CN117022398A - Urban rail transit train schedule optimization method and system considering passenger flow distribution - Google Patents
Urban rail transit train schedule optimization method and system considering passenger flow distribution Download PDFInfo
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Abstract
The invention provides a method and a system for optimizing a train schedule of urban rail transit taking passenger flow distribution into consideration, belongs to the technical field of urban rail transit operation management, establishes a schedule optimizing model taking a large and small traffic and a fast and slow train mode into consideration, takes constraint conditions such as train running interval, train service frequency, train origin and destination point and the like into consideration, aims at minimizing total travel time of passengers and enterprise kilometer cost, and establishes a multi-target schedule optimizing model based on the large and slow traffic and the fast and slow train modes. And (3) establishing a passenger flow distribution model based on analysis of passenger selection behaviors under the fast and slow vehicle operating conditions, and analyzing the passenger path selection behaviors by considering influence factors of the passenger path selection under the fast and slow vehicle operating conditions. The possible transfer and selection actions of passengers during travel are discussed in detail, and a passenger flow distribution model is established.
Description
Technical Field
The invention relates to the technical field of urban rail transit operation management, in particular to an urban rail transit train schedule optimization method and system considering passenger flow distribution.
Background
With the expansion of the road network scale, the time-space distribution of urban rail transit passenger flows is unbalanced, and the operation organization mode of station stop often cannot fully meet the diversified travel demands of passengers. Therefore, the running scheme of the fast and slow vehicles under multiple routes is researched, so that the running capacity and the demand can be effectively matched, and the running time of passengers is saved. In the face of a multi-trip fast and slow train operation organization mode, passengers have diversified trip selection behaviors, for example, the passengers can determine a trip path according to the types of trains parked to the passengers, namely, a train running scheme and a schedule directly influence passenger flow distribution, so that the passenger flow distribution results influence the optimization of a train running plan to meet the passenger trip requirements as much as possible. Currently, to achieve synergistic optimization of the two, this problem is basically solved by a "two-layer model". The passenger selection behavior is reflected by a method for establishing a passenger transfer network and searching an effective path, a double-layer planning model is established to realize cooperative optimization of a fast and slow driving scheme and a passenger flow distribution result, in an upper layer model, the section passenger flow is calculated based on the passenger flow distribution result of a lower layer model, and feedback of the upper layer model and the lower layer model is realized by taking the section passenger flow lower than the conveying capacity as a constraint. The method is characterized in that a train running scheme of combining fast and slow trains with multiple routes is taken as a research object, a lower model is built based on generalized cost of the travel origin-destination of passengers, the turn-back station and fast train stopping scheme of small-route trains and running frequencies of different types of trains are optimized, and feedback of the upper model and the lower model is embodied through generalized cost minimization of the travel origin-destination of the passengers and enterprise operation cost minimization.
Most of the existing researches at home and abroad focus on optimizing the train schedule under the mode of large and small traffic or the operation organization of the express trains, and more complicated transportation organization modes of simultaneously considering the express trains and the large and slow traffic are rarely considered; for a double-layer planning model established by solving a train running scheme, there is little correlation between an upper layer model and a lower layer model through constraint conditions.
Disclosure of Invention
The invention aims to provide an urban rail transit train schedule optimization method and system which are used for constructing a double-layer planning model combining a large-small intersection mode and a fast-slow vehicle mode, and take passenger flow distribution into consideration and reflect feedback of an optimization result of a lower model to an optimization result of the upper model through constraint conditions in the upper model so as to solve at least one technical problem in the background technology.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
in one aspect, the invention provides an urban rail transit train schedule optimization method considering passenger flow distribution, comprising the following steps:
constructing a multi-objective schedule optimization model based on a large and small road crossing and a fast and slow vehicle mode by taking the minimum total travel time of passengers and the kilometer cost of enterprises as targets;
Considering influence factors of the passenger in the path selection under the fast and slow car operation, analyzing possible transfer and path selection behaviors of the passenger in the traveling process, and establishing a passenger flow distribution model;
based on a passenger flow distribution model, acquiring average waiting time of passengers, and constructing constraint on a multi-target timetable optimization model by associating adjustable parameters with a departure interval; determining the number of train starts and the number of express stations, generating the initial number of train starts in each period, and updating the upper bound of the departure interval;
updating the lower bound of the train departure interval based on the waiting time of passengers, and setting disturbance if the maximum departure interval is exceeded;
solving a multi-target schedule optimization model based on the train running number and the lower bound of the departure interval to obtain a schedule; running a passenger flow distribution model based on the passenger flow quantity and the solved timetable to obtain new average waiting time of passengers, and updating the train running quantity in each period; and outputting the final optimal timetable until the update iteration times of the upper bound of the departure interval and the lower bound of the departure interval are met.
Further, a multi-objective schedule optimization model based on a large and small intersection and a fast and slow train mode is constructed, and the constraints comprise a train arrival safety interval constraint, an interval running time constraint, a train origin-destination constraint, a train type and stop variable constraint, a train stop constraint, a fast train and small intersection non-continuous constraint, a train service frequency constraint and a model mutual feedback constraint.
Further, the model mutual feed constraint includes: the multi-objective schedule optimization model and the passenger flow distribution model form constraint conditions of mutual feedback:
d j,k -d i,k -c·t k ≥-M·(1-y l,j,k )
wherein, i and j are the serial numbers of any two trains, k is the station serial number, d j,k D, the departure time of the train at the station i,k For the departure time of train i at station, t k For average waiting time of passenger at station k, y i,j,k Indicating that the train arrived at the station before the train, M being a constant.
Further, the method for determining the alternative set of the minor intersections comprises the following steps: using the section passenger flow { Q ] of each section along the line j Using the cross-section passenger flow variance value Var under each intersection scheme as basic data Breaking of the wire Thereby determining the alternative set { R of the small-traffic plan Alternative to }。
Further, the solution of the alternative set of minor intersections is as follows:
inputting the number N of line stations and the section passenger flow data Q of each section j And the running proportion delta of large and small transit trains l :δ s ；
According to the constraint condition, enumerating the small-crossing scheme S s ，S e ]Performing cross-path length checking, and reserving small cross-path schemes meeting constraint conditions to form an initial cross-path scheme alternative set { R } Alternative to } 0 The method comprises the steps of carrying out a first treatment on the surface of the Wherein the constraint conditions are:
calculating a passenger flow section variance value of a single large intersection scheme:
calculating the variance value Var of the passenger flow section of the intersection scheme Breaking of the wire Comprising the following steps: the small intersection section is S s ，S e ]Will Q j Middle j E S s ,S e ]Part according to delta l :δ s The ratio of the traffic signal is distributed to a large traffic path and a small traffic path respectively; respectively calculating the passenger flow volume of each section of the large intersection and the small intersectionCalculating the variance value Var of the passenger flow section of the large and small intersections respectively l ，Var s The method comprises the steps of carrying out a first treatment on the surface of the Calculating the mean value Var= (Var) of the cross-section passenger flow variance of the intersection scheme s +Var l ) And/2, taking the result as an evaluation index of the intersection scheme;
traversing the initial alternative set of the intersection scheme, judging the applicability of the intersection scheme, traversing the WT, and keeping Var less than Var dl Recording small intersection schemes of (1), arranging Var meeting the conditions in ascending order, and selecting the intersection schemes of the first 3 bits to form a small intersection scheme alternative set { R } Alternative to }。
Further, the passenger path selection behavior analysis includes:
trains are divided into two types, namely fast trains and slow trains, wherein the slow trains have two arrival conditions: direct to the destination station or indirect to the destination station; the express train comprises a front station which directly reaches the destination station and a rear station which directly reaches the destination station, and passengers can take the express train and then transfer the express train when the express train directly reaches the front station or the rear station;
calculating a route selection probability by adopting a plurality of Logit models:
wherein p is i Is the probability that the passenger will choose route i,is the waiting time of passenger selection path i, +. >Is the on-vehicle time, h, of passenger selection path i i Represents whether or not the transfer exists in the path i, if yes, taking 1, beta 1 、β 2 And beta 3 Is a regression coefficient.
In a second aspect, the present invention provides an urban rail transit train schedule optimization system taking into account passenger flow distribution, comprising:
the first construction module is used for constructing a multi-target schedule optimization model based on the size road crossing and the speed and speed modes with the aim of minimizing the total travel time of passengers and the kilometer cost of enterprises;
the second construction module is used for analyzing the transfer and path selection behaviors possibly generated by the passengers in the traveling process by considering the influence factors of the path selection of the passengers under the fast and slow car operation and establishing a passenger flow distribution model;
the updating module is used for acquiring average waiting time of passengers based on the passenger flow distribution model and forming constraint on the multi-target schedule optimization model by associating adjustable parameters with the departure interval; determining the number of train starts and the number of express stations, generating the initial number of train starts in each period, and updating the upper bound of the departure interval; updating the lower bound of the train departure interval based on the waiting time of passengers, and setting disturbance if the maximum departure interval is exceeded;
the solving module is used for solving the multi-target schedule optimizing model based on the train running number and the lower bound of the departure interval to obtain a schedule; running a passenger flow distribution model based on the passenger flow quantity and the solved timetable to obtain new average waiting time of passengers, and updating the train running quantity in each period; and outputting the final optimal timetable until the update iteration times of the upper bound of the departure interval and the lower bound of the departure interval are met.
In a third aspect, the present invention provides a non-transitory computer readable storage medium for storing computer instructions which, when executed by a processor, implement a method of urban rail transit train schedule optimization taking into account passenger flow allocation as described above.
In a fourth aspect, the present invention provides a computer program product comprising a computer program for implementing a method of urban rail transit train schedule optimisation taking into account passenger flow allocation as described above when run on one or more processors.
In a fifth aspect, the present invention provides an electronic device, comprising: a processor, a memory, and a computer program; wherein the processor is connected to the memory, and wherein the computer program is stored in the memory, said processor executing the computer program stored in said memory when the electronic device is running, to cause the electronic device to execute instructions implementing the urban rail transit train schedule optimization method taking into account the allocation of passenger flows as described above.
The invention has the beneficial effects that: the built double-layer planning model simultaneously considers a fast-slow vehicle mode and a size intersection mode, and the double-layer planning model is built based on a feedback mechanism by feeding back the optimization result of the lower model to the optimization result of the upper model according to the constraint condition in the upper model; the complex thinking is adopted, a complex model is divided into two upper and lower models, and the solving time of the model can be obviously reduced while a high-quality solving scheme is ensured.
The advantages of additional aspects of the invention will be set forth in part in the description which follows, or may be learned by practice of the invention.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a functional block diagram of a dual-layer planning model according to an embodiment of the present invention.
