CN109508814A - A kind of public transport trajectory optimization system based on Internet of Things and cloud computing - Google Patents
A kind of public transport trajectory optimization system based on Internet of Things and cloud computing Download PDFInfo
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Abstract
The public transport trajectory optimization system based on Internet of Things and cloud computing that the present invention relates to a kind of, including expert system and vehicle and bus dispatching center, the vehicle is connect with bus dispatching center and expert system respectively, and execution track performs the steps of step S1 when optimizing: obtaining the basic data of in expert system or customized input；Step S2: the real time data of in expert system or customized input is obtained；Step S3: the time interval that vehicle reaches next intersection is calculated based on public transport characteristic information and intersection information, the relationship of the time interval for reaching next intersection according to vehicle time interval corresponding with the intersection red light determines speed boot policy；Step S4: the track optimizing model for considering bus travel comfort is established according to determining speed boot policy, and solving model obtains the optimization track in each subinterval.Compared with prior art, the present invention has many advantages, such as to improve public transport riding comfort.
Description
Technical field
The present invention relates to traffic programme technical fields, more particularly, to a kind of public transport track based on Internet of Things and cloud computing
Optimization system.
Background technique
With the rapid development of our country's economy, national income and living standard are continuously improved, Urban vehicles poputation and
Resident trips rapid growth.Asking for urban development is influenced including traffic congestion, environmental pollution, energy consumption etc. in order to cope with
Topic, various regions actively implement public transport priority strategy, greatly develop urban public tranlport system.
Public transport driving procedure is mostly based on the experience of driver at present, different experiences, technical level driver exist compared with
Big difference, there are biggish optimization spaces for bus driving process.To avoid bus from frequently intersecting in the process of running
It is relied on before message signal lamp, can reduce by carrying out speed guidance to bus and rely on number, improve operational efficiency.Another party
Face, different speed guidance modes bring different riding comforts to experience due to the difference of vehicle acceleration, deceleration degree to passenger.
In order to improve bus service level, public transport share ratio is promoted, it is necessary to take into account comfort of passenger carries out bus travel track excellent
Change.
With the development of technology of Internet of things, by advanced detection, the communication technology in bus or train route communication environment, obtain in real time
Including vehicle location, the operation informations such as speed, and the road information including downstream intersection signal lamp situation can be real-time
Vehicle Speed is guided, realizes response control target.Control object is changed into vehicle from traditional signal lamp by this mode
Itself, can more actively, traffic control is effectively performed, can reduce on other public vehicles influence while, promoted
Bus running efficiency, more efficiently realization public traffic in priority.
Existing speed bootstrap technique, mainly by be distributed in urban signal control crossing dedicated trackside subsystem with
And dedicated onboard subsystem is constituted, and most operations are all concentrated on vehicle intelligent terminal, are mentioned to the performance of terminal
Higher requirement is gone out.Secondly, continually transmitting map and signal lamp data using dedicated short-distance wireless communication, original can be also occupied
Originally rare channel resource.
Chinese patent CN107067710A discloses a kind of energy-efficient city bus running track optimization method of consideration,
The city bus driving strategy dual-layer optimization computation model of energy factor is considered by building and it is solved, and is obtained current public
Hand over the optimization track of the traffic coverage to stop between a little as defined in vehicle running position to operational plan, the row including each subinterval
Sail speed, running time, position and index power and brake force.This method is with the minimum optimization mesh of bus running energy consumption
Mark, does not consider the operation comfort level of public transport, is unfavorable for improving the service level of public transport, improves public transport share rate.
Chinese patent CN107464430A disclose a kind of green wave speed bootstrap technique in lamp control crossing based on cloud service and
Then collected data are uploaded cloud service by acquisition signal lamp arrangement and real-time status data by system, system in real time
Device and by respective data storage in database；Based on the signal lamp data stored in database, Cloud Server is asked according to client
It seeks survival into signal lamp state data and green wave speed guide service Data Concurrent gives client；Client passes through after receiving data
Intelligent mobile terminal is shown to user.But it only considered the item for realizing green wave in the speed bootstrap algorithm that the system is carried out
Part does not account for comfort of passenger, may influence the ride experience of passenger.
Summary of the invention
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide one kind based on Internet of Things and
The public transport trajectory optimization system of cloud computing.
The purpose of the present invention can be achieved through the following technical solutions:
A kind of public transport trajectory optimization system based on Internet of Things and cloud computing, including expert system and vehicle and bus dispatching
Center, the vehicle are connect with bus dispatching center and expert system respectively, and execution track performs the steps of when optimizing
Step S1: the basic data of in acquisition expert system or customized input, including public transport operation route to be optimized,
Operational plan, information of vehicles and by way of intersection information；
Step S2: obtaining in expert system or customized input real time data, including vehicle current time reality
When position, real-time speed and real-time weather situation；
Step S3: calculating the time interval that vehicle reaches next intersection based on public transport characteristic information and intersection information,
The relationship of the time interval for reaching next intersection according to vehicle time interval corresponding with the intersection red light determines that speed is drawn
Lead strategy；
Step S4: establishing the track optimizing model for considering bus travel comfort according to determining speed boot policy, and
Solving model obtains the optimization track in each subinterval.
Step S5: the comfort level of passenger is recorded in detection record actual moving process；
Step S6: the comfort of passenger data feedback that will test is analyzed and counted to expert system, by expert system
It scores the track optimizing scheme works.
The expert system includes:
Man-machine interface, for realizing the interactive function of user and system；
Knowledge base, for storing the related advisory proposed including bus driving skills, passenger for driving procedure；
Model library, for storing based on the bus track optimizing model for considering comfort of passenger；
Database, for storing each public bus network information data, site information data, intersection information data, vehicle letter
Cease data, synoptic data data, history run track optimizing scheme and scoring；
Scoring library, for comfort of passenger detected in the track scheme according to final actual motion, to different marks
The track optimizing scheme of label scores.
The step S3 is specifically included:
Step S31: the distance of the lower intersection of vehicle distances is determined according to the current location of vehicle and intersection position；
Step S32: according to the present speed of vehicle, the distance at current time and the lower intersection of vehicle distances, vehicle is calculated
Reach the time interval of next intersection；
Step S33: judge that vehicle reaches the time interval time interval corresponding with the intersection red light of next intersection
Relationship, and speed boot policy is determined based on judging result.
The step S33 is specifically included:
If it is empty set that vehicle, which reaches the time interval of next intersection and the intersection in any red light section, select to accelerate
Boot policy；
Under if vehicle reaches the time interval of next intersection and the intersection in any red light section is not empty, and vehicle reaches
The time interval right margin of one intersection is located in the red light section, and left margin is located at outside the red light section, then selects to accelerate to draw
Lead strategy；
If the time interval that vehicle reaches next intersection is located in any red light section, any selection does not guide, adds
Speed guidance or one of the three kinds of strategies of guidance that slow down；
Under if vehicle reaches the time interval of next intersection and the intersection in any red light section is not empty, and vehicle reaches
The time interval left margin of one intersection is located in the red light section, and right margin is located at outside the red light section, then selects to slow down and draw
Lead strategy.
The vehicle reaches the time interval G=[G of next intersection1,G2] are as follows:
[G1, G2]=[minT 'a, maxT 'a]
Wherein: Ta' for vehicle reach next intersection at the time of, T0For current time, vsFor the guidance speed of vehicle, v0
For the present speed of vehicle, L0For the distance of the lower intersection of vehicle distances, a is acceleration, G1Reach next intersection for vehicle
Time interval left margin, G2Reach the time interval right margin of next intersection for vehicle.
In the step S4, if the time interval that vehicle reaches next intersection is located in any red light section, establish
Consider the track optimizing model of bus travel comfort are as follows:
Constraint condition are as follows:
Wherein: T is the journey time section after guidance, T0For current time, t1It is the total consumption for changing the initial velocity stage
When, t2For the total time-consuming for driving at a constant speed the stage, t3For the total time-consuming in deceleration stop stage, a1It (t) is change initial velocity stage t
The acceleration at moment, a are acceleration, a2It (t) is the acceleration for driving at a constant speed stage t moment, a3It (t) is deceleration stop stage t
The acceleration at moment, vminFor vehicle minimum speed, vmaxFor vehicle maximum speed, aminFor vehicle minimum acceleration, amaxFor vehicle
Peak acceleration, G1Reach the time interval left margin of next intersection, G for vehicle2For vehicle reach next intersection when
Between section right margin.
In the step S4, if it is sky that vehicle, which reaches the time interval of next intersection and the intersection in any red light section,
Collection then establishes the track optimizing model for considering bus travel comfort are as follows:
Constraint condition are as follows:
Wherein: T is the journey time section after guidance, T0For current time, t1It is the total consumption for changing the initial velocity stage
When, t2For the total time-consuming for driving at a constant speed the stage, a is acceleration, a1It (t) is the acceleration for changing initial velocity stage t moment, a2
It (t) is the acceleration for driving at a constant speed stage t moment, vminFor vehicle minimum speed, vmaxFor vehicle maximum speed, aminFor vehicle
Minimum acceleration, amaxFor vehicle peak acceleration, G1Reach the time interval left margin of next intersection, G for vehicle2For vehicle
Reach the time interval right margin of next intersection；
Under if vehicle reaches the time interval of next intersection and the intersection in any red light section is not empty, and vehicle reaches
The time interval right margin of one intersection is located in the red light section, and left margin is located at outside the red light section, then it is public to establish consideration
Hand over the track optimizing model of driving comfort are as follows:
Constraint condition are as follows:
Wherein: T1For the initial time in the red light section；
Under if vehicle reaches the time interval of next intersection and the intersection in any red light section is not empty, and vehicle reaches
The time interval left margin of one intersection is located in the red light section, and right margin is located at outside the red light section, then it is public to establish consideration
Hand over the track optimizing model of driving comfort are as follows:
Constraint condition are as follows:
Wherein: T2For the end time in the red light section.
The step S5 specifically:
Step S51: from the historical track prioritization scheme that selection in expert system database has identical or approximate label
It scores highest three and compares, the track optimizing scheme that model solution obtains is modified；
Step S52: final track optimizing scheme is used for actual motion after output amendment, and right in actual moving process
The comfort level of passenger carries out detection record.
The comfort level of the passenger is measured by pull spring formula acceleration transducer, and the pull spring formula acceleration transducer is set altogether
There are two groups, is respectively used to the acceleration of monitoring vehicle vertical and horizontal.
The comfort level of the passenger is measured by the accelerometer of mobile terminal, specifically, acquisition is in vehicle operation
In three-dimensional acceleration information, and calculate resultant acceleration.
Compared with prior art, the invention has the following advantages:
1) being obtained using technology of Internet of things includes vehicle real time and Intersections information etc., by bus
Speed carries out guiding in real time, reduces bus in intersection and relies on number, while when solving guide tracks, it is contemplated that vehicle
Acceleration influences passenger's riding comfort bring, and optimization obtains the optimal public transport of comfort of passenger in a variety of driving traces
Running track, to improve comfort of passenger, and then it is horizontal to promote bus service.
2) it selects different strategies to establish different track optimizing models for different situations, optimum results can be improved
Accuracy and comfort level.
3) detection of comfort of passenger score when using to practical application, helps to improve actual optimization effect
Fruit.
Detailed description of the invention
Fig. 1 is the key step flow diagram of the method for the present invention；
Fig. 2 is speed boot policy decision flowchart；
Speed guides the relationship in section and signal lamp red light section when Fig. 3 (a) is in the case of the first；
Fig. 3 (b) is the relationship under second situation between speed boot section with signal lamp red light section；
Fig. 3 (c) is the relationship in speed guidance section and signal lamp red light section in the case of the third；
Speed guides the relationship in section and signal lamp red light section in the case of Fig. 3 (d) is the 4th kind；
Fig. 4 is the total system frame diagram of the public transport trajectory optimization system based on Internet of Things and cloud computing；
Fig. 5 is the overall construction drawing of expert system.
Specific embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.The present embodiment is with technical solution of the present invention
Premised on implemented, the detailed implementation method and specific operation process are given, but protection scope of the present invention is not limited to
Following embodiments.
