CN105096584A - Traffic decision support method, device, and system - Google Patents
Traffic decision support method, device, and system Download PDFInfo
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- CN105096584A CN105096584A CN201410189626.1A CN201410189626A CN105096584A CN 105096584 A CN105096584 A CN 105096584A CN 201410189626 A CN201410189626 A CN 201410189626A CN 105096584 A CN105096584 A CN 105096584A
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/123—Traffic control systems for road vehicles indicating the position of vehicles, e.g. scheduled vehicles; Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, trams
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Abstract
Provided is a traffic decision support method which comprises the following steps of: receiving a query request sent by user through a query device; acquiring the running information of a bus in response to the query request by using a data acquisition device; calling a corresponding model from a model library and analyzing the running information of the bus in order to obtain a decision corresponding to the query request; pushing the decision corresponding to the query request to the query device. Correspondingly, the invention provides a traffic decision support device and system. The traffic decision support method, device, and system may provide a more accurate decision for a user within a shorter time in combination with the running information of the bus, and facilitates the travel decision and bus scheduling plan for users.
Description
Technical field
The present invention relates to traffic and communication technical field, particularly relate to a kind of communications policy support method, Apparatus and system.
Background technology
The necessary vehicles in people's life during bus, for alleviating urban traffic pressure and advocating Green Travel and made major contribution, and greatly facilitate that passenger goes on a journey, goes to work, the activity such as to play.
Bus information management system has been set up in some city at present, provides the basic tips of Bus information, simultaneously for bus dispatching center provides bus travel information at bus platform.This kind of public transport operation information management system, as shown in Figure 8, substantially the equipment such as GPS, video monitoring is all just utilized to monitor bus travel conditions, and be reported to bus information management system, bus information management system is pushed to corresponding Bus information inquiry unit again, as public transport stop board, bus dispatching system etc.
But, existing public transport operation information management system, the information transfer function such as substantially all can only realize the collection of bus travel information, report; User obtains the decision-making (as decision-making, bus dispatching decision-making etc. by bus) oneself needed by these actual informations, still need the self judgment process that wastes time and energy.
Summary of the invention
In view of this, the invention provides a kind of communications policy support method, comprise the steps:
Receive the inquiry request that user is sent by inquiry unit;
Corresponding to described inquiry request, gather bus running information by data collector;
From model bank, call corresponding model, analyze described public transport operation information, obtain the decision-making corresponding with described inquiry request;
The described decision-making corresponding with inquiry request is pushed to described inquiry unit.
Preferably, described bus running information comprises passenger flow information, vehicle traveling information, traffic information;
From model bank, call corresponding model, analyze described public transport operation information, the step obtaining the decision-making corresponding with described inquiry request specifically comprises:
From model bank, call passenger flow degree of crowding analytical model, utilize described passenger flow information to calculate the passenger flow degree of crowding;
From model bank, call public transport arrival time forecast model, utilize described bus travel information and traffic information prediction public transport arrival time;
From model bank, call decision model, utilize the described passenger flow degree of crowding and described public transport arrival time to calculate the decision-making corresponding with described inquiry request.
Optionally, described inquiry request comprises bus dispatching inquiry request, and described decision model comprises bus dispatching decision model;
Or described inquiry request comprises inquiry request by bus; Described decision model comprises decision model by bus.
Optionally, described passenger flow information at least comprises the one in the car that obtained by prospect harvester in passenger flow picture and platform passenger flow picture;
Accordingly, described passenger flow degree of crowding analytical model comprises on the described bus of employing without the platform background model that background model or employing bus platform in the car of background picture foundation during passenger are set up without background picture during passenger.
Preferably, when described passenger flow information comprises passenger flow picture and platform passenger flow picture in car, call passenger flow degree of crowding analytical model from model bank, the step utilizing described passenger flow information to calculate the passenger flow degree of crowding specifically comprises:
Passenger flow picture at least one Zhang Zhantai passenger flow picture and at least one car is gathered by image collecting device or video acquisition device;
Adopt corresponding model in model bank to analyze described platform passenger flow picture and Che Nei passenger flow picture respectively, obtain platform intensity of passenger flow and Che Nei intensity of passenger flow;
In conjunction with described platform intensity of passenger flow and Che Nei intensity of passenger flow, calculate the passenger flow degree of crowding.
