CN116902038A - Flexible grouping-oriented timetable and operation intersection joint adjustment method - Google Patents
Flexible grouping-oriented timetable and operation intersection joint adjustment method Download PDFInfo
- Publication number
- CN116902038A CN116902038A CN202310862205.XA CN202310862205A CN116902038A CN 116902038 A CN116902038 A CN 116902038A CN 202310862205 A CN202310862205 A CN 202310862205A CN 116902038 A CN116902038 A CN 116902038A
- Authority
- CN
- China
- Prior art keywords
- train
- station
- service
- passengers
- schedule
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 38
- 238000005457 optimization Methods 0.000 claims abstract description 19
- 238000011217 control strategy Methods 0.000 claims abstract description 10
- 230000008569 process Effects 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 7
- 230000000717 retained effect Effects 0.000 claims description 5
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 claims description 4
- 230000008859 change Effects 0.000 claims description 4
- 238000013461 design Methods 0.000 claims description 3
- 230000014759 maintenance of location Effects 0.000 abstract description 16
- 238000011160 research Methods 0.000 abstract description 7
- 230000008878 coupling Effects 0.000 abstract 1
- 238000010168 coupling process Methods 0.000 abstract 1
- 238000005859 coupling reaction Methods 0.000 abstract 1
- 230000001105 regulatory effect Effects 0.000 abstract 1
- 238000009954 braiding Methods 0.000 description 6
- 230000006399 behavior Effects 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 239000002699 waste material Substances 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 230000000052 comparative effect Effects 0.000 description 1
- 230000006837 decompression Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000013486 operation strategy Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L27/00—Central railway traffic control systems; Trackside control; Communication systems specially adapted therefor
- B61L27/10—Operations, e.g. scheduling or time tables
- B61L27/14—Following schedules
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Abstract
The invention establishes a schedule and operation intersection joint optimization adjustment Model oriented to flexible grouping based on the dynamic travel demands of passengers, and further obtains an effective operation adjustment strategy based on Model predictive control (Model PredictiveControl, MPC) algorithm. The actual passenger flow demand is comprehensively considered, and a schedule adjustment and operation route crossing adjustment model oriented to flexible grouping is established by deducing the coupling relation among train stopping, route crossing running conditions, train uncoupling states and passenger traffic. The MPC algorithm is used for designing a flexible grouping-oriented timetable and operation intersection joint adjustment model solving algorithm, converting an optimization problem into an optimal control problem, and solving the optimization problem in each control period to determine an optimal control strategy, so that the aim of minimizing passengers retention is fulfilled, and an effective operation adjustment strategy is obtained. Compared with the traditional train operation regulating method for the road crossing operation, the method effectively reduces the retention of passengers at each station, and has wide research and application values.
Description
Technical Field
The invention relates to the field of train operation adjustment, in particular to a flexible grouping-oriented schedule and operation intersection joint adjustment method.
Background
With the rapid growth and expansion of urban scale in recent years, urban rail transit systems are also facing new problems, one of which is how to better match the dynamic travel demands of passengers. The passenger flow demands have obvious unbalance in space and time dimensions, and in early rush hour, the passenger demands of some specific stations are always oversaturated, so that some passengers stay at the stations, while the passenger demands of other stations are less, and the waste of transport capacity resources is easily caused. In order to further improve the service quality of urban rail transit and reduce the operation cost, a dynamic operation adjustment strategy represented by mixed operation of a large and small road and flexible grouping of trains becomes a hot spot in research in recent years
Under the flexible marshalling technology, an operator can arrange trains in different marshalling modes to execute a size-crossing operation service strategy according to the dynamic change of the number of passengers on a line, so that the requirements of passengers are better matched and the service quality is improved. The existing research has not developed a combined optimization strategy research of a train formation mode, a size crossing operation strategy and a train schedule facing a flexible grouping mode.
Disclosure of Invention
The invention provides a schedule and operation intersection joint adjustment method for flexible grouping. A flexible grouping-oriented timetable and operation intersection joint adjustment model is established and used for obtaining an effective operation adjustment strategy. Firstly, describing the relation between train arrival and departure, traffic running conditions, train unwrapping states and passenger traffic volume by establishing a passenger dynamics equation, strictly considering the actual running of the train and passenger demand constraint, and realizing the establishment of a schedule and running traffic joint adjustment model for flexible grouping; further, the whole optimization problem is converted into a control problem, and the control problem is decomposed into sub-problems with n steps to solve, so that a real-time adjustment strategy of train operation is obtained. The results, through comparison of different strategies, show that the proposed method is more effective in reducing passenger retention.
The adjustment method comprises the steps of establishing a train schedule oriented to flexible grouping and running a road crossing joint adjustment model and designing a train schedule adjustment model solving algorithm.
The specific steps of building the train schedule and operation road crossing joint adjustment model facing the flexible grouping comprise the following steps:
establishing a constraint related to train operation;
establishing a dynamic passenger flow model based on a train schedule;
establishing relevant constraints of de-compiling and road crossing operation;
an objective function is established.
Based on the scheme, the specific mode for establishing the constraint related to the train operation is as follows: build formula (1):
wherein t is i,k Indicating departure time of train service k at station i, r i-1 Representing the run time of train service between i-1 station and i station, x i,k Indicating stop time of train service k at station i; departure time for train service at a+1 station passes through train service k 2 The sum of the time to reach the a+1 station and the time it remains at the a+1 station gives formula (2):
equation (3) represents the departure time of station b:
definition variable d i,k The time for the train service k to reach the i station is calculated by the formula (4):
train service k toThe time to station i is added by the departure time of station i-1 and the running time between stations i-1 to station i, where r i-1 Indicating that the i-1 station-to-i station runtime is a constant.
Based on the scheme, the method also comprises adding a link constraint for indicating the service k 1 ,k 2 ,k 3 A train receiving relationship between the two;
for k 2 The train service of (a) is from k 1 For train service k 2 The time to reach a+1 station is denoted train service k 1 Departure time at station a plus the running time r of the train between station a and station a+1 a Expressed as formula (5):
for k 3 Is from k 1 Train service k of (c) 3 The time to reach station b is denoted as its accepted train service k 1 Departure time at station a plus the run time T of the train between station a and station b a,b Expressed as formula (6):
for k 3 From k 2 Is represented by the following formula (7):
wherein r is b-1 Indicating the running time of the train between station b-1 to station b.
Based on the above scheme, the chase interval between train services satisfies the formula (8):
wherein the method comprises the steps ofRepresenting the smallest chase interval, +.>Indicating the maximum chase interval.
Based on the scheme, the specific mode for establishing the dynamic passenger flow model based on the train schedule is as follows:
the number of passengers arriving at station i between k-1 and k services is related to the time interval between two adjacent services and the passenger arrival rate at the current station, expressed as formula (9):
wherein mu i,k Representing the passenger arrival rate at station i during train service k-1 departure and train service k departure;
definition w i,k The number of passengers waiting for train service k at station i is represented by formula (10):
the formula (10) is composed of two parts, one part is passenger a who arrives at station i after the k-1 service leaves and before the k service arrives i,k The second part is that during peak hours, in addition to the arrival passengers, waiting passengers also include the detained passengers who have failed to successfully board the previous train due to no train service passing through the station or limited capacity, defined as s i,k ；
The terminal of the line is not waiting for passengers, i.e. w I,k ＝w 2I,k ＝0；
The retained passengers are represented by formula (11):
wherein g i,k Indicating the number of passengers successfully boarding train service k at station i;
definition p i,k Representing the number of passengers on train service k when train service k leaves station i, the passenger dynamic change process in the train defined by the boarding and alighting behavior is represented by formula (12):
based on the scheme, the specific mode of successfully boarding passengers is as follows:
wherein ε k Representing the number of fleets serviced by k trains, C representing the maximum capacity of a single fleet, the maximum capacity of the entire service being represented as ε k C, obtaining epsilon k C-p i-1,k +l i,k Representing the remaining capacity of train service k at station i;
no passenger gets on the terminal station of the line, i.e. g I,k ＝g 2I,k ＝0；
Wherein a is i,k Is a binary variable, and indicates whether the train service k passes through the i station, and if the train service k passes through the i station, the train service k can provide service for the i station; specifically represented by formula (14):
wherein the variable l i,k Indicating the number of passengers, lambda, of a train service k parked at station i i,k Representing a ratio of the number of passengers getting off to the number of passengers currently serviced by the train;
the calculation of the off-board passenger follows formula (15):
based on the scheme, the specific mode for establishing the related constraint of the de-compiling and the intersection operation is as follows:
definition of binary logic variable gamma k Indicating whether train service k is de-encoded, as in equation (16):
when the train is de-braided at the turnout, the number of the train teams forming the train service correspondingly changes, the number of the de-braided train teams is 1, the number of the train teams is not de-braided to be 2, and the calculation of the number of the train teams follows the formula (17):
based on the defined regional variables of the stops and the section conditions of train service operation, the formula (18) represents the stop condition of the train at each stop, that is, whether the stops have train service passing or not:
wherein, for the stations from station 1 to station a, there is always a train service passing; for stations of sections a+1 to b-1, the condition of train passing is that the uncoupling occurs, and gamma k =1; for b to 2I segment sites, if the service is hosted on k 1 Then there must be a train service passIf train clothing is received in k 2 Whether there is a service case and k 2 Keep consistent, i.e.)>
On the basis of the above scheme, the objective function is formula (19):
the schedule and operation intersection joint adjustment model oriented to the flexible grouping is as follows:
on the basis of the scheme, the design train schedule adjustment model solving algorithm is based on the MPC principle, and comprises the following steps:
planning a train schedule in an optimization range interval [ t, t+n ] in a t-th control period;
solving a real-time control strategy of the train in the control period, wherein the sample step length of the control period is n and is consistent with the passenger flow prediction step length;
in the t+1th control period, taking the updated actually measured departure time and stop time of the train in the t+1th stage as the prediction input of the model;
and re-estimating the control strategy of the train.
Based on the above scheme, based on the step size n of the samples, the objective function of the train schedule optimization model is re-expressed as equation (22) at each control period:
the invention has the beneficial effects that:
the invention relates to the field of train operation adjustment, and realizes a schedule and operation intersection joint adjustment method for flexible grouping, and the invention has the advantages that a schedule and operation intersection joint adjustment model for flexible grouping is established.
Drawings
The invention has the following drawings:
FIG. 1 is a layout of urban rail transit lines and a train service diagram;
FIG. 2 is a schematic diagram of an optimization model architecture;
FIG. 3 is a flow chart for solving a train operation adjustment model;
FIG. 4 adjusts a strategy model-train schedule;
FIG. 5 adjusts policy model one k 2 A passenger retention graphic;
FIG. 6 adjustment strategy model-k 3 A passenger retention graphic;
FIG. 7 adjusts a strategy model two train schedule;
FIG. 8 tuning policy model two k 1 A passenger retention graphic;
FIG. 9 tuning policy model two k 2 A passenger retention graphic;
FIG. 10 adjusts policy model two k 3 A passenger retention graphic;
FIG. 11 adjusts a strategy model three train schedule;
FIG. 12 tuning strategy model three k 1 A passenger retention graphic;
FIG. 13 tuning strategy model three k 2 A passenger retention graphic;
FIG. 14 tuning strategy model three k 3 Passenger retention illustrations.
Detailed Description
The present invention will be described in further detail with reference to fig. 1-14 and the detailed description of the invention, in order to make the objects, advantages and features of the invention more apparent.
1. Description of the problem and assumption
The invention mainly analyzes a train operation adjustment model, researches uplink and downlink road sections consisting of 2I stations in an urban rail transit system, and defines a train service set running from station 1 to station a road section as k as shown in a scene line as shown in figure 1 1 Defining a train service set running on the road sections from the station a+1 to the station b-1 as k 2 Finally, defining the train service set running on the road section from station b to station 2I as k 3 . The areas where stations 1 to a and stations b to 2I are located are defined as small intersection areas, and the areas where stations a+1 to b-1 are located are defined as large intersection areas. After passing through the station a, the train can select two routes to travel according to the passenger flow condition, wherein the two routes are small intersections, namely, all the train teams do not uncompress all the travel to the station b as shown in the figure 1 a); secondly, the train is de-encoded, the former train of the train service runs to the station a+1, and the latter train runs to the station b, as shown in fig. 1 b). In the actual running process, the situation of de-braiding and non-de-braiding can occur simultaneously, and the running state of the train at the moment is shown in fig. 1 c). The train service from the vehicle section by default consists of two trains, becomes two separate trains after de-braiding, and is identified as two train services. The train operation adjustment problem oriented to flexible grouping is studied, and the corresponding traffic scheme and schedule optimization are discussed. Finally, the matching of the passenger flow and the train resources is realized, the satisfaction of passengers is improved, and meanwhile, unnecessary waste of the train resources is reduced. Parameters known in the study are: the number of train services, the number of line stops, the maximum capacity of the fleet, the boarding and alighting rates of individual stop passengers, the start and end zone locations of the interchange, and the inter-train stop travel time.
In order to facilitate analysis and adjustment of problems, a schedule and operation intersection joint adjustment model oriented to flexible grouping is established, and based on the above scenes, the following assumptions are made for the existing research problems:
(1) Assume one: all trains do not have overtaking behaviors at the station;
(2) Suppose two: the transport capacity of each motorcade is the same and is not affected by the traffic of the driving size or the de-braiding condition;
(3) Assume three: the running time between adjacent stations is constant and known;
(4) Suppose four: passengers do not transfer between different types of train services, always selecting train services up to the destination.
2. Symbolic representation
In order to solve the problems, the variables used in the subsequent modeling need to be described, and first, basic variables and meanings of the basic variables in the scene in the modeling process are described, see table 1 in detail.
The symbols and definitions used by the train schedule deduction section are then described in table 2. Wherein the variables mainly comprise the time of stopping, running and arrival time of train service and the like, and the variables of the connection relationship among the train service, and the connection variables not only can judge that the train service is from k 1 Or k 2 The order of train services may also be identified.
TABLE 1 scene related symbols
Table 2 train schedule phase Guan Fuhao
On the basis of the schedule, the situation that passengers get on and off each station are deduced through a formula, and sign descriptions of the passenger parts are shown in a table 3, wherein the sign descriptions mainly comprise arrival rate, arrival passenger number, waiting passenger number, and stay passenger number related to the passengers; symbols involved in the road crossing and grouping operation schemes are illustrated in table 4 and mainly comprise variables such as the number of motorcades, the capacity of motorcades, the unpacking condition and the like; finally, the decision variables of the model are shown in table 5, and besides the conditions of de-compiling and train receiving, the decision variables also comprise control actions for the stop time of the train.
TABLE 3 sign for getting on/off passengers
TABLE 4 traffic and grouping related symbols
TABLE 5 decision variables
3. The combined adjustment model of train schedule and running crossing for flexible marshalling is built 1, firstly, the constraint related to train running is built,
wherein t is i,k Indicating departure time of train service k at station i, r i-1 Representing the run time of train service between i-1 station and i station, x i,k Indicating the stop time of train service k at station i. In particular, departure time for train service at a+1 station may pass through train service k 2 The sum of the time to reach the a+1 station and the time it stays at the a+1 station is obtained:
similarly, the departure time of station b may be represented:
definition variable d i,k The time for the train service k to reach the i station can be expressed by the formula:
calculated, i.e. the time for the train service k to arrive at the i station is added by the departure time at the i-1 station and the inter-station travel time from the i-1 station to the i station, where r i-1 Indicating the i-1 station to i station runtime, which is a constant.
In particular, it is also necessary to add some join constraints between different services to indicate service k 1 ,k 2 ,k 3 And the train receiving relation between the two. For k 2 The train service of (a) is from k 1 Then for train service k 2 The time to reach a+1 station can be expressed as train service k 1 Departure time at station a plus the running time r of the train between station a and station a+1 a Expressed as:
for k 3 Is from k 1 Train service k of (c) 3 The time to reach station b may be expressed as the train service k it receives 1 Departure time at station a plus the run time T of the train between station a and station b a,b Since there is no station between station a and station b, the passenger is better served, and thus the running time of this road section is also set to be constant. This process is expressed as:
for k 3 From k 2 Can be represented by the formula:
expressed, where r b-1 Indicating the running time of the train between station b-1 to station b.
In addition, the train should meet the maximum and minimum chase intervals during actual operation for passenger satisfaction and safety requirements of train operation. UsingRepresenting the smallest chase interval, +.>Indicating the maximum chase interval. The chase interval between train services should satisfy:
then, a dynamic passenger flow model is built based on the train schedule, and the number of passengers arriving at the station i between the k-1 and k services is related to the time interval between two adjacent services and the passenger arrival rate of the current station, which is expressed as:
wherein mu i,k The passenger arrival rate at station i during departure from train service k-1 and departure from train service k is shown. Definition w i,k Representing the number of passengers waiting for train service k at station i, the variable may be represented as:
it consists of two parts, one part being passenger a who arrives at station i after the k-1 service leaves and before the k service arrives i,k The second part is that during peak time, passengers waiting except for the passengers at the stationAlso includes the detained passengers which fail to successfully board the previous train due to no train service passing through the station or limited transport capacity, which is defined as s i,k In particular, the terminal of the line is not waiting for passengers, i.e. w I,k ＝w 2I,k =0. The detained passengers are expressed by the following formula:
wherein g i,k Indicating the number of passengers successfully boarding train service k at station i. Definition p i,k Representing the number of passengers on train service k when train service k leaves station i, the passenger dynamic change process in the train defined by the on-off behavior can be represented as:
regarding the calculation of passengers who successfully pick up a train, first consider whether the train service k passes through the i stops. If the service is provided through the i station, the number of arriving passengers needs to be compared with the capacity of the train, and whether the waiting passengers can get on the train or not is judged. This process can be expressed as:
wherein ε k Representing the number of fleets serviced by k trains, C representing the maximum capacity of a single fleet, the maximum capacity of the entire service being represented as ε k C, then epsilon can be derived k C-p i-1,k +l i,k Indicating the remaining capacity of train service k at station i. In particular, the terminal of the line has no passengers getting on the bus, i.e. g I,k ＝g 2I,k =0. Wherein a is i,k Is a binary variable indicating whether the train service k passes through the i station, and the passing of the i station indicates that the i station can be serviced. Specifically, the method can be expressed as:
wherein the variable l i,k Represents the number of passengers getting off the train when the train service k stays at station i, and lambda is used assuming that this number is proportional to the number of passengers on the current train service i,k To represent this ratio. The same passengers can get off the bus on the premise of having train service, so that the passengers also need to add the variable alpha i,k The calculation of the off-board passenger follows the following formula:
and then, establishing relevant constraints of the de-compiling and the intersection operation. Definition of binary logic variable gamma k For indicating whether the train service k is decompressed or not, there are
When the train is de-braided at the turnout, the number of the train teams forming the train service is correspondingly changed, the number of the de-braided train teams is 1, the number of the train teams is not de-braided to be 2, and the calculation of the number of the train teams follows the following formula:
train service k 1 The station before the turnout is cut off by providing service, so that the influence of whether a train is unwound or not is avoided, and the number of the train queues is 2; for train service k 2 The method is related to whether to de-encode, the number of the motorcades passing through the large traffic route part is 1 under the de-encode condition, and the number of the motorcades passing through the large traffic route part is 0 under the non-de-encode condition; for train service k 3 Train service is accepted by k 2 Then the fleet number and accepted k 2 Keep consistent if accepted at k 1 Then the variable is needed to passJudging that the connection is k 1 And then, carrying out specific analysis by judging whether the train service is de-compiled or not to obtain the corresponding train number. Under the condition of train de-braiding, the number of the train queues is 1, and under the condition of no de-braiding, the number of the train queues is 2.
According to the defined regional variable of the station and the condition of the section where the train service operates, the stop condition of the train at each station, namely whether the station has train service passing or not, can be represented:
wherein, for the stations from station 1 to station a, there is always a train service passing; for stations of sections a+1 to b-1, the condition of train passing is that the uncoupling occurs, and gamma k =1; for b to 2I segment sites, if the service is hosted on k 1 Then there must be a train service passIf train clothing is received in k 2 Whether there is a service case and k 2 Keep in line, i.e.)>
Finally, regarding the consideration of objective functions, in order to improve the satisfaction degree of passengers, the number of waiting passengers at each station should be reduced as much as possible, when the whole passenger flow of the line is large, the service of the train can be increased by reducing the departure interval, and for the stations in a large intersection area, the passenger flow can be balanced by deciding whether the train walks a large intersection, a small intersection or a de-organization according to the passenger flow, so that the number of waiting passengers at the station is reduced. Dynamic departure interval and train service type adjustment can fully balance the retained passengers and vehicle utilization rate of the station.
The objective function is therefore:
in summary, the schedule and operation intersection joint adjustment model for flexible grouping is as follows:
finally, the architecture of the whole optimization model is shown in fig. 2.
4. Design of solving algorithm
And (3) designing a marshalling mode, an operation intersection and train schedule joint optimization model solving algorithm. Firstly, collecting information of the current t stage, predicting the behavior of the system in a certain time in the future, and then generating an optimal control strategy by using an optimization algorithm so as to achieve a preset control target of a prediction model and meet constraint conditions. The control strategy consists of an optimal control input of the current state and an optimal control input sequence during the prediction period, and is implemented at each sampling time step.
Based on the MPC control method, the original joint optimization problem is converted into the optimal control problem. Further, optimizing the whole problem of the train schedule is converted into sub-problems with steps of n, and in each control period, the optimizing sub-problems in a given prediction range are solved on line, so that a real-time optimizing control strategy is provided. If in the t control period, planning a train schedule in an optimization range interval [ t, t+n ], solving a real-time control strategy of the train in the control period based on mixed integer planning, and adjusting the train state to reduce passenger retention. In the t+1th control period, the MPC method takes the updated actually measured departure time and stop time of the train in the t+1th stage as the prediction input of the model, and then re-estimates the control strategy of the train. In the whole process, only the first control output of the calculated optimal control sequence is applied to the system, the subsequent control period and so on. The flow of the train operation adjustment algorithm is shown in fig. 3.
And in each control period, solving the following sub-problems by adopting a mixed integer programming method. Based on the step size n of the samples, the objective function of the train schedule optimization model can be re-expressed as:
/>
by the sliding window type optimization, the timetable and the running intersection joint real-time adjustment for flexible grouping according to the real-time passenger flow situation are realized, and the real-time control effect of the whole joint optimization model solving process is ensured.
5. Analysis of results
Different train operation adjustment schemes were designed and comparative experiments were used to evaluate the effect of these schemes. Different operation adjustment model experiments are based on actual passenger flow conditions in the same day and also in the early peak period of the managerial line. The method provided by the research is based on dynamic passenger flow, fully considers the passenger flow demand, and flexibly decides the size intersection and grouping condition; in order to highlight the importance of dynamic passenger flows, the second model is not subjected to real-time adjustment, the data are derived from historical passenger flow data, and the traffic situation and the decoding situation are decided; and the third model is based on dynamic passenger flow data, only performs large and small road crossing and running, and does not consider the dynamic unlocking condition of the train. The main factors of the three train operation adjustment models are shown in table 6.
TABLE 6 different train operation adjustment model
Based on the model one, the obtained train schedule is shown in fig. 4, wherein the broken line part represents train services separated after the decompression, and the same color represents the bearing relationship among the train services.
Recording the retained passengers of each train service to obtain a train service k under a model 2 And k 3 The passenger retention at each station is shown in figures 5 and 6 below, where train service k 1 No passengers remain at the respective stations.
The second model performs train operation adjustment based on static passenger flow, and the obtained schedule is shown in fig. 7, and three services have passengers retained based on the second model operation adjustment. The number of passengers detained is shown in fig. 8, 9 and 10, respectively.
The third model is not considered in running adjustment of train uncoding, the train running diagram based on the third model is shown in fig. 11, and it can be seen from the schedule that only the large road crossing is running, no uncoding is generated, and the train running diagram and the passenger retention of the third model are shown in fig. 12, 13 and 14.
By comparing the experimental results of the model I and the model II, it is obvious that the train operation adjustment based on the actual passenger flow can effectively reduce the passenger retention of the station compared with the adjustment based on the historical static passenger flow data only. Meanwhile, the comparison result of the model one and the model three can find that the flexible intersection and grouping are taken into consideration at the same time, and compared with a method only focusing on the operation condition of the large and small intersections, the passenger retention can be further reduced.
The above embodiments are only for illustrating the present invention and not for limiting the present invention, and various changes and modifications may be made by one skilled in the relevant art without departing from the spirit and scope of the present invention, so that all equivalent technical solutions fall within the scope of the present invention, which is defined by the claims.
What is not described in detail in this specification is prior art known to those skilled in the art.
Claims (10)
1. The flexible grouping-oriented schedule and operation intersection joint adjustment method is characterized by comprising the steps of establishing a flexible grouping-oriented train schedule and operation intersection joint adjustment model and designing a joint adjustment model solving algorithm;
the specific steps of building the train schedule and operation road crossing joint adjustment model facing the flexible grouping comprise the following steps:
establishing a constraint related to train operation;
establishing a dynamic passenger flow model based on a train schedule;
establishing relevant constraints of de-compiling and road crossing operation;
an objective function is established.
2. The method for adjusting the schedule and the running cross of the flexible marshalling in combination according to claim 1, wherein the specific way for establishing the constraint related to the running of the train is as follows: build formula (1):
wherein t is i,k Indicating departure time of train service k at station i, r i-1 Representing the run time of train service between i-1 station and i station, x i,k Indicating stop time of train service k at station i; departure time for train service at a+1 station passes through train service k 2 The sum of the time to reach the a+1 station and the time it remains at the a+1 station gives formula (2):
equation (3) represents the departure time of station b:
definition variable d i,k The time for the train service k to reach the i station is calculated by the formula (4):
the time for the train service k to reach the i station is added by the departure time of the train service k from the i-1 station and the running time between the i-1 station and the i station, wherein r i-1 Indicating that the i-1 station-to-i station runtime is a constant.
3. The flexible consist-oriented schedule and run-cross joint adjustment method of claim 2, further comprising adding a join constraint to indicate service k 1 ,k 2 ,k 3 A train receiving relationship between the two;
for k 2 The train service of (a) is from k 1 For train service k 2 The time to reach a+1 station is denoted train service k 1 Departure time at station a plus the running time r of the train between station a and station a+1 a Expressed as formula (5):
for k 3 Is from k 1 Train service k of (c) 3 The time to reach station b is denoted as its accepted train service k 1 Departure time at station a plus the run time T of the train between station a and station b a,b Expressed as formula (6):
for k 3 From k 2 Is represented by the following formula (7):
wherein r is b-1 Indicating the running time of the train between station b-1 to station b.
4. A flexible consist oriented schedule and run-cross joint adjustment method as recited in claim 2, wherein,
the chase interval between train services satisfies (8):
wherein the method comprises the steps ofRepresenting the smallest chase interval, +.>Indicating the maximum chase interval.
5. The flexible grouping-oriented schedule and operation intersection joint adjustment method according to claim 1, wherein the specific way of establishing the dynamic passenger flow model based on the train schedule is as follows:
the number of passengers arriving at station i between k-1 and k services is related to the time interval between two adjacent services and the passenger arrival rate at the current station, expressed as formula (9):
wherein mu i,k Representing the passenger arrival rate at station i during train service k-1 departure and train service k departure;
definition w i,k The number of passengers waiting for train service k at station i is represented by formula (10):
the formula (10) is composed of two parts, one part is passenger a who arrives at station i after the k-1 service leaves and before the k service arrives i,k The second part is that during peak hours, in addition to the arrival passengers, waiting passengers also include the detained passengers who have failed to successfully board the previous train due to no train service passing through the station or limited capacity, defined as s i,k ；
The terminal of the line is not waiting for passengers, i.e. w I,k ＝w 2I,k ＝0；
The retained passengers are represented by formula (11):
wherein g i,k Indicating the number of passengers successfully boarding train service k at station i;
definition p i,k Representing the number of passengers on train service k when train service k leaves station i, the passenger dynamic change process in the train defined by the boarding and alighting behavior is represented by formula (12):
6. the flexible consist-oriented schedule and travel route joint adjustment method of claim 5, wherein the specific way of successfully boarding passengers is:
wherein ε k Representing the number of fleets serviced by k trains, C representing the maximum capacity of a single fleet, the maximum capacity of the entire service being represented as ε k C，To obtain epsilon k C-p i-1,k +l i,k Representing the remaining capacity of train service k at station i;
no passenger gets on the terminal station of the line, i.e. g I,k ＝g 2I,k ＝0
Wherein alpha is i,k Is a binary variable, and indicates whether the train service k passes through the i station, and if the train service k passes through the i station, the train service k can provide service for the i station; specifically represented by formula (14):
wherein the variable l i,k Indicating the number of passengers, lambda, of a train service k parked at station i i,k Representing a ratio of the number of passengers getting off to the number of passengers currently serviced by the train;
the calculation of the off-board passenger follows formula (15):
7. the flexible grouping-oriented schedule and operation intersection joint adjustment method according to claim 1, wherein the specific way of establishing the constraint related to the deconstructing and intersection operation is as follows:
definition of binary logic variable gamma k Indicating whether train service k is de-encoded, as in equation (16):
when the train is de-braided at the turnout, the number of the train teams forming the train service correspondingly changes, the number of the de-braided train teams is 1, the number of the train teams is not de-braided to be 2, and the calculation of the number of the train teams follows the formula (17):
based on the defined regional variables of the stops and the section conditions of train service operation, the formula (18) represents the stop condition of the train at each stop, that is, whether the stops have train service passing or not:
wherein, for the stations from station 1 to station a, there is always a train service passing; for stations of sections a+1 to b-1, the condition of train passing is that the uncoupling occurs, and gamma k =1; for b to 2I segment sites, if the service is hosted on k 1 Then there must be a train service passIf train clothing is received in k 2 Whether there is a service case and k 2 Keep the same, i.e.)>
8. A flexible consist oriented schedule and run-cross joint adjustment method as recited in claim 1, wherein,
the objective function is of formula (19):
the schedule and operation intersection joint adjustment model oriented to the flexible grouping is as follows:
9. the flexible grouping-oriented schedule and operation intersection joint adjustment method according to claim 1, wherein the design train schedule adjustment model solving algorithm is based on the MPC principle, and comprises the following steps:
planning a train schedule in an optimization range interval [ t, t+n ] in a t-th control period;
solving a real-time control strategy of the train in the control period, wherein the sample step length of the control period is n and is consistent with the passenger flow prediction step length;
in the t+1th control period, taking the updated actually measured departure time and stop time of the train in the t+1th stage as the prediction input of the model;
and re-estimating the control strategy of the train.
10. The flexible consist oriented schedule and run-cross joint adjustment method of claim 9,
based on the step size n of the samples, at each control cycle, the objective function of the train schedule optimization model is re-expressed as equation (22):
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310862205.XA CN116902038A (en) | 2023-07-13 | 2023-07-13 | Flexible grouping-oriented timetable and operation intersection joint adjustment method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310862205.XA CN116902038A (en) | 2023-07-13 | 2023-07-13 | Flexible grouping-oriented timetable and operation intersection joint adjustment method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116902038A true CN116902038A (en) | 2023-10-20 |
Family
ID=88355879
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310862205.XA Pending CN116902038A (en) | 2023-07-13 | 2023-07-13 | Flexible grouping-oriented timetable and operation intersection joint adjustment method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116902038A (en) |
-
2023
- 2023-07-13 CN CN202310862205.XA patent/CN116902038A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107330547B (en) | Urban public transport dynamic scheduling optimization method and system | |
| Veelenturf et al. | Passenger oriented railway disruption management by adapting timetables and rolling stock schedules | |
| US7539624B2 (en) | Automatic train control system and method | |
| US7340328B2 (en) | Scheduling system and method | |
| US5440489A (en) | Regulating a railway vehicle | |
| EP0782521B1 (en) | Scheduling system and method | |
| CN110920700B (en) | High-speed rail scheduling optimization method, system and storage medium | |
| KR102113162B1 (en) | System for scheduling of express train and method thereof | |
| CN112580866B (en) | Bus route bunching optimization method based on whole-course vehicle and inter-vehicle combined scheduling | |
| CN111859718B (en) | Method and system for calculating congestion coefficient of regional multi-standard rail transit station | |
| Wu et al. | A virtually coupled metro train platoon control approach based on model predictive control | |
| CN111098896A (en) | Weekend train operation scheme modeling method considering passenger flow fluctuation and scheme connectivity | |
| Galapitage et al. | Optimal real-time junction scheduling for trains with connected driver advice systems | |
| Qu et al. | A review of real time railway traffic management during disturbances | |
| Estrada et al. | Operation of transit corridors served by two routes: Physical design, synchronization, and control strategies | |
| Sun et al. | Redesigning rail transit short-turn operations: case study of line 2 of the shanghai metro in China | |
| Liu et al. | Modelling and simulation of cooperative control for bus rapid transit vehicle platoon in a connected vehicle environment | |
| CN116902038A (en) | Flexible grouping-oriented timetable and operation intersection joint adjustment method | |
| Chai et al. | Train schedule optimization for commuter-metro networks | |
| CN116109081A (en) | Dynamic bus scheduling optimization method and device based on Internet of things | |
| CN115879658A (en) | Customized bus route optimization method, system, storage medium and equipment | |
| CN111931386B (en) | Method and system for calculating congestion coefficient of regional multi-standard rail traffic interval | |
| Zhang et al. | A dynamic optimization model for bus schedule design to mitigate the passenger waiting time by dispatching the bus platoon | |
| Oh et al. | Railway conflict detection and resolution in the Korean railway system | |
| Alexis et al. | Minimising the travel time on congested urban rail lines with a dynamic bi-modelling of trains and users |
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 |