WO2018023331A1 - System and method for real-time evaluation of service index of regular public buses - Google Patents

Abstract

A system and method for real-time evaluation of a service index of regular public buses. The system comprises a traffic information collection platform, a traffic public information platform, a traffic simulation platform and a traffic information service platform. The traffic public information platform provides the traffic information collection platform, the traffic information service platform and the traffic simulation platform with operation support and information services. The traffic information collection platform provides original data to the traffic public information platform. The traffic simulation platform provides simulation result data to the traffic public information platform. The traffic information service platform organizes, according to the data services provided by the traffic public information platform, a function requirement of the traffic information collection platform into an information collection use-case packet. The method dynamically provides in real time operation conditions and evolving trends of regular public transport systems to government departments, industries and enterprises, and public transportation users, providing important commercial and social value.

Description

A conventional bus service index real-time evaluation system and evaluation method Technical field

The present application relates to the technical field of conventional public transportation systems, and in particular relates to a conventional bus service index real-time evaluation system and an evaluation method.

Background technique

In the period of development of new urbanization construction, with the acceleration of urbanization and the rapid development of urban motorized travel, the demand for urban roads has grown rapidly, and traffic congestion, security, and pollution problems have become increasingly prominent, and have become a bottleneck for social and economic development. As the main part of urban transportation, conventional public transportation is an artery that guarantees the normal operation of urban production and life. It is an important infrastructure for improving the comprehensive functions of cities. It is the development of various industries in the city, the prosperity of economic, social and cultural undertakings, and intercity. The connection with urban and rural areas plays an important role in promoting and promoting. Through the comprehensive evaluation of the urban public transportation system, the gap between the current situation of public transport services and social needs can be clearly recognized, so as to adjust the structure of the bus industry, further improve the service level, and promote the construction of new urbanization.

The Urban Conventional Bus Service Index is an important basis for the optimization and comprehensive evaluation of urban public transportation systems. It is a comprehensive study of the existing public transportation system layout, analysis of its characteristics, evaluation of its rationality, and summarization of its experience for the future public transportation system. Adjustment and optimization provide a scientific and reasonable decision-making basis.

For a long time, the public transport service index in the urban transportation field is usually carried out by issuing a traffic travel questionnaire to the public, summarizing the statistical analysis and sorting out the questionnaire, and publishing the bus service index. However, this method has problems such as long questionnaires, high labor costs, insufficient questionnaires and sampling ratios, inaccurate questionnaire answers, and inability to conduct real-time dynamic evaluation of regular bus services.

Summary of the invention

The invention provides a conventional bus service index real-time evaluation system and an evaluation method, aiming at solving at least one of the above technical problems in the prior art to some extent.

In order to solve the above problems, the present invention provides the following technical solutions:

A conventional bus service index real-time evaluation system, comprising a traffic information collecting platform, a traffic public information platform, a traffic simulation platform and a traffic information service platform; the traffic public information platform is oriented to a traffic information collecting platform, a traffic information service platform and a traffic simulation platform Providing operation support and information service; the traffic information collection platform provides original data for the traffic public information platform; the traffic simulation platform provides simulation result data for the traffic public information platform; the traffic information service platform is provided by the transportation public information platform The data service organizes the functional requirements of the traffic information collection platform in the information collection use case package.

The technical solution adopted by the embodiment of the present invention further includes: the traffic information collecting platform collects the point and line traffic state in the road network through the fixed point and the floating car detecting device, and processes the traffic running state data collected in real time, Store the results on the transportation public information platform.

The technical solution adopted by the embodiment of the present invention further includes: collecting, by the fixed point and the floating vehicle detecting device, the point and line traffic state in the road network by the traffic information collecting platform, including: abnormal data FCD rejection, vehicle speed calculation, FCD Data fusion and prediction, standard and historical data statistics; wherein the raw data FCD abnormal rejection includes: receiving FCD data, determining whether the data is valid, and obtaining valid FCD data.

The technical solution adopted by the embodiment of the present invention further includes: the FCD data fusion and prediction specifically includes: calculating a road speed of the road segment based on the FCD data based on the FCD vehicle speed calculation model; and calculating the current vehicle speed based on the data fusion model according to the flow rate, the location speed, and the travel speed; The vehicle speed is predicted based on the current speed based on the vehicle speed prediction model.

The technical solution adopted by the embodiment of the present invention further includes: the FCD data fusion includes fusion of real-time data and historical data, fusion of floating car data and fixed-point detector data, and the real-time data and calendar The fusion of historical data uses linear transformation, fuzzy algorithm, calibration of different weights and membership degrees, and obtains more accurate values; the fusion of the floating vehicle data and the fixed-point detector data is based on the respective characteristics of two different information sources. Heterogeneous data is isomorphized and the same parameters are fused to obtain highly reliable results.

The technical solution adopted by the embodiment of the present invention further includes: the FCD data collection specifically includes: the GetFCD is connected to the remote detector through the connect FCD Service method of the FCD Connect, and the collection frequency is set by the setTime method of the Timer; the Time class starts to pass the check. Method self-test, when the time of arrival begins to collect FCD data; GetFCD obtains the original FCD data through the Serve Handle and the remote FCD service connection, and puts the data into the OriginalVFD class through setOriginalFCD; filters the string of the original FCD data through the filter method, Get the valid FCD data into the Validated FCD, and save the valid fixed point data for the day to complete the FCD data collection.

The technical solution adopted by the embodiment of the present invention further includes: the traffic public information platform is responsible for data fusion, data dictionary, data mining based decision support, data service, and data maintenance.

Another technical solution adopted by the embodiment of the present invention is: a method for real-time evaluation of a conventional bus service index, comprising:

Step a: Obtain traffic big data and use the principal component analysis method to obtain an evaluation index system for the conventional bus service level;

Step b: Quantify the regular bus service index for real-time traffic big data evaluation according to the evaluation index system of the conventional bus service level;

Step c: Establishing a real-time index of the conventional bus service index in real time based on the quantified conventional bus service index;

Step d: collecting real-time traffic big data, and processing the collected real-time traffic big data;

Step e: According to the traffic big data, the conventional bus service index and the actual collected data types and attributes are released in real time, and relevant feature extraction of the main data is established; according to the extracted data related features, the traffic big data is published to the conventional bus service index.

The technical solution adopted by the embodiment of the present invention further includes: in the step d, processing the collected real-time traffic big data, including: establishing a data processing rule; establishing a data analysis mining process and a model algorithm supported by the road network; establishing a bus IC Card data analysis mining process and model algorithm; establishing data analysis mining process and model algorithm for regular bus travel; establishing data analysis mining process and model algorithm for taxi travel; establishing track, bus, rental, IC card database search engine; establishing Database group analysis and mining environment interface.

The technical solution adopted by the embodiment of the present invention further includes: in the step d, the collecting real-time traffic big data collects the point and line traffic state in the road network through the fixed point and the floating vehicle detecting device.

Compared with the prior art, the beneficial effects produced by the embodiments of the present invention are:

1. The conventional bus service index real-time evaluation system and evaluation method according to the embodiment of the present invention closely track the core problem of urban public transportation travel as an entry point, and is a revolutionary test of the traditional questionnaire-type evaluation of the conventional bus service index in the era of big data. Challenge; it overcomes the traditional questionnaire-based evaluation of conventional bus data static, long cycle, single-sided, statistically cumbersome and other drawbacks, through urban traffic big data modeling analysis and relevance research, real-time dynamic for government departments, industry enterprises Public travel in real time to release the operational status and evolution of the conventional public transport system has important commercial and social values.

Second, the invention can save the transit time and travel cost of urban public transportation, improve the effectiveness and convenience of public travel, and can generate both direct benefits and indirect benefits;

Third, the invention can realize the value-added service and comprehensive service of urban public transportation information, and generate the commercial value and economic benefit of the public transportation travel chain.

DRAWINGS

1 is a schematic structural diagram of a conventional bus service index real-time evaluation system according to an embodiment of the present invention;

2 is a FCC vehicle speed fusion and prediction process of a conventional bus service index real-time evaluation system according to an embodiment of the present invention;

3 is a FCD data fusion model diagram of a conventional bus service index real-time evaluation system according to an embodiment of the present invention;

4 is a sequence diagram of FCD data acquisition of a conventional bus service index real-time evaluation system according to an embodiment of the present invention;

5 is a flow chart of a method for real-time evaluation of a conventional bus service index according to an embodiment of the present invention;

Figure 6 is a diagram showing the correspondence between the indicators of the general public transportation system comprehensive evaluation system and the real-time release of the conventional bus service index evaluation indicators;

Figure 7 is a flow chart of data collection, cleaning, mining, and summary rules.

detailed description

The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It is understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

Please refer to FIG. 1 , which is a schematic structural diagram of a conventional bus service index real-time evaluation system according to an embodiment of the present invention. The conventional bus service index real-time evaluation system according to the embodiment of the present invention comprises a traffic information communication and transmission network, a traffic information collection platform, a traffic public information platform, a traffic simulation platform and a traffic information service platform.

The traffic public information platform is the core of the whole system, providing operational support and information services for the traffic information collection platform, traffic information service platform and traffic simulation platform; the traffic information collection platform provides the original data for the traffic public information platform; the traffic simulation platform is the traffic common The information platform provides simulation result data; the traffic information service platform relies on the data service provided by the traffic public information platform, and organizes the functional requirements of the traffic information collection platform in the information collection use case package, and the functional requirements of the traffic public information platform are organized in the common information use case package. The functional requirements of the traffic simulation platform are organized in the simulation use case package, and the functional requirements of the traffic information service are organized in the information service use case package.

The traffic information collection platform collects the point and line traffic status in the road network through fixed point and floating vehicle detection equipment (FCD), and combines it as the basic data of the whole system. It is responsible for collecting real-time traffic operation status data and processing it, and stores the result in the traffic public information platform. Therefore, the function is divided into two parts: traffic information collection and screening processing. In addition, the traffic information collection platform also monitors the status of external field devices and data collection. Based on the above analysis, the traffic information collection function is organized in the traffic information collection service use case package, the processing service use case package, and the information management service use case package.

1) Traffic information The core algorithms involved in FCD acquisition include seven key algorithms such as raw data FCD anomaly rejection algorithm, vehicle speed calculation, fusion and prediction algorithm, and statistical algorithms for standard and historical data (flow and vehicle speed). These algorithms realize the conversion of raw data from the field to the data of the “Urban Integrated Traffic Information Platform”, which is the key to ensuring the reliability of the “information platform” and even the entire system. The following table lists the seven core algorithms in traffic information collection, and the algorithms are described in detail in the form of pseudocode. In view of the limited space, this invention patent "real-time dynamic release of conventional bus service index and system" only introduces the most relevant FCD data related algorithms and fusion processing applications.

Traffic information collection core algorithm list

2) FCD data fusion analysis and processing

In the FCD collection information, according to the data information collected by the external field, calculate the current section speed and comprehensively collect information to predict the 15 minutes and 30 minutes of the vehicle speed. The vehicle speed fusion prediction process is divided into three processes:

· Calculate the travel speed of the road segment based on the FCD data based on the FCD vehicle speed calculation model.

• Calculate the current vehicle speed based on the data fusion model based on traffic, location speed, and travel speed.

• Forecast the vehicle speed based on the current speed based on the vehicle speed prediction model.

The core idea of this invention patent algorithm is based on multi-source traffic information and traffic flow theory, synthesizing various data information, and realizing the judgment and description of traffic state. The whole process is shown in Figure 2.

The main task of FCD data fusion is to perform data level fusion on current traffic data, FCD calculation speed and location speed. Here, data fusion is mainly divided into two aspects: one is the fusion of real-time data and historical data, using linear transformation, fuzzy algorithm, calibrating different weights and membership degrees, and obtaining more accurate values; on the other hand, floating cars The fusion of data and fixed-point detector data, based on the respective characteristics of two different information sources, through the isomorphic data isomorphization process, the same parameters are fused, and the results with higher credibility are obtained. Its fusion model is shown in Figure 3.

In the flow-rate relationship model, the data is homogenized by the flow-closed relationship model. The flow-speed relationship model is as follows (Edie multi-segment model):

Where Q is the flow rate, v _s is the speed, and the others are the model parameters.

According to the physical characteristics of the object at a certain time, the measurement device obtains n measured values z _i (i=1, 2, . . . , n) in one cycle, due to comprehensive consideration of transmission error, calculation error, environment Noise, human interference, the accuracy of the sensor itself, and the nature of the object being measured, z _i will not strictly follow the normal distribution, so some data processing methods based on the data as a normal distribution model (outliers and data fusion) Algorithms will inevitably increase the systematic error of data processing. Thus the extent of the authenticity of the measurement data can, x _2, ..., x _n x ₁ is determined by the data itself, i.e., the higher the authenticity of x _i, x _i is supported by the rest of the data on the degree of The higher, the so-called x _i is supported by x _j , that is, the degree to which the data x _i is real data is seen from the data x _j . The concept of relative distance is introduced here for the degree of support between data, and the relative distance between the measured data is defined as d _ij , which is expressed as follows:

d _ij =|x _i -x _j |,i,j=1,2,...,n

D _ij is apparent from the form of the expression, d _ij indicates that the larger the larger the difference between the two data, i.e., the smaller the degree of mutual support between the two data. The definition of relative distance is based on the implicit information of existing data, which reduces the requirements for a priori information. In turn, a support function r _ij can be defined, and r _ij itself should satisfy the following two conditions:

· r _ij should be inversely proportional to the relative distance;

r _ij ∈(0,1) enables the processing of data to take advantage of the membership functions in fuzzy set theory, avoiding the absolute degree of mutual support between data.

Then the support function r _{ij is} defined as:

Where max{d _ij } represents the maximum value among the relative distances between data, it is obvious that the greater the relative distance between data, the smaller the support between data will be from the definition of the above formula, when the relative distance between data is the largest When the value is considered, the two data can no longer support each other, then the value of the support function is zero; and the smaller the relative distance between the data, the greater the mutual support between the data, the relative distance of the data to itself. If it is zero, the data supports itself to 1. Since r _ij takes values from 1 to 0 in d _ij ∈[0,max{d _ij }], the properties that the support function should have are satisfied. Moreover, the definition form satisfying the ambiguity support function r _ij is more in line with the authenticity of the actual problem, and is convenient for implementation, so that the fusion result is more accurate and stable.

For the data fusion problem, establish a support matrix R:

r _ij in the support matrix R only indicates the degree of mutual support between the two data, and does not reflect the overall support level of a measurement data by all the data in the data group. Now we need to find out from R that the data is comprehensively supported by other data, that is, to determine the weight coefficient of the i-th measurement data in the whole measurement data.

According to the principle of information sharing, that is, the sum of the information amounts of the optimal fusion estimation can be equivalently decomposed into the sum of the information amounts of several measurement data. Or, a message can be shared by several subsystems, then

due to

The general information of r _i1 , r _i2 ,..., r _in should be synthesized, which is known from the probability source combination theory, that is, a set of non-negative numbers v ₁ , v ₂ ,..., v _{n is required} to make the next formula middle

Here, we rewrite the previous formula to the form of a matrix, so that W=RV, where

Since r _ij ≥ 0, the support matrix R is a non-negative matrix. According to the properties of the non-negative matrix, R has the largest modulus eigenvalue λ ≥ 0, and λV = RV, the corresponding eigenvector V = [ _{v 1, v 2, ...,} v n] T, so that

then

That is, the self-weight coefficient of the i-th measurement data x _i , and the fusion result of the n measurement data is

3) MVC-based FCD data acquisition and fusion

There are three control classes and two entity classes involved in the real-time dynamic release of the regular bus service index and system use case realization.

· Entity classes are as follows:

a.OriginalFCD: Record the original FCD data, including only the FCD string of the original data.

b.ValidatedFCD: Record valid FCD data, including the main attributes: taxiID (vehicle number), checkDate (test data date), latitude (vehicle position latitude), longtitude (vehicle position longitude), spotSpeed (location speed), directionAngle ( Azimuth), CpBs (computer unique), CpName (computer unique name), WithSound (no voice), UserName (user name), PassWord (password), PhoneTail (mobile phone number), ByPAss100 (not more than 100), TimeInterVal (acquisition FCD clock).

· Control classes are as follows:

a.Time: Same as the Time class in the fixed-point data collection use case.

b.FCDConnect: Responsible for connecting to the remote FCD collection service. The ConnectVFDService() method connects to the remote service and returns ServeHandle as the identifier for use by the GetFCD class.

c.GetFCD: collects the main control class of FCD data. After connecting the remote FCD acquisition service, the original FCD is obtained by the Timer driver, and the OriginalFCD is filtered by the filter() method to generate a valid ValidatedFCD.

4) Object-oriented design of event flow

GetFCD connects to the remote detector through the connect FCD Service method of FCD Connect, and sets the acquisition frequency through the setTime method of Timer. The Time class starts self-test through the check method, and starts to collect FCD data when the time is reached. GetFCD obtains the original FCD data through the Serve Handle connection with the remote FCD service, and puts the data into the OriginalVFD class through setOriginalFCD. Filter the string of the original FCD data by the filter method, obtain the valid FCD data into the Validated FCD, and save the valid fixed point data for the day to complete the FCD data collection.

The regular bus service index and system are dynamically released in real time, and the FCD data acquisition sequence diagram is shown in Figure 4.

The traffic public information platform is responsible for data fusion, data dictionary, decision support based on data mining, data service and data maintenance. Traffic data statistics query is a sub-function of data service. Based on the above analysis, the functions of the traffic public information platform are organized in a data fusion use case package, a data mining based decision support use case package, a traffic data statistical query use case package, and a data maintenance use case package.

The traffic simulation platform performs strategic level simulation analysis and project level simulation analysis through intelligent simulation components. Since the intelligent simulation component has its environment configuration data, it is necessary to calibrate the relevant parameters. In addition, for a software integration product, maintenance functions are essential, and the maintenance function of the platform needs to be added. Therefore, the functions can be divided into four parts: strategic level simulation analysis, project level simulation analysis, intelligent simulation component maintenance and simulation platform maintenance. Based on the above analysis, the traffic simulation function corresponds to the intersection Through the needs of the simulation platform, it is organized into a strategic level simulation analysis use case package, a project-level simulation analysis use case package, an intelligent simulation component maintenance use case package, and a simulation platform maintenance use case package.

The traffic information service platform will accurately and timely convey the traffic operation status data and simulation calculation results in real time to the user in an appropriate form, and realize dynamic and static traffic information release in all weather, multi-mode and multi-layer. In addition, for a software integration product, maintenance functions are essential, and the maintenance function of the platform needs to be added. Therefore, the functions can be divided into two parts: information publishing service and information management service. Based on the above analysis, the information service functions are organized in the information release service use case package and the information management service use case package.

The regular bus service index real-time evaluation system mainly reflects the information of the actual use of the conventional bus situation at every moment (5 minutes and one cycle) supported by the urban road network, including information on traffic flow, road network work status information, and traffic events. Information, etc. Traffic flow information includes traffic flow, road congestion, etc., where the congestion degree indicator can be quantified (traffic congestion index), set 0-10 levels to reflect the different degrees of urban traffic flow, congestion, congestion, respectively, marked with green, Yellow and red color expression; road network working status information mainly reflects the current congestion degree of urban road network, including congestion area, congestion status, congestion duration, congestion change trend, formation of congestion, short-term prediction of crowded road conditions, etc.; The event information mainly reflects the traffic behavior events occurring in the current urban road network, including traffic accidents, traffic control, road construction, traffic monitoring, and traffic congestion. The data of the regular bus service index real-time evaluation system should be simple and practical, and it is as convenient as possible to update and query data, so as to improve the efficiency of data usage. The data items mainly include content: number, link name, date, time, direction, traffic flow, traffic flow. Speed, congestion, road conditions, traffic events, etc.

In the design and practice process, the conventional bus service index real-time evaluation system presents different characteristics from the traditional non-real-time dynamic system, and needs good methods, tools and language support. The unified development process of the conventional bus service index real-time evaluation system, real-time dynamic unified modeling language, real-time dynamic traffic information evaluation system and the Rational Rose Real Time modeling environment are organically combined to carry out system requirements analysis and use case modeling. Static and dynamic modeling, implementation and department Interdisciplinary application of the Department's advanced software technology in traffic information engineering.

Please refer to FIG. 5 , which is a flowchart of a method for real-time evaluation of a conventional bus service index according to an embodiment of the present invention. The method for real-time evaluation of a conventional bus service index according to an embodiment of the present invention includes:

Step 10: Obtain traffic big data and use the principal component analysis method to obtain an evaluation index system for the conventional bus service level;

In the above, the conventional bus system is a very complicated system. His complexity is manifested in the following: the first component is diversified, including transportation objects, transportation vehicles, and transportation facilities; and the second is closely related to various external relationships. The comprehensive evaluation of urban conventional public transportation system is based on the conventional public transportation system. With the help of scientific methods and means, based on the analysis of the objectives, structure, environment, function and benefits of the conventional public transportation system, the index system is constructed. Establish a comprehensive evaluation model. The evaluation of the conventional public transport system is very necessary to understand, construct and develop the urban public transport industry. Through the evaluation, it clearly recognizes the gap between the regular public transport status and the social demand. This is important for adjusting the urban public transport industry structure and policies, and further improving and optimizing the public transport service. Realistic meaning.

The evaluation of urban public transportation system is the basis of urban transportation system planning. Only on the basis of fully studying the problems and development characteristics of public transportation system, can we comprehensively and systematically determine the basic ideas, development direction and planning of urban future transportation development. Targets, etc., can further improve and optimize urban traffic conditions and promote the overall development of cities and social economy. The contents of the urban public transport system evaluation mainly include the local aspects:

1) Technical evaluation of transportation network for users of public transportation systems;

2) Economic benefit evaluation for the operators and managers of the bus system (the competent department of the industry and enterprises);

3) Evaluation of service level for urban citizens representing the city and the public interest;

4) Evaluation of sustainable development for government representative cities.

The method and system for realizing the regular bus service index in real time by traffic big data is the third aspect Rong, this is the core content of the evaluation of the urban public transportation system, that is, the service level evaluation of the urban citizens and the public interest. It mainly reflects whether the bus transportation enterprises meet the passenger demand in terms of service level. It is an evaluation index for the bus transportation enterprises. The public transportation system service level refers to various public transportation services that the public transportation system can provide to residents, including the hard services provided by public transportation facilities and the soft services provided by the passengers. In view of the fact that the current urban public transport system has adopted the IC card ticketing system, the impact of the evaluation staff on the regular bus service level is of little significance. Therefore, the service level of the conventional bus system can be evaluated from two aspects, namely the bus service function and the bus service quality. The main indicators of the comprehensive evaluation of urban conventional public transport systems include the following eleven parts:

· million car accident rate;

· Safe running interval mileage;

·Direct route rate;

· Station accessibility;

· running speed;

· Passenger freight rate;

·Car service qualification rate;

· Average travel time of passengers;

· Average passenger transfer coefficient;

· Peak full load rate;

· Full-time line full load rate, etc.

The conventional bus evaluation index system is relatively complete and relatively accurate. It has considerable complexity and difficulty. According to the experience of a large number of domestic and international conventional bus optimization and evaluation, it has a deep understanding of the current status of conventional bus system evaluation at home and abroad. On the basis of the analysis, through the means of traffic big data technology, based on the selection criteria of evaluation indicators from the conventional bus service level, network technology performance, economic efficiency level, sustainable development level to establish the city's conventional bus system evaluation index system, Emphasis is placed on the evaluation of regular bus service levels. Therefore, this patent The focus of the invention is on establishing an evaluation index system for conventional bus service levels.

Conventional public transport is a public welfare undertaking in the city. It is a window for urban civilization construction. The level of service is good and poor. It is directly related to the normal and stable political, economic and social order of the society and to the reputation and image of the city. The main body of public transportation is urban residents. Whether passengers choose to use public transportation for transportation is the key to whether the services provided meet their needs. Therefore, the service level of the conventional bus system is the main aspect of the evaluation. According to the selection principle and setting function of the evaluation index, in the traffic big data environment, the principal component analysis method is used to obtain the evaluation index system of the conventional bus service level, as shown in the following table.

Table of evaluation index system for service level of conventional public transport system

The comprehensive evaluation of the conventional public transport system is based on the evaluation of the various parts, stages and sub-systems of the urban public transport system, and seeks the optimal adjustment of the overall function of the urban public transport system, and continuously in the overall optimization process of the system. Provide various related information to decision makers. The comprehensive evaluation must also be comprehensive and developed with the development goals of the conventional public transportation system. The complexity of the process and the hierarchy of the conventional public transport system itself, while the development of a new generation of information technology, especially the introduction of traffic big data technology, offers the possibility of comprehensive evaluation.

Determining the purpose of index evaluation and the reference system for evaluation, obtaining evaluation information, and forming value judgment are the general processes of index evaluation. The specific procedure of regular bus service index evaluation is as follows: Step 1: Establish index evaluation target; Step 2: Determining the index evaluation target; Step 3: Information collection and analysis; Step 4: Determining the evaluation index system; Step 5: Designing the index evaluation method; Step 6: Single item index evaluation; Step 7: Comprehensive index evaluation; Step 8: Evaluation result analysis.

Step 20: Quantify the regular bus service index for real-time traffic big data evaluation according to the evaluation index system of the conventional bus service level;

The quantitative treatment of the conventional bus system service index plays a major role in the comprehensive evaluation of the urban bus system. Reasonable quantitative processing helps to increase the scientific and accuracy of the evaluation results. For the evaluation index of the conventional public transportation system, the definition, dimension, quantitative function, evaluation criteria and indicator description of each indicator are given. Based on the relevant urban experience of different city scales, reference to relevant norms and evaluation criteria, the recommended values of the grading standards of the evaluation index system are given according to the actual situation and characteristics of the conventional public transportation system, so as to evaluate and identify the actual situation of the conventional public transportation system. .

10,000 car accident rate

Unit: times / 10,000 cars;

Definition: The 10,000 car accident rate refers to the number of annual traffic accidents per 10,000 vehicles in the city.

Quantification:

In the formula, f16 is the 10,000 car accident rate, m11 is the number of traffic accidents throughout the year, and m12 is the number of motor vehicles in the city.

Evaluation criteria: Based on the experience at home and abroad, combined with the actual situation of Shenzhen urban traffic, the recommended rating value of the vehicle accident rate is given, as shown in the table below.

10,000 car accident rate evaluation level definition recommendation value table

Bus type	First level	Secondary	Third level	Level four	Five levels
1,2,3,4,5 city bus system	≤10	(10,20)	(20,30)	(30,40)	>40

Indicator Description: The vehicle accident rate is the main indicator for measuring the traffic safety management level under certain motorization levels, and it is a comprehensive reflection of road traffic safety facilities and road traffic safety management effects.

2 passengers travel average time consumption

Unit: min;

Definition: The average travel time of passengers refers to the average one-way travel time of 90% of urban residents during the peak period of passenger traffic during the statistical period;

Quantization: f ₁₇ =f(time);

Evaluation criteria: There are significant differences in the maximum values that residents can tolerate under different city scales and different travel destinations. The larger the city size, the greater the travel time for people to tolerate travel, and the 90% travel time consumption is defined as The maximum travel time is accepted, as shown in the table below. It can be seen from the table that 90% of the residents' travel expenses reflect the convenience and accessibility of urban residents, and the smaller the travel time, the more convenient and accessible the residents are.

Residents' maximum travel time evaluation rating table

Maximum travel time	One type of city	Second class city	Three types of cities	Four types of cities	Five types of cities
90% of travel time	60	50	40	35	25

Indicator Description: Accurate data is not easy to obtain, but information can be obtained through OD reverse push. This indicator provides an overall evaluation of the general bus service level, and indirectly evaluates the overall speed of the general bus operation and the operational efficiency of the route.

3 driving punctuality rate

unit:%;

Definition: The on-time punctuality rate refers to the ratio of the number of punctual operations of public transport vehicles to the total number of trips during the statistical period.

Quantification:

In the formula, f18 is the punctuality rate of the driving, m13 is the number of running times of the operating vehicle during the statistical period, and m14 is the total number of driving times.

Evaluation criteria: The average punctuality rate is not less than 80%--90%. According to the experience at home and abroad, combined with the actual situation of Shenzhen urban traffic, the recommended value of the punctuality rate of the driving is given. See the table below for details.

Driving punctuality rate evaluation level definition recommendation value table

City bus type	First level	Secondary	Third level	Level four	Five levels
1,2,3,4,5 city conventional bus system	>95	[90,95)	[85,90)	[80,85)	<80

Indicators: Bus transportation is accurate and timely, and it is not important for passengers. Especially in big cities, to reduce traffic pressure and reduce traffic congestion, public transportation should be vigorously developed.

4 passenger freight rate

unit:%;

Definition: The passenger freight rate refers to the ratio of the actual monthly passenger fare paid by ordinary passengers to the average wage of the city's employees, which can reflect the passenger capacity of public passenger fares.

Quantification:

In the formula, f19 is the passenger freight rate, c8 is the average monthly passenger fare paid by ordinary passengers, and c9 is the average monthly salary of employees.

Evaluation criteria: Based on domestic and international experience, consider the actual situation of urban traffic in Shenzhen, and give the recommended value for the evaluation of the passenger freight rate. See the table below for details.

Passenger rating rate evaluation rating definition recommendation value table

City bus system type	First level	Secondary	Third level	Level four	Five levels
1,2,3,4,5 city conventional bus system	<3.5	[3.5, 4.5)	[4.5, 5.5)	[5.5, 6.5)	>6.5

Indicator Description: The passenger freight rate mainly refers to the cheaper price of the fare. It is an important evaluation index for the bus to attract customers, and it is also the primary consideration for the priority development of public transportation. If the fare is too high, the attraction of the bus to the customer is reduced; the fare is too low, and the operating costs of the bus company are increased; therefore, the bus fare must be kept at a reasonable price.

5 passengers are replaced by an average factor

Unit: dimensionless;

Definition: The average passenger transfer coefficient refers to the sum of passenger travel times and transfer passengers divided by passenger travel time during the statistical period. This indicator measures the passenger directness and reflects the convenience of riding.

Quantification:

Where f20 is the average passenger transfer coefficient, n1 is the passenger travel time, and n2 is the transfer passenger number.

Evaluation index: The average transfer coefficient of large cities is not more than 1.5, and the average transfer coefficient of small cities is not more than 1.3. According to the experience of domestic and foreign countries, combined with the actual situation of urban traffic in Shenzhen, the recommended value of the average rating of passengers is given. See the table below for details.

Passenger rating average rating factor

City bus system type	First level	Secondary	Third level	Level four	Five levels
1,2,3 urban conventional bus system	[1.0, 1.1)	[1.1, 1.2)	[1.2, 1.4)	[1.4, 1.5)	≥1.5
4, 5 urban conventional bus system	[1.0, 1.05)	[1.05, 1.1)	[1.1, 1.2)	[1.2, 1.3)	≥1.3

Indicator Description: Residents often have to transfer from one bus line to another during their travels, and some have to transfer multiple times. The average number of conversions is the sum of the number of transfers for all passengers divided by the total number of passengers. Transferring increases the time and effort spent on the passengers, making it inconvenient. Therefore, the city bus should be as direct and fast as possible, reducing passenger transfer.

6 full-day line full load rate

unit:%;

Definition: Full-time full load rate refers to the average full load of passengers carrying vehicles throughout the day during the statistical period.

Quantification:

Where f21 is the full-time line full load rate, q _i,i+1,k is the node i to i+1 segment traffic of k lines, and L _i,i+1,k is the node i of the kth line to The distance between passenger traffic of i+1 road segment is km, n3 is the number of regular bus lines, and n4 is the number of road network nodes that pass conventional bus.

Evaluation criteria: based on relevant domestic and international experience, considering the actual situation of urban traffic in Shenzhen, given The evaluation level of the full-day line full load rate defines the recommended value. See the table below for details.

Suggested value table for evaluation level of full-line full load rate

City bus system type	First level	Secondary	Third level	Level four	Five levels
One type of urban conventional public transport system	>90	[80,90)	[70,80)	[50,70)	<50
Second class urban public transport system	>85	[75,85)	[65,75)	[45,65)	<45
Three types of urban conventional public transport systems	>80	[70,80)	[60,70)	[40,60)	<40
Four or five types of urban conventional public transport systems	>75	[65,75)	[55,65)	[30,55)	<30

Indicator Description: The data needs to be obtained from the bus company or through the city comprehensive traffic operation command center for sample survey. The full load rate is an important indicator for evaluating the effectiveness of conventional bus transportation, verifying the capacity allocation, and adapting to the actual needs of passengers. It is also an important basis for compiling or revising operational operation plans and adjusting the number and direction of regular bus vehicles.

7 safe running interval mileage

Unit: 10,000 km/time;

Definition: The safe running interval mileage refers to the ratio of the total mileage of conventional public transport vehicles to the number of traffic accident accidents.

Quantification:

In the formula, f22 is the safe running interval mileage, l4 is the total mileage of the regular bus (10,000 km), and n5 is the number of driving accidents (times).

Evaluation criteria: According to the “Urban Traffic Management Evaluation System”, the recommended value standards for the safety operation interval mileage evaluation index level are given, as shown in the table below.

Safety operation interval mileage evaluation index level definition recommendation value table

City bus system type	First level	Secondary	Third level	Level four	Five levels
1,2,3,4,5 city conventional bus system	≥125	[100,125)	[75,100)	[50,75)	<50

Indicator Description: Calculating the indicator requires the operating company or the city's comprehensive traffic operation command center to provide the mileage of each bus and the number of traffic accidents identified by the bus management department. Therefore, through the total mileage of the bus and the total number of traffic accidents, you can know the safe running interval of the city's regular bus system.

8 peak full load rate

unit:%;

Definition: The peak full load rate refers to the ratio of the actual passenger capacity of the one-way peak section to the rated passenger capacity during the peak hours of the main operating line during the statistical period.

Quantification:

Where f23 is the peak full load rate, q2 is the actual passenger capacity of the regular bus during the statistical period, and q3 is the rated passenger capacity of the regular bus during the statistical period.

Evaluation criteria: Based on the relevant experience at home and abroad, combined with the actual situation of Shenzhen urban traffic, the recommended value of the evaluation of the peak passenger load rate is defined. See the table below for details.

Peak load factor evaluation equivalent definition recommendation value table

City bus system type	First level	Secondary	Third level	Level four	Five levels
One type of urban conventional public transport system	<60	[60,70)	[70,80)	[80,90)	>90
Second and third class urban public transport system	<63	[63,73)	[73,83)	[83,93)	>93
Four or five types of urban conventional public transport systems	<65	[65,75)	[75,85)	[85,95)	>95

Indicator Description: The peak full load rate is an important indicator for evaluating the effectiveness of conventional bus transportation, verifying the capacity allocation, and adapting to the actual needs of passengers. It is also an important basis for compiling or revising operational operations plans and adjusting the number and direction of regular bus vehicles. Through the comprehensive data of the traffic operation command center, a more accurate peak full load rate indicator can be obtained.

Step 30: Establish a real-time release of the actual indicator of the conventional bus service index according to the quantified conventional bus service index;

1 Theoretical indicators of the comprehensive evaluation system of the general public transport system

The theoretical indicators of the general public transport system comprehensive evaluation system mainly include the following eight parts:

· million car accident rate;

· Average travel time of passengers;

· On-time punctuality rate;

· Passenger freight rate;

· Average passenger transfer coefficient;

· Full line full load rate;

· Safe running interval mileage;

· Peak full load rate;

2 big data real-time release of the actual indicators of the regular bus service index

Big data real-time release The actual indicators of the regular bus service index mainly include the following ten parts:

·Science service;

·Security;

·Information service;

· Car capacity;

· Consumption when riding;

· waiting time;

·The degree of congestion;

·Change to quality;

· Facilities security;

· Walking time.

3 Establish the correspondence between theoretical indicators and actual published indicators

In order to realize the real-time automatic generation of the conventional bus service index under the traffic big data environment, it is necessary to establish a correspondence between the conventional bus system comprehensive evaluation system index and the real-time release of the regular bus service index evaluation index, as shown in Fig. 6. .

Step 40: Collect real-time traffic big data, and process the collected real-time traffic big data;

1) Collected traffic big data content and attributes

Traffic big data publishes the data types and attributes actually collected by the conventional bus service index in real time, which is the basis for further development and implementation of the platform for publishing the regular bus service index. Self-characteristic screening and feature extraction mainly include the following categories:

·General bus IC card data

"Shenzhen Tong" card passenger card data, its attributes include: card number, transaction date, transaction time, line / subway station name, industry name (bus, subway, rental, ferry, P + R parking lot), transaction amount, transaction Nature (non-preferential, preferential, no discount).

·Regular bus real-time data

The real-time data of conventional bus vehicles includes: device number, line code, site code, protocol number, entry and exit status, direction, vehicle reporting time, and code correspondence table.

·General bus network data

The regular bus line network structure and traffic geographic information data GIS-T, the first and last bus time (the city's 946 bus lines, the first and last bus schedules, the first and last bus, timetable).

·Taxis driving data

Taxi driving data, its attributes include: vehicle ID, GPS time, latitude and longitude, speed, number of satellites, operating state elevated state, braking state.

·Track traffic data

Subway operation data, its attributes include: line, station, transfer station data, timetable data of each station of the first and last bus, running time data between stations, current limit station, sealing station data, road network fare matrix, train real time to the station Time, line congestion and blocking data, exit/entry, toilet, disabled elevator data. Bus around the city track (all rail transit stations in Shenzhen, nearby bus stops, locations, names of each site).

·Road license plate identification data

Covering the license plate identification data of 460 sections of the city's urban road traffic network, its attributes include: vehicle category (car, taxi, bus, truck, etc.), license plate number, vehicle driving direction, vehicle driving speed, administrative area of the vehicle Wait.

·Traffic weather data

Traffic weather data, its attributes include: date, time, monitoring point, weather type, temperature, wind speed, wind direction, precipitation.

1) Traffic information FCD acquisition algorithm

The core algorithms involved in traffic information FCD acquisition include seven key algorithms such as raw data FCD anomaly rejection algorithm, vehicle speed calculation, fusion and prediction algorithms, and statistical algorithms for standard and historical data (flow and vehicle speed). These algorithms realize the conversion of raw data from the field to the data of the “Urban Integrated Traffic Information Platform”, which is the key to ensuring the reliability of the “information platform” and even the entire system. The following table lists the seven core algorithms in traffic information collection, and the algorithms are described in detail in the form of pseudocode. In view of the limited space, this invention patent "real-time dynamic release of conventional bus service index and system" only introduces the most relevant FCD data related algorithms and fusion processing applications.

Traffic information collection core algorithm list

2) FCD data fusion analysis and processing

In the FCD collection information, according to the data information collected by the external field, calculate the current section speed and comprehensively collect information to predict the 15 minutes and 30 minutes of the vehicle speed. The vehicle speed fusion prediction process is divided into three processes:

· Calculate the travel speed of the road segment based on the FCD data based on the FCD vehicle speed calculation model.

• Calculate the current vehicle speed based on the data fusion model based on traffic, location speed, and travel speed.

• Forecast the vehicle speed based on the current speed based on the vehicle speed prediction model.

The core idea of this invention patent algorithm is based on multi-source traffic information and traffic flow theory, synthesizing various data information, and realizing the judgment and description of traffic state.

The main task of FCD data fusion is to perform data level fusion on current traffic data, FCD calculation speed and location speed. Here, data fusion is mainly divided into two aspects: one is the fusion of real-time data and historical data, using linear transformation, fuzzy algorithm, calibrating different weights and membership degrees, and obtaining more accurate values; on the other hand, floating cars The fusion of data and fixed-point detector data, based on the respective characteristics of two different information sources, through the isomorphic data isomorphization process, the same parameters are fused, and the results with higher credibility are obtained.

In the flow-rate relationship model, the data is homogenized by the flow-closed relationship model. The flow-speed relationship model is as follows (Edie multi-segment model):

Where Q is the flow rate, v _s is the speed, and the others are the model parameters.

According to the physical characteristics of the object at a certain time, the measurement device obtains n measured values z _i (i=1, 2, . . . , n) in one cycle, due to comprehensive consideration of transmission error, calculation error, environment Noise, human interference, the accuracy of the sensor itself, and the nature of the object being measured, z _i will not strictly follow the normal distribution, so some data processing methods based on the data as a normal distribution model (outliers and data fusion) Algorithms will inevitably increase the systematic error of data processing. Thus the extent of the authenticity of the measurement data can, x _2, ..., x _n x ₁ is determined by the data itself, i.e., the higher the authenticity of x _i, x _i is supported by the rest of the data on the degree of The higher, the so-called x _i is supported by x _j , that is, the extent to which the data x _i is real data from the data x _j . The concept of relative distance is introduced here for the degree of support between data, and the relative distance between the measured data is defined as d _ij , which is expressed as follows:

d _ij =|x _i -x _j |,i,j=1,2,...,n

D _ij is apparent from the form of the expression, d _ij indicates that the larger the larger the difference between the two data, i.e., the smaller the degree of mutual support between the two data. The definition of relative distance is based on the implicit information of existing data, which reduces the requirements for a priori information. In turn, a support function r _ij can be defined, and r _ij itself should satisfy the following two conditions:

· r _ij should be inversely proportional to the relative distance;

r _ij ∈(0,1) enables the processing of data to take advantage of the membership functions in fuzzy set theory, avoiding the absolute degree of mutual support between data.

Then the support function r _{ij is} defined as:

Where max{d _ij } represents the maximum value among the relative distances between data, it is obvious that the greater the relative distance between data, the smaller the support between data will be from the definition of the above formula, when the relative distance between data is the largest When the value is considered, the two data can no longer support each other, then the value of the support function is zero; and the smaller the relative distance between the data, the greater the mutual support between the data, the relative distance of the data to itself. If it is zero, the data supports itself to 1. Since r _ij takes values from 1 to 0 in d _ij ∈[0,max{d _ij }], the properties that the support function should have are satisfied. Moreover, the definition form satisfying the ambiguity support function r _ij is more in line with the authenticity of the actual problem, and is convenient for implementation, so that the fusion result is more accurate and stable.

For the data fusion problem, establish a support matrix R:

r _ij in the support matrix R only indicates the degree of mutual support between the two data, and does not reflect the overall support level of a measurement data by all the data in the data group. Now we need to find out from R that the data is comprehensively supported by other data, that is, to determine the weight coefficient of the i-th measurement data in the whole measurement data.

According to the principle of information sharing, that is, the sum of the information amounts of the optimal fusion estimation can be equivalently decomposed into the sum of the information amounts of several measurement data. Or, a message can be shared by several subsystems, then

due to

The general information of r _i1 , r _i2 ,..., r _in should be synthesized, which is known from the probability source combination theory, that is, a set of non-negative numbers v ₁ , v ₂ ,..., v _{n is required} to make the next formula middle

Here, we rewrite the previous formula to the form of a matrix, so that W=RV, where

V = [v ₁ , v ₂ , ..., v _n ] ^T . Since r _ij ≥ 0, the support matrix R is a non-negative matrix. According to the properties of the non-negative matrix, R has the largest modulus eigenvalue λ ≥ 0, and λV = RV, the corresponding eigenvector V = [ v ₁ ,v ₂ ,...,v _n ] ^T , order

then

That is, the self-weight coefficient of the i-th measurement data x _i , and the fusion result of the n measurement data is

3) MVC-based FCD data acquisition and fusion

There are three control classes and two entity classes involved in the real-time dynamic release of the regular bus service index and system use case realization.

· Entity classes are as follows:

a.OriginalFCD: Record the original FCD data, including only the FCD string of the original data.

b.ValidatedFCD: Record valid FCD data, including the main attributes: taxiID (vehicle number), checkDate (test data date), latitude (vehicle position latitude), longtitude (vehicle position longitude), spotSpeed (location speed), directionAngle ( Azimuth), CpBs (computer unique), CpName (computer unique name), WithSound (no voice), UserName (user name), PassWord (password), PhoneTail (mobile phone number), ByPAss100 (not more than 100), TimeInterVal (acquisition FCD clock).

· Control classes are as follows:

a.Time: Same as the Time class in the fixed-point data collection use case.

b.FCDConnect: Responsible for connecting to the remote FCD collection service. The ConnectVFDService() method connects to the remote service and returns ServeHandle as the identifier for use by the GetFCD class.

c.GetFCD: collects the main control class of FCD data. After connecting the remote FCD acquisition service, the original FCD is obtained by the Timer driver, and the OriginalFCD is filtered by the filter() method to generate a valid ValidatedFCD.

4) Object-oriented design of event flow

GetFCD connects to the remote detector through the connect FCD Service method of FCD Connect, and sets the acquisition frequency through the setTime method of Timer. The Time class starts self-test through the check method, and starts to collect FCD data when the time is reached. GetFCD obtains the original FCD data through the Serve Handle connection with the remote FCD service, and puts the data into the OriginalVFD class through setOriginalFCD. Filter the string of the original FCD data by the filter method, obtain the valid FCD data into the Validated FCD, and save the valid fixed point data for the day to complete the FCD data collection.

The regular bus service index and system are dynamically released in real time, and the FCD data acquisition sequence diagram is shown in Figure 4.

5) Object-oriented collaboration diagram design for derived requirements

Real-time dynamic release of the regular bus service index and system, using automatic or manual triggering to establish a connection between taxis, bus companies and systems, taxis, bus companies can provide FCD data continuously throughout the day. The FCD data collection of Shenzhen urban traffic simulation is the large-scale GPS taxi of the city's comprehensive traffic information center and the operating enterprises such as the Shenzhen Public Transport Group. It plans to provide real-time data collection of FCD dynamic traffic of more than 15,000 taxis inside and outside the SAR. The interval is not less than 30 seconds, and the total number of vehicles is not less than 15,000.

Real-time release of data unified access and conversion platform development

With the rapid urbanization, economic globalization, and the rapid acceleration of information networking, the construction of new urbanized smart city integrated traffic information centers in Beijing, Shanghai, Guangzhou and Shenzhen in China's first-tier cities has been basically completed, and traffic big data has been connected to cities. Integrated transportation information center to realize decision support application services for urban transportation planning, construction and management integration. Faced with large-scale traffic decision-making by government, industry, enterprises, and the public to support data integration or unified access to data, as the only comprehensive traffic information center for urban traffic, transportation big data resource integration and service methods are receiving high attention.

1) Introduction to unified data access and conversion

In the practice of urban comprehensive traffic information center construction, a large number of traffic decision support systems constructed by different core technologies and related databases, such as urban integrated transportation support decision support system, urban road traffic planning decision support system, and cities, have been accessed. Traffic facilities supervision decision support system, urban public transportation service evaluation decision support system, urban traffic management and control decision support system, urban modern logistics service chain decision support system, urban traffic public travel information decision support system, etc. Heterogeneous traffic big data source. Faced with such an important and urgent traffic decision support application service demand, how to integrate and transform isomorphic and heterogeneous data sources from inside and outside the urban integrated traffic information center through an integrated system platform is the current urban traffic big data. Decision-making supports the enormous challenges facing the construction and application of the environment.

The realization of urban traffic information resource sharing mainly includes two modes, the first is data conversion, and the second is data integration. Data conversion is a physical data collection. On the one hand, it requires huge investment in hardware and related software. On the other hand, massive data migration and management also has considerable risks. The access speed is not ideal; the data is completed. Integration is a data set in a logical sense. It can make full use of existing resources for distributed storage, decentralized management, and unified access interfaces to meet the current development requirements of information technology.

Shenzhen integrated application for traffic big data decision support environment, including the federal mode, data warehouse, middleware from the model; heterogeneous database integration (migration and conversion) from integrated technology, distributed database system, Use middleware module technology. At present, the research on unified data access and conversion technology of traffic big data decision support environment is still in its infancy. Some well-known foreign database companies have developed corresponding middleware application products to solve heterogeneous data integration problems. A product needs to do a lot of data interface development work; there is still a lack of relatively complete data integration application products and technical means. At the same time, existing data programming techniques are usually designed more or less for specific data source types. Real-world applications, traffic big data come from a variety of data sources, and can ignore the expression of common data from data sources. Sets provide a simple, unified programming model for application developers.

The data unified access and conversion technology of the traffic big data decision support environment is proposed To realize the sharing and unified access of information resources of urban comprehensive traffic information center, the unified access and conversion technology of traffic big data collects, analyzes and integrates data from different data sources to provide uniform and standard for urban traffic related decision support application programmers. The data access form enables transparent access to heterogeneous data sources of various types of distribution. The goal of unified access and transformation technology for traffic big data is the unified access and application of heterogeneous data sources, that is, the access requests are decomposed into different data sources, and the returned heterogeneous results are unified and transformed for decision support. The application designer provides a unified data source access interface and lays the foundation for subsequent data analysis.

Data collection, cleaning, mining, aggregation rules and algorithms

1) Establish data processing rules

Real-time dynamic release of regular bus service index and system, data analysis: establish data collection rules, data cleaning rules, data mining rules, data summary rules, as shown in Figure 7.

2) Establishing data analysis mining process and model algorithm for road network support

Real-time dynamic release of conventional bus service index and system, data analysis design: based on FCD, establish road network data support mining data analysis mining process and model algorithm.

3) Establish data analysis mining process and model algorithm for bus IC card

Real-time dynamic release of conventional bus service index and system, data analysis design: establish data analysis mining process and model algorithm for bus IC card travel.

4) Establish data analysis mining process and model algorithm for regular bus travel

Real-time dynamic release of conventional bus service index and system, data analysis design: establish data analysis mining process and model algorithm for regular bus travel.

5) Establish data analysis mining process and model algorithm for taxi trips

Real-time dynamic release of conventional bus service index and system, data analysis design: establish data analysis mining process and model algorithm for taxi travel.

6) Establish database search engines for tracks, buses, rentals, IC cards, etc.

Real-time dynamic release of conventional bus service index and system, data analysis design: establish database search engine and database development design for track, bus, rental, IC card.

7) Establish an analysis and mining environment interface for the database group

Real-time dynamic release of conventional bus service index and system, data analysis design: establish a database group analysis and mining environment interface.

Step 50: Real-time release of the conventional bus service index and the actual collected data types and attributes according to the traffic big data, and establish relevant feature extraction of the main data;

According to the above traffic big data, the data types and attributes actually collected by the conventional bus service index are released in real time, and relevant feature extraction of the main data is established, and other data can be assisted for comparison and evaluation.

· Establish data application association structure design

Based on the collected data of the track, bus, and city bus, the application structure design of the data is established.

Step 60: Publish traffic big data to the conventional bus service index according to the extracted data related features.

The method and system of “real-time dynamic release of conventional bus service index” is based on passengers' perspective, and the overall evaluation of conventional bus services is obtained through relevant traffic big data, objective measurement and subjective perception. The technical indicators mainly reflect the two important aspects of conventional bus services:

1 accessibility of regular bus services (passengers);

2 Provides comfort and convenience to passenger service.

It is different from the road traffic index because road services are mainly for vehicles rather than passengers; they are also different from the bus industry statistics index because statistics are utility and economy, reflecting the interests of bus operators. It fully reflects the bus name "public", the name "service", and the "people-oriented" concept is on paper. It gives the evaluation of service quality and quality of bus system, line, station, transfer and so on.

Establish a general bus service state feature extraction and evaluation analysis system based on the “six types of urban traffic big data” and centered on the Shenzhen public bus service analysis query and index publishing platform. Achieve “one platform, four applications” mode, namely: regular bus service analysis query and index publishing platform, regular bus simulation, regular bus evaluation, application for government and industry management, and public travel services, through the Internet and The mobile internet method publishes regular bus information.

The invention patent has the advantages of “introducing the traffic big data environment and index mode, designing the real-time release of the conventional bus service index system, establishing a real-time release of the conventional bus service index method, and developing a real-time publishing regular bus service index platform system” to solve the general bus service index evaluation. The advantages.

The conventional bus service index real-time evaluation system and evaluation method according to the embodiment of the present invention closely track the core problem of urban public transportation travel as an entry point; it is a revolutionary challenge to the traditional questionnaire evaluation of the conventional bus service index in the era of big data; It overcomes the traditional questionnaire-based evaluation of conventional bus data static, long cycle, single-sided, statistically cumbersome and other drawbacks, through urban traffic big data modeling analysis and relevance research, real-time dynamic for government departments, industry enterprises, the public Traveling in real time to release the operational status and evolution of the conventional public transport system has important commercial and social values.

The invention can save the transit time and travel cost of the city bus travel, improve the effectiveness and convenience of the public travel, and can generate both direct benefits and indirect benefits;

The invention can realize the value-added service and comprehensive service of the urban public transportation information, and generate the commercial value and economic benefit of the public transportation travel chain.

The urban transportation priority development strategy is public transportation, and the “regular bus service index real-time evaluation system and evaluation method” is the core of this strategy. This is the priority strategy for urban bus development, bus system travel efficiency, and bus integration. It is of great social value to take over, relieve urban traffic congestion, improve public travel safety, and reduce urban traffic pollution.

The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments are obvious to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be accorded the broadest of the principles and novel features disclosed herein. range.

Claims (10)

1. A conventional bus service index real-time evaluation system is characterized in that it comprises a traffic information collection platform, a traffic public information platform, a traffic simulation platform and a traffic information service platform; the traffic public information platform faces a traffic information collection platform and a traffic information service platform. And the traffic simulation platform provides operation support and information service; the traffic information collection platform provides raw data for the traffic public information platform; the traffic simulation platform provides simulation result data for the traffic public information platform; the traffic information service platform relies on traffic common The data service provided by the information platform organizes the functional requirements of the traffic information collection platform in the information collection use case package.
2. The conventional bus service index real-time evaluation system according to claim 1, wherein the traffic information collection platform collects point and line traffic states in the road network through fixed points and floating vehicle detection devices, and collects real-time data. The traffic operation status data is processed and the results are stored in the traffic public information platform.
3. The conventional bus service index real-time evaluation system according to claim 2, wherein the traffic information collection platform collects point and line traffic states in the road network through fixed points and floating vehicle detection devices, including: original data. FCD abnormal rejection, vehicle speed calculation, FCD data fusion and prediction, standard and historical data statistics; wherein, the raw data FCD abnormal rejection includes: receiving FCD data, determining whether the data is valid, and obtaining effective FCD data.
4. The conventional bus service index real-time evaluation system according to claim 3, wherein the FCD data fusion and prediction specifically comprises: calculating a road speed of the road segment based on the FCD data based on the FCD vehicle speed calculation model; according to the flow rate, the location speed, and the travel speed The current vehicle speed is calculated based on the data fusion model; the vehicle speed is predicted based on the current vehicle speed based on the vehicle speed prediction model.
5. The conventional bus service index real-time evaluation system according to claim 4, wherein the FCD data fusion comprises fusion of real-time data and historical data, fusion of floating car data and fixed-point detector data, and real-time data and history. The data fusion adopts linear transformation, fuzzy algorithm, calibration of different weights and membership degrees, and obtains more accurate values; the fusion of the floating car data and the fixed-point detector data is based on the respective characteristics of the two different information sources. The data is isomorphically processed, and the same parameters are fused to obtain a highly credible result.
6. The conventional bus service index real-time evaluation system according to claim 4, wherein the FCD data collection comprises: the GetFCD is connected to the remote detector through the connect FCD Service method of the FCD Connect, and the collection is set by the setTime method of the Timer. The frequency occurs; the Time class starts self-test through the check method, and starts to collect FCD data when the time is up; GetFCD obtains the original FCD data through the Serve Handle and the remote FCD service connection, and puts the data into the OriginalVFD class through setOriginalFCD; Filter the string of the original FCD data, get the valid FCD data into the Validated FCD, and save the valid fixed point data for the day to complete the FCD data collection.
7. The conventional bus service index real-time evaluation system according to claim 4, wherein the traffic public information platform is responsible for data fusion, data dictionary, data mining-based decision support, data service, and data maintenance.
8. A method for real-time evaluation of a conventional bus service index, characterized in that it comprises:

   Step a: Obtain traffic big data and use the principal component analysis method to obtain an evaluation index system for the conventional bus service level;

   Step b: Quantify the regular bus service index for real-time traffic big data evaluation according to the evaluation index system of the conventional bus service level;

   Step c: Establishing a real-time index of the conventional bus service index in real time based on the quantified conventional bus service index;

   Step d: collecting real-time traffic big data, and processing the collected real-time traffic big data;

   Step e: According to the traffic big data, the conventional bus service index and the actual collected data types and attributes are released in real time, and relevant feature extraction of the main data is established; according to the extracted data related features, the traffic big data is published to the conventional bus service index.
9. The method for real-time evaluation of a conventional bus service index according to claim 8, wherein in the step d, processing the collected real-time traffic big data comprises: establishing a data processing rule; establishing a data analysis mining supported by the road network Process and model algorithm; establish data analysis mining process and model algorithm for bus IC card; establish data analysis mining process and model algorithm for regular bus travel; establish data analysis mining process and model algorithm for taxi travel; establish track, bus, lease , IC card database search engine; establish a database group analysis and mining environment interface.
10. The method for real-time evaluation of a conventional bus service index according to claim 9, wherein in the step d, the collecting real-time traffic big data passes through a fixed point and a floating vehicle detecting device to point and line traffic in the road network. Status is collected.

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