Optionally, described bus travel information comprises the public transport position and bus travel speed that are obtained by GPS device; Described traffic information comprises road congestion degree and road conditions;
From model bank, call public transport arrival time forecast model, utilize the step of described bus travel information and traffic information prediction public transport arrival time specifically to comprise:
According to described public transport position and the position obtaining target platform from corresponding inquiry request, calculate the distance that public transport arrives target platform;
Described public transport is arrived the distance of target platform, bus travel speed, road congestion degree and road conditions as variable, utilize the public transport arrival time prediction algorithm prediction public transport of calling from method base to arrive the time of target platform.
Further, the invention provides a kind of communications policy supportive device, comprising:
Request receiving module: for receiving the inquiry request that user is sent by inquiry unit;
Information acquisition module: for corresponding to described inquiry request, gather bus running information by data collector;
Analysis decision module: for calling corresponding model from model bank, analyzes described public transport operation information, obtains the decision-making corresponding with described inquiry request;
Pushing module: for the described decision-making corresponding with inquiry request is pushed to described inquiry unit.
Preferably, described bus running information comprises passenger flow information, vehicle traveling information, traffic information;
Described analysis decision module specifically comprises:
Passenger flow calculating sub module: for calling passenger flow degree of crowding analytical model from model bank, utilizes described passenger flow information to calculate the passenger flow degree of crowding;
Time Calculation submodule: for calling public transport arrival time forecast model from model bank, utilizes described bus travel information and traffic information prediction public transport arrival time;
Decision-making calculating sub module: for calling decision model from model bank, utilizes the described passenger flow degree of crowding and described public transport arrival time to calculate the decision-making corresponding with described inquiry request.
Optionally, described inquiry request comprises bus dispatching inquiry request, and described decision model comprises bus dispatching decision model;
Or described inquiry request comprises inquiry request by bus; Described decision model comprises decision model by bus.
Optionally, described passenger flow information at least comprises the one in the car that obtained by prospect harvester in passenger flow picture and platform passenger flow picture;
Accordingly, described passenger flow degree of crowding analytical model comprises on the described bus of employing without the platform background model that background model or employing bus platform in the car of background picture foundation during passenger are set up without background picture during passenger.
Preferably, when described passenger flow information comprises passenger flow picture and platform passenger flow picture in car, described passenger flow calculating sub module specifically comprises:
Picture acquiring unit: for gathering passenger flow picture at least one Zhang Zhantai passenger flow picture and at least one car by image collecting device or video acquisition device;
Picture analyzing unit: for adopting corresponding model in model bank to analyze described platform passenger flow picture and Che Nei passenger flow picture respectively, obtain platform intensity of passenger flow and Che Nei intensity of passenger flow;
Passenger flow degree of crowding projected unit: in conjunction with described platform intensity of passenger flow and Che Nei intensity of passenger flow, calculate the passenger flow degree of crowding.
Optionally, described bus travel information comprises the public transport position and bus travel speed that are obtained by GPS device; Described traffic information comprises road congestion degree and road conditions;
Described Time Calculation submodule specifically comprises:
Metrics calculation unit: for according to described public transport position and the position obtaining target platform from corresponding inquiry request, calculate the distance that public transport arrives target platform;
Time prediction unit: described public transport is arrived the distance of target platform, bus travel speed, road congestion degree and road conditions as variable, utilizes the public transport arrival time prediction algorithm prediction public transport of calling from method base to arrive the time of target platform.
Further, the present invention also provides a kind of communications policy back-up system, comprises the public transport real-time traffic information management devices provided in any one embodiment of the present invention, and for sending the inquiry unit of inquiry request.
Optionally, described inquiry unit comprises the dispatching center's inquiry unit for sending scheduling inquiry request and the inquiry unit by bus for sending inquiry request by bus.
