A passenger waiting time calculation method based on multi-element fusion in a mixed operation scenario

Abstract

The application discloses a passenger waiting time calculation method based on multi-element fusion in a mixed operation scenario, comprising: obtaining a bus schedule set and a set of passengers arriving at a station; inputting the bus schedule set and the set of passengers arriving at the station into a simulation model to obtain a simulation result, wherein the simulation model is used to depict passenger waiting behaviors and output results according to decision variables to identify passengers boarding vehicles, and the decision variables are used to represent whether the passengers arriving at the station leave by preset bus schedules; and calculating the waiting time according to the simulation result. The application not only breaks through the applicability limitations of traditional waiting time calculation methods in the mixed operation scenario, but also provides important methodological support for the operation optimization of intelligent public transport systems.

Description

Technical Field

[0001] This invention belongs to the field of public transportation technology, and in particular relates to a method for calculating passenger waiting time by integrating multiple elements in a mixed operation scenario. Background Technology

[0002] During the intelligent transformation of public transportation, a transitional phase will emerge involving the mixed operation of traditional manually driven buses and autonomous buses. This "autonomous-manual" hybrid operation model has the following typical characteristics: First, vehicles with two different control modes will operate in parallel on the same bus route; second, this model is mainly achieved through a gradual replacement, that is, gradually increasing the proportion of autonomous vehicles on existing bus routes; finally, this gradual transition strategy not only helps the smooth transformation of the operation and management system, but also provides passengers with a window of time to gradually adapt to the new travel service. It is worth noting that this hybrid operation model will become an inevitable stage in the large-scale application of autonomous buses, and its duration will depend on several key factors such as technological maturity, public acceptance, and the completeness of supporting policies.

[0003] Meanwhile, the level of information services for urban public transportation in my country has significantly improved, allowing passengers to access real-time bus operation information through diverse digital channels. On the other hand, the "Guidelines for Safe Transportation Services of Autonomous Vehicles (Trial)" explicitly requires that "operators of urban public bus and trolleybus passenger transport, taxi passenger transport, and road passenger transport using autonomous vehicles should inform passengers of the vehicle's autonomous driving capabilities through methods such as playing videos or posting signs." This comprehensive information service not only effectively enhances the certainty and convenience of public transportation travel but also creates a favorable information environment for the promotion of autonomous bus services.

[0004] In the "automatic-manual" hybrid operation model, comprehensive real-time information services will significantly influence passenger queuing behavior and boarding decisions based on vehicle type preferences. Passengers with concerns about the safety of autonomous driving technology may proactively forgo an arriving autonomous bus and choose to wait for a later-arriving manually driven bus; passengers who prefer emerging technologies may make the exact opposite choice. This "proactive delay" behavior based on vehicle type preference will disrupt the traditional "first-come, first-served" queuing rule in bus operations, rendering traditional waiting time calculation methods based on this assumption inapplicable. Summary of the Invention

[0005] To address the theoretical challenge of calculating passenger waiting time under mixed public transport operation modes, this invention proposes a multi-factor fusion method for calculating passenger waiting time in mixed operation scenarios. This invention achieves refined modeling of passenger waiting behavior in "automatic-manual" mixed operation scenarios by systematically integrating key elements such as passenger preference characteristics for vehicle type selection, decision-making behavior mechanisms under real-time public transport information conditions, and queuing dynamic characteristics under mixed operation modes.

[0006] To achieve the above objectives, this invention proposes a method for calculating passenger waiting time in a mixed operation scenario by integrating multiple factors, including:

[0007] Obtain bus schedules and arrival passenger data;

[0008] The set of bus routes and the set of arriving passengers are input into the simulation model to obtain simulation results. The simulation model is used to characterize passenger waiting behavior and identify the passenger boarding vehicle based on the output results of decision variables. The decision variables are used to characterize whether arriving passengers board the preset bus route r to leave.

[0009] Based on the simulation results, the waiting time is calculated.

[0010] Optionally, the set of bus schedules and the set of arriving passengers are input into the simulation model to obtain simulation results, including:

[0011] The decision-making mechanism is determined based on the set of bus schedules and the set of arriving passengers.

[0012] Based on the aforementioned decision-making mechanism, identify the vehicles boarded by arriving passengers;

[0013] The simulation results are obtained based on the vehicles the passengers boarded.

[0014] Optionally, the decision-making mechanism, based on the set of bus schedules and the set of arriving passengers, includes:

[0015] Based on the bus schedule set and the arriving passenger set, the arriving passenger set is divided into three categories of passengers;

[0016] Based on the three types of passengers and the information processing time constraint, the decision-making mechanism is determined.

[0017] Optionally, the three types of passengers include: those who prefer manually driven buses, those who prefer autonomous buses, and those who have no particular preference for a particular type of bus.

[0018] Optionally, based on the three types of passengers and considering information processing time constraints, the decision-making mechanism may include:

[0019] When the information processing time is less than the preset value, all types of passengers will unconditionally choose the vehicle that arrives at the station first.

[0020] When the information processing time is not less than a preset value, for passengers who prefer manually driven buses and those who prefer autonomous buses, the first arriving preferred vehicle type will be selected within the maximum acceptable waiting time range. If two consecutive buses do not have the preferred vehicle type, the first arriving vehicle will be selected directly. For passengers who do not have a specific vehicle type preference, the first arriving vehicle will be selected unconditionally.

[0021] Optionally, based on the decision-making mechanism, the method for identifying arriving passengers boarding vehicles is as follows:

[0022]

[0023] in, N represents the number of passengers waiting in line before passenger i for train r-1. r-1 m represents the remaining passenger capacity when train r-1 arrives at the station. i For passenger i, the bus route that they ultimately successfully boarded, b i,r For passenger i, b indicates whether they have not yet taken another train that arrived at the station before train r. i,r-1 For passenger i, e indicates whether they have not yet taken another train that arrived at the station before train r-1. i,r-1 Let R represent whether passenger i chooses to wait for the first bus to arrive at the station (r-1), k represent the number of passengers queuing in front of (including) passenger i, R represent the set of bus routes, and I represent the set of arriving passengers.

[0024] Optionally, based on the simulation results, the method for calculating the waiting time is as follows:

[0025]

[0026] Where ω is the total waiting time for all passengers at the station, and L r T is the departure time of train number r. i Let p be the arrival time of passenger i. i,r Whether passenger i successfully boarded flight r.

[0027] Compared with the prior art, the present invention has the following advantages and technical effects:

[0028] This invention innovatively establishes a selection algorithm under the dual constraints of "passenger preference and vehicle capacity," capable of accurately reconstructing the complete waiting process for each passenger, including their vehicle selection sequence and final boarding decision. Based on individual-level behavioral simulation technology, this invention can accurately calculate passenger waiting times under specific timetable conditions, providing a novel quantitative analysis tool for evaluating the timetable performance of hybrid public transport systems. This method not only overcomes the limitations of traditional waiting time calculation methods in hybrid operation scenarios but also provides important methodological and theoretical support for the operational optimization of intelligent public transport systems. Attached Figure Description

[0029] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0030] Figure 1 This is a flowchart of a method for calculating passenger waiting time by integrating multiple elements in a mixed operation scenario according to an embodiment of the present invention;

[0031] Figure 2 This is a schematic diagram illustrating a specific application scenario of an embodiment of the present invention. Detailed Implementation

[0032] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0033] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0034] This embodiment proposes a method for calculating passenger waiting time in a mixed operation scenario that integrates multiple factors, such as... Figure 1 As shown, the specific steps include:

[0035] Obtain bus schedules and arrival passenger data;

[0036] The set of bus routes and the set of arriving passengers are input into the simulation model to obtain the simulation results. The simulation model is used to characterize the waiting behavior of passengers and to identify the vehicles that passengers take based on the output results of the decision variables. The decision variables are used to characterize whether arriving passengers take the preset bus route r to leave.

[0037] Based on the simulation results, the waiting time is calculated.

[0038] Specifically, this embodiment focuses on a specific stop on a bus route operating under a hybrid "automatic-manual" mode, along with the bus services arriving at that stop and the waiting passengers. Let R = {1, 2, ..., |R|} represent the set of bus services arriving at that stop within the research timeframe, and r represent a specific bus service within set R. N r L represents the remaining passenger capacity when train r arrives at this station. r Let r be the departure time of train number r at this station. (Binary parameter) Indicates whether bus route r is a manually driven bus: If bus route r is a manually driven bus, then It equals 1; otherwise, it equals 0. Similarly, a binary parameter... Indicates whether bus route r is an autonomous vehicle: if bus route r is an autonomous vehicle, then It equals 1, otherwise it equals 0.

[0039] Let I = {1, 2, ..., |I|} represent a group of passengers arriving at the station within the study time frame, and let i represent a specific passenger within set I. i This represents the arrival time of passenger i. Based on passengers' vehicle type preference characteristics, passengers in set I are divided into three categories: C1 (preferring manually driven buses), C2 (preferring autonomously driven buses), and C3 (having no specific vehicle type preference). To accurately characterize the passenger category affiliation, a set of binary parameters is used for mathematical definition: for passenger i, its category attribute is determined by parameter Y. i h Y i a and Y i n The parameter is uniquely determined if and only if passenger i belongs to a certain category, in which case the corresponding parameter is 1, and all other parameters are 0. In fact, through the parameters... and {Y i h ,Y i a Cross-analysis can determine whether the service number r is the vehicle type preferred by passenger i. For ease of description, a binary parameter A is defined. i,r This directly indicates whether the train number r is the preferred train type for passenger i, i.e. In addition, let Z represent the shortest time required for passengers to understand real-time vehicle information, and W represent the longest acceptable waiting time for passengers.

[0040] Based on the above parameters, a simulation model is constructed to characterize passenger waiting behavior and accurately calculate their waiting time. The core decision variable of the model is a binary variable p. i,r The variable denoted as is used to characterize whether passenger i departs on bus r. This variable directly determines the passenger's actual bus departure time and waiting time. The sets, parameters, and variable definitions involved in the simulation model are detailed in Table 1.

[0041] The model is based on the following basic assumptions: (1) The electronic information screen at the station displays the estimated arrival time and vehicle type (autonomous driving / manual driving) information of the next two buses arriving in real time; (2) The waiting passengers are all regular passengers on this route, and their arrival time, vehicle type preference, minimum time required to understand vehicle information, and maximum acceptable waiting time can be determined in advance through surveys; (3) All passengers only update their travel choices at discrete decision points (including the passenger's arrival time and the departure time of each bus); (4) All passengers wait at the station until they successfully board the bus, i.e., there is no abandonment behavior midway.

[0042] Table 1

[0043]

[0044]

[0045] Furthermore, the set of bus schedules and the set of arriving passengers are input into the simulation model to obtain simulation results, including:

[0046] The decision-making mechanism is determined based on the combination of bus schedules and the number of arriving passengers.

[0047] Based on the decision-making mechanism, identify the vehicles boarded by arriving passengers;

[0048] Simulation results are obtained based on the vehicles passengers ride in.

[0049] Furthermore, based on the bus schedule aggregation and the arrival passenger aggregation, the decision-making mechanism includes:

[0050] Based on the bus schedule set and the arrival passenger set, the arrival passenger set is divided into three categories of passengers;

[0051] Based on the three types of passengers and the constraints of information processing time, a decision-making mechanism is determined.

[0052] Furthermore, the three categories of passengers include: those who prefer manually driven buses, those who prefer autonomous buses, and those who have no particular preference for a particular type of bus.

[0053] Furthermore, based on the three types of passengers and considering information processing time constraints, the decision-making mechanism includes:

[0054] When the information processing time is less than the preset value, all types of passengers will unconditionally choose the vehicle that arrives at the station first.

[0055] When the information processing time is not less than the preset value, for passengers who prefer manually driven buses and those who prefer autonomous buses, the first arriving preferred vehicle type will be selected within the maximum acceptable waiting time. If two consecutive buses do not have the preferred vehicle type, the first arriving vehicle will be selected directly. For passengers who do not have a specific vehicle type preference, the first arriving vehicle will be selected unconditionally.

[0056] Specifically, based on passengers' preferences for autonomous and human-driven buses, this study divides passengers into three categories: those who prefer human-driven buses (C1), those who prefer autonomous buses (C2), and those with no specific vehicle type preference (C3). These three types of passengers exhibit two decision-making mechanisms when waiting at the bus stop:

[0057] 1) When information processing time is insufficient, all types of passengers will simplify the decision-making mechanism, that is, follow the "first-arrival priority" principle, unconditionally select the vehicle that arrives first, and ignore vehicle information. This principle aims to minimize waiting time.

[0058] 2) When there is sufficient information processing time, passenger decision-making behavior shows differentiated characteristics: (1) For C1 and C2 passengers, the "preference priority" principle is preferred: within the maximum acceptable waiting time, the preferred vehicle type is selected first; if the vehicles arriving for two consecutive trains are not the preferred vehicle type, the "first arrival priority" principle is adopted. (2) For C3 passengers, the "first arrival priority" decision-making principle is always followed.

[0059] The aforementioned decision-making mechanism innovatively integrates three key influencing factors: passenger vehicle selection preferences, information processing time constraints, and waiting time tolerance thresholds, thereby more accurately depicting passengers' actual choice behavior in a mixed public transport operation environment. It should be noted that the entire waiting decision-making process has a dynamic iterative characteristic: during the waiting period, passengers will repeatedly execute the above decision-making mechanism based on real-time updated vehicle information until they complete the boarding process.

[0060] More specifically, since not all bus routes in set R are available to passenger i (for example, routes that have departed before the passenger arrives at the station are obviously unavailable), it is necessary to first determine whether passenger i has the feasible conditions to take route r. As shown in equation (1), route r is only a feasible option for passenger i if its departure time is later than the arrival time of passenger i.

[0061]

[0062] The information on two consecutive buses displayed on the platform's electronic screen directly influences passengers' waiting decisions, including their choice of bus and final boarding. Therefore, accurately identifying the bus information obtained by passengers at their initial decision-making point is a crucial prerequisite for analyzing their choice behavior. To this end, equations (2) and (3) are used to identify whether bus number r is the first bus that will arrive at the station when passenger i arrives (i.e., passenger i's initial decision-making point).

[0063]

[0064]

[0065] At each decision point, whether passengers have enough time to understand the information displayed on the platform's electronic display screen will determine their waiting time decision mechanism. To this end, equations (4) and (5) are established to determine whether passenger i has sufficient information processing time when the electronic display screen shows the information of train number r and r+1, that is, whether the time available for passenger i to process the information is not less than the minimum time Z required for him to understand the information.

[0066] Specifically, at the initial decision point, when passenger i faces the choice between train r and r+1, the time available for him to understand the information is the time difference between the departure time of train r and the arrival time of passenger i; while at subsequent decision points, if passenger i faces the choice between train r and r+1, the time available for him to process the information is determined by the travel interval between train r-1 and r.

[0067]

[0068] When there is sufficient information processing time, passengers in categories C1 and C2 tend to adopt a "preference-first" train selection principle, that is, within the maximum acceptable waiting time range, they prioritize the train type that arrives first. To this end, equation (6) is established to determine whether the waiting time for passenger i to wait for train r to arrive will exceed its maximum acceptable waiting time W.

[0069]

[0070] Ultimately, at each decision point, the bus route that the passenger chooses to wait for can be determined by equation (7).

[0071]

[0072] Furthermore, based on the decision-making mechanism, the method for identifying arriving passengers boarding vehicles is as follows:

[0073] Due to the limited passenger capacity of public buses, passengers may not be able to successfully board their chosen bus. Therefore, equation (8) is used to determine whether passenger i has not yet boarded another bus that arrives at the station before bus r. In the equation, N represents the number of passengers queuing before (and including) passenger i for flight r-1; r-1 Let b represent the remaining passenger capacity when train r-1 arrives at station r-1. Without loss of generality, let b i,1 =1 (i∈I), N R| =∞, to ensure that all passengers in set I can board a bus in set R and leave within the study time frame.

[0074]

[0075] Accordingly, the bus route m that passenger i ultimately successfully boarded. i It can be accurately identified using equation (9).

[0076]

[0077] in, N represents the number of passengers waiting in line before passenger i for train r-1. r-1 m represents the remaining passenger capacity when train r-1 arrives at the station. i For passenger i, the bus route that they ultimately successfully boarded, b i,r For passenger i, b indicates whether they have not yet taken another train that arrived at the station before train r. i,r-1 For passenger i, e indicates whether they have not yet taken another train that arrived at the station before train r-1. i,r-1 Let R represent whether passenger i chooses to wait for the first bus to arrive at the station (r-1), k represent the number of passengers queuing in front of (including) passenger i, R represent the set of bus routes, and I represent the set of arriving passengers.

[0078] Furthermore, the method for obtaining waiting time based on the passenger's vehicle is as follows:

[0079] The waiting time for passenger i is the time difference between the departure time of the actual train they are taking and their arrival time. Therefore, the total waiting time for all passengers at this station within the study time range can be calculated using equation (10). In equation (10), the binary variable p i,r The value of the equation used to determine whether passenger i is on the bus or bus route r is determined by equations (11) and (12).

[0080]

[0081]

[0082] In summary, the simulation model is composed of equations (1)-(12), which are used to depict the passenger waiting process under the "automatic-manual" hybrid operation mode and accurately calculate their waiting time.

[0083] The following is a detailed description of this embodiment: Figure 2 The diagram illustrates an implementation example in a specific scenario. The model's input data includes a set of bus routes and a set of arriving passengers, specifically a mixed bus departure timetable and arriving passenger attributes. The departure timetable includes the vehicle type (manually driven or autonomously driven) and corresponding departure time for each route, while the arriving passenger attributes include passenger types (C1, C2, or C3) and their arrival times. Using the passenger waiting time calculation method for a mixed bus fleet provided by this invention, the waiting process of passengers in a mixed bus fleet (including but not limited to behavioral characteristics such as deliberate delays) is simulated, and their waiting time is accurately calculated. Experimental results show that, under the same departure timetable conditions, the existing waiting time calculation method that ignores passenger vehicle type preference outputs 796 minutes, while this method calculates 948 minutes. This difference confirms that ignoring passenger preference leads to a systematic underestimation of waiting time, thus affecting the accuracy of timetable performance evaluation. In short, the core innovation of this invention lies in the first establishment of a simulation model and waiting time calculation method that considers passenger vehicle type preference, providing key technical support for the scientific evaluation of mixed bus fleet timetables.

[0084] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for calculating passenger waiting time by integrating multiple factors in a mixed operation scenario, characterized in that, include: Obtain bus schedules and arrival passenger data; The set of bus routes and the set of arriving passengers are input into the simulation model to obtain simulation results. The simulation model is used to characterize passenger waiting behavior and identify passenger boarding vehicles based on decision variables. These decision variables characterize whether arriving passengers board the preset bus routes. leave; Calculate the waiting time based on the simulation results; The bus schedule set and the arriving passenger set are input into the simulation model to obtain simulation results, including: The decision-making mechanism is determined based on the set of bus schedules and the set of arriving passengers. Based on the aforementioned decision-making mechanism, identify the vehicles boarded by arriving passengers; The simulation results are obtained based on the vehicles the passengers boarded; According to the aforementioned decision-making mechanism, the method for identifying arriving passengers boarding vehicles is as follows: in, Indicates that in passengers Previously waiting in line for a train The number of passengers, Indicates train number Remaining passenger capacity upon arrival For passengers The bus routes that were successfully boarded in the end. For passengers Have you not yet taken the previous train? Other trains arriving at the station, For passengers Have you not yet taken the previous train? Other trains arriving at the station, For passengers Do you want to wait for the first train to arrive at the station? , In order to include passengers Passengers currently queuing , For bus schedules to be gathered. Gathering for arriving passengers; Based on the simulation results, the method for calculating the waiting time is as follows: in, The total waiting time for all passengers at the station. For train schedule The departure time For passengers Arrival time For passengers Did you successfully board the flight? .

2. The method for calculating passenger waiting time in a mixed operation scenario based on multi-factor fusion as described in claim 1, characterized in that, Based on the aforementioned set of bus schedules and the set of arriving passengers, the decision-making mechanism includes: Based on the bus schedule set and the arriving passenger set, the arriving passenger set is divided into three categories of passengers; Based on the three types of passengers and the information processing time constraint, the decision-making mechanism is determined.

3. The method for calculating passenger waiting time in a mixed operation scenario based on multi-factor fusion as described in claim 2, characterized in that, The three categories of passengers include: those who prefer manually driven buses, those who prefer autonomous buses, and those who have no particular preference for a particular type of bus.

4. The method for calculating passenger waiting time in a mixed operation scenario based on multi-factor fusion as described in claim 3, characterized in that, Based on the three types of passengers and considering the information processing time constraints, the decision-making mechanism is determined to include: When the information processing time is less than the preset value, all types of passengers will unconditionally choose the vehicle that arrives at the station first. When the information processing time is not less than a preset value, for passengers who prefer manually driven buses and those who prefer autonomous buses, the first arriving preferred vehicle type will be selected within the maximum acceptable waiting time range. If two consecutive buses do not have the preferred vehicle type, the first arriving vehicle will be selected directly. For passengers who do not have a specific vehicle type preference, the first arriving vehicle will be selected unconditionally.

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