CN117592788B - Bus running risk identification method and device - Google Patents
Bus running risk identification method and device Download PDFInfo
- Publication number
- CN117592788B CN117592788B CN202410063890.4A CN202410063890A CN117592788B CN 117592788 B CN117592788 B CN 117592788B CN 202410063890 A CN202410063890 A CN 202410063890A CN 117592788 B CN117592788 B CN 117592788B
- Authority
- CN
- China
- Prior art keywords
- bus
- station
- passenger
- vehicle
- information
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 47
- 238000012545 processing Methods 0.000 claims abstract description 43
- 238000011156 evaluation Methods 0.000 claims abstract description 38
- 238000012163 sequencing technique Methods 0.000 claims abstract description 15
- 238000013075 data extraction Methods 0.000 claims abstract description 9
- 238000004364 calculation method Methods 0.000 claims description 61
- 238000013459 approach Methods 0.000 claims description 9
- 230000008569 process Effects 0.000 claims description 7
- 238000004422 calculation algorithm Methods 0.000 claims description 6
- 238000004891 communication Methods 0.000 description 8
- 238000010586 diagram Methods 0.000 description 4
- 230000005236 sound signal Effects 0.000 description 4
- 230000002159 abnormal effect Effects 0.000 description 3
- 238000012544 monitoring process Methods 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 description 1
- KLDZYURQCUYZBL-UHFFFAOYSA-N 2-[3-[(2-hydroxyphenyl)methylideneamino]propyliminomethyl]phenol Chemical compound OC1=CC=CC=C1C=NCCCN=CC1=CC=CC=C1O KLDZYURQCUYZBL-UHFFFAOYSA-N 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 201000001098 delayed sleep phase syndrome Diseases 0.000 description 1
- 208000033921 delayed sleep phase type circadian rhythm sleep disease Diseases 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/067—Enterprise or organisation modelling
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Educational Administration (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Traffic Control Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a method and a device for identifying bus running risk, which relate to the field of bus running risk identification, and comprise the following steps: acquiring passenger transaction payment data and compartment congestion degree evaluation information in a bus transaction system; data extraction is carried out according to the passenger transaction payment data, and vehicle information and passenger travel information are obtained; solving vehicle information and passenger travel information through a preset full rate solving model to obtain a full rate data set; processing the compartment congestion degree evaluation information through a preset congestion degree sequencing model to obtain a compartment congestion degree data set; and matching the full-load rate data set with the carriage crowding degree data set to obtain the bus running risk level. According to the method, the bus running risk level is obtained based on the station full rate calculated by the number of passengers in the bus in real time and the carriage congestion state fed back by the passengers in real time, and the bus running risk level is used for carrying out state early warning of bus running risk, so that the bus running safety level is improved.
Description
Technical Field
The invention relates to the field of public transportation operation efficiency, in particular to a method and a device for identifying public transportation operation risks, belonging to the field of public transportation operation risk identification.
Background
In the prior art, the existing bus running risk is mainly deduced and predicted on historical passenger flow data to obtain the risk level of a bus stop, so that the bus running safety level is improved. However, urban ground buses have the problems of large passenger flow, crowded passenger flow in the buses and stations in peak time periods, lack of passenger security measures and road resource sharing with other social vehicles, so that the identification of the bus running risk is poor, the timeliness is lagged, and real-time adjustment of each shift of the buses cannot be performed. Therefore, there is a need for a method for identifying bus running risk, which realizes real-time monitoring of bus running state, and obtains the risk level of a bus stop by matching the stop full rate calculated based on the number of passengers in the bus in real time with the carriage congestion state based on real-time feedback of the passengers, wherein the bus running risk level is used for carrying out state early warning of bus running risk, and improves the bus running safety level.
Disclosure of Invention
The invention aims to provide a method and a device for identifying bus running risk so as to solve the problems. In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
in a first aspect, the present application provides a method for identifying a bus running risk, where the method includes:
Acquiring passenger transaction payment data and compartment congestion degree evaluation information in a bus transaction system;
Data extraction is carried out according to the passenger transaction payment data to obtain vehicle information and passenger travel information, wherein the passenger travel information comprises passenger boarding station information and passenger disembarking station information;
solving the vehicle information and the passenger travel information through a preset full-load rate solving model to obtain a full-load rate data set;
Processing the compartment congestion degree evaluation information through a preset congestion degree sequencing model to obtain a compartment congestion degree data set;
And matching the full-load rate data set with the carriage crowding degree data set to obtain a bus running risk level, wherein the bus running risk level is used for carrying out state early warning of bus running risk.
In a second aspect, the present application further provides a device for identifying a risk of running a bus, where the device includes:
The acquisition module is used for acquiring passenger transaction payment data and compartment congestion degree evaluation information in the bus transaction system;
The first processing module is used for carrying out data extraction according to the passenger transaction payment data to obtain vehicle information and passenger travel information, wherein the passenger travel information comprises passenger boarding station information and passenger disembarking station information;
The second processing module is used for solving the vehicle information and the passenger travel information through a preset full rate solving model to obtain a full rate data set;
The third processing module is used for processing the compartment congestion degree evaluation information through a preset congestion degree sequencing model to obtain a compartment congestion degree data set;
And the fourth processing module is used for matching the full-load rate data set with the carriage crowding degree data set to obtain a bus running risk level, and the bus running risk level is used for carrying out state early warning of bus running risk.
