[go: up one dir, main page]

NL2034358A - Bus scheduling simulation system based on passenger flow big data analysis - Google Patents

Bus scheduling simulation system based on passenger flow big data analysis Download PDF

Info

Publication number
NL2034358A
NL2034358A NL2034358A NL2034358A NL2034358A NL 2034358 A NL2034358 A NL 2034358A NL 2034358 A NL2034358 A NL 2034358A NL 2034358 A NL2034358 A NL 2034358A NL 2034358 A NL2034358 A NL 2034358A
Authority
NL
Netherlands
Prior art keywords
data
passenger flow
simulation
unit
bus
Prior art date
Application number
NL2034358A
Other languages
Dutch (nl)
Other versions
NL2034358B1 (en
Inventor
Jia Ning
Li Mengyang
Li Geng
Zhong Shiquan
Ma Shoufeng
Tian Junfang
Xu Shuxian
ling Shuai
Original Assignee
Univ Tianjin
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 Univ Tianjin filed Critical Univ Tianjin
Publication of NL2034358A publication Critical patent/NL2034358A/en
Application granted granted Critical
Publication of NL2034358B1 publication Critical patent/NL2034358B1/en

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06314—Calendaring for a resource
    • 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
    • G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
    • G08G1/0112—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
    • G06F16/2462—Approximate or statistical queries
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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 OR CALCULATING; 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
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06—COMPUTING OR CALCULATING; 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
    • G06Q50/40—Business processes related to the transportation industry

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • Strategic Management (AREA)
  • Physics & Mathematics (AREA)
  • Economics (AREA)
  • General Physics & Mathematics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Theoretical Computer Science (AREA)
  • Tourism & Hospitality (AREA)
  • General Business, Economics & Management (AREA)
  • Marketing (AREA)
  • Educational Administration (AREA)
  • Development Economics (AREA)
  • Operations Research (AREA)
  • Game Theory and Decision Science (AREA)
  • Quality & Reliability (AREA)
  • Chemical & Material Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Analytical Chemistry (AREA)
  • Probability & Statistics with Applications (AREA)
  • Mathematical Physics (AREA)
  • Fuzzy Systems (AREA)
  • Software Systems (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • Databases & Information Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Traffic Control Systems (AREA)
  • Train Traffic Observation, Control, And Security (AREA)

Abstract

Disclosed is a bus scheduling simulation system based on passenger flow big data analysis, which comprises: A database management module, which is used for acquiring passenger flow statistical data and pre-processing the passenger flow statistical data to obtain processed data; a passenger flow simulation module, which is connected with the database management module and used for processing the processed data to generate passenger flow data; a route simulation module, which is connected with the database management module and used for simulating the bus route operation process based on the passenger flow data to obtain route simulation data; a system inspection module, which is used for comparing the actual passenger flow data with the line simulation data to obtain bus scheduling simulation verification results. According to the invention, the aggregation data recorded by the passenger flow meter is dispersed to the corresponding time interval by using a discretization method, so that the influence of external factors on the simulation of arrival time and departure time is avoided, and the constructed model and system can simulate the arrival situation of passengers' stations more accurately and stably..

Description

BUS SCHEDULING SIMULATION SYSTEM BASED ON PASSENGER FLOW BIG DATA
ANALYSIS
TECHNICAL FIELD
The invention belongs to the technical field of statistical analysis and simulation prediction processing of passenger flow data of public transportation, and in particular to a bus scheduling simulation system based on passenger flow big data analysis.
BACKGROUND
Traffic congestion and environmental pollution caused by the increasing number of cars has become a difficult problem in urban governance. Vigorously developing public transportation can solve these problems well, among which bus trip can not be ignored. The simulation of bus lines has high decision-making support value for the main body of bus operation-government and enterprises.
The core part of the bus line simulation is how to establish the passenger flow arrival model, how to simulate the passenger flow and how to make the simulated passenger flow as close to the real situation as possible. The above two aspects are directly related to all aspects of the simulation results of the whole line, and the specific defects are as follows: (1) Establishment of passenger arrival model: At present, there are mainly analytical models, such as Poisson distribution model, uniform distribution model, etc. Although it is mixed with probability, it is still a stable model with no unexpected factors. In fact, there are many factors affecting passenger flow, so it is difficult to use an analytical model to express the overall state of passenger flow. (2) More realistic passenger flow simulation prediction: Because there are differences between public transport and rail transit in terms of entry and exit, payment methods and punctuality of arrival, that is, it is more difficult to predict the passenger flow of public transport than to predict the passenger flow of rail transit, so there are relatively few solutions to the problems of simulating the passenger flow and the Origin-Destination of passengers at home and abroad. It mainly uses manual investigation method to record or uses the ride incomplete information to reverse, in which the information obtained by the former is too idealistic to rule out the influence of accidental factors such as weather and time in the real world, and it takes a lot of manpower and material resources, the cost is too high; the latter mostly analyses and deduces OD information based on the ride mode data by swiping the bus card, which can't handle the situation of coin-operated rides, that is, the established prediction model has a great deviation from the actual situation.
SUMMARY
The invention aims to provide a bus scheduling simulation system based on passenger flow big data analysis, so as to solve the problems existing in the prior art..
In order to achieve the above objectives, the present invention provides a bus scheduling simulation system based on passenger flow big data analysis, including:
A database management module is used for acquiring passenger flow statistical data and pre-processing the passenger flow statistical data to obtain processed data; a passenger flow simulation module is connected with the database management module and used for processing the processed data to generate passenger flow data; a route simulation module is connected with the database management module and used for simulating the bus route operation process based on the passenger flow data to obtain route simulation data; a system inspection module is used for comparing the actual passenger flow data with the line simulation data to obtain bus scheduling simulation verification results.
Preferably, the database management module comprises:
A passenger flow meter data unit is used for acquiring passenger flow statistical data in a certain period based on the passenger flow meter and pre-processing the data to generate the processed data; a bus operation unit is used for acquiring bus operation entry and exit data; a simulation result data unit is used for receiving the passenger flow data and transmitting the passenger flow data to the route simulation module.
Preferably, the passenger flow statistical data includes passenger flow meter original data, simulated passenger flow data, bus trip entry and exit data, departure schedule, entry and exit time and loading rate.
Preferably, the passenger flow simulation module comprises:
A data processing unit is used for processing the passenger flow statistical data based on the fluctuation of time and the inconsistency of passenger flow arrival laws at different times to generate a first processed data; a statistical unit is used for integrating the first processed data into different statistical intervals to generate a second processed data; a result acquisition unit is used for selecting the second processed data based on the time axis to generate a third processed data; a parameter estimation unit is used for estimating the third processed data based on a maximum likelihood estimation method to obtain the passenger flow data.
Preferably, the route simulation module comprises: a vehicle running unit is used for simulating the bus running process based on the passenger flow data to generate simulation data;
a data recording unit is used for recording the simulation data and then transmitting the simulation data to generate line operation conditions; a dynamic display unit is used for performing visual operation on the line running condition and updating the simulation data in real time to obtain the line simulation data.
Preferably, the vehicle running unit includes:
A station simulation unit is used for simulating the event that passengers arrive at the station based on the passenger flow data to generate a first simulation data; a vehicle simulation unit is used for simulating the event that the vehicle arrives at the station based on the passenger flow data to generate a second simulation data; a passenger simulation unit is used for judging whether a new passenger arrives at the station and whether the passenger can get on the bus during the boarding process, and generating a third simulation data; an integration unit is used for integrating the first simulation data, the second simulation data and the third simulation data to generate the simulation data.
Preferably, the data recording unit includes:
A time recording unit is used for recording the vehicle moving time data in the simulation data to generate time data; a loading data recording unit is used for recording the real-time loading rate of the vehicle based on a pre-set method to generate loading data; a position recording unit is used for updating and recording the vehicle position data in real time to generate the position data; a transmission unit is used for integrating the time data, the loading data and the position data and transmitting them to the dynamic display unit and the database management module.
Preferably, the system inspection module comprises:
An actual data acquisition unit for acquiring actual passenger flow data based on real-time data records; a comparison unit is used for randomly selecting the actual passenger flow data and comparing the actual passenger flow data with the line simulation data to obtain a bus scheduling simulation verification result.
The technical effects of the invention are as follows: (1) Compared with the prior art, the invention uses a discretization method to discretize the aggregated data recorded by the passenger flow meter to the corresponding time interval, thus avoiding the influence of external factors on the simulation of arrival time and departure time, and the constructed model and system can more accurately and stably simulate the arrival situation of passengers. (2) The technical method involved in the invention can automatically assume the appropriate arrival time for each passenger, effectively improve the passenger flow simulation capability of the system within the error acceptance range, and further fill the high-precision tracking and statistical application of the passenger flow meter in the bus market. (3) The application of widget movement in the invention solves the common flicker problem of the display interface of the passenger flow system, and based on big data analysis, the prediction effect of the constructed system is good, which can be further expanded and applied to the field of bus scheduling and optimization in the future.
BRIEF DESCRIPTION OF THE FIGURES
The accompanying drawings, which constitute a part of this application, are used to provide a further understanding of this application. The illustrative embodiments of this application and their descriptions are used to explain this application, and do not constitute an improper limitation of this application. In the attached drawings:
The accompanying drawings, which constitute a part of this application, are used to provide a further understanding of this application. The illustrative embodiments of this application and their descriptions are used to explain this application, and do not constitute an improper limitation of this application. In the attached drawings:
FIG. 1 is an overall structural diagram of a system in an embodiment of the present invention;
FIG. 2 is a flow chart of passenger flow data processing in an embodiment of the present invention;
FIG. 3 is an operation flow chart of the line simulation model in an embodiment of the present invention.
DESCRIPTION OF THE INVENTION
It should be noted that the embodiments in this application and the features in the embodiments can be combined with each other without conflict. The present application will be described in detail with reference to the attached drawings and examples.
It should be noted that the steps shown in the flowchart of the accompanying drawings may be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order from here.
Example 1
As shown in FIG. 1, this embodiment provides a bus scheduling simulation system based on passenger flow big data analysis, including:
In this embodiment, firstly, the passenger flow statistical data in a certain period is obtained by means of the vehicle-mounted passenger flow meter, and the obtained passenger flow big data is pre-processed; then, the inherent law of passenger flow is reversed, and the corresponding passenger flow simulation model and line simulation model which can simulate the real operation of the line are constructed, in which the route simulation module makes the simulated passenger flow in the complete bus route circulate based on the operation simulation of the line state; the operation data of G1 bus in L city is used to test the effect of the simulation system, and the results show that the simulation system is effective.
In this embodiment, the internal database of the database management module is used to 5 store passenger flow data, bus operation data and line simulation operation data, as shown in
Table 1. Among them, parameters such as station-to-station running time and average consumption time of getting on and off the bus for a line simulation model can be obtained by analysing the bus operation data; the line simulation operation data can be used for subsequent simulation optimization.
Table 1
Data Attribute Meaning Data bus_no Vehicle number int
Original data of busstop_serial Site serial number int passenger flow | passenger_up Number of boarding int
Passenger meter passenger_down Number of passengers getting int flow data pack_datetime Recording time datetime simulated reach_time arrival time datetime passenger flow origin Departure station int data destination destination station int
BUS_NO Vehicle number int
Bus Bus operation | BUSSTOP_SERIAL Current site serial number int operation entry and exit | BUSSTOP_TYPE Site identification int data data DATETIME_OUT Exit time datetime
DATETIME_IN Entry time datetime
Departure BUS_NO Vehicle number int
SCHEDULE Departure time datetime
BUS_NO Vehicle number int
Entry and exit | BUSSTOP_SERIAL Current site serial number int
Line time DATETIME_OUT Exit time datetime simulation DATETIME_IN Entry time datetime operation BUS_NO Vehicle number int data BUSSTOP_SERIAL Current site serial number int
FLR Loading rate float
Loading rate
PASSENGER_NUM Current number of passengers int
In this embodiment, based on the perspective of transportation, the passenger flow at each stop is obtained through the vehicle-mounted passenger flow meter. After a period of data collection, the obtained passenger flow data is analysed and processed, and the aggregated data recorded by the passenger flow meter is dispersed into the corresponding time interval by using the discretization method. Then, based on the data fitting theory, the designated interval data is selected for fitting, and the passenger flow arrival in a certain time interval is obtained.
Then, the fitting result, that is, the obtained distribution law, can be used for reference to generate the passenger flow at any start and end time.
In this embodiment, considering the fluctuation of the recording time of the vehicle-mounted passenger flow meter and the inconsistency of the arrival law of passengers at different times, it is necessary to preliminarily process the data of the passenger flow meter, and the processing process is shown in FIG. 2. Where, time represents the time axis, the starting point is a moment before the departure time, and the end point is the time when the last bus arrives at the terminal. Each scale on the time axis represents 1 unit time. n on the axis represents the data recorded by the vehicle-mounted passenger flow meter, the first number of the two identifiers represents the statistical date number, and the second number represents the interval number; the numbers in the axis represent the number of arrivals at each moment after the passenger flow data n11, n12,.. are dispersed, and the sum of the number of arrivals in each recording interval is equal to the number of passengers recorded in that interval; the interval of x under the axis is determined as required, including the specified length of time, and it also has two identifiers with the same meaning as n.
Further optimize the scheme, selecting the appropriate time length as the unit statistical interval, and integrating the passenger flow data of the whole day into different statistical intervals. The formula for this behaviour is: wherein Te is the operation ending time of the line; Ts is the operation starting time of the line; I is the selected time measure, and the length is selected subjectively; C is the serial number of a single statistical interval after division; YC is a collection of statistical intervals, and the symbol has double meanings, which not only indicates the number of statistical intervals, but also indicates that continuous statistical intervals cover all the operation time of a single day.
After the statistical interval is divided, it is necessary to select the statistical results of the same interval in different date serial numbers. This behaviour is expressed by the formula: wherein s is the serial number of the station, and different serial numbers indicate different stations on the journey from the originating station to the terminal station; d is the serial number of the date, and different serial numbers indicate different dates; Fie is the statistical result value of the C statistical interval of the s station on the d-th day, which indicates the total number of passengers arriving in the corresponding time interval after dividing the time interval as needed; ¢ (9 indicates that @ is the set of X , pf) is the statistical result set of the Ct statistical interval of the s station, and each item in the set is X with different d; 7 is the parameter to be estimated in the model, and there are different 7 under different (C, s).
The maximum likelihood estimation method is a classical method to observe the population with samples, and estimates the value with the maximum probability of the population parameters based on the observed values of n groups of samples extracted from the population. The overall log-likelihood function of passenger flow simulation model based on passenger flow big data analysis is:
Because the parameters of different stations and different time periods may be different, the parameter estimation needs s x YC times.
After the passenger flow simulation module is built, it can provide simulated passenger flow data for the route simulation module, that is, combined with the C# program development platform, the running flow of the line simulation model is shown in FIG. 3.
To further optimize the scheme, the module records the data of line simulation operation for dynamic display on the one hand, and records it to the corresponding data table of the database system on the other hand. The main process is as follows: 1. When the vehicle arrives at the station, call the RecordReachStationTime method to record the arrival time; 2. When the vehicle leaves the station, call the RecordLeaveTime method to record the departure time, call the RecordFullLoadRate method to record the real-time loading rate, and call the SetPosition method to update the vehicle position; 3. When the vehicle arrives at the terminal, call the SetArrangement method to update the vehicle schedule, 50 that the vehicle can use the next departure time, and call the
SetPosition method to update the vehicle position, so that the vehicle can return to the starting station.
The dynamic display unit mainly completes the visualization of the running status of the line, and adopts the way of moving widgets instead of drawing to update the vehicle position and waiting number, thus solving the problem of screen flicker. When the model is initialized, the Simulation. InitialMap method is called to generate various elements on the line, including station layout, road network drawing, and generating various widgets. The house with double spires represents the station, the number below the station represents the number of people waiting for the bus, the vehicle above or on the side of the road indicates that the vehicle is driving on the road, and the vehicle above the station indicates that the vehicle has arrived at the station and passengers are getting on and off. The buttons on the toolbar above the dynamic display interface control the running (pause), stopping and running speed of the module in turn. The time behind the button is the current time in the dynamic display interface.
The data records of G1 bus line in L city from March 1 to March 31, 2019 were detected and extracted, and the data on the 24th was selected as the training group and the data on the 7th was selected as the control group. The 25 stations of the whole line are numbered, that is, station O-station 24(0 is the starting station and 24 is the terminal station). According to the extracted passenger flow data, it is simulated that the one-way passenger flow of this line can pe regarded as 4300 people/day. Randomly selecting the data of one day in the control group (7th) as the control, and check and compare the actual passenger flow and simulated passenger flow at 25 stations in the whole line, with sim as the simulation data, as shown in
FIG. 4, and the simulated passenger flow and the actual passenger flow of the control group tend to be consistent in the total amount of one day; taking the No.10 station as an example, as shown in FIG. 4 - FIG. 2, the simulation data of the passenger flow arrival interval is highly consistent with the control data.
According to the average absolute error MAE and the average absolute percentage error
MAPE, mathematical detection is carried out, which proves that the prediction effect of this embodiment is good.
The above is only the preferred embodiment of this application, but the protection scope of this application is not limited to this. Any change or replacement that can be easily thought of by a person familiar with this technical field within the technical scope disclosed in this application should be covered by this application. Therefore, the protection scope of this application should be based on the protection scope of the claims.

Claims (8)

CONCLUSIESCONCLUSIONS 1. Een simulatiesysteem voor busplanning op basis van big data-analyse van passagiersstromen, omvattende: — een databankbeheermodule voor het verwerven van statistische gegevens over passagiersstromen en het voorbewerken van de statistische gegevens over passagiersstromen om verwerkte gegevens te verkrijgen; — een module voor het simuleren van passagiersstromen die is verbonden met de databankbeheermodule en wordt gebruikt voor het verwerken van de verwerkte gegevens om passagiersstroomgegevens te genereren; — een routesimulatiemodule die is verbonden met de databankbeheermodule die wordt gebruikt voor het simuleren van het busrouteproces op basis van de passagiersstroomgegevens om routesimulatiegegevens te verkrijgen; — een systeeminspectiemodule voor het vergelijken van de werkelijke passagiersstroomgegevens met de lijnsimulatiegegevens om verificatieresultaten van de busdienstregelingssimulatie te verkrijgen.1. A bus scheduling simulation system based on big data analysis of passenger flows, comprising: — a database management module for acquiring passenger flow statistics and preprocessing the passenger flow statistics to obtain processed data; — a passenger flow simulation module connected to the database management module and used for processing the processed data to generate passenger flow data; — a route simulation module connected to the database management module and used for simulating the bus routing process based on the passenger flow data to obtain route simulation data; — a system inspection module for comparing the actual passenger flow data with the line simulation data to obtain verification results of the bus timetable simulation. 2. Het simulatiesysteem voor busplanning op basis van big data-analyse van passagiersstromen volgens conclusie 1, waarbij de databankbeheermodule omvat: — een passagiersstroommeter-gegevenseenheid voor het verwerven van statistische gegevens over de passagiersstroom in een bepaalde periode op basis van de passagiersstroommeter en het voorbewerken van de gegevens om de verwerkte gegevens te genereren; — een busbediening-eenheid voor het verwerven van in- en uitstapgegevens van bussen; — een simulatiegegevensunit voor het ontvangen van de gegevens over de passagiersstroom en het verzenden van de gegevens over de passagiersstroom naar de routesimulatiemodule.2. The bus scheduling simulation system based on passenger flow big data analysis according to claim 1, wherein the database management module comprises: — a passenger flow meter data unit for acquiring statistical data of passenger flow in a certain period based on the passenger flow meter and preprocessing the data to generate the processed data; — a bus control unit for acquiring bus boarding and alighting data; — a simulation data unit for receiving the passenger flow data and transmitting the passenger flow data to the route simulation module. 3. Het simulatiesysteem voor busplanning op basis van big data-analyse van passagiersstromen volgens conclusie 2, waarbij de statistische gegevens over de passagiersstroom de oorspronkelijke gegevens van de passagiersstroommeter, de gesimuleerde gegevens over de passagiersstroom, de in- en uitstapgegevens van de busreis, het vertrekschema, de in- en uitstaptijd en de beladingsgraad omvatten.3. The bus scheduling simulation system based on passenger flow big data analysis according to claim 2, wherein the passenger flow statistical data includes the original passenger flow meter data, the simulated passenger flow data, the bus trip boarding and alighting data, the departure schedule, the boarding and alighting time and the load factor. 4. Het simulatiesysteem voor busplanning op basis van big data-analyse van passagiersstromen volgens conclusie 1, waarbij de simulatiemodule voor passagiersstromen omvat:4. The passenger flow big data analysis based bus scheduling simulation system according to claim 1, wherein the passenger flow simulation module comprises: — een gegevensverwerkingseenheid voor het verwerken van de statistische gegevens over de passagiersstroom op basis van de fluctuatie van de tijd en de inconsistentie van de aankomstwetten van de passagiersstroom op verschillende tijdstippen om een eerste verwerkte gegevens te genereren; — een statistische eenheid voor het integreren van de eerste verwerkte gegevens in verschillende statistische intervallen om een tweede verwerkt gegeven te genereren; — een resultaatverzamelingseenheid voor het selecteren van de tweede verwerkte gegevens op basis van de tijdas om een derde verwerkte gegevens te genereren; — een eenheid voor parameterschatting voor het schatten van de derde verwerkte gegevens op basis van een schattingsmethode op basis van maximale waarschijnlijkheid om de gegevens over de passagiersstroom te verkrijgen.— a data processing unit for processing the statistical data of the passenger flow based on the fluctuation of time and the inconsistency of the arrival laws of the passenger flow at different points in time to generate a first processed data; — a statistical unit for integrating the first processed data into different statistical intervals to generate a second processed data; — a result collection unit for selecting the second processed data based on the time axis to generate a third processed data; — a parameter estimation unit for estimating the third processed data based on a maximum likelihood estimation method to obtain the passenger flow data. 5. Het simulatiesysteem voor busplanning op basis van big data-analyse van passagiersstromen volgens conclusie 1, waarbij de routesimulatiemodule omvat: — een voertuigloopeenheid voor het simuleren van het busloopproces op basis van de passagiersstroomgegevens om simulatiegegevens te genereren; — een gegevensregistratie-eenheid voor het opnemen van de simulatiegegevens en het vervolgens doorgeven van de simulatiegegevens om de exploitatievoorwaarden van de lijn te genereren; — een dynamische display-eenheid voor het uitvoeren van visuele bewerkingen op de lijnrijtoestand en het in real time bijwerken van de simulatiegegevens om de lijnsimulatiegegevens te verkrijgen.5. The bus scheduling simulation system based on passenger flow big data analysis according to claim 1, wherein the route simulation module comprises: — a vehicle running unit for simulating the bus running process based on the passenger flow data to generate simulation data; — a data recording unit for recording the simulation data and then transmitting the simulation data to generate the line operating conditions; — a dynamic display unit for performing visual operations on the line running condition and updating the simulation data in real time to obtain the line simulation data. 6. Het simulatiesysteem voor busplanning op basis van big data-analyse van passagiersstromen volgens conclusie 5, waarbij de voertuigbesturingseenheid omvat: — een stationssimulatie-eenheid voor het simuleren van de gebeurtenis dat passagiers op het station aankomen op basis van de passagiersstroomgegevens om een eerste simulatiegegevens te genereren; — een voertuigsimulatie-eenheid voor het simuleren van de gebeurtenis dat het voertuig bij het station aankomt op basis van de passagiersstroomgegevens om een tweede simulatiegegeven te genereren; — een passagierssimulatie-eenheid voor het beoordelen of een nieuwe passagier op het station aankomt en of de passagier tijdens het instapproces in de bus kan stappen, en om een derde simulatiegegeven te genereren; — een integratie-eenheid voor het integreren van de eerste simulatiegegevens, de tweede simulatiegegevens en de derde simulatiegegevens om de simulatiegegevens te genereren.6. The bus scheduling simulation system based on passenger flow big data analysis according to claim 5, wherein the vehicle control unit comprises: — a station simulation unit for simulating the event that passengers arrive at the station based on the passenger flow data to generate a first simulation data; — a vehicle simulation unit for simulating the event that the vehicle arrives at the station based on the passenger flow data to generate a second simulation data; — a passenger simulation unit for judging whether a new passenger arrives at the station and whether the passenger can board the bus during the boarding process, and for generating a third simulation data; — an integration unit for integrating the first simulation data, the second simulation data and the third simulation data to generate the simulation data. 7. Het simulatiesysteem voor busplanning op basis van big data-analyse van passagiersstromen volgens conclusie 5, waarbij de gegevensregistratie-eenheid omvat: — een tijdregistratie-eenheid voor het registreren van de gegevens over de voertuigbewegingen in de simulatiegegevens om tijdgegevens te genereren; — een eenheid voor de registratie van laadgegevens voor de registratie van de real-time beladingssnelheid van het voertuig op basis van een vooraf ingestelde methode om laadgegevens te genereren; een positieregistratie-eenheid voor het bijwerken en registreren van de voertuigpositiegegevens in real time om de positiegegevens te genereren; — een transmissie-eenheid voor het integreren van de tijdgegevens, de laadgegevens en de positiegegevens en het doorsturen ervan naar de dynamische display-eenheid en de gegevensbankbeheermodule.7. The bus scheduling simulation system based on passenger flow big data analysis according to claim 5, wherein the data recording unit comprises: — a time recording unit for recording the vehicle movement data in the simulation data to generate time data; — a loading data recording unit for recording the real-time loading rate of the vehicle based on a preset method to generate loading data; a position recording unit for updating and recording the vehicle position data in real time to generate the position data; — a transmission unit for integrating the time data, the loading data and the position data and transmitting them to the dynamic display unit and the database management module. 8. Het simulatiesysteem voor busplanning op basis van big data-analyse van passagiersstromen volgens conclusie 1, waarbij de systeeminspectiemodule omvat: — een feitelijke gegevensverwervingseenheid voor het verwerven van feitelijke passagiersstroomgegevens op basis van real-time gegevensopnames; — een vergelijkingseenheid voor het willekeurig selecteren van de werkelijke passagiersstroomgegevens en het vergelijken van de werkelijke passagiersstroomgegevens met de lijnsimulatiegegevens om een verificatieresultaat van de busplanningssimulatie te verkrijgen.8. The bus scheduling simulation system based on passenger flow big data analysis according to claim 1, wherein the system inspection module comprises: — an actual data acquisition unit for acquiring actual passenger flow data based on real-time data recordings; — a comparison unit for randomly selecting the actual passenger flow data and comparing the actual passenger flow data with the line simulation data to obtain a verification result of the bus scheduling simulation.
NL2034358A 2023-02-23 2023-03-16 Bus scheduling simulation system based on passenger flow big data analysis NL2034358B1 (en)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310162264.6A CN116485093A (en) 2023-02-23 2023-02-23 Bus scheduling simulation system based on passenger flow big data analysis

Publications (2)

Publication Number Publication Date
NL2034358A true NL2034358A (en) 2024-09-05
NL2034358B1 NL2034358B1 (en) 2025-01-27

Family

ID=87225715

Family Applications (1)

Application Number Title Priority Date Filing Date
NL2034358A NL2034358B1 (en) 2023-02-23 2023-03-16 Bus scheduling simulation system based on passenger flow big data analysis

Country Status (2)

Country Link
CN (1) CN116485093A (en)
NL (1) NL2034358B1 (en)

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160364645A1 (en) * 2015-06-12 2016-12-15 Xerox Corporation Learning mobility user choice and demand models from public transport fare collection data
CN113962654A (en) * 2021-10-21 2022-01-21 天津大学 Simulation-based bus scheduling optimization method, system and storage medium

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160364645A1 (en) * 2015-06-12 2016-12-15 Xerox Corporation Learning mobility user choice and demand models from public transport fare collection data
CN113962654A (en) * 2021-10-21 2022-01-21 天津大学 Simulation-based bus scheduling optimization method, system and storage medium

Also Published As

Publication number Publication date
CN116485093A (en) 2023-07-25
NL2034358B1 (en) 2025-01-27

Similar Documents

Publication Publication Date Title
Ayyildiz et al. Reducing fuel consumption and carbon emissions through eco-drive training
Nesheli et al. Improved reliability of public transportation using real-time transfer synchronization
Nicholson et al. Benchmarking and evaluation of railway operations performance
Lovrić et al. Sustainable revenue management: A smart card enabled agent-based modeling approach
CN108876064B (en) Urban rail transit network key station identification method based on passenger flow
Wilson et al. The potential impact of automated data collection systems on urban public transport planning.
CN103279802B (en) Commuter's day activity-travel time prediction method
CN105550789A (en) Method for predicting bus taking passenger flow
Christoforou et al. Investigating the impact of dwell time on the reliability of urban light rail operations
CN113780396B (en) Personalized scheme generation method based on full-chain type air-railway journey travel
CN106651728A (en) Determination method for advantageous haul distances of passenger transport modes in comprehensive transport system
CN114118766A (en) Passenger flow OD algorithm based on bus passenger travel multiple matching
CN106327867A (en) Bus punctuality prediction method based on GPS data
CN118626528A (en) Personalized recommendation system for passenger travel plans based on model-as-a-service architecture
NL2034358B1 (en) Bus scheduling simulation system based on passenger flow big data analysis
Pereira et al. Generic bus route simulation model and its application to a new bus network development for caieiras city, Brazil
CN109409563B (en) Method, system and storage medium for analyzing real-time number of people in public transport operation vehicle
Karádi et al. Integrated information application on mobile devices for air passengers
Wood A framework for measuring passenger-experienced transit reliability using automated data
Gao et al. Big data analysis of beijing urban rail transit fares based on passenger flow
Shi et al. Generating synthetic passenger data through joint traffic-passenger modeling and simulation
Lingqiu et al. A LSTM based bus arrival time prediction method
JP2022006482A (en) Navigation device and navigation method
Upreti et al. Increasing transport efficiency using simulation modeling in a dynamic modeling approach
Yang et al. Temporal and spatial evolution of passenger flow in an urban rail transit network during station closure