CN109785610A - A kind of intelligent bus management method and system based on big data - Google Patents
A kind of intelligent bus management method and system based on big data Download PDFInfo
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- CN109785610A CN109785610A CN201910068534.0A CN201910068534A CN109785610A CN 109785610 A CN109785610 A CN 109785610A CN 201910068534 A CN201910068534 A CN 201910068534A CN 109785610 A CN109785610 A CN 109785610A
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Abstract
The present invention relates to a kind of intelligent bus management method and system based on big data.This method comprises: obtaining urban transportation data acquisition system, wherein the urban transportation data acquisition system includes the real time traffic data of multiple transport hubs；The real time personnel density of the transport hub is determined according to the real time traffic data；It determines whether the real time personnel density is in abnormality, when the real time personnel density is when in an abnormal state, determines the corresponding transport hub of the real time personnel density；The public bus network of transport hub described in approach is determined according to the transport hub, and bus dispatching scheme is determined according to the real time personnel density and the public bus network；According to public bus network described in the bus dispatching scheme schedules.The present invention can be based on big data analysis traffic condition adjustment public traffic management scheme, in time, effectively improve bus operation efficiency, alleviate urban traffic pressure.
Description
Technical field
The present invention relates to field of traffic control more particularly to a kind of intelligent bus management method based on big data and it is
System.
Background technique
Existing traffic system can generate the traffic data of magnanimity, data class daily all in busy work
Various, bulky.Meanwhile under the process of urbanization, a large amount of population pours in city, and transit trip is selected to become
Essential a part in people's life.However, urban traffic amount is big, personnel are unevenly distributed, and bus operation efficiency is lower,
Urban traffic pressure is huge.
Summary of the invention
In order in time, effectively improve bus operation efficiency, alleviate traffic pressure, the present invention provides a kind of based on big data
Intelligent bus management method and system.
The technical scheme to solve the above technical problems is that
In a first aspect, the embodiment of the invention provides a kind of intelligent bus management method based on big data, this method packet
It includes:
Obtain urban transportation data acquisition system, wherein the urban transportation data acquisition system includes the real-time of multiple transport hubs
Traffic data.
The real time personnel density of the transport hub is determined according to the real time traffic data.
Determine whether the real time personnel density is in abnormality, when the real time personnel density is in abnormality
When, determine the corresponding transport hub of the real time personnel density.
The public bus network of transport hub described in approach is determined according to the transport hub, and according to the real time personnel density
Bus dispatching scheme is determined with the public bus network.
According to public bus network described in the bus dispatching scheme schedules.
Second aspect, the embodiment of the invention provides a kind of intelligent traffic management system based on big data, the system packets
It includes:
Module is obtained, for obtaining urban transportation data acquisition system, wherein the urban transportation data acquisition system includes multiple friendships
The real time traffic data of logical hinge.
Extraction module, for determining the real time personnel density of the transport hub according to the real time traffic data.
Analysis module, for determining whether the real time personnel density is in abnormality, when the real time personnel density
It is when in an abnormal state, determine the corresponding transport hub of the real time personnel density.
Decision-making module, for determining the public bus network of transport hub described in approach according to the transport hub, and according to institute
It states real time personnel density and the public bus network determines bus dispatching scheme.
Application module is used for the public bus network according to the bus dispatching scheme schedules.
The beneficial effects of the present invention are: it is based on big data technology, the miscellaneous magnanimity generated from Traffic Systems
In traffic data, the useful data for influencing public traffic management, scheduling is extracted.The change of each transport hub density of personnel is paid close attention in real time
Change, finds personnel's density anomaly situation in time.According to unusual condition, analysis obtains the transport hub of density of personnel exception and effective
Bus dispatching scheme, and bus dispatching center is notified to adjust departing time interval, reduce it is empty run or alleviate congestion, reach raising
Bus operation efficiency alleviates the purpose of urban traffic pressure.
Detailed description of the invention
Fig. 1 is the flow diagram for the intelligent bus management method based on big data that one embodiment of the invention provides；
Fig. 2 is that the density of personnel grade for the intelligent bus management method based on big data that one embodiment of the invention provides is drawn
Divide schematic diagram；
Fig. 3 be another embodiment of the present invention provides the intelligent bus management method based on big data flow diagram；
Fig. 4 is the structural schematic diagram for the intelligent traffic management system based on big data that one embodiment of the invention provides；
Fig. 5 be another embodiment of the present invention provides the intelligent traffic management system based on big data structural schematic diagram；
Fig. 6 is the structural schematic diagram for the intelligent traffic management system based on big data that further embodiment of this invention provides.