The beneficial effects of the invention are as follows:
Firstly, solving according to passenger transaction payment data through a preset full-load rate solving model to obtain a full-load rate data set, wherein the full-load rate data set is used for reflecting the station full-load rate calculated based on the number of passengers in a vehicle in real time; then, the compartment congestion degree evaluation information is processed through a preset congestion degree sequencing model to obtain a compartment congestion degree data set, wherein the compartment congestion degree data set is used for reflecting a compartment congestion state based on real-time feedback of passengers; and finally, matching and calculating the full-load rate data set and the carriage crowding degree data set to obtain the bus running risk level. The method realizes real-time monitoring of the running state of the bus, can realize integrated management of transaction data, vehicle information, station full load rate, full time period of the crowded state of the carriage and multi-platform data, improves the safety level of the running of the bus and improves the identification precision of the running risk of the bus. After the running risk of each shift of the bus is identified, for a bus enterprise, real-time dynamic adjustment of a bus driving plan, such as adjustment of departure shifts, optimization of departure intervals and the like, can be realized so as to reduce the operating pressure of the shifts and improve the operating safety level; for bus passengers, fluctuation change of passenger travel demands is met, the riding risks of the passengers are reduced, and bus travel efficiency and service level are improved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the embodiments of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a method for identifying bus running risk according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a bus running risk identification device according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a first processing module according to an embodiment of the present invention;
Fig. 4 is a schematic structural diagram of a bus running risk identification device according to an embodiment of the present invention;
The marks in the figure:
1. An acquisition module; 2. a first processing module; 3. a second processing module; 4. a third processing module; 5. a fourth processing module; 21. a first processing unit; 211. a first calculation unit; 212. a second calculation unit; 213. a third calculation unit; 22. a second processing unit; 221. a first acquisition unit; 222. a fourth calculation unit; 223. a fifth calculation unit; 224. a sixth calculation unit; 2241. a second acquisition unit; 2242. a seventh calculation unit; 2243. an eighth calculation unit; 2244. a ninth calculation unit; 31. a third acquisition unit; 32. a tenth calculation unit; 33. an eleventh calculation unit; 34. a twelfth calculation unit; 41. a fourth acquisition unit; 42. a thirteenth calculation unit; 43. a fourteenth calculation unit; 44. a fifteenth calculation unit; 51. a sixteenth calculation unit; 52. a seventeenth calculation unit; 53. an eighteenth calculation unit; 800. identification equipment for bus running risk; 801. a processor; 802. a memory; 803. a multimedia component; 804. an I/O interface; 805. a communication component.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present invention, the terms "first", "second", and the like are used only to distinguish the description, and are not to be construed as indicating or implying relative importance.
Example 1:
The embodiment provides a bus running risk identification method.
Referring to fig. 1, the method is shown to include steps S1 to S5, specifically:
S1, acquiring passenger transaction payment data and compartment congestion degree evaluation information in a bus transaction system;
In step S1, the passenger transaction payment data may be passenger swipe data or passenger electronic payment data. When the vehicle-mounted hot spot is configured in the operation bus to provide the public transport private network, the signal coverage range is the interior of the carriage of the operation vehicle at the moment so as to avoid uploading invalid data by passengers who are not on the bus. After the passengers connect the private network, the car congestion degree evaluation information can be uploaded to an operation platform, wherein the car congestion degree evaluation information comprises a unique identification number of a passenger identity, a car congestion state and feedback time submission information, and the car congestion state can be divided into five grades, namely: comfort (), basic comfort (/ > ), light congestion (/ > ), medium congestion (/ > ) and heavy congestion (/ > ).
S2, data extraction is carried out according to the passenger transaction payment data to obtain vehicle information and passenger travel information, wherein the passenger travel information comprises passenger boarding station information and passenger disembarking station information;
Taking Beijing city bus transaction system as an example, when the passenger transaction payment data takes card swiping data as transaction data, the card swiping data comprises space position and time information of the upper and lower stops of the passenger, so that vehicle information and upper and lower stops of the passenger can be directly obtained;
When the passenger transaction payment data of the city does not contain the passenger travel station information, the station information of the passenger travel is deduced by using an upper station point algorithm and a lower station point algorithm, which is specifically as follows.
Deriving upper station point information:
In step S2, when data extraction is performed according to the passenger transaction payment data to obtain boarding point information of the passenger, step S2 includes steps S211 to S213, specifically includes:
s211, analyzing according to the transaction payment data of the passenger to obtain the transaction payment time and the transaction payment geographic position of the passenger;
In step S211, the transaction payment time of the passenger may be analyzed and found according to the transaction list in the transaction payment data, and the transaction payment geographic location may be analyzed and transmitted on-board map of the bus or bus GPS signal data.
S212, positioning the transaction payment geographic position to obtain target station time, wherein the target station time comprises target arrival time of a bus and target departure time of the bus;
In step S212, positioning may be performed by map positioning to match a target station of the currently operated bus in the operation interval, and the operation time of the bus is queried to obtain a target arrival time of the bus and a target departure time of the bus in a matching manner.
S213, calculating the transaction payment time of the passenger and the target station time through a preset boarding station identification model to obtain boarding station information of the passenger.
In this step, the upper station point identification model is:
；（1）
in the above formula (1), denotes a target arrival time of a bus,/> denotes a target departure time of a bus,/> denotes a transaction payment time of a passenger, and/> denotes a preset bus arrival departure elasticity threshold. In bus operation, a plurality of buses can sometimes arrive at a stop at the same time, a following bus can open a door in advance to get on a bus for saving time, in addition, in peak time, partial passengers can select a rear door to get on the bus due to front door congestion, and transaction is carried out after the bus leaves the bus, so that partial passenger transaction payment time can be outside a time interval from the vehicle to the stop, and in order to improve the recognition rate of the point of getting on the bus, the method introduces a preset elastic threshold value to expand the time interval from the stop, and considers the operation scene, and when the passenger transaction payment time/> meets the formula (1), the station S can be judged to be the station of getting on the bus.
Deducing the following station point information:
in step S2, when data extraction is performed according to the passenger transaction payment data to obtain passenger departure station information, step S2 includes steps S221 to S224, specifically includes:
S221, obtaining the number of approach stops in the bus running process and the total number of stops of the bus running in a single line;
s222, carrying out average value calculation on the number of the path stations to obtain average value information of the number of the path stations;
s223, solving the average value information of the total number of stations operated by the single bus line and the number of the approach stations through a preset getting-off probability model to obtain the getting-off probability of passengers;
In this step, the passenger getting-off probability model is:
；（2）
In the above formula (2), represents the passenger getting-off probability,/> represents a constant,/> represents average value information of the number of approach stops, and/> represents a preset stop in the operation of a single bus line, wherein the preset stop can be any stop in the operation of the single bus line; the/> represents a boarding station; and/> denotes the total number of stops that a bus is running on a single line.
S224, calculating the getting-off probability of the passengers to obtain the getting-off station information of the passengers.
In step S224, a threshold value of the getting-off probability may be preset, and when the calculated probability of getting off the passenger is greater than the threshold value of the preset getting-off probability, the getting-off station information of the passenger may be determined.
However, the preset getting-off probability threshold only considers the existing operation experience, on one hand, the attraction of the station to the passengers is not considered, and on the other hand, the historical riding flow of the station is not considered, so that the method corrects the getting-off probability of the passengers by introducing the attraction strength of the station and the riding flow historical data corresponding to the station, so that the estimated getting-off station information of the passengers is more consistent with the actual operation condition, the prediction precision is improved, and step S224 comprises steps S2241 to S2244, specifically comprising:
S2241, acquiring the number of boarding persons at a preset station and the historical data of the riding flow of the preset station in the operation of a single bus line;
In step S2241, the number of passengers on the bus at the preset station in the operation of the bus single line may be detected by the vehicle load sensor, the pressure pedal sensor, and the vehicle-mounted camera detector. The historical data of the multiplying flow rate of the preset site can be collected by inquiring the multiplying flow rate data of the site in a preset period, and the preset period can be set to be one quarter or one month.
S2242, solving the number of boarding persons at the preset stations and the total number of stations operated by the single bus route through a preset station attraction model to obtain station attraction strength;
the attraction model of the site includes:
；（3）
In the above formula (3), denotes the attraction strength of the station,/> denotes the number of boarding persons of a preset station/> in running of a single bus route, and/> denotes the total number of stations in running of a single bus route.
In the step, continuous travel and discontinuous travel are set in consideration of factors such as bus running time, waiting time and the like. For continuous travel, the departure station point of the passenger taking the car last time is mostly close to the departure station point of the passenger taking the car next time; for discontinuous travel, the passenger's departure station point when the passenger takes the bus is usually a downstream high-frequency station, i.e. the passenger selects a station with higher attraction strength to get off.
S2243, probability calculation is carried out according to the preset site multiplication flow historical data to obtain a site historical multiplication flow coefficient;
In step S2243, when the preset period is set to one month, the historical data of the multiplying flow of the preset station is ordered, then abnormal data (data of extremely changing multiplying flow after being possibly affected by weather and non-working days) is removed, and finally probability calculation is performed to obtain the historical multiplying flow coefficient of the station, and the calculation formula of the step is as follows:
;（4）
In the above formula (4), represents a site history multiplied by a flow coefficient; the/> represents the multiplied traffic history data for the first site; Multiplying traffic history data representing a second site; the/> represents the multiplication flow history data of the nth station; the/> represents the multiplying flow history data of the first site; because of abnormal data, the historical data of the multiplication flow corresponding to part of the stations are eliminated, so that/> , n is the number of the stations after the abnormal data are eliminated, and/> represents the total number of the stations operated by a single bus line.
And S2244, calculating according to the station attraction strength, the station history flow coefficient and the passenger getting-off probability to obtain the passenger getting-off station information.
In step S2244, the calculation formula is:
；（5）
In the above formula (5), represents passenger departure station point information; the/> represents the passenger getting off probability; the/> represents the site history times the flow coefficient; the/> represents the site attraction strength; and/> denotes the total number of stops that a bus is running on a single line.
After the passenger boarding station information and the passenger disembarking station information are determined, the vehicle information is determined according to bus running system matching, wherein the vehicle information comprises vehicle running shifts and vehicle running numbers.
S3, solving the vehicle information and the passenger travel information through a preset full rate solving model to obtain a full rate data set;
In step S3, the full rate solution model includes a station passenger number solution model and a station full rate solution model, the vehicle information includes a vehicle running shift and a vehicle running number, and step S3 includes steps S31 to S34, specifically includes:
S31, acquiring the total number of stations operated by a single bus line;
s32, solving the vehicle running shift, the vehicle running number and the passenger travel information through the station passenger number solving model to obtain the number of passengers of the preset vehicle in a single station;
In step S32, the station passenger number solution model is:
；（6）
In the above formula (6), represents the number of passengers of the preset/> vehicle in/> station; the/> indicates a vehicle running number; the/> represents a vehicle run shift; the/> represents the number of boarding persons at the/> site; the/> represents the number of alighting persons at/> site; and/> denotes the total number of stops that a bus is running on a single line.
S33, solving the number of passengers of the vehicles preset in the single station through the station full load rate solving model to obtain the full load rate of the vehicles preset in the single station;
in step S33, the site full rate solution model is:
；（7）
In the above formula (7), represents the full load rate of the preset vehicle in/> station; the/> denotes the number of passengers preset for vehicles in the/> station; and/> denotes the rated passenger capacity of the preset/> vehicle using the model.
And S34, sequentially calculating the full rate of the vehicles preset in the single station according to the total number of stations operated by the single bus line, and obtaining a full rate data set.
S4, processing the compartment congestion degree evaluation information through a preset congestion degree sequencing model to obtain a compartment congestion degree data set;
In step S4, the car congestion degree evaluation information includes a unique passenger identity number, a car congestion state and feedback time submission information, wherein the car congestion state can be classified into five classes, respectively: comfort (), basic comfort (), light congestion (/ > ), medium congestion (/ > ) and heavy congestion (/ > ).
Step S4 includes steps S41 to S44, including:
S41, acquiring the total number of stations operated by a single bus line;
s42, analyzing the unique identification number of the passenger identity and the feedback time submitting information to obtain the shift number of the passenger riding vehicle;
In step S42, after the passenger submits the compartment congestion degree evaluation information, the monitoring platform performs primary matching on the current day vehicle shift number according to the feedback time, and at the same time performs secondary matching on the current day vehicle shift number according to the unique identifier number of the passenger identity, so as to obtain the passenger riding vehicle shift number.
S43, processing the shift numbers of the passengers taking the vehicles through the crowdedness sequencing model to obtain a crowdedness evaluation data set of a single station;
In step S43, the congestion degree ranking model may use the existing selection ranking algorithm, that is, each time a traversal is performed, the largest item of the sequence to be ranked is found, and after the traversal is completed, it is replaced to the correct position. In the present method, the largest items of the sequence to be ordered are, in order, severe congestion (), medium congestion (/ > ), mild congestion (/ > ), basic comfort (/ > ) and comfort (/ > ). For a single station, when a plurality of passengers collect data of the congestion state of the passenger in the passenger train, the data are ordered to obtain a congestion degree evaluation data set of the single station.
And S44, sequentially calculating the congestion degree evaluation data set of the single station according to the total number of stations operated by the single bus line to obtain a carriage congestion degree data set.
Through step S4, the method establishes real-time feedback of passengers on the congestion state of the carriage, and facilitates later analysis of influence of the congestion degree of the carriage on the running risk level of the bus.
And S5, matching the full-load rate data set with the carriage crowding degree data set to obtain a bus running risk level, wherein the bus running risk level is used for carrying out state early warning of bus running risk.
When the vehicle information includes a vehicle running shift and a vehicle running number, in step S5, step S5 includes S51 to S53, specifically:
s51, calculating a risk threshold according to the maximum value in the full-load rate data set to obtain a plurality of risk level thresholds;
in step S51, the calculation formula is:
(n=1、2…5) ；（8）
In the above formula (8), denotes an nth risk level threshold, and/() denotes a maximum value in the full rate dataset; the/> indicates a vehicle running number; the/> represents a vehicle run shift; the/> represents a constant percentage, in the present method the/> ,/>,/>,/>,/> is set.
S52, matching the full rate data set with the carriage crowding degree data set one by one according to the vehicle running shift and the vehicle running number to obtain bus running data of different stations;
In step S52, since the car congestion level data set is obtained based on the selection ranking algorithm, namely: for a single station, sorting after collecting data of a plurality of passengers on the congestion state of the passenger compartment taking a shift number of a vehicle, so as to obtain a congestion degree evaluation data set of the single station; and then, sequentially calculating the congestion degree evaluation data set of the single station according to the total number of stations operated by the single bus line to obtain a carriage congestion degree data set.
Wherein, a congestion degree threshold value is set in the congestion degree evaluation data set of a single station, and the calculation formula of the congestion degree threshold value is as follows:
；（9）
In the above formula (9), denotes a congestion degree threshold value; the term/> denotes the number of feedback participants with severe congestion (/ > ) in a single site, and the term/> denotes the number of participants with congestion feedback in a single site. In this method,/> .
In step S52, the calculation formula of the bus operation data of different stations is:
；（10）
In the above formula (10), represents bus operation data of different stations; the/> indicates the number of passengers preset in the station; the expression/> is/> for presetting the rated passenger capacity of the vehicle adopting the vehicle type; and/> denotes a congestion degree threshold.
Therefore, the bus operation data of different stops not only considers the stop full rate calculated based on the number of passengers on the bus in real time, but also considers the carriage congestion state based on the real-time feedback of the passengers, so that the operation data feedback of different stops is more accurate.
And S53, calculating the bus operation data of different stations through the risk level threshold value to obtain the bus operation risk level.
In step S53, a plurality of bus running risk intervals are divided according to the risk level threshold, for example: And [/> ,/>), when the bus operation data of different stations corresponds to the bus operation risk interval, different bus operation risk levels are correspondingly obtained, namely:
when different bus operation data are received, the bus operation risk is the lowest grade I; When the bus running risk is II; when the bus running risk is class III, carrying out bus running risk on the bus at the time of carrying out bus running risk of ; when the bus running risk is class IV in the case of/> ; And when the bus running risk is the highest grade V.
In step S5, after identifying the operation risk of each shift of the bus, for the bus enterprise, real-time dynamic adjustment of the bus driving plan, such as adjusting the departure shift, optimizing the departure interval, etc., can be implemented to reduce the shift operation pressure and improve the operation safety level; for bus passengers, fluctuation change of passenger travel demands is met, the riding risks of the passengers are reduced, and bus travel efficiency and service level are improved.
Example 2:
As shown in fig. 2, this embodiment provides a device for identifying a risk of running a bus, where the device includes:
The acquisition module 1 is used for acquiring passenger transaction payment data and compartment congestion degree evaluation information in a bus transaction system;
The first processing module 2 is used for carrying out data extraction according to the passenger transaction payment data to obtain vehicle information and passenger travel information, wherein the passenger travel information comprises passenger boarding station information and passenger disembarking station information;
The second processing module 3 is used for solving the vehicle information and the passenger travel information through a preset full rate solving model to obtain a full rate data set;
The third processing module 4 is configured to process the compartment congestion evaluation information through a preset congestion ranking model, so as to obtain a compartment congestion dataset;
And the fourth processing module 5 is used for matching the full-load rate data set with the carriage crowding degree data set to obtain a bus running risk level, and the bus running risk level is used for carrying out state early warning of bus running risk.
As shown in fig. 3, in one implementation method disclosed in the present invention, the first processing module 2 includes a first processing unit 21, and the first processing unit 21 includes:
The first calculating unit 211 is configured to parse according to the transaction payment data of the passenger to obtain a transaction payment time and a transaction payment geographic location of the passenger;
A second calculating unit 212, configured to locate the transaction payment geographic location, and obtain a target stop time, where the target stop time includes a target arrival time of the bus and a target departure time of the bus;
and a third calculating unit 213, configured to calculate the transaction payment time of the passenger and the target station time through a preset boarding station identification model, so as to obtain boarding station information of the passenger.
In one disclosed implementation of the present invention, the first processing module 2 further includes a second processing unit 22, and the second processing unit 22 includes:
a first obtaining unit 221, configured to obtain the number of approach stops in the bus running process and the total number of stops running on a single bus route;
A fourth calculation unit 222, configured to perform average calculation on the number of path sites, to obtain average information of the number of path sites;
A fifth calculation unit 223, configured to solve the average information of the total number of stations operated by the single bus route and the number of approach stations by using a preset getting-off probability model, so as to obtain a getting-off probability of the passenger;
and a sixth calculating unit 224, configured to calculate the passenger getting-off probability, and obtain passenger getting-off station point information.
In one disclosed implementation of the present invention, the sixth computing unit 224 includes:
The second obtaining unit 2241 is configured to obtain the number of boarding persons at a preset station and the historical data of the boarding flow at the preset station in running of the single bus route;
the seventh calculating unit 2242 is configured to solve, by using a preset station attraction model, the number of boarding persons at the preset station and the total number of stations running on the single bus route, so as to obtain station attraction strength;
The eighth calculating unit 2243 is configured to perform probability calculation according to the preset station multiplication flow historical data to obtain a station historical multiplication flow coefficient;
The ninth calculating unit 2244 is configured to calculate according to the station attraction strength, the station history multiplying flow coefficient, and the passenger getting-off probability, and obtain passenger getting-off station information.
In one implementation method disclosed in the present invention, the full rate solution model in the second processing module 3 includes a station passenger number solution model and a station full rate solution model, and the vehicle information includes a vehicle running shift and a vehicle running number, including:
a third obtaining unit 31, configured to obtain a total number of stations running on a single bus route;
A tenth calculation unit 32, configured to solve the vehicle running shift, the vehicle running number, and the passenger travel information through the station passenger number solution model, to obtain the number of passengers of the preset vehicle in a single station;
an eleventh calculation unit 33, configured to solve the number of passengers of the vehicle preset in the single station through the station full load rate solving model, so as to obtain the full load rate of the vehicle preset in the single station;
and a twelfth calculation unit 34, configured to sequentially calculate the full rate of the preset vehicles in the single station according to the total number of stations running on the single bus route, so as to obtain a full rate data set.
In one embodiment of the present disclosure, the cabin congestion degree evaluation information in the third processing module 4 includes a unique passenger identity number, a cabin congestion state, and feedback time submission information, including:
A fourth obtaining unit 41, configured to obtain a total number of stations running on a single bus route;
A thirteenth calculating unit 42, configured to parse the unique identifier of the passenger identity and the feedback time submission information to obtain a shift number of the passenger riding vehicle;
A fourteenth calculation unit 43, configured to process the shift number of the passenger riding vehicle through the crowdedness sequencing model, to obtain a crowdedness evaluation data set of a single station;
And a fifteenth calculation unit 44, configured to sequentially calculate the congestion degree evaluation data sets of the individual stations according to the total number of stations running on the single bus route, so as to obtain a compartment congestion degree data set.
In one embodiment of the present disclosure, when the vehicle information includes a vehicle operation shift and a vehicle operation number, the fourth processing module 5 includes:
a sixteenth calculating unit 51, configured to perform risk threshold calculation according to the maximum value in the full-load rate data set, so as to obtain multiple risk level thresholds;
A seventeenth calculating unit 52, configured to match the full rate data set and the car congestion degree data set one by one according to the vehicle running shift and the vehicle running number, so as to obtain bus operation data of different stops;
An eighteenth calculating unit 53, configured to calculate bus operation data of the different stations through the risk level threshold, so as to obtain a bus operation risk level.
It should be noted that, regarding the apparatus in the above embodiments, the specific manner in which the respective modules perform the operations has been described in detail in the embodiments regarding the method, and will not be described in detail herein.
Examples
Corresponding to the above method embodiment, in this embodiment, a bus running risk identification device is further provided, and a bus running risk identification device described below and a bus running risk identification method described above may be referred to correspondingly.
Fig. 4 is a block diagram of an identification device 800 of bus running risk according to an exemplary embodiment. As shown in fig. 4, the bus running risk identification device 800 may include: a processor 801, a memory 802. The bus running risk identification device 800 may also include one or more of a multimedia component 803, an i/O interface 804, and a communication component 805.
The processor 801 is configured to control the overall operation of the bus running risk identification device 800, so as to complete all or part of the steps in the above-mentioned bus running risk identification method. The memory 802 is used to store various types of data to support the operation of the identification device 800 at the bus running risk, which may include, for example, instructions for any application or method operating on the identification device 800 at the bus running risk, as well as application related data such as contact data, messages, pictures, audio, video, and the like. The memory 802 may be implemented by any type or combination of volatile or non-volatile memory devices, such as static random access memory (Static Random Access Memory, SRAM for short), electrically erasable programmable read-only memory (ELECTRICALLY ERASABLE PROGRAMMABLE READ-only memory, EEPROM for short), erasable programmable read-only memory (Erasable Programmable Read-only memory, EPROM for short), programmable read-only memory (Programmable Read-only memory, PROM for short), read-only memory (ROM for short), magnetic memory, flash memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and an audio component. Wherein the screen may be, for example, a touch screen, the audio component being for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 802 or transmitted through the communication component 805. The audio assembly further comprises at least one speaker for outputting audio signals. The I/O interface 804 provides an interface between the processor 801 and other interface modules, which may be a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 805 is configured to perform wired or wireless communication between the identifying device 800 for the bus running risk and other devices. Wireless Communication, such as Wi-Fi, bluetooth, near field Communication (NFC for short), 2G, 3G, or 4G, or a combination of one or more thereof, and accordingly the Communication component 805 may comprise: wi-Fi module, bluetooth module, NFC module.
In an exemplary embodiment, the bus running risk identification device 800 may be implemented by one or more Application Specific Integrated Circuits (ASIC), digital signal processor (DIGITAL SIGNAL DSP), digital signal processing device (DIGITAL SIGNAL Processing Device, DSPD), programmable logic device (Programmable Logic Device, PLD), field programmable gate array (Field Programmable GATE ARRAY, FPGA), controller, microcontroller, microprocessor, or other electronic component for performing the above-described bus running risk identification method.
In another exemplary embodiment, a computer readable storage medium is also provided, comprising program instructions which, when executed by a processor, implement the steps of the above-described method of identifying bus running risk. For example, the computer readable storage medium may be the memory 802 including program instructions described above, which are executable by the processor 801 of the bus running risk identification apparatus 800 to perform the bus running risk identification method described above.
Example 3:
Corresponding to the above method embodiment, a readable storage medium is further provided in this embodiment, and a readable storage medium described below and a method for identifying a bus running risk described above may be referred to correspondingly.
A readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps of the method for identifying a bus running risk of the above method embodiment.
The readable storage medium may be a usb disk, a removable hard disk, a read-only memory (ROM), a random access memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, which may store various program codes.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (7)
1. The method for identifying the bus running risk is characterized by comprising the following steps of:
Acquiring passenger transaction payment data and compartment congestion degree evaluation information in a bus transaction system; the car congestion degree evaluation information comprises a unique identification number of a passenger identity, a car congestion state and feedback time submission information, wherein the car congestion state is divided into five grades, and the five grades are respectively: comfort (L 1), basic comfort (L 2), light congestion (L 3), medium congestion (L 4) and heavy congestion (L 5);
Data extraction is carried out according to the passenger transaction payment data to obtain vehicle information and passenger travel information, wherein the passenger travel information comprises passenger boarding station information and passenger disembarking station information;
solving the vehicle information and the passenger travel information through a preset full-load rate solving model to obtain a full-load rate data set;
processing the compartment congestion degree evaluation information through a preset congestion degree sequencing model to obtain a compartment congestion degree data set; comprising the following steps:
Acquiring the total number of stations operated by a single bus line;
analyzing the unique identification number of the passenger identity and the feedback time submitting information to obtain the shift number of the passenger riding vehicle;
processing the shift numbers of the passenger riding vehicles through the crowdedness sequencing model to obtain a crowdedness evaluation data set of a single station; the crowdedness sequencing model adopts a selection sequencing algorithm, the largest item of a sequence to be sequenced is found every time the traversal is performed, and the largest item is changed to a correct position after the traversal is completed; the largest items of the sequence to be ordered are, in order, severe congestion (L 5), medium congestion (L 4), mild congestion (L 3), basic comfort (L 2) and comfort (L 1); for a single station, sorting after collecting data of a plurality of passengers on the congestion state of the passenger compartment taking a shift number of a vehicle, so as to obtain a congestion degree evaluation data set of the single station;
sequentially calculating the crowding degree evaluation data set of the single station according to the total number of stations operated by the single bus line to obtain a carriage crowding degree data set; matching the full-load rate data set with the carriage crowding degree data set to obtain a bus running risk level, wherein the bus running risk level is used for carrying out state early warning of bus running risk; the vehicle information includes a vehicle operation shift and a vehicle operation number, including:
carrying out risk threshold calculation according to the maximum value in the full-load rate data set to obtain a plurality of risk level thresholds; wherein, the calculation formula is:
ωn＝WMm(max)×Kn(n＝1、2…5)；
In the above formula, ω n represents an nth risk level threshold, and W Mm (max) represents a maximum value in the full rate dataset; m represents a vehicle running number; m represents a vehicle running shift; k n represents a constant percentage; in the present method, K 1＝85％,K2＝89％,K3＝92％,K4＝93％,K5 =95% is set;
Matching the full rate data set with the carriage crowding degree data set one by one according to the vehicle running shift and the vehicle running number to obtain bus running data of different stations; wherein, a congestion degree threshold value is set in the congestion degree evaluation data set of a single station, and the calculation formula of the congestion degree threshold value is as follows:
In the above formula, L represents a congestion degree threshold; p (L 5) represents the number of feedback participants of severe congestion (L 5) in a single site, and p represents the number of participants of congestion feedback in a single site;
In this step, the calculation formula of the public transportation operation data of different stations is:
In the above formula, represents bus operation data of different stations; the/> indicates the number of passengers preset for an Mm vehicle in the i-station; n Mm represents the rated passenger capacity of the vehicle model adopted by the preset Mm vehicle; l represents a congestion degree threshold;
Calculating the bus operation data of different stations through the risk level threshold value to obtain a bus operation risk level; when different bus operation data are received, the bus operation risk is the lowest grade I; When the bus running risk is II; when the bus running risk is class III, carrying out bus running risk on the bus at the time of carrying out bus running risk of ; when the bus running risk is class IV in the case of/> ; And when the bus running risk is the highest grade V.
2. The method for identifying risk of bus running according to claim 1, wherein the step of extracting data according to the passenger transaction payment data to obtain passenger getting-off stop information comprises the steps of:
acquiring the number of approach stops in the bus running process and the total number of stops running on a single bus line;
Carrying out average value calculation on the number of the path stations to obtain average value information of the number of the path stations;
solving the average value information of the total number of stations operated by the single bus line and the number of the approach stations through a preset getting-off probability model to obtain the getting-off probability of passengers;
and calculating the passenger getting-off probability to obtain passenger getting-off station point information.
3. The method for identifying a bus running risk according to claim 2, wherein calculating the passenger getting-off probability to obtain passenger getting-off stop information comprises:
acquiring the number of boarding persons at a preset station and the historical data of the traffic flow of the preset station in the operation of a single bus line;
Solving the number of boarding persons at the preset stations and the total number of stations operated by the single bus line through a preset station attraction model to obtain station attraction strength;
probability calculation is carried out according to the preset station multiplication flow historical data, and a station historical multiplication flow coefficient is obtained;
And calculating according to the station attraction strength, the station history multiplying flow coefficient and the passenger getting-off probability to obtain passenger getting-off station information.
4. The method for identifying bus running risk according to claim 1, wherein the vehicle information and the passenger travel information are solved by a preset full-load rate solving model to obtain a full-load rate data set, the full-load rate solving model comprises a station passenger number solving model and a station full-load rate solving model, the vehicle information comprises a vehicle running shift and a vehicle running number, and the method comprises the following steps:
Acquiring the total number of stations operated by a single bus line;
Solving the vehicle running shift, the vehicle running number and the passenger travel information through the station passenger number solving model to obtain the number of passengers of the preset vehicle in a single station;
Solving the number of passengers of the vehicles preset in the single station through the station full load rate solving model to obtain the full load rate of the vehicles preset in the single station;
and sequentially calculating the full rate of vehicles preset in the single station according to the total number of stations operated by the single bus line, so as to obtain a full rate data set.
5. An identification device for public transportation running risk, which is characterized by comprising:
The acquisition module is used for acquiring passenger transaction payment data and compartment congestion degree evaluation information in the bus transaction system; the car congestion degree evaluation information comprises a unique identification number of a passenger identity, a car congestion state and feedback time submission information, wherein the car congestion state is divided into five grades, and the five grades are respectively: comfort (L 1), basic comfort (L 2), light congestion (L 3), medium congestion (L 4) and heavy congestion (L 5);
The first processing module is used for carrying out data extraction according to the passenger transaction payment data to obtain vehicle information and passenger travel information, wherein the passenger travel information comprises passenger boarding station information and passenger disembarking station information;
The second processing module is used for solving the vehicle information and the passenger travel information through a preset full rate solving model to obtain a full rate data set;
the third processing module is used for processing the compartment congestion degree evaluation information through a preset congestion degree sequencing model to obtain a compartment congestion degree data set; the third processing module includes:
the fourth acquisition unit is used for acquiring the total number of stations operated by a single bus line;
a thirteenth calculation unit, configured to parse the unique identifier of the passenger identity and the feedback time submission information to obtain a shift number of the passenger riding vehicle;
A fourteenth calculation unit, configured to process the shift number of the passenger riding vehicle through the crowdedness sequencing model, to obtain a crowdedness evaluation data set of a single station; the crowdedness sequencing model adopts a selection sequencing algorithm, the largest item of a sequence to be sequenced is found every time the traversal is performed, and the largest item is changed to a correct position after the traversal is completed; the largest items of the sequence to be ordered are, in order, severe congestion (L 5), medium congestion (L 4), mild congestion (L 3), basic comfort (L 2) and comfort (L 1); for a single station, sorting after collecting data of a plurality of passengers on the congestion state of the passenger compartment taking a shift number of a vehicle, so as to obtain a congestion degree evaluation data set of the single station;
a fifteenth calculation unit, configured to sequentially calculate, according to the total number of stations running on a single bus route, a congestion degree evaluation data set of the single station, to obtain a compartment congestion degree data set;
The fourth processing module is used for matching the full-load rate data set with the carriage crowding degree data set to obtain a bus running risk level, and the bus running risk level is used for carrying out state early warning of bus running risk; when the vehicle information includes a vehicle operation shift and a vehicle operation number, the fourth processing module includes:
A sixteenth calculation unit, configured to perform risk threshold calculation according to a maximum value in the full-load rate data set, so as to obtain multiple risk level thresholds; wherein, the calculation formula is:
ωn＝WMm(max)×Kn(n＝1、2…5)；
In the above formula, ω n represents an nth risk level threshold, and W Mm (max) represents a maximum value in the full rate dataset; m represents a vehicle running number; m represents a vehicle running shift; k n represents a constant percentage; in the present method, K 1＝85％,K2＝89％,K3＝92％,K4＝93％,K5 =95% is set;
seventeenth calculation unit, which is used for matching the full rate data set and the carriage crowding degree data set one by one according to the vehicle running shift and the vehicle running number to obtain bus running data of different stations; wherein, a congestion degree threshold value is set in the congestion degree evaluation data set of a single station, and the calculation formula of the congestion degree threshold value is as follows:
In the above formula, L represents a congestion degree threshold; p (L 5) represents the number of feedback participants of severe congestion (L 5) in a single site, and p represents the number of participants of congestion feedback in a single site;
In this step, the calculation formula of the public transportation operation data of different stations is:
In the above formula, represents bus operation data of different stations; the/> indicates the number of passengers preset for an Mm vehicle in the i-station; n Mm represents the rated passenger capacity of the vehicle model adopted by the preset Mm vehicle; l represents a congestion degree threshold;
An eighteenth calculation unit, configured to calculate, according to the risk level threshold, bus operation data of different stations to obtain a bus operation risk level; when different bus operation data are received, the bus operation risk is the lowest grade I; when the bus running risk is/, the bus running risk is class II; When the bus running risk is class III; when the bus running risk is class IV in the case of/> ; and when the bus running risk is/, the bus running risk is the highest grade V.
6. The bus running risk identification device of claim 5, wherein the first processing module further comprises a second processing unit, the second processing unit comprising:
the first acquisition unit is used for acquiring the number of the approach stops in the bus running process and the total number of the stops running on a single bus line;
The fourth calculation unit is used for carrying out mean value calculation on the number of the path stations to obtain mean value information of the number of the path stations;
the fifth calculation unit is used for solving the average value information of the total number of stations operated by the single bus line and the number of the approach stations through a preset getting-off probability model to obtain the getting-off probability of passengers;
and the sixth calculation unit is used for calculating the getting-off probability of the passengers to obtain the getting-off station information of the passengers.
7. The bus running risk identification device according to claim 6, wherein the sixth calculation unit includes:
The second acquisition unit is used for acquiring the number of boarding persons at a preset station and the historical data of the traffic flow of the preset station in the operation of the single bus line;
the seventh calculation unit is used for solving the number of boarding persons at the preset station and the total number of stations operated by the single bus line through a preset station attraction model to obtain station attraction strength;
the eighth calculation unit is used for carrying out probability calculation according to the preset station multiplication flow historical data to obtain a station historical multiplication flow coefficient;
And a ninth calculation unit, configured to calculate according to the station attraction strength, the station history multiplying flow coefficient, and the passenger getting-off probability, to obtain passenger getting-off station point information.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410063890.4A CN117592788B (en) | 2024-01-17 | 2024-01-17 | Bus running risk identification method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410063890.4A CN117592788B (en) | 2024-01-17 | 2024-01-17 | Bus running risk identification method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117592788A CN117592788A (en) | 2024-02-23 |
| CN117592788B true CN117592788B (en) | 2024-04-16 |
Family
ID=89910197
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410063890.4A Active CN117592788B (en) | 2024-01-17 | 2024-01-17 | Bus running risk identification method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117592788B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101615340A (en) * | 2009-07-24 | 2009-12-30 | 北京工业大学 | Real-time information processing method in the bus dynamic dispatching |
| CN104517040A (en) * | 2014-12-31 | 2015-04-15 | 青岛海信网络科技股份有限公司 | Method for calculating in-carriage congestion degree of public traffic vehicle based on IC card data |
| CN109886491A (en) * | 2019-02-26 | 2019-06-14 | 北京工业大学 | A kind of routine bus system two stages Optimization Scheduling of magnanimity operation data driving |
| CN113658025A (en) * | 2021-08-12 | 2021-11-16 | 杭州图软科技有限公司 | Intelligent bus management method, system, computer equipment and storage medium |
| CN115099721A (en) * | 2022-08-24 | 2022-09-23 | 南京智慧交通信息股份有限公司 | Bus traffic congestion degree evaluation system and method based on big data analysis |
| CN115222267A (en) * | 2022-07-21 | 2022-10-21 | 北京交通大学 | Urban rail line network passenger flow risk assessment method and system |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113920769B (en) * | 2021-10-19 | 2022-07-01 | 航天物联网技术有限公司 | Intelligent scheduling list generation method based on public transportation multi-source data analysis |
-
2024
- 2024-01-17 CN CN202410063890.4A patent/CN117592788B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101615340A (en) * | 2009-07-24 | 2009-12-30 | 北京工业大学 | Real-time information processing method in the bus dynamic dispatching |
| CN104517040A (en) * | 2014-12-31 | 2015-04-15 | 青岛海信网络科技股份有限公司 | Method for calculating in-carriage congestion degree of public traffic vehicle based on IC card data |
| CN109886491A (en) * | 2019-02-26 | 2019-06-14 | 北京工业大学 | A kind of routine bus system two stages Optimization Scheduling of magnanimity operation data driving |
| CN113658025A (en) * | 2021-08-12 | 2021-11-16 | 杭州图软科技有限公司 | Intelligent bus management method, system, computer equipment and storage medium |
| CN115222267A (en) * | 2022-07-21 | 2022-10-21 | 北京交通大学 | Urban rail line network passenger flow risk assessment method and system |
| CN115099721A (en) * | 2022-08-24 | 2022-09-23 | 南京智慧交通信息股份有限公司 | Bus traffic congestion degree evaluation system and method based on big data analysis |
Non-Patent Citations (1)
| Title |
|---|
| 常规公交乘客对车内拥挤感知阻抗调查与建模;邵敏华;李田野;孙立军;;同济大学学报(自然科学版);20120715(07) * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117592788A (en) | 2024-02-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109035770B (en) | Real-time analysis and prediction method for bus passenger capacity in big data environment | |
| CN105279955B (en) | A kind of share-car method and apparatus | |
| CN110298516B (en) | Method and device for splitting overlong bus line based on passenger flow OD data, mobile terminal equipment and server | |
| CN102509449B (en) | Vehicle scheduling method based on fuzzy decision | |
| WO2018058967A1 (en) | Ground transportation journey recommendation method, device, apparatus, and computer storage medium | |
| CN106548246B (en) | Real-time charging recommendation method and system for electric vehicle | |
| CN113194138B (en) | Travel management method and system based on AI deep learning | |
| CN109255972B (en) | Optimization method of ground public transport fixed line timetable based on big data | |
| CN110348614B (en) | Method for obtaining passenger OD and method for predicting bus passenger flow | |
| CN105825310A (en) | Taxi passenger-searching path recommendation method based on information entropy | |
| CN103177575A (en) | System and method for dynamically optimizing online dispatching of urban taxies | |
| US20200210905A1 (en) | Systems and Methods for Managing Networked Vehicle Resources | |
| CN114743401B (en) | Data visualization bus dispatching management platform based on bus digital transformation | |
| CN110254285B (en) | Method and system for providing service for mileage anxiety user based on Internet of vehicles | |
| CN110009131A (en) | A kind of net about vehicle worksheet processing method considering multifactor impact | |
| CN115587678A (en) | Intelligent map traveling method and system for commercial vehicle | |
| CN111311950A (en) | Intelligent dispatching method and system for bus in peak period | |
| CN114971136A (en) | Bus and tour bus scheduling method | |
| CN111582527A (en) | Travel time estimation method and device, electronic equipment and storage medium | |
| CN113408833A (en) | Public traffic key area identification method and device and electronic equipment | |
| CN117592788B (en) | Bus running risk identification method and device | |
| CN110347937B (en) | Intelligent passenger searching method for taxi | |
| CN113160542A (en) | Riding method and device based on information feedback | |
| CN116090785B (en) | Custom bus planning method for two stages of large-scale movable loose scene | |
| CN111339159B (en) | Analysis mining method for one-ticket public transport data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |