A method for optimizing urban bus line network considering scheduling
By analyzing bus card swiping data and constructing a real-virtual bus network, and considering passenger waiting costs, a synchronous optimization model for routes and scheduling was built. This solved the problem of neglecting the impact of passenger route selection and scheduling in existing technologies, and achieved synchronous optimization of the bus network and vehicle scheduling, reducing costs and improving travel efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SOUTH CHINA AGRICULTURAL UNIVERSITY
- Filing Date
- 2022-08-31
- Publication Date
- 2026-07-21
AI Technical Summary
Existing technologies neglect the impact of passenger route selection and scheduling in public transport network optimization, resulting in the failure to consider waiting costs and affecting travel costs and efficiency.
By analyzing bus card swiping data, vehicle frequency data, and route and station data, a real-virtual bus network is constructed. Considering passenger waiting costs, a route and scheduling synchronous optimization model is built, and Yen's shortest path traversal algorithm and genetic algorithm are used for optimization.
It has achieved simultaneous optimization of the bus network and vehicle scheduling, reducing passenger time costs and bus operating costs, and improving travel efficiency and the overall benefits of the bus system.
Smart Images
Figure CN115545403B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of intelligent transportation systems, and in particular to methods, systems, intelligent devices, and storage media for optimizing urban public transport networks that take into account scheduling. Background Technology
[0002] Currently, in major cities across the country, public transportation networks are composed of buses, subways, taxis, and trams, and their overall passenger volume is constantly increasing. While public transportation passenger volume is constantly growing, the share of public transportation passenger volume is constantly decreasing. In recent years, the daily share of public transportation and rail transit has changed significantly: In 2020, the proportion of rail transit passenger volume in the daily average passenger volume of a certain city A increased by 4.6 percentage points compared with 2019, reaching 60.7%, further strengthening its dominant position; while the proportion of bus passenger volume in the daily average passenger volume of public transportation decreased by 4.6 percentage points compared with 2019, dropping to 39.3%, and the share of public transportation further decreased. Despite the rapid development of public transportation, the share of public transportation is decreasing year by year. The reason for this phenomenon can be attributed to the fact that public transportation is less attractive and has a lower service level compared with other public transportation modes. This reason is mainly caused by the following factors: (1) high overlap between bus routes and subways; (2) long waiting time. To address the aforementioned issues, improve public transport efficiency, and balance the supply and demand of the public transport system, a redesign of the bus network is necessary. This requires avoiding significant overlap with rail lines to prevent redundant routes and resource waste. Simultaneously, it's crucial to ensure seamless connections and transfers between buses and rail transit to meet passenger travel needs and reduce costs.
[0003] At present, the optimization analysis of bus network has achieved many excellent results in terms of theoretical methods, model construction and algorithm solution. However, the optimization of bus network in the existing technology still has the following shortcomings: (1) It ignores the choice of passenger routes. When optimizing bus network, domestic and foreign scholars usually assume that passengers choose the route with the shortest time or the fewest transfers. However, when there are multiple routes between the same OD station that can meet the needs of passengers, passengers will selectively take different routes. (2) It does not take into account the impact of scheduling on bus network optimization.
[0004] Existing technologies neglect the impact of route scheduling on passenger route selection during the online network deployment phase. When calculating the total travel cost for passengers, waiting costs are not taken into account, resulting in an incomplete calculation of the total time cost for passengers. This is not conducive to providing users with optimal travel convenience or reducing travel costs.
[0005] Therefore, existing technologies still need to be improved and enhanced. Summary of the Invention
[0006] The technical problem to be solved by the present invention is to provide a method, system, intelligent device and storage medium for optimizing urban public transport network by taking into account the above-mentioned defects of the prior art, so as to optimize the layout of public transport network and vehicle scheduling simultaneously.
[0007] To address the aforementioned technical problems, the first aspect of the present invention provides a method for optimizing urban public transport networks considering scheduling, the method comprising:
[0008] A method for optimizing urban public transport networks considering shift scheduling, wherein the method includes:
[0009] Acquire and process bus card swipe data, vehicle frequency data, and route and station data, and analyze these data to identify influencing factors during passenger journeys.
[0010] We conducted an analysis of the influencing factors of passenger travel habits and vehicle frequency, and analyzed the relationship between route selection and travel habits for different types of passengers. We defined the degree of correlation using the proportion of route selection between the origin and destination (OD) of the same bus stop and the Pearson correlation coefficient, and obtained the Pearson correlation coefficients between the choices of different types of passengers and the frequency of vehicles.
[0011] By constructing a real-virtual public transport network, based on the analysis of factors influencing passenger choice and considering the route selection during the passenger's journey, a synchronous optimization model for routes and scheduling is constructed. The constructed synchronous optimization model for routes and scheduling optimizes the layout of stations and vehicle scheduling in sync, and outputs the optimization results.
[0012] The aforementioned method for optimizing urban public transport networks considering scheduling includes the following steps: acquiring collected bus card swipe data, vehicle frequency data, and route and stop data; processing and analyzing the bus card swipe data, vehicle frequency data, and route and stop data; and analyzing the influencing factors during passenger route travel.
[0013] Acquire the collected bus card swipe data, vehicle schedule data, and route and station data, and perform preprocessing: match the bus card swipe data and vehicle schedule data with the corresponding card swipe ID and route schedule ID, and merge the matched data with the sub-routes included in the entire route.
[0014] Based on the preprocessed data, card swipe data with multiple ride records between the same bus stop OD within the same time period are selected, and there are multiple accessible routes under the bus stop OD.
[0015] The data that meets the screening criteria will be analyzed for influencing factors.
[0016] The aforementioned method for optimizing urban public transport networks considering scheduling includes the following steps: analyzing the influencing factors of passenger travel habits and vehicle frequency; analyzing the relationship between route selection and travel habits for different types of passengers; defining the degree of correlation using the proportion of route selection between the origin and destination (OD) of the same bus stop and the Pearson correlation coefficient; and obtaining the Pearson correlation coefficient between different types of passenger selection and vehicle frequency.
[0017] Analysis of factors influencing riding habits: Select card swipe data of passengers who ride multiple times between the same bus stop and OD of multiple bus routes within the same time period, analyze the influence relationship between the choice of routes and riding habits of different types of passengers, and obtain the proportion of the number of routes selected in the multiple ride records of passengers to obtain riding habit data.
[0018] Analysis of factors affecting vehicle frequency: In the preprocessed data, card swiping data and route operation data under the OD of bus stops with multiple routes were selected. The proportion of card swiping volume and frequency of each route under the same bus stop OD were statistically analyzed. The results of the proportion of card swiping volume and frequency of each route were grouped by bus stop OD and correlation analysis was performed to obtain the Pearson correlation coefficient between different types of passenger choices and frequency of service.
[0019] The aforementioned method for optimizing urban public transport networks considering scheduling includes the following steps: constructing a real-virtual public transport network based on passenger choice influencing factors analysis and considering passenger route selection during travel; building a synchronous optimization model for routes and scheduling; and optimizing route and station layout and vehicle scheduling synchronously using the constructed synchronous optimization model, and outputting the optimization results.
[0020] When constructing a public transport network, the public transport network is transformed into a real-virtual public transport network based on the determination of actual and virtual stops. This method of constructing a public transport network also takes into account the waiting costs of passengers during their public transport journey.
[0021] Based on the optimization settings of urban public transport network scheduling, with the overall goal of minimizing passenger time costs and public transport operating costs, and constrained by route length, departure interval, non-repetition of route stops, and non-linearity coefficient of routes, and taking into account the route selection during passenger travel, a synchronous optimization model of network and scheduling is constructed.
[0022] The system optimizes bus routes, stops, and vehicle schedules synchronously using a route and scheduling synchronization optimization model, and outputs the optimization results.
[0023] The aforementioned method for optimizing urban public transport networks considering scheduling includes the following steps: When constructing the public transport network, the step of transforming the public transport network into a real-virtual public transport network based on the determined actual and virtual stops includes:
[0024] The stations existing in the road network are set as actual stations; the stations of each line in the network are set as virtual stations in order to determine the actual stations and virtual stations.
[0025] The weights are set based on the connectivity between stations in the real-virtual bus network: In the real-virtual bus network: when there is no connectivity between actual stations, the weight of its adjacency matrix is set to infinity; when there is connectivity between actual and virtual stations, it represents the average waiting cost for passengers choosing this route, and its weight is half the departure interval of the corresponding route; when there is connectivity between virtual and actual stations, it represents the passenger's alighting action, and its weight is 0; when there is connectivity between virtual stations, it represents the travel time between stations, and its weight is the distance between stations in the actual network.
[0026] Constructing the adjacency matrix: In the actual-virtual adjacency matrix, the number of rows and columns is the sum of the number of road station points and the number of line station points; when constructing the corresponding adjacency matrix, the actual stations are constructed before the virtual stations, and then the virtual stations are set in the order of the stations of each line to transform the bus network into an actual-virtual bus network.
[0027] The urban public transport network optimization method considering scheduling further includes the step of simultaneously optimizing bus route stops and vehicle frequency using a route and scheduling synchronization optimization model, and outputting the optimization results:
[0028] The route and scheduling synchronization optimization model is optimized and solved using Yen's shortest path traversal algorithm and genetic algorithm.
[0029] A device for optimizing urban public transport networks considering shift scheduling, wherein the system includes:
[0030] The data acquisition and processing module is used to acquire bus card swipe data, vehicle frequency data, and route and station data, and to process and analyze the bus card swipe data, vehicle frequency data, and route and station data to analyze the influencing factors in the passenger route process.
[0031] The analysis module is used to analyze the influencing factors of passenger travel habits and vehicle frequency, analyze the relationship between route selection and travel habits of different types of passengers, define the degree of correlation by the proportion of route selection between the OD of the same bus stop and the Pearson correlation coefficient, and obtain the Pearson correlation coefficient between the selection of different types of passengers and the frequency.
[0032] The model building and optimization module is used to construct a synchronous optimization model of routes and schedules by using a real-virtual public transportation network construction method, based on the analysis of factors influencing passenger choices and considering the route selection process during passenger travel. The module optimizes the route and schedule layout and vehicle scheduling in sync through the constructed synchronous optimization model and outputs the optimization results.
[0033] A smart device is characterized by comprising a memory and one or more programs, wherein one or more programs are stored in the memory and configured to be executed by one or more processors, the one or more programs comprising the method for performing any one of the methods.
[0034] A non-transitory computer-readable storage medium, wherein, when instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform any of the methods described above.
[0035] Beneficial Effects: Compared with existing technologies, this invention provides a method for optimizing urban public transport networks by considering vehicle scheduling. This invention analyzes data such as bus card swipes, passenger types, vehicle frequency, and route stops to explore the influencing factors of passenger route selection. It further analyzes passenger route selection in detail based on riding habits and vehicle frequency. Through a real-virtual public transport network construction method, it considers waiting costs when calculating total passenger travel costs. Finally, based on passenger route selection, it constructs a synchronous optimization model for routes and scheduling. This invention can optimize the public transport network by considering the impact of vehicle scheduling, optimizing the public transport network layout and vehicle scheduling simultaneously; providing convenience for passengers and reducing travel costs. Attached Figure Description
[0036] Figure 1 A flowchart illustrating a specific implementation of the urban public transport network optimization method considering scheduling, as provided in this embodiment of the invention.
[0037] Figure 2 This is a schematic diagram of the percentage of overlapping bus routes in a certain city A, which is an embodiment of the urban public transport network optimization method considering scheduling according to an embodiment of the present invention.
[0038] Figure 3 This is a schematic diagram of the OD ratio of a city A with different numbers of bus routes, representing a specific application embodiment of the present invention's method for optimizing urban public transport networks by considering scheduling.
[0039] Figure 4 This diagram illustrates the percentage of bus routes taken by passengers during multiple trips at the same time and stop, as part of a specific application embodiment of the present invention's urban public transport network optimization method that considers scheduling.
[0040] Figure 5 This diagram illustrates the theoretical and actual probability calculations of passengers in the urban public transport network optimization method considering scheduling, which is a specific application embodiment of the present invention.
[0041] Figure 6 This diagram illustrates the Pearson correlation coefficients between passenger choices and bus schedules for different numbers of routes under the same origin-destination (OD) configuration, representing a specific application embodiment of the present invention's urban public transport network optimization method considering scheduling.
[0042] Figure 7 This diagram illustrates the Pearson correlation coefficients between different types of passengers and bus schedules in the overall passenger pool for a city bus network optimization method considering scheduling, as a specific application embodiment of the present invention.
[0043] Figure 8 This is a schematic diagram of the actual-virtual bus network relationship of the urban bus network optimization method considering scheduling, which is a specific application embodiment of the present invention.
[0044] Figure 9 This is a schematic diagram of the adjacency matrix under the actual-virtual bus network, which is a specific application embodiment of the urban bus network optimization method considering scheduling in this invention.
[0045] Figure 10 The diagram illustrates the target value optimization process of the urban public transport network optimization method considering scheduling, as provided in this embodiment of the invention.
[0046] Figure 11 This is a schematic diagram comparing the results of synchronous and phased optimization of the urban public transport network optimization method considering scheduling provided in an embodiment of the present invention.
[0047] Figure 12 This diagram illustrates a comparison of optimization results under different time and operating cost conversion factors in a specific application embodiment of the urban public transport network optimization method considering scheduling of the present invention.
[0048] Figure 13 This is a schematic diagram of the urban public transport network optimization device considering scheduling provided in an embodiment of the present invention.
[0049] Figure 14 This is a block diagram illustrating the internal structure of a smart device provided in an embodiment of the present invention. Detailed Implementation
[0050] To make the objectives, technical solutions, and effects of this invention clearer and more explicit, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
[0051] At present, the optimization analysis of the bus network has achieved many excellent results in terms of theoretical methods, model construction and algorithm solution, which provides a theoretical basis for the development of the network optimization design work of this invention. However, these results still have shortcomings in practice: (1) They ignore the choice of passenger routes. When optimizing the bus network, domestic and foreign scholars usually assume that passengers choose the route with the shortest time or the fewest transfers. However, when there are multiple routes between the same OD station that can meet the passenger's needs, the passenger will selectively take different routes. (2) They do not consider the impact of the scheduling on the optimization of the bus network. Previous analyses ignored the impact of the route scheduling on the passenger's route selection during the network layout stage. When calculating the total travel cost of passengers, the waiting cost was not taken into account, which made the calculation of the total time cost of passengers incomplete. However, the passenger's waiting cost is directly related to the vehicle scheduling.
[0052] Therefore, the optimized design of the bus network needs to take into account passengers' route choices and the impact of vehicle scheduling on the deployment of the bus network.
[0053] Therefore, this embodiment provides a method for optimizing urban public transport networks considering scheduling. The method includes the following steps: Analyzing data such as bus card swiping data, passenger types, vehicle frequency, and route stops in City A to explore the influencing factors of passenger route selection. First, the duplication of bus routes in City A is analyzed. Then, the influencing factors of passenger route selection are analyzed, further specifically analyzing the relationship between passenger route selection and riding habits, and vehicle frequency. Through the construction of a real-virtual public transport network, passenger waiting costs are considered when calculating the total travel cost. Based on passenger route selection, a synchronous optimization model for routes and scheduling is constructed, thereby considering the impact of scheduling during the public transport network optimization process. This invention can achieve synchronous optimization of public transport networks and vehicle scheduling.
[0054] Exemplary methods
[0055] In specific implementation, such as Figure 1 As shown in the figure, the urban public transport network optimization method considering shift scheduling provided by this embodiment of the invention specifically includes the following steps:
[0056] Step S100: Acquire the collected bus card swiping data, vehicle frequency data, and route and station data, and process and analyze the bus card swiping data, vehicle frequency data, and route and station data to analyze the influencing factors in the passenger route process.
[0057] Step S200: Analyze the influencing factors of passenger travel habits and vehicle frequency, and define the degree of correlation using the proportion of route selection between the same bus stop OD and the Person correlation coefficient.
[0058] Step S300: Using a real-virtual public transport network construction method, based on the analysis of factors influencing passenger choice and considering the route selection during the passenger's travel process, construct a synchronous optimization model for routes and schedules. Through the constructed synchronous optimization model for routes and schedules, optimize the layout of route stops and vehicle schedules in sync, and output the optimization results.
[0059] This invention provides an in-depth analysis of urban public transport network optimization considering scheduling, taking into account the total travel time of passengers. This is crucial for analyzing the influencing factors of passenger route selection and for flexibly adjusting route stops and vehicle scheduling based on passenger demand. It also provides more accurate, comprehensive, and effective data support for public transport dispatching and network optimization management. Specifically, it processes and analyzes public transport card swiping data, vehicle frequency data, and route stop data to explore influencing factors in passenger travel. Further analysis is conducted on passenger travel habits and vehicle frequency, defining their correlation using the proportion of route selection between the same bus stop's origin-destination (OD) and the Person correlation coefficient. Through a real-virtual public transport network construction method, passenger waiting time is taken into account, and a synchronous optimization model for routes and scheduling is built based on this, optimizing route stop layout and vehicle scheduling simultaneously.
[0060] Specifically, step S100 of this embodiment of the invention, which involves acquiring and collecting bus card swiping data, vehicle frequency data, and route and stop data, and processing and analyzing the bus card swiping data, vehicle frequency data, and route and stop data to analyze the influencing factors in the passenger route process, specifically includes:
[0061] S11. Acquire the collected bus card swiping data, vehicle schedule data, and route station data, and perform preprocessing: Match the acquired bus card swiping data and vehicle schedule data with the corresponding card swiping ID and route schedule ID, and merge the matched data with the sub-routes included in the entire route.
[0062] S12. Based on the preprocessed data, filter out card swipe data that show multiple ride records between the same bus stop OD within the same time period, and where there are multiple accessible routes under the OD. The filtering criteria are: the time period selected for the riding habit influence analysis is 6:00-12:00; card swipe data with 2 ride records; OD with 2 routes and no less than 30 passengers.
[0063] S13. Conduct an influencing factor analysis on the data that meets the screening criteria. Specifically, save the screened data that meets the screening criteria for further analysis of the influencing factors of passenger route selection and travel habits, as well as vehicle frequency.
[0064] Specifically, in step S200 of this embodiment of the invention, an influencing factor analysis is further performed on passenger travel habits and vehicle frequency. The degree of correlation is defined using the proportion of route selection between the origin and destination (OD) of the same bus stop and the Pearson correlation coefficient. Here, Pearson refers to the Pearson correlation coefficient, also known as the Pearson product-moment correlation coefficient, which is a linear correlation coefficient. The Pearson correlation coefficient is a statistic used to reflect the degree of linear correlation between two variables. This step specifically includes:
[0065] S21. Analysis of factors influencing riding habits: Select the card swiping data of passengers who ride multiple times between the same bus stop and OD of multiple (more than one) bus routes within the same time period, analyze the proportion of the number of routes selected in their multiple ride records, and obtain riding habit data to reflect riding habits.
[0066] In this embodiment of the invention, the proportion of actual rides in the card-swipe records that meet the conditions is regarded as the actual probability. The probability of a passenger choosing each route is then derived from this actual proportion. Assuming that the only difference between routes is the number of services, the proportion of services between routes is considered the probability of a passenger choosing a route at that bus stop's origin-destination (OD) location, and this is regarded as a theoretical probability. The proportion of the number of routes is then calculated based on this probability and regarded as a theoretical probability. Subsequently, passenger types are analyzed separately, including: ordinary passengers, elderly passengers, students, and disabled passengers, to analyze the influence of route selection and riding habits on each passenger type.
[0067] The actual probability is compared with the theoretical probability, and the actual calculated probability is compared with the theoretical calculated probability. When the difference between the two values is small (e.g., less than or equal to 0.1), it is considered that the passenger's route selection is less influenced by habit; when the difference is large (e.g., greater than 0.1), it is considered that the passenger's choice is more influenced by habit.
[0068] The time period selected for this analysis of the impact of riding habits is 6:00-12:00; the riding records are 2 card swipe data; there are OD (Original Design Site) locations with 2 routes and no less than 30 people.
[0069] S22. Analysis of factors affecting vehicle frequency: In the preprocessed data, card swiping data and route operation data under the OD of bus stops with multiple (more than one) routes are selected. The proportion of card swiping volume and frequency of each route under the same bus stop OD is statistically analyzed. The results of the proportion of card swiping volume and frequency of each route are grouped by bus stop OD for correlation analysis to obtain the Pearson correlation coefficient between different types of passenger choices and frequency of service.
[0070] Specifically, step S300 of this embodiment of the invention, which involves constructing a real-virtual public transport network to take passenger waiting time into account and building a route and scheduling synchronization optimization model based on this, optimizes the route and station layout and vehicle scheduling simultaneously; specifically includes:
[0071] When constructing a public transport network, the public transport network is transformed into a real-virtual public transport network construction method: (1) Determine the actual and virtual stations. Set the stations existing in the road network as actual stations; set all stations of each line in the line network as virtual stations. (2) Set the connectivity and weights between stations. In the real-virtual public transport network: actual stations are not connected to each other, and the weight of their adjacency matrix is set to infinity; actual stations are connected to virtual stations, which represents the average waiting cost when passengers choose this line, and the weight is half of the departure interval of the corresponding line; virtual stations are connected to actual stations, which represents the passenger's alighting action, and the weight is 0; virtual stations are connected to each other, which represents the travel time between each station, and the weight is the distance between each station in the actual network. (3) Construct the adjacency matrix. In the real-virtual adjacency matrix, the number of rows and columns is the sum of the number of road network stations and the number of line network stations. When constructing the corresponding adjacency matrix, the actual stations are constructed before the virtual stations, and then the virtual stations are set in the order of the stations of each line.
[0072] A synchronous optimization model for routes and schedules is constructed. Considering the scheduling considerations, the optimization design of urban public transport networks aims to minimize passenger time costs and public transport operating costs. Constraints include route length, departure intervals, non-overlapping route stops, and route non-linearity coefficients. A synchronous optimization model for routes and schedules is established as follows:
[0073]
[0074]
[0075] St:
[0076] f min ≤f r ≤f max
[0077] L min ≤L k ≤L max
[0078]
[0079]
[0080] In the above formula,
[0081] Z: Represents the objective function;
[0082] X: Public transport network;
[0083] Q ij Traffic demand from station i to station j;
[0084] T ij (X): Under the X-line network, the shortest travel time required for a passenger to travel from station i to station j; λ: the emphasis coefficient between time cost and operating cost;
[0085] ω: Conversion factor between time cost and operating cost;
[0086] F: Operating costs;
[0087] The waiting time for passengers from station i to station j using the shortest route they selected;
[0088] The stopping time of the bus during its journey from station i to station j;
[0089] The time a passenger spends on the train from station i to station j;
[0090] M: Bus purchase cost;
[0091] N: Operating costs of each bus route;
[0092] δ w Passenger waiting coefficient, since passengers arrive uniformly, vehicle arrival follows a Poisson distribution, δ w =0.5;
[0093] m ij The value is 1 if there is a unique shortest path from station i to station j, otherwise the value is 0;
[0094] The shortest path from station i to station j has a value of 1 if it passes through line r, and a value of 0 if it does not.
[0095] n ij Auxiliary variable: the segment L of the shortest path between adjacent stations ij. ij The value is 1 when it is on the shortest path from i to j, and 0 when it is not.
[0096] V: Average speed of public transport vehicles;
[0097] The stopping time of the bus during its journey from station i to station j;
[0098] n: The number of stations on the selected route from station i to station j;
[0099] T s The average stopping time of buses at bus stops;
[0100] Adjacent station ij section L ij The value is 1 when on line r, and 0 when not on line r.
[0101] L ij : The length of the road segment between adjacent stations ij;
[0102] Cost per unit mileage of public buses;
[0103] Daily purchase cost per bus;
[0104] l min Minimum length of bus routes;
[0105] l r : The length of line r;
[0106] l max Maximum length of bus routes;
[0107] f min Minimum departure interval for bus routes;
[0108] f r : The departure interval of buses on route r;
[0109] f max The maximum departure interval for buses on a bus route;
[0110] l r —Length of line r (km)
[0111] d r —The straight-line distance (km) between the starting and ending stations of line r;
[0112] — Station i in line r.
[0113] Q ij Traffic demand from station i to station j;
[0114] T ij (X): Under the X-line network, the shortest travel time required for a passenger to travel from station i to station j;
[0115] λ: The weighting factor between time cost and operating cost;
[0116] ω: Conversion factor between time cost and operating cost;
[0117] F: Operating costs;
[0118] The waiting time for passengers from station i to station j using the shortest route they selected;
[0119] The stopping time of the bus during its journey from station i to station j;
[0120] The time a passenger spends on the train from station i to station j;
[0121] M: Bus purchase cost;
[0122] N: Operating costs of each bus route;
[0123] δ w Passenger waiting coefficient, since passengers arrive uniformly, vehicle arrival follows a Poisson distribution, δ w =0.5;
[0124] m ij The value is 1 if there is a unique shortest path from station i to station j, otherwise the value is 0;
[0125] The shortest path from station i to station j has a value of 1 if it passes through line r, and a value of 0 if it does not.
[0126] n ij Auxiliary variable: the segment L of the shortest path between adjacent stations ij. ij The value is 1 when it is on the shortest path from i to j, and 0 when it is not.
[0127] V: Average speed of public transport vehicles;
[0128] The stopping time of the bus during its journey from station i to station j;
[0129] n: The number of stations on the selected route from station i to station j;
[0130] T s The average stopping time of buses at bus stops;
[0131] Adjacent station ij section L ij The value is 1 when on line r, and 0 when not on line r.
[0132] L ij : The length of the road segment between adjacent stations ij;
[0133] Cost per unit mileage of public buses;
[0134] Daily purchase cost per bus;
[0135] l min Minimum length of bus routes;
[0136] l r : The length of line r;
[0137] l max Maximum length of bus routes;
[0138] f min Minimum departure interval for bus routes;
[0139] f r : The departure interval of buses on route r;
[0140] f max The maximum departure interval for buses on a bus route;
[0141] l r —Length of line r (km)
[0142] d r —The straight-line distance (km) between the starting and ending stations of line r;
[0143] — Station i in line r.
[0144] A route and schedule synchronization optimization model is used to simultaneously optimize bus routes, stops, and vehicle frequencies. Furthermore, a shortest path traversal algorithm based on Yen's algorithm and a genetic algorithm are designed to optimize and solve the route and schedule synchronization optimization model. The effectiveness of the algorithm and model is verified, and sensitivity analysis is performed on different focus areas.
[0145] The method of the present invention will be described in detail below through further specific application examples:
[0146] A specific application embodiment of the present invention provides a method for optimizing urban public transport networks considering shift scheduling, comprising the following steps:
[0147] This study analyzes data on bus card swiping, passenger types, vehicle frequency, and route stops in City A to explore the influencing factors of passenger route selection.
[0148] The bus card swiping data and route and stop data of City A were processed and analyzed to identify duplicate bus route data. This duplicate analysis mainly focused on route duplication and origin-destination (OD) duplication. For example... Figure 2 The image shows the percentage of overlapping routes on bus routes in City A. Approximately 36% of the bus routes in City A have overlapping routes. Figure 3The image shows the OD (Original Location) ratio of bus stops in Guangzhou with different numbers of bus routes. OD pairs with multiple routes account for approximately 23.6%.
[0149] When multiple routes exist to meet a passenger's travel needs, analyze the passenger's route selection and influencing factors, and further analyze travel habits and vehicle frequency.
[0150] Then, the factors influencing riding habits were analyzed: the card swiping data of passengers who took multiple rides between the same bus stop and the origin-destination (OD) of more than one bus route within the same time period were selected, and the proportion of the number of routes selected in their multiple ride records was analyzed to reflect riding habits.
[0151] Specifically, the percentage of actual routes taken from eligible card-swipe records is considered the actual probability. Based on this actual percentage, the probability of a passenger choosing each route is deduced. The actual probability is then compared with the theoretical probability, and the calculated probability is compared with the theoretical probability. A small difference between the two values indicates that the passenger's route selection is less influenced by habit; a large difference indicates a greater influence by habit. Subsequently, passenger types are analyzed separately, including: ordinary passengers, elderly passengers, students, and disabled passengers, to examine the relationship between route selection and travel habits for each passenger type.
[0152] In this embodiment of the invention, the selected time period for the analysis of the impact of riding habits is 6:00-12:00; the riding records consist of two card swipe data points; there are two routes and at least 30 passengers per route. The selection of two card swipe records is based on the amount of card swipe data that meets the conditions. The theoretical probability and actual probability results are as follows: Figure 4 As shown, Figure 4 This is a schematic diagram illustrating the percentage of routes taken by passengers multiple times at the same time and station according to an embodiment of the present invention; the calculated probability results are compared to... Figure 5 As shown, Figure 5 This is a schematic diagram illustrating the theoretical and actual probabilities of passengers in a specific embodiment of the present invention.
[0153] In this embodiment of the invention, the analysis of factors influencing vehicle frequency is as follows: Specifically, card swiping data and route operation data under ODs with more than one route can be selected from the preprocessed data. The proportion of card swiping volume and the proportion of routes under the same OD are statistically analyzed. The results of the proportion of card swiping volume and the proportion of routes are grouped by OD for correlation analysis. The Pearson correlation coefficient is used to measure the correlation between passenger choice and route frequency. The analysis is further performed by different passenger types, including: ordinary passengers, elderly passengers, students, and disabled passengers, analyzing the influence of route choice and travel habits on each type of passenger. The Pearson correlation coefficients between passenger choice and frequency under the same OD with different numbers of routes are shown below. Figure 6 As shown, Figure 6 This is a schematic diagram illustrating the Pearson correlation coefficients between passenger selection and bus frequency for different numbers of routes existing under the same bus stop OD in this embodiment of the invention; and the Pearson correlation coefficients between different types of passenger selection and bus frequency are as follows. Figure 7 As shown, Figure 7 This is a schematic diagram illustrating the Pearson correlation coefficients between different types of passengers and flight schedules in an embodiment of the present invention.
[0154] Then, the bus network is transformed into a physical-virtual bus network. This network construction method also takes into account the waiting costs for passengers during their bus journeys. Example diagrams of the physical-virtual bus network relationship and adjacency matrix are shown below. Figure 8 and Figure 9 As shown. Figure 8 The diagram shows the actual-virtual bus network relationship in an embodiment of the present invention. Figure 9 The diagram shown is a schematic diagram of the adjacency matrix structure under the actual-virtual bus network of an embodiment of the present invention.
[0155] Then, based on the analysis of factors influencing passenger choice and considering the route selection during the passenger's travel process, a synchronous optimization model for the network and scheduling is constructed.
[0156] Furthermore, in this embodiment of the invention, five routes from the two stations with the highest overlap in a certain area of City A can be selected for case analysis. Among them, the selected case network has a total of 134 stations, and the total number of road station stations is 102.
[0157] Then, the model and algorithm parameters were adjusted according to the specific circumstances of each case.
[0158] Based on the road network and demand data, model and algorithm parameters described above, a synchronous optimization model for the road network and scheduling is solved, and the road network in this case is optimized in stages. The synchronous optimization results are compared and analyzed with the staged optimization results to verify the effectiveness of the synchronous optimization model and the designed algorithm.
[0159] Algorithm effectiveness analysis. Based on the designed algorithm, the optimal solution to the optimization problem was obtained in 10 consecutive iterations in the trial cases. The designed genetic algorithm converged around 150,000 iterations, and the results remained unchanged thereafter. The algorithm's optimization process is as follows: Figure 10 As shown, Figure 10 This is a diagram illustrating the target value optimization process of the method flow of this invention.
[0160] Model Validity Analysis. This embodiment performs phased and synchronous optimization on the case route. Compared with phased optimization, synchronous optimization is superior: passenger time cost is reduced by 21.5%, bus operating cost by 13.7%, and total bus system cost by 18.0%. Therefore, it is concluded that synchronous optimization of the route network and scheduling is more effective and feasible than phased optimization. A comparison of passenger time cost and bus operating cost results after synchronous and phased optimization is provided below. Figure 11 As shown, Figure 11 This is a schematic diagram comparing the results of synchronization and phased optimization in an embodiment of the method of the present invention (unit: yuan).
[0161] This invention embodiment includes sensitivity analysis for different emphases. To further evaluate and analyze the cost of the public transportation system, different emphase coefficients for time cost and operating cost are set to highlight the different optimization emphases within the public transportation system. During the optimization process of the public transportation system, different emphases in the optimization objectives will also have a certain impact on the results. When different optimization emphases are applied to the interests of passengers and operating companies, the optimization result will have a lower cost for the one emphased benefit, while the optimized cost for the other benefit will be relatively higher. The optimization results under different time and operating cost coefficients are as follows: Figure 12 As shown, Figure 12 This is a schematic diagram comparing the optimization results under different time and operating cost conversion factors in an embodiment of the method of the present invention (unit: yuan).
[0162] As can be seen from the above, the urban public transport network optimization method considering scheduling provided by the embodiments of the present invention processes and analyzes public transport card swiping data, vehicle frequency data, and route and station data to explore the influencing factors in the passenger's route journey; further, it analyzes the influencing factors of passenger riding habits and vehicle frequency, defining their correlation degree using the proportion of route selection between the same origin and destination (OD) and the Person correlation coefficient; through a real-virtual public transport network construction method, it takes passenger waiting time into account, and on this basis, it constructs a synchronous optimization model for routes and scheduling, optimizing the layout of routes and stations and vehicle scheduling simultaneously.
[0163] Exemplary device
[0164] like Figure 13 As shown in the figure, an embodiment of the present invention provides an urban public transport network optimization device that takes into account scheduling, comprising:
[0165] The data acquisition and processing module 410 is used to acquire the collected bus card swiping data, vehicle frequency data, and route and station data, and to process and analyze the bus card swiping data, vehicle frequency data, and route and station data to analyze the influencing factors in the passenger route process.
[0166] Analysis module 420 is used to analyze the influencing factors of passenger travel habits and vehicle frequency, analyze the influence relationship between the choice of routes and travel habits of different types of passengers, define the degree of correlation by the proportion of route selection between the OD of the same bus stop and the Pearson correlation coefficient, and obtain the Pearson correlation coefficient between the choice of different types of passengers and the frequency.
[0167] The model building and optimization module 430 is used to construct a synchronous optimization model of routes and schedules by constructing a real-virtual public transportation network, based on the analysis of factors influencing passenger choices and considering the route selection during the passenger's travel process. The synchronous optimization model of routes and schedules optimizes the layout of route stops and vehicle scheduling in a synchronous manner and outputs the optimization results, as described above.
[0168] Based on the above embodiments, the present invention also provides a smart device, the principle block diagram of which can be as follows: Figure 14 As shown. The intelligent device in this embodiment of the invention can be an intelligent multimedia device, which includes a processor, a memory, a network interface, and a display screen connected via a system bus. The processor of the intelligent device provides computing and control capabilities. The memory of the intelligent device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface of the intelligent device is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for optimizing urban public transport networks considering scheduling. The display screen of the intelligent device can be a liquid crystal display (LCD).
[0169] Those skilled in the art will understand that Figure 14 The block diagram shown is merely a partial structural diagram related to the present invention and does not constitute a limitation on the smart device to which the present invention is applied. Specific smart devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.
[0170] In one embodiment, a smart device is provided, including a memory and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by one or more processors. The one or more programs include instructions for performing the following operations:
[0171] Acquire and process bus card swipe data, vehicle frequency data, and route and station data, and analyze these data to identify influencing factors during passenger journeys.
[0172] We conducted an analysis of the influencing factors of passenger travel habits and vehicle frequency, and analyzed the relationship between route selection and travel habits for different types of passengers. We defined the degree of correlation using the proportion of route selection between the origin and destination (OD) of the same bus stop and the Pearson correlation coefficient, and obtained the Pearson correlation coefficients between the choices of different types of passengers and the frequency of vehicles.
[0173] By constructing a real-virtual public transport network, based on the analysis of factors influencing passenger choice and considering the route selection during the passenger's journey, a synchronous optimization model for routes and scheduling is constructed. The constructed synchronous optimization model for routes and scheduling optimizes the layout of routes and stops and the vehicle scheduling in sync, and outputs the optimization results; as described above.
[0174] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.
[0175] In summary, this invention provides a method for optimizing urban public transport networks by considering vehicle scheduling. It analyzes data such as bus card swipes, passenger types, vehicle frequency, and route stops to explore the influencing factors of passenger route selection. Furthermore, it conducts a detailed analysis of passenger route selection based on riding habits and vehicle frequency. Through a real-virtual public transport network construction method, it considers waiting costs when calculating total passenger travel costs. Finally, based on passenger route selection, it constructs a synchronous optimization model for routes and scheduling. This invention enables the optimization of public transport networks by considering the impact of vehicle scheduling, simultaneously optimizing the layout of the public transport network and vehicle scheduling; thus providing convenience for passengers and reducing travel costs.
[0176] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. A method for optimizing urban public transport networks considering shift scheduling, characterized in that, The method includes: Acquire and process bus card swipe data, vehicle frequency data, and route and station data, and analyze these data to identify influencing factors during passenger journeys. We conducted an analysis of the influencing factors of passenger travel habits and vehicle frequency, and analyzed the relationship between route selection and travel habits for different types of passengers. We defined the degree of correlation using the proportion of route selection between the origin and destination (OD) of the same bus stop and the Pearson correlation coefficient, and obtained the Pearson correlation coefficients between the selection of different types of passengers and the frequency of vehicles. By constructing a real-virtual public transport network, based on the analysis of factors influencing passenger choice and considering the route selection during the passenger's journey, a synchronous optimization model for routes and scheduling is constructed. The constructed synchronous optimization model for routes and scheduling optimizes the layout of stops and vehicle scheduling in sync, and outputs the optimization results. The steps involved in constructing a real-virtual public transport network, based on the analysis of factors influencing passenger choice and considering route selection during the passenger's journey, to build a synchronous optimization model for routes and scheduling, and to simultaneously optimize route and station layouts and vehicle scheduling using this model, and output the optimization results, include: When constructing a public transport network, the public transport network is transformed into a real-virtual public transport network based on the determination of actual and virtual stops. This method of constructing a public transport network also takes into account the waiting costs of passengers during their public transport journey. Based on the optimization settings of urban public transport network scheduling, with the overall goal of minimizing passenger time costs and public transport operating costs, and constrained by route length, departure interval, non-repetition of route stops, and non-linearity coefficient of routes, and taking into account the route selection during passenger travel, a synchronous optimization model of network and scheduling is constructed. The bus route and schedule synchronization optimization model is used to optimize the bus route stops and vehicle frequency in sync, and the optimization results are output. The steps involved in constructing a public transport network, based on the determination of actual and virtual stops, to transform the public transport network into a real-virtual public transport network include: The stations existing in the road network are set as actual stations; the stations of each line in the network are set as virtual stations in order to determine the actual stations and virtual stations. The weights are set based on the connectivity between stations in the real-virtual bus network: In the real-virtual bus network: when there is no connectivity between actual stations, the weight of its adjacency matrix is set to infinity; when there is connectivity between actual and virtual stations, it represents the average waiting cost for passengers choosing this route, and its weight is half the departure interval of the corresponding route; when there is connectivity between virtual and actual stations, it represents the passenger's alighting action, and its weight is 0; when there is connectivity between virtual stations, it represents the travel time between stations, and its weight is the distance between stations in the actual network. Constructing the adjacency matrix: In the actual-virtual adjacency matrix, the number of rows and columns is the sum of the number of road station points and the number of line station points; when constructing the corresponding adjacency matrix, the actual stations are constructed before the virtual stations, and then the virtual stations are set in the order of the stations of each line to transform the bus network into an actual-virtual bus network.
2. The urban public transport network optimization method considering scheduling as described in claim 1, characterized in that, The steps of acquiring and processing bus card swiping data, vehicle frequency data, and route and stop data, and analyzing the influencing factors during passenger route travel, include: Acquire the collected bus card swipe data, vehicle schedule data, and route and station data, and perform preprocessing: match the bus card swipe data and vehicle schedule data with the corresponding card swipe ID and route schedule ID, and merge the matched data with the sub-routes included in the entire route. Based on the preprocessed data, card swipe data with multiple ride records between the same bus stop OD within the same time period are selected, and there are multiple accessible routes under the bus stop OD. The data that meets the screening criteria will be analyzed for influencing factors.
3. The urban public transport network optimization method considering scheduling according to claim 1, characterized in that, The steps for analyzing the influencing factors of passenger travel habits and vehicle frequency, analyzing the relationship between route selection and travel habits for different types of passengers, defining the degree of correlation using the proportion of route selection between the origin and destination (OD) of the same bus stop and the Pearson correlation coefficient, and obtaining the Pearson correlation coefficient between the selection of different types of passengers and vehicle frequency include: Analysis of factors influencing riding habits: Select card swipe data of passengers who ride multiple times between the same bus stop and OD of multiple bus routes within the same time period, analyze the influence relationship between the choice of routes and riding habits of different types of passengers, and obtain the proportion of the number of routes selected in the multiple ride records of passengers to obtain riding habit data. Analysis of factors affecting vehicle frequency: In the preprocessed data, card swiping data and route operation data under the OD of bus stops with multiple routes were selected. The proportion of card swiping volume and frequency of each route under the same bus stop OD were statistically analyzed. The results of the proportion of card swiping volume and frequency of each route were grouped by bus stop OD and correlation analysis was performed to obtain the Pearson correlation coefficient between different types of passenger choices and frequency of service.
4. The urban public transport network optimization method considering shift scheduling as described in claim 1, characterized in that, The step of simultaneously optimizing bus route stops and vehicle schedules using a route and scheduling synchronization optimization model, and outputting the optimization results, also includes: The route and scheduling synchronization optimization model is optimized and solved using Yen's shortest path traversal algorithm and genetic algorithm.
5. A device for optimizing urban public transport networks considering shift scheduling, characterized in that, The device includes: The data acquisition and processing module is used to acquire bus card swipe data, vehicle frequency data, and route and station data, and to process and analyze the bus card swipe data, vehicle frequency data, and route and station data to analyze the influencing factors in the passenger route process. The analysis module is used to analyze the influencing factors of passenger travel habits and vehicle frequency, analyze the relationship between route selection and travel habits of different types of passengers, and define the degree of correlation by the proportion of route selection between the OD of the same bus stop and the Pearson correlation coefficient, so as to obtain the Pearson correlation coefficient between the selection of different types of passengers and the frequency. The model building and optimization module is used to construct a synchronous optimization model of routes and schedules by using a real-virtual public transportation network construction method, based on the analysis of factors influencing passenger choices and considering the route selection during the passenger's travel process. The module optimizes the route and schedule layout and vehicle scheduling in sync through the constructed synchronous optimization model and outputs the optimization results. The steps involved in constructing a real-virtual public transport network, based on the analysis of factors influencing passenger choice and considering route selection during the passenger's journey, to build a synchronous optimization model for routes and scheduling, and to simultaneously optimize route and station layouts and vehicle scheduling using this model, and output the optimization results, include: When constructing a public transport network, the public transport network is transformed into a real-virtual public transport network based on the determination of actual and virtual stops. This method of constructing a public transport network also takes into account the waiting costs of passengers during their public transport journey. Based on the optimization settings of urban public transport network scheduling, with the overall goal of minimizing passenger time costs and public transport operating costs, and constrained by route length, departure interval, non-repetition of route stops, and non-linearity coefficient of routes, and taking into account the route selection during passenger travel, a synchronous optimization model of network and scheduling is constructed. The bus route and schedule synchronization optimization model is used to optimize the bus route stops and vehicle frequency in sync, and the optimization results are output. The steps involved in constructing a public transport network, based on the determination of actual and virtual stops, to transform the public transport network into a real-virtual public transport network include: The stations existing in the road network are set as actual stations; the stations of each line in the network are set as virtual stations in order to determine the actual stations and virtual stations. The weights are set based on the connectivity between stations in the real-virtual bus network: In the real-virtual bus network: when there is no connectivity between actual stations, the weight of its adjacency matrix is set to infinity; when there is connectivity between actual and virtual stations, it represents the average waiting cost for passengers choosing this route, and its weight is half the departure interval of the corresponding route; when there is connectivity between virtual and actual stations, it represents the passenger's alighting action, and its weight is 0; when there is connectivity between virtual stations, it represents the travel time between stations, and its weight is the distance between stations in the actual network. Constructing the adjacency matrix: In the actual-virtual adjacency matrix, the number of rows and columns is the sum of the number of road station points and the number of line station points; when constructing the corresponding adjacency matrix, the actual stations are constructed before the virtual stations, and then the virtual stations are set in the order of the stations of each line to transform the bus network into an actual-virtual bus network.
6. A smart device, characterized in that, It includes a memory and one or more programs, wherein one or more programs are stored in the memory and configured to be executed by one or more processors, the one or more programs comprising the means for performing the method as described in any one of claims 1-4.
7. A non-transitory computer-readable storage medium, characterized in that, When the instructions in the storage medium are executed by the processor of the electronic device, the electronic device is able to perform the method as described in any one of claims 1-4.