Traffic operation optimization method, system and device, storage medium and program product

Abstract

The invention discloses a traffic operation optimization method, system and device, a storage medium and a program product, and the method comprises the steps: collecting the real-time operation data of a rail transit system, and carrying out the data preprocessing, and obtaining the preprocessed real-time operation data; passenger behavior patterns and station characteristics are analyzed based on the preprocessed real-time operation data, and a data analysis result is obtained; formulating an optimal operation routing scheme of the rail transit according to a data analysis result; based on the optimal operation routing scheme and a rail transit system network model, obtaining an optimal vehicle allocation scheme through real-time analogue simulation; and according to the real-time operation data of the rail transit system and passenger feedback information, analyzing the operation condition of the rail transit system and the passenger demand change, and continuously optimizing the optimal operation routing scheme and the optimal vehicle allocation scheme. By optimizing the operation management of the urban rail transit system, the operation efficiency, the safety and the service level are improved, and more convenient and efficient travel experience is provided for residents.

Description

Technical Field

[0001] The present invention relates to the field of rail transit technology, and in particular to a transportation operation optimization method, system, device, storage medium and program product. Background Art

[0002] At present, the operation and management of urban rail transit systems are facing many challenges. The traditional rail transit operation and management system mainly relies on manual scheduling and experience judgment, and has the following main defects:

[0003] 1. Incomplete data collection: The existing system can usually only collect limited operational data (such as train operation status and passenger flow data of some stations), and lacks comprehensive collection and analysis of multi-dimensional data such as weather environment, track status, and station congestion.

[0004] 2. Inefficient data processing: The collected data often needs to be processed manually by staff, which is not only time-consuming and labor-intensive but also unable to provide accurate data support in real time.

[0005] 3. Unscientific dispatching decisions: Traditional manual dispatching relies on the dispatcher’s experience and intuition, lacks scientific data analysis and optimization model support, and is difficult to achieve refined operations management and resource optimization.

[0006] 4. Slow response: In the face of emergencies and peak periods, the existing system reacts slowly and is unable to quickly adjust the operation plan, resulting in increased waiting time for passengers and reduced service quality.

[0007] 5. Insufficient passenger services: The existing passenger information service system has single functions and is unable to provide passengers with detailed operating route information and information on temporarily added trains in a timely manner, affecting passengers' travel experience.

[0008] Therefore, there is an urgent need for a traffic operation optimization method and system to solve the defects of the above-mentioned existing rail transit operation management system. Summary of the invention

[0009] The purpose of the present invention is to provide a traffic operation optimization method, system, device, storage medium and program product to optimize the operation management of the urban rail transit system, improve the operation efficiency, safety and service level, and provide urban residents with a more convenient and efficient travel experience.

[0010] To achieve the above object, the present invention provides the following technical solutions:

[0011] In a first aspect, an embodiment of the present invention provides a traffic operation optimization method, comprising:

[0012] Collect real-time operation data of rail transit system and perform data preprocessing to obtain preprocessed real-time operation data;

[0013] Analyze passenger behavior patterns and station characteristics based on pre-processed real-time operation data to obtain data analysis results;

[0014] Formulate the optimal operation route plan for rail transit based on data analysis results;

[0015] Based on the optimal operation route plan and the rail transit system network model, an optimal vehicle deployment plan is obtained through real-time simulation;

[0016] According to the real-time operation data of the rail transit system and the passenger feedback information, the operation status of the rail transit system and the changes in passenger demand are analyzed, and the optimal operation route plan and the optimal vehicle deployment plan are continuously optimized.

[0017] In a second aspect, an embodiment of the present invention provides a traffic operation optimization system, the system comprising a real-time acquisition module, a route optimization module, a vehicle deployment optimization module and a continuous optimization module.

[0018] The real-time acquisition module is used to collect real-time operation data of the rail transit system and perform data preprocessing to obtain preprocessed real-time operation data;

[0019] The route optimization module is used to analyze passenger behavior patterns and station characteristics based on pre-processed real-time operation data to obtain data analysis results; and formulate an optimal operation route plan for rail transit based on the data analysis results;

[0020] The vehicle deployment optimization module is used to obtain the optimal vehicle deployment plan through real-time simulation based on the optimal operation route plan and the rail transit system network model;

[0021] The continuous optimization module is used to analyze the operation status of the rail transit system and changes in passenger demand based on the real-time operation data of the rail transit system and passenger feedback information, and continuously optimize the optimal operation route plan and the optimal vehicle deployment plan.