Fig. 2 is a flowchart of an optimization method for urban rail transit train schedule considering passenger flow distribution according to an embodiment of the invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements throughout or elements having like or similar functionality. The embodiments described below by way of the drawings are exemplary only and should not be construed as limiting the invention.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless expressly stated otherwise, as understood by those skilled in the art. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, and/or groups thereof.
In order that the invention may be readily understood, a further description of the invention will be rendered by reference to specific embodiments that are illustrated in the appended drawings and are not to be construed as limiting embodiments of the invention.
It will be appreciated by those skilled in the art that the drawings are merely schematic representations of examples and that the elements of the drawings are not necessarily required to practice the invention.
Example 1
In this embodiment 1, there is provided an urban rail transit train schedule optimizing system considering passenger flow distribution, including: the first construction module is used for constructing a multi-target schedule optimization model based on the size road crossing and the speed and speed modes with the aim of minimizing the total travel time of passengers and the kilometer cost of enterprises; the second construction module is used for analyzing the transfer and path selection behaviors possibly generated by the passengers in the traveling process by considering the influence factors of the path selection of the passengers under the fast and slow car operation and establishing a passenger flow distribution model; the updating module is used for acquiring average waiting time of passengers based on the passenger flow distribution model and forming constraint on the multi-target schedule optimization model by associating adjustable parameters with the departure interval; determining the number of train starts and the number of express stations, generating the initial number of train starts in each period, and updating the upper bound of the departure interval; updating the lower bound of the train departure interval based on the waiting time of passengers, and setting disturbance if the maximum departure interval is exceeded; the solving module is used for solving the multi-target schedule optimizing model based on the train running number and the lower bound of the departure interval to obtain a schedule; running a passenger flow distribution model based on the passenger flow quantity and the solved timetable to obtain new average waiting time of passengers, and updating the train running quantity in each period; and outputting the final optimal timetable until the update iteration times of the upper bound of the departure interval and the lower bound of the departure interval are met.
In this embodiment 1, the urban rail transit train schedule optimization method considering the distribution of the passenger flows is implemented by using the system described above.
The method comprises the steps of constructing a multi-target schedule optimization model based on a large-small intersection and a fast-slow vehicle mode, wherein the constraints comprise train arrival safety interval constraint, interval running time constraint, train origin-destination constraint, train type and stop variable constraint, train stop constraint, fast vehicle and small intersection non-continuous constraint, train service frequency constraint and model mutual feedback constraint.
The model mutual feed constraint includes: the multi-objective schedule optimization model and the passenger flow distribution model form constraint conditions of mutual feedback:
d j,k -d i,k -c·t k ≥-M·(1-y i,j,k )
wherein, i and j are the serial numbers of any two trains, k is the station serial number, d j,k D, the departure time of the train at the station i,k For the departure time of train i at station, t k For average waiting time of passenger at station k, y i,j,k Indicating that the train arrived at the station before the train, M being a constant.
The method for determining the alternative set of the minor route comprises the following steps: using the section passenger flow { Q ] of each section along the line j Using the cross-section passenger flow variance value Var under each intersection scheme as basic data Breaking of the wire Thereby determining the alternative set { R of the small-traffic plan Alternative to }。
The solution steps of the minor road alternative set are as follows:
Inputting the number N of line stations and the section passenger flow data Q of each section j And the running proportion delta of large and small transit trains l :δ s ；
According to the constraint condition, enumerating the small-crossing scheme S s ，S e ]Performing cross-path length checking, and reserving small cross-path schemes meeting constraint conditions to form an initial cross-path scheme alternative set { R } Alternative to } 0 The method comprises the steps of carrying out a first treatment on the surface of the Wherein the constraint conditions are:
calculating a passenger flow section variance value of a single large intersection scheme:
calculating the variance value Var of the passenger flow section of the intersection scheme Breaking of the wire Comprising the following steps: the small intersection section is S s ，S e ]Will Q j Middle j E S s ,S e ]Part according to delta l :δ s The ratio of the traffic signal is distributed to a large traffic path and a small traffic path respectively; respectively calculating the passenger flow volume of each section of the large intersection and the small intersectionCalculating the variance value Var of the passenger flow section of the large and small intersections respectively l ，Var s The method comprises the steps of carrying out a first treatment on the surface of the Calculating the mean value Var= (Var) of the cross-section passenger flow variance of the intersection scheme s +Var l ) And/2, taking the result as an evaluation index of the intersection scheme;
traversing the initial alternative set of the intersection scheme, judging the applicability of the intersection scheme, traversing the WT, and keeping Var less than Var dl Recording small intersection schemes of (1), arranging Var meeting the conditions in ascending order, and selecting the intersection scheme structure of the first 3 bitsAlternate set { R for small-crossing scheme Alternative to }。
The passenger path selection behavior analysis includes:
trains are divided into two types, namely fast trains and slow trains, wherein the slow trains have two arrival conditions: direct to the destination station or indirect to the destination station; the express train comprises a front station which directly reaches the destination station and a rear station which directly reaches the destination station, and passengers can take the express train and then transfer the express train when the express train directly reaches the front station or the rear station;
Calculating a route selection probability by adopting a plurality of Logit models:
wherein p is i Is the probability that the passenger will choose route i,is the waiting time of passenger selection path i, +.>Is the on-vehicle time, h, of passenger selection path i i Represents whether or not the transfer exists in the path i, if yes, taking 1, beta 1 、β 2 And beta 3 Is a regression coefficient.
Example 2
As shown in fig. 1, in this embodiment 2, a train schedule optimization method is proposed, which considers both a fast and slow train operation mode and a large and small traffic operation mode. The specific research content comprises: and establishing a schedule optimization model considering the size road crossing and the speed train mode, and establishing a multi-target schedule optimization model based on the size road crossing and the speed train mode by taking constraint conditions such as train running interval, train service frequency, train origin-destination and the like into consideration and aiming at minimizing the total travel time of passengers and the kilometer cost of enterprises. And (3) establishing a passenger flow distribution model based on analysis of passenger selection behaviors under the fast and slow vehicle operating conditions, and analyzing the passenger path selection behaviors by considering influence factors of the passenger path selection under the fast and slow vehicle operating conditions. The possible transfer and selection actions of passengers during travel are discussed in detail, and a passenger flow distribution model is established.
In this embodiment, it is assumed that all the express trains adopt the same stop scheme, the express trains travel over the slow trains without stopping at the station, all the trains adopt the same vehicle type, and the multi-group mode of the trains is not considered.
Symbol description:
(1) Aggregation
T: train set, t= {1,2, |t| }
S: station set, s= {1,2, |s| }
S R : returning station set
S b : express car must stop station collection
(2) Indexing of
i, m: train index
j, k: station index
(3) Parameters (parameters)
h d : minimum departure interval of train
h a : train minimum inter-arrival
Train i is in section [ j, j+1 ]]Interval run time of (2)
t dw : minimum stop time of train
l s′ : interval length
VT: time of passenger always in car
WT: total waiting time of passengers
Alpha, beta: weighting of passengers and businesses in objective functions
(4) Variable(s)
a ij : arrival time of train i at station j
d ij : departure time of train i at station j
x ij :0-1 variable, whether train i stops at station j or not
y imj :0-1 variable, whether the train i leaves the station j earlier than the train m, if so, the variable is 1, and if notThen is 0
z ijk :0-1 variable, whether the origin-destination of train i is [ j, k ]]If yes, 1, otherwise 0
e i :0-1 variable, whether train i is a express train or not
s i :0-1 variable, whether train i is a small-traffic slow car or not
Objective function:
The optimization target of the urban rail transit operation organization is oriented to two main bodies of a transportation demand party and a transportation supply party, namely, the shortest passenger travel time and the minimum enterprise operation cost are to be realized.
Under the multi-road-crossing speed vehicle transportation organization mode, the running of the speed vehicle can improve the traveling speed of passengers and save the traveling time of long-distance traveling passengers; but the waiting time of passengers of the small transit train, which does not cover the station and the express train does not stop, is relatively long. Therefore, the transportation organization of the multi-road-crossing fast and slow vehicle coordinates the overall travel benefits of all-line station passengers, and the shortest total travel time of all-line passengers is realized. Finally, the objectives of the passenger can be expressed as: mint=wt+vt.
Urban rail transit operation enterprises serve as operation main bodies, so that diversified travel demands of passengers are met, and economic benefits of the enterprises are considered. Enterprise costs typically consider two aspects, fixed costs and variable costs: wherein the fixed cost generally refers to construction investment of a line, acquisition cost of a vehicle bottom and the like; the variable cost comprises the running cost of the train, the stop cost and the like. Here, in this embodiment, the variable cost of the enterprise is mainly considered, and the minimum total running distance of the train is used as the objective function.
The final objective function can be expressed as:
minZ＝αT+βD
constraint conditions:
(1) Train operation constraints
To ensure operation safety, various safety intervals of a train shop need to be formulated, and the time dimension mainly comprises a departure safety interval, an arrival safety interval and a tracking safety interval of any two train places. Train departure sequence variables are introduced here to describe train departure safety intervals:
meanwhile, the departure time of any two trains at the same station is always different, so that the two trains are provided with:
(2) Interval runtime constraints
The running time of a train in an interval can be generally expressed as the difference of the arrival time of the following station minus the departure time of the preceding station:
(3) Train origin-destination constraint
Each train has only one pair of different stations as its start station and end station, and the pair of stations belong to the originating and terminating station set, the constraint can be expressed as:
the station number corresponding to the start station of the train cannot be larger than the station number corresponding to the end station, so there are:
if a station is the origin or destination of a train, the train must stop at that station, and the constraint can be expressed as:
all trains pass stations outside all operating sections without stopping in the time dimension, so there are:
(4) Train type and stop variable constraints
If the train is a small-crossing slow train, the train stops at a station in a small-crossing interval, and does not stop at a non-collinear section, namely, when s i When=1, there areAnd->Can be converted into:
similarly, if the train is a fast train, the train stops at the stop-stop station, and the rest stations are not stopped.
(5) Train stop constraint
If a train stops at a station, its stop time must be considered. In contrast, if a train is not stopped at a station, the stop time of the train at the station is 0. The constraint can be expressed as:
(6) Express car and small-traffic non-continuous-sending constraint
In order to ensure the service quality of passengers, two rows of express carts and two rows of small-traffic slow carts are not allowed in the research period, or the express carts and the small-traffic slow carts are continuously driven, and the following constraint is adopted:
(7) Train service frequency constraint
The number of trains driven on express trains and small transit trains is not too large, so that the reduction of the overall service level is avoided.
(8) Model linearization process
In the train stop time constraint, a situation occurs in which two 0-1 variables are multiplied, which is processed here.
w ij ＝(1-e i -s i )·x ij w ij ∈{0,1}
(9) The upper and lower models form constraint conditions of mutual feedback
d j,k -d i,k -c·t k ≥-M·(1-y i,j,k )
Wherein, i and j are the serial numbers of any two trains, k is the station serial number, d j,k For the departure time of the train j at the station, d i,k For the departure time of train i at station, t k For average waiting time of passenger at station k, y i,j,k Indicating that the train arrived at the station before the train, M is a very large number.
The upper layer schedule model can determine the departure interval of the train at each station, while the lower layer model can generate the average waiting time of the passengers at each station, so as to generate schedules more beneficial to the passengers to travel, and the departure interval of the train at a certain station should be greater than or equal to the average waiting time of the passengers at the station multiplied by a certain constant c.
The lower model generates new average waiting time of passengers, and the upper model can update departure time, thereby achieving the purpose of meeting the requirements of the passengers. Notably, are: the constant c is a parameter to be adjusted, which is related to the arrival of the passenger and the stop solution of the train. In the case of a stop-and-go train only, the constant c is approximately equal to 2 if the arrival process of the passenger obeys a uniform distribution. If the train is started, the value will drop; in addition, the train running number should be updated according to the average waiting time generated by the lower layer model, and if the running number of trains multiplied by the average departure interval is greater than the length of the research period, the upper layer model may have no feasible solution.
tc＝Φ(T/(c·max(t i )))
The upper model is added into the lower model, so that the train running number can be updated based on the average waiting time of passengers, tc is the train running number, T is the length of a research period, and phi is a downward rounding function.
The method for determining the alternative set of the minor routes comprises the following steps:
in the research process, the situation of too many small-traffic schemes and too large solution space is avoided. By combining the section passenger flow characteristics of the lines, a part of obviously unreasonable minor intersection schemes are removed, and the minor intersection schemes with better results are left as alternative sets for solving, so that the intersection alternative sets can be regarded as known conditions.
Using the section passenger flow { Q ] of each section along the line j Using the cross-section passenger flow variance value Var under each intersection scheme as basic data Breaking of the wire Thereby determining the alternative set { R of the small-traffic plan Alternative to The solving steps are as follows:
step1: inputting basic data
Inputting the number N of the train stations and the section passenger flow data Q of each section j And the running proportion delta of large and small transit trains l :δ s Here, 3:1 is taken.
Step2: preliminary calculation small intersection feasible scheme
The small-crossing scheme S is enumerated according to the following constraint conditions s ，S e ]The cross path length is checked according to the following steps, the small cross path scheme meeting the constraint condition is reserved, and an initial cross path scheme alternative set { R } is formed Alternative to } 0 。
Step3: calculating the variance value of passenger flow section of single large traffic scheme
Step4: calculating the variance value Var of the passenger flow section of the intersection scheme Breaking of the wire
(1) The small intersection section is S s ，S e ]Will Q j Middle j E S s ,S e ]Part according to delta l :δ s The ratio of the traffic signal is distributed to a large traffic path and a small traffic path respectively;
(2) Respectively calculate big intersectionsPassenger flow volume of each section of road and small intersection
(3) The passenger flow section variance value Var of the large and small intersections is calculated by the following method l ，Var s ；
(4) Calculating the mean value Var= (Var) of the cross-section passenger flow variance of the intersection scheme s +Var l ) And/2, using the result as an evaluation index of the intersection scheme.
Step5: traversing the initial alternative set of the intersection scheme, judging the applicability of the intersection scheme, traversing the WT, and keeping Var less than Var dl Recording small intersection schemes of (1), arranging Var meeting the conditions in ascending order, and selecting the intersection schemes of the first 3 bits to form a small intersection scheme alternative set { R } Alternative to }。
Passenger transfer behavior analysis:
when the urban rail transit line starts to drive the fast and slow cars, passengers can choose to act between the fast car and the slow car, and the fast car can be transferred to the slow car in the forward direction, the slow car in the reverse direction and the slow car in the transfer direction. The concrete expression is as follows:
case 1: the fast car is forward transferred to the slow car. In this case, the express car cannot reach the terminal station D, but may reach the station a preceding the terminal station. The passenger may first ride the express bus to express station a and then transfer the slow bus to terminal station D.
Case 2: and the fast car is reversely transferred to the slow car. In this case, the express car cannot reach the terminal D, but may reach the station B subsequent to the terminal. The passenger may first ride the express bus to express station B and then reverse transfer the slow bus to terminal D.
Case 3: and transferring the slow vehicle into the fast vehicle. A slow transfer car may occur when the following conditions are met: firstly, the station type of a starting station O is not required, and a destination station D is a fast vehicle stop station; in addition, when the passenger arrives at the station platform, the first incoming train is a slow train i, and the second incoming train is a fast train j; finally, the fast vehicle j arrives at the terminal D earlier than the slow vehicle i (i.e., the slow vehicle has been overtravel before the fast vehicle arrives at the terminal). In this case the passenger may first ride on the slow car i to the fast car stop a before the D station and then transfer the fast car j to the D station.
Passenger selection behavior analysis:
firstly, trains are divided into two types of fast trains and slow trains, wherein the slow trains have two arrival conditions, namely a direct destination station (direct destination station for short) or an indirect destination station (indirect destination station for short), and the fast trains have four arrival conditions, namely a front station (direct front station for short) of the direct destination station, a rear station (direct rear station for short) of the direct destination station and the indirect destination station. The reason for this division is that passengers may ride the fast car first and then transfer the slow car when the fast car is directed to the front station or to the rear station.
The routing probability is calculated using a multiple Logit model, as shown in the following equation. Wherein p is i Is the probability that the passenger will choose route i,is the waiting time of passenger selection path i, +.>Is the on-vehicle time, h, of passenger selection path i i Represents whether or not the transfer exists in the path i, if yes, taking 1, beta 1 、β 2 And beta 3 Is a regression coefficient.
Building a passenger flow distribution model:
the assumptions of the model mainly include: the schedule information of three trains to be arrived is known after the passengers arrive at the station; when the passengers transfer fast and slowly, the destination station must be adjacent to the transfer station; when a passenger slowly transfers to a fast vehicle, the fast vehicle is transferred as late as possible in order to obtain shorter waiting time; the capacity of the train is unlimited; and continuous express trains or continuous small transit trains are not allowed.
Based on the assumption and analysis, a fast and slow passenger flow distribution model is constructed, and the algorithm flow is shown in figure 2. The inputs to the algorithm are: train schedule data of the uplink and the downlink of the line, including train numbers, fast and slow train types, arrival and departure time of each station, and whether each station stops; and passenger flow data in time intervals, including origin-destination stops, departure time and number of people going out.
The output of the algorithm is: the uplink and downlink allocation results comprise passenger numbers, riding train numbers, forward transfer train numbers, reverse transfer train numbers, passenger numbers, waiting time, on-vehicle time, allocation types, origin stops, riding train types, departure time and arrival time; calculating the section passenger flow volume, and counting the section passenger flow volume of all sections by taking 15min as a time interval; and (5) counting the section passenger flow of all trains in each interval according to the passenger flow distribution result of the trains.
Example 3
Embodiment 3 provides a non-transitory computer readable storage medium for storing computer instructions which, when executed by a processor, implement the urban rail transit train schedule optimization method considering passenger flow allocation as described above, the method comprising:
constructing a multi-objective schedule optimization model based on a large and small road crossing and a fast and slow vehicle mode by taking the minimum total travel time of passengers and the kilometer cost of enterprises as targets;
considering influence factors of the passenger in the path selection under the fast and slow car operation, analyzing possible transfer and path selection behaviors of the passenger in the traveling process, and establishing a passenger flow distribution model;
based on a passenger flow distribution model, acquiring average waiting time of passengers, and constructing constraint on a multi-target timetable optimization model by associating adjustable parameters with a departure interval; determining the number of train starts and the number of express stations, generating the initial number of train starts in each period, and updating the upper bound of the departure interval;
updating the lower bound of the train departure interval based on the waiting time of passengers, and setting disturbance if the maximum departure interval is exceeded;
solving a multi-target schedule optimization model based on the train running number and the lower bound of the departure interval to obtain a schedule; running a passenger flow distribution model based on the passenger flow quantity and the solved timetable to obtain new average waiting time of passengers, and updating the train running quantity in each period; and outputting the final optimal timetable until the update iteration times of the upper bound of the departure interval and the lower bound of the departure interval are met.
Example 4
This embodiment 4 provides a computer program product comprising a computer program for implementing a method of urban rail transit train schedule optimization taking into account passenger flow allocation as described above when run on one or more processors, the method comprising:
constructing a multi-objective schedule optimization model based on a large and small road crossing and a fast and slow vehicle mode by taking the minimum total travel time of passengers and the kilometer cost of enterprises as targets;
considering influence factors of the passenger in the path selection under the fast and slow car operation, analyzing possible transfer and path selection behaviors of the passenger in the traveling process, and establishing a passenger flow distribution model;
based on a passenger flow distribution model, acquiring average waiting time of passengers, and constructing constraint on a multi-target timetable optimization model by associating adjustable parameters with a departure interval; determining the number of train starts and the number of express stations, generating the initial number of train starts in each period, and updating the upper bound of the departure interval;
updating the lower bound of the train departure interval based on the waiting time of passengers, and setting disturbance if the maximum departure interval is exceeded;
solving a multi-target schedule optimization model based on the train running number and the lower bound of the departure interval to obtain a schedule; running a passenger flow distribution model based on the passenger flow quantity and the solved timetable to obtain new average waiting time of passengers, and updating the train running quantity in each period; and outputting the final optimal timetable until the update iteration times of the upper bound of the departure interval and the lower bound of the departure interval are met.
Example 5
Embodiment 5 provides an electronic apparatus including: a processor, a memory, and a computer program; wherein the processor is connected to the memory, and wherein the computer program is stored in the memory, said processor executing the computer program stored in said memory when the electronic device is running, to cause the electronic device to execute instructions for implementing a method for optimizing a urban rail transit train schedule taking into account passenger flow allocation as described above, the method comprising:
constructing a multi-objective schedule optimization model based on a large and small road crossing and a fast and slow vehicle mode by taking the minimum total travel time of passengers and the kilometer cost of enterprises as targets;
considering influence factors of the passenger in the path selection under the fast and slow car operation, analyzing possible transfer and path selection behaviors of the passenger in the traveling process, and establishing a passenger flow distribution model;
based on a passenger flow distribution model, acquiring average waiting time of passengers, and constructing constraint on a multi-target timetable optimization model by associating adjustable parameters with a departure interval; determining the number of train starts and the number of express stations, generating the initial number of train starts in each period, and updating the upper bound of the departure interval;
updating the lower bound of the train departure interval based on the waiting time of passengers, and setting disturbance if the maximum departure interval is exceeded;
Solving a multi-target schedule optimization model based on the train running number and the lower bound of the departure interval to obtain a schedule; running a passenger flow distribution model based on the passenger flow quantity and the solved timetable to obtain new average waiting time of passengers, and updating the train running quantity in each period; and outputting the final optimal timetable until the update iteration times of the upper bound of the departure interval and the lower bound of the departure interval are met.
In summary, according to the urban rail transit train schedule optimization method and system considering passenger flow distribution, a schedule double-layer planning model considering passenger flow distribution is established by considering a fast-slow mode and a size-transit mode, an upper model is used for obtaining a train running scheme and a schedule aiming at minimizing passenger running time and train running distance, passenger running selection behaviors are analyzed, and a passenger flow distribution model is established as a lower planning model based on a solving result of the upper model. Meanwhile, the passenger waiting time obtained by solving the lower layer model can be used for forming constraint on the upper layer model through correlation adjustable parameters with the departure interval, namely, the C.times.WT (waiting time) is less than or equal to the departure interval, so that feedback between the upper layer model and the lower layer model is completed. The comprehensive optimization of passenger flow distribution, size intersection and speed and time table programming problems is realized, compared with other researches, the comprehensive optimization method has the advantages that various constraints of the train in actual operation are fully considered, the scheme quality is ensured, and meanwhile, the solving speed can be remarkably increased. Compared with other prior art, the invention has the advantages that the mutual feedback influence between the passenger flow distribution of the lower model and the solution of the upper model multi-intersection fast and slow time meter is more specific, the waiting time of passengers is embodied, and the solution result of the lower model can be fed back to the upper model in a constraint mode so as to carry out subsequent iterative optimization.
The invention relates to two main methods for optimizing a train running scheme: a single model is built. And placing passengers and enterprises in the game positions, seeking balance points of the passengers and the enterprises, taking the operation cost of the enterprises and the travel cost of the passengers as objective functions, and optimizing the transportation capacity, the passing capacity, the passenger demands, the train configuration number and the road-crossing plan as constraints. And establishing a double-layer planning model. The upper layer takes the minimized passenger travel cost and the enterprise operation cost as targets to construct a model, and the lower layer optimizes the problem of flow distribution balance.
In terms of algorithm calculation, a person skilled in the art can design algorithms suitable for the model according to the size of the solution space, find the minimum value by adopting a first-order derivative solving mode, and select a branch-and-bound algorithm to solve integer programming. With the increase of solution space, the model is more complex, and the traditional method is adopted to solve the problem that the calculation time is too long and the solution efficiency is low, so that heuristic algorithms such as genetic algorithm, simulated annealing algorithm, ant colony algorithm and the like can be adopted to solve in practical application.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While the foregoing description of the embodiments of the present invention has been presented in conjunction with the drawings, it should be understood that it is not intended to limit the scope of the invention, but rather, it should be understood that various changes and modifications could be made by one skilled in the art without the need for inventive faculty, which would fall within the scope of the invention.
Claims (10)
1. An urban rail transit train schedule optimization method considering passenger flow distribution, comprising:
constructing a multi-objective schedule optimization model based on a large and small road crossing and a fast and slow vehicle mode by taking the minimum total travel time of passengers and the kilometer cost of enterprises as targets;
considering influence factors of the passenger in the path selection under the fast and slow car operation, analyzing possible transfer and path selection behaviors of the passenger in the traveling process, and establishing a passenger flow distribution model;
Based on a passenger flow distribution model, acquiring average waiting time of passengers, and constructing constraint on a multi-target timetable optimization model by associating adjustable parameters with a departure interval; determining the number of train starts and the number of express stations, generating the initial number of train starts in each period, and updating the upper bound of the departure interval;
updating the lower bound of the train departure interval based on the waiting time of passengers, and setting disturbance if the maximum departure interval is exceeded;
solving a multi-target schedule optimization model based on the train running number and the lower bound of the departure interval to obtain a schedule; running a passenger flow distribution model based on the passenger flow quantity and the solved timetable to obtain new average waiting time of passengers, and updating the train running quantity in each period; and outputting the final optimal timetable until the update iteration times of the upper bound of the departure interval and the lower bound of the departure interval are met.
2. The urban rail transit train schedule optimization method considering passenger flow distribution according to claim 1, wherein the multi-objective schedule optimization model based on the size road crossing and the fast and slow train modes is constructed, and the constraints comprise train arrival safety interval constraints, interval running time constraints, train origin-destination constraints, train type and stop variable constraints, train stop constraints, fast and small road non-continuous constraints, train service frequency constraints and model mutual feedback constraints.
3. The urban rail transit train schedule optimization method considering passenger flow distribution according to claim 2, wherein the model mutual feed constraint comprises: the multi-objective schedule optimization model and the passenger flow distribution model form constraint conditions of mutual feedback:
d j，k -d i，k -c·t k ≥-M·(1-y i，j，k )
wherein, i and j are the serial numbers of any two trains, k is the station serial number, d j,k D, the departure time of the train at the station i,k For the departure time of train i at station, t k For average waiting time of passenger at station k, y i,j,k Indicating that the train arrived at the station before the train, M being a constant.
4. The urban rail transit train schedule optimization method considering passenger flow distribution according to claim 1, wherein the minor road alternative set determination method comprises: using the section passenger flow { Q ] of each section along the line j Using the cross-section passenger flow variance value Var under each intersection scheme as basic data Breaking of the wire Thereby determining the alternative set { R of the small-traffic plan Alternative to }。
5. The urban rail transit train schedule optimization method considering passenger flow distribution according to claim 2, wherein the minor road alternative set solving step is as follows:
inputting the number N of line stations and the section passenger flow data Q of each section j And the running proportion delta of large and small transit trains l :δ s ；
According to the constraint condition, enumerating the small-crossing scheme S s ，S e ]Performing cross-path length checking, and reserving small cross-path schemes meeting constraint conditions to form an initial cross-path scheme alternative set { R } Alternative to } 0 The method comprises the steps of carrying out a first treatment on the surface of the Wherein the constraint conditions are:
calculating a passenger flow section variance value of a single large intersection scheme:
calculating the variance value Var of the passenger flow section of the intersection scheme Breaking of the wire Comprising the following steps: the small intersection section is S s ，S e ]Will Q j Middle j E S s ,S e ]Part according to delta l :δ s The ratio of the traffic signal is distributed to a large traffic path and a small traffic path respectively; respectively calculating the passenger flow volume of each section of the large intersection and the small intersectionCalculating the variance value Var of the passenger flow section of the large and small intersections respectively l ，Var s The method comprises the steps of carrying out a first treatment on the surface of the Calculating the mean value Var= (Var) of the cross-section passenger flow variance of the intersection scheme s +Var l ) And/2, taking the result as an evaluation index of the intersection scheme;
traversing the initial alternative set of the intersection scheme, judging the applicability of the intersection scheme, traversing the WT, and keeping Var less than Var dl Recording small intersection schemes of (1), arranging Var meeting the conditions in ascending order, and selecting the intersection schemes of the first 3 bits to form a small intersection scheme alternative set { R } Alternative to }。
6. The urban rail transit train schedule optimization method considering passenger flow distribution according to claim 1, wherein the passenger path selection behavior analysis comprises:
Trains are divided into two types, namely fast trains and slow trains, wherein the slow trains have two arrival conditions: direct to the destination station or indirect to the destination station; the express train comprises a front station which directly reaches the destination station and a rear station which directly reaches the destination station, and passengers can take the express train and then transfer the express train when the express train directly reaches the front station or the rear station;
calculating a route selection probability by adopting a plurality of Logit models:
wherein p is i Is the probability that the passenger will choose route i,is the waiting time of passenger selection path i, +.>Is the on-vehicle time, h, of passenger selection path i i Represents whether or not the transfer exists in the path i, if yes, taking 1, beta 1 、β 2 And beta 3 Is a regression coefficient.
7. An urban rail transit train schedule optimization system that considers passenger flow distribution, comprising:
the first construction module is used for constructing a multi-target schedule optimization model based on the size road crossing and the speed and speed modes with the aim of minimizing the total travel time of passengers and the kilometer cost of enterprises;
the second construction module is used for analyzing the transfer and path selection behaviors possibly generated by the passengers in the traveling process by considering the influence factors of the path selection of the passengers under the fast and slow car operation and establishing a passenger flow distribution model;
the updating module is used for acquiring average waiting time of passengers based on the passenger flow distribution model and forming constraint on the multi-target schedule optimization model by associating adjustable parameters with the departure interval; determining the number of train starts and the number of express stations, generating the initial number of train starts in each period, and updating the upper bound of the departure interval; updating the lower bound of the train departure interval based on the waiting time of passengers, and setting disturbance if the maximum departure interval is exceeded;
The solving module is used for solving the multi-target schedule optimizing model based on the train running number and the lower bound of the departure interval to obtain a schedule; running a passenger flow distribution model based on the passenger flow quantity and the solved timetable to obtain new average waiting time of passengers, and updating the train running quantity in each period; and outputting the final optimal timetable until the update iteration times of the upper bound of the departure interval and the lower bound of the departure interval are met.
8. A computer program product comprising a computer program for implementing the urban rail transit train schedule optimization method taking into account passenger flow allocation as claimed in any one of claims 1-6 when run on one or more processors.
9. A non-transitory computer readable storage medium storing computer instructions which, when executed by a processor, implement the urban rail transit train schedule optimization method of any one of claims 1-6 taking into account passenger flow allocation.
10. An electronic device, comprising: a processor, a memory, and a computer program; wherein the processor is connected to the memory, and wherein the computer program is stored in the memory, which processor, when the electronic device is running, executes the computer program stored in the memory to cause the electronic device to execute instructions for implementing the urban rail transit train schedule optimization method taking into account passenger flow allocation according to any of claims 1-6.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117610744A (en) * | 2024-01-17 | 2024-02-27 | 西南交通大学 | Urban rail train operation scheme optimization method and system based on multiple operation strategies |
| CN117973768A (en) * | 2024-01-26 | 2024-05-03 | 福州地铁集团有限公司 | Method for compiling urban rail transit full-day multi-period train operation scheme |
| CN118358627A (en) * | 2024-04-24 | 2024-07-19 | 西南交通大学 | Train dispatching control integrated adjustment method considering passenger flow grade |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117610744A (en) * | 2024-01-17 | 2024-02-27 | 西南交通大学 | Urban rail train operation scheme optimization method and system based on multiple operation strategies |
| CN117610744B (en) * | 2024-01-17 | 2024-04-30 | 西南交通大学 | Urban rail train operation scheme optimization method and system based on multiple operation strategies |
| CN117973768A (en) * | 2024-01-26 | 2024-05-03 | 福州地铁集团有限公司 | Method for compiling urban rail transit full-day multi-period train operation scheme |
| CN118358627A (en) * | 2024-04-24 | 2024-07-19 | 西南交通大学 | Train dispatching control integrated adjustment method considering passenger flow grade |
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