CN116597638B - Intelligent traffic monitoring and scheduling system based on big data - Google Patents
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Abstract
The invention discloses an intelligent traffic monitoring and scheduling system based on big data, and belongs to the technical field of intelligent traffic; monitoring and counting the real-time waiting population of different bus stops, and determining the local personnel waiting influence of the corresponding bus stop according to the real-time waiting population of the monitoring and counting and the stop weight corresponding to the bus stop; analyzing and classifying the waiting influence of the whole personnel on the one-way bus driving route to dynamically adjust the bus operation schedule; further expanding and excavating the early traffic monitoring and scheduling data so as to dynamically adjust the running frequency of buses at different basic running interval periods in a targeted manner; the method and the device are used for solving the technical problem that in the existing scheme, different dimensionalities of monitoring and data analysis cannot be implemented on the departure operation of the vehicles in different time periods, and the overall effect of traffic vehicle monitoring and scheduling is improved by dynamically scheduling the departure of the vehicles in different time periods according to analysis results.
Description
Technical Field
The invention relates to the technical field of intelligent traffic, in particular to an intelligent traffic monitoring and scheduling system based on big data.
Background
Traffic dispatching is a command center of a traffic daily transport organization, and aims to safely transport passengers, meet the requirement of equipment maintenance and realize safe, punctual, comfortable and rapid operation service according to the requirement of a train operation diagram.
When the existing traffic monitoring and dispatching scheme is implemented, most of the traffic monitoring and dispatching schemes still stay in a manually set fixed operation interval period scheme to dynamically arrange vehicles to carry passengers, for example, the interval time of vehicle operation is shortened in a commuter period to meet the pressure generated by people flow peak time to a station, monitoring and data analysis of different dimensions cannot be implemented on the departure operation of the vehicles in different periods, and the departure of the vehicles in different periods is dynamically dispatched according to analysis results so as to improve the overall effect of traffic vehicle monitoring and dispatching.
Disclosure of Invention
The invention aims to provide a big data-based intelligent traffic monitoring and dispatching system, which is used for solving the technical problem that in the existing scheme, different dimensionalities of monitoring and data analysis cannot be implemented on the departure operation of vehicles in different time periods, and the overall effect of traffic vehicle monitoring and dispatching is improved by dynamically dispatching the departure of the vehicles in different time periods according to analysis results.
The aim of the invention can be achieved by the following technical scheme:
the intelligent traffic monitoring and scheduling system based on big data comprises a traffic line monitoring and evaluating module, a traffic line monitoring and evaluating module and a traffic line monitoring and dispatching module, wherein the traffic line monitoring and evaluating module is used for monitoring the waiting conditions of real-time personnel at different bus stops on a corresponding unidirectional driving line of a bus, and performing waiting influence verification on the driving line of the bus according to the monitored waiting data of the personnel to obtain waiting verification analysis data; comprising the following steps:
acquiring the station names and station coordinates corresponding to all bus stations on a bus unidirectional driving line, numbering and marking all bus stations according to the unidirectional driving sequence of the bus; acquiring the real-time waiting population of different bus stops by an active inquiry confirmation and active monitoring mode; the real-time waiting headcount corresponding to all marked bus stops constitutes personnel waiting data;
when waiting influence verification is carried out on the bus driving route according to the monitored waiting data of the personnel, the corresponding station names of all bus stations on the bus unidirectional driving route are traversed and matched with a station name weight table pre-stored in a database to obtain corresponding station weights;
extracting the numerical value of the real-time waiting population and the station weight of the bus station and obtaining the local waiting factor corresponding to the bus station through calculation; when analyzing and evaluating the personnel waiting influence conditions of the corresponding bus stops according to the local waiting factors, comparing the local waiting factors with a preset local waiting threshold value to judge whether the local waiting factors have low influence on the local labels, medium influence on the local labels or high influence on the local labels;
counting the total number of low-influence stations, the total number of medium-influence stations and the total number of high-influence stations, extracting the numerical values of the low-influence stations, the medium-influence stations and the total number of high-influence stations, and calculating to obtain the integral waiting factors corresponding to the bus stations;
when analyzing and evaluating the waiting influence condition of the overall personnel corresponding to the bus unidirectional running route according to the overall waiting factor, comparing and judging the overall waiting factor with a preset overall waiting range [ Zdmin, ZDmax ] to obtain an overall signal with low influence, an overall signal with medium influence or an overall signal with high influence;
the low-influence integral signal, the medium-influence integral signal or the high-influence integral signal forms the data waiting for verification analysis and is uploaded to a cloud platform and a database;
and the traffic line dispatching management module is used for carrying out self-adaptive dynamic management on the dispatching of buses on the corresponding unidirectional driving lines in the real-time basic operation interval period according to the waiting verification analysis data.
Preferably, the calculation formula of the local waiting factor Jd is: jd=dri× ZQi; DRi is the number of people waiting for different bus stops in real time; ZQi is the station weight corresponding to different bus stations; i is different bus stops, i= {1,2,3, … …, n }; n is a positive integer.
Preferably, if the local waiting factor is smaller than the local waiting threshold, generating a low-impact local label and marking the corresponding bus stop as a low-impact stop;
if the local waiting factor is not smaller than the local waiting threshold and is not larger than Y of the local waiting threshold, Y is a real number larger than one hundred, generating a middle-influence local label and marking the corresponding bus stop as a middle-influence stop;
if the local waiting factor is greater than Y of the local waiting threshold, generating a high local influence label and marking the corresponding bus stop as a high influence stop.
Preferably, the calculation formula of the overall waiting factor Zd is: zd= (zz+2×gz)/(dz+zz+gz); DZ is the total number of low impact sites; ZZ is the total number of medium-impact sites; GZ is the high impact site total.
Preferably, if Zd < ZDmin, a low impact overall signal is generated;
if Zdmin is less than or equal to Zd and less than or equal to ZDmax, generating a middle-influence integral signal;
if Zd > ZDmax, generating a high-impact overall signal; zdmin < ZDmax.
Preferably, the traffic route scheduling management module comprises the following working steps:
traversing the waiting verification analysis data, and respectively maintaining a bus running scheme corresponding to the existing basic running interval period or a free bus running scheme for dispatching the same line on the corresponding unidirectional running line according to the low-influence overall signal or the high-influence overall signal acquired by traversing; and performing scheduling necessity analysis according to the traversal acquired middle-impact overall signal.
Preferably, when the scheduling necessity analysis is implemented, a time point affecting the generation of the whole signal in the acquisition is marked as a first time point, an ending time point corresponding to a real-time basic operation interval period is marked as a second time point, a time difference between the second time point and the first time point is calculated and marked as a waiting time difference, and the waiting time difference is compared with a time difference threshold to judge and obtain an invalid scheduling signal or an effective scheduling signal;
the invalid scheduling signals or the effective scheduling signals form the scheduling necessary analysis data and are uploaded to the cloud platform and the database, and the public transport operation scheme corresponding to the existing basic operation interval period or the idle public transport vehicle scheduling the same line immediately starts the operation scheme on the corresponding unidirectional running line is maintained according to the invalid scheduling signals or the effective scheduling signals in the scheduling necessary analysis data.
Preferably, the system further comprises a traffic line dispatching traceability module, which is used for carrying out statistical analysis on the adjustment data of buses in different daily basic operation interval periods in the supervision period, and carrying out dynamic adjustment on the departure frequency corresponding to the different basic operation interval periods according to the analysis result.
Preferably, the basic operation interval period of the idle buses which implement the dispatching same route on the corresponding unidirectional driving route immediately is marked as an alternative basic operation interval period, and the week date of the alternative basic operation interval period is marked as an alternative week date;
and counting the total number of adjustment times of different alternative basic operation interval periods in different alternative period periods in turn in a monitoring period, and arranging the corresponding alternative basic operation interval periods in a descending order according to the numerical value of the counted total number of adjustment times.
Preferably, the alternative basic operation interval period corresponding to the total adjustment times greater than the adjustment threshold is marked as the selected basic operation interval period, and the alternative week date corresponding to the selected basic operation interval period is marked as the selected week date;
and increasing the departure operation frequency of buses on the corresponding unidirectional driving lines for a plurality of selected basic operation interval periods in the selected period, and continuously implementing monitoring, data analysis and departure adjustment for the rest different alternative basic operation interval periods and basic operation interval periods.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, the real-time waiting population of different bus stops is monitored and counted, and the real-time waiting population of the monitoring and counting and the stop weight corresponding to the bus stops are calculated and analyzed to determine the local waiting influence of the corresponding bus stops, so that the local waiting influence conditions corresponding to different bus stops can be intuitively obtained, reliable data support can be provided for the whole waiting influence analysis of the corresponding driving lines of the subsequent buses, and the diversity and the expansibility of the traffic local monitoring analysis are improved.
According to the invention, the bus operation scheduling is dynamically adjusted by analyzing and classifying the overall personnel waiting influence condition of the bus unidirectional running line, so that not only can the tired feeling of the overall real-time personnel waiting on the bus unidirectional running line be reduced, but also the timeliness and the necessity of the overall operation of the bus unidirectional running line can be effectively improved.
According to the invention, stability verification is implemented on scheduling adjustment occurring in different basic operation interval periods by further expanding and excavating the early traffic monitoring scheduling data, so that the running frequency of buses can be dynamically adjusted in different basic operation interval periods in a targeted manner, the influence of the irregular adjustment of the running frequency of buses in the early on the whole scheduling of buses in the same day can be reduced, and the rationality and stability of traffic monitoring scheduling in the aspect of bus scheduling are improved.
Drawings
The invention is further described below with reference to the accompanying drawings.
FIG. 1 is a block diagram of a big data based intelligent traffic monitoring and scheduling system according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The embodiment 1, as shown in fig. 1, is an intelligent traffic monitoring and dispatching system based on big data, which comprises a traffic route monitoring and evaluating module, a traffic route dispatching management module, a cloud platform and a database;
the traffic line monitoring and evaluating module is used for monitoring data of real-time personnel waiting conditions of different bus stops on the corresponding unidirectional driving lines of the buses, and carrying out waiting influence verification on the driving lines of the buses according to the monitored personnel waiting data to obtain waiting verification analysis data; comprising the following steps:
acquiring the station names and the station coordinates corresponding to all bus stations on a bus unidirectional driving line, wherein the station coordinates can be expressed based on the existing expression scheme, and numbering all bus stations according to the sequence of bus unidirectional driving and marking the bus stations as i, i= {1,2,3, … …, n }; n is a positive integer; acquiring the real-time waiting population of different bus stops by an active inquiry confirmation and active monitoring mode, and marking the real-time waiting population as DRi;
the real-time waiting headcount corresponding to all marked bus stops constitutes personnel waiting data;
wherein, the implementation of the active interrogation confirmation includes:
monitoring a bus line to be taken by a person in a waiting range of a bus stop through an induction sensor, prompting the person by voice through a stop display screen, and determining the bus line by carrying out voice recognition and keyword extraction on voice reply of the person or determining the bus line by clicking the display screen by the person; the voice recognition and keyword extraction are conventional technical means, and specific steps are not repeated here;
embodiments of active monitoring include:
when the induction sensor monitors that the personnel stays in the waiting area of the corresponding bus line in the BRT station, adding one to the total waiting number of the bus line corresponding to the waiting area at the moment;
when waiting influence verification is carried out on the bus driving route according to the monitored waiting data of the personnel, the corresponding station names of all bus stations on the bus unidirectional driving route are traversed and matched with a station name weight table pre-stored in a database to obtain corresponding station weights ZQi;
the station name weight table comprises a plurality of different station names and corresponding station weights, wherein the different station names are preset with one corresponding station weight, the station weights are used for digitally representing the station names, and specific numerical values of the station weights can be determined according to historical waiting personnel big data of bus stations;
extracting the numerical value of the real-time waiting population and the station weight of the bus station and calculating and obtaining the local waiting factor Jd corresponding to the bus station through a formula Jd=DRi× ZQi; the local waiting factor is a numerical value used for carrying out simultaneous calculation on real-time personnel waiting data and self-influence data of the bus station to analyze and evaluate the real-time personnel waiting influence condition of the bus station;
when analyzing and evaluating the personnel waiting influence conditions of the corresponding bus stops according to the local waiting factors, comparing and judging the local waiting factors with a preset local waiting threshold; the local waiting threshold value is determined according to the standard passenger carrying number of the corresponding bus;
if the local waiting factor is smaller than the local waiting threshold, generating a low-influence local label and marking the corresponding bus stop as a low-influence stop;
if the local waiting factor is not smaller than the local waiting threshold and is not larger than Y of the local waiting threshold, Y is a real number larger than one hundred, generating a middle-influence local label and marking the corresponding bus stop as a middle-influence stop;
if the local waiting factor is larger than Y of the local waiting threshold value, generating a high local influence label and marking the corresponding bus stop as a high influence stop;
in the embodiment of the invention, the local personnel waiting influence of the corresponding bus station is determined by monitoring and counting the real-time waiting population of different bus stations and calculating and analyzing the real-time waiting population of the monitoring and counting and the station weight corresponding to the bus station, so that the local personnel waiting influence condition corresponding to different bus stations can be intuitively obtained, reliable data support can be provided for the whole personnel waiting influence analysis of the corresponding driving line of the subsequent bus, and the diversity and the expansibility of the traffic local monitoring analysis are improved.
Counting the total number DZ of low-influence stations, the total number ZZ of medium-influence stations and the total number GZ of high-influence stations, and obtaining the integral waiting factor Zd corresponding to the bus station by extracting the numerical values of the low-influence stations, the total number ZZ of medium-influence stations and the total number GZ of high-influence stations and calculating through a formula Zd= (ZZ+2 XGZ)/(DZ+ZZ+GZ); the overall waiting factor is a numerical value used for carrying out simultaneous calculation on real-time personnel waiting analysis data corresponding to all bus stops on the bus unidirectional driving line so as to evaluate the overall personnel waiting influence condition of the current bus line;
when analyzing and evaluating the overall personnel waiting influence condition of the corresponding bus unidirectional travel route according to the overall waiting factor, comparing and judging the overall waiting factor with a preset overall waiting range [ Zdmin, ZDmax ]; the whole waiting range can be determined according to the standard passenger carrying number of the corresponding bus;
if Zd is less than Zdmin, generating a low-influence overall signal;
if Zdmin is less than or equal to Zd and less than or equal to ZDmax, generating a middle-influence integral signal;
if Zd > ZDmax, generating a high-impact overall signal;
the low-influence integral signal, the medium-influence integral signal or the high-influence integral signal forms the data waiting for verification analysis and is uploaded to a cloud platform and a database;
according to the embodiment of the invention, the real-time personnel waiting influence data of all bus stops of the one-way driving route of the earlier-stage bus is subjected to simultaneous calculation and analysis, so that the expansion and excavation of the monitoring analysis data of the earlier-stage bus are realized, the waiting influence condition of the whole personnel of the one-way driving route of the bus can be analyzed and classified, and meanwhile, reliable whole data support can be provided for dynamic scheduling of buses in different subsequent bus operation interval periods.
The traffic line dispatching management module is used for carrying out self-adaptive dynamic management on the dispatching of buses on the corresponding unidirectional driving lines in the real-time basic operation interval period according to the waiting verification analysis data; comprising the following steps:
traversing the waiting verification analysis data, and respectively maintaining a bus running scheme corresponding to the existing basic running interval period or a free bus running scheme for dispatching the same line on the corresponding unidirectional running line according to the low-influence overall signal or the high-influence overall signal acquired by traversing;
performing scheduling necessity analysis according to the traversed acquired middle-impact overall signals;
the method comprises the steps of obtaining a time point affecting overall signal generation, marking the time point as a first time point, marking an ending time point corresponding to a real-time basic operation interval time period as a second time point, enabling units of the first time point and the second time point to be accurate to seconds, determining the real-time basic operation interval time period according to an existing bus operation scheme, enabling interval time corresponding to the real-time basic operation interval time period to be 5 minutes, 10 minutes or 15 minutes, calculating time difference between the second time point and the first time point, marking the time difference as waiting time difference, enabling the waiting time difference to be in seconds, and comparing and judging the waiting time difference with a time difference threshold; the time difference threshold is determined according to the interval duration corresponding to the current real-time basic operation interval period;
if the waiting time difference is not greater than the time difference threshold, generating an invalid scheduling signal;
if the waiting time difference is greater than the time length time difference threshold, generating an effective scheduling signal;
the invalid scheduling signals or the effective scheduling signals form scheduling necessary analysis data and are uploaded to the cloud platform and the database, and the public transport operation scheme corresponding to the existing basic operation interval period or the idle public transport vehicle scheduling the same line immediately starts the operation scheme on the corresponding unidirectional running line is respectively maintained according to the invalid scheduling signals or the effective scheduling signals in the scheduling necessary analysis data; the idle bus refers to the bus with the longest idle duration;
in the embodiment of the invention, the bus operation scheduling is dynamically adjusted by analyzing and classifying the waiting influence of the whole personnel on the bus unidirectional running line, so that the fatigue feeling of the whole real-time personnel waiting on the bus unidirectional running line can be reduced, the timeliness and the necessity of the whole operation of the bus unidirectional running line can be effectively improved, and compared with the bus departure operation through fixed different interval periods in the prior art, the embodiment of the invention can effectively improve the efficiency and the flexibility of the whole bus operation scheduling operation.
Embodiment 2, on the basis of embodiment 1, further includes:
the traffic line scheduling traceability module is used for carrying out statistical analysis on the adjustment data of buses in different daily basic operation interval periods in the supervision period, and carrying out dynamic adjustment on the departure frequency corresponding to the different basic operation interval periods according to the analysis result; comprising the following steps:
marking a basic operation interval period of an idle bus which carries out dispatching of the same line every day on a corresponding one-way driving line to serve as an alternative basic operation interval period immediately, and marking a week date of the alternative basic operation interval period as an alternative week date;
counting the total number of adjustment times of different alternative basic operation interval periods in different alternative period periods in turn in a monitoring period, wherein the unit of the monitoring period is a day, specifically 30 days, and the corresponding alternative basic operation interval periods are arranged in a descending order according to the numerical value of the counted total number of adjustment times;
it should be noted that, the stability verification is implemented on the adjustment data of different basic operation interval periods by setting the supervision period, so that the influence of corresponding adjustment of different times, such as the difference of adjustment of different basic operation interval periods corresponding to workdays and rest days, can be reduced, and reliable data support can be provided for the dynamic adjustment of the existing basic operation interval period corresponding to buses through the adjustment data in the basic operation interval period;
marking an alternative basic operation interval period corresponding to the total adjustment times greater than the adjustment threshold as a selected basic operation interval period, and marking an alternative week date corresponding to the selected basic operation interval period as a selected week date; the adjustment threshold value can be determined by the total daily departure times on the unidirectional driving line of the corresponding bus;
and increasing the departure operation frequency of buses on the corresponding unidirectional driving lines for a plurality of selected basic operation interval periods in the selected period, and continuously implementing monitoring, data analysis and departure adjustment for the rest different alternative basic operation interval periods and basic operation interval periods.
In the embodiment of the invention, the stability verification is implemented on the scheduling adjustment which occurs in different basic operation interval periods by further expanding and excavating the early traffic monitoring scheduling data, so that the running frequency of the bus departure can be dynamically adjusted in different basic operation interval periods in a targeted manner, the influence of the irregular adjustment of the running frequency of the bus in the early on the overall scheduling of the bus on the same day can be reduced, and the rationality and stability of the traffic monitoring scheduling in the aspect of bus scheduling are improved.
In addition, the formulas related in the above are all formulas for removing dimensions and taking numerical calculation, and are one formula which is obtained by acquiring a large amount of data and performing software simulation through simulation software and is closest to the actual situation.
In the several embodiments provided by the present invention, it should be understood that the disclosed system may be implemented in other ways. For example, the above-described embodiments of the invention are merely illustrative, and for example, the division of modules is merely a logical function division, and other manners of division may be implemented in practice.
The modules illustrated as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, i.e., may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module. The integrated modules may be implemented in hardware or in hardware plus software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the essential characteristics thereof.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (4)
1. The intelligent traffic monitoring and scheduling system based on big data is characterized by comprising a traffic line monitoring and evaluating module, wherein the traffic line monitoring and evaluating module is used for monitoring data of real-time personnel waiting conditions of different bus stops on corresponding unidirectional driving lines of buses, and performing waiting influence verification on the driving lines of buses according to the monitored personnel waiting data to obtain waiting verification analysis data; comprising the following steps:
acquiring the station names and station coordinates corresponding to all bus stations on a bus unidirectional driving line, numbering and marking all bus stations according to the unidirectional driving sequence of the bus; acquiring the real-time waiting population of different bus stops by an active inquiry confirmation and active monitoring mode; the real-time waiting headcount corresponding to all marked bus stops constitutes personnel waiting data;
when waiting influence verification is carried out on the bus driving route according to the monitored waiting data of the personnel, the corresponding station names of all bus stations on the bus unidirectional driving route are traversed and matched with a station name weight table pre-stored in a database to obtain corresponding station weights;
the numerical values of the real-time waiting population and the station weights of the bus stations are extracted, local waiting factors corresponding to the bus stations are obtained through calculation, and the calculation formula of the local waiting factors Jd is as follows: jd=dri× ZQi; DRi is the number of people waiting for different bus stops in real time; ZQi is the station weight corresponding to different bus stations; i is different bus stops, i= {1,2,3, … …, n }; n is a positive integer; when analyzing and evaluating the personnel waiting influence conditions of the corresponding bus stops according to the local waiting factors, comparing the local waiting factors with a preset local waiting threshold value to judge whether the local waiting factors have low influence on the local labels, medium influence on the local labels or high influence on the local labels;
if the local waiting factor is smaller than the local waiting threshold, generating a low-influence local label and marking the corresponding bus station as a low-influence station;
if the local waiting factor is not smaller than the local waiting threshold and is not larger than Y of the local waiting threshold, Y is a real number larger than one hundred, generating a middle-influence local label and marking the corresponding bus stop as a middle-influence stop;
if the local waiting factor is larger than Y of the local waiting threshold value, generating a high local influence label and marking the corresponding bus stop as a high influence stop;
counting the total number of low-influence stations, the total number of medium-influence stations and the total number of high-influence stations, extracting the numerical values of the low-influence stations, the medium-influence stations and the total number of high-influence stations, and calculating to obtain the integral waiting factors corresponding to the bus stations; the calculation formula of the overall waiting factor Zd is: zd= (zz+2×gz)/(dz+zz+gz); DZ is the total number of low impact sites; ZZ is the total number of medium-impact sites; GZ is the total number of high impact sites;
when analyzing and evaluating the waiting influence condition of the overall personnel corresponding to the bus unidirectional running route according to the overall waiting factor, comparing and judging the overall waiting factor with a preset overall waiting range [ Zdmin, ZDmax ] to obtain an overall signal with low influence, an overall signal with medium influence or an overall signal with high influence; if Zd is less than Zdmin, generating a low-influence overall signal; if Zdmin is less than or equal to Zd and less than or equal to ZDmax, generating a middle-influence integral signal; if Zd > ZDmax, generating a high-impact overall signal; zdmin < ZDmax;
the low-influence integral signal, the medium-influence integral signal or the high-influence integral signal forms the data waiting for verification analysis and is uploaded to a cloud platform and a database;
the traffic line dispatching management module is used for carrying out self-adaptive dynamic management on the dispatching of buses on the corresponding unidirectional driving lines in the real-time basic operation interval period according to the waiting verification analysis data; comprising the following steps:
traversing the waiting verification analysis data, and respectively maintaining a bus running scheme corresponding to the existing basic running interval period or a free bus running scheme for dispatching the same line on the corresponding unidirectional running line according to the low-influence overall signal or the high-influence overall signal acquired by traversing; performing scheduling necessity analysis according to the traversed acquired middle-impact overall signals;
when the scheduling necessity analysis is implemented, a time point affecting the generation of the whole signal in the process of obtaining is marked as a first time point, an ending time point corresponding to a real-time basic operation interval period is marked as a second time point, the time difference between the second time point and the first time point is calculated and marked as a waiting time difference, and the waiting time difference is compared with a time difference threshold to judge to obtain an invalid scheduling signal or an effective scheduling signal;
the invalid scheduling signals or the effective scheduling signals form the scheduling necessary analysis data and are uploaded to the cloud platform and the database, and the public transport operation scheme corresponding to the existing basic operation interval period or the idle public transport vehicle scheduling the same line immediately starts the operation scheme on the corresponding unidirectional running line is maintained according to the invalid scheduling signals or the effective scheduling signals in the scheduling necessary analysis data.
2. The intelligent traffic monitoring and dispatching system based on big data according to claim 1, further comprising a traffic route dispatching traceability module for carrying out statistical analysis on the adjustment data of buses in different daily basic operation interval periods in a supervision period, and carrying out dynamic adjustment on departure frequencies corresponding to different basic operation interval periods according to analysis results.
3. The intelligent traffic monitoring and dispatching system based on big data according to claim 2, wherein the basic operation interval period of the departure operation scheme of the idle buses which implement the same dispatching route every day is immediately marked as an alternative basic operation interval period on the corresponding unidirectional driving route, and the week date where the alternative basic operation interval period is located is marked as an alternative week date;
and counting the total number of adjustment times of different alternative basic operation interval periods in different alternative period periods in turn in a monitoring period, and arranging the corresponding alternative basic operation interval periods in a descending order according to the numerical value of the counted total number of adjustment times.
4. The intelligent traffic monitoring and dispatching system based on big data according to claim 3, wherein the alternative basic operation interval period corresponding to the total number of adjustment greater than the adjustment threshold is marked as the selected basic operation interval period, and the alternative week date corresponding to the selected basic operation interval period is marked as the selected week date;
and increasing the departure operation frequency of buses on the corresponding unidirectional driving lines for a plurality of selected basic operation interval periods in the selected period, and continuously implementing monitoring, data analysis and departure adjustment for the rest different alternative basic operation interval periods and basic operation interval periods.
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