Specific embodiment
In being described below, for illustration and not for limitation, propose such as specific system structure, interface, technology it
The detail of class understands the present invention to cut thoroughly.However, it will be clear to one skilled in the art that there is no these specific
The present invention also may be implemented in the other embodiments of details.In other situations, omit to well-known system, circuit and
The detailed description of method, in case unnecessary details interferes description of the invention.In addition, public transport herein is not limited to common public affairs
Vehicle is handed over, can also be the public transportations sides such as taxi, subway, tramcar, passenger steamer, airline carriers of passengers or helicopter
Formula.
In a first aspect, the embodiment of the invention provides a kind of intelligent bus management method based on big data.
In a preferred embodiment, as shown in Figure 1, this method comprises:
Obtain urban transportation data acquisition system, wherein the urban transportation data acquisition system includes the real-time of multiple transport hubs
Traffic data.
The real time personnel density of the transport hub is determined according to the real time traffic data.
Determine whether the real time personnel density is in abnormality, when the real time personnel density is in abnormality
When, determine the corresponding transport hub of the real time personnel density.
The public bus network of transport hub described in approach is determined according to the transport hub, and according to the real time personnel density
Bus dispatching scheme is determined with the public bus network.
According to public bus network described in the bus dispatching scheme schedules.
Specifically, existing Traffic Systems, can generate a large amount of data daily, and the source of these big datas includes passing
Sensor data, using data, system data and service data etc..Wherein, sensing data include vehicle present position, speed,
Image and RFID etc.；It include manufacturer, the energy, performance and compatibility etc. using data；System data include equipment operation with
Maintenance record, the maintenance log of vehicle day-to-day operation etc.；Service data includes Internet data, daily high speed and mistake road and bridge data
Deng.This method is accessed in traffic system by distributed data bus, is got these mass datas and is extracted, screens and divide
Analysis.
Based on big data technology, from miscellaneous huge traffic data that traffic system generates, it is public to extract influence
The useful data of intermodulation degree.The variation of each transport hub density of personnel is paid close attention in real time, finds personnel's density anomaly situation in time.Its
In, density of personnel is a certain range of total number of persons divided by area, unit are as follows: people/square metre.According to density of personnel exception shape
Condition analyzes the transport hub for obtaining density of personnel exception and effective bus dispatching scheme, and bus dispatching center is notified to adjust
Departing time interval reduces empty race or alleviates congestion, reaches and improve bus operation efficiency, alleviate the purpose of traffic pressure.
In a preferred embodiment:
The urban transportation data acquisition system further includes the historical traffic data of multiple transport hubs, and the method also includes such as
Lower step:
The normal range (NR) of the density of personnel of the transport hub, the normal range (NR) are determined according to the historical traffic data
For judging whether the real time personnel density is in abnormality.
Specifically, according to historical empirical data, it is close that personnel corresponding to the traffic conditions such as unimpeded, moderate and congestion are marked off
Spend range.Such as: when density of personnel is 0.1 people/square metre following, the coast is clear, it may appear that the phenomenon that the race of public transport sky；Work as people
Member's density be 0.5 people/square metre or more when, congestion in road, it may appear that the phenomenon that public transport is fully loaded or overload；When density of personnel is
0.1 people/square metre to 0.5 people/square metre between when, traffic conditions are moderate, public transport passenger capacity core it is manned number left and right can expire
Sufficient demand.Reference data can not only be provided for screening, the bus dispatching solution formulation etc. of personnel's density anomaly transport hub in this way,
The mutation of density of personnel can also be coped with, for example, the temporary sharp increase of flow of the people caused by the large-scale activity temporarily held.But by
Change often in roading, for example, road is widened, the personnel that can be accommodated and vehicle become more, or originally than wider
Road, since urban construction is constructed, road narrows, then the personnel that can be accommodated and vehicle tail off.Such as: after road broadening, when
Density of personnel range 0.2 people/square metre to 0.6 people/square metre between when, traffic condition can also show as moderate；And road
After narrowing, possible density of personnel is 0.07 people/square metre be also easy the congestion that seems.Therefore, the criteria for classifying of density of personnel range,
It needs in time to be finely adjusted by big data analysis.
