CN112465396B - Bus scheduling method and system based on station events along line - Google Patents
Bus scheduling method and system based on station events along line Download PDFInfo
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Abstract
The invention discloses a bus scheduling method and system based on a station event along a line. The invention captures the things which are about to happen in a period of time in the future based on the web crawler, meanwhile, the neural network model is utilized to realize the prediction of the pedestrian volume by combining with the prior knowledge in daily life, the scheduling is carried out according to the result of the prediction of the pedestrian volume, and the targeted advance scheduling is realized according to the result of the prediction of the pedestrian volume which is likely to appear in a period of time in the future.
Description
Technical Field
The invention relates to the technical field of bus scheduling, in particular to a bus scheduling method and system based on a station event along a line.
Background
Public transport means (short for public transport) is an important component part for meeting daily travel demands, effectively schedules a public transport system, avoids traffic paralysis, improves the travel efficiency of residents, and has very important social significance.
At present, the bus dispatching method which is put into use comprises the following steps: (1) During the morning and evening peak periods, the number of vehicles on each route is increased; (2) extending the operation time, etc. However, the current scheduling method is not effective, and the reason for this is that individual and adaptive scheduling is not performed according to the characteristics of each line, and the number of buses is simply increased, which may aggravate the traffic paralysis. In addition, the method cannot accurately predict the possible pedestrian volume in a future period of time, so that targeted advance scheduling cannot be realized.
Disclosure of Invention
The invention aims to provide a bus dispatching method and system based on station events along a line, so as to accurately predict the possible pedestrian volume in a future period of time and realize targeted advance dispatching.
In order to achieve the purpose, the invention provides the following scheme:
a bus scheduling method based on station events along a line comprises the following steps:
acquiring the predicted bus scheduling related event information data of related mechanisms along each bus route and around the bus route in a future time period by adopting a web crawler technology and a priori knowledge learning method;
carrying out data cleaning and feature extraction on the public traffic scheduling related event information data to obtain public traffic scheduling related event feature data;
inputting the characteristic data of the bus scheduling related events into a trained neural network model for traffic prediction to obtain the traffic along each bus line in a future period;
and dispatching the buses along each bus line according to the pedestrian volume along each bus line in the future period.
Optionally, the acquiring, by using a web crawler technology and a priori knowledge learning method, the predicted bus scheduling related event information data of the relevant mechanisms along each bus route and around the bus route in the future period specifically includes:
acquiring expected aperiodic bus dispatching related event information data of the related mechanisms along each bus route and around the bus route from websites of the related mechanisms along each bus route and around the bus route by adopting a web crawler technology;
analyzing historical periodical bus scheduling related events by adopting a priori knowledge learning method, and determining the distribution rule of the periodical bus scheduling related events along each bus route; and comparing the future time period with the distribution rule, and determining the periodic bus scheduling related event information data of the future time period.
Optionally, data cleaning and feature extraction are performed on the bus scheduling related event information data to obtain bus scheduling related event feature data, and the method specifically includes:
carrying out missing value processing, format conversion, repeated data removal processing and noise data removal processing on the bus scheduling related event information data to obtain cleaned bus scheduling related event information data;
verifying the cleaned bus dispatching related event information data by using a data fitting method to obtain verified bus dispatching related event information data;
carrying out data abnormal value processing on the public transport scheduling related event information data passing the verification by adopting a Lauda criterion to obtain the public transport scheduling related event information data after the abnormal value processing;
denoising and smoothing the public transportation scheduling related event information data after abnormal value processing to obtain the public transportation scheduling related event information data after denoising and smoothing;
carrying out data segmentation on the bus scheduling related event information data subjected to denoising and smoothing to obtain a plurality of data segments;
extracting the characteristics of each data segment to obtain the characteristic data of each data segment;
and splicing the characteristic data of each data segment to obtain the characteristic data of the bus scheduling related events.
Optionally, the neural network model is a least squares model of attenuation.
Optionally, the bus along each bus route is scheduled according to the flow of people along each bus route in the future period, and the method specifically comprises the following steps:
according to the pedestrian volume along each bus line in the future period, a formula is utilized
Calculating the maximum section passenger flow in the future time period along each bus line; wherein p is i Represents the maximum section passenger flow in the future time interval along the ith future bus route, N l The number of passengers getting on the bus at the first platform in the pedestrian flow along the ith bus route is shown, Y l The number of people getting off at the first platform in the pedestrian flow along the ith bus line is represented, M i The number of the stations along the ith bus line is represented, and L represents the front L stations along the ith bus line;
according to the maximum section passenger flow in the future time interval along each bus line, a formula is utilized
Calculating the minimum number of vehicles in a future time period along each bus route; wherein n is i Representing the minimum number of vehicles in a future period along the ith bus route.
