CN112070372B - Bus passenger flow distribution method, system and storage medium based on interval uncertainty - Google Patents

Bus passenger flow distribution method, system and storage medium based on interval uncertainty Download PDF

Info

Publication number
CN112070372B
CN112070372B CN202010861029.4A CN202010861029A CN112070372B CN 112070372 B CN112070372 B CN 112070372B CN 202010861029 A CN202010861029 A CN 202010861029A CN 112070372 B CN112070372 B CN 112070372B
Authority
CN
China
Prior art keywords
interval
bus
passenger flow
peak
transfer
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202010861029.4A
Other languages
Chinese (zh)
Other versions
CN112070372A (en
Inventor
柳伍生
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Changsha University of Science and Technology
Original Assignee
Changsha University of Science and Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Changsha University of Science and Technology filed Critical Changsha University of Science and Technology
Priority to CN202010861029.4A priority Critical patent/CN112070372B/en
Publication of CN112070372A publication Critical patent/CN112070372A/en
Application granted granted Critical
Publication of CN112070372B publication Critical patent/CN112070372B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • G06Q50/40—

Abstract

The invention belongs to the technical field of bus passenger flow distribution, and discloses a bus passenger flow distribution method, a system and a storage medium based on interval uncertainty, wherein a travel time function and a transfer penalty function are improved based on an interval uncertainty theory and an interval number basic algorithm, and a Logit improved model of bus passenger flow distribution is constructed; and carrying out error analysis on bus passenger flow distribution before and after model improvement and average error and maximum error analysis on bus routes in morning and evening peak and flat peak periods. According to the improved Logit model, the average error and the maximum error of all lines in the early peak period are respectively reduced by about 10% and 17%, the average error and the maximum error of all lines in the late peak period are respectively reduced by about 9% and 16%, the average error and the maximum error of all lines in the flat peak period are respectively reduced by about 7% and 15%, and the average error and the maximum error of buses in all periods are effectively reduced.

