Method for evaluating spatiotemporal transfer disconnection and punctuality of bus-to-metro feeder connection

Abstract

A method for evaluating the spatiotemporal transfer disconnection and punctuality of a bus-to-metro feeder connection, the method comprising: calculating a critical punctuality time of a target bus route, and constructing a stop-based bus operation punctuality model, so as to obtain a punctuality rate of a target stop (S1); calculating the spatiotemporal transfer disconnection error for multiple groups, and combining a scenario probability distribution of individuals within a single group with the punctuality rate, obtaining an average connection success rate for the multiple groups (S2); and, with the objective of maximizing the average connection success rate for the multiple groups, on the basis of passenger flow of the target route in different time periods, calculating an optimal bus departure headway for each time period (S3). By means of comprehensively dissecting the problem of spatiotemporal transfer disconnection in a bus-to-metro feeder connection, quantitatively analyzing punctuality indicators of bus operation, and optimizing bus departure headways in combination with passenger flow, the present method may mitigate the impact of transfer disconnection and improve the bus punctuality rate and the connection success rates for different passenger groups, thereby providing a scientific scheme for intelligent scheduling and high-quality development of a bus-to-metro connection system.

Description

A method for evaluating the spatiotemporal transfer disruptions and punctuality of bus-subway connections Technical Field

[0001] This invention relates to the field of urban public transportation system optimization and operation management, and in particular to a method for evaluating the spatiotemporal transfer disruptions and punctuality of bus-subway connections. Background Technology

[0002] In urban public transportation systems, bus-subway connections are of significant social importance in improving passenger travel efficiency and promoting green travel. Currently, the public transportation system is gradually becoming outdated, increasing the time and material costs of public travel. The spatial and temporal gaps in bus-subway connections are the core cause of this problem. Therefore, focusing on the errors caused by these spatial and temporal transfer gaps and the punctuality of bus operations, and exploring optimization strategies to improve the connection probability for different travel groups, has become an important research direction in this field.

[0003] Domestic and international scholars have conducted relevant research and achieved certain results in three core areas: bus-subway connection, spatiotemporal transfer disruption analysis, and punctuality evaluation. These results are detailed below:

[0004] In the planning and design of bus connections to subways, scholars such as SunY proposed a method for classifying bus routes under given rail transit paths. With the research objectives of maximizing rail transit passenger flow and minimizing passenger travel time, they constructed a multi-objective model for the integrated network design of buses and urban rail transit. Other scholars (such as Tu Yanyu and Zhu Yongkang) have used differentiated calculation models to measure the passenger attraction range of each station by classifying different rail transit stations by function, providing data support for the route coverage and station setting of connecting buses.

[0005] In the analysis of spatiotemporal transfer discontinuities, scholars such as Du Caijun and Jiang Yukun have conducted systematic data surveys on the connection between urban rail transit and other modes of transportation such as buses and bicycles. They focused on analyzing the transfer characteristics of various connection modes, summarizing the connection time, transfer distance, and passenger flow variation patterns of different modes, and performing cluster analysis on stations. This provides basic data references for the subsequent planning, design, and construction of rail transit and municipal roads. Another scholar, Xu Yuan, analyzed the passenger flow distribution characteristics of different rail transit stations, established an optimization model for transfer station spacing with the goal of minimizing average travel time, determined the optimal transfer distance, and proposed specific design schemes for transfer facilities (such as passageways and signage) and information service systems, clarifying the basic design principles for transfer connections.

[0006] In evaluating the punctuality of public transport operations, scholars such as Chen Zhuqing selected two core indicators: travel time reliability and headway balance. They constructed a public transport service reliability evaluation system covering three levels: "stations-routes-networks," and conducted case studies and analyses using data from the Automatic Vehicle Positioning System (AVL) and Automatic Passenger Counting System (APC), achieving multi-dimensional coverage of punctuality evaluation. Scholars such as Bi Qingjun, through statistical analysis of a large amount of continuous public transport AVL data, selected detailed indicators such as total route travel time, micro-interval travel time, and station stop intervals to quantitatively evaluate and analyze the causes of punctuality fluctuations during public transport operations, providing a more precise entry point for improving operational reliability.

[0007] Although the aforementioned studies have accumulated technical expertise in their respective fields, significant research gaps and technical shortcomings remain in addressing the practical problem of coordinating the spatial and temporal transfer gaps and punctuality optimization between public transport and subway connections. Specifically:

[0008] Firstly, the spatiotemporal dimension research is singular and lacks collaborative analysis: existing studies on spatiotemporal transfer breaks mostly focus on a single dimension (either analyzing only time matching errors or optimizing only spatial transfer paths), without comprehensively considering the impact of the interaction between the time and space dimensions on the accuracy of bus-to-subway connections. This makes it difficult for technical solutions to fully cover real transfer scenarios in practical applications and fails to fundamentally reduce the risk of spatiotemporal transfer breaks.

[0009] Secondly, the punctuality evaluation is disconnected from the connection needs and lacks quantitative correlation: existing punctuality evaluations are mostly focused on the operation of buses themselves (such as the overall punctuality rate of the route and the interval between stops), and there is insufficient analysis on the "correlation between bus punctuality rate and subway connection success rate". Moreover, no specific quantitative indicators for punctuality rate have been established for the specific scenario of "bus connecting to subway", making it difficult to provide accurate technical basis for improving connection efficiency.

[0010] Thirdly, it ignores the differences in travel among different groups and the assessment of connection probability is one-sided: There are significant differences in walking speed, spatial tolerance, and time sensitivity among different travel groups (such as the elderly, pregnant women, children, and young people), but existing studies have not fully considered the impact of these differences on the spatiotemporal transfer disruptions, nor have they assessed the connection probability separately for each group and formed a comprehensive optimization plan. As a result, the technological achievements cannot meet the connection needs of different groups and have a limited effect on improving the overall connection efficiency.

[0011] In summary, existing technologies are insufficient to effectively address the issues of spatiotemporal transfer disruptions and punctuality optimization in bus-subway connections. There is an urgent need to propose a technical solution that comprehensively covers spatiotemporal collaborative analysis, punctuality quantitative correlation, and multi-group adaptation to improve the reliability and efficiency of bus-subway connections and promote the upgrading of urban public transportation system service quality. Summary of the Invention

[0012] Therefore, the technical problem to be solved by the present invention is to overcome the problems of low efficiency and insufficient punctuality in existing studies on bus-to-subway connections, which are characterized by a single spatiotemporal dimension, incomplete analysis of factors affecting bus punctuality, and insufficient consideration of connection probabilities for different travel groups.

[0013] To address the aforementioned technical problems, this invention provides a method for evaluating the spatiotemporal transfer disruptions and punctuality of bus-to-subway connections. By constructing a punctuality model, calculating the spatiotemporal transfer disruption errors for multiple groups, and using a genetic algorithm to optimize departure intervals, this method aims to mitigate the negative impact of spatiotemporal transfer disruptions and improve the punctuality rate of bus-to-subway connections and the success rate of connections for different groups.

[0014] Specifically, the method includes the following steps:

[0015] S1: Based on the floating punctuality time deviation range of the target city bus routes, calculate the critical punctuality time of the target city bus routes, and construct a station-based regular bus operation punctuality model based on the critical punctuality time to obtain the punctuality rate of buses at the target connecting stations.

[0016] S2: Calculate the spatiotemporal transfer disruption error of multiple groups. Based on the scenario probability distribution of individuals within a single group and the on-time rate, calculate the average connection success rate of multiple groups under spatiotemporal transfer disruption.

[0017] S3: With the goal of maximizing the average connection success rate for multiple groups, calculate the optimal bus departure interval scheme for each time period based on passenger flow data of the target route at different times.

[0018] In one embodiment of the present invention, S1, calculating the critical punctuality time of the target city bus route includes:

[0019] Convert the floating punctuality time deviation range of the target city's bus routes into a relative proportion range;

[0020] Based on the average running time of the current line, calculate the floating on-time time deviation and the relative proportional deviation respectively, and take the smaller of the two as the on-time floating time range;

[0021] When the absolute value of the lower limit of the on-time deviation and the absolute value of the upper limit of the on-time deviation within the on-time floating time range are equal, the corresponding average running time is calculated as the critical on-time time.

[0022] In one embodiment of the present invention, S1, constructing a station-based regular bus operation punctuality model based on the critical punctuality time, includes:

[0023] Divide any line with a total number of stations J into J-1 independent segments consisting of adjacent stations. For any j-th station, calculate the actual travel time T from its previous station to the current station. 实际，j :T 实际，j =T j -T j-1 T j T is the time to reach the j-th station. j-1 The time to reach the (j-1)th station;

[0024] By performing n operations on the road segment between station j-1 and station j... ′ The average travel time T for this road section was calculated based on the second sampling survey. 平均，j :

[0025] The on-time rate is calculated separately for each road segment. Based on the average arrival time and critical on-time time of any road segment, on-time rate calculation models are constructed for the following two scenarios:

[0026] When the average arrival time is greater than or equal to the critical on-time time, based on the average running time T 平均，j Construct the on-time floating time range for the current road segment, determine whether the actual arrival time of each train is within the on-time floating time range, and count the percentage of trains that meet the conditions, which is the on-time rate of the road segment.

[0027] When the average arrival time is less than the critical on-time time, determine whether the ratio of the actual running time to the average running time of each train is within the relative ratio range, and count the percentage of trains that meet the condition, which is the on-time rate of that section of the road.

[0028] In one embodiment of the present invention, when the average arrival time is greater than the critical on-time time, the on-time rate P 站点准点性 The calculation formula is as follows: P 站点准点性 =P{T Arr.s ∈[T 平均，j +θ1,T 平均，j +θ2]}=P{(T Arr.s -T 平均，j )∈[θ1,θ2]},

[0029] Among them, T Arr.s This represents the actual arrival time of the bus at station S, where θ1 and θ2 are time range parameters.

[0030] In one embodiment of the present invention, when the average arrival time is less than the critical on-time time, the on-time rate P 站点准点性 The calculation formula is as follows:

[0031] Where k1 and k2 are the upper and lower limits of the relative proportion range.

[0032] In one embodiment of the present invention, calculating the spatiotemporal transfer discontinuity error of multiple groups includes:

[0033] Calculate the time matching error ΔT, which measures the loss of travel efficiency due to time delays for passengers. i :

[0034] Among them, T 出站 T represents the theoretical time it takes for a passenger to exit the subway station, from where they get off the train. w Indicates the time it takes to walk from the station to the bus stop;

[0035] Simultaneously, the spatial matching error ΔS, used to measure the spatiotemporal costs incurred by passengers due to route detours and unreasonable station layout, is calculated. i : D W V represents the deviation between the passenger's actual walking distance and the ideal route. 人 The walking speed represents the walking speed of different groups of people, and α is the spatial tolerance coefficient used to reflect the sensitivity of different groups to distance deviation;

[0036] Based on the time matching error ΔT i and the spatial matching error ΔS i Calculate the individual spatiotemporal transfer discontinuity error W in each population group. i :W i =(ω T ·ΔT i +ω S ·ΔS i ), where ω T and ω S These are the weighting coefficients for time and space errors, respectively;

[0037] Based on the individual spatiotemporal transfer fragmentation error W i The spatiotemporal transfer fault error of multiple groups was obtained.

[0038] Where N is the total number of categories in the group, and ΔW represents the average spatiotemporal transfer fragmentation error of individuals within the same group. n is the total number of individuals in the same group.

[0039] In one embodiment of the present invention, calculating the average connection success rate of multiple groups under spatiotemporal transfer disruptions includes:

[0040] Combined with the bus's on-time arrival time T 准点 and the individual spatiotemporal transfer breakage error Wi The passenger journey from the subway exit to the bus stop, involving both buses and subways, is quantified into the following five scenarios:

[0041] The following conditions apply: First, the passenger arrives at the platform earlier than the bus's scheduled early arrival time; second, the passenger arrives at the platform between the bus's scheduled early arrival time and its planned on-time arrival time; third, the passenger and the bus arrive at the same time; fourth, the passenger arrives at the platform later than the bus's scheduled arrival time, but not exceeding the latest possible late arrival time for the bus to connect; fifth, the passenger arrives at the platform after the latest possible late arrival time for the bus to connect.

[0042] Based on any of the above-mentioned quantitative connection scenarios, calculate the probability P(S) of an individual within the same group being in that scenario. i ): in This represents a single group in situation S. i The number of individuals in Num 总 S represents the total number of samples in this random sampling within a single population. i Let i represent the i-th connection scenario, where i = {1, 2, 3, 4, 5};

[0043] Based on the punctuality rate P 站点准点性 Calculate the success rate TR of a single group S TR S =P 站点准点性 ·[P(S1)+P(S2)+P(S3)+P(S4)]=P 站点准点性 (1-P(S5));

[0044] According to the TR S Calculate the average successful connection probability for multiple groups.

[0045] In one embodiment of the present invention, step S3, calculating the optimal bus departure interval scheme for each time period, includes:

[0046] S31: Define the number of departure interval schemes generated in one time (sizepop), mutation rate (mp), crossover rate (cp), maximum generation (maxgen), and current generation (gen), as well as obtain the time period division of the target route and the passenger flow of each time period;

[0047] S32: Under the departure interval constraint, randomly generate sizepop individuals, each individual corresponding to an initial departure interval scheme, forming an initial population. At this time, the current genetic generation gen is 1.

[0048] S33: For each individual in the initial population, according to the fitness function... Calculate its fitness value; where, This represents the spatiotemporal transfer disruption error corresponding to the departure interval scheme for multiple groups, O. max This represents the maximum value of the spatiotemporal transfer fault error among multiple populations in the current population.

[0049] S34: Perform crossover and mutation operations on individuals in the initial population according to the preset crossover rate cp and mutation rate mp to generate a new generation of offspring population;

[0050] S35: Similarly, calculate the fitness value of each individual in the offspring population, sort the individuals in the parent and offspring populations according to their fitness values, select sizepop individuals, and perform crossover and mutation operations on the selected individuals again.

[0051] S36: Increment the value of the current generation gen by 1, and repeat steps S33 to S35 until the termination condition of the current generation gen reaching the maximum generation maxgen or the fitness improvement is lower than the threshold is met, and the individual with the largest fitness value in the population is obtained, which is the optimal departure interval scheme.

[0052] Based on the same inventive concept, the present invention also provides a computer storage medium storing a computer software product, the computer software product including several instructions for causing a computer device to execute the aforementioned method for evaluating the spatiotemporal transfer disruptions and punctuality of bus-subway connections.

[0053] Compared with the prior art, the above-described technical solution of the present invention has the following advantages:

[0054] Firstly, it breaks through the limitations of existing technologies that focus solely on the spatiotemporal dimension. It comprehensively analyzes the transfer disruption problem by integrating time and space dimensions. By constructing calculation models for time matching error and space matching error, and introducing differentiated walking speed, spatial tolerance coefficient, and weight allocation, it accurately quantifies the average spatiotemporal transfer disruption error for multiple groups (elderly, pregnant women, children, and young people), effectively mitigating the negative impact of spatiotemporal transfer disruption on passenger connections and improving the practical applicability of the solution.

[0055] Secondly, it innovatively adopts the station-based PIS punctuality evaluation index, combined with the punctuality judgment interval, and constructs a punctuality rate calculation model for different scenarios. It comprehensively and quantitatively analyzes the factors affecting the punctuality of bus operation, provides a scientific basis for improving the punctuality rate of bus connections to the subway, and helps to optimize the quality of bus services.

[0056] Third, aiming to maximize the connection probability among multiple groups, this study utilizes a genetic algorithm. By rationally designing the fitness function, genetic operators (selection, crossover, mutation), and termination conditions, and combining them with passenger flow at different times, the study optimizes the departure interval of regular buses. Validated by the Suzhou Bus Route 503 case, the optimization improved the on-time rate to 90% during peak hours and 85% during off-peak hours. The successful connection probability among multiple groups based on the on-time rate increased by approximately 30% compared to before optimization, significantly improving the connection efficiency for different groups. This provides a feasible solution for the intelligent scheduling and high-quality development of urban bus and subway connection systems, possessing both theoretical value and practical significance. Attached Figure Description

[0057] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings.

[0058] Figure 1 is a flowchart illustrating a method for evaluating the spatiotemporal transfer disruption and punctuality of public transport connecting to the subway, as provided in an embodiment of the present invention.

[0059] Figure 2 shows various scenarios of passengers being transferred in a timeline format in an embodiment of the present invention;

[0060] Figure 3 shows the frequency of bus arrivals at each preceding stop at different times in an embodiment of the present invention;

[0061] Figure 4 shows the passenger flow statistics of bus route 503 at Shihumoshe Station at different times on a certain day in an embodiment of the present invention.

[0062] Figure 5 is the fitness curve corresponding to the optimal departure interval strategy calculated by the genetic algorithm in an embodiment of the present invention;

[0063] Figure 6 shows a survey of the number of times different people were at each bus stop at different times in an embodiment of the present invention.

[0064] Figure 7 is an iterative diagram of the probability of successful connection of multiple groups during peak-hour spatiotemporal transfer breaks in an embodiment of the present invention.

[0065] Figure 8 is an iterative diagram of the probability of successful connection of multiple groups during off-peak spatiotemporal transfer breaks in an embodiment of the present invention.

[0066] Figure 9 is a comparison of the results before and after optimizing the on-time rate of departure intervals in an embodiment of the present invention.

[0067] Figure 10 is a comparison of the probability of bus-subway connection for different groups of people during off-peak hours in an embodiment of the present invention before and after optimization.

[0068] Figure 11 is a comparison chart of the probability of bus-subway connection for different groups of people during peak hours before and after optimization in an embodiment of the present invention.

[0069] Figure 12 is an iterative graph of the probability of successful connection of multiple groups of people during peak hours based on punctuality rate in an embodiment of the present invention.

[0070] Figure 13 is an iterative graph of the probability of successful connection of multiple groups of people based on punctuality rate during off-peak hours in an embodiment of the present invention.

[0071] Figure 14 is a comparison chart of the success rate of multi-group connection based on punctuality before and after optimization in an embodiment of the present invention. Detailed Implementation

[0072] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0073] Referring to Figure 1, the present invention provides a method for evaluating the spatiotemporal transfer disruptions and punctuality of bus-subway connections, comprising the following steps:

[0074] S1: Based on the floating punctuality time deviation range of the target city bus routes, calculate the critical punctuality time of the target city bus routes, and construct a station-based regular bus operation punctuality model based on the critical punctuality time to obtain the punctuality rate of buses at the target connecting stations.

[0075] S2: Calculate the spatiotemporal transfer disruption error of multiple groups. Based on the scenario probability distribution of individuals within a single group and the on-time rate, calculate the average connection success rate of multiple groups under spatiotemporal transfer disruption.

[0076] S3: With the goal of maximizing the average connection success rate for multiple groups, calculate the optimal bus departure interval scheme for each time period based on passenger flow data of the target route at different times.

[0077] Furthermore, in S1, the critical punctuality time for the target city's bus routes is calculated, including:

[0078] Convert the floating punctuality time deviation range of the target city's bus routes into a relative proportion range;

[0079] Based on the average running time of the current line, calculate the floating on-time time deviation and the relative proportional deviation respectively, and take the smaller of the two as the on-time floating time range;

[0080] When the absolute value of the lower limit of the on-time deviation and the absolute value of the upper limit of the on-time deviation within the on-time floating time range are equal, the corresponding average running time is calculated and used as the critical on-time time T. 临界 .

[0081] Taking the standard rule for on-time arrival of any bus route in a certain city as being on time if the deviation between the vehicle's arrival time and the planned time is within 1 minute earlier to 2 minutes later, the floating on-time time deviation range [-60s, 120s] is converted into a relative proportion range [-10%, +20%].

[0082] Based on the average travel time T for each bus route, calculate the floating punctuality time deviation and the relative proportional deviation, and take the smaller of the two values ​​as the punctuality time fluctuation range:

[0083] Where a1 and a2 are the lower and upper limits of the on-time floating time, respectively.

[0084] When the absolute value of the lower limit of the on-time deviation is equal to the absolute value of the upper limit, i.e., -α1 = a2, the corresponding average running time is the critical on-time time T. 临界 .

[0085] The bus route, which contains J stops, is divided into J-1 independent segments consisting of adjacent stops. For any j-th stop, the actual travel time T from its previous stop to the current stop is calculated. 实际，j :T 实际，j =T j -T j-1 T j T is the time to reach the j-th station. j-1 The time to reach the (j-1)th station;

[0086] By performing n operations on the road segment between station j-1 and station j... ′ A field sampling survey was conducted to calculate the average travel time T for this road section. 平均，j :

[0087] The on-time rate is calculated separately for each road segment. Based on the average arrival time and critical on-time time of any road segment, on-time rate calculation models are constructed for the following two scenarios:

[0088] When the average arrival time T 平均，j Greater than or equal to the critical on-time time T 临界 At that time, based on the average running time T 平均，j Construct a punctuality fluctuation range for the current road segment, determine whether the actual arrival time of each train falls within the punctuality fluctuation range, and calculate the percentage of trains meeting the criteria, which is the punctuality rate P for that road segment. 站点准点性 P 站点准点性 =P{T Arr.s ∈[T 平均，j +θ1,T 平均，j +θ2]}=P{(T Arr.s -T 平均，j)∈[θ1,θ2]},

[0089] Among them, T Arr.s θ1 and θ2 represent the actual arrival time of the bus at station S. θ1 and θ2 are time range parameters, where θ1 = -60s and θ2 = 120s.