[0022] In a third aspect, an embodiment of the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program or instructions to implement the aforementioned traffic operation optimization method.

[0023] In a fourth aspect, an embodiment of the present invention further provides a computer storage medium, wherein a computer program or instruction is stored in the computer storage medium, and when the computer program or instruction is executed by a processor, the aforementioned traffic operation optimization method is implemented.

[0024] In a fifth aspect, an embodiment of the present invention further provides a computer program product, including a computer program or instructions, which implement the aforementioned traffic operation optimization method when executed by a processor.

[0025] The technical effects and advantages of the present invention are as follows: the operation data of the rail transit system is collected in real time through a variety of data collection devices and efficient data preprocessing is performed to ensure the accuracy and completeness of the collected data; by analyzing passenger needs and optimizing the operation route plan, reasonable scheduling of trains and effective allocation of vehicles are achieved, thereby improving the overall operation efficiency of the urban rail transit system; through the passenger service function, detailed operation route information and information on temporarily added trains are provided to passengers, thereby improving the travel experience of passengers and enhancing the service level of the urban rail transit system; through refined operation route optimization and vehicle deployment optimization, congestion and conflict during train operation can be reduced, the risk of accidents is reduced, and the safety and reliability of the urban rail transit system are improved; a continuous optimization mechanism is established, and the operation route plan can be adjusted and optimized in a timely manner by continuously monitoring the operation status and performing data analysis, thereby achieving continuous improvement and optimization of the optimization system of the present invention.

[0026] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0028] Figure 1 A flow chart of a traffic operation optimization method according to an embodiment of the present invention;

[0029] Figure 2 A schematic diagram of the structure of a traffic operation optimization system according to an embodiment of the present invention;

[0030] Figure 3 The figure is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] To address the deficiencies of the prior art, the present invention discloses a method for optimizing traffic operations. Figure 1 As shown, the following steps are included:

[0033] Step S1: collecting real-time operation data of the rail transit system, and performing data preprocessing to obtain preprocessed real-time operation data;

[0034] Step S2: analyzing passenger behavior patterns and station characteristics based on the pre-processed real-time operation data to obtain data analysis results;

[0035] Step S3: Formulate an optimal operation route plan for rail transit based on the data analysis results;

[0036] Step S4: Based on the optimal operation route plan and the rail transit system network model, an optimal vehicle deployment plan is obtained through real-time simulation;

[0037] Step S5: Analyze the operation status of the rail transit system and changes in passenger demand based on the real-time operation data of the rail transit system and passenger feedback information, and continuously optimize the optimal operation route plan and the optimal vehicle deployment plan.

[0038] In some specific embodiments, step S1: collecting real-time operation data of the rail transit system and performing data preprocessing to obtain preprocessed real-time operation data, specifically includes:

[0039] Step S11: construct a real-time data acquisition module, and use the real-time data acquisition module to collect real-time operation data of the urban rail transit system; wherein the real-time operation data includes: train operation status, surrounding environment data, station passenger flow data, track status data, station congestion data and weather environment data.

[0040] The real-time data acquisition module collects real-time operation data of the urban rail transit system, specifically including the following contents:

[0041] (1) A train data acquisition device is installed on a rail transit train, and the train data acquisition device includes a GPS positioning device, a speed sensor, an acceleration sensor and a camera; the GPS positioning device, the speed sensor and the acceleration sensor are used to monitor and collect the train running status in real time, and the camera is used to monitor and collect the surrounding environment data of the train in real time.

[0042] The train running status includes the real-time position of the train, the speed of the train, the acceleration of the train and the running time of the train.

[0043] The train running status is calculated by the following formula:

[0044]

[0045] Where x(t) represents the train displacement at time t, x0 represents the initial position of the train, v0 represents the initial speed of the train, and a represents the acceleration of the train.

[0046] (2) Obtaining station passenger flow data through the entry and exit gates at the station; the station passenger flow data includes the number of passengers entering and exiting the station and the time of passengers entering and exiting the station.

[0047] The station passenger flow data is calculated by the following formula:

[0048] P(t)=P in (t)-P out (t),

[0049] Where P(t) represents the number of passengers at the station at time t, P in (t) represents the number of passengers entering the station at time t, P out (t) is the number of passengers leaving the station at time t.

[0050] (3) Using track sensors and track maintenance recording systems to obtain track status data; the track status data includes track usage status and track maintenance information.

[0051] The track status data is calculated by the following formula:

[0052] σ(t)=f(S,M,T),

[0053] Where σ(t) represents the track state at time t, S represents the track wear, M represents the track maintenance information, and T represents the time factor.

[0054] (4) Use station data collection equipment in rail transit stations (specifically, cameras inside the station) to monitor and collect station congestion data in real time.

[0055] The station congestion data include the station passenger flow, station congestion, and station environment parameters, and the station environment parameters include station temperature and station humidity.

[0056] (5) Obtain weather and environmental data through an external data interface; wherein the weather and environmental data include weather conditions and holidays.

[0057] Step S12: The real-time data acquisition module sends the collected real-time operation data to the central data processing center through the data transmission channel; the central data processing center performs data preprocessing on the received real-time operation data to obtain the preprocessed real-time operation data; the central data processing center uses real-time database technology to store the preprocessed real-time operation data.

[0058] In particular, during the transmission of the real-time operation data, a wireless network (such as Wi-Fi, 4G / 5G) or a wired network (such as Ethernet) can be used for data transmission, and the transmission protocol can be MQTT, HTTP or WebSocket, etc.

[0059] Data preprocessing includes data cleaning, data fusion and data normalization; wherein, data cleaning includes denoising, outlier processing and missing value processing. The specific operations are as follows:

[0060] 1. Data cleaning:

[0061] (1) Denoising: Use filtering algorithms (such as Kalman filtering) to remove noise from real-time operational data;

[0062] The denoising formula is as follows:

[0063]

[0064] P k =(1-K k ) k-1 +Q,

[0065] In the formula, represents the real-time operation data state estimation at the current time k, K k represents the Kalman gain, z k Indicates the measured value, P k represents the estimation error covariance, Q and R represent the process noise covariance and measurement noise covariance.

[0066] (2) Outlier processing: Statistical methods (such as the 3σ principle) can be used to identify and remove outliers in real-time operational data.

[0067] (3) Missing value filling: Use interpolation or machine learning methods to fill missing values ​​in real-time operational data.

[0068] For example, a moving average filter can be used to smooth real-time operation data during data cleaning. The specific formula is as follows:

[0069]

[0070] In the formula, x i represents the original data (i.e. real-time operational data), x i ′ represents the smoothed data, m represents the window size, x i+j Represents data point x i The offset value; j represents the offset, that is, the relative position of each offset data point in the sliding window, j∈(-m,...,0,...m).

[0071] (4) Data fusion: Use weighted average method to integrate real-time operational data from different sources;

[0072] The data fusion formula is:

[0073]

[0074] In the formula, x fused represents the fused data, x b represents the raw data from different sources (i.e., real-time operational data), λ b It represents the weight corresponding to the original data from different sources, b represents the source type serial number of the original data, and n represents the number of source types of the original data.

[0075] (5) Data normalization: The Min-Max normalization method is used to normalize the real-time operation data to the [0,1] interval;

[0076] Among them, the data normalization formula is:

[0077]

[0078] In the formula, x” i represents the normalized data, x i represents the original data (i.e. real-time operational data), x min and x max Represent the minimum and maximum values ​​of the original data respectively.

[0079] The present invention collects the operation data of the rail transit system in real time through a variety of data collection devices and performs efficient data preprocessing, thereby ensuring the accuracy and integrity of the data.

[0080] In some specific embodiments, step S2: analyzing passenger behavior patterns and station characteristics based on pre-processed real-time operation data to obtain data analysis results; specifically:

[0081] The real-time data acquisition module sends the pre-processed real-time operation data to the route optimization module; the route optimization module uses the streaming processing technology Apache Kafka to analyze the passenger behavior patterns and station characteristics of the pre-processed real-time operation data, obtains data analysis results, and provides data support for optimizing route design.

[0082] The pre-processed real-time operation data is analyzed for passenger behavior patterns and station characteristics to obtain data analysis results, including the following:

[0083] (1) Based on the pre-processed real-time operation data, the K-means clustering algorithm is used to analyze the passenger groups, identify the behavior patterns of different passenger groups, and obtain the behavior pattern data of the passenger groups. The behavior pattern data of the passenger groups includes the station passenger flow data and the station congestion degree data;

[0084] The K-means clustering algorithm formula is:

[0085]

[0086] In the formula, C i' represents the i'th cluster, M represents the number of clusters, μ i' represents the center of the i'th cluster, and x represents the data point.

[0087] (2) Based on the preprocessed real-time operation data, the ARIMA model is used to perform time series analysis on passengers to predict the changing trend of station passenger flow data.

[0088] The station passenger flow data includes the number of passengers entering and leaving the station and the time of passengers entering and leaving the station;

[0089] The time series analysis formula is:

[0090] Y t' =φ1Y t'-1 +φ2Y t'-2 +…+φ p Y t'-p +θ1ε t'-1 +θ2ε t'-2 +…+θ q ε t'-q +ε t' ,

[0091] Where Y t' represents the time series value, φ and θ represent the model parameters, ε t'Represents white noise, t' represents the position at a specific moment in the data sequence, p represents the pth past observation value used to predict the current value, and q represents the qth past error term used to predict the current error.

[0092] (3) Based on the preprocessed real-time operation data, a multivariate linear regression model is used to analyze the factors affecting passenger flow and operation efficiency.

[0093] Among them, factors that affect passenger flow and operational efficiency include: weather and environmental data, station congestion data, and track status data;

[0094] The multiple linear regression model formula is:

[0095] Y=β0+β1X1+β2X2+…+β α X α +ε,

[0096] In the formula, Y represents the dependent variable, X α represents the αth independent variable, β α represents the αth regression coefficient, ε represents the error term, and α represents the number of independent variables.

[0097] The present invention analyzes passenger behavior patterns and station site characteristics, and uses advanced data analysis and optimization models (such as K-means clustering algorithm, ARIMA model and multivariate linear regression model, etc.) to formulate a scientific operation route optimization plan.

[0098] In some specific embodiments, step S3: formulating an optimal operation route plan for rail transit according to the data analysis results includes:

[0099] Step S31: Analyze and obtain passenger demand based on the data analysis results, and establish an operation route optimization model and its objective function according to the pre-processed real-time operation data and the passenger demand;

[0100] The objective function of the operation route optimization model is established with the goal of minimizing the waiting time of passengers or maximizing the utilization rate of trains.

[0101] Exemplarily, the objective function of the operation route optimization model is:

[0102]

[0103] Where, T c represents the waiting time of passengers at the cth station, represents the weight corresponding to the passenger waiting time at the cth station, and N represents the number of stations.

[0104] Step S32: using an optimization algorithm (such as a genetic algorithm or a simulated annealing algorithm) to solve the operation route optimization model to obtain an optimal operation route solution.

[0105] Step S33: constructing a traffic dispatching integration module, integrating the traffic dispatching integration module with the urban rail transit dispatching system, and realizing data sharing between the traffic dispatching integration module and the urban rail transit dispatching system;

[0106] Based on the optimal operating route plan and actual needs, the traffic dispatch integration module is used to dynamically adjust various traffic dispatch rules and constraints in the traffic dispatch rule library and apply them to actual route operations to meet the actual route operation scenarios in different time periods and different regions, and achieve further optimization of operating routes.

[0107] Wherein, the traffic dispatch integration module includes a traffic dispatch rule library, and the traffic dispatch rule library stores various traffic dispatch rules and constraints;

[0108] Various traffic dispatching rules and constraints include: train operation rules, station stop rules, train interval constraints and transfer connection rules;

[0109] (1) Train operation rules: refers to a series of regulations and restrictions that trains must comply with when operating in the urban rail transit system;

[0110] The train operation rules include speed constraints, safety spacing constraints and signal control constraints;

[0111] The speed constraints include: the maximum speed allowed for trains between different sections or stations;

[0112] The safety distance constraint includes: a safety distance between trains or a safety distance between a train and an obstacle;

[0113] The signal control constraints include: the train must follow the signal system and control instructions to ensure the safety and smoothness of the train operation.

[0114] (2) Station stop rules: refers to the specific operational constraints on train stops and door opening at different stations;

[0115] The station stop rules include stop time constraints, door opening mode constraints and station platform constraints;

[0116] The stop time constraints include: the shortest or longest time the train stays at each station;

[0117] The door opening mode constraints include: the time interval from the train door opening to the train door closing;

[0118] The station platform constraints include: utilizing the station platform space to maximize the efficiency of passengers getting on and off the train.

[0119] (3) Train spacing rules: refers to the constraints on maintaining safe distances between different trains in urban rail transit systems;

[0120] The train spacing rule includes a minimum safety spacing constraint and an ideal running spacing constraint;

[0121] The minimum safety interval constraint includes: the minimum safety interval between two trains in the same track section; preventing two trains from being too close to each other in the same track section to prevent collision or other safety accidents;

[0122] The ideal running interval constraint includes: determining the optimal running interval between trains according to the train running schedule and passenger demand to ensure efficient transportation service.

[0123] (4) Transfer connection rules: refers to the regulations or rules that must be followed for smooth transfers between different lines or different modes of transportation in the rail transit system;

[0124] The transfer connection rules include transfer time and space rules, transfer instructions and guidance rules;

[0125] The transfer time and space rules include: the shortest transfer time and the best transfer path for passengers from one train or line to another train or line;

[0126] The transfer instructions and guidance rules include: providing clear transfer information and guidance signs so that passengers can complete the transfer conveniently and quickly.

[0127] In some specific embodiments, step S4: based on the optimal operation route plan and the rail transit system network model, obtaining the optimal vehicle deployment plan through real-time simulation; specifically includes:

[0128] Step S41: Building a rail transit system network model based on the rail transit line information and rail transit station information; wherein the nodes in the rail transit system network model represent station sites, and the edges represent lines.

[0129] Step S42: Based on the optimal operation route plan and the rail transit system network model, simulation software (such as AnyLogic, SUMO) is used to simulate route operation in real time, and the effects of different vehicle deployment plans are analyzed to obtain simulation results. Finally, the optimal vehicle deployment plan is selected according to the simulation results.

[0130] Among them, the effects of different vehicle deployment schemes are analyzed to obtain simulation results, including:

[0131] A traffic flow prediction model is established based on the long short-term memory network LSTM; the traffic flow prediction model is used to predict the passenger flow of the optimal operation route plan with different vehicle deployment plans to analyze the effects of different vehicle deployment plans and obtain simulation results; wherein the formula of the long short-term memory network LSTM is:

[0132] f t =σ(W f ·[h t-1 ,x t ]+b f ),

[0133] i t =σ(W i ·[h t-1 ,x t ]+b i ),

[0134]

[0135] o t =σ(W o ·[h t-1 ,x t ]+b o ),

[0136] h t =o t ⊙tanh(c t ),

[0137] In the formula, f t represents the forget gate, i t represents the input gate, represents candidate memory, c t Represents a memory unit, o t represents the output gate, h t represents the hidden state, σ represents the Sigmoid activation function, W f , W i , W c and W o Both represent the weights of the forget gate, input gate, candidate memory, and output gate, h t-1 represents the hidden state at the previous time step t-1, x t represents the input of the current time step t; b f 、b i 、b c and b o represents bias, and tanh represents the Tanh activation function.

[0138] The present invention obtains the optimal vehicle deployment plan through simulation and optimization; for example, increasing the frequency of trains during peak hours to improve transportation capacity, and reducing the frequency during off-peak hours to save resources.

[0139] In some specific embodiments, step S5: based on the real-time operation data of the rail transit system and the passenger feedback information, by analyzing the operation status of the rail transit system and the changes in passenger demand, the optimal operation route plan and the optimal vehicle deployment plan are continuously optimized; specifically including:

[0140] Build a continuous optimization module and use it to collect real-time operation data and passenger feedback information of the rail transit system; then use data analysis and mining technology to analyze the operation of the urban rail transit system and changes in passenger demand to obtain demand analysis results;

[0141] Based on the demand analysis results, improvement plans and optimization strategies are proposed, and the optimal operating route plan and the optimal vehicle deployment plan are continuously optimized (or other modules are optimized and monitored) using the improvement plans and optimization strategies.

[0142] In some specific embodiments, the method of the present invention further comprises constructing an interactive visualization module;

[0143] The interactive visualization module provides a user interaction interface and visualization tools, and dispatchers and managers can monitor and manage the operation status and dispatching situation of the urban rail transit system in real time through the interactive visualization module;

[0144] The interactive visualization module can also display the real-time operation data of urban rail transit through station maps, and provide real-time alarm and prompt functions to help users quickly discover and solve problems.

[0145] In some specific embodiments, the method of the present invention further comprises a passenger service module;

[0146] The passenger service module notifies passengers of the operation route information of the rail transit system through various means (station broadcasts, digital display screens, mobile applications, etc.), including:

[0147] Real-time broadcasting of train operation information and information on additional trains will be provided through the station's broadcasting system.

[0148] The digital display screens installed at stations and on trains display real-time train operation information, station arrival times, and temporary additional trains;

[0149] The mobile application provides functions such as train operation information query, real-time arrival reminder, and notification of temporary additional trains.

[0150] For example, the notification information example:

[0151] Information of the temporarily added train: Train number: 1234, departure station: Station A, destination station: Station B, expected arrival time: 14:30.

[0152] The embodiment of the present invention discloses a traffic operation optimization system, such as Figure 2 As shown, the system includes a real-time acquisition module, a route optimization module, a vehicle deployment optimization module and a continuous optimization module.

[0153] The real-time acquisition module is used to collect real-time operation data of the rail transit system and perform data preprocessing to obtain preprocessed real-time operation data;

[0154] The route optimization module is used to analyze passenger behavior patterns and station characteristics based on pre-processed real-time operation data to obtain data analysis results; and formulate an optimal operation route plan for rail transit based on the data analysis results;

[0155] The vehicle deployment optimization module is used to obtain the optimal vehicle deployment plan through real-time simulation based on the optimal operation route plan and the rail transit system network model;

[0156] The continuous optimization module is used to analyze the operation status of the rail transit system and changes in passenger demand based on the real-time operation data of the rail transit system and passenger feedback information, and continuously optimize the optimal operation route plan and the optimal vehicle deployment plan.

[0157] In some specific embodiments, the system further includes a traffic dispatch integration module, an interactive visualization module and a passenger service module.

[0158] The traffic dispatching integration module is integrated with the rail transit dispatching system to realize data sharing between the traffic dispatching integration module and the rail transit dispatching system; the traffic dispatching integration module is used to dynamically adjust various traffic dispatching rules and constraints based on the optimal operation route plan and actual needs to meet the actual route operation scenarios in different time periods and different regions;

[0159] The traffic dispatch integration module includes a traffic dispatch rule base, and the traffic dispatch rule base includes various traffic dispatch rules and constraints.

[0160] The interactive visualization module is used to display the real-time operation data of rail transit through the station map and provide real-time alarm and prompt functions.

[0161] The passenger service module is used to inform passengers of the operating route information of the rail transit system and the temporary addition of trains in various ways;

[0162] The operation route information includes train routes, train timetables, train stops, etc. The operation route information can help passengers understand the operation status of the train;

[0163] Information on the temporary addition of trains can help passengers adjust their travel plans in a timely manner in special circumstances (such as peak hours or emergencies).

[0164] In some specific embodiments, the system further includes a performance evaluation module.

[0165] The performance evaluation module is used to set and monitor the performance indicators of the traffic operation optimization system of the present invention, and can indirectly reflect the operation status of the urban rail transit system.

[0166] The performance indicators include passenger waiting time, transportation efficiency, transfer convenience and operating cost;

[0167] Passenger waiting time refers to the average waiting time for passengers from entering the station to boarding the train;

[0168] Transport efficiency refers to the volume of passengers transported per unit time;

[0169] Transfer convenience refers to the time and convenience of passengers’ transfers;

[0170] Operating costs refer to the dispatching costs of vehicles and personnel.

[0171] Therefore, a performance index formula is established, and the performance index formula mainly includes a passenger waiting time formula and a transportation efficiency formula;

[0172] The formula for passenger waiting time is:

[0173]

[0174] Where, T wait represents the average waiting time, N represents the number of passengers, t i board represents the boarding time of the i-th passenger, t i entry represents the time when the i-th passenger enters the station;

[0175] The transportation efficiency formula is:

[0176]

[0177] In the formula, E transport represents the transport efficiency, P total represents the total number of transported passengers, T total Indicates the total operating time.

[0178] Regarding the system in the above embodiment, the specific manner in which each unit module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0179] Based on the same inventive concept, an embodiment of the present invention further provides an electronic device, whose structure is as follows: Figure 3 As shown, it includes a memory, a processor and a computer program stored in the memory, and the processor executes the computer program or instructions to implement the aforementioned traffic operation optimization method.

[0180] Based on the same inventive concept, an embodiment of the present invention further provides a computer storage medium, in which a computer program or instruction is stored. When the computer program or instruction is executed by a processor, the aforementioned traffic operation optimization method is implemented.

[0181] Based on the same inventive concept, an embodiment of the present invention further provides a computer program product, including a computer program or instructions, which implement the aforementioned traffic operation optimization method when executed by a processor.

[0182] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A traffic operation optimization method, characterized in that: include: Collect real-time operation data of rail transit system and perform data preprocessing to obtain preprocessed real-time operation data; Analyze passenger behavior patterns and station characteristics based on pre-processed real-time operation data to obtain data analysis results; Formulate the optimal operation route plan for rail transit based on data analysis results; Based on the optimal operation route plan and the rail transit system network model, an optimal vehicle deployment plan is obtained through real-time simulation; According to the real-time operation data of the rail transit system and the passenger feedback information, the operation status of the rail transit system and the changes in passenger demand are analyzed, and the optimal operation route plan and the optimal vehicle deployment plan are continuously optimized.

2. A traffic operation optimization method according to claim 1, characterized in that: Collect real-time operational data of rail transit systems, including: The real-time operation data includes: train operation status, surrounding environment data, station passenger flow data, track status data, station congestion data and weather environment data; Use the train data collection equipment on rail transit trains to monitor and collect train operation status and surrounding environment data in real time; Obtain station passenger flow data through the entry and exit gates at the station site; Acquire track condition data using track sensors and track maintenance recording systems; Use station data collection equipment in rail transit stations to monitor and collect station congestion data in real time; Obtain weather and environmental data through external data interfaces; The train data collection equipment includes a GPS positioning device, a speed sensor, an acceleration sensor and a camera, and the train running status includes the real-time position of the train, the train speed, the train acceleration and the train running time; Station passenger flow data include the number of passengers entering and leaving the station and the time of passengers entering and leaving the station; Track status data includes track usage status and track maintenance information; The station congestion data includes the station passenger flow, the station congestion situation, and the station environment parameters, wherein the station environment parameters include the station temperature and the station humidity; Weather environment data includes weather conditions and holidays.

3. A traffic operation optimization method according to claim 1 or 2, characterized in that: And perform data preprocessing to obtain preprocessed real-time operation data, including: Send the collected real-time operation data to the central data processing center through the data transmission channel; The central data processing center performs data preprocessing on the received real-time operation data to obtain preprocessed real-time operation data; The central data processing center uses real-time database technology to store pre-processed real-time operation data; Among them, data preprocessing includes data cleaning, data fusion and data normalization, and the data cleaning includes denoising, outlier processing and missing value processing.

4. A traffic operation optimization method according to claim 1, characterized in that: Analyze passenger behavior patterns and station characteristics based on pre-processed real-time operational data to obtain data analysis results, including: Based on the pre-processed real-time operation data, the K-means clustering algorithm is used to analyze passenger groups and identify the behavior patterns of different passenger groups; Based on the pre-processed real-time operation data, the ARIMA model is used to conduct time series analysis on passengers to predict the changing trend of station passenger flow data; Based on the preprocessed real-time operation data, the multivariate linear regression model is used to analyze the factors affecting passenger flow and operation efficiency.

5. A traffic operation optimization method according to claim 1, characterized in that: Develop the optimal operation route plan for rail transit based on the data analysis results, including: Obtaining passenger demand based on the data analysis results, and establishing an operation route optimization model and its objective function according to the pre-processed real-time operation data and the passenger demand; Using an optimization algorithm to solve the operation route optimization model to obtain an optimal operation route plan; The objective function of the operation route optimization model is established with the goal of minimizing the waiting time of passengers or maximizing the utilization rate of trains.

6. A method for optimizing traffic operations according to claim 5, characterized in that: The method further comprises constructing a traffic dispatch integration module, Based on the optimal operation route plan, the traffic dispatch integration module is used to dynamically adjust various traffic dispatch rules and constraints in the traffic dispatch rule library according to actual needs to meet the actual route operation scenarios in different time periods and different regions.

7. A method for optimizing traffic operations according to claim 1, characterized in that: Based on the optimal operation route plan and the rail transit system network model, the optimal vehicle deployment plan is obtained through real-time simulation, including: Establish a rail transit system network model based on rail transit line information and rail transit station information; Based on the optimal operation route plan and the rail transit system network model, the route operation is simulated in real time, and the effects of different vehicle deployment plans of the optimal operation route plan are analyzed to obtain simulation results; Analyze the simulation results and select the optimal vehicle deployment plan.

8. A traffic operation optimization method according to claim 7, characterized in that: Analyze the effect of the optimal operation route plan with different vehicle deployment plans to obtain simulation results, including: Establish a traffic flow prediction model based on long short-term memory network LSTM; The traffic flow prediction model is used to predict the passenger flow of the optimal operation route scheme with different vehicle allocation schemes, so as to analyze the effects of different vehicle allocation schemes of the optimal operation route scheme and obtain simulation results; Among them, the formula of long short-term memory network LSTM is: f t =σ(W f ·[h t-1 ,x t ]+b f ), i t =σ ( W i ·[h t-1 ,x t ]+b i ), the t =σ(W o ·[h t-1 ,x t ]+b o ), h t =o t ⊙tanh(c t ), In the formula, f t represents the forget gate, i t represents the input gate, represents candidate memory, c t Represents a memory unit, o t represents the output gate, h t represents the hidden state, σ represents the Sigmoid activation function, W f , W i , W c and W o Both represent the weights of the forget gate, input gate, candidate memory, and output gate, h t-1 represents the hidden state at the previous time step t-1, x t represents the input of the current time step t; b f 、b i 、b c and b o represents bias, and tanh represents the Tanh activation function.

9. A traffic operation optimization system, characterized in that: The system includes a real-time acquisition module, a route optimization module, a vehicle deployment optimization module and a continuous optimization module. The real-time acquisition module is used to collect real-time operation data of the rail transit system and perform data preprocessing to obtain preprocessed real-time operation data; The route optimization module is used to analyze passenger behavior patterns and station characteristics based on pre-processed real-time operation data to obtain data analysis results; Formulate the optimal operation route plan for rail transit based on data analysis results; The vehicle deployment optimization module is used to obtain the optimal vehicle deployment plan through real-time simulation based on the optimal operation route plan and the rail transit system network model; The continuous optimization module is used to analyze the operation status of the rail transit system and changes in passenger demand based on the real-time operation data of the rail transit system and passenger feedback information, and continuously optimize the optimal operation route plan and the optimal vehicle deployment plan.

10. A traffic operation optimization system according to claim 9, characterized in that: The system also includes a traffic dispatch integration module, an interactive visualization module and a passenger service module. The traffic dispatching integration module is integrated with the rail transit dispatching system to realize data sharing between the traffic dispatching integration module and the rail transit dispatching system; The traffic dispatch integration module is used to dynamically adjust various traffic dispatch rules and constraints based on the optimal operation route plan and actual needs to meet the actual route operation scenarios in different time periods and different regions; The interactive visualization module is used to display the real-time operation data of rail transit through the station map and provide real-time alarm and prompt functions; The passenger service module is used to inform passengers of the operating route information of the rail transit system and the temporary addition of trains in various ways; The traffic dispatch integration module includes a traffic dispatch rule base, and the traffic dispatch rule base includes various traffic dispatch rules and constraints.

11. An electronic device, characterized in that: It comprises a memory, a processor and a computer program stored in the memory, and the processor executes the computer program or instructions to implement a traffic operation optimization method as described in any one of claims 1-8.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program or instruction, and when the computer program or instruction is executed by a processor, a traffic operation optimization method according to any one of claims 1 to 8 is implemented.

13. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, a traffic operation optimization method as described in any one of claims 1-8 is implemented.

Images