Description

Bus passenger flow distribution method, system and storage medium based on interval uncertainty
Technical Field
The invention belongs to the technical field of bus passenger flow distribution, and particularly relates to a bus passenger flow distribution method, a bus passenger flow distribution system and a storage medium based on interval uncertainty.
Background
At present, the problem of urban traffic jam in China is more and more serious, and urban public transport is an effective way for relieving traffic jam. Reasonable urban public transportation network design and public transportation scheduling are important factors for improving public transportation service quality and public transportation travel rate, and public transportation passenger flow distribution is the basis for developing network design and scheduling optimization.
The bus passenger flow distribution model is improved by a Logit model at home and abroad. Abroad, johansson et al propose a bus passenger flow distribution model for different objects. Codina E proposes a model for network balanced distribution of traffic at peak times. Sun et al propose a Logit bus passenger flow distribution model based on fixed demand. Hamdouch and the like provide a new bus passenger flow distribution model based on dispatching in consideration of supply uncertainty. In China, logit is improved by Chen Xianlong and the like, zhang Xiaoliang and the like, and the obtained model is more effective in bus passenger flow distribution. Respectively providing an unbalanced bus passenger flow distribution model and a bus random user balanced distribution model by Midi et al.
In traditional bus passenger flow distribution, the number of people getting on or off the bus at each station is determined. In practice, however, the number of passengers getting on and off each station is often uncertain and fluctuates within an interval. The uncertainty of the interval has been studied both at home and abroad since the last century. Foreign, moore et al apply the interval uncertainty theory to mathematical problems. The processing methods of the Martorell and the like are mainly based on genetic algorithm, fuzzy sets and the like. Number of intervals and probability distribution of intervals were studied by Hugo Gilbert et al, ola g. Salman Zaidi et al propose a method to simplify interval calculation. In China, the whole Vickers adopts a double-layer planning model to analyze the uncertainty problem of the interval. Zhao Ziheng, et al, zhou Heping, et al, were analyzed for interval uncertainty problems and interval impedance.
Through the above analysis, the problems and defects of the prior art are as follows: in traditional bus passenger flow distribution, the number of people getting on or off the bus at each station is determined. In practice, however, the number of passengers getting on and off each station is often uncertain and fluctuates within an interval.
The difficulty in solving the above problems and defects is:
1. the historical data is a plurality of unequal data, and how to best measure the characteristics reflected by the historical data by using uncertain numbers;
2. a method for calculating the uncertain number in the bus passenger flow distribution model;
the significance for solving the problems and the defects is as follows:
1. the historical bus passenger flow rule can be reflected more accurately and comprehensively;
2. uncertainty of passenger flow distribution is fully considered, and decision risk can be effectively avoided.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a bus passenger flow distribution method, a system and a storage medium based on interval uncertainty.
The invention is realized in such a way that a bus passenger flow distribution method based on interval uncertainty comprises the following steps:
step one, based on an interval uncertainty theory and an interval number basic algorithm, a travel time function and a transfer penalty function are improved, and a Logit improved model of bus passenger flow distribution is constructed.
And step two, carrying out error analysis on bus passenger flow distribution before and after model improvement, and carrying out average error and maximum error analysis on bus routes in the morning and evening peak and average peak periods.
Further, in the step one, the interval uncertainty theory is called interval number optimization, that is, an interval-based interval number optimization method. The interval number optimization utilizes the actual fluctuation range of the interval description numerical value. The interval number optimization method is divided into three categories: the method comprises the steps of optimizing linear interval numbers based on interval number sequence relations, optimizing linear interval numbers based on the maximum and minimum regret criterion, and optimizing nonlinear interval numbers.
Further, in the first step, the number of intervals is a range value, and is expressed as:
Figure BDA0002648134430000031
whereina、
Figure BDA0002648134430000032
Is composed of
Figure BDA0002648134430000033
The lower and upper bound values of (a),
Figure BDA0002648134430000034
are two arbitrary real numbers. When in useaAnd
Figure BDA0002648134430000035
when equal, the number of intervals becomes a fixed number.
The number of intervals can be expressed by upper and lower boundary values and an interval radius, the interval radius is half of the length of the interval number, such as the interval number
Figure BDA0002648134430000036
Lower bound valueaAnd upper bound value
Figure BDA0002648134430000037
Radius of interval
Figure BDA0002648134430000038
Median value of interval
Figure BDA0002648134430000039
Further, in the first step, the basic algorithm of the number of intervals includes:
number of any two intervals
Figure BDA00026481344300000310
The basic algorithm is as follows:
1) Adding:
Figure BDA00026481344300000311
2) Subtracting:
Figure BDA00026481344300000312
3) Multiplying:
Figure BDA00026481344300000313
(whereinaAndbis a non-negative number);
4) Removing:
Figure BDA00026481344300000314
5) Multiplication by number:
Figure BDA00026481344300000315
(where k is a real number);
6) Power:
Figure BDA00026481344300000316
7) Index:
Figure BDA00026481344300000317
8) Logarithm:
Figure BDA00026481344300000318
further, in the step one, the assumption of the bus passenger flow distribution model is as follows:
1) And obtaining an OD matrix between bus stops in a peak time period and a flat time period in the research region, and obtaining a bus line passenger flow OD matrix and a region bus passenger flow OD matrix by using Shenzhen city bus IC card data and GPS data.
2) Public transport is developed in the research area, and public transport trips in the two traffic districts can finish trips without other transportation modes.
3) Interference of special weather, holidays and major events is not considered, only interference factors of traffic jam are considered, and the driving interval is equal to the departure interval.
4) Regardless of the transfer factor of the passenger, the passenger may reach directly or transfer to the destination.
Further, in the first step, the method for establishing the Logit improved model includes:
(1) Description of the parameters
Suppose that n traffic districts, s bus lines and m bus stops exist in a research area, and w is arranged in a traffic district i i The number of the bus stops is one,
Figure BDA0002648134430000041
bus stop g i Indicating, starting point bus station a i Terminal bus station b j From a i To b j Peak hour passenger flow volume of e ij Peak hour traffic matrix E = { E = { E } ij }, flat peak hour passenger flow rate f ij Flat peak hour passenger flow matrix F = { F ij From a i To b j The passenger flow of the highway section at peak hour is u ij Traffic matrix U = { U = peak hour road section ij V is the passenger flow volume of the road section at peak-leveling hour ij The passenger flow matrix of the road section in flat peak hours is V = { V = ij And (5) setting a passenger flow matrix P capable of bearing road sections at peak hours for s bus lines d From a to a i To b j The peak hour can bear the road section passenger flow as
Figure BDA0002648134430000042
Setting bus line l c The peak hour-to-hour workshop interval is h c The interval between the flat peak hour-occurrence workshops is O c 。
(2) Adjacency matrix
The following drawings: the ordered triplets form a graph,
Figure BDA0002648134430000043
a non-empty internal element set A (G), an edge set B (G) that is disjoint from A (G),
Figure BDA0002648134430000044
for the relevance function, each edge corresponds to an unordered pair of internal elements. Adjacency matrix: graph G = { a, B }, a (G) = { c = 1 ,c 2 ,L L,c q }，c i And c j The number of edges in between is d ij Then n-order square matrix M (G) = (d) ij ) n×n Is the adjacency matrix of graph G, in which the connection c i And c j The number of pathways of length l is
Figure BDA0002648134430000051
Element of (1) th row and (j) th column
Figure BDA0002648134430000052
Taking the bus stop as a vertex, if a direct line exists between two bus stops, connecting the bus stops into a graph G, and then connecting the two bus stops into an adjacency matrix X (G) = (X) ij ) m×m 。
Figure BDA0002648134430000053
For network graph from a i To b j Of length l, i.e. from a i To b j The number of bus routes passing the transfer of k-1 times.
(3) Time of flight function T ij (l c )
Bus line l c From a to a i To b j Time of flight function T ij (l c ) Therefore, two situations of a direct line and a transfer line can be divided, and a peak time period and a flat time period are distinguished in the driving time.
(4) Transfer penalty function
Slave bus line
Figure BDA0002648134430000054
Transfer bus line
Figure BDA0002648134430000055
Has a transfer penalty function of
Figure BDA0002648134430000056
Figure BDA0002648134430000057
In the formula (I), the compound is shown in the specification,
Figure BDA0002648134430000058
for the bus line in the kth transfer scheme
Figure BDA0002648134430000059
To
Figure BDA00026481344300000510
The transfer distance of (a), km,
Figure BDA00026481344300000511
is a function of the state;
Figure BDA00026481344300000512
(5) Interval traffic distribution
According to the difference of Shenzhen conventional bus stops, the conventional small-sized transfer stops of the bus take 300 meters as the search radius, and the conventional large-sized transfer stops of the bus take 500 meters as the search radius, so that an effective path set is formed. Suppose a i To b j Has d in common ij A feasible path including d ij,1 A direct line, d ij,2 A transfer scheme, d ij,1 ＝x ij 。
Further, in step (3), the travel time function T ij (l c ) The method comprises the following steps:
a) Direct line:
function of travel time:
Figure BDA00026481344300000513
early peak travel time interval:
Figure BDA00026481344300000514
peak-flattening travel time interval:
Figure BDA00026481344300000515
late peak travel time interval:
Figure BDA0002648134430000061
in the formula (I), the compound is shown in the specification,
Figure BDA0002648134430000062
for bus line l c From a i To b j The time-of-flight interval of (a),
Figure BDA0002648134430000063
for bus route l c From a i To b j The lower bound of the early peak travel time interval,
Figure BDA0002648134430000064
for bus line l c From a to a i To b j The upper bound of the early peak travel time interval,
Figure BDA0002648134430000065
for bus line l c From a i To b j The lower bound value of the flat peak travel time interval of (a),
Figure BDA0002648134430000066
for bus line l c From a i To b j The upper bound of the flat peak travel time interval,
Figure BDA0002648134430000067
for bus route l c From a i To b j The lower bound of the late peak travel time interval,
Figure BDA0002648134430000068
for bus line l c From a i To b j The upper bound of the late peak travel time interval.
b) Transfer line: for transfer routes, T is defined due to the influence of bus arrival time, congestion in buses and road congestion f And (4) in order to transfer the penalty time, representing the influence of uncertain factors.
Passengers make m-1 transfers from a i To b j Travel time function of (d):
Figure BDA0002648134430000069
in the formula (I), the compound is shown in the specification,
Figure BDA00026481344300000610
for bus lines
Figure BDA00026481344300000611
From a i To b j The time of flight of (a) is,
Figure BDA00026481344300000612
for bus lines
Figure BDA00026481344300000613
Driving interval of T f Penalty time is traded for.
Further, in step (5), the inter-zone passenger flow allocation includes:
a) Considering the influence of departure interval on passenger selection route without travel time influence
Figure BDA00026481344300000614
Is from a i To b j The probability of the passenger flow distribution of the kth path, then its vector
Figure BDA00026481344300000615
Figure BDA00026481344300000616
The distribution probability is:
Figure BDA00026481344300000617
in the formula, h (k)c1 The departure interval of the first bus route of the kth route.
b) Considering the influence of travel time on the passenger selecting route and the influence of non-accident workshop interval
Figure BDA00026481344300000618
Is from a i To b j The probability of the traffic distribution of the kth route, then its vector
Figure BDA00026481344300000619
Figure BDA0002648134430000071
Then calculating the passenger flow distribution rate interval of each effective path by the Logit model:
Figure BDA0002648134430000072
in the formula, alpha is a sensitivity coefficient, and alpha is more than or equal to 1; when α =1, there is no conditioning effect on the model, and when α increases, it slows down
Figure BDA0002648134430000073
To pair
Figure BDA0002648134430000074
When α → + ∞ indicatesWhen used, the model had no regulating effect.
c) Comprehensively considering the influence of travel time and transfer penalty function on passenger route selection
Direct line: when x is ij Not equal to 0, no transfer, from a i To b j The probability interval of the k-th path of passenger flow distribution
Figure BDA0002648134430000075
Figure BDA0002648134430000076
No direct line, consider transfer: when x is ij ＝0,
Figure BDA0002648134430000077
From a to a i To b j Absence of direct line, presence
Figure BDA0002648134430000078
A path of one-time zero transfer when
Figure BDA0002648134430000079
From a to a i To b j There is no direct line and one zero transfer, there is
Figure BDA00026481344300000710
A path of twice zero transfer when
Figure BDA00026481344300000711
There is no path for two zero transfers. From a to a i To b j The probability interval of the k-th path of passenger flow distribution
Figure BDA00026481344300000712
Figure BDA00026481344300000713
Further, in step two, the error analysis method includes:
respectively calculate the bus route l c Uplink hourly allocation interval error ω (l) c,s ) And the error omega (l) of the allocation interval in the down direction c,x ) Mean error of global distribution
Figure BDA00026481344300000714
And maximum error ω max 。
ω(l c,s )＝|q(l c,s )-Q(l c,s )|·Q(l c,s ) -1 ；
ω(l c,x )＝|q(l c,x )-Q(l c,x )|·Q(l c,x ) -1 ；
Figure BDA00026481344300000715
ω max ＝MAX(ω(l c,s ),ω(l c,x ))；
In the formula, q (l) c,s ) For bus route l c Up-direction hour model distribution zone passenger flow volume, Q (l) c,s ) For bus line l c Upstream hourly inspection data distribution traffic, q (l) c,x ) For bus line l c Downstream hourly model assigns passenger flow, Q (l) c,x ) For bus line l c And checking data distribution passenger flow in the downlink direction in hours.
It is another object of the present invention to provide a computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of:
based on an interval uncertainty theory and an interval number basic algorithm, improving a travel time function and a transfer penalty function, and constructing a Logit improved model of bus passenger flow distribution;
and carrying out error analysis on bus passenger flow distribution before and after model improvement, and carrying out average error and maximum error analysis on bus routes in peak hours in the morning and at night and in peak hours.
Another object of the present invention is to provide a bus passenger flow distribution system based on section uncertainty for operating the bus passenger flow distribution method based on section uncertainty, comprising:
the Lomit improved model building module is used for building a Lomit improved model of bus passenger flow distribution by improving a travel time function and a transfer penalty function based on an interval uncertainty theory and an interval number basic algorithm;
and the average error and maximum error analysis module is used for carrying out error analysis on bus passenger flow distribution before and after model improvement and average error and maximum error analysis on bus routes in the peak time periods of morning and evening and peak time periods.
By combining all the technical schemes, the invention has the advantages and positive effects that: the bus passenger flow distribution method based on the interval uncertainty combines the interval uncertainty theory and bus passenger flow distribution, obtains bus interval OD data through Shenzhen city bus IC card data and GPS data, considers the interval uncertainty theory and the interval number basic algorithm, improves from two aspects of a travel time function and a transfer time function, and constructs a Logit improved model of bus passenger flow distribution; the method establishes error analysis of bus passenger flow distribution before and after model improvement, and analyzes average error and maximum error of bus routes in peak hours and peak-off hours.
The method is based on the interval uncertainty theory and the interval number basic algorithm, improves the travel time function and the transfer penalty function, and constructs a Logit improved model of bus passenger flow distribution; and carrying out error analysis on bus passenger flow distribution before and after model improvement and average error and maximum error analysis on bus routes in morning and evening peak and flat peak periods. The improved Logit model is found, the average error and the maximum error of all lines in the early peak period are respectively reduced by about 10 percent and 17 percent, the average error and the maximum error of all lines in the late peak period are respectively reduced by about 9 percent and 16 percent, the average error and the maximum error of all lines in the flat peak period are respectively reduced by about 7 percent and 15 percent, and the average error and the maximum error of buses in all periods are effectively reduced.
According to the invention, by combining the interval uncertainty theory and the basic algorithm of the interval number, the OD matrix of getting on and off the bus at the bus station is an interval value, and the actual situation is better met. The travel time function comprises two conditions of direct transfer and transfer, and then the transfer penalty function is improved for the condition of a transfer line, so that the result of bus passenger flow distribution is more accurate. The OD matrix interval value of the adopted bus line is obtained by data mining and processing through conventional bus IC card data and GPS data in Shenzhen city, so that bus OD matrixes in different time periods can be obtained, and bus passenger flow distribution is respectively carried out on different time periods according to the matrixes. Error analysis is compared from two aspects of average error and maximum error, and the improved Logit model error is smaller and is more suitable for bus passenger flow distribution under the condition that the getting-on and getting-off interval is uncertain.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments of the present application will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained from the drawings without creative efforts.
Fig. 1 is a flowchart of a bus passenger flow distribution method based on section uncertainty according to an embodiment of the present invention.
Fig. 2 is an 2015 Shenzhen conventional bus OD expected line graph provided by the embodiment of the invention.
FIG. 3 shows a and
Figure BDA0002648134430000091
a relationship graph;
in the figure: FIG. A is T f =1min; FIG. b is a drawing (b) T f =3min; FIG. C is T f =5min; FIG. d is a drawing (d) T f =7min; FIG. e is T f ＝9min。
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In view of the problems in the prior art, the present invention provides a method, a system and a storage medium for bus passenger flow distribution based on section uncertainty, which will be described in detail with reference to the accompanying drawings.
As shown in fig. 1, the bus passenger flow distribution method based on the section uncertainty provided by the embodiment of the present invention includes the following steps:
and S101, based on an interval uncertainty theory and an interval number basic algorithm, improving a travel time function and a transfer penalty function, and constructing a Logit improved model of bus passenger flow distribution.
S102, carrying out error analysis on bus passenger flow distribution before and after model improvement, and carrying out average error and maximum error analysis on bus routes in morning and evening peak and flat peak periods.
Ordinary technicians in the bus passenger flow distribution method based on the section uncertainty can also adopt other steps to implement, and the bus passenger flow distribution method based on the section uncertainty provided by the invention in fig. 1 is only a specific embodiment.
The technical solution of the present invention is further described with reference to the following examples.
Taking the data of 10 months in 2015 of Shenzhen conventional public transportation as an example, based on an interval uncertainty theory and an interval number basic algorithm, a travel time function and a transfer penalty function are improved, and a Logit improved model of public transportation passenger flow distribution is constructed; and carrying out error analysis on bus passenger flow distribution before and after model improvement and average error and maximum error analysis on bus routes in morning and evening peak and flat peak periods. The improved Logit model is found, the average error and the maximum error of all lines in the early peak period are respectively reduced by about 10% and 17%, the average error and the maximum error of all lines in the late peak period are respectively reduced by about 9% and 16%, the average error and the maximum error of all lines in the flat peak period are respectively reduced by about 7% and 15%, and the average error and the maximum error of the buses in all periods are effectively reduced.
1. Uncertainty of bus passenger flow interval
1.1 Interval uncertainty theory
Under the actual complex public transportation environment, the bus passenger flow OD changes at any moment, and more or less uncertain factors exist. And (4) combining the data processing means of the current big data, and obtaining a set of the bus passenger flow OD intervals through analysis of a large amount of bus passenger flow data. The traditional bus passenger flow OD derivation method does not consider interval uncertainty factors, uses an interval uncertainty theory, can reduce simplification and hypothesis, and enables a model to be more real and more practical.
The interval uncertainty theory is also called interval number optimization, namely an interval-based interval number optimization method. The interval number optimization utilizes the actual fluctuation range of the interval description numerical value. The interval number optimization method is divided into three categories: the first is linear interval number optimization based on interval number order relation, the second is linear interval number optimization based on the maximum and minimum regret criterion, and the third is nonlinear interval number optimization. And a third nonlinear interval number optimization method is adopted for research.
1.2 basic concept of interval number
The number of intervals is a range of values, expressed as:
Figure BDA0002648134430000111
whereina、
Figure BDA0002648134430000112
Is composed of
Figure BDA0002648134430000113
The lower and upper bound values of (a),
Figure BDA0002648134430000114
are two arbitrary real numbers. When in useaAnd
Figure BDA0002648134430000115
when equal, the number of intervals becomes a fixed number.
The number of intervals can be used as the sum of upper and lower boundary valuesBy interval radius, interval radius is half the length of the interval, e.g. the number of intervals
Figure BDA0002648134430000116
Lower bound valueaAnd upper bound value
Figure BDA0002648134430000117
Radius of interval
Figure BDA0002648134430000118
Median value of interval
Figure BDA0002648134430000119
1.3 basic Algorithm for number of intervals
Number of arbitrary two intervals
Figure BDA00026481344300001110
The basic algorithm is as follows:
1) Adding:
Figure BDA00026481344300001111
2) Subtracting:
Figure BDA00026481344300001112
3) Multiplying:
Figure BDA00026481344300001113
(whereinaAndbis not a negative number) (3)
4) Removing:
Figure BDA0002648134430000121
