CN115691164B - Intelligent traffic management method and system based on big data - Google Patents
Intelligent traffic management method and system based on big data Download PDFInfo
- Publication number
- CN115691164B CN115691164B CN202211188349.3A CN202211188349A CN115691164B CN 115691164 B CN115691164 B CN 115691164B CN 202211188349 A CN202211188349 A CN 202211188349A CN 115691164 B CN115691164 B CN 115691164B
- Authority
- CN
- China
- Prior art keywords
- time
- traffic flow
- flow data
- vehicle
- traffic
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000007726 management method Methods 0.000 title claims abstract description 48
- 238000011144 upstream manufacturing Methods 0.000 claims abstract description 64
- 238000000034 method Methods 0.000 claims description 39
- 210000002569 neuron Anatomy 0.000 claims description 28
- 239000013598 vector Substances 0.000 claims description 28
- 230000008569 process Effects 0.000 claims description 18
- 238000004364 calculation method Methods 0.000 claims description 15
- 230000007613 environmental effect Effects 0.000 claims description 12
- 230000006870 function Effects 0.000 claims description 10
- 238000012545 processing Methods 0.000 claims description 10
- 238000011217 control strategy Methods 0.000 claims description 7
- 238000005311 autocorrelation function Methods 0.000 claims description 6
- 238000013507 mapping Methods 0.000 claims description 6
- 238000013528 artificial neural network Methods 0.000 claims description 4
- 238000010606 normalization Methods 0.000 claims description 4
- 238000004458 analytical method Methods 0.000 abstract description 10
- 238000010219 correlation analysis Methods 0.000 abstract description 8
- 230000008030 elimination Effects 0.000 abstract description 6
- 238000003379 elimination reaction Methods 0.000 abstract description 6
- 230000008439 repair process Effects 0.000 abstract description 6
- 238000004891 communication Methods 0.000 description 5
- 238000011161 development Methods 0.000 description 5
- 230000000694 effects Effects 0.000 description 3
- 230000002123 temporal effect Effects 0.000 description 3
- 238000007621 cluster analysis Methods 0.000 description 2
- 238000007405 data analysis Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 206010039203 Road traffic accident Diseases 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
Landscapes
- Traffic Control Systems (AREA)
Abstract
The application provides a big data-based intelligent traffic management method and a big data-based intelligent traffic management system, which are characterized in that through carrying out space correlation and time correlation analysis on traffic flow data of a target area, vehicles at intersections in the target area can be dynamically allocated based on space correlation and time correlation analysis results, and the traffic pressure at the intersections is reduced, so that the traffic environment of the target area can be improved, the traffic efficiency of the target area is improved, the congestion areas possibly occurring at upstream intersections and downstream intersections in the target area are effectively dredged, and the elimination of the congestion areas is accelerated. The application can repair the missing traffic flow data, so that the analysis result of the application is more accurate. Meanwhile, the application can also allocate the driving relationship between the vehicle and the passenger in the target area, improve the passenger searching efficiency of the vehicles such as network taxi, taxi and the like, and reduce the waiting time of the passenger; the application can reasonably distribute the number of vehicles carried by the road, and reduce traffic pressure.
Description
Technical Field
The invention relates to the technical field of traffic management control, in particular to an intelligent traffic management method and system based on big data.
Background
At present, along with the gradual improvement of the living standard of people, automobiles become indispensable tools for riding instead of walking in the life of people, the number of automobiles is increased suddenly, along with the increase of the quantity of automobiles in cities, the roads of the cities are more and more congested, and the problem of urban traffic congestion is also one of the important problems puzzling people to go out. The intelligent traffic is a high-new IT technology integrating the Internet of things, cloud computing, big data, mobile interconnection and the like, traffic information is collected through the high-new technology, and traffic information service under real-time traffic data is provided. Through the intelligent traffic management system based on big data, urban road traffic can be managed timely, accurately and efficiently, the bearing capacity of the road traffic is improved, and the traffic management efficiency is improved.
Since there are many roads and vehicles traveling on the roads, huge traffic data is formed. However, at present, a technical scheme for carrying out statistical analysis on the traffic data is lacking, so that the traffic data cannot be fully utilized, and the effect of intelligent traffic management cannot be achieved.
Disclosure of Invention
In view of the above drawbacks of the prior art, an object of the present invention is to provide a method and a system for intelligent traffic management based on big data, which are used for solving the problem that the prior art cannot fully analyze traffic data.
To achieve the above and other related objects, the present invention provides a big data based intelligent traffic management method, comprising the steps of:
acquiring traffic flow data of an upstream intersection and a downstream intersection in a target area, wherein the target area comprises a traffic road area which is determined in advance or in real time;
Calculating the spatial correlation and the time correlation of the traffic flow data, and performing intelligent traffic management on the upstream intersection and the downstream intersection based on the spatial correlation and the time correlation;
the calculation process of the spatial correlation of the traffic flow data comprises the following steps:
Processing the traffic flow data into traffic flow time sequences with the same time interval, and marking the traffic flow time sequence of an upstream intersection as q i and the traffic flow time sequence of a downstream intersection as q j;
The cross correlation coefficient C ij of the traffic flow time sequence q i of the upstream intersection and the traffic flow time sequence q j of the downstream intersection is calculated, and as the spatial correlation of the traffic flow data, there are:
Wherein D (q i) is the variance of the traffic flow time sequence q i of the upstream intersection;
D (q j) is the variance of the traffic flow time series q j at the downstream intersection;
cov (i, j) is the covariance of the traffic flow time series q i at the upstream intersection and the traffic flow time series q j at the downstream intersection;
The calculation process of the time correlation of the traffic flow data comprises the following steps:
Acquiring a vehicle flow data set of an intersection formed by the upstream intersection and the downstream intersection, and marking the vehicle flow data set as N is a positive integer;
Calculating the vehicle flow data set The covariance of (2) is:
Where d is the time step, d=0, 1,2, 3..n-1; i=1, 2, 3..n;
acquiring the vehicle flow data set The autocorrelation function r (i) (d) of (a) has:
When the autocorrelation coefficient r (i) (d) reaches 0 for the first time, the autocorrelation coefficient r (i) (d) is noted as d (i);
When d is less than or equal to d (i), the time correlation exists between the previous d time steps and the traffic flow data at the current moment, and when d is more than d (i), the time correlation does not exist between the previous d time steps and the traffic flow data at the current moment.
Optionally, after acquiring traffic flow data of the upstream intersection and the downstream intersection in the target area, the method further includes: repairing the missing data in the traffic flow data, including:
Wherein a s represents the weight of traffic flow data;
y (h) represents traffic flow data at the time of data loss;
y (h-m) represents traffic flow data at the h-m th time;
y (h-1) represents traffic flow data at the h-1 th time;
y (h+1) represents traffic flow data at the h+1th time;
y (h+m) represents traffic flow data at the h+m-th time.
Optionally, after acquiring traffic flow data of the upstream intersection and the downstream intersection in the target area, the method further includes:
Acquiring a position point set formed by longitude, latitude and current time of a position of a target vehicle, and recording as:
p＝{longitue，latitude，time}；
The driving event of the target vehicle from the start point p u to the destination p v is recorded as
Trip (u, v), and
Trip (u, v) = { p utime,pvtime,pulocation,pv location, state }; wherein u represents a starting point of a corresponding driving event; v represents the end point of the corresponding driving event; p u time represents the start time p v time of the corresponding driving event, and p u location represents the start position of the corresponding driving event; p v location represents the end position of the corresponding driving event;
state represents the state of the target vehicle during the occurrence of the corresponding driving event; if it is
State=0, indicating that the target vehicle is in a passenger carrying state; if state=0, the target vehicle is in a passenger searching state; when p u is a passenger getting-on point and p v is a passenger getting-off point, representing that the target vehicle is a passenger carrying driving event; when p u is a passenger getting-off point and p v is a passenger getting-on point, representing that the target vehicle is a passenger searching driving event;
Mapping the driving event trip (u, v) of the target vehicle to a first region h u and a second region h v in the target region, obtaining the shortest driving time of the target vehicle from the first region h u to the second region h v in a time period t k, and recording as
Optionally, the process of determining the target vehicle includes:
Receiving a riding request initiated by a target client on a pre-established intelligent traffic management platform;
Responding to the riding request and presenting a plurality of estimated riding fees and a plurality of vehicle types with the riding request to the target client;
Generating a vehicle calling request based on the estimated riding cost and the vehicle type selected by the target client, and pushing the vehicle calling request to a plurality of vehicle terminals;
And after triggering the vehicle calling request by a certain vehicle terminal, taking the vehicle corresponding to the certain vehicle terminal as the target vehicle.
Optionally, before acquiring traffic flow data of the upstream intersection and the downstream intersection in the target area, the method further includes:
Initializing a network structure of the self-organizing map neural network, the number of neurons and weight vectors, and setting a learning rate and a domain function; wherein one neuron corresponds to one environmental impact factor;
Normalizing the weight vector and the input vector, calculating the similarity degree between each neuron and the weight vector, and selecting a winning neuron;
updating the weight vector of the winning neuron, updating the learning rate and the domain function, and judging whether the learning rate is smaller than a preset numerical value or not or judging whether the updating frequency reaches a preset iteration frequency after the updating is completed;
If the learning rate is smaller than a preset value or the update times reach the preset iteration times, marking each neuron, and searching for the environmental influence shadow of the traffic flow data based on the marked neurons;
If the learning rate is greater than or equal to a preset numerical value and the update times do not reach the preset iteration times, carrying out normalization processing on the weight vector and the input vector again, and continuing to judge the learning rate or the update times until the environment influence shadow of the traffic flow data is found.
Optionally, the environmental impact shadow of the traffic flow data includes at least one of: road environment, signal lamp control strategy, weather, accident, traffic flow, vehicle speed.
The application also provides an intelligent traffic management system based on big data, which comprises:
The data acquisition module is used for acquiring traffic flow data of an upstream intersection and a downstream intersection in a target area, wherein the target area comprises a traffic road area which is determined in advance or in real time;
the correlation calculation module is used for calculating the spatial correlation and the time correlation of the traffic flow data;
The intelligent traffic management module is used for carrying out intelligent traffic management on the upstream intersection and the downstream intersection according to the spatial correlation and the time correlation;
wherein the process of calculating the spatial correlation of the traffic flow data by the correlation calculation module comprises:
Processing the traffic flow data into traffic flow time sequences with the same time interval, and marking the traffic flow time sequence of an upstream intersection as q i and the traffic flow time sequence of a downstream intersection as q j;
The cross correlation coefficient C ij of the traffic flow time sequence q i of the upstream intersection and the traffic flow time sequence q j of the downstream intersection is calculated, and as the spatial correlation of the traffic flow data, there are:
Wherein D (q i) is the variance of the traffic flow time sequence q i of the upstream intersection;
D (q j) is the variance of the traffic flow time series q j at the downstream intersection;
cov (i, j) is the covariance of the traffic flow time series q i at the upstream intersection and the traffic flow time series q j at the downstream intersection;
the process of calculating the time correlation of the traffic flow data by the correlation calculation module comprises the following steps:
Acquiring a vehicle flow data set of an intersection formed by the upstream intersection and the downstream intersection, and marking the vehicle flow data set as
N is a positive integer;
Calculating the vehicle flow data set The covariance of (2) is:
Where d is the time step, d=0, 1,2, 3..n-1; i=1, 2, 3..n;
acquiring the vehicle flow data set The autocorrelation function r (i) (d) of (a) has:
When the autocorrelation coefficient r (i) (d) reaches 0 for the first time, the autocorrelation coefficient r (i) (d) is noted as d (i);
When d is less than or equal to d (i), the time correlation exists between the previous d time steps and the traffic flow data at the current moment, and when d is more than d (i), the time correlation does not exist between the previous d time steps and the traffic flow data at the current moment.
Optionally, the system further includes a data repairing module, configured to repair data missing in the traffic flow data after acquiring the traffic flow data of the upstream intersection and the downstream intersection in the target area, where:
Wherein a s represents the weight of traffic flow data;
y (h) represents traffic flow data at the time of data loss;
y (h-m) represents traffic flow data at the h-m th time;
y (h-1) represents traffic flow data at the h-1 th time;
y (h+1) represents traffic flow data at the h+1th time;
y (h+m) represents traffic flow data at the h+m-th time.
Optionally, the system further includes a driving matching module, configured to, after acquiring traffic flow data of an upstream intersection and a downstream intersection in the target area, acquire a set of location points formed by longitude, latitude and current time of a location where the target vehicle is located, and record as: p= { longitue, latitude, time };
The driving event of the target vehicle from the start point p u to the destination p v is recorded as
Trip (u, v), and
Trip (u, v) = { p utime,pvtime,pulocation,pv location, state }; wherein u represents a starting point of a corresponding driving event; v represents the end point of the corresponding driving event; p u time represents the start time p v time of the corresponding driving event, and p u location represents the start position of the corresponding driving event; p v location represents the end position of the corresponding driving event;
state represents the state of the target vehicle during the occurrence of the corresponding driving event; if it is
State=0, indicating that the target vehicle is in a passenger carrying state; if state=0, the target vehicle is in a passenger searching state; when p u is a passenger getting-on point and p v is a passenger getting-off point, representing that the target vehicle is a passenger carrying driving event; when p u is a passenger getting-off point and p v is a passenger getting-on point, representing that the target vehicle is a passenger searching driving event;
Mapping the driving event trip (u, v) of the target vehicle to a first region h u and a second region h v in the target region, obtaining the shortest driving time of the target vehicle from the first region h u to the second region h v in a time period t k, and recording as
Optionally, the process of determining the target vehicle by the driving matching module includes:
Receiving a riding request initiated by a target client on a pre-established intelligent traffic management platform;
Responding to the riding request and presenting a plurality of estimated riding fees and a plurality of vehicle types with the riding request to the target client;
Generating a vehicle calling request based on the estimated riding cost and the vehicle type selected by the target client, and pushing the vehicle calling request to a plurality of vehicle terminals;
And after triggering the vehicle calling request by a certain vehicle terminal, taking the vehicle corresponding to the certain vehicle terminal as the target vehicle.
As described above, the invention provides a big data-based intelligent traffic management method and system, which have the following beneficial effects:
According to the traffic flow data analysis method and the traffic flow data analysis system, the traffic flow data of the target area are subjected to spatial correlation and time correlation analysis, vehicles at the intersections in the target area can be dynamically allocated based on the spatial correlation and time correlation analysis results, and the traffic pressure at the intersections is reduced, so that the traffic environment of the target area can be improved, the traffic efficiency of the target area is improved, the congestion areas possibly occurring at the upstream intersections and the downstream intersections in the target area are effectively dredged, and the elimination of the congestion areas is accelerated. The application can repair the missing traffic flow data, so that the analysis result of the application is more accurate. Meanwhile, the application can also allocate the driving relationship between the vehicle and the passenger in the target area, improve the passenger searching efficiency of the vehicles such as network taxi, taxi and the like, and reduce the waiting time of the passenger; the intelligent traffic control system and the intelligent traffic control method can reasonably distribute the number of vehicles borne by the road, reduce traffic pressure, thereby performing intelligent traffic control on a target area, providing omnibearing traffic information service and providing more convenient, efficient, quick, economical, safe, humanized and intelligent traffic service.
Drawings
FIG. 1 is a flow chart of a smart traffic management method based on big data according to an embodiment of the present application;
FIG. 2 is a schematic hardware architecture of a big data based intelligent traffic management system according to an embodiment of the present application;
fig. 3 is a schematic circuit diagram of a hardware structure of a data acquisition module according to an embodiment of the application.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict.
It should be noted that the illustrations provided in the following embodiments merely illustrate the basic concept of the present invention by way of illustration, and only the components related to the present invention are shown in the drawings and are not drawn according to the number, shape and size of the components in actual implementation, and the form, number and proportion of the components in actual implementation may be arbitrarily changed, and the layout of the components may be more complicated.
Referring to fig. 1, the invention provides a smart traffic management method based on big data, comprising the following steps:
S110, acquiring traffic flow data of an upstream intersection and a downstream intersection in a target area, wherein the target area comprises a traffic road area which is determined in advance or in real time;
S120, calculating the spatial correlation and the time correlation of the traffic flow data. Specifically, the calculation process of the spatial correlation of the traffic flow data includes: processing the traffic flow data into traffic flow time sequences with the same time interval, and marking the traffic flow time sequence of an upstream intersection as q i and the traffic flow time sequence of a downstream intersection as q j; the cross correlation coefficient C ij of the traffic flow time sequence q i of the upstream intersection and the traffic flow time sequence q j of the downstream intersection is calculated, and as the spatial correlation of the traffic flow data, there are:
Wherein D (q i) is the variance of the traffic flow time sequence q i of the upstream intersection; d (q j) is the variance of the traffic flow time series q j at the downstream intersection; cov (i, j) is the covariance of the traffic flow time series q i at the upstream intersection and the traffic flow time series q j at the downstream intersection.
Specifically, the calculation process of the time correlation of the traffic flow data includes: acquiring a vehicle flow data set of an intersection formed by the upstream intersection and the downstream intersection, and marking the vehicle flow data set as
N is a positive integer;
Calculating the vehicle flow data set The covariance of (2) is:
Where d is the time step, d=0, 1,2, 3..n-1; i=1, 2, 3..n;
acquiring the vehicle flow data set The autocorrelation function r (i) (d) of (a) has:
When the autocorrelation coefficient r (i) (d) reaches 0 for the first time, the autocorrelation coefficient r (i) (d) is noted as d (i);
When d is less than or equal to d (i), the time correlation exists between the previous d time steps and the traffic flow data at the current moment, and when d is more than d (i), the time correlation does not exist between the previous d time steps and the traffic flow data at the current moment.
S130, intelligent traffic management is conducted on the upstream intersection and the downstream intersection based on the spatial correlation and the time correlation of the traffic flow data.
In urban road networks, certain intersections are congested, and adjacent intersections are congested to a certain extent as time passes. The congestion condition of the intersection is weakened by optimizing signal lamp control strategies, manual dredging and other modes, and the congestion state of adjacent intersections is relieved, so that certain correlation is necessarily present between the intersections. The correlation between intersections is shown in: when the radiation capability of a certain intersection is large, such as a region with dense pedestrians, stations, business offices and the like nearby, the fluctuation of traffic flow data of the intersection can influence the traffic conditions of a plurality of intersections, and even cause a large piece of congestion. The traffic state of an intersection may be affected by a plurality of external factors having uncertainty, such as sudden traffic accidents, temporary activities, road construction, etc. If a certain intersection is affected, the surrounding intersections will also be congested. The bearing capacity of an intersection in the urban road network has larger unbalance with the actual traffic flow, and slight changes of traffic flow data can cause the change of the traffic state of the intersection at the next moment. The spatial correlation between different intersections may be quantified by cross-correlation coefficients, on the other hand, autocorrelation coefficients may characterize the temporal correlation of traffic flow data.
Therefore, according to the embodiment, through the analysis of the spatial correlation and the time correlation of the traffic flow data of the target area, the vehicles at the intersections in the target area can be dynamically allocated based on the analysis results of the spatial correlation and the time correlation, and the traffic pressure at the intersections is reduced, so that the traffic environment of the target area can be improved, the traffic efficiency of the target area is improved, the congestion areas possibly occurring at the upstream intersections and the downstream intersections in the target area are effectively dredged, and the elimination of the congestion areas is accelerated.
In an exemplary embodiment, after acquiring traffic flow data of the upstream intersection and the downstream intersection in the target area, the method further includes: repairing the missing data in the traffic flow data, including:
Wherein a s represents the weight of traffic flow data;
y (h) represents traffic flow data at the time of data loss;
y (h-m) represents traffic flow data at the h-m th time;
y (h-1) represents traffic flow data at the h-1 th time;
y (h+1) represents traffic flow data at the h+1th time;
y (h+m) represents traffic flow data at the h+m-th time.
