CN112214873B - Passenger flow distribution simulation evaluation method and system under rail transit fault - Google Patents
Passenger flow distribution simulation evaluation method and system under rail transit fault Download PDFInfo
- Publication number
- CN112214873B CN112214873B CN202010945280.9A CN202010945280A CN112214873B CN 112214873 B CN112214873 B CN 112214873B CN 202010945280 A CN202010945280 A CN 202010945280A CN 112214873 B CN112214873 B CN 112214873B
- Authority
- CN
- China
- Prior art keywords
- train
- passengers
- time
- passenger
- passenger flow
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000011156 evaluation Methods 0.000 title claims abstract description 25
- 238000004088 simulation Methods 0.000 title claims abstract description 24
- 238000000034 method Methods 0.000 claims abstract description 67
- 230000008569 process Effects 0.000 claims abstract description 50
- 238000004364 calculation method Methods 0.000 claims abstract description 25
- 230000003993 interaction Effects 0.000 claims abstract description 15
- 230000002452 interceptive effect Effects 0.000 claims abstract description 7
- 238000005094 computer simulation Methods 0.000 claims abstract description 6
- 238000007781 pre-processing Methods 0.000 claims abstract description 3
- 238000010586 diagram Methods 0.000 claims description 22
- 230000001174 ascending effect Effects 0.000 claims description 7
- 238000012546 transfer Methods 0.000 claims description 7
- 230000000977 initiatory effect Effects 0.000 claims description 5
- 230000002159 abnormal effect Effects 0.000 claims description 4
- 230000006978 adaptation Effects 0.000 claims description 4
- 238000004422 calculation algorithm Methods 0.000 claims description 3
- 238000007619 statistical method Methods 0.000 claims description 3
- 238000012935 Averaging Methods 0.000 claims description 2
- 230000008859 change Effects 0.000 description 3
- 238000007405 data analysis Methods 0.000 description 3
- 238000009825 accumulation Methods 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000005206 flow analysis Methods 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000007670 refining Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Economics (AREA)
- General Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- General Business, Economics & Management (AREA)
- Development Economics (AREA)
- Marketing (AREA)
- Educational Administration (AREA)
- General Health & Medical Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Quality & Reliability (AREA)
- Health & Medical Sciences (AREA)
- Operations Research (AREA)
- Primary Health Care (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Train Traffic Observation, Control, And Security (AREA)
Abstract
The invention relates to a passenger flow distribution simulation evaluation method and a passenger flow distribution simulation evaluation system under a rail transit fault, wherein the method comprises the following steps: step 1: preprocessing basic data; step 2: modeling and simulating the interactive process of passenger flow and train: for each departure event, dividing the train waiting process, the getting-on process and the getting-off process by a passenger flow and train interaction process modeling and simulation module, sequentially traversing all departure events to form dynamic simulation, and calculating a passenger flow time-space distribution index; and step 3: and (3) passenger flow distribution evaluation: based on the passenger flow distribution calculation result, the passenger service level evaluation module compares the passenger flow distribution indexes and the passenger travel time indexes related to the train and the platform in the fault scene. Compared with the prior art, the method has the advantages of providing assistant decision support for handling large passenger flow and handling vehicle dispatching in a fault and the like.
Description
Technical Field
The invention relates to a rail transit passenger flow data analysis processing technology, in particular to a passenger flow distribution simulation evaluation method and system under a rail transit fault.
Background
As a huge complex system, the operation of urban rail transit is influenced by various factors, faults are difficult to avoid and occur randomly, train operation delay and even operation interruption are caused, and therefore passenger trip experience is reduced. With the enlargement of the scale of a rail transit road network and the increase of passenger flow, faults occur more frequently and have larger and larger influence, but because the current passenger flow is difficult to predict in real time, the influence range and the influence degree of the faults on the passenger trip are difficult to quantify in dynamic operation practice.
With the accumulation and the availability of the historical operation data of the rail transit, the method for regularly mining based on the historical passenger flow data becomes a common method in recent years, and the method mainly analyzes the influence of faults on the passenger flow by comparing the difference of the space-time distribution of the passenger flow of a section under the fault condition and the normal condition. However, the reliability of the historical passenger flow data analysis result depends on the accuracy of the network passenger flow clearing method, the transport capacity and the passenger flow are dynamically changed under abnormal conditions, and the fault process is difficult to restore by the subsequent clearing result, so that the passenger flow data analysis under the fault is easy to deviate from the actual condition.