5) Multiplication by number:
Figure BDA0002648134430000122
(where k is a real number); (5)
6) Power:
Figure BDA0002648134430000126
7) Index:
Figure BDA0002648134430000123
8) Logarithm:
Figure BDA0002648134430000124
2. bus passenger flow distribution model
2.1 model assumptions
The model assumes the following:
1) And (3) obtaining an OD matrix between bus stops in peak time periods and flat time periods in the research region, and obtaining a bus route passenger flow OD matrix and a region bus passenger flow OD matrix from Shenzhen city bus IC card data and GPS data.
2) Public transport is developed in the research area, and public transport trips in the two traffic districts can finish trips without other transportation modes.
3) Interference of special weather, holidays and major events is not considered, only interference factors of traffic jam are considered, and the driving interval is equal to the departure interval.
4) Regardless of the transfer factor of the passenger, the passenger may reach directly or transfer to the destination.
2.2Logit improved model establishment
1) Description of the parameters
Suppose that there are n traffic districts, s bus lines, m bus stops in the research area, and there are w in the traffic district i i The number of the bus stops is one,
Figure BDA0002648134430000125
g for bus stop i Indicating, starting point bus station a i Terminal bus station b j From a i To b j Peak hour passenger flow volume of e ij Peak hour passenger flow matrix E = { E = ij }, average peak hour passenger flow f ij Flat peak hour passenger flow matrix F = { F ij From a i To b j The passenger flow of the highway section at peak hour is u ij Traffic matrix U = { U = peak hour road section ij V is the passenger flow volume of the road section at peak-leveling hour ij The passenger flow matrix of the road section in flat peak hours is V = { V = ij And f, setting a road section passenger flow matrix P capable of being carried by s bus lines in peak hours d From a i To b j The peak hour can bear the road section passenger flow as
Figure BDA0002648134430000131
Setting bus line l c The peak hour-to-hour workshop interval is h c The interval between flat peak hour-off workshops is O c 。
2) Adjacency matrix
The following drawings: the ordered triplets form a graph,
Figure BDA00026481344300001310
a non-empty internal element set A (G), an edge set B (G) that is disjoint from A (G),
Figure BDA0002648134430000132
for the relevance function, each edge corresponds to an unordered pair of internal elements. Adjacency matrix: graph G = { a, B }, a (G) = { c = 1 ,c 2 ,L L,c q }，c i And c j The number of edges in between is d ij Then n-order square matrix M (G) = (d) ij ) n×n Is the adjacency matrix of graph G, in which the connection c i And c j The number of pathways of length l is
Figure BDA0002648134430000133
The element in the ith row and the jth column
Figure BDA0002648134430000134
Taking the bus stop as a vertex, if a direct line exists between two bus stops, connecting the bus stops into a graph G, and then connecting the two bus stops into an adjacency matrix X (G) = (X) ij ) m×m 。
Figure BDA0002648134430000135
For network graph from a i To b j Of length l, i.e. from a i To b j The number of bus routes passing the transfer of k-1 times.
3) Time of flight function T ij (l c )
Bus line l c From a to a i To b j Time of flight function T ij (l c ) Therefore, the two situations of a direct line and a transfer line can be distinguished, and a peak time period and a peak-smoothing time period are distinguished in the driving time.
a) Direct line:
function of travel time:
Figure BDA0002648134430000136
early peak travel time interval:
Figure BDA0002648134430000137
peak-flattening travel time interval:
Figure BDA0002648134430000138
late peak travel time interval:
Figure BDA0002648134430000139
in the formula (I), the compound is shown in the specification,
Figure BDA0002648134430000141
for bus route l c From a i To b j The time interval of the journey of (a),
Figure BDA0002648134430000142
for bus line l c From a i To b j The lower bound of the early peak travel time interval,
Figure BDA0002648134430000143
for bus line l c From a i To b j The upper bound of the early peak travel time interval,
Figure BDA0002648134430000144
for bus line l c From a to a i To b j The lower bound value of the flat peak travel time interval of (a),
Figure BDA0002648134430000145
for bus line l c From a i To b j The upper bound of the flat peak travel time interval,
Figure BDA0002648134430000146
for bus line l c From a i To b j The lower bound of the late peak travel time interval,
Figure BDA0002648134430000147
for bus line l c From a to a i To b j The upper bound of the late peak travel time interval.
b) Transfer line: for transfer routes, T is defined due to the influence of bus arrival time, congestion in the bus and road congestion f The penalty time is converted to represent the influence of uncertain factors.
Passengers make m-1 transfers from a i To b j Travel time function of (d):
Figure BDA0002648134430000148
in the formula (I), the compound is shown in the specification,
Figure BDA0002648134430000149
for bus lines
Figure BDA00026481344300001410
From a i To b j The time of flight of (a) is,
Figure BDA00026481344300001411
for bus lines
Figure BDA00026481344300001412
Driving interval of T f Penalty time is traded.
4) Transfer penalty function
Slave bus line
Figure BDA00026481344300001413
Transfer bus line
Figure BDA00026481344300001414
Is a transfer penalty function of
Figure BDA00026481344300001415
Figure BDA00026481344300001416
In the formula (I), the compound is shown in the specification,
Figure BDA00026481344300001417
for the bus line in the kth transfer scheme
Figure BDA00026481344300001418
To
Figure BDA00026481344300001419
The transfer distance of (a), km,
Figure BDA00026481344300001420
is a function of the state;
Figure BDA00026481344300001421
5) Interval traffic distribution
According to the difference of Shenzhen conventional bus stops, the conventional small-sized transfer stops of the bus take 300 meters as the search radius, and the conventional large-sized transfer stops of the bus take 500 meters as the search radius, so that an effective path set is formed. Suppose a i To b j Has d in common ij A feasible path including d ij,1 A direct line, d ij,2 A transfer scheme, d ij,1 ＝x ij 。
a) Considering the influence of departure interval on passenger selection route without travel time influence
Figure BDA0002648134430000151
Is from a i To b j The probability of the passenger flow distribution of the kth path, then its vector
Figure BDA0002648134430000152
Figure BDA0002648134430000153
The distribution probability is:
Figure BDA0002648134430000154
in the formula, h (k)c1 The departure interval of the first bus route of the kth route.
b) Considering the influence of travel time on the route selection of passengers and the influence of non-departure workshop interval
Figure BDA0002648134430000155
Is from a i To b j The probability of the passenger flow distribution of the kth path, then its vector
Figure BDA0002648134430000156
Figure BDA0002648134430000157
Then calculating the passenger flow distribution rate interval of each effective path by the Logit model:
Figure BDA0002648134430000158
in which α is sensitiveDegree coefficient, alpha is more than or equal to 1; when α =1, there is no conditioning effect on the model, and when α increases, it slows down
Figure BDA0002648134430000159
To pair
Figure BDA00026481344300001510
When α → + ∞, there is no regulatory effect on the model.
c) Comprehensively considering the influence of travel time and transfer penalty function on passenger route selection
Direct line: when x is ij When not equal to 0, no transfer is performed from a i To b j The probability interval of the k-th path of passenger flow distribution
Figure BDA00026481344300001511
Figure BDA00026481344300001512
No direct line, consider transfer: when x is ij ＝0,
Figure BDA00026481344300001513
From a to a i To b j Absence of direct line, presence
Figure BDA00026481344300001514
A path of one-time zero transfer when
Figure BDA00026481344300001515
From a to a i To b j There is no direct line and one zero transfer, there is
Figure BDA00026481344300001516
A path of twice zero transfer when
Figure BDA00026481344300001517
There are no paths of two zero transfers. From a to a i To b j The probability interval of the k-th path of passenger flow distribution
Figure BDA00026481344300001518
Figure BDA0002648134430000161
2.3 error analysis
The ascending and descending passenger flows of the same line of Shenzhen conventional public transport have large asymmetry, the asymmetry in the flat peak time period is small, and the asymmetry in the peak time period is large. For checking the effectiveness of the improved model of distributing the Logit passenger flow to the buses, respectively calculating the bus route l c Uplink direction hour allocation interval error ω (l) c,s ) And the error omega (l) of the allocation interval in the down direction c,x ) Mean error of global distribution
Figure BDA0002648134430000162
And maximum error ω max 。
ω(l c,s )＝|q(l c,s )-Q(l c,s )|·Q(l c,s ) -1 ； (19)
ω(l c,x )＝|q(l c,x )-Q(l c,x )|·Q(l c,x ) -1 ； (20)
Figure BDA0002648134430000163
ω max ＝MAX(ω(l c,s ),ω(l c,x ))； (22)
In the formula, q (l) c,s ) For bus line l c Up-direction hour model distribution zone passenger flow volume, Q (l) c,s ) For bus line l c Upstream hourly inspection data distribution traffic, q (l) c,x ) For bus line l c Downstream hourly model assigns passenger flow, Q (l) c,x ) For bus route l c And checking data distribution passenger flow in the downlink direction in hours.
3. Example analysis
A Logit improved model is distributed through passenger flow data in a Shenzhen conventional bus interval, a rock street region is taken as an example and divided into 10 traffic districts, and as shown in Table 1, OD expected line graphs of Shenzhen conventional buses 2015 are shown in FIG. 2. The stone rock street area approach comprises 97 public transportation lines, 2293 public transportation stations, 1196 uplink public transportation stations and 1097 downlink stations, and 9 types of buses including 62 trunk buses, 15 branch buses, 3 express buses, 8 special peak lines, 3 inter-class lines, 2 night buses, 2 holiday lines, 1 airport bus and 1 tourist bus are arranged in the stone rock street area according to Shenzhen bus types. According to the latest bus naming, the special lines of the peak, night buses, holiday lines, airport buses and tourist buses are classified as express buses, and the interval lines are classified as trunk buses, 65 branch buses and 19 express buses.
TABLE 1 Stone rock street traffic plot division
Figure BDA0002648134430000171
The travel time between each stop of each line can be obtained by matching Shenzhen conventional bus GPS data with stop GIS data, and a B691 line Guangdong BM5408 bus travel time table is shown in a table 2. Travel time between each station of each line in the morning and evening peak and flat peak time periods can be obtained according to the travel of the bus on one day, and travel time interval values between each station can be obtained according to data of Shenzhen conventional buses lasting six weeks and by combining an interval uncertainty theory. And combining the Shenzhen conventional bus route passenger flow interval OD and the bus area interval OD, segmenting and refining the passenger flow in the bus interval according to the early-late peak and the peak-flattening time, and dividing the refining time according to the boarding card swiping time to obtain the bus route OD and the bus area OD of the early-late peak and the peak-flattening time. According to the data of the Shenzhen conventional bus lasting for six weeks, by combining an interval uncertainty theory, a bus line interval OD and a bus region interval OD under the morning and evening peaks can be obtained, and interval passenger flows are used as bus passenger flow distribution data.
TABLE 2 B691 route yue BM5408 schedule of travel for public transport vehicles
Figure BDA0002648134430000172
TABLE 3B 691 line ascending bus stop interval passenger flow distribution
Figure BDA0002648134430000173
Figure BDA0002648134430000181
TABLE 4 quartile Range OD in rock street traffic plot
Figure BDA0002648134430000182
TABLE 5 five-place interval OD of stone rock street traffic district
Figure BDA0002648134430000183
TABLE 6 Stone rock street traffic district 90% confidence interval OD
Figure BDA0002648134430000191
TABLE 7 Stone rock street traffic plot 80% confidence interval OD
Figure BDA0002648134430000192
TABLE 8 Stone rock street traffic plot 70% confidence interval OD
Figure BDA0002648134430000193
TABLE 9 examination of proportion of data to passenger flow range in each interval
Figure BDA0002648134430000194
The Logit improved model is used for carrying out interval passenger flow distribution (four-quantile interval OD calculation is selected here) on the early and late peak and the flat peak of the research area, and the error of the distribution result, alpha and T are calculated f ，
Figure BDA0002648134430000195
The relationship is shown in FIG. 3, in which the abscissa α represents the sensitivity coefficient and the ordinate represents the sensitivity coefficient
Figure BDA0002648134430000196
Average error as a whole in%; alpha and T when two different colors in the graph respectively represent the upper and lower boundaries of the interval value f ，
Figure BDA0002648134430000197
The relationship (2) of (c). T is a unit of f 1,3,5,7,9, alpha and
Figure BDA0002648134430000201
the relationship is as follows: t when alpha is 8 f When the time is 5min, the time is less than or equal to 5min,
Figure BDA0002648134430000202
minimum, the pair of errors before and after the Logit model improvement is shown in tables 5, 6 and 7.
Table 10 comparison of errors before and after improvement of Logit at early peak period (%)
Figure BDA0002648134430000203
Table 6 comparison of errors before and after the Logit improvement at the late peak time (%)
Figure BDA0002648134430000204
Table 11 comparison of errors before and after improvement of Logit at peak-smoothing period (%)
Figure BDA0002648134430000205
Through error analysis, the improved Logit model can be known to be used for all lines in early peak period
Figure BDA0002648134430000206
ω max About 10% and 17% respectively, and all lines in late peak period
Figure BDA0002648134430000207
ω max Reduced by about 9% and 16%, respectively, and full line in flat peak period
Figure BDA0002648134430000208
ω max The decrease was about 7% and 15%, respectively. All types of public transport
Figure BDA0002648134430000209
ω max All the models are effectively reduced, and the model distribution result has higher practicability.
4. Results
1) By combining the interval uncertainty theory and the basic algorithm of the interval number, the OD matrix of getting on and off the bus at the bus station is an interval value, and the method is more suitable for the actual situation.
2) The travel time function comprises two conditions of direct transfer and transfer, and then the transfer penalty function is improved for the condition of a transfer line, so that the result of bus passenger flow distribution is more accurate.
3) The OD matrix interval value of the adopted bus line is obtained by data mining and processing through conventional bus IC card data and GPS data in Shenzhen city, so that bus OD matrixes in different time periods can be obtained, and bus passenger flow distribution is respectively carried out on different time periods according to the matrixes.
4) Error analysis is compared from two aspects of average error and maximum error, and the improved Logit model error is smaller and is more suitable for bus passenger flow distribution under the condition that the getting-on and getting-off interval is uncertain.
It should be noted that the embodiments of the present invention can be realized by hardware, software, or a combination of software and hardware. The hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the apparatus and methods described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided on a carrier medium such as a disk, CD-or DVD-ROM, programmable memory such as read only memory (firmware), or a data carrier such as an optical or electronic signal carrier, for example. The apparatus and its modules of the present invention may be implemented by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors, or by a combination of hardware circuits and software, e.g., firmware.
The above description is only for the purpose of illustrating the present invention and the appended claims are not to be construed as limiting the scope of the invention, which is intended to cover all modifications, equivalents and improvements that are within the spirit and scope of the invention as defined by the appended claims.

Claims (8)

1. A bus passenger flow distribution method based on interval uncertainty is characterized by comprising the following steps:
based on an interval uncertainty theory and an interval number basic algorithm, improving a travel time function and a transfer penalty function, and constructing a Logit improved model of bus passenger flow distribution;
carrying out error analysis of bus passenger flow distribution before and after model improvement, and carrying out average error and maximum error analysis of bus routes in morning and evening peak and flat peak periods;
the method for establishing the Loxit improved model comprises the following steps:
(1) Description of the parameters
N traffic districts, s bus lines and m bus stops exist in a research area, and w is arranged in a traffic district i i The number of the bus stops is one,
Figure FDA0003870640360000011
bus stop g i Indicating, starting point bus station a i Terminal bus station b j From a i To b j Peak hour passenger flow volume of e ij Peak hour traffic matrix E = { E = { E } ij }, average peak hour passenger flow f ij Flat peak hour passenger flow matrix F = { F ij From a i To b j The passenger flow of the peak hour road section is u ij Traffic matrix U = { U = peak hour road section ij V is the passenger flow volume of the road section at peak-leveling hour ij The passenger flow matrix of the road section in flat peak hours is V = { V = ij And f, setting a road section passenger flow matrix P capable of being carried by s bus lines in peak hours d From a i To b j The peak hour can bear the road section passenger flow as
Figure FDA0003870640360000012
Figure FDA0003870640360000013
Setting bus line l c The peak hour-to-hour workshop interval is h c The interval between flat peak hour-off workshops is O c ；
(2) Adjacency matrix
The ordered triplets form a graph,
Figure FDA0003870640360000014
a non-empty set of internal elements a (G),an edge set B (G) which does not intersect A (G),
Figure FDA0003870640360000015
for the correlation function, each edge corresponds to an unordered pair of internal elements; adjacency matrix: graph G = { a, B }, a (G) = { c = 1 ,c 2 ,……,c q }，c i And c j The number of sides in between is d ij Then n-order square matrix M (G) = (d) ij ) n×n For the adjacency matrix of FIG. G, connection c i And c j The number of pathways of length l is
Figure FDA0003870640360000016
Element of (1) th row and (j) th column
Figure FDA0003870640360000017
Taking the bus stop as a vertex, if a direct line exists between two bus stops, connecting the bus stops into a graph G, and then connecting an adjacent matrix X (G) = (X) ij ) m×m ；
Figure FDA0003870640360000021
Figure FDA0003870640360000022
For network graph from a i To b j Of length l, i.e. from a i To b j The number of bus routes passing through the transfer of k-1 times;
(3) Time of flight function T ij (l c )
Bus line l c From a i To b j Time of flight function T ij (l c ) In order to distinguish the direct line and the transfer line, the peak time period and the flat time period are distinguished in the driving time;
(4) Transfer penalty function
Slave bus line
Figure FDA0003870640360000023
Transfer bus line
Figure FDA0003870640360000024
Has a transfer penalty function of
Figure FDA0003870640360000025
Figure FDA0003870640360000026
In the formula (I), the compound is shown in the specification,
Figure FDA0003870640360000027
for the bus line in the kth transfer scheme
Figure FDA0003870640360000028
To
Figure FDA0003870640360000029
The distance of transfer of (a), km,
Figure FDA00038706403600000210
is a function of the state;
Figure FDA00038706403600000211
(5) Interval traffic distribution
According to the difference of Shenzhen conventional bus stops, the conventional small-sized transfer stops of the bus take 300 meters as the search radius, and the conventional large-sized transfer stops of the bus take 500 meters as the search radius, so that an effective path set is formed; suppose a i To b j Has d in common ij A feasible path including d ij,1 A direct line, d ij,2 A transfer scheme, d ij,1 ＝x ij ；
(3) In the time-of-flight function T ij (l c )，The method comprises the following steps:
a) Direct line:
function of travel time:
Figure FDA00038706403600000212
early peak travel time interval:
Figure FDA00038706403600000213
peak-flattening travel time interval:
Figure FDA00038706403600000214
late peak travel time interval:
Figure FDA00038706403600000215
in the formula (I), the compound is shown in the specification,
Figure FDA0003870640360000031
for bus route l c From a i To b j The time interval of the journey of (a),
Figure FDA0003870640360000032
for bus line l c From a i To b j The lower bound of the early peak travel time interval,
Figure FDA0003870640360000033
for bus line l c From a i To b j The upper bound of the early peak travel time interval,
Figure FDA0003870640360000034
for bus line l c From a i To b j The lower bound value of the flat peak travel time interval,
Figure FDA0003870640360000035
for bus line l c From a i To b j The upper bound of the flat peak travel time interval of (2),
Figure FDA0003870640360000036
for bus line l c From a to a i To b j The lower bound of the late peak travel time interval,
Figure FDA0003870640360000037
for bus line l c From a i To b j The upper bound of the late peak travel time interval;
b) Transfer line: for transfer routes, T is defined due to the influence of bus arrival time, congestion in the bus and road congestion f Penalty time for transfer, representing the influence of uncertain factors;
passengers make m-1 transfers from a i To b j Travel time function of (c):
Figure FDA0003870640360000038
in the formula (I), the compound is shown in the specification,
Figure FDA0003870640360000039
for bus lines
Figure FDA00038706403600000310
From a i To b j The time of flight of (a) is,
Figure FDA00038706403600000311
for bus lines
Figure FDA00038706403600000312
Driving interval of T f Penalty time for transfer;
(5) The inter-zone passenger flow allocation includes:
a) Considering the influence of departure interval on passenger selection route without travel time influence
Figure FDA00038706403600000313
Is from a i To b j The probability of the passenger flow distribution of the kth path, then its vector
Figure FDA00038706403600000314
Figure FDA00038706403600000315
The distribution probability is:
Figure FDA00038706403600000316
in the formula, h (k)c1 The departure interval of the first bus route of the kth route;
b) Considering the influence of travel time on the passenger selecting route and the influence of non-accident workshop interval
Figure FDA00038706403600000317
Is from a i To b j The probability of the passenger flow distribution of the kth path, then its vector
Figure FDA00038706403600000318
Figure FDA00038706403600000319
Then calculating the passenger flow distribution probability interval of each effective path by the Logit model:
Figure FDA0003870640360000041
in the formula, alpha is a sensitivity coefficient and is more than or equal to 1; when α =1, there is no conditioning effect on the model, and when α increases, it slows down
Figure FDA0003870640360000042
To pair
Figure FDA0003870640360000043
When α → + ∞, there is no regulating effect on the model;
c) Comprehensively considering the influence of travel time and transfer penalty function on passenger route selection
Direct line: when x is ij Not equal to 0, no transfer, from a i To b j The probability interval of the k-th path of passenger flow distribution
Figure FDA0003870640360000044
Figure FDA0003870640360000045
No direct line, consider transfer: when x is ij ＝0,
Figure FDA0003870640360000046
From a to a i To b j Absence of direct line, presence
Figure FDA0003870640360000047
A path of one-time zero transfer when
Figure FDA0003870640360000048
From a to a i To b j There is no direct line and one zero transfer, there is
Figure FDA0003870640360000049
A path of twice zero transfer is formed
Figure FDA00038706403600000410
When the zero transfer is not carried out, a path of two times of zero transfer does not exist;from a i To b j The k-th path of (1) in the traffic distribution probability section
Figure FDA00038706403600000411
Figure FDA00038706403600000412
2. The method for bus passenger flow distribution based on interval uncertainty as claimed in claim 1, wherein the interval uncertainty theory is also called interval number optimization, namely an interval-based interval number optimization method; the interval number optimizes and utilizes the actual fluctuation range of the interval description numerical value; the interval number optimization method is divided into three categories: the method comprises the steps of optimizing linear interval numbers based on interval number sequence relations, optimizing linear interval numbers based on the maximum and minimum regret criterion, and optimizing nonlinear interval numbers.
3. The method of claim 1, wherein the number of intervals is a range value represented as:
Figure FDA00038706403600000413
whereina、
Figure FDA00038706403600000414
Is composed of
Figure FDA00038706403600000415
The lower and upper bound values of (a),
Figure FDA00038706403600000416
two arbitrary real numbers; when in useaAnd
Figure FDA00038706403600000417
when they are equal, the interval number becomes a fixed number;
the number of intervals is represented by upper and lower boundary values and interval radius, the interval radius is half of the length of the interval number, and the interval number
Figure FDA0003870640360000051
Lower bound valueaAnd upper bound value
Figure FDA0003870640360000052
Radius of interval
Figure FDA0003870640360000053
Median value of interval
Figure FDA0003870640360000054
4. The bus passenger flow distribution method based on interval uncertainty as claimed in claim 1, wherein the basic algorithm of the interval number comprises:
number of arbitrary two intervals
Figure FDA0003870640360000055
The basic algorithm is as follows:
1) Adding:
Figure FDA0003870640360000056
2) Subtracting:
Figure FDA0003870640360000057
3) Multiplying:
Figure FDA0003870640360000058
whereinaAndbis a non-negative number;
4) Removing:
Figure FDA0003870640360000059
5) Multiplication by number:
Figure FDA00038706403600000510
wherein k is a real number;
6) Power:
Figure FDA00038706403600000511
7) Index:
Figure FDA00038706403600000512
8) Logarithm:
Figure FDA00038706403600000513
5. the bus passenger flow distribution method based on interval uncertainty as claimed in claim 1, characterized in that the bus passenger flow distribution model is assumed as follows:
1) Obtaining OD matrixes among bus stops in peak time periods and flat time periods in the region, and obtaining bus route passenger flow OD matrixes and regional bus passenger flow OD matrixes by IC card data and GPS data of Shenzhen city buses;
2) Public transport in the area is developed, and the bus in the two traffic districts can finish traveling without other transportation modes;
3) Interference of special weather, holidays and major events is not considered, only interference factors of traffic jam are considered, and the driving interval is equal to the departure interval;
4) Regardless of the transfer factor of the passenger, the passenger may reach directly or transfer to the destination.
6. The bus passenger flow distribution method based on interval uncertainty as claimed in claim 1, wherein said error analysis comprises:
respectively calculate the bus route l c Uplink hourly allocation interval error ω (l) c,s ) And the error omega (l) of the allocation interval in the down direction c,x ) Integral bodyMean error of distribution
Figure FDA0003870640360000061
And maximum error ω max ；
ω(l c,s )＝|q(l c,s )-Q(l c,s )|·Q(l c,s ) -1 ；
ω(l c,x )＝|q(l c,x )-Q(l c,x )|·Q(l c,x ) -1 ；
Figure FDA0003870640360000062
ω max ＝MAX(ω(l c,s ),ω(l c,x ))；
In the formula, q (l) c,s ) For bus line l c Up-direction hour model distribution zone passenger flow volume, Q (l) c,s ) For bus line l c Upstream hourly inspection data distribution traffic, q (l) c,x ) For bus route l c Down direction hour model assignment of passenger flow, Q (l) c,x ) For bus line l c And checking data distribution passenger flow in the downlink direction in hours.
7. A computer-readable storage medium, storing a computer program which, when executed by a processor, causes the processor to execute the method for bus passenger flow allocation based on interval uncertainty according to any one of claims 1 to 6.
8. An interval uncertainty-based bus passenger flow distribution system for operating the interval uncertainty-based bus passenger flow distribution method according to any one of claims 1 to 6, the interval uncertainty-based bus passenger flow distribution system comprising:
the Lomit improved model building module is used for building a Lomit improved model of bus passenger flow distribution by improving a travel time function and a transfer penalty function based on an interval uncertainty theory and an interval number basic algorithm;
and the average error and maximum error analysis module is used for carrying out error analysis on bus passenger flow distribution before and after model improvement and average error and maximum error analysis on bus routes in the peak time periods of morning and evening and peak time periods.
CN202010861029.4A 2020-08-25 2020-08-25 Bus passenger flow distribution method, system and storage medium based on interval uncertainty Active CN112070372B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010861029.4A CN112070372B (en) 2020-08-25 2020-08-25 Bus passenger flow distribution method, system and storage medium based on interval uncertainty

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010861029.4A CN112070372B (en) 2020-08-25 2020-08-25 Bus passenger flow distribution method, system and storage medium based on interval uncertainty

Publications (2)

Publication Number Publication Date
CN112070372A CN112070372A (en) 2020-12-11
CN112070372B true CN112070372B (en) 2022-11-18

Family

ID=73658830

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010861029.4A Active CN112070372B (en) 2020-08-25 2020-08-25 Bus passenger flow distribution method, system and storage medium based on interval uncertainty

Country Status (1)

Country Link
CN (1) CN112070372B (en)

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113112806A (en) * 2021-04-19 2021-07-13 武汉元光科技有限公司 Passenger flow analysis method for bus rapid transit special platform and related equipment
CN113705902B (en) * 2021-08-31 2024-02-27 中铁第一勘察设计院集团有限公司 Traffic hub passenger transport organization evaluation method based on crowd management
CN114639237B (en) * 2022-02-21 2023-02-14 东南大学 Method for analyzing influence effect after implementation of traffic safety management standard
CN115620525B (en) * 2022-12-16 2023-03-10 中国民用航空总局第二研究所 Short-time traffic passenger demand prediction method based on time-varying dynamic Bayesian network
CN115810271B (en) * 2023-02-07 2023-04-28 安徽交欣科技股份有限公司 Method for judging passenger flow corridor position based on card swiping data

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109615180A (en) * 2018-11-16 2019-04-12 浩鲸云计算科技股份有限公司 A kind of analysis method of public bus network website demand and supply
CN110826943A (en) * 2020-01-13 2020-02-21 武汉元光科技有限公司 Method and related equipment for judging whether bus allocation is needed or not and determining bus allocation number
CN110880052A (en) * 2019-11-22 2020-03-13 河北轨道运输职业技术学院 Urban rail transit short-term passenger flow dynamic prediction method

Family Cites Families (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101964085A (en) * 2010-09-27 2011-02-02 北京航空航天大学 Method for distributing passenger flows based on Logit model and Bayesian decision
CN103530694B (en) * 2013-04-22 2016-08-10 北京交通大学 The city underground dynamic bus traveler assignment method built based on time-space network
US20190325463A1 (en) * 2018-04-24 2019-10-24 Arizona Board Of Regents On Behalf Of Arizona State University Systems and methods for product-line pricing under discrete mixed multinomial logit demand
EP3683742A1 (en) * 2019-01-18 2020-07-22 Naver Corporation Method for computing at least one itinerary from a departure location to an arrival location
CN110119835B (en) * 2019-03-26 2021-02-19 杭州电子科技大学 Bus dynamic departure scheduling optimization method based on interval calculation
CN110288139A (en) * 2019-06-13 2019-09-27 华南理工大学 A kind of Regional Road Network method for predicting under the event of subway in short-term
CN110782070B (en) * 2019-09-25 2022-04-22 北京市交通信息中心 Urban rail transit emergency passenger flow space-time distribution prediction method

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109615180A (en) * 2018-11-16 2019-04-12 浩鲸云计算科技股份有限公司 A kind of analysis method of public bus network website demand and supply
CN110880052A (en) * 2019-11-22 2020-03-13 河北轨道运输职业技术学院 Urban rail transit short-term passenger flow dynamic prediction method
CN110826943A (en) * 2020-01-13 2020-02-21 武汉元光科技有限公司 Method and related equipment for judging whether bus allocation is needed or not and determining bus allocation number

Also Published As

Publication number Publication date
CN112070372A (en) 2020-12-11

Similar Documents

Publication Publication Date Title
CN112070372B (en) Bus passenger flow distribution method, system and storage medium based on interval uncertainty
CN106504577B (en) A kind of park and shift traffic path method and device for planning
Amirgholy et al. Optimal design of sustainable transit systems in congested urban networks: A macroscopic approach
Clarens et al. An operating strategy for a commuter bus system
Perugia et al. Designing a home-to-work bus service in a metropolitan area
CN107609677A (en) A kind of customization public bus network planing method based on taxi GPS big datas
CN103366224B (en) Passenger demand prediction system and method based on public transport network
CN105427594A (en) Bidirectional-getting-on-passenger-flow-based obtaining method and system for bus section passenger flow volume
Lavagno et al. MIS-MV: optimization of multi-level logic with multiple-values inputs
CN111667114A (en) Intelligent route planning recommendation method based on space-time big data fusion
Li et al. Using smart card data trimmed by train schedule to analyze metro passenger route choice with synchronous clustering
CN113781787B (en) Bus departure schedule generation method and system
Yuldoshev et al. City public transport and passenger traffic studying the effect of weather indicators
Situ et al. A parallel ant colony system based on region decomposition for taxi-passenger matching
CN112949987B (en) Taxi scheduling and matching method, system, equipment and medium based on prediction
Grahn et al. Optimizing first-and last-mile public transit services leveraging transportation network companies (TNC)
Hui et al. Hotspots identification and classification of dockless bicycle sharing service under electric fence circumstances
CN113947287A (en) Electric bus regional dispatching method and system
CN113408833A (en) Public traffic key area identification method and device and electronic equipment
CN111667083B (en) Method and device for determining estimated network taxi
Guo et al. Trajectory data driven transit-transportation planning
Zhang et al. Research on optimization of customized bus routes based on uncertainty theory
Deng et al. Research on bus passenger traffic forecasting model based on gps and ic card data
Mattson et al. Marginal cost pricing and subsidy of small urban transit
Yang et al. Finding most reliable path with extended shifted lognormal distribution

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant