CN111275309A - Passenger transport hub key area safety risk and bearing capacity automatic evaluation method - Google Patents

Passenger transport hub key area safety risk and bearing capacity automatic evaluation method Download PDF

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CN111275309A
CN111275309A CN202010049119.3A CN202010049119A CN111275309A CN 111275309 A CN111275309 A CN 111275309A CN 202010049119 A CN202010049119 A CN 202010049119A CN 111275309 A CN111275309 A CN 111275309A
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passenger
key area
bearing capacity
area
safety risk
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邹晓磊
徐瑞华
季晨
刘龙
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Tongji University
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services
    • G06Q50/265—Personal security, identity or safety

Abstract

The invention relates to a method for automatically evaluating safety risk and bearing capacity of a passenger transport hub key area, which comprises the following steps: step 1: obtaining passenger transport hub key area information; step 2: carrying out passenger flow simulation according to the key area information; and step 3: acquiring data in a passenger flow simulation model; and 4, step 4: judging the spatial service level in the current area; and 4, step 4: calculating a safety risk evaluation index of a key area, and evaluating the safety risk of the key area; and 5: and calculating the bearing capacity evaluation index of the key area, and evaluating the bearing capacity of the key area. Compared with the prior art, the method has the advantages of accurate evaluation result, capability of providing basis for passenger transport service organization optimization of the passenger transport hub station and the like.

Description

Passenger transport hub key area safety risk and bearing capacity automatic evaluation method
Technical Field
The invention relates to the technical field of passenger transport hub safety risk and bearing capacity evaluation, in particular to an automatic passenger transport hub safety risk and bearing capacity evaluation method in key areas.
Background
With the rapid development of social economy and the continuous improvement of the quality of life of people in China, the total amount of travel demands of passengers is continuously increased, and transportation tasks born by various transportation modes are continuously increased. Along with the continuous increase of the passenger transportation volume, the travel demand of passengers also presents diversified development changes, and the travel service which pursues service quality such as rapidness, economy, convenience, comfort, safety and the like is changed from the simple point-to-point displacement.
Meanwhile, the transportation supply of passengers is greatly changed, and the transportation infrastructure based on the travel demands of the passengers is built and rapidly developed. Due to the interaction of transportation demand and transportation supply, the road network conditions are continuously improved, the network structure is gradually complete, the resource allocation is gradually reasonable, the travel demand of passengers is stimulated, on one hand, the passenger flow is increased, and on the other hand, new travel ODs (origin and destination) of passengers are induced. The passenger transport hub is used as a large passenger flow distribution center for connecting various traffic modes in cities and connecting urban and urban traffic, the connection and transfer functions of the passenger transport hub are particularly important, and the problems of complex passenger travel processes, types and layouts of facility equipment, service schemes, safety guarantee and the like are caused.
China 'long-term development planning in Integrated traffic networks' points out that: passenger transportation hubs in railways, highways and airports are required to establish passenger collecting and transferring systems which are adaptive to the handling capacity of the passenger transportation hubs, and the passenger collecting and transferring systems are reasonably connected and transferred with various traffic modes such as urban rail transit, conventional public transit, taxis, private traffic and the like, so that traffic integration is realized. The planning connection of the passage among the highway, the transportation hub and the transportation hub is enhanced. The planning connection of intercity rails and passenger transport hubs is enhanced, and the connection of intercity rail transport and urban rails and urban public transport systems is promoted. The planning connection of urban traffic and intercity traffic and airports is perfected, and the transfer efficiency and the radiation capability of the airports are improved. Therefore, the development of the high-speed railway and the comprehensive transportation network in China clearly provides a transportation organization mode of mutual coordination and cooperation, which also provides a new challenge for the passenger transportation organization service level in a passenger transportation hub.
Under the background, the complex passenger travel process in the passenger transport hub is deeply analyzed, key areas of key links such as transfer and the like are analyzed and investigated, an automatic assessment method for establishing the safety risk and the bearing capacity of the key areas of the passenger transport hub is provided, bottleneck areas limiting the safety and the efficiency of the passenger transport hub at the present stage are assessed, and a corresponding solution is provided, so that the passenger travel needs are better met, and the method has very important theoretical and practical significance for fully playing the efficiency of the passenger transport hub.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide an automatic assessment method for the safety risk and the bearing capacity of the passenger transport hub key area, which provides a basis for passenger transport service organization optimization.
The purpose of the invention can be realized by the following technical scheme:
a passenger transport hub key area safety risk and carrying capacity automatic evaluation method is a program embedded in a computer and comprises the following steps:
step 1: obtaining passenger transport hub key area information;
step 2: carrying out passenger flow simulation according to the key area information;
and step 3: acquiring data in a passenger flow simulation model;
and 4, step 4: judging the spatial service level in the current area;
and 4, step 4: calculating a safety risk evaluation index of a key area, and evaluating the safety risk of the key area;
and 5: and calculating the bearing capacity evaluation index of the key area, and evaluating the bearing capacity of the key area.
Preferably, the key area information in step 1 includes position and layout information, passenger flow characteristic information and passenger transportation organization scheme information.
More preferably, the step 2 specifically includes:
step 2-1: establishing a passenger transport hub key area scene according to the position and the layout information;
step 2-2: passenger flow characteristic information and passenger transport organization scheme information are added in the scene established in the step 1, and a passenger travel scene is simulated.
More preferably, the passenger flow characteristic information includes:
passenger flow characteristic information and virtual passenger flow characteristic information with time distribution characteristics;
the passenger flow characteristic information with the time distribution characteristic is used for restoring the passenger flow phenomenon of a key area in passenger travel;
the virtual passenger flow characteristic information is used for simulating extreme passenger flow conditions in a key area.
Preferably, the spatial service levels include A, B, C, D, E and F-six levels, which in turn represent free, non-interfering, more constrained, crowded and congested crowd movement states.
More preferably, the safety risk assessment index includes a congestion risk probability and a congestion risk probability.
More preferably, the method for calculating the congestion risk probability and the congestion risk probability respectively comprises:
Figure RE-GDA0002469650340000031
Figure RE-GDA0002469650340000032
wherein, PEProbability of achieving a congested service level for the area, TEDuration of service level for congestion, t is peak hour duration, PFProbability of achieving a level of congestion service for the area, TFThe duration of the congestion service level.
More preferably, the bearing capacity evaluation index includes a space crowd density limit bearing capacity and a space crowd safety limit bearing capacity.
More preferably, the calculation method of the space crowd density limit bearing capacity comprises the following steps:
VD＝S*dD
Figure RE-GDA0002469650340000033
wherein, VDIs the crowd-dense ultimate bearing capacity of the region, S is the effective area of the region, dDMaximum crowd density, R, for the region at class D service levelDMaximum bearing capacity utilization, V, for the zone at class D service levelSThe number of the gathered people in the area is subjected to simulation;
the method for calculating the space crowd safety limit bearing capacity specifically comprises the following steps:
VE＝S*dE
Figure RE-GDA0002469650340000034
wherein, VEIs the crowd safety limit carrying capacity of the area, dEMaximum crowd density, R, for the area at class E service levelEThe maximum bearer capacity utilization for the area at the level E service level.
Compared with the prior art, the invention has the following advantages:
the method and the system form a key index evaluation system aiming at the safety risk and the bearing capacity of the bottleneck area of the passenger transport hub around the key area in the passenger transport hub as a main research object, and analyze and research the actual passenger transport scene by utilizing a simulation tool. The method for evaluating the safety risk and the bearing capacity of the passenger transport hub key area by combining the qualitative method and the quantitative method has accurate evaluation result and can provide basis for optimizing the passenger transport service organization of the passenger transport hub station.
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FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a schematic diagram of passenger walking speed distribution at S-junction in accordance with an embodiment of the present invention;
FIG. 3 is a schematic diagram of a simulation model of a departure layer of an S-junction station in an embodiment of the present invention;
fig. 4 is a velocity distribution diagram of the start-up simulation model of the S-junction station in the embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, shall fall within the scope of protection of the present invention.
The invention relates to a passenger transport hub key area safety risk and bearing capacity automatic evaluation method, which is a program embedded in a computer, the flow of which is shown in figure 1, and comprises the following steps:
step 1: acquiring passenger transport key area information;
step 2: carrying out passenger flow simulation according to the key area information;
and step 3: acquiring data in a passenger flow simulation model;
and 4, step 4: judging the spatial service level in the current area;
and 4, step 4: calculating a safety risk evaluation index of a key area, and evaluating the safety risk of the key area;
and 5: and calculating the bearing capacity evaluation index of the key area, and evaluating the bearing capacity of the key area.
The key area information in step 1 includes, but is not limited to, location and layout information, passenger flow characteristic information, and passenger transportation organization scheme information.
Position and layout information: including information such as geographic location, streamline design, infrastructure capabilities, etc.;
passenger flow characteristic information: the system comprises passenger flow volume, time distribution characteristics, space distribution characteristics and other information;
passenger transport organization scheme information: including information such as passenger passing flow, current limiting scheme and measures.
The step 2 specifically comprises the following steps:
step 2-1: establishing a passenger transport hub key area scene according to the position and the layout information;
step 2-2: passenger flow characteristic information and passenger transport organization scheme information are added in the scene established in the step 1, and a passenger travel scene is simulated.
The passenger flow characteristic information in the step 2-1 comprises passenger flow characteristic information which is used for restoring a passenger flow phenomenon in a key area in passenger travel and has time distribution characteristics, and virtual passenger flow characteristic information which is used for simulating an extreme passenger flow condition in the key area.
The spatial SERVICE LEVEL in step 4 is evaluated by using a general "Fruin spatial SERVICE LEVEL evaluation criterion", which is the spatial SERVICE LEVEL evaluation criterion proposed by John j. The spatial service level used in this embodiment is classified into 6 levels from a to F, and represents the movement states of free, mutually noninterference, mutually interfered, mutually greatly restricted, congested and congested crowds in turn.
The safety risk assessment indexes comprise congestion risk probability and congestion risk probability, and the specific calculation method comprises the following steps:
Figure RE-GDA0002469650340000051
Figure RE-GDA0002469650340000052
wherein, PEProbability of achieving a congested service level for the area, TEDuration of service level for congestion, t is peak hour duration, PFProbability of achieving a level of congestion service for the area, TFThe duration of the congestion service level.
The bearing capacity evaluation index comprises space crowd intensive limit bearing capacity and space crowd safety limit bearing capacity, and the specific calculation method comprises the following steps:
the calculation method of the intensive ultimate bearing capacity comprises the following steps:
VD＝S*dD
Figure RE-GDA0002469650340000053
wherein, VDIs the crowd-dense ultimate bearing capacity of the region, S is the effective area of the region, dDMaximum crowd density, R, for the region at class D service levelDMaximum bearing capacity utilization, V, for the zone at class D service levelSThe number of the gathered people in the area is subjected to simulation;
the method for calculating the space crowd safety limit bearing capacity specifically comprises the following steps:
VE＝S*dE
Figure RE-GDA0002469650340000054
wherein, VEIs the crowd safety limit carrying capacity of the area, dEMaximum crowd density, R, for the area at class E service levelEThe maximum bearer capacity utilization for the area at the level E service level.
In the embodiment, a key area of a departure floor of a station S of a certain aviation passenger terminal station is taken as a research object.
Firstly, information of a key area of a departure layer of the S station is obtained through data or literature investigation, and the information comprises but is not limited to:
1. location and layout information, such as geographic location, streamline involvement, infrastructure capabilities, etc.;
2. passenger flow characteristic information, such as passenger flow volume, time distribution characteristics, space distribution characteristics, and the like;
3. passenger transportation organization scheme information, such as passenger passing process, flow limiting scheme and measure information.
The obtained information is specifically:
in the S station starting layer, for infrastructure equipment, the number of value cabinets on one side of each value machine island is 13, the value machine time is 5min, the number of side inspection ports is 40, the number of side inspection ports is 1.5min, the number of security inspection ports is 30, the number of security inspection ports is 3min, and the service time setting obeys normal distribution setting. In the aspect of passenger flow characteristics, the passenger flow at peak hours is 4000 people; riding deviceIn the aspect of passenger space, according to investigation, nearly 60 percent of aviation passengers carry large or multiple pieces of luggage, and the occupied space is 0.63-1.13 m2. The rest of the passenger backpacks or the small draw-bar boxes are carried by 1.5 people, the light objects and the heavy objects are 50 percent respectively, and the occupied space is 0.42 to 0.63m2. It can be roughly assumed that in front of the check-in, carrying large or multiple pieces of luggage by 60% of the passengers, 40% of the passenger's backpacks or carrying small trolley cases (by 1.5 pieces), the average passenger occupancy is 0.74m2After the passenger is on-duty, the average occupied space of the passenger is 0.53m according to the calculation of 1.5 passenger average backpacks or small-sized draw-bar boxes2(ii) a In the aspect of passenger speed, speed fluctuation intervals are set according to fig. 2, fig. 2 shows speed sample distribution of pedestrians at different areas at the S junction, and square blocks are 95% of pedestrian speed distribution intervals. The passenger trip flow mainly comprises: entering-machine-side inspection-security inspection-entering other layers, wherein a current-limiting queuing area arranged by the railings exists before side inspection and security inspection.
The criteria used in the evaluation of the spatial service level of the S hub station in this embodiment are shown in table 1.
TABLE 1S division and evaluation indexes of pedestrian service level at starting layer of hub station
Figure RE-GDA0002469650340000061
And then, a microcosmic passenger flow simulation program is utilized to simulate the passenger flow traveling of the whole passenger transport hub or a key layer, and the actual passenger flow or the simulated passenger flow is loaded to restore the real scene and obtain reliable simulation statistical data so as to provide a basis for the next analysis and feedback. Based on the obtained facility equipment data, passenger flow characteristic data and passenger travel flow, a passenger flow microscopic simulation program is utilized to establish a simulation model and a corresponding passenger travel flow, as shown in fig. 3.
And performing data statistics on key areas in the process and high-density areas shown in the figure 4 in the simulation through a local area statistical function.
And finally, evaluating the safety risk and the bearing capacity of the starting layer of the S hub station by calculating a safety risk evaluation index and a bearing capacity utilization rate evaluation index, wherein the evaluation result is shown in a table 2.
Table 2S simulation evaluation result of safety risk and bearing capacity utilization rate of key area of starting layer of hub station
Figure RE-GDA0002469650340000071
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (9)

1. A passenger transport hub key area safety risk and carrying capacity automatic assessment method is characterized in that the method is a program embedded in a computer, and the assessment method comprises the following steps:
step 1: obtaining passenger transport hub key area information;
step 2: carrying out passenger flow simulation according to the key area information;
and step 3: acquiring data in a passenger flow simulation model;
and 4, step 4: judging the spatial service level in the current area;
and 4, step 4: calculating a safety risk evaluation index of a key area, and evaluating the safety risk of the key area;
and 5: and calculating the bearing capacity evaluation index of the key area, and evaluating the bearing capacity of the key area.
2. The method for automatically evaluating the safety risk and the bearing capacity of the key area of the passenger transportation hub according to claim 1, wherein the key area information in the step 1 comprises position and layout information, passenger flow characteristic information and passenger transportation organization scheme information.
3. The method for automatically evaluating the safety risk and the bearing capacity of the key area of the passenger transport hub according to claim 2, wherein the step 2 specifically comprises:
step 2-1: establishing a passenger transport hub key area scene according to the position and the layout information;
step 2-2: passenger flow characteristic information and passenger transport organization scheme information are added in the scene established in the step 1, and a passenger travel scene is simulated.
4. The method for automatically evaluating the safety risk and the carrying capacity of the key area of the passenger transportation hub according to claim 3, wherein the passenger flow characteristic information comprises:
passenger flow characteristic information and virtual passenger flow characteristic information with time distribution characteristics;
the passenger flow characteristic information with the time distribution characteristic is used for restoring the passenger flow phenomenon of a key area in passenger travel;
the virtual passenger flow characteristic information is used for simulating extreme passenger flow conditions in a key area.
5. The method as claimed in claim 1, wherein the space service levels include A, B, C, D, E and F six levels, which represent the moving states of free, non-interfering, more constrained, crowded and congested people in turn.
6. The method as claimed in claim 5, wherein the safety risk assessment indexes include a congestion risk probability and a congestion risk probability.
7. The method for automatically evaluating the safety risk and the bearing capacity of the key area of the passenger terminal according to claim 6, wherein the calculation methods of the congestion risk probability and the congestion risk probability are respectively as follows:
Figure FDA0002370486870000021
Figure FDA0002370486870000022
wherein, PEProbability of achieving a congested service level for the area, TEDuration of service level for congestion, t is peak hour duration, PFProbability of achieving a level of congestion service for the area, TFThe duration of the congestion service level.
8. The method as claimed in claim 5, wherein the load-bearing capacity evaluation index includes space crowd density limit load-bearing capacity and space crowd safety limit load-bearing capacity.
9. The method for automatically evaluating the safety risk and the bearing capacity of the key area of the passenger transportation hub according to claim 8, wherein the calculation method of the space crowd density limit bearing capacity comprises the following steps:
VD＝S*dD
Figure FDA0002370486870000023
wherein, VDIs the crowd-dense ultimate bearing capacity of the region, S is the effective area of the region, dDMaximum crowd density, R, for the region at class D service levelDMaximum bearing capacity utilization, V, for the zone at class D service levelSThe number of the gathered people in the area is subjected to simulation;
the method for calculating the space crowd safety limit bearing capacity specifically comprises the following steps:
VE＝S*dE
Figure FDA0002370486870000024
wherein, VEIs the crowd safety limit carrying capacity of the area, dEMaximum crowd density, R, for the area at class E service levelEThe maximum bearer capacity utilization for the area at the level E service level.
CN202010049119.3A 2020-01-16 2020-01-16 Passenger transport hub key area safety risk and bearing capacity automatic evaluation method Pending CN111275309A (en)

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Citations (2)

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WO2018107510A1 (en) * 2016-12-13 2018-06-21 深圳先进技术研究院 Method and apparatus for evaluating service quality of public transport system

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