CN113470364A - Bus scheduling method and system based on artificial intelligence and big data - Google Patents

Bus scheduling method and system based on artificial intelligence and big data Download PDF

Info

Publication number
CN113470364A
CN113470364A CN202111022049.3A CN202111022049A CN113470364A CN 113470364 A CN113470364 A CN 113470364A CN 202111022049 A CN202111022049 A CN 202111022049A CN 113470364 A CN113470364 A CN 113470364A
Authority
CN
China
Prior art keywords
bus
station
stop
route
departure
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.)
Granted
Application number
CN202111022049.3A
Other languages
Chinese (zh)
Other versions
CN113470364B (en
Inventor
李艳
李红
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nantong Huarui Software Technology Co ltd
Original Assignee
Nantong Huarui Software Technology Co ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Nantong Huarui Software Technology Co ltd filed Critical Nantong Huarui Software Technology Co ltd
Priority to CN202111022049.3A priority Critical patent/CN113470364B/en
Publication of CN113470364A publication Critical patent/CN113470364A/en
Application granted granted Critical
Publication of CN113470364B publication Critical patent/CN113470364B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • 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
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • 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
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/20—Monitoring the location of vehicles belonging to a group, e.g. fleet of vehicles, countable or determined number of vehicles

Landscapes

  • Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Strategic Management (AREA)
  • Human Resources & Organizations (AREA)
  • Economics (AREA)
  • Tourism & Hospitality (AREA)
  • Development Economics (AREA)
  • Analytical Chemistry (AREA)
  • Marketing (AREA)
  • Chemical & Material Sciences (AREA)
  • General Business, Economics & Management (AREA)
  • Theoretical Computer Science (AREA)
  • Primary Health Care (AREA)
  • Educational Administration (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Game Theory and Decision Science (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Traffic Control Systems (AREA)

Abstract

The invention relates to the technical field of artificial intelligence and big data, in particular to a bus dispatching method and a system based on artificial intelligence and big data, which comprises the following steps: the target route and all stops on the target route are selected, the time period of the current bus departure to reach each stop is obtained by means of a big data technology, and then the traffic pressure value of each stop is calculated, so that the score of the target route is determined. And matching and screening all stops and all bus routes on the target route, wherein the stops and all bus routes are larger than a set pressure threshold value, so that the alternative route of each stop is obtained, and the score of the target route is updated accordingly. And finally, determining the bus schedule of the current departure time period on the target route. According to the invention, the intelligent dispatching of the bus route is realized, and the accuracy of bus dispatching is improved, so that the traffic pressure of the route stop with a larger traffic pressure value is relieved, and the traffic resources are saved to a certain extent.

Description

Bus scheduling method and system based on artificial intelligence and big data
Technical Field
The invention relates to the technical field of artificial intelligence and big data, in particular to a bus scheduling method and system based on artificial intelligence and big data.
Background
With the rapid development of the economy of China, the construction of cities is changing day by day and the population of the cities is increasing year by year, more pressure is brought to the traffic system of the cities, and the urban traffic problem becomes one of the key problems in the development of many cities. Compared with private cars, the urban public transport has the advantages of large passenger capacity, less relative investment, less occupied resources, high efficiency and the like, and buses in the urban public transport are transportation means with higher use frequency in daily life of people.
Because the population is concentrated in the city and the urban traffic is relatively congested, the bus is used as a main vehicle in the city and plays an extremely important role in relieving the urban traffic pressure. In order to meet the traveling needs of citizens and improve the traveling efficiency, the bus needs to be scheduled at the time of traffic pressure peak.
At present, bus scheduling is usually a hard index, namely, each bus must complete corresponding target lap number regardless of traffic pressure of a route every day. For example, in rush hours, a certain number of buses are fixedly scheduled to relieve traffic pressure, and if the scheduled buses are not fully utilized, traffic resources are wasted to a certain extent; if the number of dispatched vehicles is small, the traffic pressure cannot be sufficiently relieved. Therefore, the existing mode is not accurate enough, and the scheduling accuracy is poor.
Disclosure of Invention
The invention aims to provide a public transport scheduling method and system based on artificial intelligence and big data, which are used for solving the problem that the conventional public transport scheduling mode is inaccurate.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows: the invention provides a bus scheduling method based on artificial intelligence and big data, which comprises the following steps:
selecting a target route from all bus routes, and acquiring each stop on the target route, the required driving time between any two stops and the number of passengers getting on and off each stop in each time period;
predicting the time period of the current bus departure to each stop and the traffic pressure value at each stop according to the required driving time between any two stops on the target route and the number of people getting on or off the bus at each stop in each time period;
determining a target route score of the bus shift of the current departure according to the predicted traffic pressure value of the bus shift of the current departure at each stop;
if the score of the target route of the current bus departure is larger than a set score threshold, respectively judging whether the traffic pressure value of the current bus departure at each stop is larger than a set pressure threshold, and obtaining each stop of which the target route is larger than the set pressure threshold;
acquiring all bus routes which can be replaced at each stop and are greater than a set pressure threshold according to the predicted time period for the bus which is sent out currently to reach each stop;
updating the traffic pressure values of the current bus departure at the stations which are larger than the set pressure threshold according to the traffic pressure values of all the alternative routes at the corresponding stations which are larger than the set pressure threshold;
and recalculating the target route score of the current bus departure time according to the updated traffic pressure value of the current bus departure time at each stop, and determining the bus dispatching time of the current departure time period on the target route according to the recalculated target route score.
Further, the time period when the current bus departure shift reaches each stop and the traffic pressure value at each stop are predicted, and the specific steps comprise:
calculating the traffic pressure value of the current bus departure at the first stop according to the number of passengers getting on and off the first stop at the current bus departure time period;
predicting the time period of the current bus departure to the next stop according to the time period of the current bus departure to the previous stop, the number of people getting on or off the bus in the time period of the current bus departure to the previous stop, the traffic pressure value of the previous stop and the required driving time from the previous stop to the next stop; predicting the number of passengers getting on and off corresponding to the next stop in the time period according to the predicted time period when the bus which is sent out currently arrives at the next stop; and predicting the traffic pressure value of the next stop according to the traffic pressure value of the current departure bus at the previous stop and the predicted number of passengers getting on and off the next stop in the time period.
Further, the time period for predicting the bus shift of the current departure to reach each stop and the corresponding calculation formula of the traffic pressure value at each stop are as follows:
Figure DEST_PATH_IMAGE001
Figure 572430DEST_PATH_IMAGE002
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE003
is at the first
Figure 302620DEST_PATH_IMAGE004
The traffic pressure value of each station is set,
Figure 613515DEST_PATH_IMAGE005
for the current bus departure
Figure 539883DEST_PATH_IMAGE004
Time period of each site is in
Figure 885414DEST_PATH_IMAGE006
The traffic pressure value of each station is set,
Figure 989767DEST_PATH_IMAGE007
for the current bus departure
Figure 787959DEST_PATH_IMAGE006
The time period of each station is,
Figure 518017DEST_PATH_IMAGE008
is as follows
Figure 718054DEST_PATH_IMAGE004
From station to station
Figure 993309DEST_PATH_IMAGE006
The travel time required for each station is,
Figure 278797DEST_PATH_IMAGE009
is as follows
Figure 546967DEST_PATH_IMAGE006
The lower-vehicle heat value of each station,
Figure 880472DEST_PATH_IMAGE010
is as follows
Figure 841475DEST_PATH_IMAGE006
The heat value of getting-on of the vehicle at each station,
Figure 614259DEST_PATH_IMAGE009
=
Figure 420541DEST_PATH_IMAGE011
，
Figure 611482DEST_PATH_IMAGE012
，
Figure 477807DEST_PATH_IMAGE013
is as follows
Figure 737887DEST_PATH_IMAGE006
Station in time slot
Figure 347860DEST_PATH_IMAGE007
The corresponding number of the people getting off the bus,
Figure 127728DEST_PATH_IMAGE014
is as follows
Figure 430533DEST_PATH_IMAGE006
Station in time slot
Figure 912330DEST_PATH_IMAGE007
The corresponding number of the passengers getting on the bus,
Figure 325994DEST_PATH_IMAGE015
the number of seats on the bus shift from which the bus is currently dispatched,
Figure 960369DEST_PATH_IMAGE016
is as follows
Figure 434075DEST_PATH_IMAGE004
The lower-vehicle heat value of each station,
Figure 403168DEST_PATH_IMAGE017
is as follows
Figure 354944DEST_PATH_IMAGE004
The heat value of getting-on of the vehicle at each station,
Figure 930694DEST_PATH_IMAGE016
=
Figure 575302DEST_PATH_IMAGE018
，
Figure 31691DEST_PATH_IMAGE019
，
Figure 787157DEST_PATH_IMAGE020
is as follows
Figure 130545DEST_PATH_IMAGE004
Station in time slot
Figure 946054DEST_PATH_IMAGE005
The corresponding number of the people getting off the bus,
Figure 889739DEST_PATH_IMAGE021
is as follows
Figure 465208DEST_PATH_IMAGE004
Station in time slot
Figure 177949DEST_PATH_IMAGE005
The corresponding number of the passengers getting on the bus,
Figure 633201DEST_PATH_IMAGE022
the time coefficient of getting on and off the vehicle is,
Figure 64183DEST_PATH_IMAGE023
for the length of time of each time segment,
Figure 161452DEST_PATH_IMAGE024
presentation pair
Figure 745011DEST_PATH_IMAGE025
Rounding down is performed.
Figure 902323DEST_PATH_IMAGE022
The pressure value of the previous station is larger, the higher the pressure value of the previous station is, the more crowded the vehicle is, the corresponding time coefficient is
Figure 820600DEST_PATH_IMAGE022
The larger.
Further, the expression of the time coefficient for getting on and off is:
Figure 469363DEST_PATH_IMAGE026
wherein the content of the first and second substances,
Figure 156696DEST_PATH_IMAGE027
the time coefficient of getting on and off the vehicle is,
Figure 484909DEST_PATH_IMAGE028
is at the first
Figure 624904DEST_PATH_IMAGE029
Traffic pressure values for individual stations.
Further, the step of acquiring all the alternative bus routes of each stop which are greater than the set pressure threshold comprises the following steps:
for the second time greater than the set pressure threshold
Figure 80287DEST_PATH_IMAGE030
A station acquiring the data passing through the first
Figure 356548DEST_PATH_IMAGE030
All bus routes of each stop, wherein the target routes are not included in all the bus routes;
acquiring the driving time required by any two stops on each bus route in all bus routes and the number of passengers getting on and off each stop in each time period;
predicting the arrival of each bus on each bus route in all bus routes in the first bus shift according to the required driving time between any two stops on each bus route in all bus routes and the number of passengers getting on and off each stop in each time period
Figure 855662DEST_PATH_IMAGE030
Time period of individual site and in
Figure 482952DEST_PATH_IMAGE030
Traffic pressure values for individual stations;
determining a preliminary alternative bus route from all bus routes, wherein the preliminary bus route of the preliminary alternative bus route reaches the first bus shift
Figure 742027DEST_PATH_IMAGE030
The time period of each station and the arrival number of bus shifts of the current departure on the target line
Figure 138373DEST_PATH_IMAGE030
The time periods of the stations are the same;
calculating the coincidence degree of each bus route in the preliminary planned alternative bus routes and the target route, and screening out the bus routes with the coincidence degree of the target route being greater than a set coincidence degree threshold value from the preliminary planned alternative bus routes;
judging whether the screened coincidence degree is greater than the preset coincidence degree threshold value or not
Figure 542809DEST_PATH_IMAGE030
And (4) whether the traffic pressure value of each station is greater than a set pressure threshold value or not, and taking the bus route smaller than the set pressure threshold value as a final bus route capable of being replaced.
Further, the step of calculating the contact ratio of each bus route in the preliminary bus routes to the target route comprises the following steps:
on the target line at
Figure 657396DEST_PATH_IMAGE030
Each station behind each station is used as a circle center, a circle is drawn by taking a set distance as a radius, and the circle corresponding to each station on the target line is used as the coincidence range of the station;
obtaining each bus route in the preliminary alternative bus routes on the second place
Figure 189003DEST_PATH_IMAGE030
Each station behind each station and judging whether each bus route is on the first station
Figure 439855DEST_PATH_IMAGE030
Whether each station behind each station is positioned in the overlapping range or not is judged, and the position of each bus route is the first position
Figure 546352DEST_PATH_IMAGE030
The number of all stations located in the overlapping range after the station is taken as the number of the station
Figure 882655DEST_PATH_IMAGE030
The coincidence degree of the bus route and the target route at each station.
Further, the formula for updating the traffic pressure value of the current bus departure at each stop point which is greater than the set pressure threshold value is as follows:
Figure 467220DEST_PATH_IMAGE031
wherein the content of the first and second substances,
Figure 585961DEST_PATH_IMAGE032
is the first after update
Figure 128938DEST_PATH_IMAGE030
The traffic pressure value of each station is set,
Figure 952538DEST_PATH_IMAGE033
is the first before update
Figure 91526DEST_PATH_IMAGE030
The traffic pressure value of each station is set,
Figure 316971DEST_PATH_IMAGE034
is as follows
Figure 499691DEST_PATH_IMAGE030
The number of all alternative bus routes for each stop,
Figure 76165DEST_PATH_IMAGE035
is as follows
Figure 753266DEST_PATH_IMAGE030
Bus route capable of replacing bus stop
Figure 833217DEST_PATH_IMAGE036
The degree of coincidence with the target route,
Figure 452417DEST_PATH_IMAGE037
in order to set the pressure threshold value,
Figure 266921DEST_PATH_IMAGE038
is as follows
Figure 996979DEST_PATH_IMAGE030
Bus route capable of replacing bus stop
Figure 665858DEST_PATH_IMAGE036
In the first place
Figure 455959DEST_PATH_IMAGE030
The traffic pressure value of each station is set,
Figure 741447DEST_PATH_IMAGE039
for the current bus departure
Figure 757420DEST_PATH_IMAGE030
Time period of each site.
Further, the calculation formula corresponding to the bus dispatching shift of the current departure time period on the target line is determined as follows:
Figure 546385DEST_PATH_IMAGE040
wherein the content of the first and second substances,
Figure 772967DEST_PATH_IMAGE041
the number of shifts to be scheduled for the bus,
Figure 280171DEST_PATH_IMAGE042
for the score of the recalculated target route,
Figure 837186DEST_PATH_IMAGE043
in order to set the pressure threshold value,
Figure 11815DEST_PATH_IMAGE044
for the time period when the current bus shift of departure arrives at the first stop,
Figure 878140DEST_PATH_IMAGE045
is a pair of
Figure 138220DEST_PATH_IMAGE046
Rounding down is performed.
Further, the maximum value of the traffic pressure value of the current bus departure at each stop is used as the target route score of the current bus departure.
The invention also provides a bus dispatching system based on artificial intelligence and big data, which comprises a processor and a memory, wherein the processor is used for processing the instructions stored in the memory so as to realize the bus dispatching method based on artificial intelligence and big data.
The invention has the following beneficial effects:
according to the method, a target route and each stop on the target route are selected from all bus routes, and the traffic pressure value and the target route score of each stop on the target route are obtained according to the running time of any two stops on the target route and the number of passengers getting on and off each stop. And selecting corresponding alternative routes for all the stations with the traffic pressure values larger than the set pressure threshold. And updating the score of the target route according to the information of the alternative route so as to determine the dispatching result of the bus.
When the bus shift of the current departure is sent out, the traffic pressure value of each stop is closely related to the traffic states of all the stops, so the traffic pressure value of the bus shift reaching each stop is predicted by using the number of people getting on or off the bus at each stop in each time period, wherein each stop comprises an initial stop. Meanwhile, the influence condition of the traffic pressure value of the starting station and the replaceable public traffic route of each station with larger traffic pressure value on the traffic pressure value of the station is fully considered, so that more accurate traffic pressure value of each station is obtained, the bus dispatching shift is determined by using the more accurate traffic pressure value, the influence of the replaceable route on the traffic pressure value of each station is fully considered, the dispatching is more accurate, the traffic pressure is effectively relieved, and meanwhile, the waste of traffic resources is also avoided.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart 1 of a bus dispatching method based on artificial intelligence and big data according to the present invention;
fig. 2 is a system flow chart 2 of the bus scheduling method and system based on artificial intelligence and big data of the present invention.
Detailed Description
To further explain the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description of the embodiments, structures, features and effects of the technical solutions according to the present invention will be given with reference to the accompanying drawings and preferred embodiments.
The embodiment provides a public transportation scheduling method based on artificial intelligence and big data, as shown in fig. 1 and 2, the specific steps are as follows:
step (ii) of
Figure 764505DEST_PATH_IMAGE047
: and selecting a target route from all bus routes, and acquiring each stop on the target route, the required driving time between any two stops and the number of people getting on or off the bus at each time period at each stop.
The main purpose of the step is to obtain some parameters in advance by using an experimental and statistical method, wherein the parameters are needed to be used in the process of realizing intelligent bus dispatching and comprise: the target route, each station on the target route, the required driving time of any two stations and the number of people getting on or off the bus at each station in each time period. The following describes in detail the method for acquiring the number of people getting on or off the bus at each station in each time slot in the target route, and the specific steps are as follows:
step (ii) of
Figure 793641DEST_PATH_IMAGE048
: first, a target bus traveling along a target route is found using a monitoring camera. The monitoring camera acquires an RGB image of each station on the target line, wherein the RGB image refers to an image represented by different proportions of each pixel R (red), G (green) and B (blue) of a color image. The RGB image of each station is converted into the HSV color space, and the HSV color space has a larger effect when used for color segmentation of the designated image, and is more suitable for judging whether the target bus appears in the image.
In the process of converting the RGB image into the HSV color space, the HSV color space model used represents hue (H), saturation sum (S), and lightness (V), and in the RGB image, three components of red (R), green (G), and blue (B) are represented. In the conversion process, the maximum value of the three components is set to be MAX, the minimum value is set to be MIN, and the conversion formula from RGB to HSV is as follows:
Figure 830867DEST_PATH_IMAGE049
Figure 578243DEST_PATH_IMAGE050
Figure 742639DEST_PATH_IMAGE051
as can be seen from the above conversion formula, the hue (H) ranges from 0 degree to 360 degrees, and the saturation (S) and lightness (V) values range from 0 to 1. When in use
Figure 360702DEST_PATH_IMAGE052
When the color (H) channel is pure gray, no color information exists; when in use
Figure 834409DEST_PATH_IMAGE053
When the temperature of the water is higher than the set temperature,
Figure 803502DEST_PATH_IMAGE054
indicating no color. The component value is artificially set according to the prior knowledge and the scene needs. The conversion formula from RGB to HSV is a well-known formula, and the reasoning process is not described herein again.
After the RGB image of each station is converted into the HSV color space, thresholds of three channels are manually set according to priori knowledge, wherein the thresholds of the three channels refer to the set threshold of H, S, V, and the LED lamp image of the bus can be segmented. The segmented LED light image reflects current bus information, i.e., the number of bus routes along the target route, e.g., 1, 2, 3, B1, B2, S108, S109, etc. And matching the segmented LED image with a template in a database to judge whether the current bus is a target bus, wherein the target bus refers to a bus along a target route. Since template matching is a prior art, it is not described herein in detail.
The purpose of the above steps is to identify the bus of the target route in the image, and a specific implementation manner is given, and those skilled in the art can also adopt other manners when the above purpose is achieved.
Step (ii) of
Figure 514799DEST_PATH_IMAGE055
: and then, shielding an irrelevant working condition by using a target detection network CenterNet technology, wherein the irrelevant working condition is to shield the background outside a bus area, so that the influence on the detection of the number of people getting on or off the bus subsequently is avoided. When the target vehicle appears in the monitoring camera, the acquired RGB image is input into the trained target detection network CenterNet to obtain the center coordinates of the bus door enclosure frame
Figure 252948DEST_PATH_IMAGE056
And length and width dimensions
Figure 631976DEST_PATH_IMAGE057
. The RGB image is cut by the enclosing frame, so that the purpose of shielding the number of people getting on or off the bus in a follow-up state under an irrelevant working condition is achieved. The target detection network centret, its training process, and the specific process of identifying a target by using the trained network are well known in the art, and are not described herein.
Step (ii) of
Figure 88366DEST_PATH_IMAGE058
: and finally, judging the number of people getting on or off the bus by detecting key points on the heads of the passengers. The steps are as follows
Figure 860144DEST_PATH_IMAGE059
Inputting the cut picture into human body key point detection network, outputting Heatmap of human body head key point
Figure 718378DEST_PATH_IMAGE060
And the function processing obtains the position information of the key point. Detecting the key points of the head by using an optical flow method to obtain the motion information of personnel, and specifying the motion direction to point to the central point of the vehicle door surrounding frame
Figure 799467DEST_PATH_IMAGE061
The person getting on the bus or the person getting off the bus. The human body key point detection network can be an existing openpos network, which is a known technology and is not described herein again.
Summarizing the above steps
Figure 743152DEST_PATH_IMAGE047
And carrying out image recognition on each stop of all bus routes by using the monitoring camera, and judging whether the segmented image is a bus along the target route according to the image recognition. Then, background factors that may affect the detection of the number of persons getting on and off need to be excluded. And finally, detecting the specific numerical value of the number of passengers getting on the bus and the specific numerical value of the number of passengers getting off the bus at each station of the target route in any period of time by using the key point information of the heads of the passengers. Thereby, canThe number of passengers getting on or off the bus at each stop in any period of time on all bus routes is detected, a plurality of time periods are set manually, then the number of passengers getting on or off the bus at each stop in each set time period can be determined by utilizing a big data statistics mode, and the determined number of passengers getting on or off the bus at each stop in each time period is stored in a corresponding database.
Step (ii) of
Figure 787462DEST_PATH_IMAGE062
: according to the driving time required by any two stops on the target route and the number of people getting on or off the bus at each stop in each time period, the time period when the current bus departure time reaches each stop and the traffic pressure value at each stop are predicted, and the specific steps are as follows:
step (ii) of
Figure 500204DEST_PATH_IMAGE063
: and calculating the traffic pressure value of the current bus departure at the starting station according to the number of passengers getting on and off the starting station at the current bus departure time period.
And calculating from the starting station to obtain the traffic pressure value of each station. The starting station is not influenced by other stations, and the departure time period
Figure 486614DEST_PATH_IMAGE064
Lower its traffic pressure value
Figure 917595DEST_PATH_IMAGE065
The difference between the getting-on heat and the getting-off heat of the personnel at the current station is as follows:
Figure 765597DEST_PATH_IMAGE066
wherein the content of the first and second substances,
Figure 332844DEST_PATH_IMAGE065
is the traffic pressure value of the starting station,
Figure 490156DEST_PATH_IMAGE067
for the time period of departure
Figure 408434DEST_PATH_IMAGE064
The heat of boarding of the passengers at the starting station,
Figure 791617DEST_PATH_IMAGE067
=
Figure 478950DEST_PATH_IMAGE068
，
Figure 807164DEST_PATH_IMAGE069
is a starting station
Figure 947158DEST_PATH_IMAGE064
The number of the passengers getting on the bus in the time period,
Figure 402541DEST_PATH_IMAGE015
the number of seats on the bus,
Figure 678802DEST_PATH_IMAGE070
for the time period of departure
Figure 177916DEST_PATH_IMAGE064
The degree of heat of alighting of passengers at the starting station,
Figure 539627DEST_PATH_IMAGE071
，
Figure 516811DEST_PATH_IMAGE072
is a starting station
Figure 929469DEST_PATH_IMAGE064
The number of people getting off in the time period,
Figure 599484DEST_PATH_IMAGE067
，
Figure 714071DEST_PATH_IMAGE070
the length of time of each time segment corresponds to a value as a function of time.
Step (ii) of
Figure 229366DEST_PATH_IMAGE073
: according to the time period of the current departure bus to the starting station, the number of people getting on or off the bus in the time period of the current departure bus, the traffic pressure value of the starting station and the required driving time from the starting station to the next station, the time period of the current departure bus to the next station is predicted, and the specific implementation calculation formula is as follows:
Figure 496530DEST_PATH_IMAGE074
wherein the content of the first and second substances,
Figure 337447DEST_PATH_IMAGE075
respectively the time periods of the bus arriving at the initial station and the current station, the unit is
Figure 939330DEST_PATH_IMAGE076
。
Figure 258316DEST_PATH_IMAGE077
The time required for the bus to travel from the initial station to the current station is given in units of
Figure 629254DEST_PATH_IMAGE078
。
Figure 123296DEST_PATH_IMAGE079
The time taken for passengers to get on or off the bus at the initial station is given by
Figure 946896DEST_PATH_IMAGE078
。
Figure 69572DEST_PATH_IMAGE023
For the time length of each time segmentHere, the
Figure 29438DEST_PATH_IMAGE080
。
Figure 212158DEST_PATH_IMAGE081
In order to round the symbol down,
Figure 539365DEST_PATH_IMAGE022
for the getting on and off time coefficient, the corresponding expression is:
Figure 200154DEST_PATH_IMAGE082
wherein the content of the first and second substances,
Figure 280105DEST_PATH_IMAGE022
the time coefficient of getting on and off the vehicle is,
Figure 633726DEST_PATH_IMAGE003
is at the first
Figure 431918DEST_PATH_IMAGE004
Traffic pressure values for individual stations. The train getting-on and getting-off coefficient reflects the time of the passengers getting on and off the train, is a piecewise function and is directly given by the personnel in the industry according to experience and related to the pressure value of the previous station, the larger the pressure value of the previous station is, the more crowded the train is, the corresponding pressure value
Figure 647130DEST_PATH_IMAGE022
The larger.
Step (ii) of
Figure DEST_PATH_IMAGE083
: predicting the number of passengers getting on and off the next stop in the time period according to the predicted time period when the bus which is sent out currently arrives at the next stop; predicting the traffic pressure value at the next stop according to the traffic pressure value at the previous stop of the current bus departure shift and the predicted number of passengers getting on and off at the next stop in the time period, and realizing the following calculation formula:
Figure 378325DEST_PATH_IMAGE084
Figure 168427DEST_PATH_IMAGE085
for the next station
Figure 939068DEST_PATH_IMAGE086
The traffic pressure value of (a) is,
Figure 738396DEST_PATH_IMAGE065
time period for previous station
Figure 792940DEST_PATH_IMAGE064
The traffic pressure value of (a) is,
Figure 488364DEST_PATH_IMAGE087
for the getting-off heat of the passenger at the latter station,
Figure 8950DEST_PATH_IMAGE088
the passenger getting on the bus at the latter station.
Step (ii) of
Figure 815232DEST_PATH_IMAGE089
: and predicting the time period of the current bus departure to the next stop according to the time period of the current bus departure to the previous stop, the number of people getting on or off the bus in the time period of the current bus departure to the previous stop, the traffic pressure value of the previous stop and the required driving time from the previous stop to the next stop. Predicting the number of passengers getting on and off the next stop and the traffic pressure value of the previous stop according to the time period for predicting the arrival of the current bus departure at the next stop, and obtaining the traffic pressure value of the next stop, wherein the concrete implementation formula is as follows:
Figure 458703DEST_PATH_IMAGE001
Figure 590607DEST_PATH_IMAGE002
wherein the content of the first and second substances,
Figure 585108DEST_PATH_IMAGE090
is at the first
Figure 476972DEST_PATH_IMAGE006
The traffic pressure value of each station is set,
Figure 506108DEST_PATH_IMAGE003
is at the first
Figure 543334DEST_PATH_IMAGE004
The traffic pressure value of each station is set,
Figure 41442DEST_PATH_IMAGE009
is as follows
Figure 455106DEST_PATH_IMAGE006
The lower-vehicle heat value of each station,
Figure 338749DEST_PATH_IMAGE010
is as follows
Figure 546876DEST_PATH_IMAGE006
The heat value of getting-on of the vehicle at each station,
Figure 266702DEST_PATH_IMAGE009
=
Figure 218477DEST_PATH_IMAGE011
，
Figure 222205DEST_PATH_IMAGE012
，
Figure 601234DEST_PATH_IMAGE013
is as follows
Figure 57623DEST_PATH_IMAGE006
Station in time slot
Figure 572611DEST_PATH_IMAGE007
The corresponding number of the people getting off the bus,
Figure 165266DEST_PATH_IMAGE014
is as follows
Figure 980775DEST_PATH_IMAGE006
Station in time slot
Figure 658881DEST_PATH_IMAGE007
The corresponding number of the passengers getting on the bus,
Figure 952460DEST_PATH_IMAGE015
the number of seats on the current bus shift of departure;
Figure 415933DEST_PATH_IMAGE007
for the current bus departure
Figure 402344DEST_PATH_IMAGE006
The time period of each station is,
Figure 833325DEST_PATH_IMAGE005
for the current bus departure
Figure 930594DEST_PATH_IMAGE004
The time period of each station is,
Figure 982995DEST_PATH_IMAGE091
is as follows
Figure 140307DEST_PATH_IMAGE004
From station to station
Figure 527426DEST_PATH_IMAGE006
The travel time required for each station is,
Figure 428385DEST_PATH_IMAGE022
the time coefficient of getting on and off the vehicle is,
Figure 115719DEST_PATH_IMAGE016
is as follows
Figure 194664DEST_PATH_IMAGE004
The lower-vehicle heat value of each station,
Figure 69079DEST_PATH_IMAGE017
is as follows
Figure 508151DEST_PATH_IMAGE006
-a boarding heating value of 1 station,
Figure 49991DEST_PATH_IMAGE016
=
Figure 296908DEST_PATH_IMAGE018
，
Figure 924198DEST_PATH_IMAGE019
，
Figure 901382DEST_PATH_IMAGE092
the number of seats on the bus shift from which the bus is currently dispatched,
Figure 563307DEST_PATH_IMAGE093
for the time length of each time segment, is based on the steps
Figure 233323DEST_PATH_IMAGE047
Determined by the length of time of the artificially set time period),
Figure 833063DEST_PATH_IMAGE024
presentation pair
Figure 613937DEST_PATH_IMAGE025
Rounding down is performed.
Here, the current departure bus shift arrival number is calculated as
Figure 395948DEST_PATH_IMAGE006
Time period of each site
Figure 971286DEST_PATH_IMAGE007
The calculation formula (c) is explained as follows: and according to the time periods of all the stops counted by the big data, dividing the time into different time periods by using the set time length of each time period, thereby judging which time period the current bus departure time is in when the current bus departure time arrives at the next stop.
Figure 58322DEST_PATH_IMAGE023
To divide the time length of each time segment, the time to the next station is in which time segment should be rounded down, and the rounding down is to ensure the accuracy of calculating the time segment to the next station. By using
Figure 642887DEST_PATH_IMAGE023
It can be determined that the time required for the previous station to reach the next station has moved by several time periods.
For example, time periods 5:00-5:30, 5:30-6:00, 6:00-6:30 … … are respectively taken as time period 1, time period 2, and time period 3 … …, when
Figure 13825DEST_PATH_IMAGE093
And =30 min. Then the data is stored according to the corresponding number of persons getting on and off the bus in time period 1, time period 2 and time period 3 … …. If the current bus shift arrives the first
Figure 25644DEST_PATH_IMAGE004
The time period of each station is time period 1, and the time period is calculated
Figure 865555DEST_PATH_IMAGE094
=32min, at this time
Figure DEST_PATH_IMAGE095
I.e. only one time period, i.e. time period 2, has to be moved from time period 1. Therefore, the time slot 2 is the arrival of the current bus shift
Figure 519390DEST_PATH_IMAGE006
Time period of each site.
Step (ii) of
Figure 10414DEST_PATH_IMAGE096
: and determining the target route score of the bus shift of the current departure according to the predicted traffic pressure value of the bus shift of the current departure at each stop.
Based on the steps
Figure 940937DEST_PATH_IMAGE062
Calculating a target route score of the current bus departure time based on the traffic pressure values of the stations on the target route obtained by the basis, wherein the calculation method of the target route score comprises the following steps:
Figure 251832DEST_PATH_IMAGE097
wherein the content of the first and second substances,
Figure 178200DEST_PATH_IMAGE098
the number of the bus stations is the number of the bus stations,
Figure 523731DEST_PATH_IMAGE099
as the traffic pressure value of each station,
Figure 628084DEST_PATH_IMAGE100
indicating a period of time
Figure 160697DEST_PATH_IMAGE064
And scoring the target route when the vehicle is sent.
Step (ii) of
Figure 890755DEST_PATH_IMAGE101
: according to the steps
Figure 356372DEST_PATH_IMAGE096
And judging the obtained target route score, if the target route score of the current bus departure time is greater than a set score threshold value, respectively judging whether the traffic pressure value of the current bus departure time at each stop is greater than a set pressure threshold value, and obtaining each stop of which the target route is greater than the set pressure threshold value.
The purpose of the step is to obtain the stations with larger traffic pressure values, and form a set
Figure 631626DEST_PATH_IMAGE102
Subsequently, will pair the sets
Figure 917114DEST_PATH_IMAGE102
The sites within are analyzed one by one. The specific implementation mode is as follows: the traffic pressure value of each station obtained in the steps is compared with an empirical threshold value
Figure 185284DEST_PATH_IMAGE037
Comparing, will be greater than a threshold
Figure 239828DEST_PATH_IMAGE037
Site entry set of
Figure 935252DEST_PATH_IMAGE102
. It should be noted that, in the following description,
Figure 458768DEST_PATH_IMAGE037
to be an empirical threshold, the implementer may set according to urban traffic needs.
Step (ii) of
Figure 265050DEST_PATH_IMAGE103
: and acquiring all alternative bus routes of all stops smaller than a set pressure threshold according to the predicted time period for the bus which is sent out currently to reach each stop.
The purpose of this step is to obtain a collection
Figure 174100DEST_PATH_IMAGE102
And the alternative routes of each stop can relieve the traffic pressure value of the target route at the current stop. Mainly, the influence of the alternative route on the traffic pressure value of the station on the target route is considered, the accuracy of the traffic pressure value and the accuracy of bus scheduling are ensured, and the specific implementation method comprises the following steps:
step (ii) of
Figure 306004DEST_PATH_IMAGE104
: and screening all bus routes for the first time. First-come-from collection
Figure 48308DEST_PATH_IMAGE102
Randomly selecting a site for analysis, and recording the site as
Figure 658281DEST_PATH_IMAGE030
. Then, the passing sites are screened out from the database
Figure 421837DEST_PATH_IMAGE030
The bus route does not include the target route. Finally, an alternative route that was preliminary screened for the first time can be obtained.
Step (ii) of
Figure 459063DEST_PATH_IMAGE105
: and screening the primary alternative routes screened in the previous step again. Predicting the arrival of each bus on each bus route in all bus routes in the first bus shift according to the required driving time between any two stops on each bus route in all bus routes and the number of passengers getting on and off each stop in each time period
Figure 940860DEST_PATH_IMAGE030
Time period of each site. In order to guarantee the travel time of passengers, the screened routes and the target route must arrive at the station
Figure 88945DEST_PATH_IMAGE030
The time periods are the same, all the preliminary alternative routes of the same time period are reserved and recorded as an alternative route set
Figure 723320DEST_PATH_IMAGE106
。
Step (ii) of
Figure 931447DEST_PATH_IMAGE107
: set of alternative routes screened out for the previous step
Figure 900540DEST_PATH_IMAGE106
Screening was performed again. Calculating a set of alternative bus routes
Figure 852316DEST_PATH_IMAGE106
The coincidence degree of each bus route and the target route in the system is collected from the alternative routes
Figure 590465DEST_PATH_IMAGE106
And screening out the bus route with the coincidence degree with the target route larger than a set coincidence degree threshold value.
The specific screening steps are as follows, the invention considers that when the straight-line distance between two stations is less than a certain value
Figure 985805DEST_PATH_IMAGE108
A time, i.e. a passenger arriving from one station to another in a short time, indicates that the two stations have a certain overlap.
Figure 442194DEST_PATH_IMAGE108
The determination of the value is related to the traffic convenience degree of the city, and the implementer sets a proper threshold value according to different cities.
Target route is located at station
Figure 932081DEST_PATH_IMAGE030
All sites thereafter are recorded as a set
Figure DEST_PATH_IMAGE109
Set of
Figure 72207DEST_PATH_IMAGE106
Each bus route is at the station
Figure 356557DEST_PATH_IMAGE030
All sites thereafter, denoted as aggregate
Figure 300243DEST_PATH_IMAGE110
. Taking each station of the target line as a circle center, and taking the numerical value as
Figure 593821DEST_PATH_IMAGE108
For drawing a circle by a radius, the invention takes
Figure 306562DEST_PATH_IMAGE111
. And taking the circle corresponding to the station on each target route as the overlapping range of the station, thereby obtaining the overlapping range of each station on the target route. When the collection
Figure 52494DEST_PATH_IMAGE109
Includes a set within the coincidence range of one station
Figure 217896DEST_PATH_IMAGE110
When the station is in, the accumulator is connected
Figure 49586DEST_PATH_IMAGE112
Plus 1, the target bus route
Figure 413571DEST_PATH_IMAGE036
In that
Figure 321615DEST_PATH_IMAGE030
After-site overlap ratio
Figure 974314DEST_PATH_IMAGE035
The calculation formula of (a) is as follows:
Figure 609694DEST_PATH_IMAGE113
wherein the content of the first and second substances,
Figure 297028DEST_PATH_IMAGE035
is a route
Figure 375973DEST_PATH_IMAGE036
In that
Figure 250388DEST_PATH_IMAGE030
The degree of coincidence after a station is,
Figure 955039DEST_PATH_IMAGE112
is an accumulator and is a gas-liquid separator,
Figure 762458DEST_PATH_IMAGE114
is a set
Figure 12305DEST_PATH_IMAGE109
The number of stations in (1).
Figure 905175DEST_PATH_IMAGE115
Is shown at the station
Figure 147937DEST_PATH_IMAGE030
The situation that the superposed station exists on the station of the target route on the primary alternative route later can be met
Figure 557665DEST_PATH_IMAGE115
Station reservation to obtain a bus route set
Figure 227681DEST_PATH_IMAGE116
。
Step (ii) of
Figure 342268DEST_PATH_IMAGE117
: judging each bus with the screened contact ratio larger than the set contact ratio threshold valueArrival of each bus shift of the route
Figure 388721DEST_PATH_IMAGE030
And (4) whether the traffic pressure value of each station is greater than a set pressure threshold value or not, and taking the bus route smaller than the set pressure threshold value as a final bus route capable of being replaced.
According to collections
Figure 124727DEST_PATH_IMAGE118
Internal bus route at station
Figure 965644DEST_PATH_IMAGE119
The traffic pressure value result of the department is set to the screened bus routes
Figure 567527DEST_PATH_IMAGE118
The last screening was performed. According to the steps
Figure 152092DEST_PATH_IMAGE120
Method (2) computing a set
Figure 273763DEST_PATH_IMAGE118
All bus routes are at the station
Figure 285581DEST_PATH_IMAGE119
And (4) setting the traffic pressure value, wherein the result of the traffic pressure value is larger than the set pressure threshold value after the traffic pressure value is cut off, considering that the current bus route is incapable of sharing the traffic pressure of the target route, and reserving the bus route of which the traffic pressure value result is smaller than the set pressure threshold value. So far, the station on the target route can be obtained
Figure 374760DEST_PATH_IMAGE119
The final set of alternative routes, denoted as set of alternative routes
Figure 497437DEST_PATH_IMAGE121
。
Thus, the collection is traversed in the same way
Figure 473614DEST_PATH_IMAGE102
For each stop, a set of alternative bus routes for each stop is obtained. The alternative routes of each stop provide more selectable routes for passengers to a certain extent, and the traffic pressure of the stop with higher traffic pressure value on the target route is relieved.
Step (ii) of
Figure 921913DEST_PATH_IMAGE122
: and updating the traffic pressure values of the current bus departure at the stations larger than the set pressure threshold according to the traffic pressure values of all the alternative routes at the corresponding stations smaller than the set pressure threshold.
When updating the traffic pressure value of each station on the target route, considering that different alternative routes have different influence degrees on the traffic pressure of the target route at the current station, obtaining a more accurate updated traffic pressure value of each station according to the alternative routes, and specifically realizing the following steps:
first, a set is obtained
Figure 232808DEST_PATH_IMAGE102
Set of alternative routes for each site within, here with site
Figure 159176DEST_PATH_IMAGE030
Set of alternative routes of
Figure 239128DEST_PATH_IMAGE123
The traffic pressure value updating method is described in detail. Then, the known site
Figure 340551DEST_PATH_IMAGE030
Alternative bus route set
Figure 138743DEST_PATH_IMAGE123
Each route comprises information of coincidence degree and traffic pressure score and is marked as a substitute route
Figure 868802DEST_PATH_IMAGE036
Respectively the contact ratio and the traffic pressure of
Figure 68839DEST_PATH_IMAGE124
If the target route is at the station
Figure 78514DEST_PATH_IMAGE030
Updated traffic pressure values
Figure 364002DEST_PATH_IMAGE032
The calculation formula of (2) is as follows:
Figure 632172DEST_PATH_IMAGE031
wherein the content of the first and second substances,
Figure 686716DEST_PATH_IMAGE032
for sites after update
Figure 398451DEST_PATH_IMAGE030
The traffic pressure value of (a) is,
Figure 171235DEST_PATH_IMAGE033
for sites before update
Figure 977517DEST_PATH_IMAGE030
The traffic pressure value of (a) is,
Figure 152147DEST_PATH_IMAGE034
as a station
Figure 752892DEST_PATH_IMAGE030
The number of all of the alternative bus routes,
Figure 498125DEST_PATH_IMAGE035
as a station
Figure 373677DEST_PATH_IMAGE030
Can be replaced by
Figure 137234DEST_PATH_IMAGE036
The degree of coincidence with the target route,
Figure 440040DEST_PATH_IMAGE038
as a station
Figure 669639DEST_PATH_IMAGE030
Alternative routes to
Figure 83303DEST_PATH_IMAGE036
In the first place
Figure 966945DEST_PATH_IMAGE030
The traffic pressure value of each station is set,
Figure 440652DEST_PATH_IMAGE037
in order to set the pressure threshold value,
Figure 160477DEST_PATH_IMAGE039
for the current bus departure
Figure 112253DEST_PATH_IMAGE030
Time period of each site. It is explained that, in order to prevent the situation that the number of the alternative routes is too large to generate negative number, when
Figure 115981DEST_PATH_IMAGE125
It is set to 0.
At the end, the sets are assembled in the same way
Figure 495010DEST_PATH_IMAGE102
And updating the traffic pressure value of each station in the system.
Step (ii) of
Figure 951399DEST_PATH_IMAGE126
: according to the updated traffic pressure values of the bus shift of the current departure at each stop, recalculating the purpose of the bus shift of the current departureAnd marking the line score. According to the recalculated updated target route score, determining the bus dispatching shift of the current departure time period on the target route, wherein the specific implementation content is as follows:
according to the updated set
Figure 457598DEST_PATH_IMAGE102
Taking the maximum value of the updated traffic pressure value as the score of the updated target route
Figure 784674DEST_PATH_IMAGE127
. Time period after update
Figure 600183DEST_PATH_IMAGE064
Target route score of
Figure 543868DEST_PATH_IMAGE127
And traffic pressure threshold
Figure 853758DEST_PATH_IMAGE037
Comparing to obtain the current time period
Figure 566499DEST_PATH_IMAGE064
Scheduling shift
Figure 287331DEST_PATH_IMAGE128
The calculation formula is as follows:
Figure 718312DEST_PATH_IMAGE040
wherein the content of the first and second substances,
Figure 575102DEST_PATH_IMAGE041
the number of shifts to be scheduled for the bus,
Figure 142350DEST_PATH_IMAGE042
for the score of the recalculated target route,
Figure 34083DEST_PATH_IMAGE043
in order to set the pressure threshold value,
Figure 952360DEST_PATH_IMAGE044
for the time period when the current bus shift of departure arrives at the first stop,
Figure 853320DEST_PATH_IMAGE045
is a pair of
Figure 25806DEST_PATH_IMAGE046
Rounding down is performed.
Thus, according to the above steps
Figure 354020DEST_PATH_IMAGE129
And finally, determining a bus dispatching scheme based on artificial intelligence and big data.
The embodiment also provides a bus dispatching system based on artificial intelligence and big data, which comprises a processor and a memory, wherein the processor is used for processing instructions stored in the memory to realize the bus dispatching method based on artificial intelligence and big data, and since the dispatching method is described in detail in the above content, the dispatching method is not described in detail here.
It should be noted that: the precedence order of the above embodiments of the present invention is only for description, and does not represent the merits of the embodiments. And specific embodiments thereof have been described above. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (7)

1. A bus dispatching method based on artificial intelligence and big data is characterized by comprising the following steps:
selecting a target route from all bus routes, and acquiring each stop on the target route, the required driving time between any two stops and the number of passengers getting on and off each stop in each time period;
predicting the time period of the current bus departure to each stop and the traffic pressure value at each stop according to the required driving time between any two stops on the target route and the number of people getting on or off the bus at each stop in each time period;
determining a target route score of the bus shift of the current departure according to the predicted traffic pressure value of the bus shift of the current departure at each stop;
if the target route score of the current bus departure time is larger than a set score threshold, respectively judging whether the traffic pressure value of the current bus departure time at each stop is larger than a set pressure threshold, and obtaining each stop of which the target route is larger than the set pressure threshold;
acquiring all bus routes which can be replaced at each stop and are smaller than a set pressure threshold according to the predicted time period for the bus which is sent out currently to reach each stop;
updating the traffic pressure values of the current bus departure at all the stations smaller than the set pressure threshold according to the traffic pressure values of all the alternative routes at all the stations smaller than the set pressure threshold;
recalculating the target route score of the current bus departure time according to the updated traffic pressure value of the current bus departure time at each stop, and determining the bus dispatching time of the current departure time period on the target route according to the recalculated target route score;
the step of acquiring all the alternative bus routes of all the stations which are greater than the set pressure threshold comprises the following steps:
for the second time greater than the set pressure threshold
Figure DEST_PATH_IMAGE002
A station acquiring the data passing through the first
Figure 63792DEST_PATH_IMAGE002
All bus routes of each stop, wherein the target routes are not included in all the bus routes;
acquiring the required running time between any two stops on each bus route in all bus routes and the number of passengers getting on and off each stop in each time period;
predicting the arrival of each bus on each bus route in all bus routes in the first bus shift according to the required driving time between any two stops on each bus route in all bus routes and the number of passengers getting on and off each stop in each time period
Figure 784755DEST_PATH_IMAGE002
Time period of individual site and in
Figure 592174DEST_PATH_IMAGE002
Traffic pressure values for individual stations;
determining a preliminary alternative bus route from all bus routes, wherein the preliminary bus route of the preliminary alternative bus route reaches the first bus shift
Figure 356867DEST_PATH_IMAGE002
The time period of each station and the arrival number of bus shifts of the current departure on the target line
Figure 470DEST_PATH_IMAGE002
The time periods of the stations are the same;
calculating the coincidence degree of each bus route in the preliminary planned alternative bus routes and the target route, and screening out the bus routes with the coincidence degree of the target route being greater than a set coincidence degree threshold value from the preliminary planned alternative bus routes;
judging whether the screened coincidence degree is greater than the preset coincidence degree threshold value or not
Figure 508811DEST_PATH_IMAGE002
Whether the traffic pressure value of each station is larger than a set pressure threshold value or not, and taking the bus route smaller than the set pressure threshold value as a final bus route capable of being replaced;
the step of calculating the contact ratio of each bus route in the preliminary bus routes and the target route comprises the following steps:
on the target line at
Figure 930258DEST_PATH_IMAGE002
Each station behind each station is used as a circle center, a circle is drawn by taking a set distance as a radius, and the circle corresponding to each station on the target line is used as the coincidence range of the station;
obtaining each bus route in the preliminary alternative bus routes on the second place
Figure 600274DEST_PATH_IMAGE002
Each station behind each station and judging whether each bus route is on the first station
Figure 980440DEST_PATH_IMAGE002
Whether each station behind each station is positioned in the overlapping range or not is judged, and the position of each bus route is the first position
Figure 777626DEST_PATH_IMAGE002
The number of all stations located in the overlapping range after the station is taken as the number of the station
Figure 294058DEST_PATH_IMAGE002
The coincidence degree of the bus route and the target route at each station;
the formula for updating the traffic pressure value of the current bus departure at each stop point which is greater than the set pressure threshold value is as follows:
Figure DEST_PATH_IMAGE004
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE006
is the first after update
Figure 10341DEST_PATH_IMAGE002
The traffic pressure value of each station is set,
Figure DEST_PATH_IMAGE008
is the first before update
Figure 159694DEST_PATH_IMAGE002
The traffic pressure value of each station is set,
Figure DEST_PATH_IMAGE010
is as follows
Figure 806576DEST_PATH_IMAGE002
The number of all alternative bus routes for each stop,
Figure DEST_PATH_IMAGE012
is as follows
Figure 456475DEST_PATH_IMAGE002
Bus route capable of replacing bus stop
Figure DEST_PATH_IMAGE014
The degree of coincidence with the target route,
Figure DEST_PATH_IMAGE016
in order to set the pressure threshold value,
Figure DEST_PATH_IMAGE018
is as follows
Figure 343660DEST_PATH_IMAGE002
Bus route capable of replacing bus stop
Figure 167259DEST_PATH_IMAGE014
In the first place
Figure 571827DEST_PATH_IMAGE002
The traffic pressure value of each station is set,
Figure DEST_PATH_IMAGE020
for the current bus departure
Figure 610321DEST_PATH_IMAGE002
Time period of each site.
2. The artificial intelligence and big data based bus scheduling method according to claim 1, wherein the step of predicting the time period for the current bus departure shift to reach each stop and the traffic pressure value at each stop comprises:
calculating the traffic pressure value of the current bus departure at the first stop according to the number of passengers getting on and off the first stop at the current bus departure time period;
predicting the time period of the current bus departure to the next stop according to the time period of the current bus departure to the previous stop, the number of people getting on or off the bus in the time period of the current bus departure to the previous stop, the traffic pressure value of the previous stop and the required driving time from the previous stop to the next stop; predicting the number of passengers getting on and off corresponding to the next stop in the time period according to the predicted time period when the bus which is sent out currently arrives at the next stop; and predicting the traffic pressure value of the next stop according to the traffic pressure value of the current departure bus at the previous stop and the predicted number of passengers getting on and off the next stop in the time period.
3. The artificial intelligence and big data based bus scheduling method according to claim 2, wherein the calculation formula corresponding to the time period for predicting the arrival of the bus shift of the current departure at each stop and the traffic pressure value at each stop is as follows:
Figure DEST_PATH_IMAGE022
Figure DEST_PATH_IMAGE024
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE026
is at the first
Figure DEST_PATH_IMAGE028
The traffic pressure value of each station is set,
Figure DEST_PATH_IMAGE030
is at the first
Figure DEST_PATH_IMAGE032
The traffic pressure value of each station is set,
Figure DEST_PATH_IMAGE034
for the current bus departure
Figure 681789DEST_PATH_IMAGE032
The time period of each station is,
Figure DEST_PATH_IMAGE036
for the current bus departure
Figure 789422DEST_PATH_IMAGE028
The time period of each station is,
Figure DEST_PATH_IMAGE038
is as follows
Figure 263260DEST_PATH_IMAGE032
From station to station
Figure 608791DEST_PATH_IMAGE028
The travel time required for each station is,
Figure DEST_PATH_IMAGE040
is as follows
Figure 772531DEST_PATH_IMAGE028
The lower-vehicle heat value of each station,
Figure DEST_PATH_IMAGE042
is as follows
Figure 118193DEST_PATH_IMAGE028
The heat value of getting-on of the vehicle at each station,
Figure 848252DEST_PATH_IMAGE040
=
Figure DEST_PATH_IMAGE044
，
Figure DEST_PATH_IMAGE046
，
Figure DEST_PATH_IMAGE048
is as follows
Figure 658076DEST_PATH_IMAGE028
Station in time slot
Figure 448177DEST_PATH_IMAGE036
The corresponding number of the people getting off the bus,
Figure DEST_PATH_IMAGE050
is as follows
Figure 15556DEST_PATH_IMAGE028
Station in time slot
Figure 549306DEST_PATH_IMAGE036
The corresponding number of the passengers getting on the bus,
Figure DEST_PATH_IMAGE052
the number of seats on the bus shift from which the bus is currently dispatched,
Figure DEST_PATH_IMAGE054
is as follows
Figure 769346DEST_PATH_IMAGE032
The lower-vehicle heat value of each station,
Figure DEST_PATH_IMAGE056
is as follows
Figure 277819DEST_PATH_IMAGE032
The heat value of getting-on of the vehicle at each station,
Figure 50603DEST_PATH_IMAGE054
=
Figure DEST_PATH_IMAGE058
，
Figure DEST_PATH_IMAGE060
，
Figure DEST_PATH_IMAGE062
is as follows
Figure 748563DEST_PATH_IMAGE032
Station in time slot
Figure 923192DEST_PATH_IMAGE034
The corresponding number of the people getting off the bus,
Figure DEST_PATH_IMAGE064
is as follows
Figure 865216DEST_PATH_IMAGE032
Station in time slot
Figure 125296DEST_PATH_IMAGE034
The corresponding number of the passengers getting on the bus,
Figure DEST_PATH_IMAGE066
the time coefficient of getting on and off the vehicle is,
Figure DEST_PATH_IMAGE068
for the length of time of each time segment,
Figure DEST_PATH_IMAGE070
presentation pair
Figure DEST_PATH_IMAGE072
Rounding down is performed.
4. The artificial intelligence and big data based bus scheduling method according to claim 3, wherein the expression of the time coefficient for getting on or off the bus is:
Figure DEST_PATH_IMAGE074
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE076
the time coefficient of getting on and off the vehicle is,
Figure DEST_PATH_IMAGE078
is at the first
Figure DEST_PATH_IMAGE080
Traffic pressure values for individual stations.
5. The artificial intelligence and big data based bus scheduling method according to claim 1, wherein the calculation formula corresponding to the bus scheduling shift at the current departure time period on the target line is determined as follows:
Figure DEST_PATH_IMAGE082
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE084
the number of shifts to be scheduled for the bus,
Figure DEST_PATH_IMAGE086
for the score of the recalculated target route,
Figure DEST_PATH_IMAGE088
in order to set the pressure threshold value,
Figure DEST_PATH_IMAGE090
for the time period when the current bus shift of departure arrives at the first stop,
Figure DEST_PATH_IMAGE092
is a pair of
Figure DEST_PATH_IMAGE094
Rounding down is performed.
6. The artificial intelligence and big data based bus scheduling method according to any one of claims 1-3, wherein the maximum value of the traffic pressure values of the current bus departure at each stop is used as the target route score of the current bus departure.
7. An artificial intelligence and big data based bus dispatching system, characterized by comprising a processor and a memory, wherein the processor is used for processing instructions stored in the memory to realize the artificial intelligence and big data based bus dispatching method as claimed in any one of claims 1-6.
CN202111022049.3A 2021-09-01 2021-09-01 Bus scheduling method and system based on artificial intelligence and big data Active CN113470364B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111022049.3A CN113470364B (en) 2021-09-01 2021-09-01 Bus scheduling method and system based on artificial intelligence and big data

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111022049.3A CN113470364B (en) 2021-09-01 2021-09-01 Bus scheduling method and system based on artificial intelligence and big data

Publications (2)

Publication Number Publication Date
CN113470364A true CN113470364A (en) 2021-10-01
CN113470364B CN113470364B (en) 2021-11-26

Family

ID=77868028

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111022049.3A Active CN113470364B (en) 2021-09-01 2021-09-01 Bus scheduling method and system based on artificial intelligence and big data

Country Status (1)

Country Link
CN (1) CN113470364B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113905215A (en) * 2021-12-07 2022-01-07 江西佳铭特实业有限公司 Bus safe driving monitoring system
CN114241770A (en) * 2021-12-21 2022-03-25 杭州图软科技有限公司 Bus scheduling method and system based on accurate real-time information

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103077605A (en) * 2012-12-14 2013-05-01 中国航天系统工程有限公司 Scheduling method and device for vehicle
CN106781432A (en) * 2016-11-16 2017-05-31 青岛海信网络科技股份有限公司 A kind of method and device of public traffic vehicles schedule
CN109859459A (en) * 2019-02-22 2019-06-07 北京航空航天大学 A kind of automatic Pilot bus dispatching optimization method
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
CN110245558A (en) * 2019-05-07 2019-09-17 平安科技（深圳）有限公司 Method for dispatching public transport vehicles, device and computer equipment based on recognition of face
CN111754757A (en) * 2020-06-24 2020-10-09 广州公交集团第三公共汽车有限公司 Bus competition line scheduling method

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103077605A (en) * 2012-12-14 2013-05-01 中国航天系统工程有限公司 Scheduling method and device for vehicle
CN106781432A (en) * 2016-11-16 2017-05-31 青岛海信网络科技股份有限公司 A kind of method and device of public traffic vehicles schedule
CN109859459A (en) * 2019-02-22 2019-06-07 北京航空航天大学 A kind of automatic Pilot bus dispatching optimization method
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
CN110245558A (en) * 2019-05-07 2019-09-17 平安科技（深圳）有限公司 Method for dispatching public transport vehicles, device and computer equipment based on recognition of face
CN111754757A (en) * 2020-06-24 2020-10-09 广州公交集团第三公共汽车有限公司 Bus competition line scheduling method

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113905215A (en) * 2021-12-07 2022-01-07 江西佳铭特实业有限公司 Bus safe driving monitoring system
CN114241770A (en) * 2021-12-21 2022-03-25 杭州图软科技有限公司 Bus scheduling method and system based on accurate real-time information

Also Published As

Publication number Publication date
CN113470364B (en) 2021-11-26

Similar Documents

Publication Publication Date Title
CN113470364B (en) Bus scheduling method and system based on artificial intelligence and big data
JP6570731B2 (en) Method and system for calculating passenger congestion
WO2021159865A1 (en) Data calibration-based bus route prediction method and system
CN110493816B (en) Real-time prediction method for rail transit subway station passenger flow volume
CN106197458B (en) A kind of mobile phone user's trip mode recognition methods based on mobile phone signaling data and navigation route data
CN110164152B (en) Traffic signal lamp control system for single-cross intersection
WO2021023310A1 (en) Method for estimating and presenting passenger flow, system, and computer readable storage medium
CN106935027A (en) A kind of traffic information predicting method and device based on running data
CN109085790A (en) A kind of underground traffic passenger guiding system based on smart city
CN107545757B (en) Urban road flow velocity measuring device and method based on license plate recognition
CN108615378A (en) A kind of traffic light time regulation and control method at two-way multilane zebra stripes crossing
CN110298516B (en) Method and device for splitting overlong bus line based on passenger flow OD data, mobile terminal equipment and server
CN108922194A (en) A kind of dynamic controllable intelligent traffic information prompting system based on big data
CN104021605A (en) Real-time statistics system and method for public transport passenger flow
CN110634299B (en) Urban traffic state fine division and identification method based on multi-source track data
CN110675032A (en) Vehicle scheduling method and device
CN105261211B (en) A kind of bus departure interval transition method under the influence of traffic composite factor
CN112016735B (en) Patrol route planning method and system based on traffic violation hotspot prediction and readable storage medium
CN105844229A (en) Method and system for calculating passenger crowdedness degree
Razavi et al. Smart traffic light scheduling in smart city using image and video processing
CN106931981A (en) A kind of generation method and device of remaining time of navigating
CN114419924B (en) AI application control management system based on wisdom city
CN116050581A (en) Smart city subway driving scheduling optimization method and Internet of things system
CN112601187B (en) Bus frequent passenger prediction method and system based on mobile phone signaling
CN108197078B (en) Method for calculating bus section passenger flow based on bus IC card 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