In a preferred embodiment, as shown in Figure 2:
The normal range (NR) includes upper limit value and lower limit value, and the abnormality includes the reality to indicate the transport hub
When very few the first abnormality of waiting personnel and indicate the transport hub wait the abnormal shape of overstaffed second in real time
State, the process whether the determination real time personnel density is in abnormality include:
When the real time personnel density is less than or equal to the lower limit value, the abnormality is the described first abnormal shape
State.
When the real time personnel density is greater than or equal to the upper limit value, the abnormality is the described second abnormal shape
State.
Specifically, the first abnormality, normal range (NR) and the second abnormality respectively correspond unimpeded, moderate and congestion friendship
Logical situation.
Classifying rationally density of personnel grade, it is specific to cooperate the variation of traffic pressure peak valley, targetedly make bus dispatching
Decision.
In a preferred embodiment, as shown in Figure 3:
The process that bus dispatching scheme is determined according to the real time personnel density and the public bus network includes:
When the abnormality is first abnormality, increase the departure time interval of the public bus network.
When the abnormality is second abnormality, reduce the departure time interval of the public bus network.
When density of personnel is normal, normal departing time interval is kept or restored.When density of personnel is small, when increase is dispatched a car
Between be spaced, reduce public transport sky run.When density of personnel is big, departing time interval is reduced, more public transport is allowed to rush to people in the short time
The intensive place of member, picks passenger, alleviates transport hub pressure.
In a preferred embodiment, the method also includes:
Determine that the corresponding relationship of the transport hub Yu the public bus network, the corresponding relationship are used for according to the traffic
Hinge determines the public bus network of transport hub described in approach.
Specifically, nearby often there is multichannel public bus network in a transport hub, sorts out this from traffic big data in advance
Information often can provide the inquiry application of subsequent long period.
The corresponding relationship of the public bus network of transport hub and each transport hub of approach is counted, in advance to do public transport tune
Quick search when spending decision.But with urban planning, public bus network is occasionally changed, and need to safeguard update in due course.
Second aspect, the embodiment of the invention provides a kind of intelligent traffic management systems based on big data.
In a preferred embodiment, as shown in figure 4, the system includes:
Module is obtained, for obtaining urban transportation data acquisition system, wherein the urban transportation data acquisition system includes multiple friendships
The real time traffic data of logical hinge.
Extraction module, for determining the real time personnel density of the transport hub according to the real time traffic data.
Analysis module, for determining whether the real time personnel density is in abnormality, when the real time personnel density
It is when in an abnormal state, determine the corresponding transport hub of the real time personnel density.
Decision-making module, for determining the public bus network of transport hub described in approach according to the transport hub, and according to institute
It states real time personnel density and the public bus network determines bus dispatching scheme.
Application module is used for the public bus network according to the bus dispatching scheme schedules.
Specifically, existing Traffic Systems, can generate a large amount of data daily, and the source of these big datas includes passing
Sensor data, using data, system data and service data etc..Wherein, sensing data include vehicle present position, speed,
Image and RFID etc.；It include manufacturer, the energy, performance and compatibility etc. using data；System data include equipment operation with
Maintenance record, the maintenance log of vehicle day-to-day operation etc.；Service data includes Internet data, daily high speed and mistake road and bridge data
Deng.
It obtains module to access in traffic system by distributed data bus, gets these mass datas.However data
Measure it is huge, need to extract by extraction module useful information for analysis module analyze.The data result that analysis module obtains
For decision-making module reference, final decision is obtained.The result of decision is notified relevant department by application module.
Based on big data technology, from miscellaneous huge traffic data that traffic system generates, it is public to extract influence
The useful data of intermodulation degree.The variation of each transport hub density of personnel is paid close attention in real time, finds personnel's density anomaly situation in time.Its
In, density of personnel is a certain range of total number of persons divided by area, unit are as follows: people/square metre.According to density of personnel exception shape
Condition analyzes the transport hub for obtaining density of personnel exception and effective bus dispatching scheme, and bus dispatching center is notified to adjust
Departing time interval reduces empty race or alleviates congestion, reaches and improve bus operation efficiency, alleviate the purpose of traffic pressure.
In a preferred embodiment, as shown in Figure 5:
The urban transportation data acquisition system further includes the historical traffic data of multiple transport hubs, the system also includes:
Division module determines the normal range (NR) of the density of personnel of the transport hub, institute according to the historical traffic data
Normal range (NR) is stated for judging whether the real time personnel density is in abnormality.
Specifically, division module is the module of a completion preprocessing function, when the result which divides can be longer
Between be applied to this system in, without frequently change.
In the embodiment, division module can mark off the traffic conditions such as unimpeded, moderate and congestion according to previous experiences data
Corresponding density of personnel range.Such as: when density of personnel be 0.1 people/square metre or less when, the coast is clear, it may appear that public transport
The phenomenon that sky is run；When density of personnel be 0.5 people/square metre or more when, congestion in road, it may appear that public transport is fully loaded or overload shows
As；When density of personnel be 0.1 people/square metre to 0.5 people/square metre between when, traffic conditions are moderate, public transport passenger capacity core carry
Number or so can meet demand.It in this way can not only be screening, the bus dispatching solution formulation of personnel's density anomaly transport hub
Deng offer reference data, moreover it is possible to the mutation of density of personnel is coped with, for example, flow of the people caused by the large-scale activity temporarily held is temporary
Property increase severely.But since roading changes often, for example, road is widened, the personnel that can be accommodated and vehicle become more, or
Person is originally than wider road, and since urban construction is constructed, road narrows, then the personnel that can be accommodated and vehicle tail off.Such as:
Road broadening after, when density of personnel range 0.2 people/square metre to 0.6 people/square metre between when, traffic conditions can also show
It is moderate；And after road narrows, possible density of personnel is 0.07 people/square metre be also easy the congestion that seems.Therefore, density of personnel model
The criteria for classifying enclosed needs in time to be finely adjusted by big data analysis.
In a preferred embodiment, as shown in Figure 6:
The normal range (NR) includes upper limit value and lower limit value, and the abnormality includes indicate the transport hub real-time
The first very few abnormality of waiting personnel waits the second overstaffed abnormality with the instruction transport hub in real time,
The analysis module includes:
First judging unit is used for when the real time personnel density is less than or equal to the lower limit value, the exception shape
State is first abnormality.
Second judgment unit is used for when the real time personnel density is greater than or equal to the upper limit value, the exception shape
State is second abnormality.
Specifically, the first abnormality, normal range (NR) and the second abnormality respectively correspond unimpeded, moderate and congestion friendship
Logical situation.
Classifying rationally density of personnel grade, it is specific to cooperate the variation of traffic pressure peak valley, targetedly make bus dispatching
Decision.
In a preferred embodiment, as shown in fig. 6, the decision-making module includes:
First decision package, for increasing the public bus network when the abnormality is first abnormality
Departure time interval.
Second decision package, for reducing the public bus network when the abnormality is second abnormality
Departure time interval.
When density of personnel is normal, normal departing time interval is kept or restored.When density of personnel is small, when increase is dispatched a car
Between be spaced, reduce public transport sky run.When density of personnel is big, departing time interval is reduced, more public transport is allowed to rush to people in the short time
The intensive place of member, picks passenger, alleviates transport hub pressure.
In a preferred embodiment, as shown in Figure 6:
Enquiry module, for determining that the corresponding relationship of the transport hub Yu the public bus network, the corresponding relationship are used
In the public bus network for determining transport hub described in approach according to the transport hub.
Specifically, nearby often there is multichannel public bus network in a transport hub, sorts out this from traffic big data in advance
Information often can provide the inquiry application of subsequent long period.
The corresponding relationship of the public bus network of transport hub and each transport hub of approach is counted, in advance to do public transport tune
Quick search when spending decision.Meanwhile with urban planning, public bus network is occasionally changed, and need to safeguard update in due course.
Reader should be understood that in the description of this specification reference term " one embodiment ", " is shown " some embodiments "
The description of example ", specific examples or " some examples " etc. mean specific features described in conjunction with this embodiment or example, structure,
Material or feature are included at least one embodiment or example of the invention.In the present specification, above-mentioned term is shown
The statement of meaning property need not be directed to identical embodiment or example.Moreover, particular features, structures, materials, or characteristics described
It may be combined in any suitable manner in any one or more of the embodiments or examples.In addition, without conflicting with each other, this
The technical staff in field can be by the spy of different embodiments or examples described in this specification and different embodiments or examples
Sign is combined.
Although the embodiments of the present invention has been shown and described above, it is to be understood that above-described embodiment is example
Property, it is not considered as limiting the invention, those skilled in the art within the scope of the invention can be to above-mentioned
Embodiment is changed, modifies, replacement and variant.
