CN117236790B - Urban rail transit capacity and passenger flow adaptability evaluation method, system and equipment - Google Patents
Urban rail transit capacity and passenger flow adaptability evaluation method, system and equipment Download PDFInfo
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Abstract
The invention provides a method, a system and equipment for evaluating urban rail transit capacity and passenger flow adaptability, and relates to the field of urban rail transit train operation diagram evaluation. The data base required by the method comprises: the method comprises the steps of obtaining four microscopic indexes of required train passenger capacity, station detention number, boarding number and waiting time by using an interactive matching algorithm, establishing four evaluation indexes of average full load rate, average riding comfort, average waiting time and station detention total number of the stations on an evaluation level, calculating the evaluation indexes corresponding to all-day time intervals according to the obtained microscopic indexes, then weighting the indexes by using a CRITIC method, and finally evaluating the adaptability degree of the traffic flow of all-day time intervals based on a TOPSIS method to obtain evaluation results of all-day time intervals.
Description
Technical Field
The invention belongs to the technical field of urban rail transit train operation diagram evaluation, and particularly relates to an urban rail transit capacity and passenger flow adaptability evaluation method, system and equipment.
Background
In recent years, urban rail transit gradually becomes a main travel mode of residents by virtue of the characteristics of rapidness, greenness, safety and large traffic, and the problem of road traffic jam caused by urbanization is well relieved. However, with the continuous improvement of the living standard of people in China, the demands of passengers for traveling are more diversified, and higher demands are put on the aspects of rapidness, safety, comfort and the like, so that the operational unit is required to increase the transport capacity investment to improve the service level. The operator, as a capacity provider, expects that capacity can be fully utilized, but this reduces the service level, while the passenger, as a demand, expects a higher service level, but this may waste capacity, and thus capacity utilization and passenger demand are somewhat contradictory. In order to meet the traveling requirements of passengers and avoid excessive waste of the transportation capacity, an operation unit needs to reasonably allocate limited transportation capacity resources, and the adaptability of the transportation capacity and passenger flow of the urban rail transit is scientifically and accurately evaluated, so that the method can provide a direction for transportation capacity adjustment, and is an important work. The urban rail transit passenger flow adaptation assessment work mainly comprises three steps: firstly, preparing related data, such as schedules, passenger flow data and the like, then selecting, defining and calculating evaluation indexes, and finally selecting a proper evaluation method for evaluation. The current assessment method for urban rail transit capacity and passenger flow adaptability has the following defects:
1. Most of the method is to directly calculate microscopic indexes such as the number of passengers getting on or off the train, the passenger capacity, the waiting time of the passengers and the like based on OD passenger flow data counted at minute intervals, and not design an interactive matching algorithm of the passengers and the train based on the card swiping data of the passengers so as to obtain the microscopic indexes, wherein the microscopic indexes are not accurate enough.
2. The quantification method of the evaluation indexes describing both the capacity utilization and the passenger demand has room for improvement, for example, the capacity utilization is described by adopting the maximum section full rate in the past, and the capacity utilization condition of other sections is not considered; the average riding comfort is only the average value of the comfort of each section, and the difference of the passenger capacity of different sections is not considered; the average waiting time of passengers is half of the waiting time of passengers under uniform passenger flows, which is not considered practically.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a method, a system and equipment for evaluating the adaptability of urban rail transit capacity and passenger flow.
The invention realizes the aim through the following technical scheme:
a city rail transit capacity and passenger flow adaptability evaluation method comprises the following steps:
s1: collecting transport capacity and passenger flow data information and preprocessing the data;
S2: determining microscopic indexes and calculating the microscopic indexes through an interactive matching algorithm based on the preprocessed acquired data;
s3: determining an evaluation index and calculating an evaluation index matrix according to the microscopic index;
s4: and carrying out urban rail transit capacity and passenger flow adaptability evaluation on the evaluation index matrix based on a CRITIC-TOPSIS method.
Further, the step S1 specifically includes the following steps:
s1.1: collecting traffic and passenger flow data information, including: the line basic information comprises operation time and site number information; train schedule; the passenger card swiping data of the whole day of the line comprises passenger numbers, station entering and exiting numbers and station entering and exiting time information; train stator and train rated capacity information;
s1.2: data preprocessing: preprocessing train schedule data and passenger card swiping data, wherein the processing flow comprises the following steps: uniformly converting time information in train schedules and passenger card swiping information into time taking seconds as a unit; removing abnormal data in the passenger card swiping data comprises the following steps: there is data with missing information, transaction data generated outside the operating time range, data with outbound time earlier than inbound time, and data with in-out of the same station.
Further, the microscopic indexes comprise train passenger capacity, station detention number, boarding number and waiting time; the evaluation indexes comprise average full load rate of the line, average riding comfort, average waiting time and total number of stay persons in the station.
