CN115410371A - Urban rail transit passenger flow data acquisition and analysis method based on non-inductive payment - Google Patents
Urban rail transit passenger flow data acquisition and analysis method based on non-inductive payment Download PDFInfo
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- CN115410371A CN115410371A CN202211111601.0A CN202211111601A CN115410371A CN 115410371 A CN115410371 A CN 115410371A CN 202211111601 A CN202211111601 A CN 202211111601A CN 115410371 A CN115410371 A CN 115410371A
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Abstract
The invention discloses an urban rail transit passenger flow data acquisition and analysis method based on non-inductive payment, which is characterized in that a non-inductive payment data acquisition unit is configured in an urban rail transit system, the travel information of passengers in the whole processes of getting-in, taking and getting-out is captured, the track data of the passengers in the whole process of rail transit travel is analyzed, and the peak time interval and the peak area of the taking are judged, so that accurate data is provided for rail transit operators, the vehicles are dispatched in real time, the passengers can pass and evacuate in time, a decision basis is provided for the design of a passenger flow line in a station, and accurate data support is provided for urban rail transit operation organizations and ticket clearing. Compared with the traditional passenger flow collection methods such as an AFC (automatic fare collection) system and a video technology for rail transit automatic fare collection, the method provided by the invention utilizes a non-inductive payment system, saves the processes of swiping cards or two-dimensional codes when passengers get in and out of the station, and further shortens the time of getting in and out of the station.
Description
Technical Field
The invention relates to the technical field of urban rail transit, in particular to an urban rail transit passenger flow data acquisition and analysis method based on non-inductive payment.
Background
With the rapid increase of urban rail transit construction mileage, urban rail transit networks and passenger traffic volume also rise, and in the face of complex rail transit operation organizations in network environments, how to effectively capture passenger travel information and realize accurate passenger flow management and control are the keys for ensuring orderly operation of rail transit.
In an urban rail transit system, the following 3 passenger flow data acquisition methods are mainly available: (1) The method can only obtain the arrival place and time and the departure place and time of each passenger through the acquisition of an AFC system of the urban rail transit, and cannot detect the travel track of the passenger in the urban rail transit system, such as waiting information, getting-on and getting-off information and transfer information of the passenger at a platform; (2) Some video analysis technical methods (such as an optical flow method) are also gradually applied to monitoring of subway passenger flow, and the method has advantages in the aspects of real-time passenger flow density monitoring and emergency response, but has the defects of limited application occasions, low accuracy and the like; (3) Some new schemes (such as a face recognition technology) improve the accuracy and the real-time performance of passenger flow monitoring to a certain extent, but the face recognition technology has the problems of privacy, information leakage and the like, and is high in popularization difficulty.
Therefore, a more comprehensive, feasible and advanced subway passenger flow data acquisition technology needs to be found, so that the passenger flow data is ensured to be accurate and have real-time performance.
Disclosure of Invention
The invention aims to provide a subway passenger flow data acquisition and analysis method based on non-inductive payment, which can conveniently record the travel track of passengers in a rail transit system in the whole process, simultaneously avoid privacy risks caused by technologies such as face recognition and the like, and provide decision support for operation organization and passenger flow management and control of urban rail transit by analyzing passenger flow data acquired by non-inductive payment.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a method for collecting and analyzing urban rail transit passenger flow data based on non-inductive payment comprises the following steps:
step 1, configuring a non-inductive payment data acquisition unit in an urban rail transit system:
step 1.1, arranging a plurality of non-inductive payment detection areas on key nodes of pedestrian circulation at each station entrance, bottleneck channels, platforms and station exits in the urban rail transit station and on each carriage of a train, and capturing position information of passengers;
step 1.2, intelligent terminal equipment carried by a passenger builds a software environment of the intelligent terminal equipment of the passenger, and information receiving and uploading between the intelligent terminal equipment and short-distance wireless communication or indoor positioning equipment are achieved;
step 1.3, building a passenger travel information database management system of the urban rail transit background at a remote background, storing the passenger travel overall process track data acquired by non-inductive payment, and managing beacon information;
step 2, the passengers enter the station, take the bus and go out of the station to obtain the travel information in the whole process
Step 2.1, matching and obtaining passenger arrival information: when a passenger enters an inbound detection area, the intelligent terminal device carried by the passenger receives a broadcast packet of a Bluetooth beacon of the inbound detection device, identifies information of the passenger, automatically uploads inbound information of the passenger and other related detailed information to a background database, and records the name of the inbound point of the passenger and inbound time information;
step 2.2 passenger platform waiting position acquisition: when a passenger enters a station area, the passenger receives a broadcast packet of a Bluetooth beacon of station detection equipment by using intelligent terminal equipment carried by the passenger, identifies the station position of the passenger, automatically uploads passenger position information to a background database, and records the position information of the passenger at the station and the residence time of the passenger at the station;
detecting and uploading information and passing time of the passengers at the positions of the bottleneck channels related to the corridor and the escalator in the same method;
step 2.3, passengers take the bus and the positions of the carriages are obtained: after a passenger gets on the train, the intelligent terminal device carried by the passenger receives the broadcast packet of the Bluetooth beacon of the platform and train detection device, judges the intensity of the platform beacon and the train beacon to judge and record the getting-on time and the position of the carriage of the passenger, and uploads and stores related information into a background database;
step 2.4, passenger transfer information acquisition: if the passenger needs to transfer, the intelligent terminal device carried by the passenger is matched with the Bluetooth beacons of the train and the platform, the detailed transfer process of the passenger is recorded, the detailed transfer process comprises the time for the passenger to get off the train platform, get off the train, transfer waiting time, transfer the train, and get on the train, the complete transfer process information of the passenger is marked, and the related information is uploaded and stored into a background database;
step 2.5, passenger outbound payment information acquisition: when a passenger leaves a station, the non-inductive payment device detects whether an intelligent terminal device carried by the passenger is in a station-leaving detection area, if so, user information and station-leaving information of the intelligent terminal device are recorded, and meanwhile, whether the passenger really forms a complete trip chain from station-entering to station-leaving is checked, the complete trip chain comprises station-entering, station-getting-on, station-getting-off and station-leaving records of the passenger, if the trip chain is complete, the user can be charged according to a trip track, and after the charging is finished, a gate is opened to release the passenger;
step 3, analyzing the whole process track passenger flow data
Step 3.1 routine analysis: the upper computer performs conventional passenger flow summarizing statistical analysis on the basis of passenger travel data collected and stored by the background database, wherein the statistical indexes comprise station passenger flow, line passenger flow and line network passenger flow; the station passenger flow indexes comprise station entrance passenger flow, station exit passenger flow and station interior passenger flow; the line passenger flow indexes comprise line passenger flow volume, line load intensity, line average distance, line passenger transport turnover, line passenger flow density and inter-station section passenger flow; the network passenger flow index comprises network passenger flow and network load intensity;
step 3.2 bottleneck channel analysis: by summarizing all passenger information of the bottleneck channel, carrying out passenger flow density analysis all day long, mining the passing behavior rule of the passenger bottleneck at the peak time, providing decision basis for the in-station passenger flow streamline organization design, or carrying out real-time bottleneck passenger flow detection;
step 3.3, platform congestion analysis: through analyzing the position data and the residence time of passengers at the platform, identifying the crowded area and the crowded time period of the platform, judging the distribution rule of the passenger platform, and providing decision support for platform passenger flow management and control;
step 3.4 train congestion analysis: the number of passengers getting on the bus at each station
The number of persons getting off
The recursion formula (1) and the recursion formula (2) can be used for accurately obtaining the number of passengers at each station when the train leaves, calculating the full load rate of the train in real time, identifying crowded trains and crowded sections, judging the peak section of the passengers when the passengers take the train, and providing decision support for passenger organization and train scheduling;
in the formula:
the number of the persons who are present when the train i leaves the starting station is represented;
step 3.5 passenger transfer information: by analyzing passenger transfer data, analyzing passenger travel rules, excavating the matching relation between the transfer passenger demands and train supply, designing a train schedule considering transfer efficiency, accurately determining the passenger flow sharing rate on each line and providing accurate ticketing liquidation.
Preferably, the setting scheme of the non-inductive payment detection area is as follows: the station hall layer center line two ports are provided with non-inductive payment detection areas, each end comprises at least one station entrance non-inductive payment detection area and one station exit non-inductive payment detection area, the station entrance non-inductive payment detection area is arranged behind the security inspection door, and the station exit non-inductive payment detection area is arranged in front of the gate; setting non-inductive payment detection areas at the inlet position of the bottleneck channel, the middle position of the channel and the outlet position of the bottleneck channel; arranging corresponding detection areas on the station layer according to the building structures of different stations; setting a non-inductive payment detection area in each carriage of the train; for capturing the passenger's position information.
Preferably, the bottleneck channel analysis in step 3.2 identifies and determines the channel service level by calculating the average passenger speed and channel density under time-varying conditions, specifically, the operation period is firstly divided into a plurality of tiny time intervals, denoted as t; then, the average speed of the passengers per period is calculated using equations (3) to (5)
And channel density
Wherein v is i,s Indicating the time for passenger i to pass through lane s,
representing the average speed of the channel s over a period t,
representing the density of the channel s at time t,
and
respectively representing the time of entry and the time of exit of passenger i on pathway s,
representing the number of passengers on the passage s during the period t, l s Represents the length of the bottleneck channel s;
using the obtained average speed
And channel density
And critical speed of bottleneck channel
And critical density
Can judge the congestion state of the bottleneck if the current situation is correct
And is
And meanwhile, the situation that the bottleneck channel is in an overcrowded state can be identified, the passenger thermodynamic diagram of the bottleneck channel is drawn by using the obtained data, and the passenger distribution state is further visually identified, so that decision support is provided for the streamline organization of station passenger flow and the improvement of passing facilities.
Preferably, in the step 3.3, the station congestion analysis uses the position of each passenger at the station and the stop time at the station, which are obtained by using the station detection device, to draw a passenger station thermodynamic diagram, so as to visually judge the passenger distribution condition, and particularly, in the case of uneven passenger distribution, the passenger flow distribution condition at the station is monitored in real time through the display of the thermodynamic diagram, so as to provide a decision basis for the station passenger flow organization.
Preferably, the short-range wireless communication device includes a bluetooth beacon.
Preferably, the intelligent terminal device comprises an intelligent watch and an intelligent wearable device capable of receiving and transmitting related signals.
Preferably, the intelligent terminal device carried by the passenger simultaneously establishes an intelligent terminal device software environment of the passenger, and can assist the passenger to complete various notification, recharging and balance inquiry functions.
With the continuous development of artificial intelligence technology, the non-inductive payment technology has been well applied in the field of intelligent traffic. The non-inductive payment can meet the 'three-nothing' payment requirement, namely, no cash, no bank card and no mobile phone. The introduction of the non-inductive payment technology into the subway passenger flow data acquisition can accurately measure the passenger flow conditions in subway stations and trains in real time, and meanwhile, the introduction of the non-inductive payment technology can further enhance the convenient and fast advantages of the subway, improve the flexibility and the safety of subway operation management, and better deal with the passenger flow management of the super-large-scale network rail transit.
The invention collects and analyzes the track data of the whole process of passenger rail transit trip based on the non-inductive payment technology, judges the time period and the region of the high peak and the low peak of the riding, provides accurate data for the operator of rail transit, carries out real-time scheduling on vehicles, ensures the timely passing and evacuation of passengers, provides decision basis for the design of the passenger flow streamline in the station, and provides accurate data support for the urban rail transit operation organization and the ticket service clearing.
Compared with the traditional passenger flow collection methods such as an AFC (automatic fare collection) system and a video technology for rail transit automatic fare collection, the method provided by the invention utilizes a non-inductive payment system, saves the processes of swiping cards or two-dimensional codes when passengers get in and out of the station, and further shortens the time of getting in and out of the station.
Drawings
FIG. 1 is a schematic diagram of a method for implementing a non-inductive payment service according to the present invention;
FIG. 2 is a schematic diagram of a station hall layer non-inductive payment detection area arrangement;
FIG. 3 is a schematic diagram illustrating the setting of a non-inductive payment detection area of a bottleneck channel of a station;
fig. 4 is a schematic diagram of a station platform layer non-inductive payment detection area;
FIG. 5 is a schematic view of an in-vehicle detection zone setup;
FIG. 6 is a diagram of a non-inductive payment passenger flow data logical storage structure;
FIG. 7 is a schematic view of a non-sensory payment record passenger transfer process;
FIG. 8 is a block diagram of a traffic data analysis based on non-sensory payments;
FIG. 9 is a thermodynamic diagram of a bottleneck passage;
fig. 10 is a thermodynamic diagram of an island platform.
Detailed Description
The present invention will be described in further detail below with reference to the accompanying drawings, taking a subway station having four entrances and exits as an example.
As shown in fig. 1, the method for collecting and analyzing urban rail transit passenger flow data based on non-inductive payment comprises the following steps:
step 1, configuring a non-inductive payment data acquisition unit in an urban rail transit system:
step 1.1 a plurality of non-inductive payment detection areas are arranged in a subway station: as shown in fig. 2, two ports of a central line of a station hall layer of a subway station are provided with a non-inductive payment detection area, each end comprises an inbound non-inductive payment detection area and an outbound non-inductive payment detection area, the non-inductive payment detection area of the inbound is arranged behind a security check door, and the non-inductive payment detection area of the outbound is arranged in front of a gate; as shown in fig. 3, at the inlet position of the bottleneck channel, the middle position of the channel and the outlet position of the bottleneck channel, non-inductive payment detection areas are arranged; as shown in fig. 4, on the station floor, corresponding detection areas are set according to the building structures of different stations; as shown in fig. 5, a non-inductive payment detection area is set in each car of the train; for capturing position information of a passenger;
step 1.2, intelligent terminal equipment carried by a passenger builds an intelligent terminal equipment software environment of the passenger, and information receiving and uploading between the intelligent terminal equipment and short-distance wireless communication or indoor positioning equipment are achieved;
the intelligent terminal equipment comprises a smart phone and a watch, and the payment function of the intelligent terminal equipment can be based on third party payment software of market mainstream or a self-customized non-inductive payment APP of urban rail transit. The method comprises the steps that a passenger only needs to open a trip function module of software before taking a subway, the function module can open a wireless positioning function of intelligent terminal equipment, the intelligent terminal equipment is enabled to receive broadcast information of a subway station beacon and transmit the position of the intelligent terminal equipment at a subway station to a background, the information of entering, waiting, getting on, transferring, getting off and leaving of the passenger is identified, whether a complete subway trip chain is formed or not is checked, a charging program is started after the checking is successful, and finally the charging information is sent to third-party payment software to realize fee deduction. Of course, the third-party software may not be selected to realize the non-inductive payment function, and the specific payment method is not limited herein.
Step 1.3, building a passenger travel information database management system of the urban rail transit background at a remote background, storing the passenger travel overall process track data acquired by non-inductive payment, and managing beacon information;
the intelligent terminal device non-inductive payment APP saves the beacon names, MAC addresses and beacon coordinate information (except for mobile vehicle-mounted beacons) in all subway stations in the city, the beacon types (such as a waiting hall identification area beacon, an in-out station area identification beacon, a vehicle-mounted beacon and the like) and the subway station access and exit geographic fence information of the city, after the non-inductive payment APP finds a new beacon, whether the beacon is newly added to a background system is verified, and if the beacon is added, the beacon information is updated.
After checking the complete travel chain of the intelligent terminal equipment, such as entering a station, waiting for a car, getting on a car, transferring, getting off a car and leaving a station, the non-inductive payment device needs to know the specific user identity and the bound account thereof when deducting time in specific implementation. Therefore, the intelligent terminal equipment identification information and the user information need to be interacted in a certain mode. In this embodiment, the interaction is performed in a database mode, and the user information and the device identification information of the passenger are prestored in the database. The user information includes identity information and account information of the user. The user information requires the passenger to register in advance. When the passenger registers, the identity information can be an identity card number or other recognized identity information, the account information can be different according to different payment modes, the passenger can select a payment account number of the third-party payment software when using the third-party payment software for payment, and can select a bank account and the like if using other modes.
Based on the implementation mode of the non-inductive payment technical scheme, the non-inductive payment device can acquire corresponding passenger flow data and store the passenger flow data in real time. In this embodiment, the data storage is stored by using a database, and the storage logic structure and the storage objects are as shown in fig. 6. The non-inductive payment passenger flow data element can comprise a data flow number, an inbound non-inductive payment device number, an inbound name, inbound time, a platform number, a platform non-inductive payment device number, a platform name, arrival platform time, an outbound non-inductive payment device number, an outbound name, outbound time and a collection equipment code. The data transmission can adopt a database interaction mode, a Socket mode, a message service mode and the like, can be used in combination according to the advantages and the disadvantages of the modes, and is not particularly limited.
Step 2, the travel information of passengers in the whole process of entering, taking, leaving and leaving
Step 2.1, matching and obtaining passenger arrival information: opening a non-inductive payment APP of an intelligent terminal device carried by a passenger before the passenger enters a station, acquiring passenger position information by the non-inductive payment APP through a non-inductive payment detection device, and starting to judge which station entrance and exit the passenger enters the geo-fence; after a passenger enters a certain station entrance geo-fence, the non-inductive payment detection equipment records that the passenger enters the station and starts to monitor Bluetooth beacon broadcasting; after a beacon matched with the non-sensitive payment APP is monitored, the non-sensitive payment detection device immediately judges that a passenger enters a station, and records and uploads passenger station entering time and station entering port information to a subway background database.
Step 2.2 passenger platform waiting position acquisition: after a passenger enters a station area, the passenger uses an intelligent terminal device carried by the passenger to make a non-sensitive payment APP to receive a broadcast packet of a Bluetooth beacon of station detection equipment, identifies the station position of the passenger, automatically uploads passenger position information to a background database, and records the position information of the passenger at the station and the residence time of the passenger at the station;
detecting and uploading information and passing time of the passengers at the positions of the bottleneck channels related to the corridor and the escalator in the same way;
step 2.3, passengers get on the bus and the position of the carriage where the passengers get on the bus: after passengers get on the train, the intelligent terminal devices carried by the passengers receive broadcast packets of Bluetooth beacons of platform and train carriage detection devices through the non-sensitive payment APP, judge and record the getting-on time and the positions of the carriages of the passengers according to the strength of the platform beacons and the train carriage Bluetooth beacons, and upload and store related information into a background database;
step 2.4, getting-off or transfer information of passengers is acquired: if the passenger needs to transfer, the intelligent terminal device carried by the passenger is used for carrying out non-inductive payment APP to be matched with Bluetooth beacons of the train and the platform, the detailed transfer process of the passenger is recorded, wherein the detailed transfer process comprises the time for the passenger to get off the train platform, the time for getting off the train, the waiting time for transfer, the time for transferring the train to get on the train, the complete transfer process information of the passenger is marked, and the related information is uploaded and stored into a background database;
step 2.5, obtaining the outbound payment information of passengers: when a passenger leaves the station, after a non-inductive payment APP of the intelligent terminal device identifies a beacon signal of a non-inductive payment detection area installed near a station exit, judging that the passenger is ready to leave the station, wherein the non-inductive payment APP of the intelligent terminal device improves the frequency of receiving beacon information, calculates the distance between the passenger and the non-inductive payment exit, releases the passenger from the station after meeting the condition of approaching the range of the beacon distance, and sends the information to a background management system; and when the non-inductive payment detection equipment detects and checks the outbound information, automatic payment is carried out, and the gate is opened for releasing.
The method comprises the steps that a non-inductive payment detection device near an exit detects whether an intelligent terminal APP carried by a passenger is in an exit detection area or not, if yes, user information and exit information of the intelligent terminal APP are recorded, meanwhile, whether the passenger really forms a complete trip chain from the entrance to the exit is checked, the complete trip chain comprises the entrance, platform boarding, platform disembarking and exit records of the passenger, if the trip chain is complete, the user can be charged according to a trip track, and after charging is completed, a gate is opened to release the passenger;
step 3, analyzing the whole process track passenger flow data
Step 3.1 conventional analysis: the upper computer performs conventional passenger flow summarizing statistical analysis on the basis of passenger travel data collected and stored by the background database, wherein the statistical indexes comprise station passenger flow, line passenger flow and line network passenger flow; the station passenger flow indexes comprise station entrance passenger flow, station exit passenger flow and station interior passenger flow; the line passenger flow indexes comprise line passenger flow volume, line load intensity, line average distance, line passenger transport turnover, line passenger flow density and inter-station section passenger flow; the network passenger flow index comprises network passenger flow and network load intensity;
step 3.2 bottleneck channel analysis: the method comprises the steps of identifying and judging the channel service level by calculating the average speed and channel density of passengers under the time-varying condition, specifically, firstly dividing an operation period into a plurality of tiny time intervals, such as 30s or 60s, and expressing t; then, the average passenger velocity for each time period is calculated using equations (3) to (5)Degree of rotation
And channel density
Wherein v is i,s Indicating the time for passenger i to pass through lane s,
representing the average speed of the channel s over a period t,
representing the density of the channel s at time t,
and
respectively representing the time of entry and the time of exit of passenger i on pathway s,
representing the number of passengers on the passage s during the period t, l s Represents the length of the bottleneck channel s;
using the obtained average speed
And channel density
And critical speed of bottleneck channel
And critical density
Can judge the congestion state of the bottleneck if the current situation is correct
And is
And meanwhile, the situation that the bottleneck channel is in an overcrowded state can be identified, the passenger thermodynamic diagram of the bottleneck channel is drawn by using the obtained data, and the passenger distribution state is further visually identified, so that decision support is provided for the streamline organization of station passenger flow and the improvement of passing facilities.
Step 3.3, platform congestion analysis: through analyzing the position data and the residence time of passengers at the platform, identifying the crowded area and the crowded time period of the platform, judging the distribution rule of the passenger platform, and providing decision support for platform passenger flow management and control;
step 3.4 train congestion analysis: the number of passengers getting on the bus at each station
The number of persons getting off
The recursion formula (1) and the recursion formula (2) can be used for accurately obtaining the number of passengers at each station when the train leaves, calculating the full load rate of the train in real time, identifying crowded trains and crowded sections, judging the peak section of the passengers when the passengers take the train, and providing decision support for passenger organization and train scheduling;
in the formula:
the number of the persons who are present when the train i leaves the starting station is represented;
step 3.5 passenger transfer information: through analyzing passenger transfer data, the transfer congestion direction and the passenger transfer rule are identified, decision support is provided for transfer streamline design, and accurate ticketing liquidation is provided.
The key nodes for pedestrian circulation comprise an entrance, a walking channel, a platform and an exit.
The short-range wireless communication device includes a bluetooth beacon.
The intelligent terminal equipment comprises intelligent watch and intelligent wearable equipment capable of receiving and transmitting related signals of the intelligent mobile phone.
The intelligent terminal device carried by the passenger establishes the software environment of the intelligent terminal device of the passenger at the same time, and can assist the passenger to complete various notification, recharging and balance inquiry functions.
The above are merely preferred examples of the present invention. It should be noted that those skilled in the art, having the benefit of the teachings of this invention, may effect numerous equivalent modifications and improvements within the scope and spirit of the invention as defined by the claims appended hereto.
Claims (7)
1. A method for collecting and analyzing urban rail transit passenger flow data based on non-inductive payment is characterized by comprising the following steps: the method comprises the following steps:
step 1, configuring a non-inductive payment data acquisition unit in an urban rail transit system:
step 1.1, arranging a plurality of non-inductive payment detection areas on key nodes of pedestrian circulation at each station entrance, bottleneck passages, platforms and station exits in an urban rail transit station and on each carriage of a train, and capturing position information of passengers;
step 1.2, intelligent terminal equipment carried by a passenger builds an intelligent terminal equipment software environment of the passenger, and information receiving and uploading between the intelligent terminal equipment and short-distance wireless communication or indoor positioning equipment are achieved;
step 1.3, building a passenger travel information database management system in the urban rail transit background at a remote background, storing the passenger travel overall process track data obtained by non-inductive payment, and managing beacon information;
step 2, the travel information of passengers in the whole process of entering, taking, leaving and leaving
Step 2.1, matching and obtaining passenger arrival information: when a passenger enters an inbound detection area, the intelligent terminal device carried by the passenger receives a broadcast packet of a Bluetooth beacon of the inbound detection device, identifies information of the passenger, automatically uploads the inbound information of the passenger and other related detailed information to a background database, and records the name of the inbound point of the passenger and the inbound time information;
step 2.2 passenger platform waiting position acquisition: when a passenger enters a station area, the passenger receives a broadcast packet of a Bluetooth beacon of station detection equipment by using intelligent terminal equipment carried by the passenger, identifies the station position of the passenger, automatically uploads passenger position information to a background database, and records the position information of the passenger at the station and the residence time of the passenger at the station;
detecting and uploading information and passing time of the passengers at the positions of the bottleneck channels related to the corridor and the escalator in the same method;
step 2.3, passengers take the bus and the positions of the carriages are obtained: after a passenger gets on the train, the intelligent terminal device carried by the passenger receives the broadcast packet of the Bluetooth beacon of the platform and train detection device, judges the intensity of the platform beacon and the train beacon to judge and record the getting-on time and the position of the carriage of the passenger, and uploads and stores related information into a background database;
step 2.4, passenger transfer information acquisition: if the passenger needs to transfer, the intelligent terminal device carried by the passenger is matched with the Bluetooth beacons of the train and the platform, the detailed transfer process of the passenger is recorded, the detailed transfer process comprises the time for the passenger to get off the train platform, get off the train, transfer waiting time, transfer the train, and get on the train, the complete transfer process information of the passenger is marked, and the related information is uploaded and stored into a background database;
step 2.5, obtaining the outbound payment information of passengers: when a passenger leaves a station, the non-inductive payment device detects whether an intelligent terminal device carried by the passenger is in a station-leaving detection area, if so, user information and station-leaving information of the intelligent terminal device are recorded, and meanwhile, whether the passenger really forms a complete trip chain from station-entering to station-leaving is checked, the complete trip chain comprises station-entering, station-getting-on, station-getting-off and station-leaving records of the passenger, if the trip chain is complete, the user can be charged according to a trip track, and after the charging is finished, a gate is opened to release the passenger;
step 3, analyzing the whole process track passenger flow data
Step 3.1 conventional analysis: the upper computer performs conventional passenger flow summarizing statistical analysis on the basis of passenger travel data collected and stored by the background database, wherein the statistical indexes comprise station passenger flow, line passenger flow and line network passenger flow; the station passenger flow indexes comprise station entrance passenger flow, station exit passenger flow and station interior passenger flow; the line passenger flow indexes comprise line passenger flow volume, line load intensity, line average distance, line passenger transport turnover, line passenger flow density and inter-station section passenger flow; the network passenger flow index comprises network passenger flow and network load intensity;
step 3.2 bottleneck channel analysis: by summarizing all passenger information of the bottleneck channel, carrying out passenger flow density analysis all day long, mining the passing behavior rule of the passenger bottleneck at the peak time, providing decision basis for the in-station passenger flow streamline organization design, or carrying out real-time bottleneck passenger flow detection;
step 3.3, platform congestion analysis: through analyzing the position data and the residence time of passengers at the platform, identifying the crowded area and the crowded time period of the platform, judging the distribution rule of the passenger platform, and providing decision support for platform passenger flow management and control;
step 3.4 train congestion analysis: by the aid of the obtained number of getting-on passengers and the number of getting-off passengers at each station, the number of passengers at each station can be accurately obtained in real time by means of a recursion formula (1) and a recursion formula (2), crowded trains and crowded sections are identified, passengers are judged to take a high peak section, and decision support is provided for passenger organization and train scheduling;
in the formula:
the number of the persons who are present when the train i leaves the starting station is represented;
step 3.5 passenger transfer information: through detailed passenger transfer data, the travel rule of passengers is analyzed, the matching relation between the demand of the passengers for transfer and the train supply is mined, a train schedule with the transfer efficiency taken into consideration is designed, the passenger flow sharing rate on each line is accurately determined, and accurate ticketing clearing is provided.
2. The urban rail transit passenger flow data collection and analysis method based on non-inductive payment according to claim 1, characterized in that: the setting scheme of the non-inductive payment detection area is as follows: the non-inductive payment detection regions are arranged at two ports of a central line of a station hall layer, each port comprises at least one inbound non-inductive payment detection region and one outbound non-inductive payment detection region, the non-inductive payment detection region of the inbound port is arranged behind a security inspection door, and the non-inductive payment detection region of the outbound port is arranged in front of a gate; setting non-inductive payment detection areas at the inlet position of the bottleneck channel, the middle position of the channel and the outlet position of the bottleneck channel; arranging corresponding detection areas on the station layer according to the building structures of different stations; setting a non-inductive payment detection area in each carriage of the train; for capturing the passenger's position information.
3. The urban rail transit passenger flow data acquisition and analysis method based on non-inductive payment according to claim 1 or 2, characterized in that: in the step 3.2, the passenger average speed and the channel density under the time-varying condition are calculated to identify and judge the channel service level, and firstly, the operation time interval is divided into a plurality of tiny time intervals which are represented as t; then, the average speed of the passengers per period is calculated using equations (3) to (5)
And channel density
Wherein v is i,s Indicating the time for passenger i to pass through lane s,
representing the average speed of the channel s over a period t,
representing the density of the channel s at time t,
and
respectively representing the time of entry and the time of exit of passenger i on pathway s,
representing the number of passengers on the passage s during the period t, l s Represents the length of the bottleneck channel s;
using the obtained average speed
And channel density
And critical speed of bottleneck channel
And critical density
Can judge the congestion state of the bottleneck if the current situation is correct
And is
And meanwhile, the situation that the bottleneck channel is in an overcrowded state can be identified, the passenger thermodynamic diagram of the bottleneck channel is drawn by using the obtained data, and the passenger distribution state is further visually identified, so that decision support is provided for the streamline organization of station passenger flow and the improvement of passing facilities.
4. The urban rail transit passenger flow data collection and analysis method based on non-inductive payment according to claim 3, characterized in that: in the step 3.3, the station congestion analysis uses the position of each passenger at the station and the stop time of each passenger at the station, which are obtained by the station detection equipment, to draw a passenger station thermodynamic diagram, so that the passenger distribution condition is visually judged, and particularly, under the condition that the passenger distribution is not uniform, the passenger flow distribution condition of the station is monitored in real time through the display of the thermodynamic diagram, so that a decision basis is provided for the passenger flow organization of the station.
5. The urban rail transit passenger flow data collection and analysis method based on non-inductive payment according to claim 4, characterized in that: the short-range wireless communication device includes a bluetooth beacon.
6. The urban rail transit passenger flow data collection and analysis method based on non-inductive payment according to claim 5, characterized in that: the intelligent terminal device comprises an intelligent watch and an intelligent wearable device capable of receiving and transmitting related signals of the intelligent watch and the intelligent mobile phone.
7. The urban rail transit passenger flow data collection and analysis method based on non-inductive payment according to claim 6, characterized in that: the intelligent terminal device carried by the passenger simultaneously builds the software environment of the intelligent terminal device of the passenger, and can assist the passenger to complete various notification, recharging and balance inquiry functions.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116167787A (en) * | 2023-04-25 | 2023-05-26 | 深圳市深圳通有限公司 | Rail transit sorting system and method |
| CN117808184A (en) * | 2024-02-29 | 2024-04-02 | 成都汇辙科技有限公司 | Urban rail transit service management system driven by full production elements |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116167787A (en) * | 2023-04-25 | 2023-05-26 | 深圳市深圳通有限公司 | Rail transit sorting system and method |
| CN117808184A (en) * | 2024-02-29 | 2024-04-02 | 成都汇辙科技有限公司 | Urban rail transit service management system driven by full production elements |
| CN117808184B (en) * | 2024-02-29 | 2024-05-10 | 成都汇辙科技有限公司 | Urban rail transit service management system driven by full production elements |
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