CN113393355A - Method and system for calculating relative passenger flow distribution of rail transit, electronic device and medium - Google Patents

Method and system for calculating relative passenger flow distribution of rail transit, electronic device and medium Download PDF

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CN113393355A
CN113393355A CN202110473408.0A CN202110473408A CN113393355A CN 113393355 A CN113393355 A CN 113393355A CN 202110473408 A CN202110473408 A CN 202110473408A CN 113393355 A CN113393355 A CN 113393355A
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passenger flow
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flow distribution
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CN113393355B (en
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汪晓臣
李樊
王志飞
杜呈欣
王明哲
孙同庆
田源
赵伟慧
黄志威
郭悦
宫玉昕
吴卉
孟宇坤
阚庭明
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China Railway Network Co ltd
China Academy of Railway Sciences Corp Ltd CARS
Institute of Computing Technologies of CARS
Beijing Jingwei Information Technology Co Ltd
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China Railway Network Co ltd
China Academy of Railway Sciences Corp Ltd CARS
Institute of Computing Technologies of CARS
Beijing Jingwei Information Technology Co Ltd
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Abstract

The invention provides a method, a system, electronic equipment and a medium for calculating relative passenger flow distribution of rail transit, wherein the method comprises the following steps: obtaining passenger flow volume data; calculating to obtain a passenger flow distribution function according to the passenger flow data; calculating to obtain an average passenger flow distribution function according to the passenger flow data; calculating to obtain a relative passenger flow distribution function according to the average passenger flow distribution function and the passenger flow distribution function; obtaining a calculation conclusion of the relative passenger flow distribution of the rail transit according to the relative passenger flow distribution function; the invention realizes the integral consideration of the passenger flow data in the waiting area and the transportation means by comprehensively considering the passenger flow density in the waiting area and the passenger flow density data of the transportation means, and can accurately reflect the passenger flow congestion condition during transfer.

Description

Method and system for calculating relative passenger flow distribution of rail transit, electronic device and medium
Technical Field
The invention relates to the technical field of rail transit information, in particular to a method, a system, electronic equipment and a medium for calculating relative passenger flow distribution of rail transit.
Background
The rail transit belongs to a large-traffic, rapid and punctual transportation system, and along with the acceleration of an urbanization process and the formation of rail transit networking, platform waiting and transfer congestion aggravation often occur in a key station and a junction station of the rail transit at ordinary times and in early and late peaks, so that the station operation organization and the train transportation efficiency are influenced. With the development and application of information technology, the station PIS system and the vehicle-mounted PIS system realize the release of train arrival and operation information, and dynamic display of train passenger flow density data is added in part of cities and lines, but the prior art has the following defects:
in the prior art, the collection and analysis are mostly carried out from a single dimension of passenger flow density data of a station platform and a train carriage. On one hand, the passenger flow density data of the platform and the passenger flow density data of the train compartment can not realize the data linkage of the train and the ground, and the correlation and the integral operation are carried out through the train organization, so that the actual conditions of the getting-on and getting-off links are difficult to reflect; on the other hand, in the aspect of calculating the passenger flow congestion degree index, factors such as the waiting area of a station platform, the area of a carriage, the change of the passenger flow volume along with time and the like cannot be considered, so that the finally calculated result has a more comprehensive guiding significance for the transfer of the upper carriage and the lower carriage.
Therefore, various factors of passenger congestion during getting on and off of the platform are difficult to be reflected comprehensively in the prior art, the passenger congestion degree condition is difficult to be calculated accurately aiming at conditions such as a super-large platform riding link and the like, and the efficiency improvement of the boarding and alighting transfer organization is not facilitated.
Disclosure of Invention
Aiming at the problems in the prior art, the embodiment of the invention provides a method and a system for calculating the relative passenger flow distribution of rail transit, electronic equipment and a medium.
The invention provides a method for calculating the relative passenger flow distribution of rail transit, which comprises the following steps:
obtaining passenger flow volume data;
calculating to obtain a passenger flow distribution function according to the passenger flow data;
calculating to obtain an average passenger flow distribution function according to the passenger flow data;
calculating to obtain a relative passenger flow distribution function according to the average passenger flow distribution function and the passenger flow distribution function;
wherein the relative passenger flow distribution function is a function relating to a ratio of real-time passenger flow distribution to average passenger flow distribution; the passenger flow data comprises passenger flow data of a waiting area and/or passenger flow data of a vehicle; the waiting area passenger flow data comprises any one or any combination of more than one of the total passenger flow of the waiting area, the total area of the waiting area, the passenger flow set of a plurality of areas in the waiting area and the area set of the plurality of areas in the waiting area; the vehicle passenger flow data includes any one or any combination of a total vehicle passenger flow, a total vehicle area, a set of passenger flows for a plurality of zones within the vehicle, and a set of areas for a plurality of zones within the vehicle.
