CN103043084A - Method and system for optimizing urban railway transit transfer - Google Patents
Method and system for optimizing urban railway transit transfer Download PDFInfo
- Publication number
- CN103043084A CN103043084A CN2012105911454A CN201210591145A CN103043084A CN 103043084 A CN103043084 A CN 103043084A CN 2012105911454 A CN2012105911454 A CN 2012105911454A CN 201210591145 A CN201210591145 A CN 201210591145A CN 103043084 A CN103043084 A CN 103043084A
- Authority
- CN
- China
- Prior art keywords
- pedestrian
- attribute
- train
- station
- transfer
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Abstract
The invention discloses a method and a system for optimizing urban railway transit transfer. The method includes configuring an Agent object attribute on the basis of an Agent method, establishing a subway station artificial system and demonstrating in 2D (two dimension) and/or 3D (three dimension); computing average transfer time and analyzing affection on the average transfer time from the attribute of the subway station artificial system to obtain an optimizing result; and adjusting running of trains according to the optimizing result. The system comprises a system establishing module, a transfer optimizing module and a train running optimizing module. By establishing the subway station artificial system to display dynamic scenes of passenger flow in a subway station, flow and distribution states of pedestrians in the subway station are clearly displayed, and particularly, on the basis of a real station mirror image model and a pedestrian dynamics model, behavior characteristics of the pedestrians are parameterized so as to realize optimization of the urban railway transit transfer, and the method and the system for optimizing the urban transit transfer are quite creative and high in scientific research value.
Description
Technical field
The present invention relates to urban track traffic.More specifically, the present invention relates to a kind of urban track traffic transfer optimization method and system.
Background technology
Enter 2l century, along with improving constantly of Chinese Urbanization level, urban population density will strengthen rapidly, and consequent traffic jam issue also can be more and more severeer, and this will become one of key factor of restriction economy, society, cultural development.Urban track traffic will become effective mode of transportation of solving urban traffic blocking as the important component part of public transport.Because that urban track traffic has is energy-conservation, economize ground, freight volume is large, round-the-clock, on schedule, the characteristics such as pollution-free again safety, can draw such conclusion: the development of urban track traffic meets continuable development principle fully.Therefore, the following a very long time will be the flourish golden period of urban track traffic.Show according to reliable data, by the end of the end of the year 2010, the rail transportation operation total kilometrage that tens cities such as Beijing of China, Shanghai have had has reached 2700 kilometers.According to long term planning, to reach 6100 kilometers to the year two thousand twenty National urban track traffic total kilometrage, wherein the total kilometrage of Beijing's urban track traffic will be above 1000 kilometers, public transport will become civic, 80% left and right sides vehicle of mainly going on a journey, and wherein have the people about 50% will often use urban track traffic as the trip instrument.Along with the continuous expansion of urban rail transit in China network size, the height of urban track traffic transfer efficient will become the key factor that affects its efficiency of operation and service quality.Yet, China's big city track traffic ubiquity is changed to the lower phenomenon of efficient at present, this low efficiency has limited the high efficiency operation of urban mass transit network, for alleviating urban public transport pressure and improving existing current situation of traffic very important meaning is arranged so how research improves transfer efficient.
The method of traditional research orbit traffic transfer optimization problem mainly relies on observed data and adds up, and based on statistics match transfer time distribution function, and then makes up the transfer Optimized model and finds the solution optimum transfer plan.Since 20 century 70s, abroad just to rise gradually for the research of pedestrian behavior feature, development has formed comparatively ripe theoretical system so far, has also accumulated certain experiment experience simultaneously.At present existing research mainly lays particular emphasis on theoretical analysis and the experimental design of pedestrian's walking behavior, and is still less for the behavioural characteristic research of pedestrian in the urban track traffic transfer stop.With abroad compare, domestic research focuses mostly in the topological design of urban track traffic transfer stop and transfer manner analysis, and the research of changing in the station aspect the concrete behavior optimization for the pedestrian is less.
Forefathers scholar is studied orbit traffic transfer is time-optimized by setting up various models.But, carry out research that the transfer time optimizes and few based on the three-dimensional subway station realistic model of true station mirror image and pedestrian's kinetic model, especially in the situation that the almost true reappearance of interior environment of standing is few especially with the research of the mutual all combinations between pedestrian, train and the environment comprehensively and accurately.The method of traditional research orbit traffic transfer optimization problem is macroscopical, do not take into full account pedestrian's behavioural characteristic to the impact of transfer optimization problem from microcosmic angle, be statistics rough on macroscopic view, can not comprehensively, flexibly and accurately show the situation of change that pedestrian's transfer time is dynamic and real-time.
Summary of the invention
The object of the invention is a kind of urban track traffic transfer optimization method and system, is used for from microcosmic angle by building the manual system realization to the optimization of traffic transfer.
Concrete technical scheme is as follows:
Urban track traffic transfer optimization method may further comprise the steps:
Step S1, Agent-base modeling method, the interior Agent attribute in configuration station and modeling, thus forming the subway station manual system also by 2D and/or 3D demonstration, described Agent object classification is pedestrian, train, stand interior environment and station equipment; Step S2, calculate the average transfer time and analyze the attribute of subway station manual system to the impact of average transfer time, draw optimum results, the attribute of described subway station manual system comprises described Agent attribute and macroscopical attribute; And, step S3, adjust the operation of train according to described optimum results.
Described step S1 further comprises:
Step S11, collection affect the metadata of transfer time, and described metadata comprises passenger flow data, pedestrian's data, train data; Step S12, described metadata is analyzed, filters, extracted, and be parameter with described metadata conversion and according to the type of described Agent object described parametric classification stored; Step S13, according to described parameter, utilize pedestrian's kinetic model to configure described pedestrian's attribute, described pedestrian is carried out modeling, described pedestrian's attribute comprises the pedestrian density under pedestrian's traveling speed and each trip time section; Step S14, according to real conditions in the station, utilize the Agent model to configure the attribute of environment and station equipment in the described station, and environment and station equipment in the described station carried out respectively the mirror image modeling; Step S15, according to described parameter, utilize the Agent model to configure the attribute of described train, and described train carried out modeling, the attribute of described train comprises the train arrival time gap; And, step S16, demonstrate described subway station manual system by 2D and/or 3D.
Described step S2 further comprises:
Step S21, utilize the discrete sampling algorithm to calculate and predict the average transfer time; Step S22, the described macroscopical attribute of configuration, this macroscopic view attribute comprises pedestrian density, transfer number and the number out of the station under each trip time section, analyzes the pedestrian density under described pedestrian's traveling speed, described each trip time section, the described train arrival time gap impact on the average transfer time; And, step S23, obtain the optimum results of average transfer time.
Described step S3 further comprises: according to described optimum results, each described Agent object is carried out distributed control, adjust train arrival time gap and train diagram in the pedestrian density under each trip time section, the zone of reasonableness of pedestrian's traveling speed.
Correspondingly, urban track traffic transfer optimization system comprises with lower module:
The system building module is used for the Agent-base modeling method, configures the interior Agent attribute in station and modeling, thereby forms the subway station manual system and pass through 2D and/or the 3D demonstration, and described Agent object classification is pedestrian, train, stand interior environment and station equipment; Module is optimized in transfer, is used for calculating the average transfer time and analyzes the attribute of subway station manual system to the impact of average transfer time, draws optimum results, and the attribute of described subway station manual system comprises described Agent attribute and macroscopical attribute; Optimize the train operation module, be used for adjusting according to described optimum results the operation of train.
Described system building module further comprises:
Data acquisition module is used for gathering the metadata that affects the transfer time, and described metadata comprises passenger flow data, pedestrian's data, train data; Data are compiled processing module, are used for described metadata is analyzed, filters, extracted, and are parameter with described metadata conversion and according to the type of described Agent object described parametric classification are stored; Pedestrian's MBM is used for according to described parameter, utilizes pedestrian's kinetic model to configure described pedestrian's attribute, and described pedestrian is carried out modeling, and described pedestrian's attribute comprises pedestrian's traveling speed; Environment and equipment modeling module in standing are used for according to real conditions in the station, utilize the Agent model to configure the attribute of environment and station equipment in the described station, and environment and station equipment in the described station are carried out respectively the mirror image modeling; The Train modeling module is used for according to described parameter, utilizes the Agent model to configure the attribute of described train, and described train is carried out modeling, and the attribute of described train comprises the train arrival time gap; And demonstration module is used for demonstrating described subway station manual system by 2D and/or 3D.
Described transfer is optimized module and is further comprised:
Average transfer time computing module is used for utilizing the calculating of discrete sampling algorithm and predicts the average transfer time; The experiment with computing module, be used for configuring described macroscopical attribute, this macroscopic view attribute comprises pedestrian density, transfer number and the number out of the station under each trip time section, analyzes the pedestrian density under described pedestrian's traveling speed, described each trip time section, the described train arrival time gap impact on the average transfer time; Acquisition module is used for obtaining the optimum results of average transfer time as a result.
Described optimization train operation module is further used for each described Agent object is carried out distributed control, adjusts train arrival time gap and train diagram in the pedestrian density under each trip time section, the zone of reasonableness of pedestrian's traveling speed.
The present invention represents the activity scene of passenger flow in the station by building the subway station manual system, flowing in the station one skilled in the art AT STATION, distribution situation have clearly been shown, especially based on true station mirror image model and pedestrian's kinetic model, cybernetics control number with the pedestrian, realize urban track traffic transfer optimization with this, have very much novelty and scientific research and be worth.
Description of drawings
Below with reference to accompanying drawings and in conjunction with the embodiments the present invention is specifically described.
Fig. 1 is the basic flow sheet of the inventive method;
Fig. 2 is the schematic diagram of the present invention's subway station manual system of building;
Fig. 3 is the structural representation of system of the present invention;
Fig. 4 is that each Agent object and unit interaction thereof concern schematic diagram;
Fig. 5 is the affect figure of arrival time in the period interval, flat peak of specific embodiment on the average transfer time;
The train arrival time gap was on the figure that affects of average transfer time when Fig. 6 was the different densities of specific embodiment.
The specific embodiment
With reference to the accompanying drawings and by embodiments of the invention, technical scheme of the present invention is described in detail.
The present invention adopts Agent(or is called the agency) thought build subway station manual system (abbreviation manual system), according to attribute and function whole system is classified.Being controlled on the design concept from traditional control system based on the agency has the different of essence: control policy no longer is towards algorithm, realizes and be based on the agency.Except having certain control function, intelligent, independence and interactivity are several essential characteristics of agency, can realize real distributed intelligence control, are fit to very much the distributed characteristics of each quasi-controller of complex process system.
According to shown in Figure 1, the invention provides a kind of urban track traffic transfer optimization method, may further comprise the steps: step S1, Agent-base modeling method, Agent attribute and modeling in the configuration station, thereby form the subway station manual system and pass through 2D and/or the 3D demonstration, described Agent object classification is pedestrian, train, stand interior environment and station equipment; Step S2, calculate the average transfer time and analyze the attribute of subway station manual system to the impact of average transfer time, draw optimum results, the attribute of described subway station manual system comprises described Agent attribute and macroscopical attribute; Step S3, adjust the operation of train according to described optimum results.
Correspondingly, according to Fig. 3, the invention provides a kind of urban track traffic transfer optimization system, comprise with lower module:
Optimize train operation module 3, be used for execution in step S3, adjust the operation of train according to described optimum results.
Further, described system building module 1 comprises that data acquisition module, data are compiled processing module, pedestrian's MBM, stand interior environment and equipment modeling module, Train modeling module and demonstration module.
Wherein said data acquisition module is used for execution in step S11, gathers the metadata that affects the transfer time, and described metadata comprises passenger flow data, pedestrian's data, train data.Described metadata comprises passenger flow data, pedestrian's data, train data, the collection of this metadata can obtain or gather by watch-dog, acquisition range comprise different pedestrians' traveling speed, pedestrian density, per capita floor area, whether carry luggage and age, and actual time of arrival of train, time departure, arrival interval etc.And this metadata is to gather respectively for different positions, different time sections, for example in the gangway, transfer stop with in get on or off the bus passenger flow, the transfer number at place, in the booking number of the booking number at ticket vending machine place, artificial ticket office, the direct number that enters the station etc. of swiping the card, also can grasp the flow of traffic of the different time sections such as peak, Ping Feng, ebb, Holiday.
Described data are compiled processing module, be used for execution in step S12, described metadata is analyzed, is filtered and extracts for the influential factor of transfer by image processing techniques, and with described metadata conversion be parameter and according to the type of described Agent object be the pedestrian, the interior environment of stand, train, station equipment store described parametric classification.Described parameter is used for configuring each Agent attribute.
Described pedestrian's MBM, be used for execution in step S13, compile described parameter in the processing module according to described data, utilize pedestrian's kinetic models such as social force model, field of magnetic forece model, queue theory model, cellular automata to configure described pedestrian's attribute, described pedestrian is carried out modeling, described pedestrian's attribute comprises pedestrian's traveling speed, also comprise to the familiarity of environment in station equipment and the station, by bus custom, whether carry luggage, whether team, have or not mass transit card and floor area etc. per capita.For pedestrian's setup of attribute, can use a lot of methods, arranging of this attribute of pedestrian among the present invention is preferred by the configuration of social force model modeling method.
Environment and equipment modeling module in the described station are used for execution in step S14, according to real conditions in the station, utilize the Agent model to configure the attribute of environment and station equipment in the described station, and environment and station equipment in the described station are carried out respectively the mirror image modeling.Environment refers to the physical environment in the subway station in standing, and can be divided into lower unit: pillar, platform etc.Can accurately configure its attribute according to the CAD drawing of design planning in the actual station.And station equipment is further divided into lower unit: the media devices such as ticket vending machine, artificial ticket machine, screening machine, gate, water conservancy diversion guardrail, oriented identification, passage, stair, elevator, shielded gate and broadcasting.The present invention is by the attribute of Agent model to each unit of stand interior environment and station equipment, and such as the pass of gate with open two attributes, open and close state and the running velocity of elevator are configured respectively, thereby have finally realized the mirror image modeling of subway station internal environment.
Described Train modeling module, be used for execution in step S15, compile described parameter and timetable in the processing module according to described data, utilize the Agent model to configure the attribute of described train, and described train carried out modeling, the attribute of described train comprises the train arrival time gap; And, the parking period that arrives at a station, pedestrian's load factor, timetable, the pedestrian number etc. of getting on or off the bus.The arrival time interval between the train arrival time gap train that refers to participate in changing to wherein, such as from A alignment car A1 arrival time to time that B alignment car B1 arrival time ends.
Equipment room and peripheral facility in considering to arrive at a station, the pedestrian is difficult to enter and carries out activity, therefore the present invention does not do detailed description.
Because the randomness of each Agent object can be compiled described data parameter input manual system and the process of preserving in the processing module and simply calculate the output statistics as the basis of analyzing the transfer time.
By each Agent object and each unit are carried out the setting of attribute, finished building of subway station manual system, as shown in Figure 2.For example: gate is divided into and enters the station and set off two types, the pedestrian by gate out of the station the time, delay that can be for some time; Escalator is divided into uplink and downlink; The pedestrian need to just can be entered the station by safety check; Enter the station the stage in booking, the pedestrian selects artificial ticketing, automatic ticketing or directly swipes the card the to some extent embodiments of these details of entering the station.
Owing to adopting social force model that pedestrian's attribute is set, therefore in manual system, can reproduce the activity scene of pedestrian in AT STATION, such as self organization phenomenon, arch obstruction, population effect etc.Simulated out of the station, the booking of pedestrian in the station (manually and ticket vending machine), advanced/set off gate, stair, escalator, a series of actions such as change to, wait, get on or off the bus, thereby showed interactive relation between pedestrian and other Agent objects.By this interactive relation, can clearly represent the factor relevant with pedestrian's transfer time, such as, shown in Figure 4, enter the station traveling speed and the traveling path that obviously affect the pedestrian understood in arranging of water conservancy diversion guardrail, and gate, screening machine, ticket machine and artificial ticket machine also can have influence on pedestrian's enter the station traveling speed and the path of entering the station.In order to keep the interval between normal traveling speed and the pedestrian, have obvious interaction between the pedestrian, final, have influence on speed and direction of travel.Oriented identification and passage have obvious impact to pedestrian's sense of motion in standing.Stair, elevator and have or not mass transit card to have a direct impact pedestrian's traveling speed.Obviously, be interactional between train and the pedestrian, when station one skilled in the art density was larger, the train departure interval can obviously shorten, and whether train arrives at a station and has determined that directly can the pedestrian leave, and has determined pedestrian's the state of waiting.When train arrival, the open state of shielded gate has also produced direct impact to pedestrian's by bus state.The setting of platform can be so that the pedestrian rides in order, and pedestrian's waiting time is had a direct impact.By with the representing of co-relation, can find out which factor to pedestrian's traveling speed, direction of travel has a direct impact, and can provide useful reference to the mode of optimizing the transfer time.
Described demonstration module is used for execution in step S16, demonstrates described subway station manual system by 2D and/or 3D.
Further, described transfer is optimized module 2 and is used for execution in step S2, calculate the average transfer time and analyze the impact of the attribute of subway station manual system on the average transfer time, draw optimum results, the attribute of described subway station manual system comprises described Agent attribute and macroscopical attribute, comprising: average transfer time computing module, experiment with computing module and acquisition module as a result.
Further, described average transfer time computing module is used for execution in step S21, under different time sections passenger flow situation, utilizes the calculating of discrete sampling algorithm and predicts the average transfer time.。
Transfer time generally be the pedestrian when the vehicle of a circuit is got off time gap when climbing up another circuit vehicle.This time gap can be divided into two parts, and a part is pedestrian's travel time, and a part is pedestrian's wait time.The pedestrian is called passage travel time T from the time that the delivery conduit platform begins to run to bond wire way station platform
Walk, the pedestrian arrives the time of ending behind the bond wire way station platform when nearest one class of train arrives be transfer wait time T
Wait
T
int erchange=T
walk+T
wait (1.1)
Because pedestrian's transfer travel time is mainly according to the distance travelled decision of transfer, transfer optimization mainly is to shorten the transfer wait time.
The pedestrian who participates in transfer is a colony, is subjected to the factor affecting such as environment and self, there are differences between individuality in addition, so the present invention is the shortest in optimization aim with all pedestrians' the average transfer time.Influence factor for average transfer time of pedestrian has a lot, such as: train arrival time gap, pedestrian's traveling speed, pedestrian density, different time sections, queuing situation, the traveling comfort of facility, pedestrian go on a journey pressure, personal like etc.The present invention will be limited to the major influence factors of average transfer time train arrival time gap, pedestrian's traveling speed, pedestrian density and trip time section.Wherein pedestrian's traveling speed and pedestrian density also have substantial connection from pedestrian's trip time section different, specific embodiments of the invention by will the section of trip and the pedestrian density to bind be that pedestrian density under each trip time section is described the impact of pedestrian's traveling speed.
Based on the characteristics of track traffic scheduling operation as can be known, by the running chart of two circuits of reasonable adjustment, two train arrival time gaps in succession just in time are consistent with pedestrian's travel time, then change to the wait time minimum, and then make the transfer time minimum.
Consider the transfer of two circuits, then all pedestrian's transfer time summations:
T wherein
DivideThe sample time interval; N is the number at sample time interval; N (t
i) be t
iNumber on the moment platform.
n(t
i)=n
walk(t
i)+n
wait(t
i) (1.3)
So, the average transfer time
T＝T
total/N (1.4)
Wherein N is the sum of finishing the transfer pedestrian.
Described experiment with computing module, be used for execution in step S22, configure described macroscopical attribute, this macroscopic view attribute comprises pedestrian density, transfer number and the number that enters the station under each trip time section, analyzes the pedestrian density under described pedestrian's traveling speed, described each trip time section, the described train arrival time gap impact on the average transfer time.Described macroscopical attribute also comprises the number of vehicle-mounted number, regional, the attribute of the manual systems such as expert number of standing itself.The basis of these macroscopical attributes also derives from the metadata of described data acquisition module and described data and compiles parameter in the processing module.
Because pedestrian's traveling speed is to passage travel time T
WalkAffect larger, and to transfer wait time T
WaitImpact also comparatively complicated.The different periods, pedestrian's density also can be different.The present invention will choose different velocity conditions in specific embodiment, impact on the average transfer time of pedestrian is described, and have the pedestrian under the prerequisite of equal traveling speed, analyze different pedestrians' density for the impact of average transfer time, in order to obtain so that the optimal solution at two train arrival intervals in the average the shortest situation of transfer time of pedestrian.
Described as a result acquisition module is used for execution in step S23, according to the analysis in the described experiment with computing module, obtains the optimum results of average transfer time.
The present invention is with the formal description transfer optimizing process of an example, and this example is take transfer subway station X as prototype, and by 1 pair of subway station modeling of described system building module, at this subway station X, the pedestrian can change between A line and B line.In described pedestrian's MBM, based on social force model the pedestrian in the subway station is carried out modeling, pedestrian in the model shows certain intelligent, for example can automatically keep suitably distance to collide avoiding between the pedestrian, and they understand the shortest travel path of Automatic-searching etc.In environment and equipment modeling module, the described Train modeling module, the different Agent objects that utilize Agent thought that classification is finished carry out modeling, finally build the manual system of having finished subway station X in described station.
Based on the manual system of X subway station, the data such as transfer number and transfer time are being carried out on the basis of measurements and calculations, proposition transfer optimized algorithm was optimized the average transfer time of the pedestrian from the A line to the B line.
Particularly, social force model used among the present invention is take Newtonian mechanics as the basis, the pedestrian is according to different motivation and suffered impact in environment, be subject to altogether the impact of three kinds of application forces: propulsive effort, subjective consciousness can turn to individual suffered self-imposed " social force " to the impact of individual behavior, has embodied the pedestrian moves to the destination with the speed of thirsting for motivation; Interpersonal application force is attempted " power " that keeps certain distance to apply with other pedestrians; Application force between people and the border, border and obstacle are similar to interpersonal effect to people's impact.Social force model can represent with following set of equations:
Wherein, r
αRepresent the position vector of pedestrian α, its differential is tried to achieve v
α(t) be the speed vector of pedestrian α; F in the formula (1.6)
α(t) representing the suffered social force of pedestrian α is that subjective consciousness is to the propulsive effort of people's motion, ξ
α(t) be distracter, both with the vector acceleration that consists of the pedestrian; In the formula (1.7)
Be acceleration force, f
α B(r
α) be the application force of pedestrian and border (Boundary),
Be the vectorial sum of pedestrian with other pedestrians' of periphery application force;
Be the attraction effect of suffered other things of pedestrian α, these four the social force f that consist of pedestrian α
α(t).
(1) attribute configuration
Humanized as example take configuration line, pedestrian's traveling speed is that the obedience aviation value is 1.19 in the artificial system of the present invention, and standard deviation is 0.35 normal distribution.Pedestrian's traveling speed is obeyed this normal distribution between 0.49～1.89.Specific object configures such as table 1:
Table 1 attribute configuration
(2) analyze the impact of the arrival time interval of two trains on the average transfer time
Change the descending arrival time interval T with the B line up train of A line
d, on subway station X manual system take 0.5 minute as the transformation period step-length, draw pedestrian's the average transfer time.Transformation period step-length of every change, moving model nine times is finally got the transfer time aviation value of nine operation results, is the more accurately average transfer time of pedestrian.To drawing diagram of curves 5 after the data analysis, the selected time period of Fig. 5 is flat peak period, and transverse axis is train arrival time gap T
d, the longitudinal axis is the average transfer time.
Can be obtained by Fig. 5, the best average transfer time is 7min, corresponding T
d1min.
(3) analyze the impact of pedestrian density on the average transfer time
The pedestrian is different the average transfer time on the peak, when Ping Feng, ebb, Holiday, according to the data that gather, respectively to each in period different pedestrian densities the pedestrian calculate the transfer time and analyze, obtain each the trip time section shown in the table 2 and pedestrian density's relation.
The pedestrian density of table 2 different times
| Time period | Ebb period | Flat peak period | Peak time | Holiday |
| No. 4 line intensitys of passenger flow of No. 2 line transfers (people/minute) | 10 | 44 | 80 | 120 |
According to the pedestrian density under each trip time section, repeat that above-mentioned computational analysis draws density-the time gap curve is shown in Figure 6, transverse axis is the train arrival time gap, the longitudinal axis is the average transfer time.By Fig. 6 can table 3 the result.When the passenger flow flow not simultaneously, the best arrival time interval of two trains also is not quite similar, the average transfer time is different, namely the time period not simultaneously, two arrival time intervals that train is identical, the average transfer time is also different.
Best train arrival time gap and the shortest average transfer time of table 3 different densities
| Density | Ebb period | Flat peak period | Peak time | Holiday |
| Best T d(min) | 1 | 2 | 1 | 0.5 |
| The shortest average transfer time (min) | 7.21 | 8.99 | 11.20 | 15.41 |
Can draw by Fig. 6 and table 3, take flat peak period as example, adopt best train arrival time gap T
dDuring for 2min, the average transfer time is than train arrival time gap T
dSaved 12.4% when being 0min.Also shortened the transfer time accordingly other period when corresponding best train arrival time gap.
In sum, thereby consist of the transfer optimized algorithm by the result who calculates and the analyze scheme that is optimized, namely the pedestrian density under the different trip time sections and train arrival time gap have inevitable impact to the length of average transfer time: the pedestrian density exerts an influence to pedestrian's traveling speed, and the train arrival time gap exerts an influence to pedestrian's wait time.
Further, optimize train operation module 3 and be used for execution in step S3, adjust the operation of train according to described optimum results.Particularly, according to described optimum results each described Agent object is carried out distributed control, adjust train arrival time gap and train diagram in the pedestrian density under each trip time section, the zone of reasonableness of pedestrian's traveling speed.
Building on the manual system of finishing, optimizing module 2 by described transfer and analyzed the impact of the variation of ebb period, flat peak period, peak period, period Holiday pedestrian's traveling speed and density on the average transfer time.When pedestrian's traveling speed changes, can effectively shorten the average transfer time of pedestrian by the arrival time interval of adjusting the transfer train in the reasonable scope; When the pedestrian density changed in the reasonable scope, the best of two trains pitch time that arrives at a station changed thereupon, therefore can select suitable transfer train arrival interval according to different passenger flow volume of different periods.
Transfer manner at first depends on the trend of two circuits and the form that is interweaved, and square crossing, oblique, the parallel various ways such as interweave are generally arranged.Can be divided into interchange on the same platform, node transfer, subway concourse transfer, passage and change to, mix the multiple base models such as transfer outside changing to, standing.Average transfer time algorithm among the present invention is applicable to the platform of form of ownership.
By the present invention, can some Optimizing Suggestions be proposed on the adaptive dynamic analysis of transfer basis.Analysis data and optimum results that the metro operation personnel provide in can be according to the present invention are optimized the train operation scheme, effectively shorten pedestrian's the average transfer time, improve efficiency of operation.Meanwhile, the optimum results that calculates can provide decision support to operation and the planning of supervisory management zone.
In addition, by the displaying at 2D, two visual angles of 3D in the manual system, increased visuality and the intuitive at the required visual angle that makes a policy for the decision maker.
Should be appreciated that the above detailed description of technical scheme of the present invention being carried out by preferred embodiment is illustrative and not restrictive.Those of ordinary skill in the art is reading on the basis of specification sheets of the present invention and can make amendment to the technical scheme that each embodiment puts down in writing, and perhaps part technical characterictic wherein is equal to replacement; And these modifications or replacement do not make the essence of appropriate technical solution break away from the spirit and scope of various embodiments of the present invention technical scheme.Protection scope of the present invention is only limited by the claims of enclosing.
Claims (8)
1. a urban track traffic transfer optimization method is characterized in that, may further comprise the steps:
Step S1, Agent-base modeling method, the interior Agent attribute in configuration station and modeling, thus forming the subway station manual system also by 2D and/or 3D demonstration, described Agent object classification is pedestrian, train, stand interior environment and station equipment;
Step S2, calculate the average transfer time and analyze the attribute of subway station manual system to the impact of average transfer time, draw optimum results, the attribute of described subway station manual system comprises described Agent attribute and macroscopical attribute; And,
Step S3, adjust the operation of train according to described optimum results.
2. urban track traffic transfer optimization method according to claim 1 is characterized in that,
Described step S1 further comprises:
Step S11, collection affect the metadata of transfer time, and described metadata comprises passenger flow data, pedestrian's data, train data;
Step S12, described metadata is analyzed, filters, extracted, and be parameter with described metadata conversion and according to the type of described Agent object described parametric classification stored;
Step S13, according to described parameter, utilize pedestrian's kinetic model to configure described pedestrian's attribute, described pedestrian is carried out modeling, described pedestrian's attribute comprises pedestrian's traveling speed;
Step S14, according to real conditions in the station, utilize the Agent model to configure the attribute of environment and station equipment in the described station, and environment and station equipment in the described station carried out respectively the mirror image modeling;
Step S15, according to described parameter, utilize the Agent model to configure the attribute of described train, and described train carried out modeling, the attribute of described train comprises the train arrival time gap; And,
Step S16, demonstrate described subway station manual system by 2D and/or 3D.
3. urban track traffic transfer optimization method according to claim 1 is characterized in that,
Described step S2 further comprises:
Step S21, utilize the discrete sampling algorithm to calculate and predict the average transfer time;
Step S22, the described macroscopical attribute of configuration, this macroscopic view attribute comprises pedestrian density, transfer number and the number that enters the station under each trip time section, analyzes the pedestrian density under described pedestrian's traveling speed, described each trip time section, the described train arrival time gap impact on the average transfer time; And,
Step S23, obtain the optimum results of average transfer time.
4. urban track traffic transfer optimization method according to claim 3 is characterized in that,
Described step S3 further comprises:
According to described optimum results, each described Agent object is carried out distributed control, adjust train arrival time gap and train diagram in the pedestrian density under each trip time section, the zone of reasonableness of pedestrian's traveling speed.
5. a urban track traffic transfer optimization system is characterized in that, comprises with lower module
The system building module is used for the Agent-base modeling method, configures the interior Agent attribute in station and modeling, thereby forms the subway station manual system and pass through 2D and/or the 3D demonstration, and described Agent object classification is pedestrian, train, stand interior environment and station equipment;
Module is optimized in transfer, is used for calculating the average transfer time and analyzes the attribute of subway station manual system to the impact of average transfer time, draws optimum results, and the attribute of described subway station manual system comprises described Agent attribute and macroscopical attribute;
Optimize the train operation module, be used for adjusting according to described optimum results the operation of train.
6. urban track traffic transfer optimization system according to claim 5 is characterized in that,
Described system building module further comprises:
Data acquisition module is used for gathering the metadata that affects the transfer time, and described metadata comprises passenger flow data, pedestrian's data, train data;
Data are compiled processing module, are used for described metadata is analyzed, filters, extracted, and are parameter with described metadata conversion and according to the type of described Agent object described parametric classification are stored;
Pedestrian's MBM is used for according to described parameter, utilizes pedestrian's kinetic model to configure described pedestrian's attribute, and described pedestrian is carried out modeling, and described pedestrian's attribute comprises pedestrian's traveling speed;
Environment and equipment modeling module in standing are used for according to real conditions in the station, utilize the Agent model to configure the attribute of environment and station equipment in the described station, and environment and station equipment in the described station are carried out respectively the mirror image modeling;
The Train modeling module is used for according to described parameter, utilizes the Agent model to configure the attribute of described train, and described train is carried out modeling, and the attribute of described train comprises the train arrival time gap; And,
Demonstration module is used for demonstrating described subway station manual system by 2D and/or 3D.
7. urban track traffic transfer optimization system according to claim 5 is characterized in that,
Described transfer is optimized module and is further comprised:
Average transfer time computing module is used for utilizing the calculating of discrete sampling algorithm and predicts the average transfer time;
The experiment with computing module, be used for configuring described macroscopical attribute, this macroscopic view attribute comprises pedestrian density, transfer number and the number that enters the station under each trip time section, analyzes the pedestrian density under described pedestrian's traveling speed, described each trip time section, the described train arrival time gap impact on the average transfer time;
Acquisition module is used for obtaining the optimum results of average transfer time as a result.
8. urban track traffic transfer optimization system according to claim 7 is characterized in that,
Described optimization train operation module is further used for each described Agent object is carried out distributed control, adjusts train arrival time gap and train diagram in the pedestrian density under each trip time section, the zone of reasonableness of pedestrian's traveling speed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2012105911454A CN103043084A (en) | 2012-12-31 | 2012-12-31 | Method and system for optimizing urban railway transit transfer |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2012105911454A CN103043084A (en) | 2012-12-31 | 2012-12-31 | Method and system for optimizing urban railway transit transfer |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN103043084A true CN103043084A (en) | 2013-04-17 |
Family
ID=48056003
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN2012105911454A Pending CN103043084A (en) | 2012-12-31 | 2012-12-31 | Method and system for optimizing urban railway transit transfer |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103043084A (en) |
Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103500499A (en) * | 2013-09-25 | 2014-01-08 | 青岛海信网络科技股份有限公司 | Induction method and system for comprehensive passenger transport hub passenger transportation mode selection |
| CN106828545A (en) * | 2015-03-31 | 2017-06-13 | 江苏理工学院 | A kind of flow-optimized control method of subway transportation based on Robust Strategies |
| WO2018023611A1 (en) * | 2016-08-04 | 2018-02-08 | 汤隆初 | Method for pushing information during passageway direction change, and subway ticket gate |
| WO2018023612A1 (en) * | 2016-08-04 | 2018-02-08 | 汤隆初 | Direction change technology usage data collection method and subway ticket gate |
| CN107832779A (en) * | 2017-12-11 | 2018-03-23 | 北方工业大学 | Track station classification system |
| CN109819038A (en) * | 2019-01-30 | 2019-05-28 | 成都智元汇信息技术股份有限公司 | A kind of subway comprehensive managing and control system uploaded in real time based on passenger flow information and method |
| CN110222912A (en) * | 2019-06-21 | 2019-09-10 | 清华大学 | Railway stroke route method and device for planning based on Time Dependent model |
| CN110395298A (en) * | 2019-07-26 | 2019-11-01 | 中国安全生产科学研究院 | A kind of passenger flow intelligent guidance system and method |
| CN111114595A (en) * | 2019-12-13 | 2020-05-08 | 交控科技股份有限公司 | Adjustment method for switching in train number running chart |
| CN112163701A (en) * | 2020-09-23 | 2021-01-01 | 佳都新太科技股份有限公司 | Station hub transfer management method and device |
| CN112347654A (en) * | 2020-11-11 | 2021-02-09 | 郑州捷安高科股份有限公司 | Simulation method and device based on subway passenger flow system |
| CN115423220A (en) * | 2022-11-03 | 2022-12-02 | 南京国电南自轨道交通工程有限公司 | Operation and maintenance monitoring system based on subway rail transit vehicle |
| CN115782985A (en) * | 2022-11-30 | 2023-03-14 | 北京轨道交通路网管理有限公司 | Method and device for adjusting rail transit transfer connection train schedule |
| CN116691741A (en) * | 2023-08-03 | 2023-09-05 | 江苏飞梭智行设备有限公司 | Remote control system of railway car |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4838384A (en) * | 1988-06-21 | 1989-06-13 | Otis Elevator Company | Queue based elevator dispatching system using peak period traffic prediction |
| CN101826200A (en) * | 2010-04-02 | 2010-09-08 | 北京交通大学 | Method for evaluating operating effect of urban track traffic hub |
| CN102750427A (en) * | 2012-07-23 | 2012-10-24 | 中国地质大学（武汉） | Urban traffic planning simulation method and system based on multi-agent activity model |
-
2012
- 2012-12-31 CN CN2012105911454A patent/CN103043084A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4838384A (en) * | 1988-06-21 | 1989-06-13 | Otis Elevator Company | Queue based elevator dispatching system using peak period traffic prediction |
| CN101826200A (en) * | 2010-04-02 | 2010-09-08 | 北京交通大学 | Method for evaluating operating effect of urban track traffic hub |
| CN102750427A (en) * | 2012-07-23 | 2012-10-24 | 中国地质大学（武汉） | Urban traffic planning simulation method and system based on multi-agent activity model |
Non-Patent Citations (4)
| Title |
|---|
| HAIRONG DONG: "Urban Rail Emergency Response Using Pedestrimi Dynamics", 《INTELLIGENT TRANSPORTATION SYSTEMS》, 5 March 2012 (2012-03-05) * |
| XUBIN SUN: "Pedestrian Transfer Time Optimization of Urban Rail Transit based on ACP Approach", 《THE IEEE INTERNATIONAL CONFERENCE ON AUTOMATION AND LOGISTICS》, 17 August 2012 (2012-08-17) * |
| 何韬: "地铁换乘站线路间列车到站间隔优化问题研究", 《物流技术》, vol. 30, no. 6, 15 June 2011 (2011-06-15) * |
| 杜鹏: "地铁通道换乘乘客走行时间规律研究", 《交通运输系统工程与信息》, vol. 9, no. 4, 15 August 2009 (2009-08-15) * |
Cited By (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103500499A (en) * | 2013-09-25 | 2014-01-08 | 青岛海信网络科技股份有限公司 | Induction method and system for comprehensive passenger transport hub passenger transportation mode selection |
| CN106828545A (en) * | 2015-03-31 | 2017-06-13 | 江苏理工学院 | A kind of flow-optimized control method of subway transportation based on Robust Strategies |
| CN106828546A (en) * | 2015-03-31 | 2017-06-13 | 江苏理工学院 | A kind of subway transportation method of flow control |
| WO2018023611A1 (en) * | 2016-08-04 | 2018-02-08 | 汤隆初 | Method for pushing information during passageway direction change, and subway ticket gate |
| WO2018023612A1 (en) * | 2016-08-04 | 2018-02-08 | 汤隆初 | Direction change technology usage data collection method and subway ticket gate |
| CN107832779A (en) * | 2017-12-11 | 2018-03-23 | 北方工业大学 | Track station classification system |
| CN109819038A (en) * | 2019-01-30 | 2019-05-28 | 成都智元汇信息技术股份有限公司 | A kind of subway comprehensive managing and control system uploaded in real time based on passenger flow information and method |
| CN110222912A (en) * | 2019-06-21 | 2019-09-10 | 清华大学 | Railway stroke route method and device for planning based on Time Dependent model |
| CN110222912B (en) * | 2019-06-21 | 2021-06-11 | 清华大学 | Railway travel route planning method and device based on time dependence model |
| CN110395298B (en) * | 2019-07-26 | 2020-06-16 | 中国安全生产科学研究院 | Passenger flow intelligent guiding system and method |
| CN110395298A (en) * | 2019-07-26 | 2019-11-01 | 中国安全生产科学研究院 | A kind of passenger flow intelligent guidance system and method |
| CN111114595A (en) * | 2019-12-13 | 2020-05-08 | 交控科技股份有限公司 | Adjustment method for switching in train number running chart |
| CN111114595B (en) * | 2019-12-13 | 2021-11-12 | 交控科技股份有限公司 | Adjustment method for switching in train number running chart |
| CN112163701A (en) * | 2020-09-23 | 2021-01-01 | 佳都新太科技股份有限公司 | Station hub transfer management method and device |
| CN112347654A (en) * | 2020-11-11 | 2021-02-09 | 郑州捷安高科股份有限公司 | Simulation method and device based on subway passenger flow system |
| CN115423220A (en) * | 2022-11-03 | 2022-12-02 | 南京国电南自轨道交通工程有限公司 | Operation and maintenance monitoring system based on subway rail transit vehicle |
| CN115423220B (en) * | 2022-11-03 | 2023-03-03 | 南京国电南自轨道交通工程有限公司 | Operation and maintenance monitoring system based on subway rail transit vehicle |
| CN115782985A (en) * | 2022-11-30 | 2023-03-14 | 北京轨道交通路网管理有限公司 | Method and device for adjusting rail transit transfer connection train schedule |
| CN115782985B (en) * | 2022-11-30 | 2023-10-13 | 北京轨道交通路网管理有限公司 | Method and device for adjusting track traffic transfer connection train schedule |
| CN116691741A (en) * | 2023-08-03 | 2023-09-05 | 江苏飞梭智行设备有限公司 | Remote control system of railway car |
| CN116691741B (en) * | 2023-08-03 | 2023-10-10 | 江苏飞梭智行设备有限公司 | Remote control system of railway car |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103043084A (en) | Method and system for optimizing urban railway transit transfer | |
| Dong et al. | Integrated optimization of train stop planning and timetabling for commuter railways with an extended adaptive large neighborhood search metaheuristic approach | |
| Tirachini | The economics and engineering of bus stops: Spacing, design and congestion | |
| CN104282162B (en) | A kind of crossing self-adapting signal control method based on real-time vehicle track | |
| Guo et al. | Timetable coordination of first trains in urban railway network: a case study of Beijing | |
| Sun et al. | Multi-objective optimization of train routing problem combined with train scheduling on a high-speed railway network | |
| Sicre et al. | A method to optimise train energy consumption combining manual energy efficient driving and scheduling | |
| CN103116608A (en) | Method of reproducing traffic flow on express way | |
| CN107704950A (en) | A kind of city rail train figure optimization method based on trip requirements and energy saving of system | |
| CN103456163B (en) | The city expressway interchange traffic capacity and running status method of discrimination and system | |
| Luo et al. | Stop plan of express and local train for regional rail transit line | |
| CN103995915A (en) | Crowd evacuation simulation system based on composite potential energy field | |
| Enayatollahi et al. | Modelling bus bunching under variable transit demand using cellular automata | |
| CN108009973A (en) | A kind of cavitation modeling method of holography urban track traffic object | |
| CN110390437A (en) | A kind of Regional Rail Network network timetable coordination optimizing method | |
| CN103000026A (en) | Bus arrival distribution analysis method of bus station | |
| Gao et al. | Modeling and simulation of passenger flow distribution in urban rail transit hub platform | |
| Nüñez et al. | Simulating railway and metropolitan rail networks: From planning to on-line control | |
| Ye et al. | Modeling influencing factors for passenger flow growth of modern trams using system dynamics method | |
| Darbari et al. | N-dimensional self organizing petrinets for urban traffic modeling | |
| Chen et al. | Emergency evacuation with unbalanced utilization of exits at platform level: A simulation study | |
| Sun et al. | Pedestrian transfer time optimization of urban rail transit based on ACP approach | |
| Zhou et al. | Integrated optimization of service-oriented train plan and schedule on intercity rail network with varying demand | |
| Birol et al. | A modelling and simulation study of a metro line as a time-delayed switched system | |
| Zhang et al. | Coordination and Optimization of Long-Distance Passenger Departure Timetable Connected to High-Speed Railway Station: Considering the Heterogeneity of Transfer Passengers Demand |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C02 | Deemed withdrawal of patent application after publication (patent law 2001) | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20130417 |