In practice, because present each city is not popularized yet completely to the timely propelling movement of fault information, most commuting passengers go on a journey according to usual fixed time, just know fault information after arriving the subway station usually. Therefore, the passenger flow at a period of time after the fault is started is not much different from the previous day, and more passengers are detained in the train, the train is crowded in the train, the platform waits, the passengers enter the train and queue the passengers out of the train because the transportation energy is reduced and the passenger flow distribution speed is slowed down. The passenger flow analysis after the fault cannot reflect the interactive process of train operation and platform passenger flow during the fault period and the distribution condition and the supply condition of transportation energy of the passenger flow at each time-space position under the interaction.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides a passenger flow distribution simulation evaluation method and a passenger flow distribution simulation evaluation system under a rail transit fault, which are used for calculating the space-time distribution of passenger flow under the fault and the meeting condition of transport capacity on the demand, and providing auxiliary decision support for handling large passenger flow and handling driving scheduling under the fault.
The purpose of the invention can be realized by the following technical scheme:
according to one aspect of the invention, a passenger flow distribution simulation evaluation method under rail transit fault is provided, which comprises the following steps:
step 1: preprocessing basic data: calling OD passenger flow grouped data obtained by a standard passenger flow calculating module, and arranging the OD passenger flow grouped data according to the ascending sequence of the station-entering time; selecting the subsequent stage starting event data obtained by the adjustment chart calculation module, and arranging the starting event data in ascending order according to the direction and the time; setting train capacity data and limiting parameter values of the longest waiting time of passengers;
step 2: modeling and simulating the interactive process of passenger flow and train: for each departure event, dividing the train waiting process, the getting-on process and the getting-off process by a passenger flow and train interaction process modeling and simulation module, sequentially traversing all departure events to form dynamic simulation, and calculating a passenger flow time-space distribution index;
and step 3: and (3) passenger flow distribution evaluation: based on the passenger flow distribution calculation result, the passenger service level evaluation module compares the passenger flow distribution index and the passenger travel time index related to the train and the platform in the fault scene.
As a preferred technical solution, the specific process of calculating the passenger flow space-time distribution index in step 2 is as follows:
1) Waiting for the vehicle: explicitly waiting for a set of passengers and a set of passengers leaving because the maximum waiting time is exceeded; consider newly inbound passengers and passengers who have left a preceding car; for a small-traffic-road turn-back station, if a train in front of the small-traffic-road turn-back station turns back in advance, the condition that a passenger getting off in advance enters into waiting or leaves is also considered, an attribute of getting off in advance is added to the passenger, the passenger is considered as a new 'station-entering moment', waiting or leaving is judged according to the maximum waiting time as the same as the first two classes of passengers, wherein the maximum waiting time is set as D g ；
2) The getting-on process: determining the number of available passengers based on the remaining capacity of the train and the number of waiting passengers, and if the number of available passengers is equal to the number of waiting passengers, enabling all waiting passengers to be available for getting on the train; if the number of passengers getting on the train is equal to the remaining capacity of the train, the passengers get on the train in the principle of first-come first-serve until a certain group of passengers cannot get on the train completely, and at the moment, the passengers at different destinations in the group are considered to get on the train in proportion, so that the passenger collection getting on the train, the passenger collection left to take the train and the passenger collection in the train are calculated in an accumulated mode;
3) A getting-off process: and defining a set of passengers getting off, determining the passengers getting off at the station in advance according to the destination of the passengers and the starting event of the station, and distinguishing the conditions of all the passengers getting off at the turn-back station of the small traffic road.
As a preferred techniqueScheme, said maximum waiting time D g The value of the time interval is set under the condition that most passengers are willing to wait more due to longer trip time in a trip habit or other modes under the condition of considering faults, and is more than or equal to two planned driving intervals;
the calculation of the train residual capacity needs to distinguish whether the current starting event is an initiating event, and if the current starting event is the initiating event, the train has complete train capacity; otherwise, subtracting the number of the passengers on the train when the train departs from the previous station and the number of the passengers getting off the train at the current station from the capacity of the train; the number of the passengers on the train when the train departs firstly comprises the number of the passengers on the train at the station, and secondly, the number of the passengers on the train when the train departs from the previous station and the number of the passengers off the train when the train arrives at the station are added to the non-starting event.
As a preferred technical solution, the passenger flow distribution index and the passenger travel time index in step 3 include:
1) The train full load rate: the number of people in each space-time position compartment can be known through the passenger set in the train corresponding to each departure event, and the space-time full load rate of each train is calculated by combining the train passenger deciding capacity;
2) Station congestion situation: analyzing the distribution and evolution characteristics of passengers staying and waiting for passengers, calculating the occurrence rate of all-line passengers staying, and measuring the station congestion by combining the station capacity; .
3) Passenger exit condition: analyzing the number and distribution characteristics of passengers leaving the train, and checking the overall adaptation condition of train service and passenger flow requirements;
4) Passenger waiting time: the method comprises the time for waiting for getting on the train at a starting station and the time for waiting for the next shift of the train after being forced to get off in advance at a small traffic route return station; in the case of temporary adjustment to the folding back of the minor traffic route, the latter belongs to increased waiting time, so that a penalty coefficient larger than 1 is multiplied, and the waiting time before the passengers leave is also counted;
5) Travel time of the passenger: including waiting time and ride time, and also considering both being serviced and not being serviced by departure, for a departing passenger, consider its subsequent travel time increase, i.e., equal to the planned ride time multiplied by a penalty factor greater than 1.
As a preferred technical solution, the getting-on and getting-off attributes of each passenger group are recorded during simulation of the passenger flow and train interaction process in step 2, and include a start station getting-on time, a transfer station getting-off time, a transfer station getting-on time, and a terminal station getting-off time, so that the passenger waiting time and the riding time associated with each train departure event are calculated in step 3, and the total passenger waiting time and the total passenger riding time are calculated.
According to another aspect of the invention, a passenger flow distribution simulation evaluation system under rail transit fault is provided, which comprises:
and a standard passenger flow volume calculation module: the system is used for obtaining the standard passenger flow volume of each space-time granularity of a certain characteristic day according to the statistical analysis of historical passenger flow data of a plurality of same characteristic days under normal operation conditions;
an adjustment operation diagram calculation module: the adjustment operation diagram data is used for generating a fit plan operation diagram according to the fault information and the scheduling strategy;
the passenger flow and train interactive process modeling and simulation module comprises: the system is used for constructing a passenger flow distribution index calculation method according to the interaction state of platform passenger flow and the train running process, loading standard passenger flow and adjusting operation diagram data, and calculating a passenger flow space-time distribution result through dynamic simulation by combining with the train capacity limit;
a passenger service level assessment module: and the passenger travel service level evaluation system is used for comparing the normal scene and evaluating the passenger travel service level index in the fault scene according to the passenger flow space-time distribution calculation result.
As a preferred technical solution, the multiple days with the same characteristics under the normal operation condition refer to a week with the same practical meaning in a period of time when no abnormal event occurs; each space-time granularity refers to each OD group s and unit time t, and the corresponding standard passenger flow is expressed as
The calculation formula is as follows:
wherein Dx is a set of normal days,
a clustering algorithm is adopted for calculating the function; when the fault date and the national holiday are eliminated, an averaging method can be adopted.
As a preferred technical solution, the fault information includes fault time, location, cause, and estimated fault duration; and the dispatching strategy is confirmed according to the fault information, a station stopping time and interval running time adjusting strategy is adopted for slight faults, and a corresponding intersection and interval adjusting strategy is searched from the emergency plan for serious faults.
As a preferred technical scheme, the interaction state of the platform passenger flow and the train running process is divided into an waiting process, an getting-on process and a getting-off process; the passenger flow distribution indexes comprise the number of waiting persons, the number of leaving persons, the number of getting-on persons, the number of remaining persons, the number of getting-on persons and the number of getting-off persons corresponding to each train at each station.
Preferably, the passenger service level index includes a train full load rate, a platform congestion condition, a passenger leaving condition, a passenger waiting time, and a passenger travel time.
Compared with the prior art, the invention has the following advantages:
1. the standard passenger flow volume can be calculated and stored in advance, and the passenger flow demand characteristics similar to the characteristic days with the same characteristics at the beginning of the fault and the previous day are reflected, so that the quick calling and handling under the fault are facilitated, and the quick handling under the fault is facilitated.
2. And an adjusting operation diagram is quickly generated according to the scheduling experience and the emergency plan, so that the change situation of the transport capacity can be conveniently mastered, and the operation situation is comprehensively known by combining the standard passenger flow for further adjusting and optimizing reference.
3. By refining the interactive process of passenger flow and trains, the distribution condition of the demand of each station and each class of passengers is predicted, and reference is provided for traffic scheduling and passenger transport organizations.
4. The maximum waiting time is set, the change of the passenger trip behavior under the influence of faults is reflected, the value of the parameter is adjusted by combining different line conditions, and the change of the passenger waiting behavior in practice is reflected.
Drawings
FIG. 1 is a system architecture diagram of the present invention;
FIG. 2 is a schematic diagram of the interaction result between passenger flow and train according to the present invention;
FIG. 3 is a schematic diagram of the full load of the train under the normal operation scenario in the embodiment of the present invention, in which (a) is the ascending (D) g =9 min), and (b) is downlink (D) g ＝9min)；
FIG. 4 is a schematic diagram of the train full load rate under the fault operation scenario in the embodiment of the present invention, where (a) is the uplink (D) g =9 min), and (b) is downlink (D) g =9 min), and (c) is uplink (D) g =15 min), and (D) is downlink (D) g ＝15min)；
Fig. 5 is a schematic diagram illustrating a sequence of the number of waiting passengers at the platform for all train services according to an embodiment of the present invention, wherein (a) is the number of waiting passengers at the platform in a normal operation scenario, and (b) is the number of waiting passengers at the platform in a fault operation scenario.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, shall fall within the scope of protection of the present invention.
The invention provides a passenger flow distribution simulation evaluation method under a rail transit fault and a corresponding system. In short, the method adjusts the operation diagram data based on normal standard passenger flow data and fault stages, and calculates and evaluates passenger flow distribution indexes and passenger travel time indexes related to the train and the platform under the fault through modeling in a sub-process and simulating the interaction process of the passenger flow and the train according to time sequence.
The invention is further illustrated below, with reference to fig. 1, and comprises the following implementation steps:
step S1: before a fault occurs, standard passenger flow is calculated and stored in advance based on historical normal passenger flow data in a standard passenger flow calculation module.
Aiming at a certain characteristic day, selecting a period of week with the same actual meaning in the historical time without abnormal events, carrying out statistical analysis on historical passenger flow data, and calculating the standard passenger flow of each OD group s in unit time t, wherein the calculation formula is as follows:
wherein Dx is a set of normal days,
in order to calculate the function, a clustering algorithm is generally adopted, and when a fault date and a national holiday are eliminated, a mean value calculation method can be adopted.
Particularly, the step can be carried out at any time period of normal operation, and if the passenger flow characteristics are stable, the step can be calculated and stored at one time for standby in a subsequent period.
Step S2: after a fault occurs, an adjustment operation diagram calculation module rapidly generates adjustment operation diagram data according to fault information, a plan operation diagram and a scheduling strategy, and the adjustment operation diagram data of a subsequent stage are directly called during fault treatment.
The fault information comprises fault time, position, reason and estimated fault duration; and the dispatching strategy is confirmed according to the fault information, the minor fault mainly adopts a stop time and interval running time adjusting strategy, and the major fault searches a corresponding intersection and interval adjusting strategy from the emergency plan.
And step S3: and calling the standard passenger flow data obtained in the step S1, selecting the starting event data of the subsequent-stage adjustment operation diagram in the step S2, and setting the values of the train capacity and the longest waiting time limit parameter of the passengers.
Wherein, the standard passenger flow data are grouped and arranged in ascending order according to OD and station-entering time, and the adjusted departure event is arranged according to directionThe parameters such as the time ascending sequence, the train capacity, the longest waiting time of passengers and the like are respectively taken according to the line conditions. Wherein the maximum waiting time is set to D g 。
And step S4: based on the data preprocessed in the step S3, for each departure event, a model is built and a simulation module is used in the passenger flow and train interaction process, the passenger flow and train interaction process is refined in the vehicle classifying process, the getting-on process and the getting-off process, all departure events are traversed in sequence to form dynamic simulation, and passenger flow space-time distribution indexes including the number of waiting persons, the number of leaving persons, the number of getting-on persons, the number of remaining persons, the number of getting-on persons and the number of getting-off persons corresponding to each train at each station are calculated, and the reference chart 2 is used for referring to the data.
1) Waiting for the vehicle: explicitly waiting for the passenger set and the passenger set that departed because the maximum waiting time was exceeded. Consideration is generally given to newly arriving passengers and passengers who have left the lead car; for a small-traffic-road turning-back station, if a train in front of the small-traffic-road turning-back station turns back in advance, the situation that a passenger getting off in advance enters or leaves needs to be considered, an attribute of getting off in advance is added for the passenger, the new 'getting-on time' is considered, and waiting or leaving is judged according to the maximum waiting time as the first two classes of passengers, namely: the passengers wait when the passengers get into the station and the next train arrives within the maximum waiting time; otherwise, the passenger leaves.
The value of the maximum waiting time influences the calculation result of the passenger flow distribution index, most passengers are willing to wait more and can bear longer waiting time due to longer trip time in the trip habit or other modes under the condition of considering the fault, and therefore D g The value range is set to be more than or equal to two planned driving intervals.
2) The getting-on process: and accumulating and calculating the boarding passenger set, the reserved passenger set and the in-vehicle passenger set. Determining the number of available passengers based on the remaining capacity of the train and the number of waiting passengers, and if the number of available passengers is equal to the number of waiting passengers, enabling all the waiting passengers to be available; if the number of passengers capable of getting on the train is equal to the remaining capacity of the train, the passengers get on the train in the principle of first-come first-serve until a group of passengers arriving at the same time can not get on the train completely, at the moment, the passengers of different destinations in the group are considered to get on the train in proportion to obtain a part of getting on the train and a part of remaining passengers in the group, the total passengers getting on the train corresponding to the event are composed of all the passengers grouped in the past and the passengers grouped in the part of getting on the train, and the total remaining passengers are composed of all the passengers grouped in the past and all the passengers grouped in the next.
The calculation of the train residual capacity needs to distinguish whether the current starting event is an initiating event, and if the current starting event is the initiating event, the train has complete train capacity; or subtracting the number of the passengers getting on the train when the train departs from the last station and the number of the passengers getting off the train from the current station from the capacity of the train. The number of the passengers on the train when the train departs comprises the number of the passengers on the train at the station, and the number of the passengers on the train when the train departs from the previous station is added to the non-starting event, and the number of the passengers off the train when the train arrives at the station is subtracted.
3) A getting-off process: the set of alighting passengers is specified. And determining the passengers getting off at the station in advance according to the destination of the passengers and the starting event of the station, and distinguishing the conditions of all the passengers getting off at the turn-back station of the small traffic road.
Step S5: and comparing the passenger flow distribution calculation result in the step S4 with a normal scene in a passenger service level evaluation module, and evaluating passenger flow distribution indexes and passenger travel time indexes related to the train and the platform in a fault scene.
1) The train full load rate: the number of people in each space-time position compartment can be known through the passenger set in the train corresponding to each departure event, and the space-time full load rate of each train is calculated by combining the train passenger deciding capacity;
2) Station congestion situation: and analyzing the distribution and evolution characteristics of the passengers staying and waiting for passengers, calculating the occurrence rate of all-line staying, and measuring the congestion degree of the platform by combining the capacity of the platform.
3) Passenger exit condition: and analyzing the number and distribution characteristics of passengers leaving the train, and checking the overall adaptation condition of the train service and the passenger flow demand.
4) Passenger waiting time: the method comprises the time for waiting for getting on at a starting station and the time for waiting for the next shift of traffic trains after the small traffic route return station is forced to get off in advance. In the case of temporary adjustment to minor-crossing foldback, the latter is of increased latency, and is therefore multiplied by a penalty factor greater than 1, set to δ. The waiting time before the passenger leaves is also taken into account.
5) Travel time of the passenger: including waiting time and riding time, as well as being serviced and not serviced by leaving. For the leaving passenger, the subsequent travel time is considered to be increased, namely equal to the planned riding time multiplied by a penalty coefficient which is larger than 1, and is set as rho.
In step S4, the getting-on and getting-off attributes of each passenger group are recorded during the simulation of the passenger flow and train interaction process, including the getting-on time at the start station, the getting-off time at the transfer station, the getting-on time at the transfer station, and the getting-off time at the end station, so that the passenger waiting time and the riding time associated with each train departure event are calculated in step S5, and the total passenger waiting time and the total passenger riding time are calculated.
Further, as shown in fig. 1, the results of the passenger service level evaluation module can be used to further optimize and adjust the operation diagram, such as finding the space-time position where the capacity of the train or platform is not enough to meet the passenger flow demand, for the car scheduling and the passenger organization to take countermeasures in time.
Taking an actual line as an example, the system and the method of the invention are used for evaluating the passenger flow distribution under the fault. Selecting the passenger flow data under the normal operation condition, and converting the network OD passenger flow data into local line OD passenger flow data in advance to form standard passenger flow. The train passenger capacity is 840 persons, and the maximum allowable load rate of the train is calculated by 120%. The transfer latency penalty factor δ is set to 2. And (3) testing two normal and fault operation scenes by taking the following steps of 8. The normal scene is orderly operated according to a plan, and a plan operation diagram is used; the fault scenario is that assuming that 8. Taking into account the maximum waiting time D of the passengers g And the penalty coefficient rho of subsequent trips of passengers leaving has obvious influence on the calculation of passenger flow distribution indexes, and different values are respectively set for discussion and analysis: (1) D g ＝9/10/15min；(2)ρ＝1.5/2。
The train full load rate is as shown in fig. 3 and 4, and the upper and lower rows are respectively displayed in each operation scene; the platform related passenger flow distribution index statistics are shown in table 1 and fig. 5; the passenger travel time index is shown in table 2.
TABLE 1
TABLE 2
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (8)
1. A passenger flow distribution simulation evaluation method under a rail transit fault is characterized by comprising the following steps:
step 1: preprocessing basic data: calling OD passenger flow grouped data obtained by a standard passenger flow calculating module, and arranging the OD passenger flow grouped data according to the ascending sequence of the station-entering time; selecting the subsequent stage starting event data obtained by the adjustment chart calculation module, and arranging the starting event data in ascending order according to the direction and the time; setting train capacity data and limiting parameter values of the longest waiting time of passengers;
and 2, step: modeling and simulating the interactive process of passenger flow and train: for each departure event, dividing the train waiting process, the getting-on process and the getting-off process by a passenger flow and train interaction process modeling and simulation module, sequentially traversing all departure events to form dynamic simulation, and calculating a passenger flow time-space distribution index;
and step 3: and (3) passenger flow distribution evaluation: comparing a normal scene in a passenger service level evaluation module based on a passenger flow distribution calculation result, and calculating passenger flow distribution indexes and passenger travel time indexes related to trains and platforms in a fault scene;
the specific process of calculating the passenger flow space-time distribution index in the step 2 is as follows:
1) Waiting for the vehicle: explicitly waiting for a set of passengers and a set of passengers leaving because the maximum waiting time is exceeded; considering newly inbound passengers and passengers left for riding in a preceding car; for a small-traffic-road retracing station, if a train in front of the small-traffic-road retracing station turns back in advance, the condition that passengers getting off the train in advance join in waiting or leave is also considered, an attribute of getting off the train in advance is added for the passengers, the attribute is regarded as a new 'station entering moment', waiting or leaving is judged according to the maximum waiting time as the same as the first two classes of passengers, wherein the maximum waiting time is set as D g ；
2) The getting-on process: determining the number of available passengers based on the remaining capacity of the train and the number of waiting passengers, and if the number of available passengers is equal to the number of waiting passengers, enabling all waiting passengers to be available for getting on the train; if the number of passengers getting on the train is equal to the remaining capacity of the train, passengers get on the train in the principle of first-come first-serve until a certain group of passengers cannot get on the train completely, and at the moment, the passengers at different destinations in the group are considered to get on the train in proportion, so that the passenger set getting on the train, the passenger set staying and the passenger set in the train are calculated in an accumulated manner;
3) The getting-off process comprises the following steps: defining a set of passengers getting off, determining the passengers getting off at the station in advance according to the destination of the passengers and the starting event of the station, and distinguishing the conditions of all the passengers getting off at the turn-back station of the small traffic route;
the passenger flow distribution index and the passenger travel time index in the step 3 comprise:
1) The train full load rate: the number of people in each space-time position compartment can be known through the passenger set in the train corresponding to each departure event, and the space-time full load rate of each train is calculated by combining the train passenger deciding capacity;
2) Station congestion situation: analyzing the distribution and evolution characteristics of passengers staying and waiting for passengers, calculating the occurrence rate of all-line passengers staying, and measuring the station congestion by combining the station capacity;
3) Passenger exit condition: analyzing the number and distribution characteristics of passengers leaving the train, and checking the overall adaptation condition of train service and passenger flow requirements;
4) Waiting time of passengers: the method comprises the time for waiting for getting on the train at a starting station and the time for waiting for the next shift of the train after being forced to get off in advance at a small traffic route return station; in the case of temporary adjustment to the folding back of the small traffic road, the latter belongs to the increased waiting time, so the penalty factor which is larger than 1 is multiplied, and the waiting time before the passengers leave is also counted;
5) Travel time of the passenger: including waiting time and ride time, and also considering both being serviced and not being serviced by departure, for a departing passenger, consider its subsequent departure time to increase, i.e., equal to the planned ride time multiplied by a penalty factor greater than 1.
2. The method as claimed in claim 1, wherein the maximum waiting time D is a maximum waiting time g The value of the time interval is set under the condition that most passengers are willing to wait more due to longer trip time in a trip habit or other modes under the condition of considering faults, and is more than or equal to two planned driving intervals;
the calculation of the remaining train capacity is to distinguish whether the current starting event is an initiating event, if so, the train has complete train capacity; or else, the capacity of the train is obtained by subtracting the number of the people on the train when the train starts at the previous station and adding the number of the people getting off the train at the current station; the number of the passengers on the train when the train departs firstly comprises the number of the passengers on the train at the station, and secondly, the number of the passengers on the train when the train departs from the previous station and the number of the passengers off the train when the train arrives at the station are added to the non-starting event.
3. The method as claimed in claim 1, wherein in step 2, the getting-on/off attributes of each passenger group are recorded during simulation of the interaction process between the passenger flow and the train, including the getting-on time at the start station, the getting-off time at the transfer station, the getting-on time at the transfer station, and the getting-off time at the end station, so that the waiting time and the riding time of the passengers associated with each train departure event are calculated in step 3, and the total waiting time and the total riding time of the passengers are calculated.
4. A passenger flow distribution simulation evaluation system under rail transit fault is characterized by comprising:
and a standard passenger flow volume calculation module: the system is used for obtaining the standard passenger flow volume of each space-time granularity of a certain characteristic day according to the statistical analysis of historical passenger flow data of a plurality of same characteristic days under normal operation conditions;
an adjustment operation diagram calculation module: the adjustment operation diagram data is used for generating a fit plan operation diagram according to the fault information and the scheduling strategy;
the passenger flow and train interactive process modeling and simulation module comprises: the system is used for constructing a passenger flow distribution index calculation method according to the interaction state of platform passenger flow and the train running process, loading standard passenger flow and adjusting operation diagram data, and calculating a passenger flow space-time distribution result through dynamic simulation by combining with the train capacity limit;
a passenger service level assessment module: the passenger travel service level evaluation system is used for comparing normal scenes according to the passenger flow space-time distribution calculation result and evaluating passenger travel service level indexes under fault scenes;
the specific process for calculating the passenger flow space-time distribution index is as follows:
1) Waiting for the vehicle: explicitly waiting for a set of passengers and a set of passengers leaving because the maximum waiting time is exceeded; considering newly inbound passengers and passengers left for riding in a preceding car; for a small-traffic-road retracing station, if a train in front of the small-traffic-road retracing station turns back in advance, the condition that passengers getting off the train in advance join in waiting or leave is also considered, an attribute of getting off the train in advance is added for the passengers, the attribute is regarded as a new 'station entering moment', waiting or leaving is judged according to the maximum waiting time as the same as the first two classes of passengers, wherein the maximum waiting time is set as D g ；
2) The getting-on process: determining the number of available passengers based on the remaining capacity of the train and the number of waiting passengers, and if the number of available passengers is equal to the number of waiting passengers, enabling all the waiting passengers to be available; if the number of passengers getting on the train is equal to the remaining capacity of the train, the passengers get on the train in the principle of first-come first-serve until a certain group of passengers cannot get on the train completely, and at the moment, the passengers at different destinations in the group are considered to get on the train in proportion, so that the passenger collection getting on the train, the passenger collection left to take the train and the passenger collection in the train are calculated in an accumulated mode;
3) A getting-off process: defining a set of passengers getting off, determining the passengers getting off at the station in advance according to the destination of the passengers and the starting event of the station, and distinguishing the conditions of all the passengers getting off at the turn-back station of the small traffic route;
the passenger flow distribution index and the passenger travel time index comprise:
1) The train full load rate: the number of people in each space-time position compartment can be known through the passenger set in the train corresponding to each departure event, and the space-time full load rate of each train is calculated by combining the train passenger deciding capacity;
2) Station congestion situation: analyzing the distribution and evolution characteristics of passengers staying and waiting for passengers, calculating the occurrence rate of all-line passengers staying, and measuring the station congestion by combining the station capacity;
3) Passenger exit condition: analyzing the number and distribution characteristics of passengers leaving the train, and checking the overall adaptation condition of the train service and the passenger flow demand;
4) Passenger waiting time: the method comprises the time for waiting for getting on the train at a starting station and the time for waiting for the next shift of the train after being forced to get off in advance at a small traffic route return station; in the case of temporary adjustment to the folding back of the minor traffic route, the latter belongs to increased waiting time, so that a penalty coefficient larger than 1 is multiplied, and the waiting time before the passengers leave is also counted;
5) Travel time of the passenger: including waiting time and ride time, and also considering both being serviced and not being serviced by departure, for a departing passenger, consider its subsequent departure time to increase, i.e., equal to the planned ride time multiplied by a penalty factor greater than 1.
5. The passenger flow distribution simulation evaluation system under the rail transit fault according to claim 4, wherein the multiple same characteristic days under the normal operation condition refer to weeks of the same practical meaning within a period of time when no abnormal event occurs; each space-time granularity refers to each OD group s and unit time t, and the corresponding standard passenger flow is expressed as
The calculation formula is as follows:
wherein Dx is a set of normal days,
for calculating the function, a clustering algorithm is adopted; when the fault date and the national holiday have been eliminated, an averaging method can be adopted.
6. The system for the simulation evaluation of the passenger flow distribution under the rail transit fault as claimed in claim 4, wherein the fault information comprises fault time, location, reason and estimated fault duration; and the dispatching strategy is confirmed according to the fault information, a station stopping time and interval running time adjusting strategy is adopted for slight faults, and a corresponding intersection and interval adjusting strategy is searched from the emergency plan for serious faults.
7. The passenger flow distribution simulation evaluation system under the rail transit fault according to claim 4, wherein the interaction state of the platform passenger flow and the train running process is divided into an waiting process, an getting-on process and a getting-off process; the passenger flow distribution indexes comprise the number of waiting persons, the number of leaving persons, the number of getting-on persons, the number of remaining persons, the number of getting-on persons and the number of getting-off persons corresponding to each train at each station.
8. The system of claim 4, wherein the passenger service level indicators include train occupancy, platform congestion, passenger departure, passenger waiting time, and passenger travel time.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010945280.9A CN112214873B (en) | 2020-09-10 | 2020-09-10 | Passenger flow distribution simulation evaluation method and system under rail transit fault |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010945280.9A CN112214873B (en) | 2020-09-10 | 2020-09-10 | Passenger flow distribution simulation evaluation method and system under rail transit fault |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112214873A CN112214873A (en) | 2021-01-12 |
| CN112214873B true CN112214873B (en) | 2022-10-04 |
Family
ID=74049938
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010945280.9A Active CN112214873B (en) | 2020-09-10 | 2020-09-10 | Passenger flow distribution simulation evaluation method and system under rail transit fault |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112214873B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112785071B (en) * | 2021-01-29 | 2023-02-28 | 华南理工大学 | Enterprise vehicle passenger flow simulation and prediction system |
| CN113570148B (en) * | 2021-08-02 | 2024-04-09 | 上海市城市建设设计研究总院（集团）有限公司 | Urban rail station stop time optimization setting method based on passenger simulation |
| CN114118775A (en) * | 2021-11-19 | 2022-03-01 | 北京市轨道交通建设管理有限公司 | Rail transit operation scheme analysis method and device and readable storage medium |
| CN114298669B (en) * | 2021-12-22 | 2024-04-09 | 交控科技股份有限公司 | Adjustment method and device for train running chart and train |
| CN115689154B (en) * | 2022-09-15 | 2023-07-25 | 合肥市轨道交通集团有限公司 | Urban rail transit scheduling auxiliary decision-making system based on dynamic passenger flow |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002037076A (en) * | 2000-07-27 | 2002-02-06 | Kawasaki Heavy Ind Ltd | Method and device for simulating train operation |
| CN103413433A (en) * | 2013-07-26 | 2013-11-27 | 浙江工业大学 | Traffic-jam bus transferring method based on passenger flow volume information |
| CN110329319A (en) * | 2019-06-28 | 2019-10-15 | 卡斯柯信号有限公司 | A kind of fully automatic operation system towards wisdom urban rail |
-
2020
- 2020-09-10 CN CN202010945280.9A patent/CN112214873B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002037076A (en) * | 2000-07-27 | 2002-02-06 | Kawasaki Heavy Ind Ltd | Method and device for simulating train operation |
| CN103413433A (en) * | 2013-07-26 | 2013-11-27 | 浙江工业大学 | Traffic-jam bus transferring method based on passenger flow volume information |
| CN110329319A (en) * | 2019-06-28 | 2019-10-15 | 卡斯柯信号有限公司 | A kind of fully automatic operation system towards wisdom urban rail |
Non-Patent Citations (3)
| Title |
|---|
| A stochastic transit assignment model considering differences in passengers utility functions;Otto Anker Nielsen;《Transportation Research Part B: Methodological》;20000630;第34卷(第5期);第377-402页 * |
| 城市轨道交通系统故障时的客流动态分布仿真研究;张知青等;《城市轨道交通研究》;20060830(第04期);全文 * |
| 基于事件驱动的城市轨道交通应急处置微观仿真系统;王志强;《城市轨道交通研究》;20151110(第11期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112214873A (en) | 2021-01-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112214873B (en) | Passenger flow distribution simulation evaluation method and system under rail transit fault | |
| CN107194497B (en) | Method for planning travel path of urban rail transit passenger in emergency | |
| CN111401614B (en) | Dynamic passenger flow distribution method and system for urban rail transit | |
| CN112793631B (en) | Subway running adjusting method and system under condition that train exits main line operation | |
| CN111401643B (en) | Urban rail transit passenger flow loop self-adaptive intelligent train scheduling method | |
| US20170232976A1 (en) | Method for directing passengers of public means of transport | |
| CN112949078B (en) | Matching degree calculation method for urban rail transit passenger flow and traffic flow | |
| CN112819316B (en) | Hub transportation energy identification method of comprehensive passenger transport hub rail transit system | |
| CN112918523B (en) | Passenger flow collaborative accurate control method for crowded subway line based on train schedule optimization | |
| CN114707709A (en) | Safety early warning method and system for comprehensive passenger transport hub of railway | |
| CN113888387A (en) | Multi-objective operation optimization method based on passenger flow demand | |
| CN113408859A (en) | Urban rail transit line passenger flow and train matching method considering passenger flow management and control measures | |
| CN111723963B (en) | Subway vehicle off-station operation energy optimization method | |
| CN110135721B (en) | Train passenger ticket supplementing scheduling method based on overload limitation of dynamic reverse calculation | |
| CN117094506A (en) | Subway transfer station passenger flow bottleneck control method based on platform congestion degree | |
| CN112784204B (en) | Train schedule and passenger flow control robust optimization method oriented to uncertain demands | |
| CN115983543A (en) | Scheduling method, system, terminal and storage medium for urban rail transit | |
| CN114021796B (en) | Urban rail transit flow control method and device and nonvolatile storage medium | |
| CN114655281A (en) | Train operation diagram processing method and device, electronic equipment and storage medium | |
| CN113962616A (en) | Coordinated current limiting method and system based on passenger accumulated travel cost analysis | |
| CN112906926B (en) | Method, device, equipment and storage medium for predicting railway transportation flow direction | |
| van Oort et al. | Impact of rail terminal design on transit service reliability | |
| CN118710478A (en) | Subway burst operation interruption passenger flow dredging method based on improved sparrow searching algorithm | |
| CN117808184B (en) | Urban rail transit service management system driven by full production elements | |
| Luo et al. | Study on the Propagation Mechanism of Large Passenger Flow in Urban Rail Transit |
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 | ||
| REG | Reference to a national code |
Ref country code: HK Ref legal event code: DE Ref document number: 40035927 Country of ref document: HK |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |