WO2018023331A1 - 一种常规公交服务指数实时评价系统及评价方法 - Google Patents

一种常规公交服务指数实时评价系统及评价方法 Download PDF

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WO2018023331A1
WO2018023331A1 PCT/CN2016/092669 CN2016092669W WO2018023331A1 WO 2018023331 A1 WO2018023331 A1 WO 2018023331A1 CN 2016092669 W CN2016092669 W CN 2016092669W WO 2018023331 A1 WO2018023331 A1 WO 2018023331A1
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data
traffic
platform
real
fcd
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关金平
关志超
须成忠
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Shenzhen Institute of Advanced Technology of CAS
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Shenzhen Institute of Advanced Technology of CAS
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    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles

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  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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;
  • 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.
  • FIG. 1 is a schematic structural diagram of a conventional bus service index real-time evaluation system according to an embodiment of the present invention
  • FIG. 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
  • FIG. 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
  • FCD data acquisition of a conventional bus service index real-time evaluation system according to an embodiment of the present invention
  • FIG. 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.
  • FIG. 1 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.
  • FCD fixed point and floating vehicle detection equipment
  • FCD acquisition includes 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.
  • 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.
  • 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:
  • FCD data fusion The main task of FCD data fusion is to perform data level fusion on current traffic data, FCD calculation speed and location speed.
  • 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.
  • the flow-speed relationship model is as follows (Edie multi-segment model):
  • 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 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.
  • 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
  • max ⁇ d ij ⁇ represents the maximum value among the relative distances between data
  • 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
  • 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.
  • 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.
  • 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.
  • 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
  • W RV, where Since r ij ⁇ 0, the support matrix R is a non-negative matrix.
  • ⁇ Entity classes are as follows:
  • a.OriginalFCD Record the original FCD data, including only the FCD string of the original data.
  • FCD 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).
  • 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
  • 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.
  • 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 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • FIG. 5 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;
  • 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:
  • 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:
  • 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.
  • 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.
  • 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.
  • 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.
  • the evaluation index of the conventional public transportation system the definition, dimension, quantitative function, evaluation criteria and indicator description of each indicator are given.
  • 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. .
  • the 10,000 car accident rate refers to the number of annual traffic accidents per 10,000 vehicles in the city.
  • f16 is the 10,000 car accident rate
  • m11 is the number of traffic accidents throughout the year
  • 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.
  • 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.
  • 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;
  • 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.
  • 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.
  • 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.
  • f18 is the punctuality rate of the driving
  • m13 is the number of running times of the operating vehicle during the statistical period
  • m14 is the total number of driving times.
  • 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.
  • 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.
  • 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.
  • f19 is the passenger freight rate
  • c8 is the average monthly passenger fare paid by ordinary passengers
  • 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.
  • 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.
  • 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.
  • f20 is the average passenger transfer coefficient
  • n1 is the passenger travel time
  • 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.
  • Full-time full load rate refers to the average full load of passengers carrying vehicles throughout the day during the statistical period.
  • 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
  • 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
  • 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.
  • 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.
  • 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.
  • f22 is the safe running interval mileage
  • l4 is the total mileage of the regular bus (10,000 km)
  • n5 is the number of driving accidents (times).
  • 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.
  • 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.
  • 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.
  • 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
  • the actual indicators of the regular bus service index mainly include the following ten parts:
  • Step 40 Collect real-time traffic big data, and process the collected real-time traffic big data
  • 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:
  • “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).
  • 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.
  • 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).
  • Taxi driving data its attributes include: vehicle ID, GPS time, latitude and longitude, speed, number of satellites, operating state elevated state, braking state.
  • 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).
  • 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 its attributes include: date, time, monitoring point, weather type, temperature, wind speed, wind direction, precipitation.
  • 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.
  • 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.
  • 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:
  • 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.
  • FCD data fusion The main task of FCD data fusion is to perform data level fusion on current traffic data, FCD calculation speed and location speed.
  • 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.
  • the flow-speed relationship model is as follows (Edie multi-segment model):
  • 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.
  • 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
  • max ⁇ d ij ⁇ represents the maximum value among the relative distances between data
  • 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
  • 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.
  • 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.
  • 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.
  • 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
  • ⁇ Entity classes are as follows:
  • a.OriginalFCD Record the original FCD data, including only the FCD string of the original data.
  • FCD 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).
  • 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
  • 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.
  • 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.
  • 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.
  • 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.
  • heterogeneous database integration miration and conversion
  • 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.
  • 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.
  • 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.
  • 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.
  • 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;
  • 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.
  • Step 60 Publish traffic big data to the conventional bus service index according to the extracted data related features.
  • 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 are “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 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.

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Abstract

一种常规公交服务指数实时评价系统及评价方法。所述系统包括交通信息采集平台、交通公用信息平台、交通仿真平台与交通信息服务平台；所述交通公用信息平台面向交通信息采集平台、交通信息服务平台和交通仿真平台提供运行支撑和信息服务；所述交通信息采集平台为交通公用信息平台提供原始数据；所述交通仿真平台为交通公用信息平台提供仿真结果数据；所述交通信息服务平台依托交通公用信息平台提供的数据服务，将交通信息采集平台的功能需求组织在信息采集用例包。本方法实时动态地面向政府部门、行业企业、公众出行实时发布常规公交系统运行状态与演变态势，具有重要的商业价值与社会价值。

Description

一种常规公交服务指数实时评价系统及评价方法 技术领域
本申请涉及常规公交系统技术领域，特别涉及一种常规公交服务指数实时评价系统及评价方法。
背景技术
进入新型城镇化建设发展时期，随着城市化进程的不断加快和城市机动化出行的迅猛发展，城市道路需求迅速增长，交通拥堵、安全、污染问题日益突出，并成为社会经济发展的瓶颈问题。常规公共交通作为城市交通的主体部分，是保证城市生产、生活正常运转的动脉，是提高城市综合功能的重要基础设施，它对城市各产业的发展，经济、社会、文化事业的繁荣、城际与城乡间联系起着重要的纽带和促进作用。通过城市公共交通系统的综合评价，可以清楚地认识到公交服务现状和社会需求的差距，以便调整公交产业结构，进一步提高服务水平，促进新型城镇化建设。
城市常规公交服务指数是城市公共交通系统优化与综合评价的重要依据，是对现有公共交通系统布局进行综合研究、分析其特点、评价其布局合理性，总结其经验，为今后公共交通系统的调整优化提供科学合理的决策依据。
长期以来，城市交通领域中公共交通服务指数通常是通过向市民发放交通出行问卷、归纳统计分析整理问卷后，来发布公交服务指数方式进行的。但是这种方式存在着发放问卷调查周期长、耗费人力成本大、问卷发放范围与取样比例不足、问卷回答的问题不够准确、不能进行实时动态对常规公交服务进行评价等问题。
发明内容
本发明提供了一种常规公交服务指数实时评价系统及评价方法，旨在至少在一定程度上解决现有技术中的上述技术问题之一。
为了解决上述问题，本发明提供了如下技术方案：
一种常规公交服务指数实时评价系统，包括交通信息采集平台、交通公用信息平台、交通仿真平台与交通信息服务平台；所述交通公用信息平台面向交通信息采集平台、交通信息服务平台和交通仿真平台提供运行支撑和信息服务；所述交通信息采集平台为交通公用信息平台提供原始数据；所述交通仿真平台为交通公用信息平台提供仿真结果数据；所述交通信息服务平台依托交通公用信息平台提供的数据服务，将交通信息采集平台的功能需求组织在信息采集用例包。
本发明实施例采取的技术方案还包括：所述交通信息采集平台通过固定点和浮动车检测设备对路网中的点和线交通状态进行采集，并对实时采集的交通运行状态数据进行处理，将结果存储到交通公用信息平台。
本发明实施例采取的技术方案还包括：所述交通信息采集平台通过固定点和浮动车检测设备对路网中的点和线交通状态进行采集具体包括：原始数据FCD异常剔除、车速计算、FCD数据融合和预测、标准和历史数据的统计；其中，所述原始数据FCD异常剔除包括：接收FCD数据，判断数据是否有效，得到有效FCD数据。
本发明实施例采取的技术方案还包括：所述FCD数据融合和预测具体包括：根据FCD数据基于FCD车速计算模型计算路段行程车速；根据流量、地点车速和行程车速基于数据融合模型计算当前车速；根据当前车速基于车速预测模型对车速进行预测。
本发明实施例采取的技术方案还包括：所述FCD数据融合包括实时数据和历史数据的融合、浮动车数据和定点检测器数据的融合，所述实时数据和历 史数据的融合采用线性变换，模糊算法，标定不同的权值和隶属度，得出较为精确的数值；所述浮动车数据和定点检测器数据的融合根据两种不同信息源的各自特点，通过异构数据同构化处理，对同一参数进行融合，得出可信度高的结果。
本发明实施例采取的技术方案还包括：所述FCD数据采集具体包括：GetFCD通过FCD Connect的connect FCD Service方法连接远端检测器，通过Timer的setTime方法设定采集发生频率；Time类开始通过check方法自检，当到达时间时开始采集FCD数据；GetFCD通过Serve Handle与远端FCD服务连接获得原始FCD数据，并通过setOriginalFCD将数据放入OriginalVFD类中；通过filter方法过滤原始FCD数据的字符串，得到有效FCD数据放入Validated FCD，并保存当天有效定点数据，完成FCD数据采集工作。
本发明实施例采取的技术方案还包括：所述交通公用信息平台负责数据融合、数据字典、基于数据挖掘的决策支持、数据服务和数据维护。
本发明实施例采取的另一技术方案为：一种常规公交服务指数实时评价方法，包括：
步骤a：获取交通大数据，利用主成分分析法，得到常规公交服务水平的评价指标体系；
步骤b：根据常规公交服务水平的评价指标体系对实时交通大数据评价常规公交服务指数进行量化；
步骤c：根据量化的常规公交服务指数建立大数据实时发布常规公交服务指数实际指标；
步骤d：采集实时交通大数据，并对采集的实时交通大数据进行处理；
步骤e：根据交通大数据实时发布常规公交服务指数与实际采集的数据类型与属性，建立主要数据的相关特征提取；根据提取的数据相关特征，发布交通大数据对常规公交服务指数。
本发明实施例采取的技术方案还包括：在所述步骤d中，对采集的实时交通大数据进行处理包括：建立数据处理规则；建立道路网络支撑的数据分析挖掘流程与模型算法；建立公交IC卡的数据分析挖掘流程与模型算法；建立常规公交出行的数据分析挖掘流程与模型算法；建立出租车出行的数据分析挖掘流程与模型算法；建立轨道、巴士、出租、IC卡数据库搜索引擎；建立数据库群的分析与挖掘环境界面。
本发明实施例采取的技术方案还包括：在所述步骤d中，所述采集实时交通大数据通过固定点和浮动车检测设备对路网中的点和线交通状态进行采集。
相对于现有技术，本发明实施例产生的有益效果在于：
一、本发明实施例的常规公交服务指数实时评价系统及评价方法，紧密跟踪城市公交通出行核心问题做为切入点，是在大数据时代，对传统问卷式评价常规公交服务指数发布的革命性挑战；它克服了传统问卷式评价常规公交的数据静态、周期较长、单一片面、统计繁琐等弊端，通过城市交通大数据的建模分析与关联性研究，实时动态地面向政府部门、行业企业、公众出行实时发布常规公交系统运行状态与演变态势，具有重要的商业价值与社会价值。
二、本发明可以节省城市公交出行的在途时间与出行成本，提高公众出行的实效性与便捷性，既可以产生直接效益，又可以产生间接效益；
三、本发明可以实现城市公交信息的增值服务与综合服务，产生公交出行链的商业价值与经济效益。
附图说明
图1是本发明实施例的常规公交服务指数实时评价系统的结构示意图；
图2是本发明实施例的常规公交服务指数实时评价系统FCD车速融合、预测流程；
图3是本发明实施例的常规公交服务指数实时评价系统FCD数据融合模型图；
图4是本发明实施例的常规公交服务指数实时评价系统FCD数据采集序列图；
图5是本发明实施例的常规公交服务指数实时评价方法的流程图；
图6是常规公交系统综合评价体系指标与实时发布常规公交服务指数评价指标对应关系图；
图7是数据采集、清洗、挖掘、汇总规则流程图。
具体实施方式
为了使本发明的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本发明进行进一步详细说明。应当理解，此处所描述的具体实施例仅用以解释本发明，并不用于限定本发明。
请参阅图1，是本发明实施例的常规公交服务指数实时评价系统的结构示意图。本发明实施例的常规公交服务指数实时评价系统包括交通信息通信与传输网络、交通信息采集平台、交通公用信息平台、交通仿真平台与交通信息服务平台构成。
交通公用信息平台是整个系统的核心，面向交通信息采集平台、交通信息服务平台和交通仿真平台提供运行支撑和信息服务；交通信息采集平台为交通公用信息平台提供原始数据；交通仿真平台为交通公用信息平台提供仿真结果数据；交通信息服务平台依托交通公用信息平台提供的数据服务，将交通信息采集平台的功能需求组织在信息采集用例包，交通公用信息平台的功能需求组织在公用信息用例包，交通仿真平台的功能需求组织在仿真用例包，交通信息服务的功能需求组织在信息服务用例包。
交通信息采集平台通过固定点和浮动车检测设备(FCD)对路网中的点和线交通状态进行采集，融合后作为整个系统的基础数据。它负责采集实时交通运行状态数据并进行处理，将结果存储到交通公用信息平台，因此功能分成交通信息采集与筛选处理两部分。另外，交通信息采集平台还要对外场设备和数据采集状态进行监控。基于上述分析，将交通信息采集功能组织在交通信息采集服务用例包与处理服务用例包、信息管理服务用例包中。
1)交通信息FCD采集中所涉及的核心算法包括原始数据FCD异常剔除算法，车速计算、融合和预测算法，标准和历史数据(流量和车速)的统计算法等七个关键算法。这些算法实现了从外场原始数据到“城市综合交通信息平台”使用数据的转换，是保证“信息平台”乃至整个系统基础数据可靠性的关键。下表列出了交通信息采集中七个核心算法，其后均以伪码形式对算法进行详细的描述。鉴于篇幅所限，本次发明专利“实时动态发布常规公交服务指数与系统”只介绍最核心的FCD数据的相关算法及融合处理应用。
交通信息采集核心算法列表
Figure PCTCN2016092669-appb-000001
2)FCD数据融合分析与处理
在FCD采集信息中，根据外场采集的数据信息，计算当前路段车速并综合采集信息预测15分钟、30分钟车速。车速融合预测过程分为三个过程：
·根据FCD数据基于FCD车速计算模型计算路段行程车速。
·根据流量、地点车速和行程车速基于数据融合模型计算当前车速。
·根据当前车速基于车速预测模型对车速进行预测。
本次发明专利算法的核心思想是基于多源的交通信息和交通流理论，综合各种数据信息，实现对交通状态的判断和描述。其整个过程如图2所示。
FCD数据融合的主要任务是对当前流量数据，FCD计算车速和地点车速进行数据级的融合。这里数据融合主要分为两个方面：一方面是实时数据和历史数据的融合，采用线性变换，模糊算法，标定不同的权值和隶属度，得出较为精确的数值；另一方面是浮动车数据和定点检测器数据的融合，根据两种不同信息源的各自特点，通过异构数据同构化处理，对同一参数进行融合，得出可信度较高的结果。其融合模型图3所示。
在交通流理论中的流密速关系模型，通过流密速关系模型对数据进行同构化处理。流密速关系模型如下式(伊迪多段式模型):
Figure PCTCN2016092669-appb-000002
式中Q为流量，vs为速度，其它为模型参数。
针对被研究对象某一时刻的物理特性,利用测量设备在一个周期内得到n个测量值zi(i＝1,2,...,n)，由于综合考虑了传输误差、计算误差、环境噪声、人为干扰、传感器自身的精度及被测对象自身性质等因素，zi将并不严格服从正态分布，所以基于数据为正态分布模型的一些数据处理方法(异常值剔出和数据融合算法)将不可避免地增加数据处理的系统误差。这样对测量数据的真伪程度只能由数据的自身x1,x2,...,xn来确定，即xi的真实性越高，则xi被其余的数据所支持的程度就越高，即所谓xi被xj支持程度即从数据xj来 看数据xi为真实数据的可能程度。针对数据间支持程度问题这里引入相对距离的概念，定义测量数据间的相对距离为dij，其表达形式如下：
dij＝|xi-xj|,i,j＝1,2,...,n
由dij的表达形式可知，dij越大则表明两数据间的差别越大，即两数据间的相互支持程度就越小。相对距离的定义形式完全建立在现有数据隐含信息的基础上，降低了对于先验信息的要求。进而可以定义一个支持度函数rij，rij本身应满足以下两个条件：
·rij应与相对距离成反比关系；
·rij∈(0,1)使数据的处理能够利用模糊集合理论中隶属函数的优点，避免数据之间相互支持程度的绝对化。
则支持度函数rij定义为：
Figure PCTCN2016092669-appb-000003
其中max{dij}表示数据间相对距离中的最大值，很明显数据间相对距离越大，则数据间的支持度将越小从上式的定义形式可知，当数据间的相对距离取最大值时，可认为两数据已经不再相互支持，则此时支持度函数的值为零；而数据间的相对距离越小，则数据间的相互支持度就越大，数据对自身的相对距离为零，则数据对自身的支持度为1。由于rij在dij∈[0,max{dij}]上取值从1至0依次递减，所以满足支持度函数应具有的性质。而且，这种满足模糊性支持度函数rij的定义形式更符合实际问题的真实性，同时便于具体实施，使得融合的结果更加精确和稳定。
对于数据融合问题，建立支持度矩阵R：
Figure PCTCN2016092669-appb-000004
支持度矩阵R中rij仅表示两数据间的相互支持程度，并不能反映一个测量数据被数据组中所有数据的总体支持程度。现在要从R中求出某个数据受到其他数据的综合支持程度，也即确定第i个测量数据在全体测量数据中自身的权系数
Figure PCTCN2016092669-appb-000005
根据信息分享原理即最优融合估计的信息量之和可等效分解为若干个测量数据的信息量之和。或者说，一个信息可被若干个子系统所分享，则
Figure PCTCN2016092669-appb-000006
由于
Figure PCTCN2016092669-appb-000007
应综合ri1,ri2,...,rin的总体信息，则由概率源合并理论得知，即要求一组非负数v1,v2,...,vn，使得下一个公式中的
Figure PCTCN2016092669-appb-000008
在这里，我们将上个公式改写为矩阵的形式，则使W＝RV，其中
Figure PCTCN2016092669-appb-000009
因为rij≥0，所以支持度矩阵R是一个非负矩阵,根据非负矩阵的性质可知R存在最大模特征值λ≥0，并且由λV＝RV,可得到其对应的特征向量V＝[v1,v2,...,vn]T,令
Figure PCTCN2016092669-appb-000010
则
Figure PCTCN2016092669-appb-000011
即为第i个测量数据xi的自身权系数，对n个测量数据的融合结果为
Figure PCTCN2016092669-appb-000012
3)基于MVC的FCD数据采集与融合
在实时动态发布常规公交服务指数与系统用例实现中涉及到三个控制类，两个实体类。
·实体类如下：
a.OriginalFCD：记录原始FCD数据，仅包括原始数据的FCD字符串。
b.ValidatedFCD：记录有效FCD数据，包括的主要属性有taxiID(车辆编号)、checkDate(测试数据所属日期)、latitude(车辆位置纬度)、longtitude(车辆位置经度)、spotSpeed(地点车速)、directionAngle(方位角)、CpBs(计算机唯一标设)、CpName(计算机唯一名)、WithSound(是否声控)、UserName(用户名)、PassWord(密码)、PhoneTail(手机尾号)、ByPAss100(超过100不要)、TimeInterVal(采集FCD时钟)。
·控制类如下：
a.Time：与定点数据采集用例中的Time类相同。
b.FCDConnect：负责连接远端FCD采集服务，ConnectVFDService()方法连接远端服务，并返回ServeHandle作为标识供GetFCD类使用。
c.GetFCD：采集FCD数据主要控制类，在连接远端FCD采集服务后，由Timer驱动获得原始FCD，并通过filter()方法将OriginalFCD过滤，生成有效的ValidatedFCD。
4)事件流的面向对象设计
GetFCD通过FCD Connect的connect FCD Service方法连接远端检测器，通过Timer的setTime方法设定采集发生频率；Time类开始通过check方法自检，当到达时间时开始采集FCD数据。GetFCD通过Serve Handle与远端FCD服务连接获得原始FCD数据，并通过setOriginalFCD将数据放入OriginalVFD类中。通过filter方法过滤原始FCD数据的字符串，得到有效FCD数据放入Validated FCD，并保存当天有效定点数据，完成FCD数据采集工作。
实时动态发布常规公交服务指数与系统，FCD数据采集序列图详见图4所示。
交通公用信息平台负责数据融合、数据字典、基于数据挖掘的决策支持、数据服务和数据维护，交通数据统计查询是数据服务的一个子功能。基于上述分析，将交通公用信息平台的功能组织在数据融合用例包、基于数据挖掘的决策支持用例包、交通数据统计查询用例包和数据维护用例包中。
交通仿真平台通过智能仿真组件进行战略级仿真分析和项目级仿真分析。由于智能仿真组件有其环境配置数据，所以需要标定相关参数。另外，对于一个软件集成产品来说，维护功能必不可少，还需要增加平台的维护功能。因此，功能可以分成战略级仿真分析、项目级仿真分析、智能仿真组件维护和仿真平台维护四块。基于上述分析，将交通仿真功能对应于交 通仿真平台的需要，将其组织为战略级仿真分析用例包、项目级仿真分析用例包、智能仿真组件维护用例包和仿真平台维护用例包。
交通信息服务平台将实时检测、处理后的交通运行状态数据以及仿真计算结果，以适当的形式准确、及时地传达至用户，实现全天候、多方式、多层面的动态、静态交通信息发布。另外，对于一个软件集成产品来说，维护功能必不可少，还需要增加平台的维护功能。因此，功能可以分成信息发布服务、信息管理服务两块。基于上述分析，将信息服务功能组织在信息发布服务用例包和信息管理服务用例包中。
常规公交服务指数实时评价系统主要反映城市道路网络支撑的每时每刻(5分钟一个周期)实际使用常规公交情况的信息，主要包括交通流方面的信息、道路网络工作状况信息、交通事件方面的信息等。交通流信息包括车流量、道路拥挤程度等，其中拥挤程度指标可进行量化(交通拥挤指数)，设置0-10十个级别来反映城市交通畅通、拥挤、阻塞的不同程度，分别标以绿、黄、红三种颜色表达；道路网络工作状况信息主要反映城市道路网络目前拥挤程度，包括拥挤区域、拥挤状态、拥挤持续时间、拥挤变化趋势、形成拥挤的成因、拥挤路况的短期预测等；交通事件方面信息主要反映城市道路网络中当前时刻发生的交通行为事件，主要包括交通事故、交通管制、道路施工、交通监控、交通疏解等。常规公交服务指数实时评价系统数据应该设计简单、实用，尽可能方便数据更新与查询，从而提高数据使用效率；其数据项主要包括内容：编号、路段名称、日期、时刻、方向、车流量、车流速度、拥挤度、路况状态、交通事件等。
常规公交服务指数实时评价系统在设计与实践过程中，与传统的非实时动态系统呈现不同的特性，需要良好的方法、工具、语言的支持。将常规公交服务指数实时评价系统、实时动态统一建模语言、实时动态交通信息评价系统的统一开发过程和Rational Rose Real Time建模环境有机地结合起来，进行了系统的需求分析与用例建模、静态与动态建模、实现与部 署的先进软件技术在交通信息工程中的跨学科应用。
请参阅图5，是本发明实施例的常规公交服务指数实时评价方法的流程图。本发明实施例的常规公交服务指数实时评价方法包括：
步骤10：获取交通大数据，利用主成分分析法，得到常规公交服务水平的评价指标体系；
上述中，常规公交系统是一个非常复杂的系统，他的复杂性表现在：第一构成要素多样化，包括运输对象、运输工具、交通设施；第二与多种外部关系存在紧密关系。城市常规公交系统综合评价是以常规公共交通系统为主要研究对象，借助于科学方法和手段，对于常规公交系统的目标、结构、环境、功能、效益等要素进行分析的基础上，构建指标体系，建立综合评价模型。常规公交系统评价对认识、建设、发展城市公交产业非常必要，通过评价清楚地认识到常规公交现状和社会需求的差距，这对调整城市公交产业结构与政策，进一步完善与优化公交服务具有重要的现实意义。
城市公共交通系统的评价是城市交通系统规划的基础，只有在充分研究公共交通系统中存在的问题和发展特点的基础上，才能全面、系统地确定城市未来交通发展的基本思想、发展方向、规划目标等，才能进一步改善和优化城市交通条件，促进城市和社会经济的全面发展。城市公交系统评价的内容主要包括地个方面：
1)面向公交系统使用者的交通网络技术评价；
2)面向公交系统经营者和管理者(行业与企业的主管部门)的经济效益评价；
3)面向城市市民代表城市与公众利益的服务水平评价；
4)面向政府代表城市的可持续发展评价。
交通大数据实时发布常规公交服务指数的方法与系统是第三个方面内 容，这是城市公共交通系统的评价核心内容，即面向城市市民代表城市与公众利益的服务水平评价。主要反映公交运输企业在服务水平方面是否满足乘客需求，它是面向公交运输企业类的评价指标。公共交通系统服务水平指公共交通系统能给居民提供的各种公交服务，包括公交设施提供的硬服务和司乘人员所提供的软服务两个方面。鉴于当前城市常规公交系统已经采用IC卡售票系统，因此评价司乘人员对常规公交服务水平的影响也是没有多大的意义。因此，常规公交系统服务水平可从两个方面来评价，即公交服务功能和公交服务质量。城市常规公交系统综合评价的主要指标包括以下十一个部分：
·万车事故率；
·安全运行间隔里程；
·路线直达率；
·车站可达性；
·运行速度；
·客运费率；
·车厢服务合格率；
·乘客出行平均时耗；
·乘客平均换乘系数；
·高峰满载率；
·全日线路满载率等。
常规公交评价指标体系比较完整、比较精确的建立，具有相当的复杂性和困难度，根据大量国内外常规公交优化与评价方面的经验，在对于目前国内外常规公交系统评价现状有了深入地了解的基础上，通过分析调查，借助于交通大数据技术手段，依据评价指标的选取原则从常规公交服务水平、网络技术性能、经济效益水平、可持续发展水平来建立城市常规公交系统评价指标体系，重点放在常规公交服务水平的评价上。因此，本专利 发明重点围绕常规公交服务水平建立评价指标体系。
常规公交是城市公益事业，是城市文明建设的窗口，服务水平的好与差，直接关系到社会的政治、经济、社会秩序的正常和稳定，关系到城市的声誉和形象。公共交通的主体是城市居民，对于乘客来说是否选择公交为出行的交通工具关键在于所提供的服务是否满足他们的需求。因此，常规公交系统的服务水平是评价的主要方面。依据评价指标的选取原则和设置功能，在交通大数据环境下，利用主成分分析法，得到常规公交服务水平的评价指标体系，详见下表所示。
常规公交系统服务水平的评价指标体系表
Figure PCTCN2016092669-appb-000013
常规公交系统的综合评价，就是在对城市公共交通系统各个部分、各个阶段、各个层次子系统评价的基础上，谋求城市公共交通系统整体功能的最优调节，并在系统整体优化过程中，不断向决策者提供各种关联信息。综合评价之所以必须也是有常规公共交通系统发展目标的综合性、发展过 程的复杂性、常规公交系统本身的层次性所决定的，同时新一代信息技术的发展，特别是交通大数据技术的引入为综合评价地开展提供了可能。
确定指数评价的目的和评价的参考系统、获取评价信息、形成价值判断，是指数评价问题的一般性过程，常规公交服务指数评价的具体程序流程如下：步骤1：确立指数评价对象；步骤2：确定指数评价目标；步骤3：信息收集与分析；步骤4：确定评价指标体系；步骤5：设计指数评价方法；步骤6：单项指数评价；步骤7：综合指数评价；步骤8：评价结果分析。
步骤20：根据常规公交服务水平的评价指标体系对实时交通大数据评价常规公交服务指数进行量化；
常规公交系统服务指数的量化处理在城市公交系统综合评价中起着主要作用，合理地量化处理有助于增加评价结果的科学性和准确性。针对常规公交系统评价指标，给出每个指标的定义、量纲、量化函数、评价标准、指标说明。针对在不同城市规模、参考有关规范、评价标准等相关城市经验基础上，根据常规公交系统的实际情况与特点给出评价指标体系的分级标准建议值，以便于评价与识别常规公交系统的实际状况。
①万车事故率
单位：次/万车；
定义：万车事故率指全市每万辆机动车的年交通事故次数。
量化：
Figure PCTCN2016092669-appb-000014
式中f16为万车事故率，m11为全年交通事故次数，m12为全市机动车数量。
评价标准：根据国内外的经验，结合深圳城市交通的实际情况，给出万车事故率的评价等级建议值，详见下表所示。
万车事故率评价等级界定建议值表
公交类型 一级 二级 三级 四级 五级
1,2,3,4,5类城市公交系统 ≤10 (10,20] (20,30] (30,40] ＞40
指标说明：万车事故率是衡量一定机动化水平下的交通安全管理水平的主要指标，是道路交通安全设施、道路交通安全管理效果的综合反映。
②乘客出行平均时耗
单位：min；
定义：乘客出行平均时耗指统计期内，客运高峰期90％城市居民的平均单程出行时间；
量化：f17＝f(time)；
评价标准：不同城市规模、不同出行目的下居民能够忍受的最大值存在明显差异，城市规模越大，人们出行容忍的最大出行时耗也相对越大，将居民90％的出行时耗定义为可接受最大出行时耗，详见下表所示。从表中可见，居民90％的出行时耗反映了城市居民出行方便性和可达性，出行时耗越小，居民出行越方便、可达性高。
居民最大出行时耗评价等级界定表
最大出行时耗 一类城市 二类城市 三类城市 四类城市 五类城市
90％的出行时耗 60 50 40 35 25
指标说明：精确数据不易获得，但可以通过OD反推获取信息，该指标对常规公交服务水平进行了总体评价，间接地评估了常规公交运行总体速度和路线的运行效率。
③行车准点率
单位：％；
定义：行车准点率指统计期内，公交运营车辆正点运行次数与全部行车次数之比。
量化：
Figure PCTCN2016092669-appb-000015
式中f18为行车准点率，m13为统计期内运营车辆正 点运行次数，m14为全部行车次数。
评价标准：通常正点率平均不低于80％--90％，根据国内外经验，结合深圳城市交通实际情况给出出行车准点率的评价建议值。详见下表所示。
行车准点率评价等级界定建议值表
城市公交类型 一级 二级 三级 四级 五级
1,2,3,4,5类城市常规公交系统 ＞95 [90,95) [85,90) [80,85) ＜80
指标说明：公交运送准确及时，对乘客来说也别重要，特别是大城市，要减少交通压力、减少交通拥堵，就应该大力发展公共交通。
④客运费率
单位：％；
定义：客运费率指统计期内，普通票乘客平均每月个人实际支付的乘车费与该城市职工平均工资之比，可反映公共客运票价乘客承受能力。
量化：
Figure PCTCN2016092669-appb-000016
式中f19为客运费率，c8为普通乘客平均每月实际支付乘车费，c9为职工平均月薪。
评价标准：根据国内外经验，考虑深圳城市交通实际情况，给出客运费率的评价等级界定建议值，详见下表所示。
客运费率的评价等级界定建议值表
城市公交系统类型 一级 二级 三级 四级 五级
1,2,3,4,5类城市常规公交系统 ＜3.5 [3.5,4.5) [4.5,5.5) [5.5,6.5) ＞6.5
指标说明：客运费率主要指票价的便宜程度，是公交吸引顾客的一个重要评价指标，也是公交优先发展首要考虑的问题。票价过高，公交对顾客的吸引力降低；票价过低，公交企业的运营成本加大；所以公交票价要保持合理的价位。
⑤乘客平均换成系数
单位：无量纲；
定义：乘客平均换乘系数指统计期内，乘客出行人次与换乘人次之和除以乘客出行人次，该指标衡量乘客直达程度，反映乘车方便程度。
量化：
Figure PCTCN2016092669-appb-000017
式中f20为乘客平均换乘系数，n1为乘客出行人次，n2为换乘人次。
评价指标：大城市平均换乘系数不大于1.5，小城市平均换乘系数不大于1.3，根据国内外经验，结合深圳城市交通实际情况，给出乘客平均换成系数的评价等级建议值。详见下表所示。
乘客平均换成系数的评价等级建议值表
城市公交系统类型 一级 二级 三级 四级 五级
1,2,3类城市常规公交系统 [1.0,1.1) [1.1,1.2) [1.2,1.4) [1.4,1.5) ≥1.5
4,5类城市常规公交系统 [1.0,1.05) [1.05,1.1) [1.1,1.2) [1.2,1.3) ≥1.3
指标说明：居民出行途中常要从一条公交线路换乘另一条线路，有的还要多次换乘。平均转换次数指全部乘客的换乘次数总和除以全部乘客人数。换乘要增加乘客途中耗费的时间和精力，使之感到不便。所以城市公交尽量做到直达、快捷，减少乘客换乘。
⑥全天线路满载率
单位：％；
定义：全天满载率指统计期内，运营车辆全天运载乘客的平均满载程度。
量化：
Figure PCTCN2016092669-appb-000018
式中f21为全天线路满载率，qi,i+1,k为k条线路的节点i至i+1路段客流量，Li,i+1,k为第k条线路的节点i至i+1路段客流量间距离km，n3为常规公交线路数，n4为通行常规公交车辆的道路网节点数。
评价标准：根据国内外相关经验，考虑深圳城市交通实际情况，给出 全天线路满载率的评价等级界定建议值。详见下表所示。
全天线路满载率的评价等级界定建议值表
城市公交系统类型 一级 二级 三级 四级 五级
一类城市常规公交系统 ＞90 [80,90) [70,80) [50,70) ＜50
二类城市常规公交系统 ＞85 [75,85) [65,75) [45,65) ＜45
三类城市常规公交系统 ＞80 [70,80) [60,70) [40,60) ＜40
四，五类城市常规公交系统 ＞75 [65,75) [55,65) [30,55) ＜30
指标说明：数据需要从公交企业获得，或者通过城市综合交通运行指挥中心进行抽样调查。满载率是评价常规公交工具投放效益、验证运力配备、运用是否适应乘客实际需求的重要指标，也是编制或修订运营作业计划、调整常规公交运载工具投放数量和投放方向的重要依据。
⑦安全运行间隔里程
单位：万km/次；
定义：安全运行间隔里程指常规公交车辆总行驶里程与行车责任事故次数的比率。
量化：
Figure PCTCN2016092669-appb-000019
式中f22为安全运行间隔里程，l4为常规公交车辆总行驶里程(万km)，n5为行车责任事故次数(次)。
评价标准：根据“城市交通管理评价体系”，给出安全运行间隔里程评价指标等级界定建议值标准，详见下表所示。
安全运行间隔里程评价指标等级界定建议值表
城市公交系统类型 一级 二级 三级 四级 五级
1,2,3,4,5类城市常规公交系统 ≥125 [100,125) [75,100) [50,75) ＜50
指标说明：计算该指标要求运营公司或城市综合交通运行指挥中心提供各路公交车行驶里程，以及公交管理部门认定的行车责任事故次数。所以通过公交车行驶总里程的数据和总的行车责任事故次数，就可以知道城市常规公交系统的安全运行间隔里程。
⑧高峰满载率
单位：％；
定义：高峰满载率指统计期内主要运营线路高峰小时内，单向高峰路段车辆实际载客量与额定载客量之比。
量化：
Figure PCTCN2016092669-appb-000020
式中f23为高峰满载率，q2为统计期内常规公交车辆实际载客量，q3为统计期内常规公交车辆额定载客量。
评价标准：根据国内外相关经验，结合深圳城市交通的实际情况，给出高峰载客率的评价等价界定建议值。详见下表所示。
高峰载客率的评价等价界定建议值表
城市公交系统类型 一级 二级 三级 四级 五级
一类城市常规公交系统 ＜60 [60,70) [70,80) [80,90) ＞90
二,三类城市常规公交系统 ＜63 [63,73) [73,83) [83,93) ＞93
四,五类城市常规公交系统 ＜65 [65,75) [75,85) [85,95) ＞95
指标说明：高峰满载率是评价常规公交工具投放效益、验证运力配备、运用是否适应乘客实际需求的重要指标，也是编制或修订运营作业计划、调整常规公交运载工具投放数量和投放方向的重要依据。通过综合交通运行指挥中心大量数据，才能得到比较准确的高峰满载率指标。
步骤30：根据量化的常规公交服务指数建立大数据实时发布常规公交服务指数实际指标；
①常规公交系统综合评价体系的理论指标
常规公交系统综合评价体系的理论指标主要包括以下八个部分：
·万车事故率；
·乘客出行平均时耗；
·行车准点率；
·客运费率；
·乘客平均换乘系数；
·全天线路满载率；
·安全运行间隔里程；
·高峰满载率；
②大数据实时发布常规公交服务指数实际指标
大数据实时发布常规公交服务指数实际指标主要包括以下十个部分：
·司乘服务；
·安全保障；
·信息服务；
·车容车况；
·乘车时耗；
·等候时间；
·拥挤程度；
·换成质量；
·设施保障；
·步行时间。
③建立理论指标与实际发布指标对应关系
为了实现在交通大数据环境下，建立实时全自动生成常规公交服务指数，必须建立常规公交系统综合评价体系指标与实时发布常规公交服务指数评价指标两者之间对应关系，详见图6所示。
步骤40：采集实时交通大数据，并对采集的实时交通大数据进行处理；
1)采集的交通大数据内容及属性
交通大数据实时发布常规公交服务指数实际采集的数据类型与属性，是进一步开发研制实施发布常规公交服务指数平台的方法基础，根据其各 自特征进行筛选与特征提取，主要包括以下几类：
·常规公交IC卡数据
“深圳通“一卡通乘客刷卡数据，其属性内容包含：卡号、交易日期、交易时间、线路/地铁站点名称、行业名称(公交、地铁、出租、轮渡、P+R停车场)、交易金额、交易性质(非优惠、优惠、无优惠)。
·常规公交车辆实时数据
常规公交车辆实时数据，其属性包含：设备号码，线路编码，站点编码，协议编号，进出站状态，方向，车载上报时间、编码对应表。
·常规公交线网数据
常规公交线路网络结构与交通地理信息数据GIS-T，首末班车时间(全市946条公交线路、上行首末班车时刻表、下行首末班车、时刻表)等。
·出租车行车数据
出租车行车数据，其属性包含：车辆ID、GPS时间、经纬度、速度、卫星颗数、营运状态高架状态、制动状态。
·轨道交通运行数据
地铁运行数据，其属性包含：线路、车站、换乘站数据、首末班车各站时刻表数据、站间运行时间数据、限流车站、封站数据、路网票价矩阵、列车实时到发站台时刻、线路拥挤及阻塞数据、出/入口、厕所、残疾电梯数据。城市轨道周边公交(深圳市所有轨道交通站点、附近的公交车站、位置、各站点的名称)。
·道路车牌识别数据
覆盖全市域城市道路交通网络460个断面的车牌识别数据，其属性包含：车辆类别(小汽车、出租车、公交车、货车等)、车牌号、车辆行驶方向、车辆行驶速度、车辆所属行政区域等。
·交通气象数据
交通气象数据，其属性包含：日期、时间、监测点、天气类型、温度、风速、风向、降水量。
1)交通信息FCD采集算法
交通信息FCD采集中所涉及的核心算法包括原始数据FCD异常剔除算法，车速计算、融合和预测算法，标准和历史数据(流量和车速)的统计算法等七个关键算法。这些算法实现了从外场原始数据到“城市综合交通信息平台”使用数据的转换，是保证“信息平台”乃至整个系统基础数据可靠性的关键。下表列出了交通信息采集中七个核心算法，其后均以伪码形式对算法进行详细的描述。鉴于篇幅所限，本次发明专利“实时动态发布常规公交服务指数与系统”只介绍最核心的FCD数据的相关算法及融合处理应用。
交通信息采集核心算法列表
Figure PCTCN2016092669-appb-000021
2)FCD数据融合分析与处理
在FCD采集信息中，根据外场采集的数据信息，计算当前路段车速并综合采集信息预测15分钟、30分钟车速。车速融合预测过程分为三个过程：
·根据FCD数据基于FCD车速计算模型计算路段行程车速。
·根据流量、地点车速和行程车速基于数据融合模型计算当前车速。
·根据当前车速基于车速预测模型对车速进行预测。
本次发明专利算法的核心思想是基于多源的交通信息和交通流理论，综合各种数据信息，实现对交通状态的判断和描述。
FCD数据融合的主要任务是对当前流量数据，FCD计算车速和地点车速进行数据级的融合。这里数据融合主要分为两个方面：一方面是实时数据和历史数据的融合，采用线性变换，模糊算法，标定不同的权值和隶属度，得出较为精确的数值；另一方面是浮动车数据和定点检测器数据的融合，根据两种不同信息源的各自特点，通过异构数据同构化处理，对同一参数进行融合，得出可信度较高的结果。
在交通流理论中的流密速关系模型，通过流密速关系模型对数据进行同构化处理。流密速关系模型如下式(伊迪多段式模型):
Figure PCTCN2016092669-appb-000022
式中Q为流量，vs为速度，其它为模型参数。
针对被研究对象某一时刻的物理特性,利用测量设备在一个周期内得到n个测量值zi(i＝1,2,...,n)，由于综合考虑了传输误差、计算误差、环境噪声、人为干扰、传感器自身的精度及被测对象自身性质等因素，zi将并不严格服从正态分布，所以基于数据为正态分布模型的一些数据处理方法(异常值剔出和数据融合算法)将不可避免地增加数据处理的系统误差。这样对测量数据的真伪程度只能由数据的自身x1,x2,...,xn来确定，即xi的真实性越高，则xi被其余的数据所支持的程度就越高，即所谓xi被xj支持程度即从数据xj来看数据xi为真实数据的可能程度。针对数据间支持程度问题这里引入相对距离的概念，定义测量数据间的相对距离为dij，其表达形式如下：
dij＝|xi-xj|,i,j＝1,2,...,n
由dij的表达形式可知，dij越大则表明两数据间的差别越大，即两数据间的相互支持程度就越小。相对距离的定义形式完全建立在现有数据隐含 信息的基础上，降低了对于先验信息的要求。进而可以定义一个支持度函数rij，rij本身应满足以下两个条件：
·rij应与相对距离成反比关系；
·rij∈(0,1)使数据的处理能够利用模糊集合理论中隶属函数的优点，避免数据之间相互支持程度的绝对化。
则支持度函数rij定义为：
Figure PCTCN2016092669-appb-000023
其中max{dij}表示数据间相对距离中的最大值，很明显数据间相对距离越大，则数据间的支持度将越小从上式的定义形式可知，当数据间的相对距离取最大值时，可认为两数据已经不再相互支持，则此时支持度函数的值为零；而数据间的相对距离越小，则数据间的相互支持度就越大，数据对自身的相对距离为零，则数据对自身的支持度为1。由于rij在dij∈[0,max{dij}]上取值从1至0依次递减，所以满足支持度函数应具有的性质。而且，这种满足模糊性支持度函数rij的定义形式更符合实际问题的真实性，同时便于具体实施，使得融合的结果更加精确和稳定。
对于数据融合问题，建立支持度矩阵R：
Figure PCTCN2016092669-appb-000024
支持度矩阵R中rij仅表示两数据间的相互支持程度，并不能反映一个测量数据被数据组中所有数据的总体支持程度。现在要从R中求出某个数据受到其他数据的综合支持程度，也即确定第i个测量数据在全体测量数据中自身的权系数
Figure PCTCN2016092669-appb-000025
根据信息分享原理即最优融合估计的信息量之和可等效分解为若干个测量数据的信息量之和。或者说，一个信息可被若干个子系统所分享，则
Figure PCTCN2016092669-appb-000026
由于
Figure PCTCN2016092669-appb-000027
应综合ri1,ri2,...,rin的总体信息，则由概率源 合并理论得知，即要求一组非负数v1,v2,...,vn，使得下一个公式中的
Figure PCTCN2016092669-appb-000028
在这里，我们将上个公式改写为矩阵的形式，则使W＝RV，其中
Figure PCTCN2016092669-appb-000029
V＝[v1,v2,...,vn]T。因为rij≥0，所以支持度矩阵R是一个非负矩阵,根据非负矩阵的性质可知R存在最大模特征值λ≥0，并且由λV＝RV,可得到其对应的特征向量V＝[v1,v2,...,vn]T,令
Figure PCTCN2016092669-appb-000030
则
Figure PCTCN2016092669-appb-000031
即为第i个测量数据xi的自身权系数，对n个测量数据的融合结果为
Figure PCTCN2016092669-appb-000032
3)基于MVC的FCD数据采集与融合
在实时动态发布常规公交服务指数与系统用例实现中涉及到三个控制类，两个实体类。
·实体类如下：
a.OriginalFCD：记录原始FCD数据，仅包括原始数据的FCD字符串。
b.ValidatedFCD：记录有效FCD数据，包括的主要属性有taxiID(车辆编号)、checkDate(测试数据所属日期)、latitude(车辆位置纬度)、longtitude(车辆位置经度)、spotSpeed(地点车速)、directionAngle(方位角)、CpBs(计算机唯一标设)、CpName(计算机唯一名)、WithSound(是否声控)、UserName(用户名)、PassWord(密码)、PhoneTail(手机尾号)、ByPAss100(超过100不要)、TimeInterVal(采集FCD时钟)。
·控制类如下：
a.Time：与定点数据采集用例中的Time类相同。
b.FCDConnect：负责连接远端FCD采集服务，ConnectVFDService()方法连接远端服务，并返回ServeHandle作为标识供GetFCD类使用。
c.GetFCD：采集FCD数据主要控制类，在连接远端FCD采集服务后，由Timer驱动获得原始FCD，并通过filter()方法将OriginalFCD过滤，生成有效的ValidatedFCD。
4)事件流的面向对象设计
GetFCD通过FCD Connect的connect FCD Service方法连接远端检测器，通过Timer的setTime方法设定采集发生频率；Time类开始通过check方法自检，当到达时间时开始采集FCD数据。GetFCD通过Serve Handle与远端FCD服务连接获得原始FCD数据，并通过setOriginalFCD将数据放入OriginalVFD类中。通过filter方法过滤原始FCD数据的字符串，得到有效FCD数据放入Validated FCD，并保存当天有效定点数据，完成FCD数据采集工作。
实时动态发布常规公交服务指数与系统，FCD数据采集序列图详见图4所示。
5)派生需求的面向对象协作图设计
实时动态发布常规公交服务指数与系统，采用自动或人工触发方式建立出租车、公交车公司与系统之间的连接，出租车、公交车公司能够全天连续提供FCD数据。深圳市城市交通仿真的FCD数据采集，就是城市综合交通信息中心的大规模GPS出租车及深圳公交集团等运营企业，计划在特区内外将提供超过15000辆出租车的FCD动态交通实时采集数据，采集间隔不小于30秒，车总量不少于15000辆。
实时发布数据统一访问与转换平台研发
随着快速城市化、经济全球化、信息网络化进程的不断加快，中国一线城市北京、上海、广州、深圳的新型城镇化智慧城市综合交通信息中心建设已经基本完成，交通大数据已经接入城市综合交通信息中心，实现面向城市交通规划、建设、管理一体化的决策支持应用服务。面对政府、行业、企业、公众大规模的交通决策支持数据集成或数据的统一访问，作为城市交通唯一的综合交通信息中心交通大数据资源整合和服务方式正在受到高度关注。
1)数据统一访问与转换简介
在城市综合交通信息中心建设实践中，大量地接入了由不同核心技术构造的交通决策支持系统以及相关的数据库，如：城市综合交通运输辅助决策支持系统、城市道路交通规划决策支持系统、城市交通设施监管决策支持系统、城市公共交通服务评估决策支持系统、城市交通管理与控制决策支持系统、城市现代物流服务链决策支持系统、城市交通公众出行信息决策支持系统等，由此构成了一个个异构交通大数据源。面对如此重要且紧迫的交通决策支持应用服务需求，如何通过一个集成系统平台，将来自城市综合交通信息中心内部和外部的同构、异构数据源进行整合与转换，是当前城市交通大数据决策支持环境建设与应用所面临的巨大挑战。
实现城市交通信息资源共享主要包括两种模式，第一是数据转换，第二是数据集成。进行数据转换是物理意义上的数据集中，一方面需要在硬件及相关软件上进行巨大投入，另一方面进行海量数据迁移和管理，也存在相当大的风险，有关访问速度并不理想；完成数据集成是逻辑意义上的数据集中，能够充分利用现有资源进行分布式存储、分散管理、统一访问接口，适应新一代信息技术发展现状要求。
深圳市针对交通大数据决策支持环境的集成应用，实现了从模型上包括联邦方式、数据仓库、中间件三种方式；从集成技术上异构数据库集成(迁移和转换)、分布式数据库系统、使用中间件模块技术。当前，交通大数据决策支持环境的通用数据统一访问和转换技术的研究仍处于起步阶段，国外一些著名的数据库公司开发了相应的中间件应用产品，用于解决异构数据集成问题，使用这些中间件产品需要做大量的数据接口开发工作；国内目前还缺乏比较完整的数据整合应用产品与技术手段。与此同时，现有的数据编程技术通常是或多或少地针对特定数据源类型而设计的，现实环境的应用，交通大数据都来自于多种数据源，可以忽略数据来源普通数据的表达集，能够为应用开发者提供一种简单、统一的编程模型。
交通大数据决策支持环境的数据统一访问与转换技术的提出，正是为 了实现城市综合交通信息中心信息资源的共享与统一访问，交通大数据的统一访问与转换技术采集、分析、集成来自不同的数据源的数据，为城市交通相关决策支持应用程序员提供统一、规范的数据访问形式，实现对各类分布的异构数据源进行透明访问。交通大数据的统一访问与转换技术的目标是对异构数据源的统一访问和应用，即将访问请求分解到各个不同的数据源中，再将返回的异构结果进行统一整合转换，给决策支持应用程序的设计者提供了一个统一的数据源访问接口，并为后续的数据分析奠定基础。
数据采集、清洗、挖掘、汇总规则与算法
1)建立数据处理规则
实时动态发布常规公交服务指数与系统，数据分析：建立数据采集规则、数据清洗规则、数据挖掘规则、数据汇总规则，详见图7所示。
2)建立道路网络支撑的数据分析挖掘流程与模型算法
实时动态发布常规公交服务指数与系统，数据分析设计：基于FCD，建立道路网络支撑的公交出行数据分析挖掘流程与模型算法。
3)建立公交IC卡的数据分析挖掘流程与模型算法
实时动态发布常规公交服务指数与系统，数据分析设计：建立公交IC卡出行的数据分析挖掘流程与模型算法。
4)建立常规公交出行的数据分析挖掘流程与模型算法
实时动态发布常规公交服务指数与系统，数据分析设计：建立常规公交出行的数据分析挖掘流程与模型算法。
5)建立出租车出行的数据分析挖掘流程与模型算法
实时动态发布常规公交服务指数与系统，数据分析设计：建立出租车出行的数据分析挖掘流程与模型算法。
6)建立轨道、巴士、出租、IC卡等数据库搜索引擎
实时动态发布常规公交服务指数与系统，数据分析设计：建立轨道、巴士、出租、IC卡等数据库搜索引擎与数据库开发设计。
7)建立数据库群的分析与挖掘环境界面
实时动态发布常规公交服务指数与系统，数据分析设计：建立数据库群的分析与挖掘环境界面。
步骤50：根据交通大数据实时发布常规公交服务指数与实际采集的数据类型与属性，建立主要数据的相关特征提取；
根据上述交通大数据实时发布常规公交服务指数实际采集的数据类型与属性，建立主要数据的相关特征提取，其他数据可以进行辅助较验与评估。
·建立数据应用关联结构设计
基于采集的轨道、巴士、出租城市公交相关数据，建立数据的应用关联结构设计。
步骤60：根据提取的数据相关特征，发布交通大数据对常规公交服务指数。
“实时动态发布常规公交服务指数”方法与系统是从乘客角度出发，通过相关交通大数据，客观量测与主观感知得出对常规公交服务的整体评价。通过技术指标主要反映常规公交服务的两个方面重要内容：
①常规公交服务(乘客)的可达性；
②提供给乘客服务的舒适性与便捷性。
它不同于道路交通指数，因为道路服务主要面向车辆而不是乘客；也不同于公交行业统计指数，因为统计的是效用与经济，反映公交运营商利益。它充分体现公交姓“公”、名“服务”，“以人为本”的理念跃然纸上。它给出的是公交系统、线路、场站、换乘等服务质量与品质出行评价。
建立以“六类城市交通大数据”为基础、以深圳市常规公交服务分析查询与指数发布平台为中心的常规公交服务状态特征提取与评价分析系统， 实现“一个平台、四个应用”模式，即：常规公交服务分析查询与指数发布平台，常规公交仿真、常规公交评价、面向政府与行业管理、面向公众出行服务的四个领域应用，通过互联网与移动互联网方式发布常规公交信息。
本发明专利具有“引入交通大数据环境与指数模式、设计实时发布常规公交服务指数体系、建立实时发布常规公交服务指数方法、开发了实时发布常规公交服务指数平台系统”整体解决常规公交服务指数评价的优点。
本发明实施例的常规公交服务指数实时评价系统及评价方法，紧密跟踪城市公交通出行核心问题做为切入点；是在大数据时代，对传统问卷式评价常规公交服务指数发布的革命性挑战；它克服了传统问卷式评价常规公交的数据静态、周期较长、单一片面、统计繁琐等弊端，通过城市交通大数据的建模分析与关联性研究，实时动态地面向政府部门、行业企业、公众出行实时发布常规公交系统运行状态与演变态势，具有重要的商业价值与社会价值。
本发明可以节省城市公交出行的在途时间与出行成本，提高公众出行的实效性与便捷性，既可以产生直接效益，又可以产生间接效益；
本发明可以实现城市公交信息的增值服务与综合服务，产生公交出行链的商业价值与经济效益。
城市交通优先发展战略方针是公共交通，而“常规公交服务指数实时评价系统及评价方法”又是实现这一战略的核心所在，这对于城市公交优先发展战略、公交系统出行效率、公交一体化换乘接驳、缓解城市交通拥堵、提高公众出行安全、降低城市交通污染等都具有重要的社会价值。
对所公开的实施例的上述说明，使本领域专业技术人员能够实现或使用本发明。对这些实施例的多种修改对本领域的专业技术人员来说将是显而易见的，本文中所定义的一般原理可以在不脱离本发明的精神或范围的情况下，在其它实施例中实现。因此，本发明将不会被限制于本文所示的这些实施例，而是要符合与本文所公开的原理和新颖特点相一致的最宽的 范围。

Claims (10)

  1. 一种常规公交服务指数实时评价系统，其特征在于，包括交通信息采集平台、交通公用信息平台、交通仿真平台与交通信息服务平台；所述交通公用信息平台面向交通信息采集平台、交通信息服务平台和交通仿真平台提供运行支撑和信息服务；所述交通信息采集平台为交通公用信息平台提供原始数据；所述交通仿真平台为交通公用信息平台提供仿真结果数据；所述交通信息服务平台依托交通公用信息平台提供的数据服务，将交通信息采集平台的功能需求组织在信息采集用例包。
  2. 根据权利要求1所述的常规公交服务指数实时评价系统，其特征在于，所述交通信息采集平台通过固定点和浮动车检测设备对路网中的点和线交通状态进行采集，并对实时采集的交通运行状态数据进行处理，将结果存储到交通公用信息平台。
  3. 根据权利要求2所述的常规公交服务指数实时评价系统，其特征在于，所述交通信息采集平台通过固定点和浮动车检测设备对路网中的点和线交通状态进行采集具体包括：原始数据FCD异常剔除、车速计算、FCD数据融合和预测、标准和历史数据的统计；其中，所述原始数据FCD异常剔除包括：接收FCD数据，判断数据是否有效，得到有效FCD数据。
  4. 根据权利要求3所述的常规公交服务指数实时评价系统，其特征在于，所述FCD数据融合和预测具体包括：根据FCD数据基于FCD车速计算模型计算路段行程车速；根据流量、地点车速和行程车速基于数据融合模型计算当前车速；根据当前车速基于车速预测模型对车速进行预测。
  5. 根据权利要求4所述的常规公交服务指数实时评价系统，其特征在于，所述FCD数据融合包括实时数据和历史数据的融合、浮动车数据和定点检测器数据的融合，所述实时数据和历史数据的融合采用线性变换，模糊算法，标定不同的权值和隶属度，得出较为精确的数值；所述浮动车数据和定点检测器数据的融合根据两种不同信息源的各自特点，通过异构数据同构化处理，对同一参数进行融合，得出可信度高的结果。
  6. 根据权利要求4所述的常规公交服务指数实时评价系统，其特征在于，所述FCD数据采集具体包括：GetFCD通过FCD Connect的connect FCD Service方法连接远端检测器，通过Timer的setTime方法设定采集发生频率；Time类开始通过check方法自检，当到达时间时开始采集FCD数据；GetFCD通过Serve Handle与远端FCD服务连接获得原始FCD数据，并通过setOriginalFCD将数据放入OriginalVFD类中；通过filter方法过滤原始FCD数据的字符串，得到有效FCD数据放入Validated FCD，并保存当天有效定点数据，完成FCD数据采集工作。
  7. 根据权利要求4所述的常规公交服务指数实时评价系统，其特征在于，所述交通公用信息平台负责数据融合、数据字典、基于数据挖掘的决策支持、数据服务和数据维护。
  8. 一种常规公交服务指数实时评价方法，其特征在于，包括：
    步骤a：获取交通大数据，利用主成分分析法，得到常规公交服务水平的评价指标体系；
    步骤b：根据常规公交服务水平的评价指标体系对实时交通大数据评价常规公交服务指数进行量化；
    步骤c：根据量化的常规公交服务指数建立大数据实时发布常规公交服务指数实际指标；
    步骤d：采集实时交通大数据，并对采集的实时交通大数据进行处理；
    步骤e：根据交通大数据实时发布常规公交服务指数与实际采集的数据类型与属性，建立主要数据的相关特征提取；根据提取的数据相关特征，发布交通大数据对常规公交服务指数。
  9. 根据权利要求8所述的常规公交服务指数实时评价方法，其特征在于，在所述步骤d中，对采集的实时交通大数据进行处理包括：建立数据处理规则；建立道路网络支撑的数据分析挖掘流程与模型算法；建立公交IC卡的数据分析挖掘流程与模型算法；建立常规公交出行的数据分析挖掘流程与模型算法；建立出租车出行的数据分析挖掘流程与模型算法；建立轨道、巴士、出租、IC卡数据库搜索引擎；建立数据库群的分析与挖掘环境界面。
  10. 根据权利要求9所述的常规公交服务指数实时评价方法，其特征在于，在所述步骤d中，所述采集实时交通大数据通过固定点和浮动车检测设备对路网中的点和线交通状态进行采集。
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