A bus dispatching system based on along-line station events comprises:
the knowledge acquisition module is used for acquiring the information data of the predicted bus dispatching related events of the related mechanisms along each bus route and around the bus route in the future period by adopting a web crawler technology and a priori knowledge learning method;
the data cleaning and feature extraction module is used for cleaning and extracting the data of the event information related to the bus dispatching to obtain the feature data of the event related to the bus dispatching;
the passenger flow prediction module is used for inputting the characteristic data of the bus scheduling related events into a trained neural network model for passenger flow prediction to obtain the passenger flow along each bus line in the future period;
and the scheduling module is used for scheduling the buses along each bus line according to the pedestrian volume along each bus line in a future period.
Optionally, the knowledge acquiring module specifically includes:
the network crawler submodule is used for acquiring the expected aperiodic bus dispatching related event information data of the related mechanisms along each bus route and around from the websites of the related mechanisms along each bus route and around by adopting the network crawler technology;
the prior knowledge learning submodule is used for analyzing historical periodical bus scheduling related events by adopting a prior knowledge learning method and determining the distribution rule of the periodical bus scheduling related events along each bus route; and comparing the future time period with the distribution rule, and determining the periodic bus scheduling related event information data of the future time period.
Optionally, the data cleaning and feature extracting module specifically includes:
the data cleaning submodule is used for carrying out missing value processing, format conversion, repeated data removal processing and noise data removal processing on the public transportation scheduling related event information data to obtain cleaned public transportation scheduling related event information data;
the data verification submodule is used for verifying the cleaned bus dispatching related event information data by using a data fitting method to obtain the verified bus dispatching related event information data;
the data abnormal value processing submodule is used for carrying out data abnormal value processing on the verified bus dispatching related event information data by adopting a Lauda criterion to obtain the bus dispatching related event information data after the abnormal value processing;
the denoising and smoothing sub-module is used for denoising and smoothing the bus scheduling related event information data after the abnormal value processing to obtain the bus scheduling related event information data after the denoising and smoothing processing;
the data segmentation submodule is used for carrying out data segmentation on the bus scheduling related event information data subjected to denoising and smoothing processing to obtain a plurality of data segments;
the characteristic extraction submodule is used for extracting the characteristics of each data segment to obtain the characteristic data of each data segment;
and the data splicing submodule is used for splicing the characteristic data of each data segment to obtain the characteristic data of the bus scheduling related events.
Optionally, the neural network model is a least squares model of attenuation.
Optionally, the scheduling module specifically includes:
a maximum section passenger flow volume calculation submodule for utilizing a formula according to the passenger flow volume along each bus line in a future time period
Calculating the maximum section passenger flow in the future time period along each bus route; wherein p is i Represents the maximum section passenger flow in the future time interval along the ith future bus route, N l The number of passengers getting on the bus at the first platform in the pedestrian flow along the ith bus route is shown, Y l The number of people getting off the first platform in the flow of people along the ith bus line is represented, M i The number of the stations along the ith bus line is represented, and L represents the front L stations along the ith bus line;
a minimum vehicle number calculation submodule for utilizing a formula according to the maximum section passenger flow in the future time period along each bus route
Calculating the minimum number of vehicles in a future time period along each bus line; wherein n is i Representing the minimum number of vehicles in a future period along the ith bus route.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention discloses a bus scheduling method and system based on station events along a line, wherein the bus scheduling method comprises the following steps: acquiring the predicted bus scheduling related event information data of related mechanisms along each bus route and around the bus route in a future time period by adopting a web crawler technology and a priori knowledge learning method; carrying out data cleaning and feature extraction on the public traffic scheduling related event information data to obtain public traffic scheduling related event feature data; inputting the characteristic data of the bus scheduling related events into a trained neural network model for traffic prediction to obtain the traffic along each bus line in a future period; and dispatching the buses along each bus line according to the pedestrian flow along each bus line in the future period. The invention captures the things which are about to happen in a period of time in the future based on the web crawler, meanwhile, the neural network model is utilized to realize the prediction of the pedestrian volume by combining the daily prior knowledge, the scheduling is carried out according to the result of the prediction of the pedestrian volume, and the targeted advance scheduling is realized according to the result of the prediction of the pedestrian volume which is likely to appear in a period of time in the future.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flow chart of a bus scheduling method based on along-line station events according to the present invention;
FIG. 2 is a block diagram of a bus dispatching system based on along-line station events according to the present invention;
fig. 3 is a 870 buses along the station point diagram in taiyuan city of shanxi province provided in embodiment 1 of the present invention;
fig. 4 is a diagram of a result of prediction of a pedestrian volume during an entrance peak time period in a national complex network conference according to embodiment 1 of the present invention;
fig. 5 is a diagram illustrating a result of prediction of a human traffic during an entrance peak time period at a national computer conference according to embodiment 2 of the present invention;
fig. 6 is a result diagram of prediction of pedestrian flow during peak entrance periods in the sports center in shanxi province according to embodiment 2 of the present invention.