Claims (10)
1. a kind of intelligent bus management method based on big data, which is characterized in that described method includes following steps:
Obtain urban transportation data acquisition system, wherein the urban transportation data acquisition system includes the real-time traffic of multiple transport hubs
Data；
The real time personnel density of the transport hub is determined according to the real time traffic data；
Determine whether the real time personnel density is in abnormality, when the real time personnel density is when in an abnormal state, really
Determine the corresponding transport hub of the real time personnel density；
The public bus network of transport hub described in approach is determined according to the transport hub, and according to the real time personnel density and institute
It states public bus network and determines bus dispatching scheme；
According to public bus network described in the bus dispatching scheme schedules.
2. the intelligent bus management method according to claim 1 based on big data, which is characterized in that the urban transportation
Data acquisition system further includes the historical traffic data of multiple transport hubs, and the method also includes following steps:
Determine that the normal range (NR) of the density of personnel of the transport hub, the normal range (NR) are used for according to the historical traffic data
Judge whether the real time personnel density is in abnormality.
3. the intelligent bus management method according to claim 2 based on big data, which is characterized in that the normal range (NR)
Including upper limit value and lower limit value, the abnormality includes indicating very few first different of the real-time waiting personnel of the transport hub
Normal state waits the second overstaffed abnormality, the determination real time personnel with the instruction transport hub in real time
The process whether density is in abnormality includes:
When the real time personnel density is less than or equal to the lower limit value, the abnormality is first abnormality；
When the real time personnel density is greater than or equal to the upper limit value, the abnormality is second abnormality.
4. the intelligent bus management method according to claim 3 based on big data, which is characterized in that described according to
Real time personnel density and the public bus network determine that the process of bus dispatching scheme includes:
When the abnormality is first abnormality, increase the departure time interval of the public bus network；
When the abnormality is second abnormality, reduce the departure time interval of the public bus network.
5. the intelligent bus management method according to any one of claims 1 to 4 based on big data, which is characterized in that
The method also includes following steps:
Determine that the corresponding relationship of the transport hub Yu the public bus network, the corresponding relationship are used for according to the transport hub
Determine the public bus network of transport hub described in approach.
6. a kind of intelligent traffic management system based on big data, which is characterized in that the system comprises:
Module is obtained, for obtaining urban transportation data acquisition system, wherein the urban transportation data acquisition system includes multiple traffic pivots
The real time traffic data of knob；
Extraction module, for determining the real time personnel density of the transport hub according to the real time traffic data；
Analysis module, for determining whether the real time personnel density is in abnormality, when the real time personnel density is in
When abnormality, the corresponding transport hub of the real time personnel density is determined；
Decision-making module, for determining the public bus network of transport hub described in approach according to the transport hub, and according to the reality
When density of personnel and the public bus network determine bus dispatching scheme；
Application module is used for the public bus network according to the bus dispatching scheme schedules.
7. the intelligent traffic management system according to claim 6 based on big data, which is characterized in that the urban transportation
Data acquisition system further includes the historical traffic data of multiple transport hubs, the system also includes:
Division module, determines the normal range (NR) of the density of personnel of the transport hub according to the historical traffic data, it is described just
Normal range is for judging whether the real time personnel density is in abnormality.
8. the intelligent traffic management system according to claim 7 based on big data, which is characterized in that the normal range (NR)
Including upper limit value and lower limit value, the abnormality includes indicating very few first different of the real-time waiting personnel of the transport hub
Normal state waits the second overstaffed abnormality with the instruction transport hub in real time, and the analysis module includes:
First judging unit, for when the real time personnel density is less than or equal to the lower limit value, the abnormality to be
First abnormality；
Second judgment unit, for when the real time personnel density is greater than or equal to the upper limit value, the abnormality to be
Second abnormality.
9. the intelligent traffic management system according to claim 8 based on big data, which is characterized in that the decision-making module
Include:
First decision package, for increasing going out for the public bus network when the abnormality is first abnormality
Send out time interval；
Second decision package, for reducing going out for the public bus network when the abnormality is second abnormality
Send out time interval.
10. based on the intelligent traffic management system of big data according to claim 6 to 9 any one, which is characterized in that
The system also includes:
Enquiry module, for determining that the corresponding relationship of the transport hub Yu the public bus network, the corresponding relationship are used for root
The public bus network of transport hub described in approach is determined according to the transport hub.
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