As can be seen from above, communications policy support method provided by the invention, Apparatus and system, can Real-time Collection bus running information, and utilize the model in model bank, public transport operation information is analyzed, and then provide the decision-making corresponding to its inquiry request to user, for user provides convenience.The communications policy support method of the embodiment of the present invention, Apparatus and system, can analyze the multidimensional information of public transport operation, obtain towards passenger or decision-making or scheduling decision are by bus provided towards dispatching center, be not only passenger to choose bus decision references is provided, reference Help can also be provided for the despatching work at bus dispatching center.
Accompanying drawing explanation
Fig. 1 be the embodiment of the present invention support method flow schematic diagram based on communications policy;
Fig. 2 is the sub-process schematic diagram of a step in the embodiment of the present invention;
Fig. 3 is the passenger flow degree of crowding calculation process schematic diagram of an embodiment of the present invention;
Fig. 4 is the passenger flow degree of crowding calculation process schematic diagram of the another kind of embodiment of the present invention;
Fig. 5 is the passenger flow degree of crowding calculation process schematic diagram of the preferred embodiment of the present invention;
Fig. 6 is the time flow schematic diagram of the public transport arrival target platform of an embodiment of the present invention;
Fig. 7 is the communications policy supportive device structural representation of the embodiment of the present invention;
Fig. 8 is the public transport operation information management system structural representation of prior art.
Embodiment
In order to provide effective implementation, embodiments providing following examples, below in conjunction with Figure of description, embodiments of the invention being described.
According to the public transport real-time traffic information management method based on passenger flow provided by the invention, comprise the following steps:
Receive the inquiry request that user is sent by inquiry unit;
Corresponding to described inquiry request, gather bus running information by data collector;
From model bank, call corresponding model, analyze described public transport operation information, obtain the decision-making corresponding with described inquiry request;
The described decision-making corresponding with inquiry request is pushed to described inquiry unit.
With reference to Fig. 1, embodiments of the invention are described, comprise the steps:
Step 101: receive the inquiry request that user is sent by inquiry unit.
Concrete, described inquiry unit can be arranged at the mobile terminal of user, also can be arranged at bus platform, can also be arranged at dispatching center.
Described inquiry request can be the request that user sends based on oneself demand, such as ride inquiry request or dispatch request.
Generally, target platform positional information is included in described inquiry request.
Step 102: corresponding to described inquiry request, gathers bus running information by data collector.
Described bus running information, when comprising the transmission of described inquiry request, by the bus running information of the data collector Real-time Collections such as GPS; Also can be the bus routes information prestored.
Step 103: call corresponding model from model bank, analyzes described public transport operation information, obtains the decision-making corresponding with described inquiry request.
Corresponding to described inquiry request, from model bank, call corresponding model, and from method base, call the method for described model employing, the public transport operation information collected is analyzed, obtains the decision-making corresponding with described inquiry request.
Step 104: the described decision-making corresponding with inquiry request is pushed to described inquiry unit.
Above-mentioned decision-making can be sent by wired or wireless mode.
Communications policy support method provided by the invention, corresponding to the inquiry request that user sends, corresponding model is utilized to analyze the public transport operation information gathered, and obtain the decision-making corresponding with described inquiry request, achieve intelligent query function, the plan such as decision-making trip, bus dispatching that user can provide with reference to the inventive method.
The public transportation enquiry system of prior art is only the prediction providing a public transport arrival time according to the bus travel speed estimated and inception point to the distance of terminus.But under actual conditions, because the reason such as the road degree of crowding, the passenger flow degree of crowding, bus travel speed often differs more with the travel speed estimated.The suggestion that existing public transportation enquiry system provides often is not inconsistent with actual conditions.
In view of this, in some embodiments of the invention, based on multidimensional information for user provides communications policy support, concrete scheme is as follows:
Described bus running information comprises passenger flow information, vehicle traveling information, traffic information;
From model bank, call corresponding model, analyze described public transport operation information, the step obtaining the decision-making corresponding with described inquiry request specifically comprises sub-step as shown in Figure 2.
Step 201: call passenger flow degree of crowding analytical model from model bank, utilizes described passenger flow information to calculate the passenger flow degree of crowding.
Described passenger flow information can be the picture of the reflection volume of the flow of passengers, is obtained by picture collection device or video acquisition device.
Step 202: call public transport arrival time forecast model from model bank, utilizes described bus travel information and traffic information prediction public transport arrival time.
Described bus travel information can comprise public transport current location, bus routes, bus travel speed etc.Described traffic information can comprise the road degree of crowding, road conditions etc.
Step 203: call decision model from model bank, utilizes the described passenger flow degree of crowding and described public transport arrival time to calculate the decision-making corresponding with described inquiry request.
Concrete, different weights can be given to the passenger flow degree of crowding and public transport arrival time, obtain a communications policy based on multidimensional information.
In the above-described embodiments, comprehensively analyze the passenger flow information collected, vehicle traveling information, traffic information, obtain the communications policy based on multidimensional information, this communications policy can be combined with actual conditions better, has higher accuracy.
In some embodiments of the invention, described inquiry request comprises inquiry request by bus; Described decision model comprises decision model by bus, or described inquiry request comprises bus dispatching inquiry request, and described decision model comprises bus dispatching decision model.
In prior art, for the people often taken pubic transport, most all can have such worries, stands in a bus platform waiting for bus, although bus has arrived sometimes, very crowded; This situation brings puzzlement to most of passenger, more especially because the old,weak,sick and disabled is pregnant etc., reason needs seat or is not suitable for walking in intensive passen-gers the passenger shuttled back and forth, be not easy to take and just arrived but incomparably crowded public transport, but do not know that whether next regular bus is equally crowded, this get on the bus or etc. next class, be usually difficult to choice.
For bus dispatching center, general dispatching method is, arranges vehicle scheduling according to fixed time interval.But actual conditions are, due to various factors, the number of vehicles required for concrete time specific circuit often has difference.And this bus dispatching method of prior art, cause the number of vehicles of sometimes dispatching to be greater than demand number, the number of vehicles of sometimes dispatching can not practical requirement.
The communications policy support method provided by the embodiment of the present invention, user sends inquiry request by bus by the inquiry unit being arranged at mobile terminal or bus platform, utilize decision model and corresponding algorithm by bus, calculate the variablees such as passenger flow information, vehicle traveling information, traffic information, obtain the decision-making by bus based on multidimensional information, not only revenue passenger can obtain useful reference information from described decision-making by bus, and the special passenger being not easy to take crowded vehicle also described decision-making by bus can make the selection adapting to oneself demand.
Meanwhile, the communications policy support method provided by the embodiment of the present invention, according to actual traffic situation, for dispatching center provides scheduling decision support flexibly, can be convenient to the quantity that bus dispatching center arranges to dispatch buses according to the actual requirements.
In some embodiments of the invention, described passenger flow information at least comprises the one in the car that obtained by prospect harvester in passenger flow picture and platform passenger flow picture.
Concrete, when described passenger flow information comprises passenger flow picture in the car obtained by prospect harvester, described passenger flow degree of crowding analytical model comprises on the described bus of employing without background model in the car of background picture foundation during passenger.
More specifically, when described passenger flow information comprises passenger flow picture in the car obtained by prospect harvester, and described passenger flow degree of crowding analytical model comprises and adopting on described bus without in the car of the background picture foundation during passenger during background model, from model bank, call passenger flow degree of crowding analytical model, the step utilizing described passenger flow information to calculate the passenger flow degree of crowding comprises sub-step as shown in Figure 3:
Step 301: by passenger flow picture in image acquisition device at least one car.
Step 302: passenger flow picture in described car is compared to background model in the corresponding car preset, extracts foreground data.
By picture in described car with to preset and in the identical car of acquisition angles, background picture compares, filter less difference as required, extract foreground data.Described prospect comprises the passenger in car here.
Step 303: analyze described foreground data, calculates intensity of passenger flow in car.
The modes such as image analysis can be adopted to analyze described foreground data, calculate intensity of passenger flow in car.
Step 304: calculate the passenger flow degree of crowding according to passenger flow picture in described car.
In an advantageous embodiment, consider that in car, intensity of passenger flow changes in each bus station, therefore, adopt the algorithm of setting to be combined by intensity of passenger flow in bus positional information and current vehicle and calculate, obtain passenger flow crowding estimated value.