Detailed Description
The invention aims to provide a bus dispatching method and system based on station events along a line, so as to accurately predict the possible pedestrian volume in a future period of time and realize targeted advance dispatching.
In order to make the aforementioned objects, features and advantages of the present invention more comprehensible, the present invention is described in detail with reference to the accompanying drawings and the detailed description thereof.
Public transportation routes may give priority to mechanisms with relatively high population density or people flow along the route when making station selections, such as: stations, hospitals, schools, conference centers, hotels, and the like. These mechanisms typically occur in a regular series of events, such as: the flow of people at station stations during weekends and holidays is usually higher than usual; the flow of people at school sites in the open school season is greatly increased; in a large conference (academic conference, concert, sporting event, etc.), the traffic in the conference center and the hotel sites around the conference center is also greatly increased.
Therefore, the method can effectively utilize the prior knowledge of the bus stations along the bus and surrounding mechanisms which may have events to design a set of self-adaptive bus operation scheduling strategy.
As shown in fig. 1, the present invention provides a bus scheduling method based on events at stations along a line, wherein the bus scheduling method comprises the following steps:
The related surrounding institutions include stations, hospitals, schools, conference centers, hotels and the like along the bus.
And 102, performing data cleaning and feature extraction on the public transportation scheduling related event information data to obtain public transportation scheduling related event feature data.
And 103, inputting the characteristic data of the bus dispatching related events into the trained neural network model for people flow prediction to obtain the people flow along each bus line in a future period.
The neural network model is a least squares model of the attenuation.
And 104, dispatching the buses along each bus line according to the pedestrian volume along each bus line in the future time period.
The minimum value of the number of buses can be derived in the case of a full load rate of 120%. The maximum section passenger flow on all buses in each time period is calculated according to the data of the number of passengers getting on and off the buses, the minimum number of the buses required in the time period is calculated according to the maximum number of the passengers, the passengers in each time period are assumed to uniformly arrive at the station, each passenger in each time period can take the bus, and the upper limit of the capacity of each bus is 120.
As shown in fig. 2, the present invention further provides a bus dispatching system based on the events at the stations along the line, wherein the bus dispatching system comprises:
and the knowledge acquisition module is used for acquiring the predicted bus scheduling related event information data of the related mechanisms along each bus route and around the bus route in the future period by adopting a web crawler technology and a priori knowledge learning method.
The knowledge acquisition module specifically comprises: the network crawler submodule is used for acquiring the expected aperiodic bus dispatching related event information data of the related mechanisms along each bus route and around from the websites of the related mechanisms along each bus route and around by adopting the network crawler technology; the prior knowledge learning submodule is used for analyzing historical periodic bus dispatching related events by adopting a prior knowledge learning method and determining the distribution rule of the periodic bus dispatching related events along each bus route; and comparing the future time period with the distribution rule, and determining the periodic bus scheduling related event information data of the future time period.
The main function of the knowledge acquisition module is to search information related to bus route stops by using various channels.
Firstly, using a network crawler submodule to take all along-line stations (such as stations, schools, hospitals, meeting centers, hotels and the like) of a bus line as search terms, and searching relevant events (such as spring fortune, school seasons, large conferences, performances and the like) which are about to occur in a future period of events of all along-line stations through various channels (such as official websites, news portals, microblogs, weChat public numbers and the like).
Secondly, the prior knowledge learning submodule is used for predicting relevant events (such as weekends, festivals, holidays, travel laws and the like) which are about to occur in a period of time in the future. The method specifically comprises the steps of analyzing a frequency spectrum of an event occurrence through an FFT algorithm to obtain a distribution rule of related data of pedestrian flow of each bus stop, then carrying out maximum overlapping discrete wavelet decomposition on a time sequence, integrating data to establish an ARIMA model, comparing a future time point with a time sequence of historical events, analyzing the occurrence probability of the event by utilizing a Fourier spectrum, and further predicting the related event which is about to occur in a period of time in the future. The regularity of the people flow can be predicted by the model with a priori knowledge, such as on holidays or on-duty hours.
And the data cleaning and feature extraction module is used for cleaning and extracting the data of the event information data related to the bus dispatching to obtain the characteristic data of the event related to the bus dispatching.
The data cleaning and feature extraction module has the main functions of cleaning and feature extraction of the data retrieved by the knowledge acquisition module, and the FFT software is used for analyzing the obtained spectrogram result and eliminating the deviation of the fitting curve from the interference data, so that the event information and the features really related to the bus line stations are obtained. Such as: the address of the meeting, the size of the meeting, the distribution of the participants, the peak event section of travel (entrance and exit), the time of the first and last bus, and the like. The data cleaning method comprises the following steps: 1) Processing missing values, namely removing fields, filling missing values and re-fetching data according to the missing rate and the importance of events; 2) Processing formats and contents; 3) Removing duplicate data; 4) And (4) processing noise data. The data feature extraction steps are as follows: 1) Judging the authenticity of the data, and verifying the authenticity of the data by means of fitting technology and the like; 2) Data abnormal value processing, namely adopting data abnormal value processing based on Lauda criterion; 3) Carrying out data denoising and smoothing; 4) Data segmentation; 5) And extracting characteristic data.
Specifically, the data cleaning and feature extraction module specifically includes: the data cleaning submodule is used for carrying out missing value processing, format conversion, repeated data removal processing and noise data removal processing on the public transportation scheduling related event information data to obtain cleaned public transportation scheduling related event information data; the data verification submodule is used for verifying the cleaned bus dispatching related event information data by using a data fitting method to obtain the verified bus dispatching related event information data; the data abnormal value processing submodule is used for processing the data abnormal value of the public transport scheduling related event information data passing the verification by adopting a Lauda criterion to obtain the public transport scheduling related event information data after the abnormal value processing; the denoising and smoothing sub-module is used for denoising and smoothing the bus scheduling related event information data after the abnormal value processing to obtain the bus scheduling related event information data after the denoising and smoothing processing; the data segmentation submodule is used for carrying out data segmentation on the bus scheduling related event information data subjected to denoising and smoothing processing to obtain a plurality of data segments; the characteristic extraction submodule is used for extracting the characteristics of each data segment to obtain the characteristic data of each data segment; and the data splicing submodule is used for splicing the characteristic data of each data segment to obtain the characteristic data of the bus scheduling related events.
And the passenger flow prediction module is used for inputting the characteristic data of the bus scheduling related events into the trained neural network model for passenger flow prediction to obtain the passenger flow along each bus line in the future period.
The main function of the people flow prediction module is to predict the people flow required for travel in the time period of occurrence of an event based on effective data collected, cleaned and extracted by the data cleaning and feature extraction module. In the process of dispatching buses, as the variation trend of the pedestrian volume has strong randomness and obvious nonlinear characteristics, the pedestrian volume prediction by using the traditional prediction method is not suitable. Therefore, the invention adopts a data mining method, adopts an artificial neural network system and utilizes historical data to carry out modeling and prediction. The artificial neural network can realize various linear and nonlinear mapping capabilities by changing the types of the excitation functions, and meanwhile, the neural network model has extremely strong learning capability and self-adaptive capability, so that the artificial neural network can adapt to the characteristics of short-time passenger flow to a great extent, and the reliability of prediction can be ensured. The invention selects the attenuated least squares algorithm as the algorithm of the artificial neural network. The first step of establishing the prediction model is to obtain data, so that on the basis of the effective data obtained in the module 2, data corresponding to two adjacent weeks, two adjacent days and two adjacent time intervals are selected as input samples, the artificial neural network model is used for passenger flow prediction, and in order to verify the correctness of the prediction model, the data samples can be divided into a training set and a test set, wherein the proportion of the training set to the test set is 3:1. Before prediction is carried out by using a neural network model, the data needs to be normalized to the range of [ -1,1] so as to simplify prediction and prevent the problem of neuron output saturation. After the current work is finished, a prediction model based on an artificial neural network can be written through simulation software such as MATLAB and the like, then training parameters of the network are set, the network is initialized, then training of the prediction network is carried out, training samples are input, and finally the trained network is tested.
