of retrieval from the departure place to the destination automatically; Go out from one of the origin-to-destination close together or some paths as traffic route at electronic map marker; Running time that is spent based on the every paths of these path computing then; The user has just accomplished path planning through man-machine interface after based on a certain traffic routes of Information Selection such as path that is shown and running time, and navigator gets into navigational state;

Performing step is following,

S1; Step 1: the user specifies departure place and destination through man-machine interface; Described cloud computing server parses service content and starting point, endpoint information, generates one or some alternative paths automatically according to starting point, endpoint information, and a paths of every generation all is the set Route of several SegmentID; The chained list of representing several SegmentID annexations, the algorithm use A* algorithm of generation pass have also been generated simultaneously;

S2, step 2: from some alternative paths, confirm to select the path;

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

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

S5; Step 5: from the estimation of the vehicle average running speed of this SegmentID through this SegmentID needed time of distance the last SegmentID to the chained list; Owing in each SegmentID, all comprised the spatial positional information of this SegmentID; Therefore, distance can directly calculate between two adjacent SegmentID;

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

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

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

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

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

S11, step 11: calculating path is from the prediction cumulative time that starting point is breasted the tape, and preserves these data and be used to export to the user;

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

According to claim 1 or claim 2 realize system based on the Traffic Information cloud computing of technology of Internet of things and cloud service; It is characterized in that: described dynamic route planning cloud service pattern; Be used for continuous variation, cook up optimal path according to user designated destination and real-time traffic; Specific practice is: the GPS navigation device obtains the position and the travel direction of present vehicle automatically; Described cloud computing server automatically retrieval from now to the path of destination; The running time that is spent according to the every paths of these path computing then; This service mode is mainly considered the dynamic change of road condition, as situation such as traffic hazard has taken place, and therefore need be used for dynamically adjustment driving path according to present road traffic state; The information of vehicle present position is to be brought in constant renewal in by the location of GPS navigation appearance; This cloud service pattern is to belong to a kind of minimum running time dynamic route planning, thereby helps the continuous variation of traveler according to real-time traffic, cooks up optimal path.

According to claim 1 or claim 2 realize system based on the Traffic Information cloud computing of technology of Internet of things and cloud service; It is characterized in that: the road service level cloud service pattern in described city based on GIS is used to make a general survey of the Assessment of Service Level for Urban Roads state of browsing; Specific practice is: the user browses the road service level situation of certain point, certain bar line, certain zone and entire city on the urban road through generalized information system; At first be to confirm its corresponding SegmentID according to selected zone; Read the state-of-the-art record in the described cloud computing server according to these SegmentID then; With the road service state in the corresponding SegmentID record; Be about to ServiceLevel value and be indicated on the highway section of the pairing generalized information system of these SegmentID with color, make the user from urban road CIS very clear obtain the urban road macroscopic view, the road service level visual information of sight and microcosmic; The service level grade and the marker color correspondence table of road section are as shown in table 2,

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

Table 2.

10. road traffic cloud service implementation method that 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 process of said road traffic cloud service implementation method is following: the user sets terminal through 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 the user; Generate a URL, and this URL is sent to described Traffic Information Platform Server and GIS server, wait for the result that described Traffic Information Platform Server and GIS server are beamed back then.Described Traffic Information Platform Server and GIS server are after receiving the URL that the user sends; This URL is resolved; Obtain traffic cloud service type; Like path computing, map overview, location, information inquiry, CMMB service etc., the cloud service type can obtain through the value of type among the URL; Then according to which type of operation of COS decision carrying out; Then according among the URL { start} is with { value of end} is judged the SegmentID value of given start-stop node; After obtaining SegmentID; Judge whether given start-stop SegmentID is effective; Confirming it is under the situation of effective SegmentID, server calculates two the shortest internodal and some second shortest paths, provides the running time confession user selection in different paths then according to the path; Navigating instrument just got into navigational state after the user had selected certain paths, and according to the real-time condition correction guidance path of road.

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