According to the method for calculating the relative passenger flow distribution of the rail transit, provided by the invention, the step of acquiring the passenger flow data comprises the following steps:
obtaining passenger flow volume data corresponding to a specific vehicle; the passenger flow data corresponding to the specific vehicle comprises specific waiting area data and specific vehicle data;
the step of acquiring the data of the specific waiting area comprises the following steps:
dividing a specific waiting area into m 'waiting areas, and respectively collecting the real-time passenger flow densities of m set waiting areas in the m' waiting areas to form a first passenger flow density set which is marked as { xi|i∈[1，m]}; respectively collecting the areas of m set waiting areas in the m' waiting areas to form a first area set, and recording the first area set as { si|i∈[1，m]}；
Wherein m' is an integer greater than 1; i is an integer; x is the number ofiThe real-time passenger flow density in the ith waiting area; siThe area of the ith waiting area; m is a positive integer no greater than m';
the step of obtaining the vehicle-specific data comprises:
dividing a specific vehicle into n 'vehicle areas, respectively collecting the real-time passenger flow density of n set vehicle areas in the n' vehicle areas to form a second passenger flow density set which is marked as { yj|j∈[1，n]}; respectively collecting the areas of n set vehicle areas in the n' vehicle areas to form a second surfaceProduct set, denoted as { t }j|j∈[1，n]}；
Wherein n' is an integer greater than 1; j is an integer; y isjReal-time passenger flow density in the jth vehicle zone; t is tjIs the area of the jth vehicle zone; n is a positive integer not greater than n';
according to the method for calculating the relative passenger flow distribution of the rail transit, provided by the invention, the step of calculating and obtaining the average passenger flow distribution function according to the passenger flow data comprises the following steps:
calculating to obtain an average passenger flow distribution function according to passenger flow data corresponding to a specific vehicle; the step of calculating the average passenger flow distribution function comprises the following steps:
calculating a passenger flow distribution function S in the ith waiting area by a first formula1i(ii) a The first formula is:
S1i＝xi×si
calculating passenger flow distribution function S in jth vehicle area through second formula2j(ii) a The second formula is:
S2j＝yj×tj
calculating an average passenger flow distribution function S through a third formula; the third formula is:
Figure BDA0003046415060000031
according to the method for calculating the relative passenger flow distribution of the rail transit, provided by the invention, the step of calculating and obtaining the passenger flow distribution function according to the passenger flow data comprises the following steps:
calculating to obtain a passenger flow distribution function according to passenger flow data corresponding to a specific vehicle;
the step of calculating the passenger flow distribution function comprises the following steps:
if n is less than or equal to m, then:
calculating passenger flow function Z of the transportation means area corresponding to the ith waiting area by a fourth formulai'; what is needed isThe fourth formula is:
Figure BDA0003046415060000041
the waiting area corresponding to the jth vehicle area comprises an ith waiting area;
calculating the passenger flow distribution function Z of the ith waiting area by a fifth formulai(ii) a The fifth formula is:
Zi＝S1i+Zi′
if n > m, then:
calculating the passenger flow function Z of the transportation means zone corresponding to the ith waiting zone by a sixth formulai'; the sixth formula is:
Figure BDA0003046415060000042
the set I is a set of serial numbers j of the vehicle areas corresponding to the ith waiting area;
calculating the passenger flow distribution function Z of the ith waiting area by a fifth formulai(ii) a The fifth formula is:
Zi＝S1i+Zi′。
according to the method for calculating the relative passenger flow distribution of the rail transit, the step of calculating the relative passenger flow distribution function according to the average passenger flow distribution function and the passenger flow distribution function comprises the following steps:
calculating a relative passenger flow distribution function theta of the ith waiting area by a seventh formulai(ii) a The seventh formula is:
Figure BDA0003046415060000043
wherein, delta is a set regulating factor;
the adjusting factor delta is obtained by calculating an eighth formula; the eighth formula is:
Figure BDA0003046415060000051
wherein, f (x) is a set passenger flow function with an independent variable as time, and f (x) is the passenger flow at the time of x; t is t1The time when the vehicle starts to operate; t is t2The time when the vehicle stops operating; t is the current time; t' is the set end of the cycle time.
According to the method for calculating the relative passenger flow distribution of the rail transit, provided by the invention, the waiting area is an area where vehicles pass through and stop; the vehicle is the specific vehicle with the shortest time to reach the waiting area.
The invention also provides a relative passenger flow distribution computing system of the rail transit, which comprises an acquisition module and a passenger flow density analysis module;
the acquisition module comprises an acquisition terminal; the acquisition terminals are arranged in a waiting area and a vehicle area and can acquire passenger flow volume data;
the passenger flow density analysis module is capable of:
calculating to obtain a passenger flow distribution function according to the passenger flow data;
calculating to obtain an average passenger flow distribution function according to the passenger flow data;
calculating to obtain a relative passenger flow distribution function according to the average passenger flow distribution function and the passenger flow distribution function;
wherein the relative passenger flow distribution function is a function relating to a ratio of real-time passenger flow distribution to average passenger flow distribution; the passenger flow data comprises passenger flow data of a waiting area and/or passenger flow data of a vehicle; the waiting area passenger flow data comprises any one or any combination of more than one of the total passenger flow of the waiting area, the total area of the waiting area, the passenger flow set of a plurality of areas in the waiting area and the area set of the plurality of areas in the waiting area; the vehicle passenger flow data includes any one or any combination of a total vehicle passenger flow, a total vehicle area, a set of passenger flows for a plurality of zones within the vehicle, and a set of areas for a plurality of zones within the vehicle.
The relative passenger flow distribution computing system for the rail transit further comprises an ATS analysis module, a data processing module and a display module;
the ATS analysis module can analyze the number information of the coming vehicles of the specific transportation means to be analyzed;
the data processing module can receive passenger flow volume data and information of number of vehicles coming to a station of a specific vehicle, and extract the passenger flow volume data corresponding to the specific vehicle; the passenger flow data corresponding to the specific vehicle comprises specific waiting area data and specific vehicle data;
the passenger flow density analysis module can calculate a relative passenger flow distribution function corresponding to a specific vehicle and a specific waiting area according to the specific waiting area data and the specific vehicle data, and obtain display information corresponding to the specific vehicle and the specific waiting area according to the relative passenger flow distribution function corresponding to the specific vehicle and the specific waiting area;
the display module comprises a display terminal; the display terminal is arranged in a waiting area and/or a vehicle area and can display information;
the data processing module can also receive display information and distribute the display information to the display terminal of the display module.
The invention also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the steps of the rail transit relative passenger flow distribution calculation method.
The invention also provides a non-transitory computer-readable storage medium on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the method for calculating a relative rail transit passenger flow distribution as defined in any one of the above.
According to the method, the system, the electronic equipment and the medium for calculating the relative passenger flow distribution of the rail transit, provided by the invention, the passenger flow data in the waiting area and the traffic means are realized by comprehensively considering the passenger flow density of the waiting area and the passenger flow density data of the traffic means, and the congestion condition of the passenger flow during transfer can be accurately reflected.
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In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for calculating the relative passenger flow distribution of rail transit according to the present invention;
FIG. 2 is a schematic diagram of a rail transit relative passenger flow distribution computing system provided by the present invention;
FIG. 3 is a schematic diagram of a computing system for calculating the relative passenger flow distribution of rail transit according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a network structure of a track traffic relative passenger flow distribution computing system according to an embodiment of the present invention;
fig. 5 is a schematic flow chart of a processing method of a rail transit relative passenger flow distribution computing system according to an embodiment of the present invention;
FIG. 6 is a schematic diagram illustrating the effect of displaying passenger flow congestion indexes of platforms and trains according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of an electronic device provided by the present invention.
Reference numerals:
11: an acquisition module; 12: an ATS analysis module;
13: a data processing module; 14: a passenger flow density analysis module;
15: a display module; 21: a station server;
22: a platform PIS broadcast controller; 23: a station switch;
24: a station; 25: an edge intelligent terminal;
26: a carriage; 27: a vehicle-mounted display screen;
28: station AP 29: vehicle-mounted camera.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The method for calculating the relative passenger flow distribution of rail transit according to the invention is described below with reference to the accompanying drawings.
As shown in fig. 1, the present embodiment provides a method for calculating a relative passenger flow distribution of rail transit, including:
step 1, obtaining passenger flow volume data;
step 2, calculating to obtain a passenger flow distribution function according to the passenger flow data;
step 3, calculating to obtain an average passenger flow distribution function according to the passenger flow data;
step 4, calculating to obtain a relative passenger flow distribution function according to the average passenger flow distribution function and the passenger flow distribution function;
wherein the relative passenger flow distribution function is a function relating to a ratio of real-time passenger flow distribution to average passenger flow distribution; the passenger flow data comprises passenger flow data of a waiting area and/or passenger flow data of a vehicle; the waiting area passenger flow data comprises any one or any combination of more than one of the total passenger flow of the waiting area, the total area of the waiting area, the passenger flow set of a plurality of areas in the waiting area and the area set of the plurality of areas in the waiting area; the vehicle passenger flow data includes any one or any combination of a total vehicle passenger flow, a total vehicle area, a set of passenger flows for a plurality of zones within the vehicle, and a set of areas for a plurality of zones within the vehicle.
The beneficial effect of this embodiment lies in:
by comprehensively considering the passenger flow density of the waiting area and the passenger flow density data of the vehicles, the passenger flow data in the waiting area and the passengers in the vehicles are realized, and the situation of passenger flow congestion during transfer can be accurately reflected.
According to the above embodiment, in the present embodiment:
the step of obtaining passenger flow volume data comprises:
obtaining passenger flow volume data corresponding to a specific vehicle; the passenger flow data corresponding to the specific vehicle comprises specific waiting area data and specific vehicle data;
the step of acquiring the data of the specific waiting area comprises the following steps:
dividing a specific waiting area into m 'waiting areas, and respectively collecting the real-time passenger flow densities of m set waiting areas in the m' waiting areas to form a first passenger flow density set which is marked as { xi|i∈[1，m]}; respectively collecting the areas of m set waiting areas in the m' waiting areas to form a first area set, and recording the first area set as { si|i∈[1，m]}；
Wherein m' is an integer greater than 1; i is an integer; x is the number ofiThe real-time passenger flow density in the ith waiting area; siThe area of the ith waiting area; m is a positive integer no greater than m';
the step of obtaining the vehicle-specific data comprises:
dividing a specific vehicle into n 'vehicle areas, respectively collecting the real-time passenger flow density of n set vehicle areas in the n' vehicle areas to form a second passenger flow density set which is marked as { yj|j∈[1，n]}; respectively collecting the areas of n set vehicle areas in the n' vehicle areas to form a second area set, and recording the second area set as { tj|j∈[1，n]}；
Wherein n' is an integer greater than 1; j is an integer; y isjReal-time passenger flow density in the jth vehicle zone; t is tjIs the area of the jth vehicle zone; n is a positive integer not greater than n；
The beneficial effect of this embodiment lies in:
the factors such as waiting area of a station platform, carriage area, change of passenger flow along with time and the like are fully considered, the calculation result is accurate, and the method has extremely strong guiding significance for calculating the crowdedness of the passenger flow on and off the train at the time point and the passenger flow evacuation.
According to the above embodiment, in the present embodiment:
the step of calculating the average passenger flow distribution function according to the passenger flow data comprises the following steps:
calculating to obtain an average passenger flow distribution function according to passenger flow data corresponding to a specific vehicle; the step of calculating the average passenger flow distribution function comprises the following steps:
calculating a passenger flow distribution function S in the ith waiting area by a first formula1i(ii) a The first formula is:
S1i＝xi×si
calculating passenger flow distribution function S in jth vehicle area through second formula2j(ii) a The second formula is:
S2j＝yj×tj
calculating an average passenger flow distribution function S through a third formula; the third formula is:
Figure BDA0003046415060000091
the beneficial effect of this embodiment lies in:
the factors such as waiting area of a station platform, carriage area, change of passenger flow along with time and the like are fully considered, the calculation result is accurate, and the method has extremely strong guiding significance for calculating the crowdedness of the passenger flow on and off the train at the time point and the passenger flow evacuation.
According to any of the embodiments described above, in this embodiment:
the step of calculating the passenger flow distribution function according to the passenger flow data comprises the following steps:
calculating to obtain a passenger flow distribution function according to passenger flow data corresponding to a specific vehicle;
the step of calculating the passenger flow distribution function comprises the following steps:
if n is less than or equal to m, then:
calculating passenger flow function Z of the transportation means area corresponding to the ith waiting area by a fourth formulai'; the fourth formula is:
Figure BDA0003046415060000101
the waiting area corresponding to the jth vehicle area comprises an ith waiting area;
calculating the passenger flow distribution function Z of the ith waiting area by a fifth formulai(ii) a The fifth formula is:
Zi＝S1i+Zi′
if n > m, then:
calculating the passenger flow function Z of the transportation means zone corresponding to the ith waiting zone by a sixth formulai'; the sixth formula is:
Figure BDA0003046415060000102
the set I is a set of serial numbers j of the vehicle areas corresponding to the ith waiting area;
calculating the passenger flow distribution function Z of the ith waiting area by a fifth formulai(ii) a The fifth formula is:
Zi＝S1i+Zi′。
the beneficial effect of this embodiment lies in:
by subdividing the waiting area and discussing the calculation method of the passenger flow distribution function of the congestion index under different corresponding relations between the waiting area and the vehicle area, the application range of the method is enlarged, and a hardware system can be more flexibly collocated.
According to any of the embodiments described above, in this embodiment:
the step of calculating a relative passenger flow distribution function according to the average passenger flow distribution function and the passenger flow distribution function comprises:
calculating a relative passenger flow distribution function theta of the ith waiting area by a seventh formulai(ii) a The seventh formula is:
Figure BDA0003046415060000111
wherein, delta is a set regulating factor;
the adjusting factor delta is obtained by calculating an eighth formula; the eighth formula is:
Figure BDA0003046415060000112
wherein, f (x) is a set passenger flow function with an independent variable as time, and f (x) is the passenger flow at the time of x; t is t1The time when the vehicle starts to operate; t is t2The time when the vehicle stops operating; t is the current time; t' is the set end of the cycle time.
The beneficial effect of this embodiment lies in:
by increasing the adjusting factor, the influence of passenger flow change in the peak/low peak period on the congestion index is further considered, and the accuracy of the conclusion is increased.
According to any of the embodiments described above, in this embodiment:
the step of obtaining a calculation conclusion of the rail transit relative passenger flow distribution according to the relative passenger flow distribution function comprises single-critical-value analysis or multi-critical-value analysis;
the single-cut-value analysis comprises:
setting a critical value and a relative passenger flow distribution function theta for the ith waiting areaiAnd (4) judging: if thetaiIf the number of the waiting areas is not more than the critical value, the calculation conclusion of the rail transit relative passenger flow distribution in the ith waiting area is comfortFitting; if thetaiIf the value is larger than the critical value, the calculation conclusion of the rail traffic relative to the passenger flow distribution in the ith waiting area is congestion;
the multi-threshold analysis comprises:
setting a critical values of a, thetaiThe whole numeric area of the division is divided into a +1 intervals; recording the a +1 intervals as a first interval to an a +1 th interval from small to large according to the numerical range;
judging the relative passenger flow distribution function theta of the ith waiting areaiIn the interval according to thetaiCorrespondingly obtaining a calculation conclusion of the relative passenger flow distribution of the rail transit in the ith waiting area in the section;
the first interval to the a +1 th interval correspond to a set first conclusion to a +1 th conclusion; the comfort degree from the first conclusion to the a +1 th conclusion is decreased in sequence, and the crowding degree is increased in sequence.
The beneficial effect of this embodiment lies in:
by adopting the technical means of single/multi-critical value judgment, different hardware computing power and different judgment precision requirements are compatible, and the applicability of the method is enhanced.
According to any of the embodiments described above, in this embodiment:
the waiting area is an area where vehicles pass by and stop; the vehicle is the specific vehicle with the shortest time to reach the waiting area.
The beneficial effect of this embodiment lies in:
by preferentially calculating the specific transportation means with the shortest time to reach the waiting area, the method for calculating the relative passenger flow distribution of the rail transit can calculate and calculate the crowdedness of each transportation means and the corresponding waiting area thereof timely and comprehensively.
The following describes a track traffic relative passenger flow distribution calculation system provided by the present invention, and the track traffic relative passenger flow distribution calculation system described below and the track traffic relative passenger flow distribution calculation method described above may be referred to in correspondence with each other.
As shown in fig. 2, the system for calculating relative passenger flow distribution of rail transit provided by the present embodiment includes an acquisition module 11 and a passenger flow density analysis module 14;
the acquisition module 11 comprises an acquisition terminal; the acquisition terminals are arranged in a waiting area and a vehicle area and can acquire passenger flow volume data;
the passenger flow density analysis module 14 is capable of:
calculating to obtain a passenger flow distribution function according to the passenger flow data;
calculating to obtain an average passenger flow distribution function according to the passenger flow data;
calculating to obtain a relative passenger flow distribution function according to the average passenger flow distribution function and the passenger flow distribution function;
wherein the relative passenger flow distribution function is a function relating to a ratio of real-time passenger flow distribution to average passenger flow distribution; the passenger flow data comprises passenger flow data of a waiting area and/or passenger flow data of a vehicle; the waiting area passenger flow data comprises any one or any combination of more than one of the total passenger flow of the waiting area, the total area of the waiting area, the passenger flow set of a plurality of areas in the waiting area and the area set of the plurality of areas in the waiting area; the vehicle passenger flow data includes any one or any combination of a total vehicle passenger flow, a total vehicle area, a set of passenger flows for a plurality of zones within the vehicle, and a set of areas for a plurality of zones within the vehicle.
The beneficial effect of this embodiment lies in:
by adopting the technical means of subdividing the collected data, the problem that the passenger flow distribution in a waiting area and a vehicle area is not uniform when the rail transit is changed, but the traditional congestion measuring and calculating method only considers the overall congestion degree is solved.
According to the above embodiment, in the present embodiment:
the embodiment further comprises an ATS analysis module 12, a data processing module 13 and a display module 15;
the ATS analysis module 12 can analyze the number information of the coming vehicles of the specific transportation means to be analyzed;
the data processing module 13 can receive passenger flow volume data and information of the number of upcoming vehicles of a specific vehicle, and extract the passenger flow volume data corresponding to the specific vehicle; the passenger flow data corresponding to the specific vehicle comprises specific waiting area data and specific vehicle data;
the passenger flow density analysis module 14 can calculate a relative passenger flow distribution function corresponding to the specific vehicle and the specific waiting area according to the specific waiting area data and the specific vehicle data, and obtain display information corresponding to the specific vehicle and the specific waiting area according to the relative passenger flow distribution function corresponding to the specific vehicle and the specific waiting area;
the display module 15 comprises a display terminal; the display terminal is arranged in a waiting area and/or a vehicle area and can display information;
the data processing module 13 is also capable of receiving display information and distributing it to the display terminals of the display module 15.
The beneficial effect of this embodiment lies in:
by adopting the technical means of subdividing the collected data, the problem that the passenger flow distribution in a waiting area and a vehicle area is not uniform when the rail transit is changed, but the traditional congestion measuring and calculating method only considers the overall congestion degree is solved.
Embodiments of a method and a system for analyzing and processing passenger flow density of rail transit stations in a public rail transit scene are provided below, and specifically, the following are provided.
Fig. 3 shows an urban rail transit relative passenger flow distribution calculation system in the embodiment, which includes: the system comprises a passenger flow density acquisition module 11, a data processing module 13, a station passenger flow density analysis module 14, an ATS analysis module 12 and a station passenger flow congestion index display module 15.
The passenger flow density acquisition module 11 acquires images acquired by intelligent terminals at each edge of a platform and a plurality of video cameras in a train carriage, and acquires passenger flow volume data in real time by a video image analysis method;
the ATS analysis module 12 acquires train arrival data from an ATS system and analyzes the train number of a station train to be arrived;
the data processing module 13 is responsible for collecting and forwarding passenger flow data of each area of the platform and the train carriages, acquiring the number of trains to arrive at the station from the ATS analysis module 12, pairing and binding the number with the station panoramic passenger flow congestion index, and distributing the panoramic passenger flow congestion index to each intelligent terminal of the platform and the trains;
the station passenger flow density analysis module 14 calls a passenger flow density analysis algorithm to realize the calculation of the transfer congestion index of each carriage, and feeds back the calculation result to the data processing module 13.
The passenger flow congestion index display module 15 realizes subscription, publication and display of the panoramic passenger flow congestion index information through the platform and train intelligent display terminal.
Further, the data processing module 13 implements interactive transmission of train data through a train-ground wireless transmission channel, associates and binds the train number of the train to be arrived at with train passenger flow density data and a station train panoramic passenger flow congestion index, and receives and transmits data by adopting a "publish-subscribe" model with the train number as a theme, thereby implementing association and reliable transmission of the station train data. Due to the characteristics of high-speed mobile transmission between a train and a ground, the traditional TCP/IP message communication mode has the possibility of incomplete data or serial packet, the embodiment adopts a 'publish-subscribe' model mode to receive and transmit messages, a train vehicle-mounted end server or an application program is not directly connected with the data processing module 13, and message agent transmission is carried out through a third-party message middleware, so that the train can accurately upload carriage passenger flow density data in the running process, and meanwhile, the station panoramic passenger flow congestion index sent to a train from a station can be received.
In the data processing process, in order to ensure the relevance between the train data processed by the data processing module 13 and the platform data and ensure the accuracy of the data displayed by the train to be arrived and the platform to be arrived, the embodiment processes the data in a manner of taking the train number as a key and a theme, and transmits the data to the data processing module 13 in a manner of taking { key, value } ═ the train number and the passenger flow density object data } when uploading the passenger flow density data of the compartment; when the train panoramic congestion index is issued, the data of the train number is pushed to the subscription subject number, namely the train number. And carrying out platform and train data association through the train number, thereby realizing the consistency of data displayed in the train compartment to be arrived and data displayed by the platform intelligent terminal.
Fig. 4 shows a network structure diagram of a system for calculating relative passenger flow distribution of urban rail transit in this embodiment, where the system implements processing of images of passengers at a platform and a train, completes operations such as acquisition of passenger flow density data at the station, forwarding of data, analysis of passenger flow density, display of passenger flow congestion index, and implements automatic analysis and processing of panoramic passenger flow congestion index at the platform and the train. The system consists of three parts: a hardware system, a software platform and a passenger flow density analysis algorithm. The hardware system comprises a platform edge intelligent terminal 25, a vehicle-mounted display screen 27, a carriage camera 29, a station server 21, a platform PIS broadcast controller 22, a station switch 23, a station AP28 and the like, and all hardware devices are connected with a train-ground wireless network through a station transmission network. Respectively acquiring passenger flow images of the platform 24 area and the train compartment 26 through the edge intelligent terminal 25 and the compartment camera 29, and analyzing the passenger flow by using an image recognition and analysis method; the platform edge intelligent terminal 25 and the vehicle-mounted display screen 27 are used for displaying the platform transfer panoramic passenger flow congestion index in real time, so that the edge intelligent terminal 25 is not only a display terminal of information, but also a collection terminal of passenger images. The software platform is used for processing, analyzing and calculating the collected passenger flow density data, taking the passenger flow density data collected by the platform and the carriage as input parameters, and calculating by using an intelligent density analysis algorithm, thereby calculating the station panorama passenger flow congestion index of the train which is about to arrive at the station at the current time point.
Fig. 5 shows an algorithm flowchart of a method for calculating a relative passenger flow distribution of urban rail transit in the embodiment, which includes the following steps:
step S1: acquiring station passenger flow density data: and acquiring passenger flow volume data of each area of the platform and each carriage of the train in real time by using a video image analysis method through edge intelligent terminal equipment of the platform and each carriage of the train.
Step S2: station vehicle data receiving, sending and processing: the passenger flow data of each area of the platform and each carriage of the train are collected, and the data are forwarded to the station train passenger flow density analysis module 14 for analysis and calculation; the real-time station panoramic passenger flow congestion index calculated by the station passenger flow density analysis module 14 is obtained, the number of trains to arrive at the station is obtained from the ATS analysis module 12 and is paired and bound with the station panoramic passenger flow congestion index, and finally the station panoramic passenger flow congestion index is published.
Step S3: and (3) calculating the passenger flow distribution mean value of the station: and calculating the passenger flow distribution mean value of the platform and the train carriage at the current moment of the station.
Step S4: and (3) calculating passenger flow distribution values of stations corresponding to each carriage: and calculating passenger flow distribution values of the platform and the train carriages corresponding to each carriage.
Step S5: and (3) calculating the station and vehicle panoramic passenger flow congestion degree index: and comparing the station passenger flow distribution value corresponding to each carriage with the station passenger flow distribution mean value to calculate the station transfer panoramic passenger flow crowdedness index distributed according to the position of each carriage.
Step S3 passenger flow distribution average value calculation:
let the intelligent terminal collect the area passenger flow volume of coverage area as xiThe area of the platform in the coverage area of the acquisition range is siAnd the passenger flow of the carriage where the train is located is collected as yjAnd the area of the carriage where the train is located is tj. In the scheme, a single carriage corresponds to at least more than 1 edge intelligent terminals (n is more than or equal to m, the sum of the product of the area of a station platform collection range and the corresponding passenger flow and the product of the area of each carriage passing through the station train and the corresponding passenger flow is obtained, and then, the expression S of the passenger flow area of the side of the station platform to be analyzed in a single carriage is a formula (1):
Figure BDA0003046415060000171
therefore, in the embodiment, the two variables of the area passenger flow and the area are used as multiplication factors, the product of the passenger flow and the area of the platform and the train related to the transfer area is calculated and summed, and finally, each carriage of the train is averaged, so that the average value of the passenger flow area of the platform side of the single carriage is calculated and further used as a reference for the congestion degree of each carriage.
Step S4 passenger flow distribution average value calculation:
the passenger flow distribution average value of each carriage and the corresponding platform is calculated, so that the transfer congestion degree condition of each carriage is represented in an arithmetic way. Thus, the passenger traffic area and the expression Z representing a single car and corresponding platformiIs formula (2):
Figure BDA0003046415060000172
considering the periodicity of passenger flow of a station, let the function of passenger flow and time be f (x), and the operation starting time be t1End time t2Calculating the passenger flow rate of the t time point in the (t, t +1) time period range as
Figure BDA0003046415060000181
The whole day operation time interval is
Figure BDA0003046415060000182
In progress, the calculation formula (3) of the adjustment factor δ:
Figure BDA0003046415060000183
furthermore, due to the fact that the passenger flow of rail transit presents a typical 'peak-valley' distribution characteristic, the passenger flow of the early peak and the late peak is large, and the passenger flow of the other times is smaller in working hours. Therefore, in order to objectively reflect the congestion index condition of station transfer, an adjusting factor delta is specially introduced, the ratio of the passenger flow of one hour at the current time point to the passenger flow of the operation time period of the day is calculated, so that the adjustment of the passenger flow distribution mean value of each carriage station at the current time point is realized, and the calculated passenger flow distribution mean value of each carriage station can reflect the actual relative passenger flow congestion condition of the station at the calculation time.
Step S5 passenger flow distribution average value calculation:
and finally, calculating the passenger flow crowding condition at the current time point by calculating the ratio of the passenger flow in the single carriage and the corresponding platform to the average daily passenger flow. The calculation formula is
Figure BDA0003046415060000184
Figure BDA0003046415060000185
Analyzing and judging corresponding to the calculation result:
theta is less than or equal to 0.8, so that the pillow is comfortable; theta is more than or equal to 0.8 and less than or equal to 1.2, and is crowded; theta is more than or equal to 1.2, and severe crowding is caused.
Fig. 6 shows an actual effect diagram of the station panoramic passenger flow congestion index displayed on the platform display screen and the vehicle-mounted display screen by applying the system and the method of the embodiment.
To sum up, according to the method and the system for calculating the relative passenger flow distribution of the urban rail transit, the passenger flow density of each area of the platform and the carriages is collected through the intelligent terminal at the edge of the platform and the vehicle-mounted camera, and the accurate calculation of the panoramic passenger flow crowding index of each carriage transfer is realized by applying a passenger flow density analysis algorithm. On one hand, the passenger flow density data of the platform and the train compartment are comprehensively considered, the integral consideration of the passenger flow data of the platform and the train is realized, and the passenger flow congestion condition during transfer can be accurately reflected; on the other hand, in the passenger flow congestion degree index calculation algorithm, factors such as waiting area of a station platform, carriage area and change of passenger flow along with time are fully considered, the calculation result is accurate, and the method has a great guiding significance for calculating the passenger flow congestion condition of the train at the time point and the passenger flow evacuation.
Fig. 7 illustrates a physical structure diagram of an electronic device, and as shown in fig. 7, the electronic device may include: a processor (processor)810, a communication Interface 820, a memory 830 and a communication bus 840, wherein the processor 810, the communication Interface 820 and the memory 830 communicate with each other via the communication bus 840. Processor 810 may invoke logic instructions in memory 830 to perform a method of urban rail transit relative passenger flow distribution calculation, the method comprising:
obtaining passenger flow volume data;
calculating to obtain a passenger flow distribution function according to the passenger flow data;
calculating to obtain an average passenger flow distribution function according to the passenger flow data;
calculating to obtain a relative passenger flow distribution function according to the average passenger flow distribution function and the passenger flow distribution function;
and obtaining a calculation conclusion of the relative passenger flow distribution of the urban rail transit according to the relative passenger flow distribution function.
In addition, the logic instructions in the memory 830 may be implemented in software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product, the computer program product includes a computer program stored on a non-transitory computer readable storage medium, the computer program includes program instructions, when the program instructions are executed by a computer, the computer can execute the method for calculating the relative passenger flow distribution of urban rail transit provided by the above methods, the method includes:
obtaining passenger flow volume data;
calculating to obtain a passenger flow distribution function according to the passenger flow data;
calculating to obtain an average passenger flow distribution function according to the passenger flow data;
calculating to obtain a relative passenger flow distribution function according to the average passenger flow distribution function and the passenger flow distribution function;
and obtaining a calculation conclusion of the relative passenger flow distribution of the urban rail transit according to the relative passenger flow distribution function.
In yet another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored, the computer program being implemented by a processor to execute the above-mentioned urban rail transit relative passenger flow distribution calculation method, the method comprising:
obtaining passenger flow volume data;
calculating to obtain a passenger flow distribution function according to the passenger flow data;
calculating to obtain an average passenger flow distribution function according to the passenger flow data;
calculating to obtain a relative passenger flow distribution function according to the average passenger flow distribution function and the passenger flow distribution function;
and obtaining a calculation conclusion of the relative passenger flow distribution of the urban rail transit according to the relative passenger flow distribution function.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (10)

1. A rail transit relative passenger flow distribution calculation method is characterized by comprising the following steps:
obtaining passenger flow volume data;
calculating to obtain a passenger flow distribution function according to the passenger flow data;
calculating to obtain an average passenger flow distribution function according to the passenger flow data;
calculating to obtain a relative passenger flow distribution function according to the average passenger flow distribution function and the passenger flow distribution function;
wherein the relative passenger flow distribution function is a function relating to a ratio of real-time passenger flow distribution to average passenger flow distribution; the passenger flow data comprises passenger flow data of a waiting area and/or passenger flow data of a vehicle; the waiting area passenger flow data comprises any one or any combination of more than one of the total passenger flow of the waiting area, the total area of the waiting area, the passenger flow set of a plurality of areas in the waiting area and the area set of the plurality of areas in the waiting area; the vehicle passenger flow data includes any one or any combination of a total vehicle passenger flow, a total vehicle area, a set of passenger flows for a plurality of zones within the vehicle, and a set of areas for a plurality of zones within the vehicle.
2. The rail transit relative passenger flow distribution calculation method of claim 1, wherein the step of obtaining passenger flow volume data comprises:
obtaining passenger flow volume data corresponding to a specific vehicle; the passenger flow data corresponding to the specific vehicle comprises specific waiting area data and specific vehicle data;
the step of acquiring the data of the specific waiting area comprises the following steps:
dividing a specific waiting area into m 'waiting areas, and respectively collecting the real-time passenger flow densities of m set waiting areas in the m' waiting areas to form a first passenger flow density set which is marked as { xi|i∈[1，m]}; respectively collecting the areas of m set waiting areas in the m' waiting areas to form a first area set, and recording the first area set as { si|i∈[1，m]}；
Wherein m' is an integer greater than 1; i is an integer; x is the number ofiThe real-time passenger flow density in the ith waiting area; siThe area of the ith waiting area; m is a positive integer no greater than m';
the step of obtaining the vehicle-specific data comprises:
dividing a specific vehicle into n 'vehicle areas, respectively collecting the real-time passenger flow density of n set vehicle areas in the n' vehicle areas to form a second passenger flow density set which is marked as { yj|j∈[1，n]}; respectively collecting the areas of n set vehicle areas in the n' vehicle areas to form a second area set, and recording the second area set as { tj|j∈[1，n]}；
Wherein n' is an integer greater than 1; j is an integer; y isjReal-time passenger flow density in the jth vehicle zone; t is tjIs the area of the jth vehicle zone; n is a positive integer not greater than n'.
3. The method for calculating relative passenger flow distribution of rail transit according to claim 2, wherein the step of calculating the average passenger flow distribution function according to the passenger flow data comprises:
calculating to obtain an average passenger flow distribution function according to passenger flow data corresponding to a specific vehicle; the step of calculating the average passenger flow distribution function comprises the following steps:
calculating a passenger flow distribution function S in the ith waiting area by a first formula1i(ii) a The first formula is:
S1i＝xi×si
calculating passenger flow distribution function S in jth vehicle area through second formula2j(ii) a The second formula is:
S2j＝yj×tj
calculating an average passenger flow distribution function S through a third formula; the third formula is:
Figure FDA0003046415050000021
4. the method for calculating relative passenger flow distribution of rail transit according to claim 3, wherein the step of calculating the passenger flow distribution function according to the passenger flow data comprises:
calculating to obtain a passenger flow distribution function according to passenger flow data corresponding to a specific vehicle;
the step of calculating the passenger flow distribution function comprises the following steps:
if n is less than or equal to m, then:
calculating passenger flow volume function Z 'of a vehicle area corresponding to the ith waiting area through a fourth formula'i(ii) a The fourth formula is:
Figure FDA0003046415050000031
the waiting area corresponding to the jth vehicle area comprises an ith waiting area;
calculating the passenger flow distribution function Z of the ith waiting area by a fifth formulai(ii) a The fifth formula is:
Zi＝S1i+Z′i
if n > m, then:
calculating passenger flow volume function Z 'of the vehicle area corresponding to the ith waiting area through a sixth formula'i(ii) a The sixth formula is:
Figure FDA0003046415050000032
the set I is a set of serial numbers j of the vehicle areas corresponding to the ith waiting area;
calculating the passenger flow distribution function Z of the ith waiting area by a fifth formulai(ii) a The fifth formula is:
Zi＝S1i+Z′i。
5. the method for calculating relative passenger flow distribution of rail transit according to claim 4, wherein the step of calculating the relative passenger flow distribution function according to the average passenger flow distribution function and the passenger flow distribution function comprises:
calculating a relative passenger flow distribution function theta of the ith waiting area by a seventh formulai(ii) a The seventh formula is:
Figure FDA0003046415050000033
wherein, delta is a set regulating factor;
the adjusting factor delta is obtained by calculating an eighth formula; the eighth formula is:
Figure FDA0003046415050000041
wherein, f (x) is a set passenger flow function with an independent variable as time, and f (x) is the passenger flow at the time of x; t is t1The time when the vehicle starts to operate; t is t2The time when the vehicle stops operating; t is the current time; t' is the set end of the cycle time.
6. The rail transit relative passenger flow distribution calculation method according to any one of claims 2 to 5, wherein the waiting area is an area where vehicles pass by and stop; the vehicle is the specific vehicle with the shortest time to reach the waiting area.
7. A relative passenger flow distribution calculation system for rail transit is characterized by comprising an acquisition module and a passenger flow density analysis module;
the acquisition module comprises an acquisition terminal; the acquisition terminals are arranged in a waiting area and a vehicle area and can acquire passenger flow volume data;
the passenger flow density analysis module is capable of:
calculating to obtain a passenger flow distribution function according to the passenger flow data;
calculating to obtain an average passenger flow distribution function according to the passenger flow data;
calculating to obtain a relative passenger flow distribution function according to the average passenger flow distribution function and the passenger flow distribution function;
wherein the relative passenger flow distribution function is a function relating to a ratio of real-time passenger flow distribution to average passenger flow distribution; the passenger flow data comprises passenger flow data of a waiting area and/or passenger flow data of a vehicle; the waiting area passenger flow data comprises any one or any combination of more than one of the total passenger flow of the waiting area, the total area of the waiting area, the passenger flow set of a plurality of areas in the waiting area and the area set of the plurality of areas in the waiting area; the vehicle passenger flow data includes any one or any combination of a total vehicle passenger flow, a total vehicle area, a set of passenger flows for a plurality of zones within the vehicle, and a set of areas for a plurality of zones within the vehicle.
8. The track traffic relative passenger flow distribution computing system according to claim 7, further comprising an ATS parsing module, a data processing module and a display module;
the ATS analysis module can analyze the number information of the coming vehicles of the specific transportation means to be analyzed;
the data processing module can receive passenger flow volume data and information of number of vehicles coming to a station of a specific vehicle, and extract the passenger flow volume data corresponding to the specific vehicle; the passenger flow data corresponding to the specific vehicle comprises specific waiting area data and specific vehicle data;
the passenger flow density analysis module can calculate a relative passenger flow distribution function corresponding to a specific vehicle and a specific waiting area according to the specific waiting area data and the specific vehicle data, and obtain display information corresponding to the specific vehicle and the specific waiting area according to the relative passenger flow distribution function corresponding to the specific vehicle and the specific waiting area;
the display module comprises a display terminal; the display terminal is arranged in a waiting area and/or a vehicle area and can display information;
the data processing module can also receive display information and distribute the display information to the display terminal of the display module.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method for calculating a rail transit relative passenger flow distribution according to any one of claims 1 to 6 are implemented when the processor executes the program.
10. A non-transitory computer readable storage medium, on which a computer program is stored, wherein the computer program, when being executed by a processor, implements the steps of the rail transit relative passenger flow distribution calculation method according to any one of claims 1 to 6.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114266010A (en) * 2022-03-01 2022-04-01 华东交通大学 Method and system for calculating congestion coefficient of regional multi-standard rail traffic interval

Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20160132233A (en) * 2015-05-07 2016-11-17 주식회사 엘비씨소프트 Based on the response force sensors carriage/trains congestion management system and service methods
CN107220724A (en) * 2017-04-21 2017-09-29 北京航空航天大学 Passenger flow forecast method and device
CN107844893A (en) * 2017-10-10 2018-03-27 东南大学 Station operation security appraisal procedure occurs for the large passenger flow based on Matter element Extension
CN108229400A (en) * 2018-01-04 2018-06-29 东南大学 Subway station large passenger flow identification method for early warning and system
US20190156678A1 (en) * 2017-11-20 2019-05-23 Tony Mitchell Cole Systems and methods for coordinated passenger offloading of autonomous vehicles at large scale parking destinations
CN109858670A (en) * 2018-12-24 2019-06-07 哈尔滨工业大学 A kind of rail traffic station large passenger flow real time early warning method
CN109961164A (en) * 2017-12-25 2019-07-02 比亚迪股份有限公司 Passenger flow forecast method and device
US10501343B1 (en) * 2018-08-08 2019-12-10 Evoqua Water Technologies Llc Method of treating water with an ion exchange bed in a water treatment system
CN110991394A (en) * 2019-12-17 2020-04-10 中国建设银行股份有限公司 People flow distribution determination method and system based on multi-feature fusion
CN111859718A (en) * 2020-09-22 2020-10-30 北京全路通信信号研究设计院集团有限公司 Method and system for calculating congestion coefficient of regional multi-standard rail transit station
CN112288122A (en) * 2019-11-28 2021-01-29 南京行者易智能交通科技有限公司 Public transport rapid commuting line design method based on passenger flow OD big data

Patent Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20160132233A (en) * 2015-05-07 2016-11-17 주식회사 엘비씨소프트 Based on the response force sensors carriage/trains congestion management system and service methods
CN107220724A (en) * 2017-04-21 2017-09-29 北京航空航天大学 Passenger flow forecast method and device
CN107844893A (en) * 2017-10-10 2018-03-27 东南大学 Station operation security appraisal procedure occurs for the large passenger flow based on Matter element Extension
US20190156678A1 (en) * 2017-11-20 2019-05-23 Tony Mitchell Cole Systems and methods for coordinated passenger offloading of autonomous vehicles at large scale parking destinations
CN109961164A (en) * 2017-12-25 2019-07-02 比亚迪股份有限公司 Passenger flow forecast method and device
CN108229400A (en) * 2018-01-04 2018-06-29 东南大学 Subway station large passenger flow identification method for early warning and system
US10501343B1 (en) * 2018-08-08 2019-12-10 Evoqua Water Technologies Llc Method of treating water with an ion exchange bed in a water treatment system
CN109858670A (en) * 2018-12-24 2019-06-07 哈尔滨工业大学 A kind of rail traffic station large passenger flow real time early warning method
CN112288122A (en) * 2019-11-28 2021-01-29 南京行者易智能交通科技有限公司 Public transport rapid commuting line design method based on passenger flow OD big data
CN110991394A (en) * 2019-12-17 2020-04-10 中国建设银行股份有限公司 People flow distribution determination method and system based on multi-feature fusion
CN111859718A (en) * 2020-09-22 2020-10-30 北京全路通信信号研究设计院集团有限公司 Method and system for calculating congestion coefficient of regional multi-standard rail transit station

Non-Patent Citations (7)

* Cited by examiner, † Cited by third party
Title
BENDER ET AL.: "The effect of relative flow on the asymmetric structure in the interior of hurricanes", JOURNAL OF THE ATMOSPHERIC SCIENCES *
张敏等: "城市轨道交通站台等候区客流时空特性研究", 山东交通学院学报, vol. 22, no. 4 *
李传成;周希霖;杨依林;: "铁路客运站站前公交车场规模预测方法", 中国铁道科学, no. 05 *
杨永泰: "城市轨道交通网络列车运行图评估方法研究", 中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑, no. 1 *
王淑伟: "站点周边用地特性对轨道客流影响机理研究", 中国博士学位论文全文数据库 工程科技Ⅱ辑, no. 3 *
王玉萍等: "城市轨道交通客流预测结果的技术分析体系", 长安大学学报 (自然科学版) *
田蕾;王随继;: "近60年来辽河流域径流量变化及其主控因素分析", 水土保持研究, no. 01 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114266010A (en) * 2022-03-01 2022-04-01 华东交通大学 Method and system for calculating congestion coefficient of regional multi-standard rail traffic interval
CN114266010B (en) * 2022-03-01 2022-05-13 华东交通大学 Method and system for calculating congestion coefficient of regional multi-standard rail traffic interval

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