Further, the step S2 specifically includes the following steps:
s2.1: inputting the preprocessed train schedule data, passenger card swiping data, line basic information and train capacity information, and constructing an empty data container platform representing a platform for accommodating platform passenger information; each passenger information is represented by a number ID, and the passenger information arrives at a station D and arrives at a station T in And outbound time T out Five fields are formed;
s2.2: initializing k=1, k e {1,2, …, n } as a train set;
s2.3: an empty data container car representing the train is constructed for receiving information about passengers on board the train. Similarly, a train acts as a carrier for passengers and can also be viewed as a matrix of vectors representing passengers on the train. Initializing the train capacity to C N ，C N The rated capacity of the train is represented, the remaining capacity C of the train is a variable, let i=s k [1]，i∈S k ={S k [1],S k [2],…,S k [end]The 'k' is the set of reachable stations of the k trains, S k Is a subset of all station sets, S k ∈{1,2,…,m}；
S2.4: if i is the final destination of the train and is the line terminal, turning to S2.10; otherwise, according to the time T of the passenger in And the departure station O screens out passengers waiting k trains to the i station, and adds the passengers to the corresponding platform container platform [ i ] ]In (2), the screening conditions are as follows:
and o=i, indicating that the outbound O is the current i-station
Wherein t is begin dT represents the train operation start time i k Indicating that the kth train is at the ith stationDeparture time;
calculating the waiting number W of the trains waiting k times at the station i i k ，W i k The value is equal to platform container platform]The amount of data at this time is represented by the following formula:
；
s2.5: if i is the origin station of the train, turning to S2.6; if i is the destination station of the minor intersection, turning to S2.7; otherwise, the arrival station D of the passenger on the vehicle is the current station i, which is expressed asThe passengers getting off at the station i are deleted from the train container car, and then the remaining capacity of the k train at this time is calculated, the remaining capacity of the train at this time being the difference between the rated capacity of the train and the number of passengers remaining in the train container car at this time, as follows:
；
s2.6: counting the number of k trains on i stationsAnd the sum of these passenger waiting times +.>The number of people on the train is determined by the residual capacity of the train at the moment, and the calculation formula is as follows:
；
the sum of waiting time of waiting k train passengers at the station i is the sum of waiting time of all boarding passengers, and the calculation formula is as follows:
；
wherein,indicating the arrival time of passenger j;
then adding the passengers capable of getting on in the platform container platform [ i ] into the train container car, updating the residual capacity of the train at the moment, and finally deleting the passengers just getting on from the platform container platform [ i ] to finish the getting on process of the passengers, and turning to S2.8;
S2.7: according to arrival of passengersScreening out passengers in the train container car which are not in the stop i, waiting for the train to get off after the passengers need to get off, and adding the passengers to the platform container platform [ i ]]Then go to S2.10;
s2.8: calculating the number of people staying in the station I due to failure to get on the train kAnd k passenger capacity of train in section (i, i+1)>The number of retained people is the platform container platform [ i ]]The number of passengers remaining, expressed as waiting number +.>And the number of people getting on the busThe difference is calculated as follows:
；
after the boarding and disembarking process of passengers at the station i, the passenger carrying calculation formula of the k train in the section (i, i+1) is as follows:
；
s2.9: let i=i+1, go to S2.4 until all reachable stops of k trains are traversed;
s2.10: judging whether k is the last train, if yes, ending calculation; if not, let k=k+1, go to S2.3 until all trains are traversed;
four microscopic indexes of the passenger capacity of all trains in all intervals, the total waiting time of all stations, the number of detained passengers and the number of boarding passengers are obtained through the processes from S2.1 to S2.10.
Further, in the step S3, an evaluation index matrix is calculated according to the microscopic index, specifically, the following steps are adopted:
S3.1: the initialization period is train operation start time;
s3.2: screening microscopic indexes within the time period according to the occurrence time of the four microscopic indexes;
s3.3: calculating the average full rate of the line in the period, and the full rate of the train in the interval (i, i+1) of k trainsThe formula of (2) is as follows:
；
in the method, in the process of the invention,for the passenger capacity of k trains in section (i, i+1), C 0 Determining a train for a train operator;
the calculation formula of the average full load rate ρ of the line in the study period is as follows:
；
wherein Train is a collection of related trains in a research period, S k For a collection of stations contained in the journey of k trains within a study period, |s k I is set S k The number of stations involved;
s3.4: calculating average riding comfort, wherein the comfort is 1 when the passenger capacity is lower than the train seat number; comfort decreases as passenger capacity increases;when the passenger capacity reaches the train operator, the comfort level is 0; when the passenger capacity exceeds the train operator, the comfort level is negative, and the riding comfort level of k trains in the interval (i, i+1) is improved for simplifying calculationExpressed by a "decreasing half trapezoidal function", the calculation formula is as follows:
；
wherein, the seat is the number of seats,for the passenger capacity of k trains in section (i, i+1), C 0 C for train coaching N Rated capacity of the train and is larger than train staffs;
The average riding comfort level is obtained by carrying out weighted average on riding comfort levels of all trains in all sections, the weight of each section is determined according to the proportion of the section passenger capacity to the total passenger capacity of all sections, and the calculation formula of the average riding comfort level f in the research period is as follows:
；
in the method, in the process of the invention,comfort level for k trains in section (i, i+1), +.>Weights for this comfort level;
s3.5: the average waiting time of passengers is calculated, and the average waiting time of all boarding passengers of the whole line in the research period is calculated as follows:
；
wherein t is w In order to average the waiting time of the vehicle,for the sum of waiting times of passengers sitting on k trains at station i, +.>The number of people riding k trains at station i;
s3.6: calculating the total number of people detained at the station, wherein the calculation formula of the total number of people detained at the station in the research period is as follows:
；
in the method, in the process of the invention,the number of detainers after k trains leave the station i;
s3.7: judging whether the time period is an operation final vehicle receiving time period, if so, ending calculation; if not, continuing the next time period, and turning to S3.2 until four evaluation indexes corresponding to all operation time periods are calculated;
finally, an n×4 evaluation index matrix is obtained through the above processes from S3.1 to S3.7, where n represents the number of operation periods of the whole day, and each period corresponds to 4 evaluation indexes.
Further, the final evaluation in the step S4 is performed based on the CRITIC-TOPSIS method, and the specific steps are as follows:
s4.1: the CRITIC method is used for weighting the evaluation index, and the specific steps are as follows:
providing n samples to be evaluated, and m evaluation indexes to form an original evaluation index matrix:
；
s4.1.1 performs standardization processing on each index, so that the values of each standardized index are all located in the [0,1] interval, and the average riding comfort index is a benefit index, and the standardization processing formula is as follows:
；
for two indexes of average waiting time and total residence time of a station, the two indexes are cost type indexes, and a standardized processing formula is as follows:
；
for the average full load rate index of the line, which is an intermediate index, an ideal value is set asThe standardized formula is as follows:
；
the normalized evaluation index matrix becomes:
；
s4.1.2 index variability is calculated, and the standard deviation calculation formula of the j-th index is as follows:
；
in the method, in the process of the invention,is the average value of the j-th index, j=1, 2, … m, n is the number of evaluation samples;
s4.1.3 the index conflict is calculated, and the conflict calculation formula of the jth index and other indexes is as follows:
；
in the method, in the process of the invention,is index i and index jCorrelation coefficients between;
s4.1.4 the information quantity and weight are calculated, and the calculation formula of the information quantity of the j index is as follows:
；
The j-th index is assigned a weight of:
；
s4.2: the adaptability of the traffic passenger flow in different time periods is evaluated by using a TOPSIS method, and the specific steps are as follows:
s4.2.1 constructing a standardized weighting matrix, and multiplying each evaluation index by a corresponding weight on the basis of the standardized evaluation index matrix to obtain the standardized weighting matrix:
；
s4.2.2 determining the distance of the index of each period from the positive and negative ideal solutions: let the positive ideal of the j-th index beNegative ideal solution is->；
Positive ideal solution:
；
negative ideal solution:
；
the i-th period is separated from the positive ideal solution and the negative ideal solution by:
；
s4.2.3 calculate the capacity and passenger flow adaptability evaluation value of the ith period:
；
wherein Z is i Is the evaluation value of the traffic capacity and passenger flow adaptability in the ith period, and the value range is [0,1]The larger the value, the better the adaptability of the transport capacity and passenger flow in the i period.
