CN115034622B - Input and output risk quantitative evaluation method for public transport system - Google Patents
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Abstract
The invention provides a quantitative evaluation method for input and output risks of a bus system. According to the invention, the waiting process and the taking process in the bus travel process of passengers are considered, the input-output risk assessment model of the bus system is built by considering the input-output relation of the community-bus system-community taking the bus station as an import-export link, the influence factors of the input-output risk assessment model are comprehensively considered, the optimization of the departure interval and the station-jump strategy is provided, the input-output risk of the bus system is reduced, and the normal travel of residents and the normal operation of cities are ensured.
Description
Technical Field
The invention relates to a quantitative evaluation method for input and output risks of a public transportation system, which is a method for describing and evaluating input and output risks of the public transportation system by establishing a community-public transportation system-community input and output relation taking a public transportation station as an import and export link by using passenger card swiping data, bus geographic information data and bus station and line data.
Background
Public health event emergency management and disposal capabilities are extremely important. The research of public transportation system transmission and infection risk is an important premise and basis for ensuring normal travel of residents, maintaining normal operation of traffic infrastructure and ensuring normal operation of cities.
Current research on bus system transmission and infection risk is mainly focused on two aspects: firstly, researching optimization based on public transport operation strategies; and secondly, researching propagation tracking based on a public transportation system. 1. Research based on bus operation strategies is mainly directed to: balance the public transportation supply and demand, social economy and transmission prevention and control, analyze the prevention and control strategy to appoint the factor that needs to consider additionally, propose various tactics to optimize models and methods based on this, offer the theoretical support for prevention and control tactics to optimize, most research takes public transportation route as the unit to consider, many researches have ignored the key transmission link of public transportation stop; 2. propagation tracking based on public transportation systems is mainly focused on: based on space-time big data and artificial intelligence technology, the space-time law and relation map of residents are explored, the infection characteristics of infectious diseases are combined, the individual infection risk probability is calculated, and the like, the research is mostly based on the overlapping of space-time tracks of passengers and infected persons in the process of bus travel, and the dynamic reconstruction of the relation network caused by the dynamic space-time characteristics and the individual space-time behaviors in the process of passenger travel is not considered. In addition, the current research ignores that in the actual situation, the states of passengers entering and exiting the public transportation system are unknown, and according to the existing policy means such as body temperature measurement, green code holding, riding and the like, the passengers entering the public transportation system by default are in a healthy state, and how to effectively formulate public transportation management policies under the condition of unknown infected persons is one of the problems to be solved urgently.
The existing public transportation system transmission and infection risk research achieves a certain effect, but has the following problems:
1. ignoring the key propagation link of bus stops
The passenger bus travel process mainly comprises two stages: a riding stage and an waiting stage. The existing research mainly focuses on the infection risk of passengers in the riding process, and ignores the infection risk of passengers in the waiting process.
2. Ignoring relationships between public transportation systems and surrounding community risks
The existing research mainly focuses on infection risk transmission in a public transportation system caused by the passenger taking process, and ignores the input-output relationship between the public transportation system and surrounding communities.
3. Ignoring the fact that passengers entering the public transportation system by default are in a healthy state
When the existing research is used for making public transportation management strategies (such as optimizing departure intervals, jumping stops and the like), on the premise that an infected person exists in a public transportation system (1 infection case is generally assumed), the existing research neglects that in the actual situation, the states of passengers entering and exiting the public transportation system are unknown, and passengers entering the public transportation system are in a healthy state by default according to the existing strategy means of measuring the body temperature, taking a bus with a green code and the like.
Disclosure of Invention
The invention aims to provide an input and output risk assessment method for a public transportation system, which provides a new thought for infection risk assessment, prevention and control and emergency response mechanisms of the public transportation system under sudden public health events.
The present invention is first proposed in view of the above,
1. by referring to the input and output risk concepts of 'country-country', 'province-province', and 'region-region', the input and output relationship of 'community-public transportation system-community' taking public transportation stations as import and export links is provided;
2. providing input and output risk concepts of the public transport system; the input risk refers to the possibility that passengers get on the bus and get into the public transportation system, and describes the influence degree of different bus stops on the whole public transportation system; output risk refers to the likelihood that a get-off passenger is infected and affects communities around the get-off passenger, for evaluating the risk imposed by each bus stop on the surrounding communities;
3. establishing a community-public transportation system-community input-output relationship taking a public transportation station as an import and export link, and establishing a public transportation system input-output risk assessment model; the input risk is specifically represented by: the risk of people carried by passengers getting on the bus from a bus stop after waiting for the bus is mainly related to factors such as stop demand, departure interval, arrival rate of the passengers and the like; the output risk is embodied as follows: the passenger gets off the bus and gets off the bus from the bus stop to carry the average risk after waiting for the bus and riding, the output risk is made up of input risk and riding risk output risk; in addition to the impact factors of input risk, output risk is mainly related to factors such as passenger origin-destination, stop demand, and bus travel time.
The invention solves the technical problems by adopting the technical proposal that,
the quantitative evaluation method of the input and output risk of the bus system is used for evaluating the input and output risk of the bus system based on the OD data of passengers and the GPS data of the buses through the following processes: according to the method, OD data and bus travel time data of passengers are obtained according to card swiping data, bus GPS data and bus stop and line data of the passengers, an input/output risk assessment model is built based on the data and combined with the bus travel process of the passengers, and input/output risks of a bus system in different periods are calculated, and the method specifically comprises the following steps:
1) Constructing passenger OD data and travel time data of buses at each station according to the acquired data;
2) Based on the definition of the input and output risks of the public transportation system, the input and output risks are associated with the travel process (including waiting process and riding process) of passengers;
3) According to random arrival assumptions in the waiting process of passengers, establishing accumulated contact time of each passenger with other passengers in the waiting process, and taking the accumulated contact time of passengers on buses at the moment of arrival at a bus stop as input risk;
4) According to the OD data of the passengers and the travel time data of the buses, the accumulated contact time of each passenger with other passengers in the riding process is established, and the accumulated contact time of passengers getting off at the moment when the buses arrive at bus stops is taken as an output risk by combining the input risk;
5) And (3) combining the established input/output risk models, and optimizing the bus dispatching strategy according to the input/output risk assessed by each station of the bus system.
In the above technical scheme, further, in step 1), based on the acquired passenger card swiping data, bus GPS data and bus stop and line data, the acquired data can be subjected to data processing and matching to obtain bus IC card passenger boarding point data including card number, card swiping date, card swiping time, line number, bus arrival time, boarding point number/name, and longitude and latitude of the stop; the passenger transfer behavior can be identified through the data, meanwhile, the passenger can be subjected to departure station calculation based on a travel chain, and finally, the passenger OD identification is realized; this can be determined by existing probability theory models, markov chain methods, iterative proportional fitting methods, and the like.
Further, the input risk in step 2) is specifically shown as follows: the risk of people carried by passengers getting on the bus from the bus stop after waiting for the bus, namely the possibility that the passengers get on the bus are infected into the public transportation system, describes the influence degree of different bus stops on the whole public transportation system; the output risk is embodied as follows: the risk of people carried by passengers getting off from bus stops after waiting and taking the bus, namely the possibility that the passengers get off are infected and influence communities around the getting-off stops, is used for evaluating the risk applied to the communities around the bus stops by each bus stop.
Further, in step 3), assuming that the arrival of passengers at the bus stop is a batch poisson arrival process, the accumulated contact time between the passengers is determined by the passengers arriving at the bus stop later, and modeling an input risk assessment model according to the OD data of the passengers and the batch poisson arrival process; the input risk is mainly related to factors such as the station demand, departure interval, and passenger arrival rate.
Further, in step 4), based on the accumulated contact time length of the passengers with other passengers during the riding process as the riding risk of the passengers during the riding process, and combining the difference of the boarding sites of the passengers during the alighting, namely the difference of the input risks, an output risk model is established; the output risk consists of an input risk and a riding risk; in addition to the impact factors of input risk, output risk is mainly related to factors such as passenger origin-destination, stop demand, and bus travel time.
Furthermore, in step 5), the input and output risks of the bus system are obtained through calculation based on the passenger OD data, the bus GPS data and the input and output risk quantitative evaluation model, and the input and output risks can be effectively reduced by presetting and optimizing the departure interval and the stop jump strategy in combination with influencing factors of the model.
The beneficial effects of the invention are as follows:
the method can evaluate the input and output risk conditions of the public transportation system under sudden public health events and provide ideas for optimizing the public transportation scheduling scheme. Compared with the existing research, the invention focuses on: 1. combining the traveling process of passengers: namely, the waiting process and the riding process are two aspects; 2. evaluating input and output risks of the public transportation system by combining public transportation stops and public transportation line propagation risks; 3. the mutual influence condition of the public transportation system and the surrounding community risk is more comprehensively considered by combining with the study of the real condition that passengers entering the public transportation system by default are in a health state.
How to evaluate the input and output risks of a bus stop is not yet studied. Therefore, the invention provides two risk quantitative evaluation indexes, namely an input risk and an output risk, aiming at the public transportation system. The input-output risk assessment modeling is based primarily on the number of passengers and duration of contact by the commuters, taking into account cumulative contingencies between the commuters. The input risk refers to the possibility that passengers get on the bus and get into the public transportation system, and describes the influence degree of different bus stops on the whole public transportation system; outputting risks, namely the possibility that passengers get off and influence the adjacent communities of the get-off station, for evaluating risks applied by each bus station to the adjacent communities; the two risk indexes fully reflect the input-output relation between a public transportation system taking a bus station as an import and export link and the infection risk of surrounding communities, and are influenced by a plurality of factors, such as the number of passengers contacted by a commuter in the public transportation system, the origin and destination points of the commuter, a travel chain, a bus travel schedule, road conditions and the like. The invention can provide reference for formulating an emergency response mechanism of the bus, thereby not only enhancing the trust of passengers on the public transportation, but also increasing the confidence of public transportation enterprises.
Drawings
FIG. 1 input/output Risk propagation schematic
FIG. 2 is a schematic diagram showing the relationship between travel process and input/output risk
FIG. 3 is a schematic diagram showing the process of waiting for passengers to get in batches and the infection situation
FIG. 4 schematic illustration of the same batch passenger contact duration calculation
FIG. 5 schematic view of a non-identical batch of passenger contact duration calculations
FIG. 6 schematic diagram of a ride risk assessment
FIG. 7 is a schematic diagram of an input risk model for site 3 as an example
FIG. 8 input/output risk factor graph
FIG. 9 influence of departure interval on input risk
FIG. 10 input a comparison of risk at Gao Fengqi and peaked period
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
Fig. 1 is a schematic diagram of input/output risk propagation, mainly embodying a community-public transportation system-community input/output relationship taking a bus stop as an import/export link, further illustrating the possibility that an boarding passenger is infected to enter a public transportation system, and describing the influence degree of different bus stops on the whole public transportation system; output risk refers to the likelihood that a departure passenger will become infected and affect the vicinity of the departure stop for assessing the risk imposed by each bus stop on the vicinity.
Fig. 2 is a schematic diagram showing a relationship between a travel process and an input/output risk, and mainly shows a relationship between an input/output risk and a waiting process and a riding process in the travel process. The input risk is embodied as follows: the risk of people carried by passengers getting on the bus from the bus stop after waiting for the bus, namely the possibility that the passengers get on the bus are infected into the public transportation system, describes the influence degree of different bus stops on the whole public transportation system; the output risk is embodied as follows: the risk of people carried by passengers getting off from bus stops after waiting and taking the bus, namely the possibility that the passengers get off are infected and influence communities around the getting-off stops, is used for evaluating the risk applied to the communities around the bus stops by each bus stop.
Fig. 3 is a schematic diagram showing the process of waiting for passengers to get in batches and the infection condition. Based on the assumption that passengers arrive at a bus stop as a batch poisson arrival process, the accumulated contact time between the passengers is determined by the passengers arriving at the bus stop later, and an input risk assessment model is modeled according to the OD data of the passengers and the batch poisson process; the input risk is mainly related to factors such as the station demand, departure interval, and passenger arrival rate. For a target passenger, the cumulative contact time period may be composed of three parts: 1. for the same batch of passengers (see in detail fig. 4), 2 for the first arrival (see in detail fig. 5) and 3 for the later arrival (see in detail fig. 5).
The method specifically comprises the following steps:
for each bus stop j, if the passenger batch arrival rate is assumed to be b j The number of arriving passengers per batch isThe arrival time of each batch of passengers is->The bus arrival rate is lambda, and the interval time between the front and back buses is G j (mainly depending on departure interval of buses +.>And delays between stops of buses).
1. For the case of the same batch of passengers, the accumulated contact time length of the target passenger and other passengers is as follows:
2. for the first arrival situation, the accumulated contact duration of the target passenger and other passengers is as follows:
3. for the case of a post arrival, the accumulated contact duration of the target passenger with other passengers is:
to sum up, input risk RI j As shown:
fig. 6 is a schematic diagram showing a ride risk assessment. In the riding process, the accumulated time length between passengers is mainly related to factors such as the start and stop points of the passengers, the station requirements, the travel time of the buses and the like. The method specifically comprises the following steps:
for the accumulated contact time between passengers in different bus stops in the riding process, if the number of passengers on bus stop j and the number of passengers off bus stop j are respectively O j And D j The travel time of the bus between stations is tau j,j+1 Bus taking risk RB of passengers between bus stop x and bus stop y x,y The method comprises the following steps:
fig. 7 is a schematic diagram of an input risk model for example of site 3. The output risk defined by the invention is the people-average risk carried by the passengers getting off from the bus stops after going through the waiting process and the taking process, so that the requirements of the passengers on the bus stops are focused in the modeling process, and the passenger exchange condition of the passengers on the bus stops along the way in the traveling process is focused; for example, taking bus stop 3 as an example, a passenger getting off at bus stop 3 may get on bus stop 1 or get on bus stop 2, and based on the definition of the input risk according to the present invention, the input risk and the riding risk of these two passengers are different, and details can be explained with reference to fig. 7, specifically including the following steps:
if assume a 1,3 For the number of passengers getting on bus stop 1 to get off bus stop 3, RE 1,3 For outputting risk from the part of passengers getting on bus stop 1 to getting off bus stop 3, RE 2,3 Similarly, the other parameters are the same as above, and since the input risk consists of two parts of input risk and riding risk, RE 1,3 And RE (RE) 2,3 The method comprises the following steps:
then the output risk RE of bus stop 3 3 The method comprises the following steps:
similarly, export risk RE j The method comprises the following steps:
fig. 8 is a diagram showing input/output risk factors. According to the input/output risk model, the input risk is mainly related to factors such as site requirements, departure intervals, passenger arrival rates and the like; in addition to the impact factors of input risk, output risk is mainly related to factors such as passenger origin-destination, stop demand, and bus travel time. Based on this, fig. 9 and 10 compare the variation of input risk in case of different departure intervals and the comparison of output risk in peak period and peaked period, respectively, based on an example, the specific analysis is as follows:
FIG. 9 shows the variation of input risk for different departure intervals, wherein different broken lines represent different bus stops, and the graphical results show that the input risk is in a fluctuating state for different departure intervals, and the output risk is minimal for the departure intervals in the virtual coil, so that the input risk can be dynamically evaluated to determine the optimal departure interval;
fig. 10 shows that the output risk is Gao Fengqi and the peak period is compared, and the traffic conditions are poor due to the large demand of passenger flow in the peak period, so that the traffic is often jammed to cause delay of the bus, and the result of the drawing shows that the output risk in the peak period is higher than the output risk in the Yu Pingfeng period.
Claims (3)
1. The method is characterized by calculating accumulated travel duration among passengers arriving randomly in a bus travel process comprising a waiting process and a taking process according to card swiping data of the passengers, geographic information data of the buses and bus stop and line data, modeling random input and output risks of the bus system based on accumulated contact duration among the passengers by taking the bus stop as an import and export link, and carrying out quantitative evaluation on the risks; the input risk refers to the possibility that passengers get in the bus and get into the public transportation system when waiting for the bus, and is used for describing the influence degree of different bus stops on the whole public transportation system; the output risk refers to the possibility that the getting-off passengers are infected in the waiting process and the taking process and influence the adjacent communities of the getting-off station, and is used for evaluating the risk applied by each bus station to the adjacent communities;
1) Constructing passenger OD data and travel time data of buses at each station according to acquired card swiping data of passengers, bus geographic information data and bus station and line data;
2) Based on definition of input and output risks of a bus system, establishing a connection between the input and output risks and waiting and taking processes of passengers, wherein the input risks are average risks carried by passengers getting on the bus from a bus stop after the passengers go on the bus after the waiting processes, and the output risks are average risks carried by passengers getting off the bus from the bus stop after the passengers go on the bus after the waiting processes and the taking processes;
3) According to random arrival assumptions in the waiting process of passengers, establishing accumulated contact time of each passenger with other passengers in the waiting process, and constructing an input risk model based on the accumulated contact time of people of passengers getting on the bus at the moment that the bus arrives at a bus stop;
4) According to the OD data of the passengers and the travel time data of the buses, the accumulated contact time of each passenger with other passengers in the riding process is established, and an output risk model is established based on the accumulated contact time of all passengers of the passengers when the buses arrive at a bus stop by combining input risks;
5) Combining the established input and output risk models, and optimizing a bus dispatching strategy according to the input and output risks evaluated by each station of the bus system;
the step 3) specifically comprises the following steps:
assuming that passengers arrive at a bus stop as a batch poisson arrival process, the accumulated contact time between the passengers is determined by the passengers arriving at the bus stop later, and modeling the input risk according to the OD data of the passengers and the batch poisson process; for bus stop j, its input risk RI j The following is shown:
where N is the total batch that passengers at the station arrive in batches,for each batch arrival multiplicationNumber of guests, k= {1,2,..,for each batch of passenger arrival time, G j For the interval time between the front bus and the rear bus, r is the belonging batch of the target passenger arriving at the station j, and the value range of r is [1, k ] U (k, N)]；
The step 4) specifically comprises the following steps:
based on the accumulated contact time length of the passengers with other passengers in the riding process as the riding risk of the passengers in the riding process, and combining the difference of boarding sites of the passengers in the getting-off process, namely the difference of input risks, an output risk model is established; the output risk consists of an input risk and a riding risk; the method comprises the following steps:
output risk RE of bus stop j j The method comprises the following steps:
wherein RE v，j For the part of the passenger getting on from the bus station v to getting off from the bus station j, the RE is used for outputting the risk, because the output risk consists of two parts of the input risk and the riding risk v，j ＝RI v +RB v，j ，RB v，j A is the bus taking risk of bus stops v to j v，j For the number of passengers getting off at bus stop j from bus stop v, D j For the number of passengers getting off at bus stop j, the passenger's taking risk RB between bus stop x and bus stop y x，y The method comprises the following steps:
O u for the number of passengers getting on bus stop u, tau j-1，j Travel time of buses between stops.
2. The method for quantitatively evaluating the input and output risks of the bus system according to claim 1, wherein the obtaining OD data and bus travel time data of the passengers according to the acquired card swiping data, bus GPS data and bus stop and line data specifically comprises:
based on the acquired passenger card swiping data, bus GPS data and bus stop and line data, the acquired data can be subjected to data processing and matching to obtain bus IC card passenger boarding point data comprising card numbers, card swiping dates, card swiping time, taking line numbers, taking bus numbers, bus arrival stop time, boarding point numbers or names and station longitude and latitude;
the bus travel time data can be determined through the data, the passenger transfer behavior can be identified, meanwhile, the departure point calculation can be carried out on the passengers based on the travel chain, and finally the OD identification of the passengers is realized.
3. The method for quantitatively evaluating the input and output risks of the bus system according to claim 1, wherein the optimizing the bus dispatching policy specifically comprises:
and calculating based on the passenger OD data, the bus GPS data and the quantitative evaluation model of the input and output risks to obtain the input and output risks of the bus system, and presetting and optimizing departure intervals and stop-jump strategies by combining influence factors of the model to reduce the input and output risks.
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