A kind of public transport trajectory optimization system based on Internet of Things and cloud computing, including expert system and vehicle and bus dispatching
Center, the vehicle are connect with bus dispatching center and expert system respectively,
This system based on public transport operation route, site information, intersection information, by acquire in real time vehicle location,
The data such as speed, weather condition form bus driving trace based on the bus track optimizing model for considering comfort of passenger
Model optimization scheme, then the history Management plan and model counted from the expert system database established in cloud platform is excellent
Change scheme compares and analyzes, and is adjusted to the unreasonable part of model optimization scheme perfect, exports final track optimizing
Scheme.During the actual travel of each suboptimization track, comfort of passenger is detected, with every label of the program
It inputs in expert system and is scored it together, continuous area is carried out to expert system in the daily operation accumulated over a long period
It is tired abundant.
As shown in Figure 1, execution track performs the steps of when optimizing
Step S1: the basic data of in acquisition expert system or customized input, including public transport operation route to be optimized,
Site information, operational plan, vehicle model information and by way of intersection information；
Wherein, public transport operation route, site information, operational plan and vehicle model are obtained, be by 3G, 4G, 5G or
The wireless communication techniques such as UWB are communicated with bus dispatching center, obtain information needed, including vehicle departure interval, vehicle fortune
Walking along the street diameter, stand between runing time, next point that stops, reach next station at the time of and vehicle model.
Wherein, it is described by way of intersection information include intersection position and intersection signal timing scheme, it is special by inquiring
The method of database obtains in family's system.When the intersection signal timing scheme information includes the Intersections period
It is long, long green light time and red light duration.
Wherein, since bus according to the present invention travels on public transportation lane, not by other lane speed-limiting messages
Limitation, road section speed limit information is not considered.
Step S2: the real time data of in expert system or customized input is obtained, including vehicle is at the current T_0 moment
Real time position, real-time speed v_0 and real-time weather situation；
Wherein, vehicle real time position and real time speed information are fixed by vehicle GPS, Beidou satellite alignment system, UWB
Position system or the fusion of millimetre-wave radar, laser radar or vision positioning system one of which positioning system or several location technologies are fixed
Pre-stored offline map data in the location information and vehicle of positioning system output are carried out coordinate to obtain by position
Match, obtains bus current location information.
Wherein, the real-time weather situation refers to that sunny, cloudy, light rain, moderate rain, heavy rain, slight snow, heavy snow, typhoon etc. are real
When weather condition.
Step S3: calculating the time interval that vehicle reaches next intersection based on public transport characteristic information and intersection information,
The relationship of the time interval for reaching next intersection according to vehicle time interval corresponding with the intersection red light determines that speed is drawn
Strategy is led, as shown in Fig. 2, specifically including:
Step S31: the distance of the lower intersection of vehicle distances is determined according to the current location of vehicle and intersection position；
Step S32: according to the present speed of vehicle, the distance at current time and the lower intersection of vehicle distances, vehicle is calculated
Reach the time interval of next intersection, wherein vehicle reaches the time interval G=[G of next intersection1,G2] are as follows:
[G1,G2]=[minTa′,maxTa′]
Wherein: Ta' for vehicle reach next intersection at the time of, T0For current time, vsFor the guidance speed of vehicle, v0
For the present speed of vehicle, L0For the distance of the lower intersection of vehicle distances, a is acceleration, G1Reach next intersection for vehicle
Time interval left margin, G2Reach the time interval right margin of next intersection for vehicle.
Step S33: judge that vehicle reaches the time interval time interval corresponding with the intersection red light of next intersection
Relationship, and speed boot policy is determined based on judging result.
The judgement of the step S33 has 4 kinds of possible outcomes to specifically include:
If a) vehicle reaches the time interval of next intersection and the intersection in any red light section is empty set, selection plus
Fast boot policy works as G that is, as shown in Fig. 3 (a)1< G2< T1When, bus can pass through intersection by guidance, should select
Accelerate boot policy, it is made to pass through intersection earlier, improves operational efficiency.Journey time T should meet T ∈ [G after guiding at this time1-
T0,G2-T0].At this point, bootup process includes that speed changes stage and to guide speed to drive at a constant speed two stages, wherein speed changes
The change stage can be divided into acceleration again and pass through, at the uniform velocity passes through and slow down through three kinds of possible situations.
If b) vehicle reaches the time interval of next intersection and the intersection in any red light section is not empty, and vehicle reaches
The time interval right margin of next intersection is located in the red light section, and left margin is located at outside the red light section, then selects to accelerate
Boot policy works as G that is, as shown in Fig. 3 (b)1< T1< G2When, bus can pass through intersection by guidance, should select to add
Fast boot policy guarantees that it passes through intersection during green light.Journey time T should meet T ∈ [G after guiding at this time1-T0,T1-
T0]。
At this point, bootup process includes that speed changes stage and to guide speed to drive at a constant speed two stages, wherein speed changes
The change stage can be divided into acceleration again and pass through, at the uniform velocity passes through and slow down through three kinds of possible situations.
C) as shown in Fig. 3 (c), if the time interval that vehicle reaches next intersection is located in any red light section, appoint
Meaning selection does not guide, accelerates one of the three kinds of strategies of guidance that guide or slow down, and still can stop, should be selected at the intersection after guidance
It does not guide, accelerate one of the three kinds of strategies of guidance that guide or slow down, achieve the purpose that reduce bus in intersection berthing time.This
When guidance after journey time T should meet T ∈ [G1-T0,G2-T0]。
Wherein, boot policy does not refer to that bus is driven at a constant speed with initial velocity, and stop is decelerated to when closing on bus stop, should
Process includes that bus drives at a constant speed stage and bus deceleration two stages of stop.
Wherein, after accelerating boot policy to refer to that bus accelerates to guidance speed, a distance is travelled with the speed, then subtract
Speed is to stopping, which includes that bus changes the initial velocity stage, and bus drives at a constant speed the stage and bus slows down
Stop three phases.
Wherein, after deceleration boot policy refers to that bus is decelerated to guidance speed, a distance is travelled with the speed, then subtract
Speed is to stopping, which includes that bus changes the initial velocity stage, and bus drives at a constant speed the stage and bus slows down
Stop three phases.
If d) vehicle reaches the time interval of next intersection and the intersection in any red light section is not empty, and vehicle reaches
The time interval left margin of next intersection is located in the red light section, and right margin is located at outside the red light section, then selects to slow down
Boot policy works as G as shown in Fig. 3 (d)1< T2< G2When, bus can pass through intersection by guidance, should select to slow down
Boot policy achievees the purpose that reduce bus in intersection berthing time.Journey time T should meet T ∈ after guiding at this time
[T2-T0,G2-T0]。
At this point, bootup process includes that speed changes stage and to guide speed to drive at a constant speed two stages, wherein speed changes
The change stage is guidance of slowing down.
Step S4: establishing the track optimizing model for considering bus travel comfort according to determining speed boot policy, and
Solving model obtains the optimization track in each subinterval.
In the step S4, if the time interval that vehicle reaches next intersection is located in any red light section, establish
Consider the track optimizing model of bus travel comfort are as follows:
Constraint condition are as follows:
Wherein: T is the journey time section after guidance, T0For current time, t1It is the total consumption for changing the initial velocity stage
When, t2For the total time-consuming for driving at a constant speed the stage, t3For the total time-consuming in deceleration stop stage, a1It (t) is change initial velocity stage t
The acceleration at moment, a are acceleration, a2It (t) is the acceleration for driving at a constant speed stage t moment, a3It (t) is deceleration stop stage t
The acceleration at moment, vminFor vehicle minimum speed, vmaxFor vehicle maximum speed, aminFor vehicle minimum acceleration, amaxFor vehicle
Peak acceleration, G1Reach the time interval left margin of next intersection, G for vehicle2For vehicle reach next intersection when
Between section right margin.
In the step S4, if it is sky that vehicle, which reaches the time interval of next intersection and the intersection in any red light section,
Collection then establishes the track optimizing model for considering bus travel comfort are as follows:
Constraint condition are as follows:
Wherein: T is the journey time section after guidance, T0For current time, t1It is the total consumption for changing the initial velocity stage
When, t2For the total time-consuming for driving at a constant speed the stage, a is acceleration, a1It (t) is the acceleration for changing initial velocity stage t moment, a2
It (t) is the acceleration for driving at a constant speed stage t moment, vminFor vehicle minimum speed, vmaxFor vehicle maximum speed, aminFor vehicle
Minimum acceleration, amaxFor vehicle peak acceleration, G1Reach the time interval left margin of next intersection, G for vehicle2For vehicle
Reach the time interval right margin of next intersection；
Under if vehicle reaches the time interval of next intersection and the intersection in any red light section is not empty, and vehicle reaches
The time interval right margin of one intersection is located in the red light section, and left margin is located at outside the red light section, then it is public to establish consideration
Hand over the track optimizing model of driving comfort are as follows:
Constraint condition are as follows:
Wherein: T1For the initial time in the red light section；
Under if vehicle reaches the time interval of next intersection and the intersection in any red light section is not empty, and vehicle reaches
The time interval left margin of one intersection is located in the red light section, and right margin is located at outside the red light section, then it is public to establish consideration
Hand over the track optimizing model of driving comfort are as follows:
Constraint condition are as follows:
Wherein: T2For the end time in the red light section.
The expert system includes man-machine interface, knowledge base, model library, database and scoring library 5 modules.Wherein, know
Knowing library includes the knowledge such as the related advisory that bus driving skills, passenger propose driving procedure；Model library is based on consideration
The bus track optimizing model of comfort of passenger respectively travels section to bus and carries out track optimizing；There are each public affairs in database
Intersection road information data, site information data, intersection information data, vehicle information data, synoptic data data, history run
Track scheme and scoring etc.；Scoring library is with occupant comfort detected in the track optimizing scheme according to final actual motion
Degree, scores to track optimizing scheme.
Wherein, man-machine interface for realizing user and system interactive function: all types of user, as transit operator manage
Member etc. accesses the interface of this system by browser, and inputs bus routes information to expert system, site information, intersects message
Breath etc.；Vehicle real time position and real-time vehicle speed information are exported to user by man-machine interface, and exports to obtain optimization gained to user
Driving trace.
As shown in figure 4, be this who total system frame diagram for asking, by by public transport operation route, site information, by way of intersection
Message ceases these basic informations and these real time information of vehicle real time position, real-time speed and weather condition carry out comprehensive analysis and obtain
Each traveling section locus model prioritization scheme out recycles the history operation data in cloud platform in expert system to be verified,
New track optimizing scheme is generated to correct, carries out the publication of guiding in real time speed.
Step S5: the comfort level of passenger is recorded in detection record actual moving process, is specifically included:
Step S51: according to labels such as traveling section to be optimized, real-time weather, bus vehicle models, from expert system
It chooses to score highest three in the historical track prioritization scheme with identical or approximate label in database and compare.Compare
The vehicle driving acceleration value for the more excellent scheme that scores in the track optimizing scheme and expert system that model solution obtains, if difference is larger,
Then the track optimizing scheme that model solution obtains is modified；
Step S52: final track optimizing scheme is used for actual motion after output amendment, and right in actual moving process
The comfort level of passenger carries out detection record.
Wherein, comfort level during bus travel of passenger can be detected at least one of following manner:
1) it is detected with the pull spring formula acceleration transducer being fixed on bus.By the way that one piece of movable mass block is connected
It connects among two sections of resistance wires, in bus driving process, since acceleration change leads to two resistance wires, one tension, one
It being pressurized, caused resistance change is equal in magnitude, and it is contrary, the two neighboring bridge arm of half-bridge differential circuit is accessed, is converted into
Voltage output, to monitor the acceleration change of bus traveling.The device is divided into Longitudinal Surveillance and laterally two groups of monitoring, respectively
Monitor the acceleration change of bus vertical and horizontal.Acceleration change is bigger, and comfort of passenger is lower.
2) it is detected using the motion sensor of smart phone.Call the fortune of train crew personnel or dedicated smart phone
Dynamic sensor and GPS module acquire three-dimensional acceleration information during bus running, to collected original number in real time
Segment processing is carried out according to by traveling section, obtains the longitudinal acceleration when driving in each traveling section.Three-dimensional close adds in traveling
Speed is bigger, and comfort of passenger is lower.