The invention also provides an urban rail transit capacity and passenger flow adaptability evaluation system, which uses the urban rail transit capacity and passenger flow adaptability evaluation method, and comprises the following steps:
the data acquisition and preprocessing module is used for acquiring transport capacity and passenger flow data information, preprocessing the data and eliminating abnormal data in passenger card swiping data;
The micro index generation module is used for calculating micro index data by adopting an interactive matching algorithm based on the preprocessed data information;
the evaluation index generation module is used for calculating the evaluation index matrix according to the microscopic indexes;
and the evaluation module is used for evaluating the adaptability of the traffic and the passenger flow in different time periods through CRITIC-TOPSIS comprehensive evaluation.
The invention also provides computer equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the computer program is executed by the processor to realize the urban rail transit capacity and passenger flow adaptability assessment method.
The invention also provides a storage medium, on which a computer program is stored, which when being executed by a processor, implements the above-mentioned urban rail transit capacity and passenger flow adaptability assessment method.
Compared with the prior art, the invention has the following beneficial effects:
the invention comprehensively considers the indexes of both transportation capacity utilization and passenger flow demand, the assignment of the indexes adopts a comprehensive CRITIC method, and the actual microscopic indexes are obtained by realizing the interactive matching algorithm of the passenger train, so that the quantification mode of the evaluation indexes is more reasonable. The method can carry out scientific, reasonable and effective analysis and evaluation on the adaptability degree of the traffic capacity and the passenger flow demand of the urban rail transit train, reflect whether the traffic capacity configuration is reasonable, provide reference for the adjustment of the traffic capacity of the train and provide theoretical support for the quality optimization of the train running diagram.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It will be apparent to those skilled in the art from this disclosure that the drawings described below are merely exemplary and that other embodiments may be derived from the drawings provided without undue effort.
FIG. 1 is a flow chart of an evaluation method of the present invention;
FIG. 2 is a flowchart of a microscopic index calculation process;
FIG. 3 is a flowchart of an evaluation index calculation process;
FIG. 4 is a graph of evaluation results for each period;
fig. 5 is a graph of relative values of the evaluation index for each period after normalization.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other. The invention will be described in detail below with reference to the drawings in connection with embodiments.
The invention discloses an urban rail transit capacity and passenger flow adaptability evaluation method, which is shown in fig. 1 and comprises the following steps:
step S1: and collecting traffic and passenger flow data information and preprocessing the data.
S1.1: first, the following data are collected, including: the line basic information comprises operation time and site number information; a train schedule, which is a technical file describing the arrival and departure times of each train at each station; the passenger card swiping data of the whole day of the line comprises passenger numbers, station entering and exiting numbers and station entering and exiting time information; train-setting personnel and train rated capacity information.
The known evaluation line uplink direction comprises 35 stations, and the operation time is 5:00-23:00; the train schedule comprises arrival and departure time of 273 trains at 35 stations in the whole day, the format is HMMSS, and the arrival and departure time of the trains with the large and small routes outside the routes is empty; the passenger card swiping data comprises five fields of a passenger one-card through number, a passenger arrival number, a passenger departure number, a passenger arrival time and a passenger departure time, and the time format is HHMSS, and the total card swiping information is forty tens of thousands of pieces; the line train adopts 6B grouping all the day, the number of the operators is 1460, and the rated capacity is 1660.
S1.2: preprocessing data: converting the time format in the train schedule from HMMSS to S (seconds), removing abnormal data in the passenger card swiping data, and comprising the following steps: there is data with missing information, transaction data generated outside the operating time range, data with outbound time earlier than inbound time, and data with in-out of the same station.
Step S2: determining microscopic indexes and calculating the microscopic indexes through an interactive matching algorithm based on the preprocessed acquired data.
The microscopic indexes comprise the passenger capacity of the train, the number of detained passengers at the station, the number of passengers on the train and the waiting time, and are calculated through an interactive matching algorithm, and the flow is shown in figure 2, and specifically comprises the following steps:
s2.1: inputting the pretreated train schedule data, passenger card swiping data and line baseThe information and train information, and constructing an empty data container platform representing the platform for accommodating the platform passenger information; each passenger information is represented by a number ID, and the passenger information arrives at a station D and arrives at a station T in And outbound time T out Five fields so that each passenger information can be regarded as a five-dimensional row vector, and a station as one of the carriers for accommodating passengers can be regarded as a matrix of vectors representing the passengers of the station;
S2.2: initializing k=1, k e {1,2, …, n } as a train set;
s2.3: an empty data container car representing the train is constructed for receiving information about passengers on board the train. Similarly, a train acts as a carrier for passengers and can also be viewed as a matrix of vectors representing passengers on the train. Initializing the train capacity to C N ，C N The rated capacity of the train is represented, the remaining capacity C of the train is a variable, let i=s k [1]，i∈S k ={S k [1],S k [2],…,S k [end]The 'k' is the set of reachable stations of the k trains, S k Is a subset of all station sets, S k ∈{1,2,…,m}；
S2.4: if i is the final destination of the train and is the line terminal, turning to S2.10, otherwise, according to the time T of the passenger in And departure station O screens out passengers waiting k trains to station i, and then adds these passengers to the corresponding platform containers platform [ i ]]In (2), the screening conditions are as follows:
and o=i, indicating that the outbound O is the current i-station
Wherein t is begin Represents the operation start time dT i k Indicating the departure time of the kth train at the ith station.