More specifically, without background model in the car of background picture foundation during passenger on described bus, adopt in the car of several representative angle shot and set up without background picture during passenger.
Concrete, when described passenger flow information comprises the platform passenger flow picture obtained by prospect harvester, described passenger flow degree of crowding analytical model comprises the platform background model adopting bus platform to set up without background picture during passenger.
More specifically, when described passenger flow information comprises the platform passenger flow picture obtained by prospect harvester, and described passenger flow degree of crowding analytical model comprise adopt bus platform without during passenger background picture set up platform background model time, from model bank, call passenger flow degree of crowding analytical model, the step utilizing described passenger flow information to calculate the passenger flow degree of crowding comprises sub-step as shown in Figure 4:
Step 401: by least one Zhang Zhantai passenger flow of image acquisition device picture.
Step 402: described platform passenger flow picture is compared to the corresponding platform background model preset, extracts foreground data.
By described platform picture with to preset and the identical platform background picture of acquisition angles compares, filter less difference as required, extract foreground data.Described prospect is included in the passenger that platform is waited here.
Step 403: analyze described foreground data, calculates platform intensity of passenger flow.
The modes such as image analysis can be adopted to analyze described foreground data, calculate platform intensity of passenger flow.
Step 404: calculate the passenger flow degree of crowding according to described platform passenger flow picture.
In an advantageous embodiment, therefore, the intensity of passenger flow of all platforms that the algorithm that employing sets will pass through before bus positional information and public transport being reached terminal combines and calculates, and obtains passenger flow crowding estimated value.
In an advantageous embodiment, platform passenger flow information and Che Nei passenger flow information can be combined, calculate the passenger flow degree of crowding, as shown in Figure 5:
Step 501: gather passenger flow picture at least one Zhang Zhantai passenger flow picture and at least one car by image collecting device or video acquisition device.
Step 502: adopt corresponding model in model bank to analyze described platform passenger flow picture and Che Nei passenger flow picture respectively, obtain platform intensity of passenger flow and Che Nei intensity of passenger flow.
Step 503: in conjunction with described platform intensity of passenger flow and Che Nei intensity of passenger flow, calculates the passenger flow degree of crowding.
In some embodiments of the invention, described bus travel information comprises the public transport position and bus travel speed that are obtained by GPS device; Described traffic information comprises road congestion degree and road conditions; Now, from model bank, call public transport arrival time forecast model, utilize the step of described bus travel information and traffic information prediction public transport arrival time specifically to comprise:
According to described public transport position and the position obtaining target platform from corresponding inquiry request, calculate the distance that public transport arrives target platform;
Described public transport is arrived the distance of target platform, bus travel speed, road congestion degree and road conditions as variable, utilize the public transport arrival time prediction algorithm prediction public transport of calling from method base to arrive the time of target platform.
Concrete, public transport arrives time of target platform can according to workflow management as shown in Figure 6:
Step 601: obtain position of bus by GPS device.
Step 602: arrive target platform distance according to described position of bus and the target platform position calculation vehicle that obtains from described inquiry request.
Described target platform can be the platform that will pass through before public transport is reached terminal from current location; Also can be only terminal platform.
Step 603: obtain traffic information, road congestion degree, and obtain bus travel speed by GPS device.
Step 604: arrive the distance of target platform, traffic information and road congestion degree in conjunction with described bus travel velocity information, public transport and calculate that public transport arrives the time of target platform.
In certain embodiments, first condition of road surface and road congestion degree information can be obtained by third-party application such as GIS.
In certain embodiments, first can calculate according to bus travel velocity information and the distance between public transport and target platform the theoretical time that public transport arrives target platform; Give certain weight to condition of road surface and the road degree of crowding again, and described public transport arrives the theoretical time combination of target platform, calculates that public transport arrives the time of target platform.
Further, the present invention also provides a kind of communications policy supportive device, and structure as shown in Figure 7, comprising:
Request receiving module: for receiving the inquiry request that user is sent by inquiry unit;
Information acquisition module: for corresponding to described inquiry request, gather bus running information by data collector;
Analysis decision module: for calling corresponding model from model bank, analyzes described public transport operation information, obtains the decision-making corresponding with described inquiry request;
Pushing module: for the described decision-making corresponding with inquiry request is pushed to described inquiry unit.
Concrete, described request receiver module can receive user by inquiry unit, the inquiry request sent in a wireless or wired way.Generally, target platform positional information is comprised in described inquiry request.
Concrete, described bus running information, when comprising the transmission of described inquiry request, by the bus running information of the data collector Real-time Collections such as GPS; Also can be the bus routes information prestored.
Concrete, described decision-making sends by wired or wireless mode.
Communications policy supportive device provided by the present invention, corresponding to the inquiry request that user sends, utilizes corresponding model to analyze the public transport operation information gathered, obtains the decision-making corresponding with described inquiry request.For user provides decision-making more accurately within the shorter time, without the need to user, information can be judged, facilitates user and formulate the plans such as trip, bus dispatching.
In certain embodiments, described bus running information comprises passenger flow information, vehicle traveling information, traffic information; Still with reference to Fig. 7, described analysis decision module specifically comprises:
Passenger flow calculating sub module: for calling passenger flow degree of crowding analytical model from model bank, utilizes described passenger flow information to calculate the passenger flow degree of crowding;
Time Calculation submodule: for calling public transport arrival time forecast model from model bank, utilizes described bus travel information and traffic information prediction public transport arrival time;
Decision-making calculating sub module: for calling decision model from model bank, utilizes the described passenger flow degree of crowding and described public transport arrival time to calculate the decision-making corresponding with described inquiry request.
Concrete, described passenger flow information can be the picture of the reflection volume of the flow of passengers, is obtained by picture collection device or video acquisition device.
Concrete, described bus travel information can comprise public transport current location, bus routes, bus travel speed etc.Described traffic information can comprise the road degree of crowding, road conditions etc.
Concrete, described decision-making calculating sub module is when utilizing the described passenger flow degree of crowding and described public transport arrival time calculates the decision-making corresponding with described inquiry request, different weights can be given to the passenger flow degree of crowding and public transport arrival time, obtain a communications policy based on multidimensional information.
In certain embodiments, described inquiry request comprises bus dispatching inquiry request, and described decision model comprises bus dispatching decision model;
In certain embodiments, described inquiry request comprises inquiry request by bus; Described decision model comprises decision model by bus.
In certain embodiments, described passenger flow information at least comprises the one in the car that obtained by prospect harvester in passenger flow picture and platform passenger flow picture.
In certain embodiments, when described passenger flow information comprises passenger flow picture in the car obtained by prospect harvester, described passenger flow degree of crowding analytical model comprises on the described bus of employing without background model in the car of background picture foundation during passenger.
In certain embodiments, when described passenger flow information comprises the platform passenger flow picture obtained by prospect harvester, described passenger flow degree of crowding analytical model comprises the platform background model adopting bus platform to set up without background picture during passenger.
In an advantageous embodiment, platform passenger flow information and Che Nei passenger flow information can be combined, calculate the passenger flow degree of crowding, in this case, described passenger flow calculating sub module comprises:
Picture acquiring unit: for gathering passenger flow picture at least one Zhang Zhantai passenger flow picture and at least one car by image collecting device or video acquisition device.
Picture analyzing unit: for adopting corresponding model in model bank to analyze described platform passenger flow picture and Che Nei passenger flow picture respectively, obtain platform intensity of passenger flow and Che Nei intensity of passenger flow.
Passenger flow degree of crowding projected unit: in conjunction with described platform intensity of passenger flow and Che Nei intensity of passenger flow, calculate the passenger flow degree of crowding.
In one embodiment, described bus travel information comprises the public transport position and bus travel speed that are obtained by GPS device; Described traffic information comprises road congestion degree and road conditions;
Described Time Calculation submodule specifically comprises:
Metrics calculation unit: for according to described public transport position and the position obtaining target platform from corresponding inquiry request, calculate the distance that public transport arrives target platform;
Time prediction unit: described public transport is arrived the distance of target platform, bus travel speed, road congestion degree and road conditions as variable, utilizes the public transport arrival time prediction algorithm prediction public transport of calling from method base to arrive the time of target platform.
Further, the invention provides a kind of communications policy back-up system, comprise the public transport real-time traffic information management devices of any one embodiment of the present invention, and for sending the inquiry unit of inquiry request.
In certain embodiments, described inquiry unit comprises the dispatching center's inquiry unit for sending scheduling inquiry request and the inquiry unit by bus for sending inquiry request by bus.
As can be seen from above, communications policy provided by the invention supports methods, devices and systems, by utilizing corresponding model to analyze public transport operation information, can provide decision support for user.The communications policy of the embodiment of the present invention supports methods, devices and systems, can also the comprehensive multidimensional public transport operation information such as bus travel speed, traffic information, the passenger flow degree of crowding, for providing a decision-making more tallied with the actual situation.
Should be appreciated that multiple embodiments described by this instructions are only for instruction and explanation of the present invention, are not intended to limit the present invention.And when not conflicting, the embodiment in the application and the feature in embodiment can combine mutually.
Obviously, those skilled in the art can carry out various change and modification to the present invention and not depart from the spirit and scope of the present invention.Like this, if these amendments of the present invention and modification belong within the scope of the claims in the present invention and equivalent technologies thereof, then the present invention is also intended to comprise these change and modification.
Claims (14)
1. a communications policy support method, is characterized in that, comprise the steps:
Receive the inquiry request that user is sent by inquiry unit;
Corresponding to described inquiry request, gather bus running information by data collector;
From model bank, call corresponding model, analyze described public transport operation information, obtain the decision-making corresponding with described inquiry request;
The described decision-making corresponding with inquiry request is pushed to described inquiry unit.
2. method according to claim 1, is characterized in that, described bus running information comprises passenger flow information, vehicle traveling information, traffic information;
From model bank, call corresponding model, analyze described public transport operation information, the step obtaining the decision-making corresponding with described inquiry request specifically comprises:
From model bank, call passenger flow degree of crowding analytical model, utilize described passenger flow information to calculate the passenger flow degree of crowding;
From model bank, call public transport arrival time forecast model, utilize described bus travel information and traffic information prediction public transport arrival time;
From model bank, call decision model, utilize the described passenger flow degree of crowding and described public transport arrival time to calculate the decision-making corresponding with described inquiry request.
3. method according to claim 2, is characterized in that, described inquiry request comprises bus dispatching inquiry request, and described decision model comprises bus dispatching decision model;
Or described inquiry request comprises inquiry request by bus; Described decision model comprises decision model by bus.
4. method according to claim 2, is characterized in that, described passenger flow information at least comprises the one in the car that obtained by prospect harvester in passenger flow picture and platform passenger flow picture;
Accordingly, described passenger flow degree of crowding analytical model comprises on the described bus of employing without the platform background model that background model or employing bus platform in the car of background picture foundation during passenger are set up without background picture during passenger.
5. method according to claim 4, it is characterized in that, when described passenger flow information comprises passenger flow picture and platform passenger flow picture in car, call passenger flow degree of crowding analytical model from model bank, the step utilizing described passenger flow information to calculate the passenger flow degree of crowding specifically comprises:
Passenger flow picture at least one Zhang Zhantai passenger flow picture and at least one car is gathered by image collecting device or video acquisition device;
Adopt corresponding model in model bank to analyze described platform passenger flow picture and Che Nei passenger flow picture respectively, obtain platform intensity of passenger flow and Che Nei intensity of passenger flow;
In conjunction with described platform intensity of passenger flow and Che Nei intensity of passenger flow, calculate the passenger flow degree of crowding.
6. method according to claim 2, is characterized in that, described bus travel information comprises the public transport position and bus travel speed that are obtained by GPS device; Described traffic information comprises road congestion degree and road conditions;
From model bank, call public transport arrival time forecast model, utilize the step of described bus travel information and traffic information prediction public transport arrival time specifically to comprise:
According to described public transport position and the position obtaining target platform from corresponding inquiry request, calculate the distance that public transport arrives target platform;
Described public transport is arrived the distance of target platform, bus travel speed, road congestion degree and road conditions as variable, utilize the public transport arrival time prediction algorithm prediction public transport of calling from method base to arrive the time of target platform.
7. a communications policy supportive device, is characterized in that, comprising:
Request receiving module: for receiving the inquiry request that user is sent by inquiry unit;
Information acquisition module: for corresponding to described inquiry request, gather bus running information by data collector;
Analysis decision module: for calling corresponding model from model bank, analyzes described public transport operation information, obtains the decision-making corresponding with described inquiry request;
Pushing module: for the described decision-making corresponding with inquiry request is pushed to described inquiry unit.
8. device according to claim 7, is characterized in that, described bus running information comprises passenger flow information, vehicle traveling information, traffic information;
Described analysis decision module specifically comprises:
Passenger flow calculating sub module: for calling passenger flow degree of crowding analytical model from model bank, utilizes described passenger flow information to calculate the passenger flow degree of crowding;
Time Calculation submodule: for calling public transport arrival time forecast model from model bank, utilizes described bus travel information and traffic information prediction public transport arrival time;
Decision-making calculating sub module: for calling decision model from model bank, utilizes the described passenger flow degree of crowding and described public transport arrival time to calculate the decision-making corresponding with described inquiry request.
9. device according to claim 8, is characterized in that, described inquiry request comprises bus dispatching inquiry request, and described decision model comprises bus dispatching decision model;
Or described inquiry request comprises inquiry request by bus; Described decision model comprises decision model by bus.
10. device according to claim 8, is characterized in that, described passenger flow information at least comprises the one in the car that obtained by prospect harvester in passenger flow picture and platform passenger flow picture;
Accordingly, described passenger flow degree of crowding analytical model comprises on the described bus of employing without the platform background model that background model or employing bus platform in the car of background picture foundation during passenger are set up without background picture during passenger.
11. devices according to claim 10, is characterized in that, when described passenger flow information comprises passenger flow picture and platform passenger flow picture in car, described passenger flow calculating sub module specifically comprises:
Picture acquiring unit: for gathering passenger flow picture at least one Zhang Zhantai passenger flow picture and at least one car by image collecting device or video acquisition device;
Picture analyzing unit: for adopting corresponding model in model bank to analyze described platform passenger flow picture and Che Nei passenger flow picture respectively, obtain platform intensity of passenger flow and Che Nei intensity of passenger flow;
Passenger flow degree of crowding projected unit: in conjunction with described platform intensity of passenger flow and Che Nei intensity of passenger flow, calculate the passenger flow degree of crowding.
12. devices according to claim 8, is characterized in that, described bus travel information comprises the public transport position and bus travel speed that are obtained by GPS device; Described traffic information comprises road congestion degree and road conditions;
Described Time Calculation submodule specifically comprises:
Metrics calculation unit: for according to described public transport position and the position obtaining target platform from corresponding inquiry request, calculate the distance that public transport arrives target platform;
Time prediction unit: described public transport is arrived the distance of target platform, bus travel speed, road congestion degree and road conditions as variable, utilizes the public transport arrival time prediction algorithm prediction public transport of calling from method base to arrive the time of target platform.
13. 1 kinds of communications policy back-up systems, comprise as the communications policy supportive device in claim 7-12 as described in any one, and for sending the inquiry unit of inquiry request.
14. systems according to claim 13, is characterized in that, described inquiry unit comprises the dispatching center's inquiry unit for sending scheduling inquiry request and the inquiry unit by bus for sending inquiry request by bus.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410189626.1A CN105096584A (en) | 2014-05-06 | 2014-05-06 | Traffic decision support method, device, and system |
| PCT/CN2014/080347 WO2015168976A1 (en) | 2014-05-06 | 2014-06-19 | Traffic decision support method, device and system |
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| Application Number | Priority Date | Filing Date | Title |
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| CN201410189626.1A CN105096584A (en) | 2014-05-06 | 2014-05-06 | Traffic decision support method, device, and system |
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| CN105096584A true CN105096584A (en) | 2015-11-25 |
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| CN201410189626.1A Withdrawn CN105096584A (en) | 2014-05-06 | 2014-05-06 | Traffic decision support method, device, and system |
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| CN (1) | CN105096584A (en) |
| WO (1) | WO2015168976A1 (en) |
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