Step S6: the comfort of passenger data feedback that will test is analyzed and counted to expert system, by expert system
It scores the track optimizing scheme works.
The comfort of passenger data back that will test is analyzed and counted to expert system, by expert system to the track
Prioritization scheme effect scores.Simultaneously by the corresponding traveling section of the Management plan, real-time weather, bus vehicle model etc.
Label also inputs expert system, carries out to experts database further perfect.
Wherein, comfort of passenger detected during actual travel is lower, the prioritization scheme comment it is lower, otherwise more
It is high.
Claims (10)
1. a kind of public transport trajectory optimization system based on Internet of Things and cloud computing, which is characterized in that including expert system and vehicle
With bus dispatching center, the vehicle is connect with bus dispatching center and expert system respectively, execution track optimize when realize with
Lower step:
Step S1: the basic data of in expert system or customized input, including public transport operation route to be optimized, operation are obtained
Plan, information of vehicles and by way of intersection information；
Step S2: the real time data of in expert system or customized input is obtained, including vehicle in the real-time position at current time
It sets, real-time speed and real-time weather situation；
Step S3: calculating the time interval that vehicle reaches next intersection based on public transport characteristic information and intersection information, according to
The relationship that vehicle reaches the time interval time interval corresponding with the intersection red light of next intersection determines that speed guides plan
Slightly；
Step S4: the track optimizing model for considering bus travel comfort is established according to determining speed boot policy, and is solved
Model obtains the optimization track in each subinterval.
Step S5: the comfort level of passenger is recorded in detection record actual moving process；
Step S6: the comfort of passenger data feedback that will test is analyzed and counted to expert system, by expert system to this
Track optimizing scheme works score.
2. a kind of public transport trajectory optimization system based on Internet of Things and cloud computing according to claim 1, which is characterized in that
The expert system includes:
Man-machine interface, for realizing the interactive function of user and system；
Knowledge base, for storing the related advisory proposed including bus driving skills, passenger for driving procedure；
Model library, for storing based on the bus track optimizing model for considering comfort of passenger；
Database, for storing each public bus network information data, site information data, intersection information data, information of vehicles number
According to, synoptic data data, history run track optimizing scheme and scoring；
Scoring library, for comfort of passenger detected in the track scheme according to final actual motion, to different labels
Track optimizing scheme scores.
3. a kind of public transport trajectory optimization system based on Internet of Things and cloud computing according to claim 1, which is characterized in that
The step S3 is specifically included:
Step S31: the distance of the lower intersection of vehicle distances is determined according to the current location of vehicle and intersection position；
Step S32: it according to the present speed of vehicle, the distance at current time and the lower intersection of vehicle distances, calculates vehicle and reaches
To the time interval of next intersection；
Step S33: judge that vehicle reaches the pass of the time interval time interval corresponding with the intersection red light of next intersection
System, and speed boot policy is determined based on judging result.
4. a kind of public transport trajectory optimization system based on Internet of Things and cloud computing according to claim 3, which is characterized in that
The step S33 is specifically included:
If it is empty set that vehicle, which reaches the time interval of next intersection and the intersection in any red light section, select to accelerate to guide
Strategy；
If vehicle reaches the time interval of next intersection and the intersection in any red light section is not empty, and vehicle reaches next friendship
The time interval right margin of prong is located in the red light section, and left margin is located at outside the red light section, then selects to accelerate guidance plan
Slightly；
If the time interval that vehicle reaches next intersection is located in any red light section, any selection does not guide, accelerates to draw
It leads or slows down and guide one of three kinds of strategies；
If vehicle reaches the time interval of next intersection and the intersection in any red light section is not empty, and vehicle reaches next friendship
The time interval left margin of prong is located in the red light section, and right margin is located at outside the red light section, then selects guidance plan of slowing down
Slightly.
5. a kind of public transport trajectory optimization system based on Internet of Things and cloud computing according to claim 3, which is characterized in that
The vehicle reaches the time interval G=[G of next intersection1, G2] are as follows:
[G1, G2]=[minT 'a, maxT 'a]
Wherein: T 'aAt the time of reaching next intersection for vehicle, T0For current time, vsFor the guidance speed of vehicle, v0For vehicle
Present speed, L0For the distance of the lower intersection of vehicle distances, a is acceleration, G1Reach the time of next intersection for vehicle
Section left margin, G2Reach the time interval right margin of next intersection for vehicle.
6. a kind of public transport trajectory optimization system based on Internet of Things and cloud computing according to claim 3, which is characterized in that
In the step S4, if the time interval that vehicle reaches next intersection is located in any red light section, consideration public transport is established
The track optimizing model of driving comfort are as follows:
Constraint condition are as follows:
Wherein: T is the journey time section after guidance, T0For current time, t1It is the total time-consuming for changing the initial velocity stage, t2
For the total time-consuming for driving at a constant speed the stage, t3For the total time-consuming in deceleration stop stage, a1It (t) is change initial velocity stage t moment
Acceleration, a are acceleration, a2It (t) is the acceleration for driving at a constant speed stage t moment, a3(t) adding for deceleration stop stage t moment
Speed, vminFor vehicle minimum speed, vmaxFor vehicle maximum speed, aminFor vehicle minimum acceleration, amaxMost greatly for vehicle
Speed, G1Reach the time interval left margin of next intersection, G for vehicle2The time interval for reaching next intersection for vehicle is right
Boundary.
7. a kind of public transport operation track optimizing method for considering comfort level according to claim 4, which is characterized in that described
In step S4, if it is empty set that vehicle, which reaches the time interval of next intersection and the intersection in any red light section, foundation is examined
Consider the track optimizing model of bus travel comfort are as follows:
Constraint condition are as follows:
Wherein: T is the journey time section after guidance, T0For current time, t1It is the total time-consuming for changing the initial velocity stage, t2
For the total time-consuming for driving at a constant speed the stage, a is acceleration, a1It (t) is the acceleration for changing initial velocity stage t moment, a2(t) it is
Drive at a constant speed the acceleration of stage t moment, vminFor vehicle minimum speed, vmaxFor vehicle maximum speed, aminAdd for vehicle minimum
Speed, amaxFor vehicle peak acceleration, G1Reach the time interval left margin of next intersection, G for vehicle2Under reaching for vehicle
The time interval right margin of one intersection；
If vehicle reaches the time interval of next intersection and the intersection in any red light section is not empty, and vehicle reaches next friendship
The time interval right margin of prong is located in the red light section, and left margin is located at outside the red light section, then establishes and consider public transport row
Sail the track optimizing model of comfort are as follows:
Constraint condition are as follows:
Wherein: T1For the initial time in the red light section；
If vehicle reaches the time interval of next intersection and the intersection in any red light section is not empty, and vehicle reaches next friendship
The time interval left margin of prong is located in the red light section, and right margin is located at outside the red light section, then establishes and consider public transport row
Sail the track optimizing model of comfort are as follows:
Constraint condition are as follows:
Wherein: T2For the end time in the red light section.
8. a kind of public transport trajectory optimization system based on Internet of Things and cloud computing according to claim 2, which is characterized in that
The step S5 specifically:
Step S51: it scores from being chosen in expert system database in the historical track prioritization scheme with identical or approximate label
Highest three compare, and are modified to the track optimizing scheme that model solution obtains；
Step S52: final track optimizing scheme is used for actual motion after output amendment, and to passenger in actual moving process
Comfort level carry out detection record.
9. a kind of public transport trajectory optimization system based on Internet of Things and cloud computing according to claim 5, which is characterized in that
The comfort level of the passenger is measured by pull spring formula acceleration transducer, and the pull spring formula acceleration transducer is equipped with two groups altogether,
It is respectively used to the acceleration of monitoring vehicle vertical and horizontal.
10. a kind of public transport trajectory optimization system based on Internet of Things and cloud computing according to claim 5, feature exist
In the comfort level of the passenger is measured by the accelerometer of mobile terminal, specifically, acquiring three in vehicle operation
Acceleration information is tieed up, and calculates resultant acceleration.
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