And the scheduling module is used for scheduling the buses along each bus line according to the pedestrian volume along each bus line in a future period.
The main function of the scheduling module is to establish an optimized bus scheduling model based on a people flow prediction model established by the people flow prediction module. The invention uses the web crawler to capture the data of the pedestrian flow and the bus shift in the past period of time, cleans and extracts the data, combines the actual situation, and constructs a structural equation model according to the characteristics of each bus line stop, for example, can establish a least bus model: the mathematical expression of the model is as follows:
the minimum vehicle values during this time period are:
p i represents the maximum section passenger flow in the future time interval along the ith future bus route, N l The number of passengers getting on the bus at the first station along the ith bus route is shown as Y l Represents the ithNumber of people getting off at the first station along bus line, M i The number of the stations along the ith bus line is represented, and L represents the first L stations along the ith bus line.
The structural equation model is a statistical method for analyzing the relation between variables based on the variables, the model is established, firstly, common parameters are determined and calculated, all influence factors (morning and evening peaks, special time points, road conditions, weather, passenger comfort and the like) are brought into the model to be quantitatively calculated, the required minimum number of cars is obtained, and an optimization strategy and a recommendation are provided according to the model result. For example, taking 1 bus in taiyuan city of shanxi as an example, the data of the bus route, departure timetable, station distance table and the like are established, the model is constructed, and the current bus route scheduling method is adjusted and optimized by analyzing and combining the model result with reality. The scheduling arrangement in a future period of time is made by adjusting 1) whether or not the number of the regular buses needs to be increased, and 2) if the number of the regular buses needs to be increased in which period of time.
Specifically, the scheduling module specifically includes: according to the pedestrian volume along each bus line in the future period, the formula is utilized
Calculating the maximum section passenger flow in the future time period along each bus line; wherein p is i Represents the maximum section passenger flow in the future time interval along the ith future bus route, N l The number of passengers getting on the bus at the first station in the passenger flow along the ith bus line is shown, Y l The number of people getting off the first platform in the flow of people along the ith bus line is represented, M i The number of the stations along the ith bus line is represented, and L represents the front L stations along the ith bus line; a minimum vehicle number calculation submodule for utilizing a formula to make a decision on the maximum section passenger flow in a future time period along each bus line>
Calculating the minimum of each bus line in future time periodThe number of vehicles; wherein n is i Representing the minimum number of vehicles in a future period along the ith bus route.
The technical solution of the present invention is explained below with reference to two specific examples.
Example 1:
in this embodiment, 870 bus routes in taiyuan city of shanxi province are taken as research objects, and the scheduling strategy of the routes is researched by using the method provided by the invention. As shown in fig. 3, 870 buses contain 26 stations, including stations with a typical traffic volume, such as: train stations, schools (university of Shanxi, department of justice, finance university, etc.), companies (Fushikang), hospitals (Hospital of Shanxi province and Hospital of Shanxi province are located near the North road and east street crossing site of Shanxi province) and conference centers/hotels (International conference center of Shanxi province and hotels are located near the five square site), etc.
Taking 2016 and 9 as an example, 2016, 10 months are related to 870 buses along a bus stop, which is searched by the web crawler submodule, the upcoming events are two national academic conferences respectively held in Longchen international restaurants and lakeside international hotels (main meeting places, which simultaneously comprise 5 branch meeting places) at 14-17 days (national complex network meetings) and 20-22 days (national computer meetings) of 10 months, and meeting addresses are all near five square stops. Meanwhile, the information searched by the network crawler submodule is displayed: (1) The undertaking units of the two conferences are Shanxi university; (2) the scale of the national complex network congress is about 1000 persons; the size of the computer conference in China is about 5000 people. By combining daily priori knowledge, a large number of teachers and students can come and go between a meeting place and a school during a meeting, and the university in Shanxi is also on 870 buses along the line, so that the passenger flow of 870 buses during the meeting can be deduced to be obviously higher than that in normal times.
In addition, according to the meeting schedule searched by the web crawler module, the meeting period is from 8 am to 30 pm and ends at 18 pm. As known by the prior knowledge module, the participants will arrive at the meeting place within half an hour before the meeting begins, namely the peak time period of entering the meeting in the morning is 8-00-8. Without loss of generality, it is assumed that the traffic in the peak time period of admission meets the normal distribution, and the number of participants who come and go between the university of shanxi and the five-quarter yard during two meetings in the morning accounts for 20% of the total (200 and 1000 respectively). According to the people flow prediction module, the number of people going out at each time point in the peak time period can be obtained, as shown in fig. 4 and 5 respectively.
In addition, consider that there are other public transportation routes (e.g., 103, 812, etc.) between Shanxi university and Wuyi Square. Thus, based on the above analysis, a bus schedule strategy of 870 lines during two meetings may be formulated, as listed in table 1.
Table 1 870 bus scheduling strategies meeting the travel demands of participants during two conferences
As can be seen from table 1, during a national complex network conference, there is substantially no need to additionally schedule more buses, because the conference is relatively small in size; during the computer conference in China, buses need to be additionally developed according to an approximately normal distribution rule so as to meet the travel demands of the participants. It should be noted that the time period in table 1 refers to the time of arriving at a station of five squares, and each bus route needs to make an departure strategy in advance according to the whole route time.
Example 2:
in the embodiment, a sports center in Shanxi province is taken as a research object, and the method provided by the invention is utilized to research the bus dispatching strategy of the station. As is known, the sports centre of shanxi province is also the home of the game of shanxi fen wine men basket. During the CBA season (10 months per year to 2 months following year), there are dense events. For example, 2016. Month 11, the web crawler module searches for a home course for shanxi fen wine boy basket as listed in table 2. As can be seen from the table, all 6 games start at 19.
Meanwhile, the following two prior knowledge are combined: (1) The general audience can enter the scene 30-60 minutes in advance, namely the audience has an entrance peak time period of 18; (2) Normally (excluding overtime) a CBA match (including mid-break) lasts approximately 2 hours or so, so the audience is known to be in the off-peak time period of 21-22.
TABLE 2 CBA2016-2017 Saturn Shanxi Fenjiu boy basket home course table (2016 year 11 month)
| Number of rounds | Array (leading team) | Time |
| 3 | Shanxi vs Shenzhen | 11-04 19:35 |
| 4 | Shanxi vs Tianjin | 11-06 19:35 |
| 6 | Shanxi vs Beijing | 11-13 19:35 |
| 7 | Shanxi vs Liaoning | 11-16 19:35 |
| 11 | Shanxi's vjian Fujian | 11-27 19:35 |
| 12 | Shanxi vs Zhejiang river | 11-30 19:35 |
The bus routes around the sports center of shanxi province searched by the web crawler sub-module are listed in table 3. As can be seen from the table, all lines end up before 20.
TABLE 3 Table of buses around sports center of Shanxi province
Meanwhile, the information about the seating rate of the shanxi fen wine man basket searched by the web crawler sub-module is displayed after data cleaning and characteristic analysis: the total number of the receivable audiences is 8000, the seat taking rate per field is about 7, namely, about 5600 audiences per field. Conservative assumptions about 60% of the spectators choose public transportation, and therefore can infer that there is a travel demand for about 3360 people on both the incoming and outgoing peak event segments.
In addition, without loss of generality, assuming that the flow of people satisfies an approximately normal distribution in two peak time periods of entrance and exit, the flow of people at each time point can be obtained by using a flow of people prediction model, and the result is shown in fig. 6, where the flow of people is separated by 5 minutes.
Therefore, based on the above analysis, a bus dispatching strategy can be formulated to meet the entrance requirement of the shanxi fen men basket audience (here, it is assumed that the audience number of 5 bus lines in table 3 is equal, i.e. the dispatching strategy of 5 lines is the same), as listed in table 4, i.e. the bus dispatching strategy also approximately meets the normal distribution. It should be noted that the time period in table 4 refers to the time of arriving at the sports center in shanxi province, and each bus route needs to make an departure strategy in advance according to the whole route time.
Table 4 bus scheduling strategy for meeting entrance requirements of Shanxi Fenjiu men basket audiences
| Time period | The number of vehicles dispatched by each line in the time period |
| 18:30–18:40 | 1 |
| 18:40–18:55 | 2 |
| 18:55–19:05 | 3 |
| 19:05–19:20 | 2 |
| 19:20–19:35 | 1 |
The public traffic scheduling strategy in the departure peak time period is approximately the same as that in the entrance peak time period. Compared with the current operation plan, the scheduling strategy provided by the embodiment is mainly embodied in two aspects: (1) Increase evening class cars on the main field day (21; (2) And the departure frequency and the departure interval of the shift cars are adaptively adjusted according to the approximate normal distribution in the peak time periods of the entry and the exit.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention discloses a bus scheduling method and system based on station events along a line, wherein the bus scheduling method comprises the following steps: acquiring the predicted bus scheduling related event information data of related mechanisms along each bus route and around the bus route in a future time period by adopting a web crawler technology and a priori knowledge learning method; carrying out data cleaning and feature extraction on the public traffic scheduling related event information data to obtain public traffic scheduling related event feature data; inputting the characteristic data of the bus scheduling related events into a trained neural network model for traffic prediction to obtain the traffic along each bus line in a future period; and dispatching the buses along each bus line according to the pedestrian volume along each bus line in the future period. The invention captures the things which are about to happen in a period of time in the future based on the web crawler, meanwhile, the neural network model is utilized to realize the prediction of the pedestrian volume by combining the daily prior knowledge, the scheduling is carried out according to the result of the prediction of the pedestrian volume, and the targeted advance scheduling is realized according to the result of the prediction of the pedestrian volume which is likely to appear in a period of time in the future.
In the present specification, the embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
The principle and embodiments of the present invention are explained by using specific examples, the above examples are only used to help understanding the method of the present invention and the core idea thereof, the described examples are only a part of examples of the present invention, not all examples, and all other examples obtained by a person of ordinary skill in the art without making creative efforts based on the examples of the present invention belong to the protection scope of the present invention.
Claims (6)
1. A bus scheduling method based on along-line station events is characterized by comprising the following steps:
acquiring predicted bus scheduling related event information data of related mechanisms along each bus route and around the bus route in a future period by adopting a web crawler technology and a priori knowledge learning method;
the method for acquiring the predicted bus scheduling related event information data of the related mechanisms along each bus route and around in the future period by adopting the web crawler technology and the priori knowledge learning method specifically comprises the following steps:
acquiring expected aperiodic bus dispatching related event information data of the related mechanisms along each bus route and around the bus route from websites of the related mechanisms along each bus route and around the bus route by adopting a web crawler technology;
analyzing historical periodic bus scheduling related events by adopting a priori knowledge learning method, and determining the distribution rule of the periodic bus scheduling related events along each bus route; comparing the future time period with the distribution rule, and determining the periodic bus scheduling related event information data of the future time period;
performing data cleaning and feature extraction on the bus scheduling related event information data to obtain bus scheduling related event feature data;
carrying out data cleaning and feature extraction on the public transportation scheduling related event information data to obtain public transportation scheduling related event feature data, and specifically comprising the following steps:
carrying out missing value processing, format conversion, repeated data removal processing and noise data removal processing on the bus scheduling related event information data to obtain cleaned bus scheduling related event information data;
verifying the cleaned bus scheduling related event information data by using a data fitting method to obtain verified bus scheduling related event information data;
carrying out data abnormal value processing on the public transport scheduling related event information data passing the verification by adopting a Lauda criterion to obtain the public transport scheduling related event information data after the abnormal value processing;
denoising and smoothing the public transportation scheduling related event information data after abnormal value processing to obtain the public transportation scheduling related event information data after denoising and smoothing;
carrying out data segmentation on the bus scheduling related event information data subjected to denoising and smoothing to obtain a plurality of data segments;
extracting the characteristics of each data segment to obtain the characteristic data of each data segment;
splicing the characteristic data of each data segment to obtain the characteristic data of the bus dispatching related events;
inputting the characteristic data of the bus scheduling related events into a trained neural network model for traffic prediction to obtain the traffic along each bus line in a future period;
and dispatching the buses along each bus line according to the pedestrian volume along each bus line in the future period.
2. The method of claim 1, wherein the neural network model is a least squares model of attenuation.
3. The bus scheduling method based on the bus stop events along the line as claimed in claim 1, wherein the bus scheduling method for the bus along each bus line according to the pedestrian volume along each bus line in the future time period specifically comprises:
according to the pedestrian volume along each bus line in the future period, a formula is utilized
Calculating the maximum section passenger flow in the future time period along each bus line; wherein p is i Represents the maximum section passenger flow in the future time interval along the ith future bus route, N l The number of passengers getting on the bus at the first station in the passenger flow along the ith bus line is shown, Y l The number of people getting off the first platform in the flow of people along the ith bus line is represented, M i The number of the stations along the ith bus line is represented, and L represents the front L stations along the ith bus line;
according to the maximum section passenger flow in the future time interval along each bus line, the formula is utilized
Calculating the future time interval along each bus lineMinimum number of vehicles in; wherein n is i Representing the minimum number of vehicles in a future period along the ith bus route.
4. A bus dispatching system based on station events along the line is characterized by comprising:
the knowledge acquisition module is used for acquiring the information data of the predicted bus dispatching related events of the related mechanisms along each bus route and around the bus route in the future period by adopting a web crawler technology and a priori knowledge learning method;
the knowledge acquisition module specifically comprises:
the network crawler submodule is used for acquiring the predicted aperiodic bus scheduling related event information data of the related mechanisms along each bus route and around from the websites of the related mechanisms along each bus route and around by adopting the network crawler technology;
the prior knowledge learning submodule is used for analyzing historical periodical bus scheduling related events by adopting a prior knowledge learning method and determining the distribution rule of the periodical bus scheduling related events along each bus route; comparing the future time period with the distribution rule, and determining the periodic bus scheduling related event information data of the future time period;
the data cleaning and feature extraction module is used for cleaning and extracting the data of the event information related to the bus dispatching to obtain the feature data of the event related to the bus dispatching;
the data cleaning and feature extraction module specifically comprises:
the data cleaning submodule is used for carrying out missing value processing, format conversion, repeated data removal processing and noise data removal processing on the public transportation scheduling related event information data to obtain cleaned public transportation scheduling related event information data;
the data verification submodule is used for verifying the cleaned bus dispatching related event information data by using a data fitting method to obtain the verified bus dispatching related event information data;
the data abnormal value processing submodule is used for carrying out data abnormal value processing on the verified bus dispatching related event information data by adopting a Lauda criterion to obtain the bus dispatching related event information data after the abnormal value processing;
the denoising and smoothing sub-module is used for denoising and smoothing the bus scheduling related event information data after the abnormal value processing to obtain the bus scheduling related event information data after the denoising and smoothing processing;
the data segmentation submodule is used for carrying out data segmentation on the bus scheduling related event information data subjected to denoising and smoothing processing to obtain a plurality of data segments;
the characteristic extraction submodule is used for extracting the characteristics of each data segment to obtain the characteristic data of each data segment;
the data splicing submodule is used for splicing the characteristic data of each data segment to obtain the characteristic data of the bus dispatching related events;
the passenger flow prediction module is used for inputting the characteristic data of the bus scheduling related events into a trained neural network model for passenger flow prediction to obtain the passenger flow along each bus line in a future period;
and the scheduling module is used for scheduling the buses along each bus line according to the pedestrian volume along each bus line in a future time period.
5. The bus dispatching system based on events along line stops of claim 4, wherein the neural network model is a least squares model of attenuation.
6. The bus scheduling system based on events along line stops as claimed in claim 4, wherein the scheduling module specifically comprises:
a maximum section passenger flow volume calculation submodule for utilizing a formula according to the passenger flow volume along each bus line in a future time period
Calculating the maximum section passenger flow in the future time period along each bus line; wherein p is i Represents the maximum section passenger flow quantity N in the future time interval along the ith bus route l The number of passengers getting on the bus at the first station in the passenger flow along the ith bus line is shown, Y l The number of people getting off at the first platform in the pedestrian flow along the ith bus line is represented, M i The number of the stations along the ith bus line is represented, and L represents the front L stations along the ith bus line;
a minimum vehicle number calculation submodule for utilizing a formula according to the maximum section passenger flow in the future time period along each bus line
Calculating the minimum number of vehicles in a future time period along each bus line; wherein n is i Representing the minimum number of vehicles in a future period along the ith bus route. />
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