CN117495204B - Urban bus running efficiency evaluation method and system based on data analysis - Google Patents
Urban bus running efficiency evaluation method and system based on data analysis Download PDFInfo
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Abstract
The invention relates to the technical field of public transportation management, in particular to a method and a system for evaluating urban public transportation operation efficiency based on data analysis, which improve the capability of comprehensively evaluating and finely optimizing the urban public transportation operation efficiency; the method comprises the following steps: collecting operation data of urban bus lines in an evaluation time period; dividing the operation data in the evaluation time period according to preset time windows, and calculating efficiency evaluation indexes in each time window, wherein the efficiency evaluation indexes comprise running speed, departure frequency, arrival time rate, passenger capacity, vehicle residence time and passenger getting-on and getting-off time; carrying out sequential adjustment on each efficiency evaluation index in different time windows according to time to obtain a bus running efficiency characteristic matrix; evaluating a bus running efficiency characteristic matrix by using a pre-constructed bus running efficiency evaluation model to obtain a bus running efficiency evaluation index; and acquiring traffic jam information in the evaluation time period.
Description
Technical Field
The invention relates to the technical field of bus transportation management, in particular to a city bus running efficiency evaluation method and system based on data analysis
Background
Urban buses are taken as important components of urban traffic, the running efficiency of the urban buses directly influences the smoothness of the urban traffic and the traveling experience of passengers, and the evaluation and optimization of the running efficiency of the urban buses are one of important tasks of urban traffic management.
The traditional bus running efficiency evaluation method is mainly based on manual observation and statistical data, and has the problems of strong subjectivity, incomplete data, poor real-time performance and the like; meanwhile, the existing evaluation method only considers single efficiency evaluation indexes such as running speed, departure frequency and the like, and influences of traffic jams on bus running efficiency are ignored. Therefore, there is a need for an urban bus operation efficiency assessment method based on data analysis.
Disclosure of Invention
In order to solve the technical problems, the invention provides the urban public transportation operation efficiency assessment method based on data analysis, which improves the capability of comprehensively assessing and finely optimizing the urban public transportation operation efficiency.
In a first aspect, the present invention provides a method for evaluating urban bus running efficiency based on data analysis, the method comprising:
collecting operation data of urban bus lines in an evaluation time period;
Dividing operation data in an evaluation time period according to preset time windows, and calculating efficiency evaluation indexes in each time window, wherein the efficiency evaluation indexes comprise running speed, departure frequency, arrival time rate, passenger capacity, vehicle residence time and passenger getting-on and getting-off time;
carrying out sequential adjustment on each efficiency evaluation index in different time windows according to time to obtain a bus running efficiency characteristic matrix;
Evaluating a bus running efficiency characteristic matrix by using a pre-constructed bus running efficiency evaluation model to obtain a bus running efficiency evaluation index;
Acquiring traffic jam information in an evaluation time period;
Collecting bus running efficiency evaluation indexes in historical data and traffic congestion information in the historical data, and carrying out correlation analysis on the bus running efficiency evaluation indexes in the historical data and the traffic congestion information in a corresponding historical time period to obtain correlation coefficient comparison tables between different traffic congestion information and different bus running efficiency evaluation indexes;
Traversing a correlation coefficient comparison table according to traffic congestion information in an evaluation time period, and determining a corresponding correlation coefficient in the evaluation time period;
And calculating the corrected bus running efficiency evaluation index of the urban bus in the evaluation time period according to the bus running efficiency evaluation index in the evaluation time period and the corresponding correlation coefficient in the evaluation time period.
Further, the method for acquiring the bus route operation data comprises the following steps:
Installing a GPS device on a bus, and collecting position data of the bus in real time, wherein the position data comprises the position, the speed and the direction of the bus;
Acquiring data related to bus operation, including vehicle state and intersection signal lamp information, by using an intelligent traffic management system of a city;
the passenger counter is used for monitoring the number of passengers on the bus in real time and evaluating the passenger carrying capacity and the passenger getting-on and getting-off time;
Acquiring user feedback and real-time bus position data by using a mobile application program;
acquiring health status and performance data of a vehicle using a vehicle diagnostic system;
And the encryption technology and the anonymization method are used for processing information, so that the data privacy is ensured, and the safety of the data is ensured.
Further, the construction method of the bus running efficiency feature matrix comprises the following steps:
Aiming at the efficiency evaluation indexes calculated in each time window, carrying out time series adjustment on the efficiency evaluation indexes, and arranging the efficiency evaluation indexes according to the time sequence;
Processing the efficiency evaluation index by using a sliding window technology, and capturing operation characteristics on different time scales;
Periodically adjusting the efficiency evaluation index by calculating the relative change in each time window;
Normalizing and standardizing the serialized efficiency evaluation index;
The efficiency evaluation indexes subjected to serialization adjustment are combined to form a feature matrix, each column represents a specific efficiency evaluation index, and each row represents a time window.
Further, the method for constructing the bus running efficiency evaluation model comprises the following steps:
Collecting historical bus operation data, and preprocessing the collected bus operation data, wherein the preprocessing comprises processing missing values and abnormal values;
Carrying out characteristic engineering on the original data;
selecting a machine learning model for evaluation, including a regression model, a decision tree model, a support vector machine, and a neural network;
Training the model by using the historical data; dividing the data set into a training set and a verification set, training the model with the training set, and evaluating the performance of the model using the verification set;
selecting an evaluation index to measure the performance of the model, wherein the evaluation index comprises a mean square error and a root mean square error;
After model training is completed, the model is deployed into an actual running environment; and taking the bus running efficiency characteristic matrix as the input of a model, and taking the bus running efficiency evaluation index as the output of the model.
Further, the traffic congestion information acquisition method comprises the following steps:
using a traffic monitoring camera to collect real-time traffic conditions; analyzing the camera images through a computer vision technology, identifying the density and flow of vehicles on the road, and judging the traffic jam degree;
The vehicle track data is used for acquiring real-time position information of buses and other vehicles through a GPS device, analyzing the movement speed of the vehicles and deducing the traffic jam condition;
and using unmanned aerial vehicles and satellite data to monitor urban traffic at high altitude, and providing large-scale traffic jam information.
Further, the correlation coefficient comparison table obtaining method includes:
Acquiring a bus running efficiency evaluation index and traffic jam information in an evaluation time period from historical data;
Cleaning and processing the collected data;
taking the bus running efficiency evaluation index as a dependent variable, taking traffic jam information as a dependent variable, obtaining a correlation coefficient through statistical analysis, and reflecting the association degree between the bus running efficiency evaluation index and the traffic jam information;
And the obtained correlation coefficients are arranged into a correlation coefficient comparison table, and the comparison table shows the influence of traffic jams with different degrees on various indexes of the bus running efficiency.
Further, the setting influence factors of the time window include traffic fluidity, bus running speed, bus departure frequency, station density, station spacing and accuracy of data acquisition equipment.
On the other hand, the application also provides an urban public transportation operation efficiency evaluation system based on data analysis, which comprises:
the data acquisition module is used for collecting and transmitting the operation data of the urban bus line in the evaluation time period;
The data processing module is used for receiving the operation data in the evaluation time period, dividing the operation data in the evaluation time period according to preset time windows, calculating an efficiency evaluation index in each time window and transmitting the efficiency evaluation index; the efficiency evaluation indexes comprise running speed, departure frequency, arrival time rate, passenger capacity, vehicle residence time and passenger getting-on and getting-off time;
The characteristic matrix construction module is used for receiving the efficiency evaluation indexes in each time window, carrying out sequential adjustment on each efficiency evaluation index in different time windows according to time, obtaining a bus running efficiency characteristic matrix, and sending the bus running efficiency characteristic matrix;
The operation efficiency evaluation module is used for receiving the bus operation efficiency characteristic matrix, evaluating the bus operation efficiency characteristic matrix by utilizing a pre-constructed bus operation efficiency evaluation model, obtaining a bus operation efficiency evaluation index and transmitting the bus operation efficiency evaluation index;
The congestion information acquisition module is used for acquiring and transmitting traffic congestion information in the evaluation time period;
The correlation analysis module is used for collecting bus running efficiency evaluation indexes in the historical data and traffic congestion information in the historical data, carrying out correlation analysis on the bus running efficiency evaluation indexes in the historical data and the traffic congestion information in the corresponding historical time period, obtaining correlation coefficient comparison tables between different traffic congestion information and different bus running efficiency evaluation indexes, and sending the correlation coefficient comparison tables;
The correlation coefficient determining module is used for receiving the traffic congestion information and the correlation coefficient comparison table in the evaluation time period, traversing the traffic congestion information in the evaluation time period to the correlation coefficient comparison table, determining the corresponding correlation coefficient in the evaluation time period and transmitting the correlation coefficient;
the result output module is used for receiving the bus running efficiency evaluation index in the evaluation time period and the corresponding correlation coefficient in the evaluation time period, and calculating the corrected bus running efficiency evaluation index of the urban bus in the evaluation time period according to the bus running efficiency evaluation index in the evaluation time period and the corresponding correlation coefficient in the evaluation time period.
In a third aspect, the present application provides an electronic device comprising a bus, a transceiver, a memory, a processor and a computer program stored on the memory and executable on the processor, the transceiver, the memory and the processor being connected by the bus, the computer program when executed by the processor implementing the steps of any of the methods described above.
In a fourth aspect, the application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of any of the methods described above.
Compared with the prior art, the invention has the beneficial effects that: according to the method, a large amount of bus operation data can be objectively collected through data acquisition and processing, and a plurality of efficiency evaluation indexes are calculated, so that the objectivity and the comprehensiveness of evaluation are improved, and the problems of strong subjectivity and incomplete data in the traditional method are avoided; based on the analysis of the real-time data, more accurate and timely bus running efficiency evaluation can be provided;
According to the method, a single efficiency evaluation index is considered, and a bus running efficiency characteristic matrix is constructed through sequential adjustment of the efficiency evaluation index; the influence of different factors on the bus running efficiency can be comprehensively considered, and the accuracy of evaluation is improved; the method considers the influence of traffic jam on the bus running efficiency more comprehensively; by acquiring traffic jam information and carrying out correlation analysis with the bus running efficiency evaluation index, how the traffic jam affects the bus running efficiency can be more accurately understood, and corresponding correction is carried out; by collecting the bus running efficiency evaluation index and the traffic jam information in the historical data and carrying out correlation analysis, a correlation coefficient comparison table can be established; the system can consider past experience in the evaluation, so that the reliability and stability of the evaluation are improved; the finally output corrected bus running efficiency evaluation index provides more accurate information for a decision maker, so that transportation management and optimization decision can be carried out more pertinently;
In summary, the method improves the capability of comprehensively evaluating and finely optimizing the running efficiency of the urban buses by comprehensively considering a plurality of factors including real-time data, traffic jam information and historical data.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a flow chart of a method for constructing a bus running efficiency evaluation model;
fig. 3 is a block diagram of an urban bus running efficiency evaluation system based on data analysis.
Detailed Description
In the description of the present application, those skilled in the art will appreciate that the present application may be embodied as methods, apparatus, electronic devices, and computer-readable storage media. Accordingly, the present application may be embodied in the following forms: complete hardware, complete software (including firmware, resident software, micro-code, etc.), a combination of hardware and software. Furthermore, in some embodiments, the application may also be embodied in the form of a computer program product in one or more computer-readable storage media, which contain computer program code.
Any combination of one or more computer-readable storage media may be employed by the computer-readable storage media described above. The computer-readable storage medium includes: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of the computer readable storage medium include the following: portable computer magnetic disks, hard disks, random access memories, read-only memories, erasable programmable read-only memories, flash memories, optical fibers, optical disk read-only memories, optical storage devices, magnetic storage devices, or any combination thereof. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, device.
The technical scheme of the application obtains, stores, uses, processes and the like the data, which all meet the relevant regulations of national laws.
The application provides a method, a device and electronic equipment through flow charts and/or block diagrams.
It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions. These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in a computer readable storage medium that can cause a computer or other programmable data processing apparatus to function in a particular manner. Thus, instructions stored in a computer-readable storage medium produce an instruction means which implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
The present application will be described below with reference to the drawings in the present application.
Embodiment one: as shown in fig. 1 to 2, the urban bus running efficiency evaluation method based on data analysis of the invention specifically comprises the following steps:
s1, collecting operation data of urban bus lines in an evaluation time period;
the method for acquiring the bus line operation data comprises the following steps:
S11, installing a GPS device on a bus, and collecting position data of the bus in real time, wherein the position data comprise the position, the speed and the direction of the bus;
S12, acquiring data related to bus operation, including vehicle state and intersection signal lamp information, by using an intelligent traffic management system of a city;
s13, monitoring the number of passengers on the bus in real time by using a passenger counter, and evaluating the passenger capacity and the passenger getting-on and getting-off time;
s14, acquiring user feedback and real-time bus position data by using a mobile application program;
S15, acquiring health state and performance data of a vehicle by using a vehicle diagnosis system;
S16, information is processed by using an encryption technology and an anonymization method, so that data privacy is ensured, and data safety is ensured.
In the step, the GPS device is used for collecting the position, speed and direction information of the vehicle, so that the real-time position of the vehicle can be monitored, and the dispatching accuracy and efficiency are improved; the intelligent traffic management system provides vehicle state data and traffic light information, is beneficial to optimizing route planning, reducing congestion and improving the overall transportation efficiency; the number of passengers on the vehicle can be known in real time through the passenger counter, and the passenger capacity and the time for passengers to get on or off the vehicle can be estimated, so that reasonable capacity allocation is carried out, and the service level of the passengers is improved;
Through the mobile application, the passenger can provide feedback, which helps to understand passenger demand, improve quality of service, and adjust the transportation strategy in time; the real-time bus position data is provided, so that passengers can know the arrival time of the vehicles, the traveling experience of the passengers is enhanced, and the traveling efficiency is improved; the vehicle diagnosis system monitors the vehicle health state and performance data, is beneficial to preventing faults and maintaining in advance, and ensures the safety and reliability of the vehicle operation; the encryption technology and the anonymization method ensure the data privacy and safety, prevent sensitive information from revealing, and simultaneously obey relevant privacy regulations and protect the integrity and safety of users and system data;
in summary, the operation efficiency of the public transportation system can be optimized, the service quality of passengers can be improved, the operation safety of vehicles can be enhanced, the user experience can be improved, and the data privacy and safety can be guaranteed.
S2, dividing the operation data in the evaluation time period according to preset time windows, and calculating efficiency evaluation indexes in each time window, wherein the efficiency evaluation indexes comprise running speed, departure frequency, arrival time rate, passenger capacity, vehicle residence time and passenger getting-on and getting-off time;
In S2, the setting of the time window is a key step for evaluating the running efficiency of the urban bus, and the reasonable setting of the time window can capture the change of the running of the bus more accurately, so as to ensure the accuracy of the evaluation index, and the setting influence factors of the time window include:
A. the traffic fluidity, the traffic characteristics of different urban areas are different, and the city center and suburban areas are different, so that the size of a time window is adjusted according to the fluidity of a specific road section, and a shorter time window is considered to be used in an area with high congestion degree;
B. The bus running speed, if the bus speed changes more frequently, selecting a shorter time window to reflect the fluctuation of the running speed in a finer granularity; conversely, if the vehicle speed is relatively stable, a longer time window is selected;
C. The bus departure frequency can be changed due to different time periods, and the shorter time window can better capture the change of the departure frequency, but the complexity of data processing is increased;
D. station density, in the area with higher station density, the indexes of vehicle stay time and passenger capacity change more frequently, so a shorter time window is needed;
E. The distance between stations, if the distance between stations is larger, the running speed and the residence time of the vehicle are more stable, and a relatively longer time window can be selected;
F. The accuracy of the data acquisition equipment can select a shorter time window if the high-frequency data acquisition equipment is used, so that the change of bus operation can be more accurately captured;
the calculation method of the efficiency evaluation index comprises the following steps:
s21, driving speed is obtained by calculating the average speed of the vehicle in each time window;
s22, departure frequency, and departure times in each time window;
S23, calculating the arrival time rate, namely calculating whether the vehicle arrives at a station according to a preset moment in each time window;
S24, passenger capacity, and the average number of passengers in each time window;
s25, vehicle stay time, and average stay time of the vehicle at the station in each time window;
s26, passenger getting on/off time, and the times and average time of getting on/off of passengers in each time window.
In the step, the time window is set by considering the factors such as traffic fluidity, bus running speed, departure frequency and the like, so that the time-space change of urban traffic can be reflected more accurately, and the capturing of traffic conditions and dynamic changes of bus running in different time periods is facilitated; multiple efficiency evaluation indexes are considered, so that evaluation is more comprehensive, the running condition of the bus system can be known in depth, and the evaluation is not limited to evaluation of a single index; according to the characteristics of different urban areas, the size of the time window is flexibly adjusted, so that the assessment method is more suitable for areas with different topological structures and different densities, and the universality and the adaptability of the assessment method can be improved; by dividing the data according to time windows, the information of the bus running efficiency can be obtained in real time, timely and effective data support is provided for urban traffic management and decision making, and problems such as traffic jam can be rapidly solved;
By calculating a plurality of indexes, comprehensive images of the bus running efficiency can be obtained, and a more scientific optimization strategy is facilitated to be formulated by an urban traffic manager so as to improve the overall efficiency and the service quality of a bus system; by collecting historical data and comparing the historical data with the current data, deeper analysis can be performed, long-term trend and periodic change of bus running efficiency are identified, future traffic conditions are further predicted, and urban traffic planning and management are further guided;
In summary, the step S2 establishes a comprehensive, real-time and flexible urban bus running efficiency evaluation system, and provides more powerful data support for urban traffic management.
S3, carrying out sequential adjustment on each efficiency evaluation index in different time windows according to time to obtain a bus running efficiency feature matrix;
The construction method of the bus running efficiency feature matrix comprises the following steps:
s31, aiming at the efficiency evaluation indexes calculated in each time window, carrying out time series adjustment on the efficiency evaluation indexes, and arranging the efficiency evaluation indexes according to the time sequence to ensure that the data in the feature matrix can reflect the time change trend;
S32, processing the efficiency evaluation index by using a sliding window technology, capturing operation characteristics on different time scales, and calculating statistical information in each window by sliding a window with a fixed size on a time sequence to serve as the characteristics after serialization;
s33, periodically adjusting the efficiency evaluation index by calculating the relative change in each time window;
s34, normalizing and standardizing the serialized efficiency evaluation indexes to ensure that the efficiency evaluation indexes have similar numerical ranges, and avoiding deviation caused by different data scales in subsequent model evaluation;
S35, combining the efficiency evaluation indexes subjected to serialization adjustment to form a feature matrix, wherein each column represents a specific efficiency evaluation index, and each row represents a time window.
In the step, the efficiency evaluation indexes are arranged according to the time sequence, so that the data in the feature matrix can reflect the time change trend, the operation condition of the bus system in different time periods can be conveniently analyzed and understood, and the possible daily, seasonal or annual changes are captured; by utilizing the sliding window technology, the characteristics of bus operation can be captured on different time scales, so that operation fluctuation in a short period can be found, and more comprehensive operation state information can be provided;
The periodic adjustment of the efficiency evaluation index is realized by calculating the relative change, so that the system is better adapted to the periodic change in the operation of the public transportation system, and the evaluation result is ensured to be more accurate and close to reality; normalizing and standardizing the efficiency evaluation indexes to ensure that the indexes have similar numerical ranges, eliminating deviation caused by different measurement scales, and enabling different indexes to be compared and comprehensively analyzed on the same scale; the efficiency evaluation indexes after serialization and adjustment are combined into a feature matrix, the subsequent data analysis and modeling are more convenient due to the structural matrix form, and clear input is provided for model evaluation;
In summary, the step enables the constructed bus running efficiency feature matrix to have more practicability and interpretability, provides a reliable basis for further analysis and decision, and the data-based method can evaluate the running condition of the urban bus system more comprehensively and objectively and provides scientific basis for urban traffic management.
S4, evaluating a bus running efficiency characteristic matrix by using a pre-constructed bus running efficiency evaluation model to obtain a bus running efficiency evaluation index;
the method for constructing the bus running efficiency evaluation model comprises the following steps:
S41, collecting historical bus operation data, and preprocessing the collected bus operation data, wherein the preprocessing comprises processing missing values and abnormal values;
S42, performing feature engineering on the original data, and converting the original data into a feature set suitable for model training;
s43, selecting a machine learning model to evaluate, wherein the machine learning model comprises a regression model, a decision tree model, a support vector machine and a neural network;
S44, training a model by using the historical data; dividing the data set into a training set and a verification set, training the model with the training set, and evaluating the performance of the model using the verification set;
S45, selecting an evaluation index to measure the performance of the model, wherein the evaluation index comprises a mean square error and a root mean square error;
S46, after model training is completed, deploying the model training into an actual running environment; and taking the bus running efficiency characteristic matrix as the input of a model, and taking the bus running efficiency evaluation index as the output of the model.
In the step, a plurality of evaluation indexes are combined, so that the comprehensiveness of evaluation is improved; the model established based on the historical data can more accurately predict the bus running efficiency, provide more reliable data support for decision makers, and guide the decision makers to formulate reasonable management and optimization strategies;
The evaluation model can evaluate the bus running efficiency in real time, so that targeted optimization can be implemented more quickly, and the bus running condition can be improved in a short time; through correlation analysis in the historical data, the relation between different traffic congestion information and the bus running efficiency evaluation index can be found, so that the influence of traffic congestion on a bus system can be better understood; the constructed model can be deployed in an actual running environment, new data are continuously evaluated, and real-time running efficiency evaluation is provided; selecting a proper machine learning model, and using proper evaluation indexes to ensure the accuracy of the model, wherein the feature engineering and the data preprocessing in the model construction process ensure the interpretability and the robustness of the model;
in summary, the step provides a comprehensive and data-driven evaluation framework, so that the bus running condition can be more comprehensively understood, the method is favorable for fine management and timely adjustment of the operation strategy, and the overall efficiency and the service quality of the urban bus system are improved.
S5, acquiring traffic jam information in an evaluation time period;
S5, acquiring traffic jam information in an evaluation time period, wherein the traffic jam has a remarkable influence on the bus running efficiency, and acquiring the traffic jam information by adopting real-time traffic data and historical traffic data is a common technical scheme in the field of bus transportation management; the traffic jam information acquisition method comprises the following steps:
s51, acquiring real-time traffic conditions by using a traffic monitoring camera; analyzing the camera images through a computer vision technology, and identifying the density and flow of vehicles on the road so as to judge the traffic jam degree;
S52, vehicle track data, namely acquiring real-time position information of buses and other vehicles through a GPS device, wherein the position information is used for analyzing the movement speed of the vehicles so as to infer traffic jam conditions;
and S53, using unmanned aerial vehicles and satellite data to monitor urban traffic at high altitude, wherein a high-altitude data source can provide traffic jam information in a larger range.
In the step, through the integration and use of the traffic monitoring camera, the vehicle track data and the unmanned aerial vehicle and satellite data, real-time and comprehensive traffic jam information can be obtained, and the application of the comprehensive data can reflect the traffic condition in the evaluation time period more accurately, so that the real-time traffic condition can be mastered;
The camera images are analyzed by utilizing a computer vision technology, so that the density and the flow of vehicles on a road and the running state of the vehicles can be accurately identified, and the traffic jam condition can be accurately judged; the real-time position information of buses and other vehicles can be provided by the vehicle track data acquired by the GPS device, so that the movement speed of the vehicles is analyzed, and the traffic jam degree can be inferred more accurately; the unmanned aerial vehicle and satellite data provide high-altitude monitoring, so that traffic jam information of a wider area can be provided, the high-altitude visual angle can acquire the whole condition of traffic jam in the urban range, and the urban traffic jam condition can be more comprehensively known;
The richness of the data dimension can be increased by combining the information acquired by the different data sources; the condition of traffic jam can be more comprehensively mastered by comprehensively analyzing various data, so that the bus running efficiency in the evaluation time period can be accurately evaluated; the comprehensive and accurate traffic jam information is acquired, so that urban traffic managers and planners can formulate more effective bus running optimization schemes; the data support can guide and adjust strategies such as bus lines, departure frequency and the like so as to cope with different traffic jam conditions and improve bus running efficiency;
In summary, the step can enable the public transportation management department to optimize public transportation operation more pertinently so as to provide more efficient and smoother service, and simultaneously provide more scientific data support for urban traffic planning and management.
S6, collecting bus running efficiency evaluation indexes in historical data and traffic congestion information in the historical data, and carrying out correlation analysis on the bus running efficiency evaluation indexes in the historical data and the traffic congestion information in a corresponding historical time period to obtain correlation coefficient comparison tables between different traffic congestion information and different bus running efficiency evaluation indexes;
In the S6 step, collecting the bus running efficiency evaluation index in the historical data and the traffic jam information in the historical data, and then carrying out correlation analysis so as to establish the correlation between different traffic jam information and the bus running efficiency evaluation index, and further deducing the influence degree of the traffic jam on the bus running efficiency; the correlation coefficient comparison table acquisition method comprises the following steps:
s61, acquiring a bus running efficiency evaluation index and traffic jam information in an evaluation time period from historical data;
s62, cleaning and processing the collected data to ensure the accuracy and consistency of the data;
S63, performing correlation analysis by using a statistical method, and quantifying linear and nonlinear relations between two groups of variables by calculating correlation coefficients; taking the bus running efficiency evaluation index as a dependent variable, taking traffic jam information as the independent variable, obtaining a correlation coefficient through statistical analysis, and reflecting the correlation degree between the two coefficients;
S64, the obtained correlation coefficients are arranged into a correlation coefficient comparison table, and the table can show the influence of traffic jams with different degrees on various indexes of bus running efficiency.
In the step, the influence degree of traffic jams with different degrees on the bus running efficiency evaluation index can be quantified through correlation analysis; the specific contribution of the traffic jam to each index is further determined, so that a manager can more comprehensively know the actual influence of the traffic jam on a public transport system; the influence of different traffic jam conditions on the bus running is known, and the bus scheduling strategy can be optimized; the manager can adjust departure frequency, route planning and the like according to actual conditions so as to improve the running efficiency of the public transport system to the greatest extent;
The correlation coefficient comparison table provides actual data support for decision making; based on the data, a more scientific and accurate traffic policy and a public transportation system optimization scheme are formulated so as to improve the overall efficiency of urban traffic; by arranging the correlation analysis result into a correlation coefficient comparison table, the accurate adjustment of different time periods can be realized; under the condition of different traffic jams, the bus running efficiency is more accurately estimated and adjusted, and the timeliness and the accuracy of decision making are improved; the correlation coefficient comparison table can provide powerful support for urban planning; in urban planning, traffic is a key factor, and the roles of public traffic in an urban traffic system can be better considered through a data driving method, so that more intelligent and sustainable urban development is realized;
in summary, the step S6 is beneficial to improving understanding of influencing factors of bus running efficiency, providing data support for urban traffic management, and promoting more scientific and fine bus system operation and urban planning decisions;
s7, traversing a correlation coefficient comparison table according to traffic jam information in an evaluation time period, and determining a corresponding correlation coefficient in the evaluation time period;
In the S7 step, traversing a correlation coefficient comparison table according to traffic congestion information in an evaluation time period, and determining a corresponding correlation coefficient in the evaluation time period so as to more accurately reflect the influence of the traffic congestion on the bus running efficiency; the following is a detailed description of step S7:
s71, aiming at the real-time traffic congestion situation in each evaluation time period, traversing Shi Xiangguan coefficient comparison tables to find out the correlation coefficient which is most matched with the current congestion situation;
S72, adopting a real-time updating strategy to cope with the situation of the traffic condition at any time; when the real-time congestion condition changes, the new correlation coefficient can be timely adjusted and applied;
In actual operation, the accuracy and applicability of the selected correlation coefficient are verified, necessary calibration and adjustment are carried out on the algorithm, and the accuracy and predictability of the evaluation index are improved.
In the step, by traversing the correlation coefficient comparison table for the real-time traffic congestion situation in each evaluation time period, the correlation coefficient which is most matched with the current congestion situation can be more accurately found; such precision improvement is helpful for more truly reflecting the actual influence of traffic jams on the bus running efficiency; the real-time updating strategy is adopted, and new correlation coefficients are timely adjusted and applied, so that the assessment method can flexibly cope with the change of traffic conditions at any time; the evaluation result is more practical, and the dynamic change of the urban traffic environment can be more effectively dealt with;
The accuracy and the applicability of the selected correlation coefficient are verified in actual operation, necessary calibration and adjustment are carried out on the algorithm, the accuracy and the predictability of the evaluation index are improved, and the reliability and the stability of the evaluation method are ensured; through a history correlation coefficient comparison table, comprehensively considering the relationship between the bus running efficiency evaluation index and congestion information in history data, and improving the understanding of the evaluation method on the urban traffic evolution trend;
In summary, the step S7 has the advantages that the evaluation method has better real-time performance, adaptability and accuracy, thereby better serving the evaluation and optimization of the urban bus running efficiency;
S8, calculating and obtaining the corrected bus running efficiency evaluation index of the urban bus in the evaluation time period according to the bus running efficiency evaluation index in the evaluation time period and the corresponding correlation coefficient in the evaluation time period;
Step S8 aims at correcting the bus running efficiency evaluation index in the evaluation time period according to the traffic congestion information and the correlation coefficient comparison table so as to more accurately reflect the influence of the traffic congestion on the bus running efficiency; the following is a detailed description of S8:
S81, for the bus running efficiency evaluation index of each time window, corresponding correlation coefficients are applied to carry out correction, and the specific calculation mode is as follows:
corrected evaluation index = original evaluation index x correlation coefficient;
the original evaluation index is adjusted by considering the actual influence of traffic jam, so that the original evaluation index is more in line with the actual running condition;
s82, for each time window, obtaining a final bus running efficiency evaluation index through the corrected evaluation index;
and S83, summarizing the corrected evaluation indexes of all the time windows to form an evaluation index of the bus running efficiency in the evaluation time period.
In the step, the influence of traffic jam on actual operation is considered by correcting the bus operation efficiency evaluation index, so that the evaluation is more accurate and comprehensive, and the evaluation index is more close to the actual situation; the corrected evaluation index can provide more reliable data support for urban traffic management and planning; government departments and traffic managers can make decision-making according to the more accurate data, optimize bus routes, departure frequency and operation strategies, so as to cope with traffic jams and promote the traveling experience of passengers;
by considering traffic jam factors, the evaluation index more comprehensively reflects the actual condition of bus operation; the manager can better know the bus running conditions of different time periods or road sections, and a more targeted scheme is provided for solving the problems and improving the service; the correlation analysis of the traffic jam information in the historical data and the running efficiency index can help to predict the possible bus running condition in the future; the method provides data support for long-term traffic planning, and is helpful for formulating long-term public traffic development strategies;
In summary, the step S8 provides more accurate and comprehensive bus running efficiency evaluation, provides more reliable data support for decision makers, helps optimize urban bus systems, and provides reasonable reference basis for future planning;
embodiment two: as shown in fig. 3, the urban bus running efficiency evaluation system based on data analysis of the invention specifically comprises the following modules;
the data acquisition module is used for collecting and transmitting the operation data of the urban bus line in the evaluation time period;
The data processing module is used for receiving the operation data in the evaluation time period, dividing the operation data in the evaluation time period according to preset time windows, calculating an efficiency evaluation index in each time window and transmitting the efficiency evaluation index; the efficiency evaluation indexes comprise running speed, departure frequency, arrival time rate, passenger capacity, vehicle residence time and passenger getting-on and getting-off time;
The characteristic matrix construction module is used for receiving the efficiency evaluation indexes in each time window, carrying out sequential adjustment on each efficiency evaluation index in different time windows according to time, obtaining a bus running efficiency characteristic matrix, and sending the bus running efficiency characteristic matrix;
The operation efficiency evaluation module is used for receiving the bus operation efficiency characteristic matrix, evaluating the bus operation efficiency characteristic matrix by utilizing a pre-constructed bus operation efficiency evaluation model, obtaining a bus operation efficiency evaluation index and transmitting the bus operation efficiency evaluation index;
The congestion information acquisition module is used for acquiring and transmitting traffic congestion information in the evaluation time period;
The correlation analysis module is used for collecting bus running efficiency evaluation indexes in the historical data and traffic congestion information in the historical data, carrying out correlation analysis on the bus running efficiency evaluation indexes in the historical data and the traffic congestion information in the corresponding historical time period, obtaining correlation coefficient comparison tables between different traffic congestion information and different bus running efficiency evaluation indexes, and sending the correlation coefficient comparison tables;
The correlation coefficient determining module is used for receiving the traffic congestion information and the correlation coefficient comparison table in the evaluation time period, traversing the traffic congestion information in the evaluation time period to the correlation coefficient comparison table, determining the corresponding correlation coefficient in the evaluation time period and transmitting the correlation coefficient;
the result output module is used for receiving the bus running efficiency evaluation index in the evaluation time period and the corresponding correlation coefficient in the evaluation time period, and calculating the corrected bus running efficiency evaluation index of the urban bus in the evaluation time period according to the bus running efficiency evaluation index in the evaluation time period and the corresponding correlation coefficient in the evaluation time period.
The system can collect wide operation data through the data acquisition and processing module, and calculate a plurality of evaluation indexes, the objectivity and the comprehensiveness of evaluation are improved by multi-dimensional data consideration, and the problem of subjectivity and incomplete data in the traditional method is avoided; based on the collection and processing of real-time data, the system can provide more accurate and timely operation efficiency evaluation, and can take necessary adjustment and optimization measures more quickly;
The traffic jam factors are taken into consideration through the jam information acquisition module and the correlation analysis module, so that the bus running efficiency can be estimated more accurately, and corresponding correction is carried out in the estimation;
The finally output corrected bus running efficiency evaluation index can provide powerful support for urban traffic management, and a decision maker can more intelligently formulate improvement measures based on the evaluation result of data so as to optimize the bus running efficiency and improve the smoothness of urban traffic and the traveling experience of passengers;
In summary, the system utilizes the data analysis method to comprehensively consider a plurality of evaluation indexes and traffic jam factors, provides a more objective and comprehensive evaluation means for urban traffic management, provides a scientific basis for decision making, and is beneficial to optimizing urban bus running efficiency.
The various variations and specific embodiments of the urban bus running efficiency evaluation method based on data analysis in the first embodiment are equally applicable to the urban bus running efficiency evaluation system based on data analysis in this embodiment, and by the foregoing detailed description of the urban bus running efficiency evaluation method based on data analysis, those skilled in the art can clearly know the implementation method of the urban bus running efficiency evaluation system based on data analysis in this embodiment, so that the details of this embodiment will not be described in detail herein for the sake of brevity of description.
In addition, the application also provides an electronic device, which comprises a bus, a transceiver, a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the transceiver, the memory and the processor are respectively connected through the bus, and when the computer program is executed by the processor, the processes of the method embodiment for controlling output data are realized, and the same technical effects can be achieved, so that repetition is avoided and redundant description is omitted.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that it will be apparent to those skilled in the art that modifications and variations can be made without departing from the technical principles of the present invention, and these modifications and variations should also be regarded as the scope of the invention.
Claims (9)
1. The urban bus running efficiency evaluation method based on data analysis is characterized by comprising the following steps of:
collecting operation data of urban bus lines in an evaluation time period;
Dividing operation data in an evaluation time period according to preset time windows, and calculating efficiency evaluation indexes in each time window, wherein the efficiency evaluation indexes comprise running speed, departure frequency, arrival time rate, passenger capacity, vehicle residence time and passenger getting-on and getting-off time;
carrying out sequential adjustment on each efficiency evaluation index in different time windows according to time to obtain a bus running efficiency characteristic matrix;
Evaluating a bus running efficiency characteristic matrix by using a pre-constructed bus running efficiency evaluation model to obtain a bus running efficiency evaluation index;
Acquiring traffic jam information in an evaluation time period;
Collecting bus running efficiency evaluation indexes in historical data and traffic congestion information in the historical data, and carrying out correlation analysis on the bus running efficiency evaluation indexes in the historical data and the traffic congestion information in a corresponding historical time period to obtain correlation coefficient comparison tables between different traffic congestion information and different bus running efficiency evaluation indexes;
Traversing a correlation coefficient comparison table according to traffic congestion information in an evaluation time period, and determining a corresponding correlation coefficient in the evaluation time period;
According to the bus running efficiency evaluation index in the evaluation time period and the corresponding correlation coefficient in the evaluation time period, calculating to obtain the corrected bus running efficiency evaluation index of the urban bus in the evaluation time period;
the method for constructing the bus running efficiency evaluation model comprises the following steps:
Collecting historical bus operation data, and preprocessing the collected bus operation data, wherein the preprocessing comprises processing missing values and abnormal values;
Carrying out characteristic engineering on the original data;
Selecting a machine learning model for evaluation, including any one of a regression model, a decision tree model, a support vector machine, and a neural network;
Training the model by using the historical data; dividing the data set into a training set and a verification set, training the model with the training set, and evaluating the performance of the model using the verification set;
selecting an evaluation index to measure the performance of the model, wherein the evaluation index comprises a mean square error and a root mean square error;
After model training is completed, the model is deployed into an actual running environment; and taking the bus running efficiency characteristic matrix as the input of a model, and taking the bus running efficiency evaluation index as the output of the model.
2. The urban bus running efficiency evaluation method based on data analysis according to claim 1, wherein the bus line running data acquisition method comprises:
Installing a GPS device on a bus, and collecting position data of the bus in real time, wherein the position data comprises the position, the speed and the direction of the bus;
Acquiring data related to bus operation, including vehicle state and intersection signal lamp information, by using an intelligent traffic management system of a city;
the passenger counter is used for monitoring the number of passengers on the bus in real time and evaluating the passenger carrying capacity and the passenger getting-on and getting-off time;
Acquiring user feedback and real-time bus position data by using a mobile application program;
acquiring health status and performance data of a vehicle using a vehicle diagnostic system;
And the encryption technology and the anonymization method are used for processing information, so that the data privacy is ensured, and the safety of the data is ensured.
3. The urban bus running efficiency evaluation method based on data analysis according to claim 1, wherein the method for constructing the bus running efficiency feature matrix comprises the following steps:
Aiming at the efficiency evaluation indexes calculated in each time window, carrying out time series adjustment on the efficiency evaluation indexes, and arranging the efficiency evaluation indexes according to the time sequence;
Processing the efficiency evaluation index by using a sliding window technology, and capturing operation characteristics on different time scales;
Periodically adjusting the efficiency evaluation index by calculating the relative change in each time window;
Normalizing and standardizing the serialized efficiency evaluation index;
The efficiency evaluation indexes subjected to serialization adjustment are combined to form a feature matrix, each column represents a specific efficiency evaluation index, and each row represents a time window.
4. The urban bus running efficiency evaluation method based on data analysis according to claim 1, wherein the traffic congestion information acquisition method comprises:
using a traffic monitoring camera to collect real-time traffic conditions; analyzing the camera images through a computer vision technology, identifying the density and flow of vehicles on the road, and judging the traffic jam degree;
The vehicle track data is used for acquiring real-time position information of buses and other vehicles through a GPS device, analyzing the movement speed of the vehicles and deducing the traffic jam condition;
and using unmanned aerial vehicles and satellite data to monitor urban traffic at high altitude, and providing large-scale traffic jam information.
5. The urban bus running efficiency evaluation method based on data analysis according to claim 1, wherein the correlation coefficient comparison table acquisition method comprises:
Acquiring a bus running efficiency evaluation index and traffic jam information in an evaluation time period from historical data;
Cleaning and processing the collected data;
taking the bus running efficiency evaluation index as a dependent variable, taking traffic jam information as a dependent variable, obtaining a correlation coefficient through statistical analysis, and reflecting the association degree between the bus running efficiency evaluation index and the traffic jam information;
And the obtained correlation coefficients are arranged into a correlation coefficient comparison table, and the comparison table shows the influence of traffic jams with different degrees on various indexes of the bus running efficiency.
6. The method for evaluating urban bus running efficiency based on data analysis according to claim 1, wherein the time window setting influencing factors include traffic fluidity, bus running speed, bus departure frequency, station density, station spacing and accuracy of data acquisition equipment.
7. An urban bus running efficiency evaluation system based on data analysis, the system comprising:
the data acquisition module is used for collecting and transmitting the operation data of the urban bus line in the evaluation time period;
The data processing module is used for receiving the operation data in the evaluation time period, dividing the operation data in the evaluation time period according to preset time windows, calculating an efficiency evaluation index in each time window and transmitting the efficiency evaluation index; the efficiency evaluation indexes comprise running speed, departure frequency, arrival time rate, passenger capacity, vehicle residence time and passenger getting-on and getting-off time;
The characteristic matrix construction module is used for receiving the efficiency evaluation indexes in each time window, carrying out sequential adjustment on each efficiency evaluation index in different time windows according to time, obtaining a bus running efficiency characteristic matrix, and sending the bus running efficiency characteristic matrix;
The operation efficiency evaluation module is used for receiving the bus operation efficiency characteristic matrix, evaluating the bus operation efficiency characteristic matrix by utilizing a pre-constructed bus operation efficiency evaluation model, obtaining a bus operation efficiency evaluation index and transmitting the bus operation efficiency evaluation index;
The congestion information acquisition module is used for acquiring and transmitting traffic congestion information in the evaluation time period;
The correlation analysis module is used for collecting bus running efficiency evaluation indexes in the historical data and traffic congestion information in the historical data, carrying out correlation analysis on the bus running efficiency evaluation indexes in the historical data and the traffic congestion information in the corresponding historical time period, obtaining correlation coefficient comparison tables between different traffic congestion information and different bus running efficiency evaluation indexes, and sending the correlation coefficient comparison tables;
The correlation coefficient determining module is used for receiving the traffic congestion information and the correlation coefficient comparison table in the evaluation time period, traversing the traffic congestion information in the evaluation time period to the correlation coefficient comparison table, determining the corresponding correlation coefficient in the evaluation time period and transmitting the correlation coefficient;
The result output module is used for receiving the bus running efficiency evaluation index in the evaluation time period and the corresponding correlation coefficient in the evaluation time period, and calculating to obtain the corrected bus running efficiency evaluation index of the urban bus in the evaluation time period according to the bus running efficiency evaluation index in the evaluation time period and the corresponding correlation coefficient in the evaluation time period;
the method for constructing the bus running efficiency evaluation model comprises the following steps:
Collecting historical bus operation data, and preprocessing the collected bus operation data, wherein the preprocessing comprises processing missing values and abnormal values;
Carrying out characteristic engineering on the original data;
Selecting a machine learning model for evaluation, including any one of a regression model, a decision tree model, a support vector machine, and a neural network;
Training the model by using the historical data; dividing the data set into a training set and a verification set, training the model with the training set, and evaluating the performance of the model using the verification set;
selecting an evaluation index to measure the performance of the model, wherein the evaluation index comprises a mean square error and a root mean square error;
After model training is completed, the model is deployed into an actual running environment; and taking the bus running efficiency characteristic matrix as the input of a model, and taking the bus running efficiency evaluation index as the output of the model.
8. Urban bus operation efficiency assessment electronic device based on data analysis, comprising a bus, a transceiver, a memory, a processor and a computer program stored on the memory and executable on the processor, the transceiver, the memory and the processor being connected by the bus, characterized in that the computer program when executed by the processor implements the steps of the method according to any of claims 1-6.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method according to any of claims 1-6.
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