Toll lane service level evaluation index system construction method based on highway toll lane global data resources

Abstract

The invention relates to an expressway toll lane global data resource-based toll lane service level evaluation index system construction method, which comprises the following steps of: sorting a lane service level evaluation index set, and classifying each index in the lane service level evaluation index set according to functions and entities for implementing services; according to an SERVQUAL model, carrying out preliminary classification on each index; screening the indexes by adopting an expert scoring method to obtain comprehensive scores of the indexes, and deleting the indexes with low scores; adopting a correlation coefficient method to merge indexes meeting conditions; and classifying the indexes again according to an SERVQUAL model, and constructing and obtaining a toll lane service level evaluation index system. According to the method, toll lane global data resources are fully converged, the indexes are classified according to the SERVQUAL model, and the indexes are screened and merged by using an expert scoring method and a correlation coefficient method, so that the toll lane service level is objectively and accurately reflected and presented.

Description

Technical Field

[0001] The present invention relates to a method for constructing a toll lane service level evaluation index system based on global data resources of toll lanes on expressways, and belongs to the technical field of traffic toll service evaluation. Background Art

[0002] With the construction and development of expressways, the global data resources of toll lanes at toll stations are massive and continue to increase rapidly. At the same time, there has always been an objective need for the evaluation and management of the service level of toll lanes at expressway toll stations, and some evaluation and management measures have been implemented to maintain the service level of toll lanes at a high level. However, based on the applicant's practical experience, the evaluation and management measures currently adopted by expressway toll stations have not fully tapped and utilized the global data resources of toll lanes, and it is urgent to develop a method that can fully gather the global data of toll lanes and form a construction method for the toll lane service evaluation index system.

[0003] After searching, it was found that the invention patent application with application number 202311059532.8 and application publication number CN117078201A disclosed a business evaluation system based on high-speed business data. By establishing a set of operation management work quality evaluation index systems applicable to various types of high-speed companies, using existing business data for multi-dimensional analysis, and formulating full-factor evaluation indicators covering the overall operation business scope. However, this technical solution is not directly aimed at the evaluation of the service level of toll lanes, nor does it make full use of the full-domain data of toll lanes, and cannot be directly applied to solve the above-mentioned existing technical problems. Summary of the invention

[0004] The main purpose of the present invention is to overcome the problems existing in the prior art and provide a method for constructing a toll lane service level evaluation index system based on the global data resources of toll lanes on expressways.

[0005] The technical solution of the present invention to solve the technical problem is as follows:

[0006] The method for constructing a toll lane service level evaluation index system based on the global data resources of toll lanes on expressways includes the following steps:

[0007] The first step is to sort out the lane service level evaluation indicator set and classify each indicator according to its function and the entity that implements the service.

[0008] The second step is to preliminarily classify the indicators of the lane service level evaluation index set according to the SERVQUAL model.

[0009] The third step is to use the expert scoring method to screen the indicators of the lane service level evaluation index set, obtain the comprehensive score of each indicator, and delete the indicators with low scores.

[0010] The fourth step is to use the correlation coefficient method to merge the qualified indicators of the lane service level evaluation index set obtained in the third step.

[0011] Step 5: Reclassify the indicators of the lane service level evaluation index set obtained in the fourth step according to the SERVQUAL model, so as to construct a toll lane service level evaluation index system.

[0012] This construction method fully integrates the global data resources of toll lanes, classifies indicators according to the SERVQUAL model according to the actual service business needs and capabilities of toll lanes, and uses expert scoring method and correlation coefficient method to screen and merge indicators. Finally, a lane service level evaluation index system is constructed to objectively and accurately reflect and present the service level of toll lanes.

[0013] The technical solution further improved by the present invention is as follows:

[0014] Preferably, in the third step, the expert scoring method includes the following steps:

[0015] S1. Invite several evaluation experts to score each indicator of the lane service level evaluation indicator set, and obtain the expert scoring matrix:

[0016]

[0017] Where n is the number of evaluation experts, m is the number of indicators; let i be 1 to n, j be 1 to m, then a ij It represents the score given by the i-th expert to the j-th evaluation index.

[0018] S2. According to the specific situation of each evaluation expert, the expert weight matrix is ​​obtained:

[0019]

[0020] Where n is the number of evaluation experts; let i be 1 to n, then b i represents the weight of the i-th expert.

[0021] S3, based on the results of S1 and S2, the final scoring matrix is ​​obtained:

[0022]

[0023] Then the comprehensive score matrix of each indicator is obtained:

[0024]

[0025] More preferably, in S1, when scoring, the evaluation experts score each indicator after comprehensive consideration of three aspects: representativeness, independence, and relevance to the service quality of the toll station.

[0026] In S2, the process of calculating the weight of the i-th expert is as follows:

[0027] Determine whether the work of the expert is related to the highway toll station. If so, use the following formula to calculate the weight of the expert:

[0028] b i =0.4·education+0.6·workAge,

[0029] If not relevant, the following formula is used to calculate the expert's weight:

[0030] b i =0.2·education+0.3·job+0.3·workAge+drive? (0.2·driveAge): 0,

[0031] In the above two formulas, education is the expert's education level, job is the expert's occupation, and workAge is the expert's working years; drive? (0.2·driveAge): 0 means first judging whether the expert can drive. If not, this item is assigned a value of 0. If so, this item is assigned a value of 0.2·driveAge, where driveAge is the expert's driving years.

[0032] More preferably, the value assignment standard for education is: 1 for PhD, 0.95 for master's, 0.9 for bachelor's, 0.85 for junior college, and 0.8 for others.

[0033] The job assignment standard is: highway traffic related work is 0.9, highway traffic research work is 0.8, and others are 0.7.

[0034] The value assignment standard for workAge is: 1 for more than 20 years, 0.9 for 10 to 20 years, 0.8 for 5 to 10 years, and 0.7 for less than 5 years.

[0035] The driveAge value is 1 for more than 20 years, 0.9 for 10 to 20 years, 0.8 for 5 to 10 years, and 0.7 for less than 5 years.

[0036] By adopting the above preferred scheme, the specific technical features of the third step can be further optimized.

[0037] Preferably, in the fourth step, the specific process of merging includes the following steps:

[0038] T1. Assume that the number of samples is k, and each sample has data under each indicator of the lane service level evaluation indicator set; where the sample data set of the i-th indicator is X = {x1, x2, …, x k}, the mean is The sample data set of the jth indicator is Y = {y1, y2, ..., y k}, the mean is

[0039] T2. Calculate the Pearson correlation coefficient between the i-th indicator and the j-th indicator by the following formula:

[0040]

[0041] Calculate the Pearson correlation coefficient between each two indicators and get the correlation matrix:

[0042]

[0043] Among them, when r uj = ±1, it means that the i-th index is completely linearly correlated with the j-th index; when r ij When it is close to 1 or -1, the i-th indicator is approximately correlated with the j-th indicator.

[0044] T3. According to the preset limit α, determine r ij The relationship between the size of α and |r ij When |≥α, the i-th index and the j-th index are merged.

[0045] More preferably, the value of α is 0.95±0.02.

[0046] By adopting the above preferred scheme, the specific technical features of the fourth step can be further optimized.

[0047] Preferably, in the first step, each indicator is divided into the following five categories: networked toll collection infrastructure capacity, traffic capacity, service efficiency, traffic management, and traffic experience.

[0048] The indicators of networked toll collection infrastructure capabilities include: IC1 gantry connectivity qualification rate, IC2 gantry license plate recognition accuracy rate, IC3 lane connectivity qualification rate, IC4 other electromechanical equipment failure rate, and IC5 cloud agent system application level.

[0049] The indicators of divided traffic capacity include: TC1 travel service information service level, TC2 number of toll lanes, TC3 lane ratio level, TC4 total transaction volume of entrance toll lanes, TC5 total transaction volume of exit toll lanes, TC6 total weighing and detection data of entrance toll lanes, TC7 total weighing and detection data matched with entrance transactions, and TC8 maximum vehicle flow per unit time per month.

[0050] The indicators of entry service efficiency include: SE1 average passing time of ETC lanes, SE2 average passing time of mixed lanes, SE3 matching rate of weighing detection data with entry transactions, SE4 total number of special situations, and SE5 emergency services.

[0051] The indicators of entry and exit traffic management include: TM1 gantry license plate recognition data upload timeliness rate, TM2 gantry transaction data upload timeliness rate, TM3 OBU status list download timeliness rate, TM4 user card status list download timeliness rate, TM5 entrance toll transaction data upload timeliness rate, TM6 exit toll transaction data upload timeliness rate, TM7 entrance toll lane weighing detection data upload timeliness rate, TM8 cumulative number of vehicles handled: cumulative working hours ratio, TM9 average time for special situation handling.

[0052] The indicators of entry and exit experience include: TE1 average queue length, TE2 service standards, TE3 toll station environment, and TE4 driver and passenger satisfaction with toll services.

[0053] By adopting the above preferred scheme, the specific technical features of the first step can be further optimized, and the specific indicators contained in the lane service level evaluation index set can be clarified.

[0054] Preferably, in the second step, the indicators are divided into the following four categories: tangibility, reliability, responsiveness, and recognition.

[0055] The tangible indicators include: IC5 cloud seat system application level, TC1 travel service information service level, TC2 number of toll lanes, TC3 lane ratio level, TC4 total transaction volume of entrance toll lanes, TC5 total transaction volume of exit toll lanes, TC6 total weighing and detection data of entrance toll lanes, TC7 total weighing and detection data matched with entrance transactions, TC8 maximum vehicle flow per unit time per month, and TE3 toll station environment.

[0056] The indicators of entry reliability include: IC1 gantry connection qualification rate, IC2 gantry license plate recognition accuracy rate, IC3 lane connection qualification rate, IC4 other electromechanical equipment failure rate, SE3 weighing detection data matching rate with entrance transactions, SE4 total number of special situations, SE5 emergency services.

[0057] The indicators of response speed include: average passing time of SE1 ETC lane, average passing time of SE2 mixed lane, timely upload rate of TM1 gantry license plate recognition data, timely upload rate of TM2 gantry transaction data, timely download rate of TM3 OBU status list, timely download rate of TM4 user card status list, timely upload rate of TM5 entrance toll transaction data, timely upload rate of TM6 exit toll transaction data, timely upload rate of TM7 entrance toll lane weighing and detection data, average time for TM9 special situation handling, and average queue length of TE1.

[0058] The recognition indicators include: TE2 service standards, TE4 driver and passenger satisfaction with traffic services, TM8 ratio of the cumulative number of vehicles handled to the cumulative working hours.

[0059] By adopting the above preferred scheme, the specific technical features of the second step can be further optimized, and the SERVQUAL model classification to which each indicator belongs can be clarified.

[0060] More preferably, in the third step, the indicators with low scores that are finally deleted are: the number of TC2 toll lanes and the TC3 lane ratio level.

[0061] By adopting the above preferred scheme, the specific technical features of the third step can be further optimized, and the results obtained by executing the third step can be clarified.

[0062] More preferably, in the fourth step, the result of the merger is: the IC1 gantry connectivity qualification rate, IC2 gantry license plate recognition accuracy rate, IC3 lane connectivity qualification rate, IC4 other electromechanical equipment failure rate, SE3 and entrance transaction matching weighing detection data matching rate are merged into RL1 equipment operation status; the IC5 cloud seat system application level and the TC1 travel service information service level are merged into TA1 travel service level; the total transaction volume of TC4 entrance toll lane, the total transaction volume of TC5 exit toll lane, and the total weighing detection data volume of TC6 entrance toll lane are merged into TA3 toll lane transaction volume; the TM1 gantry license plate recognition data upload timeliness rate, the TM2 gantry transaction data upload timeliness rate, the TM3 OBU status list download timeliness rate, the TM4 user card status list download timeliness rate, the TM5 entrance toll transaction data upload timeliness rate, the TM6 exit toll transaction data upload timeliness rate, and the TM7 entrance toll lane weighing detection data upload timeliness rate are merged into RS1 data transmission capacity; the SE1 ETC lane average passing time and the SE2 mixed lane average passing time are merged into the RS2 lane average passing time.

[0063] Among them, the indicators IC1, IC2, IC3, IC4, and SE3 are standardized and calculated as real values ​​between [0,1], and the score of the RL1 equipment operation status is calculated based on these values, that is:

[0064]

[0065] The indicators IC5 and TC1 are standardized and calculated as real values ​​between [0,1], and the score of TA1 travel service level is calculated based on these values, that is:

[0066]

[0067] The indicators TC4, TC5, and TC6 are standardized and calculated as real values ​​between [0, 1], and the score of the total transaction volume of the TA3 toll lane is calculated based on these values, that is:

[0068]

[0069] The indicators TM1, TM2, TM3, TM4, TM5, TM6, and TM7 are standardized and calculated as real numbers between [0, 1], and the scores of RS1 data transmission capacity are calculated based on these values, namely:

[0070]

[0071] Assume that in a toll station, the number of ETC lanes is n1, and the average passing time SE1 of each ETC lane is e1, e2, …, e n1 ; The number of MTC lanes is n2, and the average passing time SE2 of each mixed lane is m1, m2, …, m n2 ; Based on these values, the average passing time score of RS2 lane is calculated, namely:

[0072]

[0073] By adopting the above preferred scheme, the specific technical features of the fourth step can be further optimized, and the results obtained by executing the fourth step can be clarified.

[0074] Preferably, in the fifth step, each indicator is still divided into the following four categories: tangibility, reliability, responsiveness, and recognition.

[0075] The tangible indicators include: TA1 travel service level, TA2 toll station environment, TA3 total transactions in toll lanes, TA4 total weighing and detection data matching entrance transactions, and TA5 maximum vehicle flow per unit time per month; among them, TA2 toll station environment is TE3 toll station environment, TA4 total weighing and detection data matching entrance transactions is TC7 total weighing and detection data matching entrance transactions, and TA5 maximum vehicle flow per unit time per month is TC8 maximum vehicle flow per unit time per month.

[0076] The indicators of input reliability include: RL1 equipment operation status, RL2 total number of special situations, and RL3 emergency services; among them, the total number of RL2 special situations is the total number of SE4 special situations, and the RL3 emergency services is the SE5 emergency services.

[0077] The indicators of response speed include: RS1 data transmission capacity, RS2 average lane passing time, RS3 average time for special situation handling, RS4 average queue length; among them, RS3 average time for special situation handling is the average time for TM9 special situation handling, and RS4 average queue length is the average queue length of TE1.

[0078] The indicators for recognition include: AS1 service standards, AS2 driver and passenger satisfaction with toll service, and AS3 the ratio of the cumulative number of vehicles handled to the cumulative working hours; among them, AS1 service standards are TE2 service standards, AS2 driver and passenger satisfaction with toll service is TE4 driver and passenger satisfaction with toll service, and AS3 the ratio of the cumulative number of vehicles handled to the cumulative working hours is TM8 the ratio of the cumulative number of vehicles handled to the cumulative working hours.

[0079] By adopting the above preferred scheme, the specific technical features of the fifth step can be further optimized, and the results obtained by executing the fifth step can be clarified.

[0080] Preferably, the construction method further comprises the following steps:

[0081] For the constructed toll lane service level evaluation index system, the rationality of each indicator is judged through theoretical analysis and simulation, and the defects and shortcomings of each indicator are determined through review by evaluation experts. Then, we return to the first step to re-sort the lane service level evaluation index set, and execute sequentially to the fifth step to construct a complete toll lane service level evaluation index system.

[0082] By adopting the above preferred scheme, the overall construction method can be further improved, so that the toll lane service level evaluation index system can be improved.

[0083] The present invention adheres to the principles of unity of scientificity and practicality, combination of qualitative and quantitative, and combination of dynamics and relative stability. It fully gathers the global data resources of toll lanes, classifies the indicators according to the actual service business needs and capabilities of toll lanes according to the SERVQUAL model, and uses expert scoring method and correlation coefficient method to screen and merge the indicators. Finally, it constructs a lane service level evaluation index system to achieve objective and accurate reflection and presentation of toll lane service levels. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Figure 1 It is a schematic diagram of the main flow chart of the specific implementation technical solution of the present invention.

[0085] Figure 2This is an example diagram of the process of calculating the correlation matrix using Python's Scipy library in Example 4 of the present invention. DETAILED DESCRIPTION

[0086] The principles of constructing the toll lane service level evaluation index system of the present invention are as follows:

[0087] 1. Unification of scientificity and practicality

[0088] Based on a full understanding of toll station services and the needs of drivers and passengers, we select evaluation indicators, use relevant scientific theories and technologies to construct an evaluation system, and establish a highway toll station service evaluation model. This can objectively and impartially reflect the service quality and service level of toll stations, and can promptly reflect the problems existing in toll stations. The evaluation model is highly operational.

[0089] 2. Combining qualitative and quantitative analysis

[0090] When selecting evaluation indicators, the principle of combining qualitative and quantitative factors should be considered. It is necessary to comprehensively consider all subjective and objective factors related to the service quality of toll stations, including factors that can be actually measured, such as traffic capacity and queue length, as well as qualitative factors such as the environmental sanitation, service attitude, and evaluation of drivers and passengers at toll stations. Only in this way can an objective and fair evaluation result be obtained.

[0091] 3. Combination of dynamics and relative stability

[0092] Some evaluation indicators will get real-time results, while others will be updated regularly, with an update cycle of one week, one month, half a year or one year. When building a service level evaluation model, it is necessary to consider different processing methods for different types of evaluation factors so that dynamic indicators and relatively stable indicators can be organically combined.

[0093] like Figure 1 As shown, the method for constructing a toll lane service level evaluation index system based on the global data resources of highway toll lanes specifically implemented by the present invention includes the following steps:

[0094] The first step is to sort out the lane service level evaluation indicator set and classify each indicator according to its function and the entity that implements the service.

[0095] Specifically, the indicators are divided into the following five categories: networked toll collection infrastructure capabilities, traffic capacity, service efficiency, traffic management, and traffic experience.

[0096] The indicators of networked toll collection infrastructure capabilities include: IC1 gantry connectivity qualification rate, IC2 gantry license plate recognition accuracy rate, IC3 lane connectivity qualification rate, IC4 other electromechanical equipment failure rate, and IC5 cloud agent system application level.

[0097] The indicators of divided traffic capacity include: TC1 travel service information service level, TC2 number of toll lanes, TC3 lane ratio level, TC4 total transaction volume of entrance toll lanes, TC5 total transaction volume of exit toll lanes, TC6 total weighing and detection data of entrance toll lanes, TC7 total weighing and detection data matched with entrance transactions, and TC8 maximum vehicle flow per unit time per month.

[0098] The indicators of entry service efficiency include: SE1 average passing time of ETC lanes, SE2 average passing time of mixed lanes, SE3 matching rate of weighing detection data with entry transactions, SE4 total number of special situations, and SE5 emergency services.

[0099] The indicators of entry and exit traffic management include: TM1 gantry license plate recognition data upload timeliness rate, TM2 gantry transaction data upload timeliness rate, TM3 OBU status list download timeliness rate, TM4 user card status list download timeliness rate, TM5 entrance toll transaction data upload timeliness rate, TM6 exit toll transaction data upload timeliness rate, TM7 entrance toll lane weighing detection data upload timeliness rate, TM8 cumulative number of vehicles handled: cumulative working hours ratio, TM9 average time for special situation handling.

[0100] The indicators of entry and exit experience include: TE1 average queue length, TE2 service standards, TE3 toll station environment, and TE4 driver and passenger satisfaction with toll services.

[0101] The second step is to preliminarily classify the indicators of the lane service level evaluation index set according to the SERVQUAL model.

[0102] Specifically, the indicators are divided into the following four categories: tangibility, reliability, responsiveness, and recognition.

[0103] The tangible indicators include: IC5 cloud seat system application level, TC1 travel service information service level, TC2 number of toll lanes, TC3 lane ratio level, TC4 total transaction volume of entrance toll lanes, TC5 total transaction volume of exit toll lanes, TC6 total weighing and detection data of entrance toll lanes, TC7 total weighing and detection data matched with entrance transactions, TC8 maximum vehicle flow per unit time per month, and TE3 toll station environment.

[0104] The indicators of entry reliability include: IC1 gantry connection qualification rate, IC2 gantry license plate recognition accuracy rate, IC3 lane connection qualification rate, IC4 other electromechanical equipment failure rate, SE3 weighing detection data matching rate with entrance transactions, SE4 total number of special situations, SE5 emergency services.

[0105] The indicators of response speed include: average passing time of SE1 ETC lane, average passing time of SE2 mixed lane, timely upload rate of TM1 gantry license plate recognition data, timely upload rate of TM2 gantry transaction data, timely download rate of TM3 OBU status list, timely download rate of TM4 user card status list, timely upload rate of TM5 entrance toll transaction data, timely upload rate of TM6 exit toll transaction data, timely upload rate of TM7 entrance toll lane weighing and detection data, average time for TM9 special situation handling, and average queue length of TE1.

[0106] The recognition indicators include: TE2 service standards, TE4 driver and passenger satisfaction with traffic services, TM8 ratio of the cumulative number of vehicles handled to the cumulative working hours.

[0107] The third step is to use the expert scoring method to screen the indicators of the lane service level evaluation index set, obtain the comprehensive score of each indicator, and delete the indicators with low scores.

[0108] Specifically, the expert scoring method includes the following steps:

[0109] S1. Invite several evaluation experts to score each indicator of the lane service level evaluation indicator set to obtain the expert scoring matrix:

[0110]

[0111] Where n is the number of evaluation experts, m is the number of indicators; let i be 1 to n, j be 1 to m, then a ij It represents the score given by the i-th expert to the j-th evaluation index.

[0112] When scoring, the evaluation experts scored each indicator after comprehensive consideration of three aspects: representativeness, independence, and relevance to the toll station service quality.

[0113] S2. According to the specific situation of each evaluation expert, the expert weight matrix is ​​obtained:

[0114]

[0115] Where n is the number of evaluation experts; let i be 1 to n, then b i represents the weight of the i-th expert.

[0116] The process of calculating the weight of the i-th expert is as follows:

[0117] Determine whether the work of the expert is related to the highway toll station. If so, use the following formula to calculate the weight of the expert:

[0118] b i=0.4·education+0.6·workAge,

[0119] If not relevant, the following formula is used to calculate the expert's weight:

[0120] b i =0.2·education+0.3·job+0.3·workAge+drive? (0.2·driveAge): 0,

[0121] In the above two formulas, education is the expert's education level, job is the expert's occupation, and workAge is the expert's working years; drive? (0.2·driveAge): 0 means first judging whether the expert can drive. If not, this item is assigned a value of 0. If so, this item is assigned a value of 0.2·driveAge, where driveAge is the expert's driving years.

[0122] The standard for assigning values ​​for education is: 1 for PhD, 0.95 for master's, 0.9 for bachelor's degree, 0.85 for junior college, and 0.8 for others.

[0123] The job assignment standard is: highway traffic related work is 0.9, highway traffic research work is 0.8, and others are 0.7.

[0124] The value assignment standard for workAge is: 1 for more than 20 years, 0.9 for 10 to 20 years, 0.8 for 5 to 10 years, and 0.7 for less than 5 years.

[0125] The driveAge value is 1 for more than 20 years, 0.9 for 10 to 20 years, 0.8 for 5 to 10 years, and 0.7 for less than 5 years.

[0126] S3, based on the results of S1 and S2, the final scoring matrix is ​​obtained:

[0127]

[0128] Then the comprehensive score matrix of each indicator is obtained:

[0129]

[0130] In the third step, the indicators with low scores that were finally deleted were: the number of TC2 toll lanes and the TC3 lane ratio level.

[0131] The fourth step is to use the correlation coefficient method to merge the qualified indicators of the lane service level evaluation index set obtained in the third step.

[0132] Specifically, the specific process of the merger includes the following steps:

[0133] T1. Assume that the number of samples is k, and each sample has data under each indicator of the lane service level evaluation indicator set; where the sample data set of the i-th indicator is X = {x1, x2, …, x k}, the mean is The sample data set of the jth indicator is Y = {y1, y2, ..., y l}, the mean is

[0134] T2. Calculate the Pearson correlation coefficient between the i-th indicator and the j-th indicator by the following formula:

[0135]

[0136] Calculate the Pearson correlation coefficient between each two indicators and get the correlation matrix:

[0137]

[0138] Among them, when r ij = ±1, it means that the i-th index is completely linearly correlated with the j-th index; when r ij When it is close to 1 or -1, the i-th indicator is approximately correlated with the j-th indicator.

[0139] T3. According to the preset limit α, determine r ij The relationship between the size of α and |r ij When |≥α, the i-th index and the j-th index are merged.

[0140] The value of α is 0.95±0.02, preferably α=0.95.

[0141] In the fourth step, the results of the merger are as follows: the qualified rate of IC1 gantry connection, the accuracy rate of IC2 gantry license plate recognition, the qualified rate of IC3 lane connection, the failure rate of IC4 other electromechanical equipment, and the matching rate of SE3 weighing and detection data matching with the entrance transaction are merged into the RL1 equipment operation status; the application level of IC5 cloud agent system and the service level of TC1 travel service information are merged into the TA1 travel service level; the total transaction volume of TC4 entrance toll lane, the total transaction volume of TC5 exit toll lane, and the total weighing and detection data volume of TC6 entrance toll lane are merged into the total transaction volume of TA3 toll lane; the timely upload rate of TM1 gantry license plate recognition data, the timely upload rate of TM2 gantry transaction data, the timely download rate of TM3 OBU status list, the timely download rate of TM4 user card status list, the timely upload rate of TM5 entrance toll transaction data, the timely upload rate of TM6 exit toll transaction data, and the timely upload rate of TM7 entrance toll lane weighing and detection data are merged into the RS1 data transmission capacity; the average passing time of SE1 ETC lane and the average passing time of SE2 mixed lane are merged into the average passing time of RS2 lane.

[0142] Among them, the indicators IC1, IC2, IC3, IC4, and SE3 are standardized and calculated as real values ​​between [0,1], and the score of the RL1 equipment operation status is calculated based on these values, that is:

[0143]

[0144] The indicators IC5 and TC1 are standardized and calculated as real values ​​between [0,1], and the score of TA1 travel service level is calculated based on these values, that is:

[0145]

[0146] The indicators TC4, TC5, and TC6 are standardized and calculated as real values ​​between [0, 1], and the score of the total transaction volume of the TA3 toll lane is calculated based on these values, that is:

[0147]

[0148] The indicators TM1, TM2, TM3, TM4, TM5, TM6, and TM7 are standardized and calculated as real numbers between [0, 1], and the scores of RS1 data transmission capacity are calculated based on these values, namely:

[0149]

[0150] Assume that in a toll station, the number of ETC lanes is n1, and the average passing time SE1 of each ETC lane is e1, e2, …, e n1; The number of MTC lanes is n2, and the average passing time SE2 of each mixed lane is m1, m2, …, m n2 Based on these values, the average passing time score of RS2 lane is calculated, namely:

[0151]

[0152] Step 5: Reclassify the indicators of the lane service level evaluation index set obtained in the fourth step according to the SERVQUAL model, so as to construct a toll lane service level evaluation index system.

[0153] Specifically, the indicators are still divided into the following four categories: tangibility, reliability, responsiveness, and recognition.

[0154] The tangible indicators include: TA1 travel service level, TA2 toll station environment, TA3 total transactions in toll lanes, TA4 total weighing and detection data matching entrance transactions, and TA5 maximum vehicle flow per unit time per month; among them, TA2 toll station environment is TE3 toll station environment, TA4 total weighing and detection data matching entrance transactions is TC7 total weighing and detection data matching entrance transactions, and TA5 maximum vehicle flow per unit time per month is TC8 maximum vehicle flow per unit time per month.

[0155] The indicators of input reliability include: RL1 equipment operation status, RL2 total number of special situations, and RL3 emergency services; among them, the total number of RL2 special situations is the total number of SE4 special situations, and the RL3 emergency services is the SE5 emergency services.

[0156] The indicators of response speed include: RS1 data transmission capacity, RS2 average lane passing time, RS3 average time for special situation handling, RS4 average queue length; among them, RS3 average time for special situation handling is the average time for TM9 special situation handling, and RS4 average queue length is the average queue length of TE1.

[0157] The indicators for recognition include: AS1 service standards, AS2 driver and passenger satisfaction with toll service, and AS3 the ratio of the cumulative number of vehicles handled to the cumulative working hours; among them, AS1 service standards are TE2 service standards, AS2 driver and passenger satisfaction with toll service is TE4 driver and passenger satisfaction with toll service, and AS3 the ratio of the cumulative number of vehicles handled to the cumulative working hours is TM8 the ratio of the cumulative number of vehicles handled to the cumulative working hours.

[0158] In addition, the method for constructing a toll lane service level evaluation index system based on the global data resources of highway toll lanes specifically implemented by the present invention may also include the following steps:

[0159] For the constructed toll lane service level evaluation index system, the rationality of each indicator is judged through theoretical analysis and simulation, and the defects and shortcomings of each indicator are determined through review by evaluation experts. Then, we return to the first step to re-sort the lane service level evaluation index set, and execute sequentially to the fifth step to construct a complete toll lane service level evaluation index system.

[0160] Example 1

[0161] This embodiment is a compilation of lane service level evaluation indicator sets.

[0162] The specific contents of this embodiment are as follows:

[0163] The indicators can be classified into five categories according to their functions and the entities implementing the services, as follows.

[0164] 1. Infrastructure Capability

[0165] There are five specific indicators under the toll station operating status perception level: IC1 to IC5.

[0166] IC1 gantry interconnection qualified rate

[0167] Data acquisition method: provided by the Internet center.

[0168] Data format: real number between [0,1].

[0169] Data description: This indicator refers to the connectivity between the equipment on the gantry and the data center, and is related to whether the data on the gantry can be uploaded to the data center in a timely manner (Note: the equipment on the gantry sends at least one heartbeat every 15 minutes, and only one valid heartbeat is recorded). It is objective and relatively stable data, and can be updated after each inspection.

[0170] IC2 gantry license plate recognition accuracy

[0171] Data acquisition method: provided by the Internet center.

[0172] Data format: real number between [0,1].

[0173] Data description: This indicator measures the recognition of license plates by the equipment on the gantry. It is objective and relatively stable data and can be updated monthly.

[0174] IC3 lane connectivity pass rate

[0175] Data acquisition method: provided by the Internet center.

[0176] Data format: real number between [0,1].

[0177] Data description: This indicator refers to the connectivity between the equipment on each toll lane and the data center. It is objective and relatively stable data and can be updated after each inspection.

[0178] IC4 Other electromechanical equipment failure rate

[0179] Data acquisition method: provided by the Internet center.

[0180] Data format: real number between [0,1].

[0181] Data description: This indicator refers to the connectivity between the equipment on each toll lane and the data center. It is objective and relatively stable data and can be updated after each inspection.

[0182] IC5 Cloud Agent System Application Level

[0183] Data acquisition method: provided by the Internet center.

[0184] Data format: can be quantized into integers between [1,10].

[0185] Data description: This indicator refers to the application level of the customer service platform based on cloud computing technology at toll stations, measuring the overall level of integration of telephone call centers, online services and multiple customer service channels at toll stations. It is objective and relatively stable data, which can be updated every six months and depends on specific circumstances.

[0186] 2. Traffic Capacity

[0187] There are 8 specific indicators: TC1 to TC8.

[0188] TC1 Travel Service Information Service Level

[0189] Data acquisition method: provided by the Internet center.

[0190] Data format: can be quantized into integers between [1,10].

[0191] Data description: This indicator refers to whether the traffic conditions of toll stations provided by toll stations or expressway operating departments to users are comprehensive and timely. It is objective and relatively stable data, which can be updated every six months and depends on the specific situation.

[0192] Number of TC2 toll lanes

[0193] Data acquisition method: provided by toll stations.

[0194] Data format: integer.

[0195] Data description: These are objective and stable data and generally will not change once determined unless the toll station is rebuilt or expanded.

[0196] TC3 lane ratio level

[0197] Data acquisition method: provided by toll stations.

[0198] Data format: can be quantized into integers between [1,10].

[0199] Data description: This indicator refers to the proportion of ETC, MTC and other types of lanes at toll stations. It is an objective and stable data, and generally will not change after being determined unless the toll station is rebuilt or expanded.

[0200] Total transaction volume of TC4 entrance toll lane

[0201] Data acquisition method: provided by the Internet center.

[0202] Data format: vehicles / year, vehicles / month, can be quantified into integers between [1,10].

[0203] Data description: This indicator refers to the total transaction volume of the entrance toll lane in a year or a month. It is objective and dynamic data, which can be used as a basis for judging the level of a toll station. The larger the total transaction volume, the higher the level. The differentiation of toll stations of different levels should be reflected in the model.

[0204] Total transaction volume of TC5 exit toll lane

[0205] Data acquisition method: provided by the Internet center.

[0206] Data format: vehicles / year, vehicles / month, can be quantified into integers between [1,10].

[0207] Data description: This indicator refers to the total transaction volume of the entrance toll lane of a toll station in a year or a month. It is objective and dynamic data, which can be used as a basis for judging the level of a toll station. The larger the total transaction volume, the higher the level. The differentiation of toll stations of different levels should be reflected in the model.

[0208] Total amount of weighing test data for TC6 entrance toll lane (this indicator only counts trucks and special operation vehicles)

[0209] Data acquisition method: provided by the Internet center.

[0210] Data format: vehicles / year, vehicles / month, can be quantified into integers between [1,10].

[0211] Data description: This indicator is the total number of weighing data recorded and reported by the toll lanes in a year or a month. It is objective and dynamic data.

[0212] TC7 Total amount of weighing test data matched with entry transactions (this indicator only counts trucks and special operation vehicles)

[0213] Data acquisition method: provided by the Internet center.

[0214] Data format: vehicles / year, vehicles / month, can be quantified into integers between [1,10].

[0215] Data description: This indicator is the total amount of weighing test data collected and reported at the entrance. It is objective and dynamic data.

[0216] Maximum traffic volume per unit time per month for TC8

[0217] Data acquisition method: Calculated through toll station traffic data provided by the networking center.

[0218] Data format: integer, which is the maximum traffic flow data within an hour.

[0219] Data description: This indicator is the maximum traffic volume passing through the toll station within one hour each month, reflecting the peak traffic volume of the toll station within a month. It is objective and relatively stable data and can be counted on a monthly basis. This indicator measures the maximum traffic capacity and service capacity of the toll station. Together with the number of lanes and the ratio of lane types, it can be used as an important indicator for evaluating the service quality of the toll station. When evaluating the service level at a certain point in time, this indicator can use the data of the previous month. The M / G / K queuing theory model can be used.

[0220] 3. Service Efficiency

[0221] There are 5 specific indicators: SE1 to SE5.

[0222] Average passing time of SE1 ETC lane (service time)

[0223] Data acquisition method: provided by the Internet center.

[0224] Data format: average number of seconds passed.

[0225] Data description: This indicator is the average time for each ETC lane to receive service within a unit of time (1 day), specifically the time it takes for a vehicle to enter a toll station, receive service, and leave the toll station. It is objective and dynamic data. Based on the traffic records provided by the network center, the passing time of each vehicle can be calculated through regular sampling: the arrival time of the two vehicles before and after is subtracted. If it is within a certain range, it can be used as the passing time.

[0226] Average passing time of SE2 mixed traffic lane (MTC)

[0227] Data acquisition method: provided by the Internet center.

[0228] Data format: average number of seconds passed.

[0229] Data description: This indicator is the average time for each MTC lane to receive service within a unit of time (1 day), specifically the time it takes for a vehicle to enter a toll station, receive service, and leave the toll station. It is objective and dynamic data. Based on the traffic records provided by the network center, the passing time of each vehicle can be calculated through regular sampling: the arrival time of the two vehicles before and after is subtracted. If it is within a certain range, it can be used as the passing time.

[0230] SE3 weighing test data matching rate with entry transaction (this indicator only counts trucks and special operation vehicles)

[0231] Data acquisition method: provided by the Internet center.

[0232] Data format: real number between [0,1].

[0233] Data description: This indicator refers to the data where the difference between the transaction time and the detection time of the weighing detection data of the same license plate entrance transaction at the same entrance station is within 1 hour. It is objective and dynamic data.

[0234] SE4 Special Situation Total

[0235] Data acquisition method: provided by toll booths, or estimated through data provided by the networking center.

[0236] Data format: number of special situations / month, integer.

[0237] Data description: This indicator is the total number of special situations that occur in a unit of time (one month). It is objective and dynamic data and is updated monthly. When calculating through the toll station traffic records provided by the network center, if the service time of the front and rear vehicles is greater than a certain threshold (different types of lanes have different thresholds), it is considered a special situation.

[0238] SE5 Emergency Services

[0239] Data acquisition method: The higher-level department will regularly check the emergency service situation of each toll station.

[0240] Data format: real number between [1,5].

[0241] Data description: This indicator measures the emergency handling capability of toll stations, including whether the emergency equipment is complete, whether the emergency plan is reasonable, whether the emergency response is timely, etc. The higher-level department regularly inspects the emergency service of toll stations, including emergency support, emergency drills and emergency plans, and uploads the inspection results in real time through the APP. The backend obtains the level of emergency service of the service station through weighted scoring. It is subjective and dynamic data.

[0242] 4. Traffic Management

[0243] There are 9 specific indicators: TM1 to TM9.

[0244] Timely upload rate of TM1 gantry license plate recognition data

[0245] Data acquisition method: provided by the Internet center.

[0246] Data format: real number between [0,1].

[0247] Data description: This indicator refers to whether the license plate information obtained by the gantry can be uploaded to the data center in a timely manner. It is objective and dynamic data and can be updated regularly every day.

[0248] Timely upload rate of TM2 gantry transaction data

[0249] Data acquisition method: provided by the Internet center.

[0250] Data format: real number between [0,1].

[0251] Data description: This indicator refers to whether the transaction information obtained by the gantry can be uploaded to the data center in a timely manner. It is objective and dynamic data and can be updated regularly every day.

[0252] Timely download rate of TM3 OBU status list

[0253] Data acquisition method: provided by the Internet center.

[0254] Data format: real number between [0,1].

[0255] Data description: This indicator refers to whether the OBU information of vehicles newly applying for ETC services can be downloaded to the toll station in time. It is objective and dynamic data and can be updated regularly every day.

[0256] Timely download rate of TM4 user card status list

[0257] Data acquisition method: provided by the Internet center.

[0258] Data format: real number between [0,1].

[0259] Data description: This indicator refers to whether the information of the user card issued by the MTC lane can be downloaded in time. It is objective and dynamic data and can be updated regularly every day.

[0260] Timely upload rate of TM5 entry fee transaction data

[0261] Data acquisition method: provided by the Internet center.

[0262] Data format: real number between [0,1].

[0263] Data description: This indicator refers to whether the information of the user card issued by the MTC lane can be downloaded in time. It is objective and dynamic data and can be updated regularly every day.

[0264] Timely upload rate of TM6 export fee transaction data

[0265] Data acquisition method: provided by the Internet center.

[0266] Data format: real number between [0,1].

[0267] Data description: This indicator refers to whether the export fee transaction information can be uploaded in time. It is objective and dynamic data and can be updated regularly every day.

[0268] Timely upload rate of weighing test data of TM7 entrance toll lane (this indicator only counts trucks and special operation vehicles)

[0269] Data acquisition method: provided by the Internet center.

[0270] Data format: real number between [0,1].

[0271] Data description: This indicator refers to whether the weighing information of the entrance lane can be uploaded in time. It is objective and dynamic data and can be updated regularly every day.

[0272] TM8: Cumulative number of vehicles handled / Cumulative working hours (used to assess and evaluate the work of toll collectors)

[0273] Data acquisition method: provided by the Internet center.

[0274] Data format: number of vehicles processed per unit time, real number.

[0275] Data description: This indicator is the number of vehicles handled by a toll collector in a unit of time. It is objective and dynamic data and can be updated regularly.

[0276] Average time to handle TM9 special situations

[0277] Data acquisition method: provided by toll booths, or estimated through data provided by the networking center.

[0278] Data format: average number of seconds to handle special situations, integer.

[0279] Data description: This indicator refers to the average time (in seconds) to handle special situations per unit time. It is objective and dynamic data and can be updated monthly. After calculation based on the toll station traffic records provided by the network center, if it is determined to be a special situation, the special situation handling event will be recorded.

[0280] 5. Travel Experience

[0281] There are 4 specific indicators: TE1 to TE4.

[0282] TE1 average queue length

[0283] Data acquisition method: Based on the time information of vehicle arrival at the gantry and toll station provided by the networking center, the average queue length at the toll station is calculated; or based on the camera at the toll station, the queue length of each lane is calculated according to the vehicle identification and counting method.

[0284] Data format: integer.

[0285] Data description: This indicator refers to the average queue length at the toll station in a unit of time (one day). It is objective and dynamic data. The passing vehicles can be sampled and calculated according to the license plate number at regular intervals to obtain the queue length every minute.

[0286] TE2 Service Specifications

[0287] Data acquisition method: Artificial intelligence technology can be used to conduct real-time detection of videos uploaded by cameras in toll booths, and judge indicators such as employee appearance, standard language, and standard movements.

[0288] Data format: integer between [1,5].

[0289] Data description: This indicator refers to whether the toll collectors' appearance, standardized terms, and standardized actions during the toll collection process meet the requirements. It is objective and dynamic data. The data is obtained by comprehensively scoring the toll collectors through expression recognition, language recognition, and posture recognition.

[0290] TE3 Toll Station Environment

[0291] Data acquisition method: The higher-level management department can regularly score the environment of each toll station.

[0292] Data format: integer between [1,5].

[0293] Data description: This indicator measures the comfort level of the toll station environment by measuring whether the environment is clean and tidy, whether the equipment is placed in a standardized manner, etc. It is subjective and dynamic data. An environmental inspection module can be set up on the APP, and authorized managers can score online, which can be divided into sub-items such as clean and tidy environment and standardized equipment placement. Finally, the evaluation score of the toll station environment is obtained through weighted sum.

[0294] TE4 Passenger and Driver Satisfaction with Passenger Services

[0295] Data acquisition method: online questionnaire survey and online complaint channel.

[0296] Data format: real number between [1,5].

[0297] Data description: This indicator refers to the satisfaction evaluation of drivers and passengers on the toll station service. It is subjective and dynamic data. After the drivers and passengers leave the toll station, they can ask users to rate their experience of passing through the toll station through APP or push text messages, WeChat, etc., which can be divided into sub-items such as satisfaction with travel time, satisfaction with service attitude, satisfaction with convenience, and satisfaction with service consultation. The overall satisfaction of drivers and passengers is obtained through weighted sum of each sub-item.

[0298] So far, the lane service level evaluation index set obtained in this embodiment includes five categories of indicators:

[0299] (1) Networked charging infrastructure capabilities: IC1 to IC5.

[0300] (2) Traffic capacity: TC1 to TC8.

[0301] (3) Service efficiency: SE1 to SE5.

[0302] (4) Traffic management: TM1 to TM9.

[0303] (5) Traffic experience: TE1 to TE4.

[0304] Example 2

[0305] This embodiment is based on the lane service level evaluation index set obtained in Embodiment 1, and preliminarily classifies the indicators according to the SERVQUAL model.

[0306] The specific contents of this embodiment are as follows:

[0307] SERVQUAL is a model for evaluating the service quality of a company or enterprise. Based on the SERVQUAL concept and the characteristics of toll station service evaluation, the lane service level evaluation index set obtained in Example 1 can be classified as follows.

[0308] (1) Tangible

[0309] Tangible indicators are indicators that can be used to measure the infrastructure, equipment, hardware and software quality and configuration of toll stations, as well as their operation. Indicators of this type include:

[0310] IC5 Cloud Agent System Application Level

[0311] TC1 Travel Service Information Service Level

[0312] Number of TC2 toll lanes

[0313] TC3 lane ratio level

[0314] Total transaction volume of TC4 entrance toll lane

[0315] Total transaction volume of TC5 exit toll lane

[0316] Total amount of weighing test data for TC6 entrance toll lane (this indicator only counts trucks and special operation vehicles)

[0317] TC7 Total amount of weighing test data matched with entry transactions (this indicator only counts trucks and special operation vehicles)

[0318] Maximum traffic volume per unit time per month for TC8

[0319] TE3 Toll Station Environment

[0320] (2) Reliability

[0321] Reliability indicators are indicators that can be used to measure whether errors will occur during the toll collection process at toll stations, including whether the data is accurate, whether the equipment is operating normally, and how to handle unexpected situations. Indicators in this category include:

[0322] IC1 gantry interconnection qualified rate

[0323] IC2 gantry license plate recognition accuracy

[0324] IC3 lane connectivity pass rate

[0325] IC4 Other electromechanical equipment failure rate

[0326] SE3 weighing test data matching rate with entry transaction (this indicator only counts trucks and special operation vehicles)

[0327] SE4 Special Situation Total

[0328] SE5 Emergency Services

[0329] (3) Responsiveness

[0330] Response speed indicators are indicators that can be used to measure the efficiency of vehicles passing through toll booths, including total traffic volume, travel time, queue length, etc. Indicators belonging to this category include:

[0331] Average passing time of SE1 ETC lane (service time)

[0332] Average passing time of SE2 mixed traffic lane (MTC)

[0333] Timely upload rate of TM1 gantry license plate recognition data

[0334] Timely upload rate of TM2 gantry transaction data

[0335] Timely download rate of TM3 OBU status list

[0336] Timely download rate of TM4 user card status list

[0337] Timely upload rate of TM5 entry fee transaction data

[0338] Timely upload rate of TM6 export fee transaction data

[0339] Timely upload rate of weighing test data of TM7 entrance toll lane (this indicator only counts trucks and special operation vehicles)

[0340] Average time to handle TM9 special situations

[0341] TE1 average queue length

[0342] (4) Assurance

[0343] Recognition indicators are indicators that can be used to measure the direct experience of drivers and passengers on the service, including the appearance and attitude of toll booth staff. Indicators in this category include:

[0344] TE2 Service Specifications

[0345] TE4 Passenger and Driver Satisfaction with Passenger Services

[0346] TM8: Cumulative number of vehicles handled / Cumulative working hours (used to assess and evaluate the work of toll collectors)

[0347] At this point, this embodiment has completed the preliminary classification of indicators according to the SERVQUAL model.

[0348] Example 3

[0349] This embodiment screens the evaluation indicators based on the embodiment 2.

[0350] The specific contents of this embodiment are as follows:

[0351] This embodiment follows the principles of being concise, scientific, feasible, and economical, and selects representative, independent, and closely related indicator factors to the service quality of toll stations from the indicator set through the expert scoring method, and can reflect the service effect of highway toll stations.

[0352] (1) Expert scoring method

[0353] By inviting experts or front-line staff in the field of transportation or transportation-related fields as evaluation experts, scoring each indicator factor of the evaluation system, and then statistically analyzing the experts' scoring results, the comprehensive weight of each evaluation indicator is obtained, thereby screening the highway toll station service evaluation indicator factors.

[0354] The expert scoring matrix is ​​as follows:

[0355]

[0356] Among them, n is the number of evaluation experts, m is the number of evaluation indicators, and a ij It represents the score given by the i-th expert to the j-th evaluation index.

[0357] Since the level and comprehensive ability of scoring experts vary, it is necessary to multiply the scores of different experts for each item by a weight. The expert weight is determined based on the expert's education, occupation, and work age. If the expert is not engaged in toll station-related work, whether he drives a car and his driving age also need to be considered.

[0358] The education classification and its weights are as follows: PhD (1), Master (0.95), Bachelor (0.9), Associate (0.85), and Other (0.8).

[0359] The job classifications and their weights are as follows: toll station-related jobs (1), highway traffic-related jobs (0.9), highway traffic research jobs (0.8), and others (0.7).

[0360] The work age categories and their weights are as follows: more than 20 years (1), 10-20 years (0.9), 5-10 years (0.8), and less than 5 years (0.7).

[0361] The driving age (driveAge) categories and their weights are as follows: more than 20 years (1), 10-20 years (0.9), 5-10 years (0.8), less than 5 years (0.7).

[0362] For the i-th scoring expert, if he is not engaged in toll station-related work, the weight calculation formula is:

[0363] b i =0.2·eduction+0.3·job+0.3·workAge+drive? (0.2·driveAge): 0

[0364] If you are engaged in toll booth related work, the weight is:

[0365] b i =0.4·education+0.6·workAge Based on the above data, the expert weight matrix can be obtained as follows:

[0366]

[0367] Get the final scoring matrix:

[0368]

[0369] Note: Scoring Matrix C n×m Scoring matrix A for experts n×m With the expert weight matrix B n×m The expert weight matrix is ​​constructed as a matrix with m columns of the same elements b for convenience of representation. i The matrix B n×m .

[0370] Thus, the comprehensive score of each indicator is obtained:

[0371]

[0372] (2) Screening of evaluation index factors

[0373] The expert scoring method described above is used to score each index after classification in Example 2. Then, based on the comprehensive score of each index, an in-depth comprehensive analysis of each index is performed, and finally the two lowest-scoring indexes are deleted:

[0374] a) Number of TC2 toll lanes

[0375] Toll lanes cannot be used as a direct indicator for evaluating the service quality of toll station lanes. This indicator cannot provide any help for single lane evaluation and can only be used when conducting a comprehensive evaluation of toll stations.

[0376] b) TC3 lane ratio level

[0377] The lane ratio level cannot be used as a direct indicator for evaluating the lane service quality of toll stations. This indicator cannot provide any help for the evaluation of single lanes. Similarly, this indicator can only be used when conducting a comprehensive evaluation of toll stations.

[0378] So far, this embodiment completes the screening of evaluation indicators through the expert scoring method.

[0379] Example 4

[0380] This embodiment combines the indicators that meet the conditions in the evaluation indicators based on Embodiment 3, and finally constructs a toll lane service level evaluation indicator system.

[0381] The specific contents of this embodiment are as follows:

[0382] The redundancy of the indicator set will affect the efficiency of lane service level evaluation. Therefore, in addition to deleting indicators that are not highly correlated with the lane service level in Example 3, it is also necessary to merge related indicators.

[0383] The method of index merging adopts the correlation coefficient method, and those strongly correlated indicators are merged according to the correlation between the indicators.

[0384] Assume that the number of samples is k, and the sample data of the i-th indicator is X = {x1, x2, ..., x k}, the mean is The sample data of the jth indicator is Y = {y1, y2, ..., y k}, the mean is The Pearson correlation coefficient between indicator i and indicator j can be calculated using the following formula:

[0385]

[0386] Calculate the correlation coefficient of each two indicators and get the following correlation matrix:

[0387]

[0388] The correlation matrix R can be calculated using Python's Scipy library m×m The calculation process is as follows Figure 2 As shown in the example, those skilled in the art can understand the specific calculation process based on the figure and the existing usage method of the Scipy library, which will not be repeated here.

[0389] When ij = ±1, indicating that the indexes i and j are completely linearly correlated. ij When it is close to 1 or -1, the index i and the index j are approximately correlated. In this embodiment, α = 0.95, then when |r ij |≥α, merge index i and index j.

[0390] The above method is used to calculate the various indicators screened in Example 3. According to the calculation results of the correlation matrix, the indicators that satisfy |r ij |≥α are combined to obtain the following five new indices.

[0391] (1) Equipment operation status (RL1)

[0392] Combine the "IC1 gantry connectivity qualification rate", "IC2 gantry license plate recognition accuracy rate", "IC3 lane connectivity qualification rate", "IC4 other electromechanical equipment failure rate", and "SE3 weighing detection data matching rate with entry transaction" to obtain a new indicator: equipment operation status (RL1).

[0393] The above indicators IC1, IC2, IC3, IC4, and SE3 are standardized and calculated as real values ​​between [0, 1], and the score of "equipment operation status (RL1)" is calculated based on these values, that is:

[0394]

[0395] (2) Travel service level (TA1)

[0396] Combine "IC5 cloud seat system application level" and "TC1 travel service information service level" to get a new indicator: travel service level (TA1).

[0397] The above indicators IC5 and TC1 are standardized and calculated as real values ​​between [0,1], and the score of "travel service level (TA1)" is calculated based on these values, that is:

[0398]

[0399] (3) Total transaction volume of toll lanes (TA3)

[0400] The "total transaction volume of TC4 entrance toll lane", "total transaction volume of TC5 exit toll lane" and "total weighing detection data of TC6 entrance toll lane" are combined to obtain a new indicator: the total transaction volume of toll lane (TA3), which is used as one of the criteria for evaluating the level of toll stations.

[0401] The above indicators TC4, TC5, and TC6 are standardized and calculated as real values ​​between [0, 1], and the score of "toll lane transaction volume (TA3)" is calculated based on these values, that is:

[0402]

[0403] (4) Data transmission capability (RS1)

[0404] The "TM1 gantry license plate recognition data upload timeliness rate", "TM2 gantry transaction data upload timeliness rate", "TM3 OBU status list download timeliness rate", "TM4 user card status list download timeliness rate", "TM5 entrance toll transaction data upload timeliness rate", "TM6 exit toll transaction data upload timeliness rate", and "TM7 entrance toll lane weighing detection data upload timeliness rate" are combined to obtain a new indicator: data transmission capacity (RS1), which can measure the data transmission capacity of toll stations.

[0405] The above indicators TM1, TM2, TM3, TM4, TM5, TM6, and TM7 are standardized and calculated as real numbers between [0, 1], and the score of "data transmission capacity (RS1)" is calculated based on these values, that is:

[0406]

[0407] (5) Average lane passing time (RS2)

[0408] Combine the "SE1 ETC lane average passing time" and "SE2 mixed lane (MTC) average passing time" to get a new indicator: lane average passing time (RS2).

[0409] Indicators SE1 and SE2 measure the average passing time of different lanes. For a single lane, if the lane is an ETC lane, SE1 is used, and if the lane is a mixed lane (MTC), SE2 is used. When considering the service level of a toll station, these indicators need to be considered comprehensively.

[0410] Assume that in a toll station, the number of ETC lanes is n1, and the average passing time SE1 of each ETC lane is e1, e2, …, e n1 ; The number of MTC lanes is n2, and the average passing time SE2 of each MTC lane is m1, m2, …, m n2 ; Based on these values, the score of "lane average passing time (RS2)" is calculated, namely:

[0411]

[0412] After the above-mentioned operation of merging the indicators that meet the conditions, the indicators are classified again according to the SERVQUAL model, and the toll lane service level evaluation index system is finally constructed as follows:

[0413] (1) Tangible

[0414] Tangible indicators are indicators that can be used to measure the infrastructure, equipment, hardware and software quality and configuration of toll stations, as well as their operation. Indicators of this type include (the original numbers of the indicators are in brackets):

[0415] TA1 (IC5, TC1): Travel service level. This indicator refers to whether the toll station or highway operation department provides users with comprehensive and timely information on toll station traffic conditions, including the application level of the cloud agent system and the travel service information service level.

[0416] TA2(TE3): Toll station environment. This indicator measures the comfort level of the toll station environment, such as whether the toll station environment is clean and tidy, and whether the equipment is placed in a standardized manner.

[0417] TA3 (TC4, TC5, TC6): Total transaction volume of toll lanes. This indicator refers to the total transaction volume of toll stations per unit time, including the total transaction volume of entrance toll lanes, the total transaction volume of exit toll lanes, and the total volume of weighing and detection data of entrance toll lanes.

[0418] TA4(TC7): The total amount of weighing detection data matching the entry transaction. This indicator is the total amount of weighing data recorded by the data center.

[0419] TA5(TC8): Maximum traffic volume per unit time per month. This indicator is the maximum traffic volume passing through the toll station within one hour each month, reflecting the peak traffic volume at the toll station within a month.

[0420] (2) Reliability

[0421] Reliability indicators are indicators that can be used to measure whether errors will occur during the toll collection process at toll stations, including whether the data is accurate, whether the equipment is operating normally, and how to handle unexpected situations. Indicators in this category include (the original numbers of the indicators are in brackets):

[0422] RL1 (IC1, IC2, IC3, IC4, SE3): Equipment operation status. This indicator measures the operation status of toll station equipment and the accuracy of data transmission, including the gantry interconnection qualification rate, gantry license plate recognition accuracy rate, lane connectivity qualification rate, other electromechanical equipment failure rate, and the weight detection data matching rate that matches the entry transaction.

[0423] RL2(SE4): Total number of special situations. This indicator is the total number of special situations that occurred within a unit of time (one month).

[0424] RL3(SE5): Emergency service. This indicator is used to measure the emergency handling capability of the toll station, including whether the emergency equipment is complete, whether the emergency plan is reasonable, and whether the emergency response is timely.

[0425] (3) Responsiveness

[0426] Response speed indicators are indicators that can be used to measure the efficiency of vehicles passing through toll booths, including total traffic volume, travel time, queue length, etc. Indicators in this category include (the original indicator numbers are in brackets):

[0427] RS1 (TM1, TM2, TM3, TM4, TM5, TM6, TM7): Data transmission capability. This indicator measures the accuracy and timeliness of data transmission between the data center and the toll station and gantry, including the timeliness of gantry license plate recognition data upload, gantry transaction data upload, OBU status list download, user card status list download, entrance toll transaction data upload, exit toll transaction data upload, and entrance toll lane weighing detection data upload.

[0428] RS2 (SE1, SE2): Average lane passing time. This indicator is the average passing time of vehicles passing through each toll station per unit time, including the average passing time of ETC lanes and the average passing time of mixed lanes (MTC).

[0429] RS3(TM9): Average time for handling special situations. This indicator refers to the average time (in seconds) for handling special situations per unit time.

[0430] RS4(TE1): Average queue length. This indicator refers to the average queue length at a toll station per unit time (one day).

[0431] (4) Assurance

[0432] Recognition type indicators are indicators that can be used to measure the direct experience of drivers and passengers on the service, including the appearance and attitude of toll booth staff. Indicators belonging to this category are (the original numbers of the indicators are in brackets):

[0433] AS1(TE2): Service Standards. This indicator refers to whether the toll collectors’ appearance, standardized terms, and standardized actions during the toll collection process meet the requirements.

[0434] AS2(TE4): Driver and passenger satisfaction with toll station services. This indicator refers to the driver and passenger's satisfaction evaluation of toll station services after receiving them.

[0435] AS3 (TM8): Cumulative number of vehicles handled / Cumulative working hours. This indicator is the number of vehicles handled by a toll collector in a unit of time, and is used to assess the work efficiency of the toll collector.

[0436] At this point, the construction of the toll lane service level evaluation index system is completed. On the basis of this evaluation index system, the relevant evaluation models in the prior art can be used to evaluate the toll lane service level, and finally the toll lane service level can be calculated; at the same time, the rationality of the indicators can be judged through theoretical analysis and simulation, and the defects and shortcomings of the indicators can be determined through expert review, and the initial indicator set can be re-established and a more complete toll lane service level evaluation index system can be finally constructed according to the above-mentioned construction method of the present invention.

[0437] From the above embodiments, it can be seen that the present invention follows the principles of unity of scientificity and practicality, combination of qualitative and quantitative, and combination of dynamics and relative stability, fully gathers the global data resources of toll lanes, classifies the indicators according to the SERVQUAL model according to the actual service business needs and capabilities of toll lanes, and uses expert scoring method and correlation coefficient method to screen and merge the indicators, and finally constructs a lane service level evaluation index system to achieve objective and accurate reflection and presentation of toll lane service levels.

[0438] In addition to the above embodiments, the present invention may also have other implementation modes. Any technical solution formed by equivalent replacement or equivalent transformation falls within the protection scope required by the present invention.

Claims

1. A method for constructing a toll lane service level evaluation index system based on the global data resources of highway toll lanes includes the following steps: The first step is to sort out the lane service level evaluation indicator set and classify each indicator according to its function and the entity that implements the service. The second step is to preliminarily classify the indicators of the lane service level evaluation index set according to the SERVQUAL model. The third step is to use the expert scoring method to screen the indicators of the lane service level evaluation index set, obtain the comprehensive score of each indicator, and delete the indicators with low scores. Step 4: Use the correlation coefficient method to merge the qualified indicators of the lane service level evaluation index set obtained in the third step. Step 5: Reclassify the indicators of the lane service level evaluation index set obtained in the fourth step according to the SERVQUAL model, so as to construct a toll lane service level evaluation index system.

2. The method for constructing a toll lane service level evaluation index system based on the global data resources of highway toll lanes according to claim 1 is characterized in that: In the third step, the expert scoring method includes the following steps: S1. Invite several evaluation experts to score each indicator of the lane service level evaluation indicator set to obtain the expert scoring matrix: Where n is the number of evaluation experts, m is the number of indicators; let i be 1 to n, j be 1 to m, then a uj It represents the score given by the i-th expert to the j-th evaluation index. S2. According to the specific situation of each evaluation expert, the expert weight matrix is ​​obtained: Where n is the number of evaluation experts; let i be 1 to n, then b i represents the weight of the i-th expert. S3, based on the results of S1 and S2, the final scoring matrix is ​​obtained: Then the comprehensive score matrix of each indicator is obtained:

3. The method for constructing a toll lane service level evaluation index system based on the global data resources of highway toll lanes according to claim 2 is characterized in that: In S1, when scoring, the evaluation experts scored each indicator after comprehensive consideration of three aspects: representativeness, independence, and relevance to the toll station service quality; In S2, the process of calculating the weight of the i-th expert is as follows: Determine whether the work of the expert is related to the highway toll station. If so, use the following formula to calculate the weight of the expert: b=0.4·education+0.6·workAge If not relevant, the following formula is used to calculate the expert's weight: b = 0.2·education+0.3·job+0.3·workAge+drive?(0.2·driveAge):0 In the above two formulas, education is the expert's academic qualifications, job is the expert's occupation, and workAge is the expert's length of service; drive?(0.2·driveAge):0 means first judging whether the expert can drive. If not, this item is assigned a value of 0. If so, this item is assigned a value of 0.2·driveAgedriveAge is the expert's driving age.

4. The method for constructing a toll lane service level evaluation index system based on the global data resources of highway toll lanes according to claim 3 is characterized in that: The standard for assigning values ​​for education is: 1 for PhD, 0.95 for master's, 0.9 for bachelor's degree, 0.85 for junior college, and 0.8 for others. The job assignment standard is: highway traffic related work is 0.9, highway traffic research work is 0.8, and others are 0.

7. The value assignment standard for workAge is: 1 for more than 20 years, 0.9 for 10 to 20 years, 0.8 for 5 to 10 years, and 0.7 for less than 5 years. The driveAge value is 1 for more than 20 years, 0.9 for 10 to 20 years, 0.8 for 5 to 10 years, and 0.7 for less than 5 years.

5. The method for constructing a toll lane service level evaluation index system based on the global data resources of highway toll lanes according to claim 1 is characterized in that: In the fourth step, the specific process of merging includes the following steps: T1. Assume that the number of samples is k, and each sample has data under each indicator of the lane service level evaluation indicator set; where the sample data set of the i-th indicator is X = {x1, x2, …, x k }, the mean is The sample data set of the jth indicator is Y = {y1, y2, ..., y l }, the mean is T2. Calculate the Pearson correlation coefficient between the i-th indicator and the j-th indicator by the following formula: Calculate the Pearson correlation coefficient between each two indicators and get the correlation matrix: Among them, when r uj = ±1, it means that the i-th index is completely linearly correlated with the j-th index; when r ij When it is close to 1 or -1, the ith indicator is approximately correlated with the jth indicator; T3. According to the preset limit α, determine r ij The relationship between the size of α and |r ij When |≥α, the i-th indicator and the j-th indicator are combined; the value of α is 0.95±0.

02.

6. The method for constructing a toll lane service level evaluation index system based on the global data resources of highway toll lanes according to any one of claims 1 to 5, Its characteristics are that, in the first step, each indicator is divided into the following five categories: networked toll infrastructure capacity, traffic capacity, service efficiency, traffic management, and traffic experience; The indicators of the capacity of the networked toll collection infrastructure include: IC1 gantry interconnection qualification rate, IC2 gantry license plate recognition accuracy rate, IC3 lane connectivity qualification rate, IC4 other electromechanical equipment failure rate, IC5 cloud seat system application level; The indicators of traffic capacity include: TC1 travel service information service level, TC2 number of toll lanes, TC3 lane ratio level, TC4 total transaction volume of entrance toll lanes, TC5 total transaction volume of exit toll lanes, TC6 total volume of weighing and detection data of entrance toll lanes, TC7 total volume of weighing and detection data matched with entrance transactions, TC8 maximum vehicle flow per unit time per month; The indicators of entry service efficiency include: SE1 average vehicle passing time in ETC lanes, SE2 average vehicle passing time in mixed lanes, SE3 matching rate of weighing and detection data with entry transactions, SE4 total number of special situations, SE5 emergency services; The indicators of entry and exit management include: TM1 gantry license plate recognition data upload timeliness rate, TM2 gantry transaction data upload timeliness rate, TM3 OBU status list download timeliness rate, TM4 user card status list download timeliness rate, TM5 entrance toll transaction data upload timeliness rate, TM6 exit toll transaction data upload timeliness rate, TM7 entrance toll lane weighing test data upload timeliness rate, TM8 cumulative number of vehicles handled: cumulative working hours ratio, TM9 average time for special situation handling; The indicators of entry and exit experience include: TE1 average queue length, TE2 service standards, TE3 toll station environment, and TE4 driver and passenger satisfaction with toll services.

7. The method for constructing a toll lane service level evaluation index system based on the global data resources of highway toll lanes according to claim 6 is characterized in that: In the second step, the indicators were divided into the following four categories: tangibility, reliability, responsiveness, and recognition; The tangible indicators include: IC5 cloud agent system application level, TC1 travel service information service level, TC2 number of toll lanes, TC3 lane ratio level, TC4 total transaction volume of entrance toll lanes, TC5 total transaction volume of exit toll lanes, TC6 total volume of weighing and detection data of entrance toll lanes, TC7 total volume of weighing and detection data matching with entrance transactions, TC8 maximum vehicle flow per unit time per month, TE3 toll station environment; The indicators of entry reliability include: IC1 gantry connection qualification rate, IC2 gantry license plate recognition accuracy rate, IC3 lane connection qualification rate, IC4 other electromechanical equipment failure rate, SE3 weighing test data matching rate with entry transaction, SE4 total number of special situations, SE5 emergency service; The indicators of response speed include: average vehicle passing time of SE1 ETC lane, average vehicle passing time of SE2 mixed lane, timely upload rate of TM1 gantry license plate recognition data, timely upload rate of TM2 gantry transaction data, timely download rate of TM3 OBU status list, timely download rate of TM4 user card status list, timely upload rate of TM5 entrance toll transaction data, timely upload rate of TM6 exit toll transaction data, timely upload rate of TM7 entrance toll lane weighing detection data, average time for TM9 special situation handling, and average queue length of TE1; The recognition indicators include: TE2 service standards, TE4 driver and passenger satisfaction with traffic services, TM8 ratio of the cumulative number of vehicles handled to the cumulative working hours.

8. The method for constructing a toll lane service level evaluation index system based on the global data resources of highway toll lanes according to claim 7 is characterized in that: In the third step, the indicators with low scores that were finally deleted were: the number of TC2 toll lanes and the level of TC3 lane ratio; In the fourth step, the merged results are: IC1 gantry connection qualified rate, IC2 gantry license plate recognition accuracy, IC3 lane connection qualified rate, IC4 other electromechanical equipment failure rate, SE3 and entrance transaction matching weighing detection data matching rate are merged into RL1 equipment operation status; IC5 cloud seat system application level and TC1 travel service information service level are merged into TA1 travel service level; TC4 entrance toll lane transaction volume, TC5 exit toll lane transaction volume, TC6 entrance toll lane weighing detection data volume are merged into TA3 toll lane transaction volume; Combine the timely upload rate of TM1 gantry license plate recognition data, the timely upload rate of TM2 gantry transaction data, the timely download rate of TM3 OBU status list, the timely download rate of TM4 user card status list, the timely upload rate of TM5 entrance toll transaction data, the timely upload rate of TM6 exit toll transaction data, and the timely upload rate of TM7 entrance toll lane weighing detection data into RS1 data transmission capacity; combine the average passing time of SE1 ETC lane and the average passing time of SE2 mixed lane into RS2 lane average passing time; Among them, the indicators IC1, IC2, IC3, IC4, and SE3 are standardized and calculated as real values ​​between [0,1], and the score of the RL1 equipment operation status is calculated based on these values, that is: The indicators IC5 and TC1 are standardized and calculated as real values ​​between [0,1], and the score of TA1 travel service level is calculated based on these values, that is: The indicators TC4, TC5, and TC6 are standardized and calculated as real values ​​between [0, 1], and the score of the total transaction volume of the TA3 toll lane is calculated based on these values, that is: The indicators TM1, TM2, TM3, TM4, TM5, TM6, and TM7 are standardized and calculated as real numbers between [0, 1], and the scores of RS1 data transmission capacity are calculated based on these values, namely: Assume that in a toll station, the number of ETC lanes is n1, and the average passing time SE1 of each ETC lane is e1, e2, …, e n1 ; The number of MTC lanes is n2, and the average passing time SE2 of each mixed lane is m1, m2, …, m n2 ; Based on these values, the average passing time score of RS2 lane is calculated, namely:

9. The method for constructing a toll lane service level evaluation index system based on the global data resources of toll lanes on expressways according to claim 8, Its characteristics are that, in the fifth step, each indicator is still divided into the following four categories: tangibility, reliability, responsiveness, and recognition; The indicators classified into tangibles include: TA1 travel service level, TA2 toll station environment, TA3 total transactions in toll lanes, TA4 total weighing detection data matching with entrance transactions, TA5 maximum vehicle flow per unit time per month; among them, TA2 toll station environment is TE3 toll station environment, TA4 total weighing detection data matching with entrance transactions is TC7 total weighing detection data matching with entrance transactions, TA5 maximum vehicle flow per unit time per month is TC8 maximum vehicle flow per unit time per month; The indicators of input reliability include: RL1 equipment operation status, RL2 total number of special situations, and RL3 emergency services; among them, RL2 total number of special situations is the total number of SE4 special situations, and RL3 emergency services is the SE5 emergency services; The indicators of response speed include: RS1 data transmission capacity, RS2 lane average vehicle passing time, RS3 average time for handling special situations, RS4 average queue length; among them, RS3 average time for handling special situations is the average time for handling special situations of TM9, and RS4 average queue length is the average queue length of TE1; The indicators for recognition include: AS1 service standards, AS2 driver and passenger satisfaction with toll service, and AS3 the ratio of the cumulative number of vehicles handled to the cumulative working hours; among them, AS1 service standards are TE2 service standards, AS2 driver and passenger satisfaction with toll service is TE4 driver and passenger satisfaction with toll service, and AS3 the ratio of the cumulative number of vehicles handled to the cumulative working hours is TM8 the ratio of the cumulative number of vehicles handled to the cumulative working hours.

10. The method for constructing a toll lane service level evaluation index system based on the global data resources of highway toll lanes according to any one of claims 1 to 9, characterized in that: The construction method also includes the following steps: For the constructed toll lane service level evaluation index system, the rationality of each indicator is judged through theoretical analysis and simulation, and the defects and shortcomings of each indicator are determined through review by evaluation experts. Then, we return to the first step to re-sort the lane service level evaluation index set, and execute sequentially to the fifth step to construct a complete toll lane service level evaluation index system.

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