Calculating the waiting number W of the trains waiting k times at the station i i k ，W i k The value is equal to platform container platform]The amount of data at this time is represented by the following formula:
；
s2.5: if i is the origin station of the train, turning to S2.6; if i is the terminal station of the train, but not the terminal station of the line, namely the terminal station of the small-crossing mode, turning to S2.7; otherwise, the arrival station D of the passenger on the vehicle is the current station i, which is expressed as The passengers getting off at the station i are deleted from the train container car, and then the remaining capacity C of the k train at this time is updated, and the remaining capacity of the train at this time is the difference between the rated capacity of the train and the number of passengers remaining in the train container car at this time, as follows:
；
s2.6: counting the number of k trains on i stationsAnd the sum of these passenger waiting times +.>The number of people on the train is determined by the residual capacity of the train at the moment, and the calculation formula is as follows:
；
the sum of waiting time of waiting k train passengers at the station i is the sum of waiting time of all boarding passengers, and the calculation formula is as follows:
；
wherein,indicating the arrival time of passenger j.
Then adding the passengers capable of getting on in the platform container platform [ i ] into the train container car, updating the residual capacity of the train at the moment, and finally deleting the passengers just getting on from the platform container platform [ i ] to finish the getting on process of the passengers, and turning to S2.8;
s2.7: according to arrival of passengersScreening out passengers in the train container car which are not in the stop i, waiting for the train to get off after the passengers need to get off, and adding the passengers to the platform container platform [ i ]]Then go to S2.10;
s2.8: calculating the number of people staying in the station I due to failure to get on the train kAnd k passenger capacity of train in section (i, i+1) >The number of people in the residence is the platform container platform [ i ]]The number of passengers remaining, which can also be indicated as waiting +.>Is>The difference is calculated as follows:
；
when the boarding and disembarking process of passengers is completed at the station i, the passenger capacity of the k train in the running interval (i, i+1) is determined, and the calculation formula is as follows:
；
s2.9: let i=i+1, go to S2.4 until all reachable stops of k trains are traversed;
s2.10: judging whether k is the last train, if yes, ending calculation; if not, let k=k+1, go to S2.3 until all trains have been traversed.
And obtaining four microscopic indexes of the total waiting time, the number of detained persons and the number of boarding persons of all the trains in all the intervals through the processes from S2.1 to S2.10.
Realizing the algorithms S2.1 to S2.10 by using MATLAB, and storing the data information such as the number of passes, the number of stations, train staffs, rated capacity and the like of the trains on the whole day by adopting variables; constructing a 1 multiplied by 5 cell array, wherein each cell is a 273 multiplied by 34 two-dimensional array, and the two-dimensional array is respectively used for storing four microscopic indexes of the passenger capacity of each car in each section, the number of passengers on each station, the total waiting time of passengers on each station, the number of detained passengers on each station and the occurrence time of the microscopic indexes; constructing a 1 x 34 array of cells representing 34 stations (35 th without consideration), the train being represented by the array; and then, according to the interactive matching algorithm of the passengers-trains, all trains and all stations are traversed from the first station of the first train in the whole day in sequence, the waiting process of the passengers and the getting-on and getting-off and the entering and leaving processes of the trains are deduced, the interactive matching of the passengers and the trains is completed, and the program finally returns a 1 multiplied by 5 cell array which stores four microscopic indexes and time information.
Step S3: an evaluation index is determined and an evaluation index matrix is calculated from the microscopic indices.
The evaluation indexes comprise average full load rate, average riding comfort level, average waiting time and total number of stations detained, an evaluation index matrix is calculated according to microscopic indexes, the flow is shown in figure 3, and the specific steps are as follows:
s3.1: the initialization period is train operation start time;
s3.2: screening microscopic indexes within the time period according to the occurrence time of the four microscopic indexes;
s3.3: calculating the average full rate of the line in the period, wherein the average full rate of the line refers to the average value of the full rate of all trains in the research period, which is an index for describing the utilization condition of the transportation capacity of the train, and the full rate of the train refers to the ratio of the passenger capacity of the train when the train operates in a certain section to the train operator, which reflects the transportation capacity of the train in the sectionThe higher the train full rate, the better the utilization of the transport capacity of the train in the section, and the train full rate of k trains in the section (i, i+1)The formula of (2) is as follows:
；
in the method, in the process of the invention,for the passenger capacity of k trains in section (i, i+1), C 0 To order train operators.
For the whole line in the research period, the condition of the transportation capacity of the train in each section needs to be considered, so the average value of the full load rate of all trains involved in the research period can be used for measuring, namely the average full load rate of the line is the higher, the better the utilization condition of the transportation capacity of the whole line is indicated, each train bears as many passengers as possible in each section, which is expected by an operation unit, but the phenomenon that passengers are overcrowded and stations are detained possibly is considered when the average full load rate of the line is too high, so that the passengers run off or cause safety problems, which are unwilling to happen by the operation unit, the average full load rate of the line should belong to an intermediate type index, the ideal value of the average full load rate of the line in the research period should be different according to the characteristics of the passenger flows of different lines ρThe formula of (2) is as follows:
；
wherein Train is a collection of related trains in a research period, S k For a collection of stations contained in the journey of k trains within a study period, |s k I is set S k Number of stations involved.
S3.4: calculating average ride comfort, which refers to ride comfort over all intervals involving all trains over a study periodAverage value, which is an index describing the passenger's demand for ride comfort. According to related researches, many factors influencing the riding comfort level of passengers, such as passenger psychology, air humidity, air temperature, space density and the like, but mainly depend on the space comfort level related to the crowding degree, and the invention considers the riding comfort level of the passengers to depend on the number of passengers carried by a train based on the angle of the space comfort level: when the passenger capacity is lower than the number of seats of the train, people have seats, and the comfort level is 1; when more passengers get up and passengers stand, the comfort level is reduced along with the increase of the passenger capacity; when the passenger capacity reaches the train operator, only the basic space of the passengers can be ensured, the passengers are crowded, and the comfort level is 0; when the passenger capacity exceeds the train's staff, the passengers are obviously mutually extruded, the passengers are fully ill, the comfort level is negative, and the riding comfort level of k trains in the section (i, i+1) is used for simplifying calculation Expressed by a "decreasing half trapezoidal function", the calculation formula is as follows:
；
wherein, the seat is the number of seats,for the passenger capacity of k trains in section (i, i+1), C 0 C for train coaching N Is the rated capacity of the train and is larger than the train staffs.
The average riding comfort is the average of riding comfort of all trains related to the whole line in a research period in all sections, and the average riding comfort needs to reflect riding comfort of most passengers in consideration of large actual difference of the riding comfort of different sections, so the riding comfort of all trains in all sections is weighted and averaged, and each section weight is determined according to the proportion of the riding comfort of the section in the sum of the riding comfort of all sections, so the calculation formula of the average riding comfort f in the research period is as follows:
；
in the method, in the process of the invention,comfort level for k trains in section (i, i+1), +.>Is a weight for this comfort level.
S3.5: and calculating average waiting time, wherein the average waiting time refers to the average value of the waiting time of all boarding passengers of the whole line in a research period, and the average waiting time is an index for describing the requirements of passengers on riding rapidness. When passengers go out through the rail transit train, certain requirements are placed on the rapidness, namely the travel time is as short as possible, and the travel time consists of the waiting time and the on-vehicle time of the passengers. When the starting and ending point of the passenger is fixed, the time of the passenger is generally fixed, so that the travel time of the passenger mainly depends on the waiting time, the waiting time directly influences the travel rapidness of the passenger, and the average waiting time of all the passengers in the research period reflects the rapidness of the whole line. It should be noted that, the waiting time exists only when the passengers get on the train, so the average waiting time of the passengers is the average value of the waiting time of all the passengers getting on the train in the whole line in the research period, and the calculation formula is as follows:
；
Wherein t is w In order to average the waiting time of the vehicle,for the sum of waiting times of passengers sitting on k trains at station i, +.>The number of people riding k trains at station i;
s3.6: the total number of station stops is calculated, which is the sum of the number of stops at all stations involved in the study period, which is an index describing the safety requirements of passengers for the stations. In the rail transit system, after the train arrives at the platform, the interaction process of getting on and off the train is completed with passengers, but due to the capacity limitation of the train, passengers waiting for the train can not get on all the trains at any condition, and the passengers incapable of getting on the train can only stay at the platform to wait for the subsequent trains. Particularly, in peak time and hot stations, the phenomenon of passenger detention is serious, so that extra waiting time is added for detention of passengers, the crowding degree in the stations is increased, and certain potential safety hazards are brought, therefore, the safety of the passengers at the stations can be reflected to a certain extent by the detention number of stations, and the calculation formula of the total number L of the detention of the stations in the research period is as follows:
；
in the method, in the process of the invention,the number of detainers after k trains leave the station i;
s3.7: judging whether the time period is an operation final vehicle receiving time period, if so, ending calculation; if not, continuing the next time period, and turning to S3.2 until four evaluation indexes corresponding to all operation time periods are calculated;
Through the processes of S3.1 to S3.7, an n multiplied by 4 evaluation index matrix is finally obtained, n represents the number of time periods of the whole day, and each time period corresponds to 4 evaluation indexes.
Starting from the period of 5:00-6:00, stopping to the period of 22:00-23:00, screening microscopic indexes contained in the current period according to the occurrence time of the microscopic indexes, and further calculating four evaluation indexes of average full load rate, average riding comfort, average waiting time and total retention people of the line in the current period; four evaluation indexes of 18 time periods of the whole day are sequentially calculated to form an 18×4 evaluation index matrix, and the results of the four evaluation indexes of 18 time periods of the whole day are shown in table 1:
table 1 index results for each time period
Step S4: and carrying out urban rail transit capacity and passenger flow adaptability evaluation on the evaluation index matrix based on a CRITIC-TOPSIS method.
Step S4.1: and (5) weighting the index by using a CRITIC method.
When multi-index comprehensive evaluation is performed, reasonable weighting of each index is a key problem, and the method can be divided into a subjective weighting method and an objective weighting method. The CRITIC method is a relatively comprehensive objective weighting method, and simultaneously considers variability and conflict of indexes, and is a better objective weighting method than the entropy weighting method and the standard deviation method. The basic idea of the CRITIC method is to comprehensively determine the index weight according to the variability and the conflict of the indexes, and measure the variability of a certain index by using the standard deviation, wherein the larger the standard deviation is, the stronger the variability of the index is represented; the correlation between indexes is used for measuring the conflict, and the stronger the correlation is, the lower the conflict between indexes is.
The CRITIC method comprises the following specific steps of:
providing n samples to be evaluated, and m evaluation indexes to form an original evaluation index matrix:
；
s4.1.1: data standardization processing: in order to eliminate the influence of different dimensions of different indexes on the evaluation result, each index needs to be standardized, so that the values of each standardized index fall in the [0,1] interval. For the average riding comfort index, which is a benefit index (the larger and the better), the standardized processing formula is as follows:
；
for the average waiting time and the total number of standing passengers in the station, the two indexes are cost indexes (smaller and better), and the standardized processing formula is as follows:
；
the average full load rate index of the line is an intermediate index (the closer to the ideal value is, the better) and the ideal value isThe standardized formula is as follows:
；
the normalized evaluation index matrix becomes:
；
s4.1.2: calculating index variability: in the CRITIC method, the variability of the index is represented by standard deviations of different values of the same index, and the calculation formula of the standard deviation of the j index is as follows:
；
in the method, in the process of the invention,is the average value of the j-th index, j=1, 2, … m, n is the number of evaluation samples.
S4.1.3: calculating index conflict: in CRITIC method, the conflict between indexes is based on the correlation between a certain index and other indexes, and the calculation formula of the conflict between the j-th index and other indexes is as follows:
；
In the method, in the process of the invention,is the correlation coefficient between index i and index j.
S4.1.4: calculating information quantity and weight: based on the variability and conflict of the index, the information amount contained in the index can be obtained, and the larger the information amount is, the larger the effect of the index in the evaluation system is, and more weight should be allocated. The calculation formula of the information quantity of the j-th index is as follows:
；
the j-th index is assigned a weight of:
；
firstly, carrying out standardized elimination dimension on four evaluation indexes; the average riding comfort adopts a standardized formula of benefit indexes (larger and better), the average waiting time and the total residence time of a station adopt standardized formulas of cost indexes (smaller and better), the average full load rate of a line adopts a standardized formula of intermediate indexes (closer to an ideal value and better), the average full load rate of the line when no residence condition happens is considered to be an ideal value, the condition that the transportation capacity is fully utilized and the residence does not occur and seriously influence the service level of passengers is ensured, and the ideal value of the average full load rate of the line in the example is ρbest=0.4; then, the conflict and variability of each index are calculated according to the CRITIC method, so that the weight of each evaluation index is obtained, and the weight result is shown in Table 2:
Table 2 weight of each evaluation index
And multiplying the normalized evaluation indexes by the weights to obtain a normalized weighting matrix so as to carry out TOPSIS evaluation later.
Step S4.2: the suitability of traffic flow at different time intervals was evaluated using the TOPSIS method.
TOPSIS (distance between good and bad solutions) is a common comprehensive evaluation method, and the basic idea is that after positive and negative ideal solutions are selected, the relative closeness of the positive ideal solution to the negative ideal solution is taken as a standard for judging the good or bad of the positive ideal solution according to the current index value. The TOPSIS method has strong operability, can fully utilize the original data, and is widely applied in a plurality of fields. In the invention, the TOPSIS method is utilized to evaluate the adaptability of the traffic passenger flow in different time periods, and the specific steps are as follows:
s4.2.1: constructing a standardized weighting matrix, and multiplying each evaluation index by a corresponding weight on the basis of the standardized evaluation index matrix to obtain the standardized weighting matrix:
；
s4.2.2: determining the distance between the indexes of positive and negative ideal solutions and each period of time from the positive and negative ideal solutions: let the positive ideal of the j-th index beNegative ideal solution is->；
Positive ideal solution:
；
negative ideal solution:
；
the i-th period is separated from the positive ideal solution and the negative ideal solution by:
；
S4.2.3: calculating an estimated value of the adaptability of the traffic flow in the ith period:
；
wherein Z is i The estimated value of the traffic and passenger flow adaptability in the ith period is generally called the closeness, and the value range is [0,1]The larger the value, the better the adaptability of the transport capacity and passenger flow in the i period.
The evaluation results of finally evaluating the capacity and passenger flow adaptability of the study object in each period of the whole day by using the TOPSIS method are shown in table 3:
table 3 evaluation results for each period
As shown in fig. 4, the adaptive assessment result of the traffic and passenger flow in each period can be intuitively known through the result graph, so as to obtain: the worst adaptability of the whole day traffic passenger flow is that the operation starting period is 5:00-6:00, the time period of the receiving vehicle is 22:00-23:00, and the time period of the early peak is 8:00-9:00, which are only about 47 percent. The adaptability fluctuation range of each period of the early peak 6:00-10:00 is large, the adaptability degrees of the early peak 6:00-7:00 and the early peak 9:00-10:00 are close to 70%, and the adaptability is good; however, the time periods of 7:00-8:00 and 8:00-9:00 are less than 55%, and the adaptability is poor. The adaptability value of the flat peak in the period of 10:00-16:00 is not high but is relatively stable, and the adaptability value is 57-60 percent. The adaptability of the night peak is better from 16:00 to 19:00, and the adaptability is more than 65 percent; wherein 17:00-18:00 reach an optimal value of 77%, and the adaptability is good.
As shown in FIG. 5, in order to deeply analyze the change reason of the adaptability degree of the line uplink all day traffic capacity and passenger flow, the evaluation indexes are uniformly drawn after the dimension is normalized and eliminated, so that the cooperative change condition of the indexes along with time can be conveniently seen. The average full load rate of the line reflects the utilization efficiency of the transport capacity, the other 3 indexes reflect the service level of passengers, and as can be seen from fig. 5, the operation starts at the time of 5:00-6:00 and the time of 22:00-23:00, besides no passenger retention phenomenon and good riding comfort, the average full load rate of the line is very low, the utilization degree of the transport capacity is poor, the average waiting time is very long, and the travel time of passengers is long; in the early peak period 8:00-9:00, except that the average full load rate of the line is close to the ideal full load rate, the riding comfort is low, a plurality of passengers are detained at the station, the average waiting time is long, the passenger service level is low, and the passenger flow requirement cannot be met; the capacity and passenger flow adaptability for these three periods is therefore worst throughout the day. The adaptability degree is close to 70% because the 4 indexes are in a better state at the beginning and the end of the early peak in two periods of 6:00-7:00 and 9:00-10:00; the average full load rate of the line reaches 48% in the early peak period 7:00-8:00, the average waiting time is relatively short as compared with the ideal full load rate, but the riding comfort is low and passengers are detained, so the adaptability is not high, and only about 54%. Although the peak time period 10:00-16:00 is better than 3 indexes reflecting the passenger service level, the average full load rate of the line reflecting the transport capacity utilization efficiency is very low and is close to the operation starting and vehicle receiving time period which are the lowest in the whole day, so the adaptability degree is not high and is between 57 and 60 percent. The indexes are in a better state in the period of 16:00-19:00 of the late peak, so that the adaptability degree of the traffic passenger flow is higher, wherein the adaptability degree of 17:00-18:00 reaches the highest 77%. From the analysis, the adaptability degree of the traffic passenger flow is comprehensively determined by 4 indexes, the advantages of one or two indexes alone cannot make up the disadvantages of other indexes, and the adaptability of the traffic passenger flow can be best only when the 4 indexes are good.
From the above analysis, it can be derived that: the operation starts from 5:00 to 6:00, the time period for receiving the bus is 22:00 to 23:00, the time period for early peak is 7:00 to 9:00, and the time period for the flat peak is 10:00 to 16:00, and certain indexes are poor, so that the adaptability degree of the traffic and passenger flow is not high, and corresponding measures should be taken for improvement. However, for the operation start and the receiving period, as the passenger flow requirement is very small, the main contradiction is that the average full load rate and the average waiting time of the line are poor, and the two indexes cannot reach the optimal level at the same time no matter how the two indexes are adjusted, which is unavoidable objectively, so that the adjustment is unnecessary. For the early peak of 7:00-9:00, the reason that the adaptability of the transport capacity and the passenger flow is poor is that the provided transport capacity can not meet the passenger flow requirement, so that the conditions of train crowding, poor passenger riding comfort and retention of a large number of passengers are caused, and the transport capacity in the period can be considered to be improved, for example, the departure interval is shortened or a small-traffic mode is started in a passenger flow concentrated interval; for peak levels 10:00-16:00, the low traffic adaptability is caused by low average full load rate of the line and poor traffic utilization, so that it is considered to reduce the traffic of the period, such as prolonging the departure interval or opening a small intersection mode in the traffic concentration interval.
The invention also discloses an urban rail transit capacity and passenger flow adaptability evaluation system, which uses the urban rail transit capacity and passenger flow adaptability evaluation method, and comprises the following steps:
the data acquisition and preprocessing module is used for acquiring transport capacity and passenger flow data information, preprocessing the data and eliminating abnormal data in passenger card swiping data;
the micro index generation module is used for calculating micro index data by adopting an interactive matching algorithm based on the preprocessed data information;
the evaluation index generation module is used for calculating the evaluation index matrix according to the microscopic indexes;
and the evaluation module is used for evaluating the adaptability of the traffic and the passenger flow in different time periods through CRITIC-TOPSIS comprehensive evaluation.
The invention also discloses a computer device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the computer program is executed by the processor to realize the urban rail transit capacity and passenger flow adaptability evaluation method.
The invention also discloses a storage medium, on which a computer program is stored, which when being executed by a processor, realizes the urban rail transit capacity and passenger flow adaptability assessment method.
Claims (6)
1. The urban rail transit capacity and passenger flow adaptability evaluation method is characterized by comprising the following steps of:
s1: the method for collecting the traffic and passenger flow data information and preprocessing the data specifically comprises the following steps:
s1.1: collecting traffic and passenger flow data information, including: the line basic information comprises operation time and site number information; train schedule; the passenger card swiping data of the whole day of the line comprises passenger numbers, station entering and exiting numbers and station entering and exiting time information; train stator and train rated capacity information;
s1.2: data preprocessing: preprocessing train schedule data and passenger card swiping data, wherein the processing flow comprises the following steps: uniformly converting time information in train schedules and passenger card swiping information into time taking seconds as a unit; removing abnormal data in the passenger card swiping data comprises the following steps: data with information missing, transaction data generated outside the operation time range, data with outbound time earlier than inbound time and data with the same station in and out;
s2: determining microscopic indexes and calculating the microscopic indexes through an interactive matching algorithm based on the preprocessed collected data, wherein the microscopic indexes comprise train passenger capacity, station detention number, boarding number and waiting time; the microcosmic index is calculated by the following steps:
S2.1: inputting the preprocessed train schedule data, passenger card swiping data, line basic information and train capacity information, and constructing an empty data container platform representing a platform for accommodating platform passenger information; each passenger information is represented by a number ID, and the passenger information arrives at a station D and arrives at a station T in And outbound time T out Five fields are formed;
s2.2: initializing k=1, k e {1,2, …, n } as a train set;
s2.3: constructing an empty data container car representing the train for accommodating passenger information on the train; initializing train capacity=c N ，C N The rated capacity of the train is represented, the remaining capacity C of the train is a variable, let i=s k [1]，i∈S k ={S k [1],S k [2],…,S k [end]The 'k' is the set of reachable stations of the k trains, S k Is a subset of all station sets, S k ∈{1,2,…,m}；
S2.4: if i is the final destination of the train and is the line terminal, turning to S2.10; otherwise, according to the time T of the passenger in Departure O screening outPassengers waiting k trains to station i add these passengers to the corresponding platform containers platform i]In (2), the screening conditions are as follows:
and o=i, indicating that the outbound O is the current i-station
Wherein t is begin dT represents the train operation start time i k Indicating the departure time of the kth train at the ith station;
calculating the waiting number W of the trains waiting k times at the station i i k ，W i k The value is equal to the container platform [ i ]]The amount of data at this time is represented by the following formula:
；
s2.5: if i is the origin station of the train, turning to S2.6; if i is the destination station of the minor intersection, turning to S2.7; otherwise, the arrival station D of the passenger on the vehicle is the current station i, which is expressed asThe passengers getting off at the station i are deleted from the train container car, and then the remaining capacity C of the k train at this time is updated, and the remaining capacity of the train at this time is the difference between the rated capacity of the train and the number of passengers remaining in the train container car at this time, as follows:
；
s2.6: counting the number of k trains on i stationsAnd the sum of these passenger waiting times +.>The number of people on the train is determined by the residual capacity of the train at the moment, and the calculation formula is as follows:
；
the sum of waiting time of waiting k train passengers at the station i is the sum of waiting time of all boarding passengers, and the calculation formula is as follows:
；
wherein,indicating the arrival time of passenger j;
then adding the passengers capable of getting on in the platform container platform [ i ] into the train container car, updating the residual capacity of the train at the moment, and finally deleting the passengers just getting on from the platform container platform [ i ] to finish the getting on process of the passengers, and turning to S2.8;
s2.7: according to arrival of passengersScreening out passengers in the train container car which are not in the stop i, waiting for the train to get off after the passengers need to get off, and adding the passengers to the platform container platform [ i ] ]Then go to S2.10;
s2.8: calculating the number of people staying in the station I due to failure to get on the train kAnd k passenger capacity of train in section (i, i+1)>The number of retained people is the platform container platform [ i ]]The number of passengers remaining, expressed as waiting number +.>Is>The difference is calculated as follows:
；
after the boarding and disembarking process of passengers at the station i, the passenger carrying calculation formula of the k train in the section (i, i+1) is as follows:
；
s2.9: let i=i+1, go to S2.4 until all reachable stops of k trains are traversed;
s2.10: judging whether k is the last train, if yes, ending calculation; if not, let k=k+1, go to S2.3 until all trains are traversed;
four microscopic indexes of the passenger capacity of all trains in all intervals, the total waiting time of all stations, the number of detained passengers and the number of boarding passengers are obtained through the processes from S2.1 to S2.10;
s3: determining an evaluation index and calculating an evaluation index matrix according to the microscopic index, wherein the evaluation index comprises an average full load rate of a line, average riding comfort level, average waiting time and total number of people detained in a station;
s4: and carrying out urban rail transit capacity and passenger flow adaptability evaluation on the evaluation index matrix based on a CRITIC-TOPSIS method.
2. The urban rail transit capacity and passenger flow adaptability assessment method according to claim 1, wherein the step S3 calculates an assessment index matrix according to microscopic indexes, specifically comprising the following steps:
s3.1: the initialization period is train operation start time;
s3.2: screening microscopic indexes within the time period according to the occurrence time of the four microscopic indexes;
s3.3: calculating the average full rate of the line in the period, and the full rate of the train in the interval (i, i+1) of k trainsThe formula of (2) is as follows:
；
in the method, in the process of the invention,for the passenger capacity of k trains in section (i, i+1), C 0 Determining a train for a train operator;
mean line full rate during study periodρThe formula of (2) is as follows:
；
wherein Train is a collection of related trains in a research period, S k For a collection of stations contained in the journey of k trains within a study period, |s k I is set S k The number of stations involved;
s3.4: calculating average riding comfort, wherein the comfort is 1 when the passenger capacity is lower than the train seat number; comfort decreases as passenger capacity increases; when the passenger capacity reaches the train operator, the comfort level is 0; when the passenger capacity exceeds the train operator, the comfort level is negative, and the riding comfort level of k trains in the interval (i, i+1) is improved for simplifying calculation Expressed by a decreasing half-trapezoidal function, the calculation formula is as follows:
；
wherein, the seat is the number of seats,for the passenger capacity of k trains in section (i, i+1), C 0 C for train coaching N Rated capacity of the train and is larger than train staffs;
the average riding comfort level is obtained by carrying out weighted average on riding comfort levels of all trains in all sections, the weight of each section is determined according to the proportion of the section passenger capacity to the total passenger capacity of all sections, and the calculation formula of the average riding comfort level f in the research period is as follows:
；
in the method, in the process of the invention,comfort level for k trains in section (i, i+1), +.>Weights for this comfort level;
s3.5: the average waiting time of passengers is calculated, and the average waiting time of all boarding passengers of the whole line in the research period is calculated as follows:
；
wherein t is w In order to average the waiting time of the vehicle,for the sum of waiting times of passengers sitting on k trains at station i, +.>The number of people riding k trains at station i;
s3.6: calculating the total number of people detained at the station, wherein the calculation formula of the total number of people detained at the station in the research period is as follows:
；
in the method, in the process of the invention,the number of detainers after k trains leave the station i;
s3.7: judging whether the time period is an operation final vehicle receiving time period, if so, ending calculation; if not, continuing the next time period, and turning to S3.2 until four evaluation indexes corresponding to all operation time periods are calculated;
Finally, an n×4 evaluation index matrix is obtained through the above processes from S3.1 to S3.7, where n represents the number of operation periods of the whole day, and each period corresponds to 4 evaluation indexes.
3. The urban rail transit capacity and passenger flow adaptability assessment method according to claim 2, wherein the final assessment based on CRITIC-TOPSIS method in step S4 comprises the following specific steps:
s4.1: the CRITIC method is used for weighting the evaluation index, and the specific steps are as follows:
providing n samples to be evaluated, and m evaluation indexes to form an original evaluation index matrix:
；
s4.1.1 performs standardization processing on each index, so that the values of each standardized index are all located in the [0,1] interval, and the average riding comfort index is a benefit index, and the standardization processing formula is as follows:
；
for two indexes of average waiting time and total residence time of a station, the two indexes are cost type indexes, and a standardized processing formula is as follows:
；
mean full rate for lineThe mark is an intermediate index, and is set to be ideal valueThe standardized formula is as follows:
；
the normalized evaluation index matrix becomes:
；
s4.1.2 index variability is calculated, and the standard deviation calculation formula of the j-th index is as follows:
；
In the method, in the process of the invention,is the average value of the j-th index, j=1, 2, … m, n is the number of evaluation samples;
s4.1.3 the index conflict is calculated, and the conflict calculation formula of the jth index and other indexes is as follows:
；
in the method, in the process of the invention,is the correlation coefficient between index i and index j;
s4.1.4 the information quantity and weight are calculated, and the calculation formula of the information quantity of the j index is as follows:
；
the j-th index is assigned a weight of:
；
s4.2: the adaptability of the traffic passenger flow in different time periods is evaluated by using a TOPSIS method, and the specific steps are as follows:
s4.2.1 constructing a standardized weighting matrix, and multiplying each evaluation index by a corresponding weight on the basis of the standardized evaluation index matrix to obtain the standardized weighting matrix:
；
s4.2.2 determining the distance of the index of each period from the positive and negative ideal solutions: let the positive ideal of the j-th index beNegative ideal solution is->；
Positive ideal solution:
；
negative ideal solution:
；
the i-th period is separated from the positive ideal solution and the negative ideal solution by:
；
s4.2.3 calculate the capacity and passenger flow adaptability evaluation value of the ith period:
；
wherein Z is i Is the evaluation value of the traffic capacity and passenger flow adaptability in the ith period, and the value range is [0,1]The larger the value, the better the adaptability of the transport capacity and passenger flow in the i period.
4. A system for estimating urban rail transit capacity and passenger flow adaptability using the method for estimating urban rail transit capacity and passenger flow adaptability according to any one of claims 1-3, the system comprising:
the data acquisition and preprocessing module is used for acquiring transport capacity and passenger flow data information, preprocessing the data and eliminating abnormal data in passenger card swiping data;
the micro index generation module is used for calculating micro index data by adopting an interactive matching algorithm based on the preprocessed data information;
the evaluation index generation module is used for calculating the evaluation index matrix according to the microscopic indexes;
and the evaluation module is used for evaluating the adaptability of the traffic and the passenger flow in different time periods through CRITIC-TOPSIS comprehensive evaluation.
5. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the computer program when executed by the processor implements the urban rail transit capacity and passenger flow suitability assessment method according to any one of claims 1-3.
6. A storage medium having stored thereon a computer program, which when executed by a processor, implements the urban rail transit capacity and passenger flow suitability assessment method according to any one of claims 1-3.
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