[0090] When the average arrival time T 平均，j Less than the critical on-time time T 临界 This method is suitable for stations with small inter-station spacing and short average travel time (such as densely packed stations in the city center). The on-time rate for any road segment is calculated using the proportional deviation method, as follows:

[0091] Determine whether the ratio of the actual travel time to the average travel time for each bus trip falls within the specified relative ratio range, and count the number of trips Num out of n' buses. 准点 Divide this by the total number of trips to get the on-time rate P for that route. 站点准点性 The calculation formula is as follows:

[0092] Where k1 and k2 are the upper and lower limits of the relative proportion interval, k1 = 90% and k2 = 120%.

[0093] Furthermore, in S2, the calculation of the spatiotemporal transfer discontinuity error for multiple groups includes:

[0094] Calculate the time matching error ΔT, which measures the loss of travel efficiency due to time delays for passengers. i (Unit: seconds):

[0095] Among them, T 出站 T represents the theoretical time it takes for a passenger to exit the subway station, from where they get off the train. w Indicates the time taken to walk from the station to the bus stop; when T 出站 +T w When the value is less than 0, it indicates that there is no time delay. In this case, we take ΔT = 0 to ensure that the error ΔT is zero. i Nonnegativity;

[0096] Simultaneously, the spatial matching error ΔS, used to measure the spatiotemporal costs incurred by passengers due to route detours and unreasonable station layout, is calculated. i (Unit: seconds): D W V represents the deviation between the passenger's actual walking distance and the ideal route, expressed in meters. 人This represents the walking speed of different groups, including the elderly, pregnant women, young people, and children; 'a' is a spatial tolerance coefficient used to reflect the sensitivity of different groups to distance deviations. For example, 'a' is 0.2 for the elderly and pregnant women; 0.4 for children; and 0.7 for young people.

[0097] Based on the time matching error ΔT i and the spatial matching error ΔS i Calculate the individual spatiotemporal transfer discontinuity error W in each population group. i :W i =(ω T ·ΔT i +ω S ·ΔS i ), where ω T and ω S These are the weighting coefficients for time and space errors, ω. T =0.4, ω S =0.6;

[0098] Based on the individual spatiotemporal transfer fragmentation error W i The spatiotemporal transfer fault error of multiple groups was obtained.

[0099] Where N is the total number of categories in the group, and ΔW represents the average spatiotemporal transfer fragmentation error of individuals within the same group. n is the total number of individuals in the same group.

[0100] The average probability of bus-subway connections for multiple groups is calculated based on punctuality. The time-space transfer gap error involved is an average value for multiple groups. Therefore, the survey subjects based on the average probability of bus-subway connections for multiple groups under punctuality will be the elderly, pregnant women, children, and young people.

[0101] The initial conditions are assumed to be: (1) the research object is the regular bus connecting to the subway; (2) the average probability of the bus connecting to the subway is based on punctuality. In order to make the data more authentic, the punctuality rate calculation and punctuality evaluation are based on the station (PIS) dimension evaluation system.

[0102] Given: the time T required for the bus to arrive at a certain stop on time. 准点 And individual spatiotemporal transfer discontinuity error W i Combined with T 准点 and W i The process of getting a passenger from the subway exit to the bus stop involves hypothesizing various scenarios regarding whether different individuals can seamlessly connect to the bus. These scenarios can be quantified into the following five situations:

[0103] The following are considered valid conditions: 1) The passenger arrives at the platform earlier than the bus's scheduled arrival time; 2) The passenger arrives at the platform between the bus's scheduled arrival time and its planned on-time arrival time; 3) The passenger and the bus arrive at the same time; 4) The passenger arrives at the platform later than the bus's scheduled arrival time, but not exceeding the latest possible arrival time for the bus to connect; 5) The passenger arrives at the platform after the latest possible arrival time for the bus to connect.

[0104] Specifically, as shown in Figure 2, the total error W of spatiotemporal transfer breaks for different groups of people is... i and the on-time arrival time of the bus T 准点 The matching relationships are used to accurately determine whether passengers can successfully connect to a bus after exiting the subway station. Specific explanations for each scenario are as follows:

[0105] Scenario 1: W i <T 准点 -1 means that the total time it takes for a passenger to reach the bus stop after experiencing a time-space transfer interruption (including the time error of exiting the subway station and walking to the bus stop) is less than the time it takes for the bus to arrive 1 minute ahead of schedule. If the time required for the time-space transfer interruption is within this range, the probability that the passenger can be connected is 100%.

[0106] Scenario 2: T 准点 -1≤W i <T 准点 This means that if the total time it takes for a passenger to arrive at the bus stop falls within the time range between the bus arriving 1 minute early and the bus arriving on time, the probability of the passenger being able to be connected within this range is 80%.

[0107] Scenario 3: T 准点 =W i This means that the total time it takes for passengers to arrive at the bus stop is exactly the same as the scheduled arrival time of the bus.

[0108] Scenario 4: T 准点 <W i ≤T 准点 +2 means that the total time it takes for a passenger to arrive at the bus stop falls within the time range between the bus arriving on time and the bus arriving 2 minutes late. Within this range, the probability of the passenger being able to be connected is less than 50%.

[0109] Scenario 5: W i >T 准点 +2 means that if the total time it takes for the passenger to arrive at the bus stop exceeds the bus's scheduled arrival time by 2 minutes, and the time required for the time-space transfer to break is within this range, then the passenger has missed this bus, indicating that the connection has failed.

[0110] Based on any of the above-mentioned quantitative connection scenarios, calculate the probability P(S) of an individual within the same group being in that scenario.i ): in This represents a single group in situation S. i The number of individuals in Num 总 S represents the total number of samples in this random sampling within a single population. i Let i represent the i-th connection scenario, where i = {1, 2, 3, 4, 5};

[0111] Based on the punctuality rate P 站点准点性 Calculate the success rate TR of a single group S TR S =P 站点准点性 ·[P(S1)+P(S2)+P(S3)+P(S4)]=P 站点准点性 (1-P(S5));

[0112] According to the TR S Calculate the average successful connection probability for multiple groups.

[0113] Furthermore, in S3, the goal is to maximize the average connection success rate across multiple groups. With the objective of optimizing the bus departure intervals for different time periods based on passenger flow data for the target route, a genetic algorithm is used to calculate the optimal bus departure intervals for each time period, including:

[0114] S31: Define the number of departure interval schemes generated in one time (sizepop), mutation rate (mp), crossover rate (cp), maximum genetic generation (maxgen), and current genetic generation (gen=0), as well as obtain the time period division of the target route and the passenger flow of each time period;

[0115] S32: Under the departure interval constraint, randomly generate sizepop individuals, each individual corresponding to an initial departure interval scheme, forming an initial population. At this time, the current genetic generation gen is 1.

[0116] S33: For each individual in the initial population, according to the fitness function... Calculate its fitness value; where, This represents the spatiotemporal transfer disruption error corresponding to the departure interval scheme for multiple groups, O. max This represents the maximum value of the spatiotemporal transfer fault error among multiple populations in the current population.

[0117] S34: Perform crossover and mutation operations on individuals in the initial population according to the preset crossover rate cp and mutation rate mp to generate a new generation of offspring population;

[0118] S35: Similarly, calculate the fitness value of each individual in the offspring population, sort the individuals in the parent and offspring populations according to their fitness values, select sizepop individuals, and perform crossover and mutation operations on the selected individuals again.

[0119] S36: Increment the value of the current generation gen by 1, and repeat steps S33 to S35 until the termination condition of the current generation gen reaching the maximum generation maxgen or the fitness improvement is lower than the threshold is met, and the individual with the largest fitness value in the population is obtained, which is the optimal departure interval scheme.

[0120] This study focuses on the Shihumoshe Metro Station (Exit 7, Southwest) and Shihumoshe Bus Station in Suzhou, selecting the 503 regular bus route as the target line. The research revolves around the spatiotemporal transfer disruption problem and punctuality evaluation of bus-metro connections. Basic information such as passenger flow, bus arrival time, and passenger walking data were obtained through field surveys. Combined with a constructed punctuality model, a multi-group spatiotemporal transfer disruption error calculation method, and a genetic algorithm optimization strategy, the changes in bus connection efficiency and punctuality rate before and after optimization were compared and analyzed to verify the effectiveness of the proposed method.

[0121] To calculate the average arrival time of the bus, a systematic survey of all stops before the target station (Shihumoshe Bus Station) is needed to record the theoretical arrival time from different preceding stops to the target station. After passengers exit the subway, the four preceding stops of bus route 503 before Shihumoshe Station—Wangshan Industrial Park, Ruiyi Senior Living Community, Wenzheng College, and Xiwen Road East—provide a spatial reference framework for subsequent on-site statistics of the bus's location and for calculating the average arrival time.

[0122] Using a field random sampling survey method, the location of bus route 503 when different individuals got off the subway at different times was recorded 30 times (if the bus was located between two stops, the nearest stop was used as the statistical benchmark, and buses that had already passed the target bus stop were excluded), thus forming the frequency data of bus stop distribution.

[0123] Considering the differences in the real-time location of bus route 503 when different individuals exit the subway at different times, which leads to variations in the estimated arrival time at the target station (Note: If the bus is between two stations, the nearest station is used for statistical purposes; buses that have already passed Shihumoshe Station are automatically excluded), a field survey was conducted to obtain the frequency of bus appearances at each preceding station for 30 different individuals at different times. The specific data is shown in Figure 3.

[0124] Based on the frequency statistics in Figure 3, and combined with the theoretical arrival time intervals corresponding to each preceding stop, the average arrival time of the bus was calculated. Table 1 clearly lists the arrival time range, frequency of occurrence, and final calculated average arrival time for each preceding stop. Furthermore, before optimization, the bus departure interval was fixed, and the arrival probability of each stop remained consistent during peak and off-peak hours.

[0125] Table 1 shows the average arrival time of buses based on the frequency statistics of each stop.

[0126] The calculation of the spatial-temporal transfer gap error needs to consider the actual walking path parameters of passengers from the subway station exit to the bus stop. Through on-site measurements and map data calibration, it was determined that passengers need to walk 385m from the Shihhumoshe subway station exit and 40m from the exit to the Shihhuhuacheng bus stop. Therefore, the sum of these two distances is the total walking distance D required for the spatial-temporal transfer gap. W The value is 425m, and this parameter serves as the core foundational data for subsequent error calculations.

[0127] According to the formula Calculate the time matching error ΔT i T 出站 This represents the theoretical time it takes for a passenger to exit the subway station from where they get off; T w This indicates the time it takes for a passenger to walk from the station to the bus stop.

[0128] According to the formula Calculate the spatial matching error ΔS i D W V represents the deviation between the passenger's actual walking distance and the ideal route; 人 The walking speed of different groups needs to be distinguished according to the group type (elderly and pregnant women, children, and young people); 'a' is the spatial tolerance coefficient used to reflect the sensitivity of different groups to distance deviation. The value is determined according to the characteristics of the group. For the elderly and pregnant women, 'a' is 0.2; for the children, 'a' is 0.4; and for the young people, 'a' is 0.7.

[0129] Before optimization, the average walking speed of different groups of people was used for calculation. The specific values ​​are shown in Table 2, which provides key speed parameters for spatial matching error calculation.

[0130] Table 2. Average walking speed within stations for each population group (before optimization)

[0131] The total error of individual spatiotemporal transfer gap time is calculated using a weighted summation model, as shown in the following formula:

[0132] W i =(ω T ·ΔT i +ω S ·ΔSi In the formula, ω T and ω S The weights for time error and spatial error are not specified, and their values ​​range from 0.4 to 0.6. In this study, the median value of 0.5 is used to ensure that the contributions of the two types of errors to the total error are balanced.

[0133] Through manual statistical surveys, spatiotemporal transfer disruption error data were obtained for multiple groups (elderly, children, and youth) at the time level. Combined with spatial error calculation results, the total error for each group was obtained. Tables 3, 4, and 5 show the field statistical data for the elderly, children, and youth groups, respectively, covering T 出站 T w ΔT i ΔS i W i Key indicators such as these provide basic data support for subsequent calculations of average error and connection probability among multiple groups. The sample size for each group is 10 to ensure the representativeness and reliability of the data.

[0134] Table 3 shows the field statistics for various groups and individuals among the elderly population regarding the spatial-temporal travel disruptions.

[0135] Table 4 shows the field statistics for various individuals in multiple groups regarding spatiotemporal travel disruptions among children.

[0136] Table 5 shows the field statistics for various individuals in multiple groups regarding the spatiotemporal travel disruptions among the youth population.

[0137] The average arrival time T of the bus 准点 Based on this, a "fast one, slow two" punctuality time range evaluation system is adopted, combined with the station (PIS) information hierarchy, and compared with T 准点 and time range threshold T 临界 To determine the time interval for punctuality evaluation, this study adopted... As a standard for judging on-time arrival time, if the actual arrival time of the bus falls within this range, it is judged as on-time; otherwise, it is not on-time.

[0138] By randomly sampling the actual arrival times of 20 different bus trips on Route 503 at different times, the time range, actual arrival time, and on-time determination results were recorded. The specific data are shown in Table 6. The statistical results show that 15 out of the 20 trips were on time. Before optimization, the on-time rate of buses based on the stops was 15 / 20 = 75%, and because the departure interval was fixed, the on-time rate was consistent between peak and off-peak hours.

[0139] Table 6 shows the actual arrival times of each random bus route 503, compiled manually on-site.

[0140] By statistically analyzing the distribution probability of individuals from different groups in various scenarios, the successful connection probability of multi-group spatiotemporal transfers (i.e., the sum of probabilities for scenarios one through four) was calculated. Combined with the bus punctuality rate, the successful connection probability of multi-groups based on punctuality was obtained (the product of the successful connection probability and the punctuality rate). Table 7 lists the successful connection probability, punctuality rate, and successful connection probability based on punctuality for each group. The final calculated average successful connection probability of multi-groups based on punctuality is 57.5%.

[0141] Table 7. Average Probability of Multiple Groups Based on Punctuality of Bus and Subway Connections

[0142] To address the issue that the original fixed departure interval (approximately 20 minutes) of bus route 503 did not take into account the differences in passenger flow during different time periods, a departure interval optimization strategy based on passenger flow during different time periods is proposed. This strategy involves dynamically adjusting the departure interval according to the passenger flow at different times to improve the efficiency of bus operation and the degree of connection matching.

[0143] As shown in Figure 4, through field research, passenger flow data of bus route 503 at Shihumoshe Station during different time periods (5:30-7:00, 7:00-9:00, 9:00-12:30, 12:30-16:00, 16:00-17:30, 17:30-19:00, 19:00-20:00) on a certain day were obtained. The difference in passenger flow between peak hours (7:00-9:00, 17:30-19:00) and off-peak hours was clarified, providing data support for optimizing departure intervals.

[0144] A genetic algorithm is used to obtain the optimal departure interval for each time period, defining the different passenger flow rates at different times of the day within the genetic algorithm. The vertical axis represents fitness, which means the degree of fit of the optimal departure interval for each time period in the final output of the genetic algorithm. The higher the fitness, the better it fits the decision variable, thus determining the intermediate variable T. 准点 The algorithm outputs the results shown in Figure 5, where the optimal departure intervals for each time period are ['5.6 minutes', '5.0 minutes', '5.4 minutes', '11.3 minutes', '5.7 minutes', '5.0 minutes', '7.7 minutes'], with a corresponding fitness value of 8709.5025.

[0145] Based on the passenger flow at different times, the optimized departure interval can be obtained. When the passenger flow is low, it is the off-peak period. The optimized off-peak departure interval is longer than the peak period, which is in line with the operation rules of public transportation. The fitness curve and the final fitness value can also be obtained. The higher the fitness, the closer the optimization result is to the optimal solution. Here, since the initial definition of full fitness is 10000, the result is 8709, so the fitness of the result is close to 90%.

[0146] The optimized departure intervals for different time periods obtained above are based on the passenger flow at different times of the day. Therefore, the optimized data should also be studied based on the peak and off-peak periods. For the Shihhumoshe Station in the case study, the peak period from 7:00 to 9:00 and the off-peak period from 12:30 to 4:00 will be selected as the manual data collection period.

[0147] Since the change in the departure interval of regular buses after optimization will directly affect the location of the bus stop after passengers get off the subway each time, the average arrival time of buses needs to be manually counted again according to different time periods, which is consistent with the on-site statistics method before optimization, as shown in Figure 6.

[0148] Similar to the data statistics before optimization, statistics were conducted in two time periods, with a sample size of 30. The statistical frequency revealed that during peak hours, after 30 randomly selected passengers exited the subway, the bus stops at that moment were mainly concentrated between Wangshan Industrial Park Station and Ruiyi Senior Living Community Station; while during off-peak hours, the stops were mainly concentrated between Ruiyi Senior Living Community Station and Wenzheng College Station. Therefore, the average arrival time of different buses during peak and off-peak hours after optimizing the departure interval can be denoted as T. 准点-高 and T 准点-平 As shown in Table 8.

[0149] Table 8 shows the average arrival time of buses after optimization, based on the frequency statistics of each stop.

[0150] The above yields optimized peak and off-peak T values. 准点 (denoted as T) 准点-高 and T 准点-平 Write probability simulation and visualization program code, calculate the probability of meeting the conditions using Monte Carlo simulation method, and visualize the iterative process as shown in Figures 7 and 8.

[0151] As can be seen from the aforementioned method, only when the time judgment interval corresponding to scenario five is the connection considered to have failed; all other intervals are considered successful connections. Then, the Monte Carlo simulation method is used to perform probability simulation iterations, thereby obtaining the iterative diagram of the success rate of connection for multiple groups during off-peak and peak-peak time-space transfer breaks. After 1000 iterations, the success rates of connection for multiple groups under peak and off-peak time-space transfer breaks are 83.90% and 86.39%, respectively.

[0152] The variation in departure intervals results in different average arrival times for buses during peak and off-peak periods. Before optimizing departure intervals, calculating the on-time rate of regular buses requires first considering its T... 准点The results are compared with the obtained critical value, and then the corresponding mathematical expression is selected to calculate the accuracy rate over the time range. This process is even more rigorous after optimization, but the difference is that the time range now needs to be calculated twice, simply because the research period has changed from one to two: peak and off-peak periods.

[0153] According to the formula The actual arrival times of bus route 503 were statistically analyzed for 20 trips each, and the results are shown in Table 9 (peak hours) and Table 10 (off-peak hours), respectively.

[0154] Table 9. Data on the actual arrival times of each random bus route 503 during peak hours, compiled manually.

[0155] Table 10: Off-peak period manual statistics of actual arrival times for each random bus route 503.

[0156] In Table 9, during peak hours, 18 out of 20 statistical samples were on-time, with an on-time rate of 90%. In Table 10, during off-peak hours, 17 out of 20 statistical samples were on-time, with an on-time rate of 85%. Figure 9 is a bar chart comparing the on-time rates before and after optimization during peak and off-peak hours, visually demonstrating the improvement in on-time rate after optimization. Statistical calculations show that before optimization, the on-time rate of buses during both peak and off-peak hours was 75%, while after optimization, the on-time rates during peak and off-peak hours were 90% and 85%, respectively.

[0157] Whether it's T 准点 The determination of on-time performance and the calculation of punctuality rates are both based on the optimized departure intervals and passenger flow at different times, using peak and off-peak periods as the statistical periods for the study. This section studies the calculation and data statistics of the connection probability for various groups after optimization, so it will also analyze the data from both peak and off-peak periods. Daily travel experiences have revealed that different groups walk at different speeds during peak and off-peak periods. For example, passengers often walk faster during off-peak periods than during peak periods. This is because the large passenger flow causes congestion, which in turn reduces walking speed. Table 11 below shows the different walking speeds for different groups during peak and off-peak periods:

[0158] Table 11 Average walking speed within stations for individuals in different population groups

[0159] After optimization, when calculating the spatiotemporal transfer time error of each group, the walking speed is different during peak and off-peak periods, which changes the spatiotemporal transfer time error at the spatial level, and thus changes the total average spatiotemporal transfer time error of a single group. Finally, the successful connection probability of each group before and after optimization is obtained, as shown in Figures 10 and 11.

[0160] As shown in Figures 10 and 11, the curves after optimizing the departure intervals are all above the curves before optimization. Although there are equal data, the overall probability of successful connection for all groups of people has been greatly improved, whether during off-peak or peak hours.

[0161] To obtain the average probability of successful connection for multiple groups based on punctuality, the punctuality rate should be multiplied by the connection probability for each group. According to the formula:

[0162] In the formula, P(S5) is W i In the "W" i >T 准点 The probability of the interval "+2" N = 3 represents the sum of the probabilities of successful connection based on punctuality for different groups.

[0163] Through 1000 iterations of calculation, the probabilities of successful multi-group connections based on punctuality during peak and off-peak hours were obtained, as shown in Figures 12 and 13, respectively. The final results were 75.76% during peak hours and 73.45% during off-peak hours. Figure 14 shows a comparison of the probabilities of successful multi-group connections based on punctuality before and after optimization. The average probability before optimization was 57.5%, while after optimization, the probabilities during peak and off-peak hours increased by approximately 18.26% and 15.95%, respectively, with an overall improvement of nearly 30%. This verifies the effectiveness of the dynamic departure interval optimization strategy in improving the efficiency of bus-subway connections.

[0164] Furthermore, based on the same inventive concept as the method described above, the present invention also provides a computer storage medium storing a computer software product, the computer software product including several instructions for causing a computer device to execute the method for evaluating the spatiotemporal transfer disruptions and punctuality of bus-subway connections.

[0165] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0166] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in one or more flowchart illustrations and / or one or more block diagrams.

[0167] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more block diagrams.

[0168] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more block diagrams.

[0169] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

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