In this embodiment, the missing data refers to that the missing data or part of the key content is missing at a certain moment due to equipment failure, weather influence, manual operation error and other reasons. Meanwhile, the phenomenon of data loss in the urban traffic field generally causes problems in the time dimension of the data, and can not provide more accurate information for scientific research. Therefore, on the basis of repairing the missing data in the traffic flow data, the embodiment can analyze based on all traffic flow data, so that an analysis result is more accurate, and higher accuracy can be provided for intelligent traffic management in the later stage.
In an exemplary embodiment, after acquiring traffic flow data of the upstream intersection and the downstream intersection in the target area, the method further includes:
Acquiring a position point set formed by longitude, latitude and current time of a position of a target vehicle, and recording as:
p＝{longitue，latitude，time}；
The driving event of the target vehicle from the start point p u to the destination p v is recorded as
Trip (u, v), and
Trip (u, v) = { p utime,pvtime,pulocation,pv location, state }; wherein u represents a starting point of a corresponding driving event; v represents the end point of the corresponding driving event; p u time represents the start time p v time of the corresponding driving event, and p u location represents the start position of the corresponding driving event; p v location represents the end position of the corresponding driving event;
state represents the state of the target vehicle during the occurrence of the corresponding driving event; if it is
State=0, indicating that the target vehicle is in a passenger carrying state; if state=0, the target vehicle is in a passenger searching state; when p u is a passenger getting-on point and p v is a passenger getting-off point, representing that the target vehicle is a passenger carrying driving event; when p u is a passenger getting-off point and p v is a passenger getting-on point, representing that the target vehicle is a passenger searching driving event;
Mapping the driving event trip (u, v) of the target vehicle to a first region h u and a second region h v in the target region, obtaining the shortest driving time of the target vehicle from the first region h u to the second region h v in a time period t k, and recording as
According to the above, the process of determining the target vehicle of the present embodiment includes: receiving a riding request initiated by a target client on a pre-established intelligent traffic management platform; responding to the riding request and presenting a plurality of estimated riding fees and a plurality of vehicle types with the riding request to the target client; generating a vehicle calling request based on the estimated riding cost and the vehicle type selected by the target client, and pushing the vehicle calling request to a plurality of vehicle terminals; and after triggering the vehicle calling request by a certain vehicle terminal, taking the vehicle corresponding to the certain vehicle terminal as the target vehicle. The target vehicle of the embodiment includes, but is not limited to, a network taxi, a taxi, and the like.
Therefore, the driving relationship between the vehicle and the passenger in the target area can be allocated, when the passenger wants to take a vehicle in the target area, the passenger only needs to initiate a riding request on a pre-established intelligent traffic management platform, then the estimated riding expense and the type of the vehicle are selected, and the intelligent traffic management platform can match the corresponding network taxi or the corresponding taxi for the passenger, so that the passenger seeking efficiency of the vehicles such as the network taxi and the taxi can be improved, and the passenger riding waiting time is also reduced; meanwhile, the number of vehicles borne by the road can be reasonably distributed, and the traffic pressure is reduced.
In an exemplary embodiment, before acquiring traffic flow data of the upstream intersection and the downstream intersection in the target area, the method further includes:
Initializing a network structure of the self-organizing map neural network, the number of neurons and weight vectors, and setting a learning rate and a domain function; wherein one neuron corresponds to one environmental impact factor;
Normalizing the weight vector and the input vector, calculating the similarity degree between each neuron and the weight vector, and selecting a winning neuron;
updating the weight vector of the winning neuron, updating the learning rate and the domain function, and judging whether the learning rate is smaller than a preset numerical value or not or judging whether the updating frequency reaches a preset iteration frequency after the updating is completed;
If the learning rate is smaller than a preset value or the update times reach the preset iteration times, marking each neuron, and searching for the environmental influence shadow of the traffic flow data based on the marked neurons;
If the learning rate is greater than or equal to a preset numerical value and the update times do not reach the preset iteration times, carrying out normalization processing on the weight vector and the input vector again, and continuing to judge the learning rate or the update times until the environment influence shadow of the traffic flow data is found. The environmental impact shadow of the traffic flow data in this embodiment includes, but is not limited to: road environment, signal lamp control strategy, weather, accident, traffic flow, vehicle speed, etc.
Due to the complexity of urban traffic scenes, infrastructure around urban intersections, road environments, signal lamp control strategies, weather and sudden accidents are all important factors affecting traffic states. The traditional traffic state estimation is determined by empirical numerical division of traffic parameters such as traffic flow, average speed, occupancy and the like, and the influence of various factors is not comprehensively considered. The method and the device have the advantages that factors such as road environment and weather are difficult to collect and are characterized by specific numerical values, but the influences of the factors can be represented in a large amount of data, so that the method and the device define clustered results as environment influence factors by carrying out cluster analysis on intersection flow data, and then predict traffic flows under different environment influence factors, so that environment influence molecules of the traffic flow data in a target area can be determined, and corresponding interference noise can be eliminated when the traffic flow data in the target area is analyzed in a later period.
In summary, the invention provides a big data-based intelligent traffic management method, by performing spatial correlation and time correlation analysis on traffic flow data of a target area, vehicles at intersections in the target area can be dynamically allocated based on the spatial correlation and time correlation analysis results, and the traffic pressure at the intersections is reduced, so that the traffic environment of the target area can be improved, the traffic efficiency of the target area is improved, the congestion areas possibly occurring at the upstream intersections and the downstream intersections in the target area are effectively dredged, and the elimination of the congestion areas is accelerated. The method can also repair missing traffic flow data, so that the analysis result of the method is more accurate. Meanwhile, the method can also allocate the driving relation between the vehicle and the passenger in the target area, improve the passenger searching efficiency of the vehicles such as network taxi, taxi and the like, and reduce the waiting time of the passenger; the method is equivalent to reasonably distributing the number of vehicles borne by the road, reducing traffic pressure, thereby carrying out intelligent traffic management on a target area, providing omnibearing traffic information service and being capable of providing more convenient, efficient, quick, economical, safe, humanized and intelligent traffic service.
As shown in fig. 2, the present application further provides a big data-based intelligent traffic management system, which includes:
the data acquisition module 210 is configured to acquire traffic flow data of an upstream intersection and a downstream intersection in a target area, where the target area includes a predetermined or real-time traffic road area. The collection mode of the traffic flow data in this embodiment may be an existing mode, and the data collection mode is not limited in this embodiment. As an example, the hardware circuit for collecting traffic flow data in this embodiment is shown in fig. 3, and in fig. 3, the circuit mainly comprises a dc regulated power supply circuit, a core board S3C2440, a minimum system board formed by a development board, and a 485 communication circuit. Firstly, a direct-current stabilized power supply part adopts a direct-current stabilized power supply of 5V, and converts the 5V power supply voltage into a 3.3V power supply voltage through an LM1117 stabilized circuit to supply power for an S3C2440 development board. Secondly, an S3C2440 processor taking the ARM920T chip as a core, a clock circuit, a reset source, a power supply circuit, a standard SD card interface, a NAND FLASH storage device marked with 256M and a plurality of programmable full duplex serial communication interfaces jointly form an S3C2440 minimum system. In addition, the 485 communication circuit is used for leading out an RS-485 communication interface of the S3C23440 development board, so that 485 data communication between the lower computer collector and the conversion module is realized. The MAX485 chip mainly uses a driver and a receiver as an input end of the driver and an output end of the receiver respectively, and is connected with the S3C2440 development board only by being connected with RXD and TXD on the S3C2440 development board respectively.
The correlation calculation module 220 is configured to calculate a spatial correlation and a temporal correlation of the traffic flow data. Specifically, the process of calculating the spatial correlation of the traffic flow data by the correlation calculation module 220 includes: processing the traffic flow data into traffic flow time sequences with the same time interval, and marking the traffic flow time sequence of an upstream intersection as q i and the traffic flow time sequence of a downstream intersection as q j; the cross correlation coefficient C ij of the traffic flow time sequence q i of the upstream intersection and the traffic flow time sequence q j of the downstream intersection is calculated, and as the spatial correlation of the traffic flow data, there are:
Wherein D (q i) is the variance of the traffic flow time sequence q i of the upstream intersection; d (q j) is the variance of the traffic flow time series q j at the downstream intersection; cov (i, j) is the covariance of the traffic flow time series q i at the upstream intersection and the traffic flow time series q j at the downstream intersection.
Wherein, the process of calculating the time correlation of the traffic flow data by the correlation calculation module 220 includes: acquiring a vehicle flow data set of an intersection formed by the upstream intersection and the downstream intersection, and marking the vehicle flow data set as
N is a positive integer;
Calculating the vehicle flow data set The covariance of (2) is:
Where d is the time step, d=0, 1,2, 3..n-1; i=1, 2, 3..n;
acquiring the vehicle flow data set The autocorrelation function r (i) (d) of (a) has:
When the autocorrelation coefficient r (i) (d) reaches 0 for the first time, the autocorrelation coefficient r (i) (d) is noted as d (i);
When d is less than or equal to d (i), the time correlation exists between the previous d time steps and the traffic flow data at the current moment, and when d is more than d (i), the time correlation does not exist between the previous d time steps and the traffic flow data at the current moment.
And the intelligent traffic management module 230 is configured to perform intelligent traffic management on the upstream intersection and the downstream intersection according to the spatial correlation and the temporal correlation.
Therefore, according to the embodiment, through the analysis of the spatial correlation and the time correlation of the traffic flow data of the target area, the vehicles at the intersections in the target area can be dynamically allocated based on the analysis results of the spatial correlation and the time correlation, and the traffic pressure at the intersections is reduced, so that the traffic environment of the target area can be improved, the traffic efficiency of the target area is improved, the congestion areas possibly occurring at the upstream intersections and the downstream intersections in the target area are effectively dredged, and the elimination of the congestion areas is accelerated.
In an exemplary embodiment, the system further includes a data repairing module, configured to repair data missing in traffic flow data after acquiring traffic flow data of an upstream intersection and a downstream intersection in a target area, where:
Wherein a s represents the weight of traffic flow data;
y (h) represents traffic flow data at the time of data loss;
y (h-m) represents traffic flow data at the h-m th time;
y (h-1) represents traffic flow data at the h-1 th time;
y (h+1) represents traffic flow data at the h+1th time;
y (h+m) represents traffic flow data at the h+m-th time.
In this embodiment, the missing data refers to that the missing data or part of the key content is missing at a certain moment due to equipment failure, weather influence, manual operation error and other reasons. Meanwhile, the phenomenon of data loss in the urban traffic field generally causes problems in the time dimension of the data, and can not provide more accurate information for scientific research. Therefore, on the basis of repairing the missing data in the traffic flow data, the embodiment can analyze based on all traffic flow data, so that an analysis result is more accurate, and higher accuracy can be provided for intelligent traffic management in the later stage.
In an exemplary embodiment, the system further includes a driving matching module, configured to, after acquiring traffic flow data of an upstream intersection and a downstream intersection in the target area, acquire a set of location points formed by longitude, latitude, and current time of a location where the target vehicle is located, and record as: p= { longitue, latitude, time };
The driving event of the target vehicle from the start point p u to the destination p v is recorded as
Trip (u, v), and
Trip (u, v) = { p utime,pvtime,pulocation,pv location, state }; wherein u represents a starting point of a corresponding driving event; v represents the end point of the corresponding driving event; p u time represents the start time p v time of the corresponding driving event, and p u location represents the start position of the corresponding driving event; p v location represents the end position of the corresponding driving event;
state represents the state of the target vehicle during the occurrence of the corresponding driving event; if it is
State=0, indicating that the target vehicle is in a passenger carrying state; if state=0, the target vehicle is in a passenger searching state; when p u is a passenger getting-on point and p v is a passenger getting-off point, representing that the target vehicle is a passenger carrying driving event; when p u is a passenger getting-off point and p v is a passenger getting-on point, representing that the target vehicle is a passenger searching driving event;
Mapping the driving event trip (u, v) of the target vehicle to a first region h u and a second region h v in the target region, obtaining the shortest driving time of the target vehicle from the first region h u to the second region h v in a time period t k, and recording as
According to the above, the process of determining the target vehicle of the present embodiment includes: receiving a riding request initiated by a target client on a pre-established intelligent traffic management platform; responding to the riding request and presenting a plurality of estimated riding fees and a plurality of vehicle types with the riding request to the target client; generating a vehicle calling request based on the estimated riding cost and the vehicle type selected by the target client, and pushing the vehicle calling request to a plurality of vehicle terminals; and after triggering the vehicle calling request by a certain vehicle terminal, taking the vehicle corresponding to the certain vehicle terminal as the target vehicle. The target vehicle of the embodiment includes, but is not limited to, a network taxi, a taxi, and the like.
Therefore, the driving relationship between the vehicle and the passenger in the target area can be allocated, when the passenger wants to take a vehicle in the target area, the passenger only needs to initiate a riding request on a pre-established intelligent traffic management platform, then the estimated riding expense and the type of the vehicle are selected, and the intelligent traffic management platform can match the corresponding network taxi or the corresponding taxi for the passenger, so that the passenger seeking efficiency of the vehicles such as the network taxi and the taxi can be improved, and the passenger riding waiting time is also reduced; meanwhile, the number of vehicles borne by the road can be reasonably distributed, and the traffic pressure is reduced.
In an exemplary embodiment, before acquiring traffic flow data for an upstream intersection and a downstream intersection in a target area, the system further comprises:
Initializing a network structure of the self-organizing map neural network, the number of neurons and weight vectors, and setting a learning rate and a domain function; wherein one neuron corresponds to one environmental impact factor;
Normalizing the weight vector and the input vector, calculating the similarity degree between each neuron and the weight vector, and selecting a winning neuron;
updating the weight vector of the winning neuron, updating the learning rate and the domain function, and judging whether the learning rate is smaller than a preset numerical value or not or judging whether the updating frequency reaches a preset iteration frequency after the updating is completed;
If the learning rate is smaller than a preset value or the update times reach the preset iteration times, marking each neuron, and searching for the environmental influence shadow of the traffic flow data based on the marked neurons;
If the learning rate is greater than or equal to a preset numerical value and the update times do not reach the preset iteration times, carrying out normalization processing on the weight vector and the input vector again, and continuing to judge the learning rate or the update times until the environment influence shadow of the traffic flow data is found. The environmental impact shadow of the traffic flow data in this embodiment includes, but is not limited to: road environment, signal lamp control strategy, weather, accident, traffic flow, vehicle speed, etc.
Due to the complexity of urban traffic scenes, infrastructure around urban intersections, road environments, signal lamp control strategies, weather and sudden accidents are all important factors affecting traffic states. The traditional traffic state estimation is determined by empirical numerical division of traffic parameters such as traffic flow, average speed, occupancy and the like, and the influence of various factors is not comprehensively considered. The method and the device have the advantages that factors such as road environment and weather are difficult to collect and are characterized by specific numerical values, but the influences of the factors can be represented in a large amount of data, so that the method and the device define clustered results as environment influence factors by carrying out cluster analysis on intersection flow data, and then predict traffic flows under different environment influence factors, so that environment influence molecules of the traffic flow data in a target area can be determined, and corresponding interference noise can be eliminated when the traffic flow data in the target area is analyzed in a later period.
In summary, the invention provides a big data-based intelligent traffic management system, by performing spatial correlation and time correlation analysis on traffic flow data of a target area, vehicles at intersections in the target area can be dynamically allocated based on the spatial correlation and time correlation analysis results, and the traffic pressure at the intersections is reduced, so that the traffic environment of the target area can be improved, the traffic efficiency of the target area is improved, the congestion areas possibly occurring at the upstream intersections and the downstream intersections in the target area are effectively dredged, and the elimination of the congestion areas is accelerated. And the system can repair missing traffic flow data, so that the analysis result of the system is more accurate. Meanwhile, the system can also allocate the driving relationship between the vehicle and the passenger in the target area, improve the passenger searching efficiency of the vehicles such as network taxi, taxi and the like, and reduce the waiting time of the passenger; the intelligent traffic control system can reasonably distribute the number of vehicles borne by the road, reduce traffic pressure, thus carrying out intelligent traffic control on a target area, providing omnibearing traffic information service and providing more convenient, efficient, quick, economical, safe, humanized and intelligent traffic service.
It should be noted that, the intelligent traffic management system based on big data provided in the above embodiment and the intelligent traffic management method based on big data provided in the above embodiment belong to the same concept, and the specific manner in which each module and unit perform the operation has been described in detail in the method embodiment, which is not repeated here. In practical application, the intelligent traffic management system based on big data provided in the above embodiment can allocate the functions to different functional modules according to needs, that is, the internal structure of the system is divided into different functional modules to complete all or part of the functions described above, which is not limited herein.
The above embodiments are merely illustrative of the principles of the present invention and its effectiveness, and are not intended to limit the invention. Modifications and variations may be made to the above-described embodiments by those skilled in the art without departing from the spirit and scope of the invention. Accordingly, it is intended that all equivalent modifications and variations of the invention be covered by the claims, which are within the ordinary skill of the art, be within the spirit and scope of the present disclosure.
Claims (4)
1. An intelligent traffic management method based on big data, which is characterized by comprising the following steps:
acquiring traffic flow data of an upstream intersection and a downstream intersection in a target area, wherein the target area comprises a traffic road area which is determined in advance or in real time;
Calculating the spatial correlation and the time correlation of the traffic flow data, and performing intelligent traffic management on the upstream intersection and the downstream intersection based on the spatial correlation and the time correlation;
the calculation process of the spatial correlation of the traffic flow data comprises the following steps:
Processing the traffic flow data into traffic flow time sequences with the same time interval, and marking the traffic flow time sequence of an upstream intersection as q i and the traffic flow time sequence of a downstream intersection as q j;
The cross correlation coefficient C ij of the traffic flow time sequence q i of the upstream intersection and the traffic flow time sequence q j of the downstream intersection is calculated, and as the spatial correlation of the traffic flow data, there are:
Wherein D (q i) is the variance of the traffic flow time sequence q i of the upstream intersection;
D (q j) is the variance of the traffic flow time series q i at the downstream intersection;
cov (i, j) is the covariance of the traffic flow time series q i at the upstream intersection and the traffic flow time series q j at the downstream intersection;
The calculation process of the time correlation of the traffic flow data comprises the following steps:
Acquiring a vehicle flow data set of an intersection formed by the upstream intersection and the downstream intersection, and marking the vehicle flow data set as N is a positive integer;
Calculating the vehicle flow data set The covariance of (2) is:
Where d is the time step, d=0, 1,2, 3..n-1; i=1, 2, 3..n;
acquiring the vehicle flow data set The autocorrelation function r (i) (d) of (a) has:
When the autocorrelation coefficient r (i) (d) reaches 0 for the first time, the autocorrelation coefficient r (i) (d) is noted as d (i);
When d is less than or equal to d (i), the time correlation exists between the previous d time steps and the traffic flow data at the current moment, and when d is more than d (i), the time correlation does not exist between the previous d time steps and the traffic flow data at the current moment;
after the traffic flow data of the upstream intersection and the downstream intersection in the target area are obtained, repairing the missing data in the traffic flow data,
The method comprises the following steps:
Wherein a s represents the weight of traffic flow data;
y (h) represents traffic flow data at the time of data loss;
y (h-m) represents traffic flow data at the h-m th time;
y (h-1) represents traffic flow data at the h-1 th time;
y (h+1) represents traffic flow data at the h+1th time;
y (h+m) represents traffic flow data at the h+m-th time;
after obtaining traffic flow data of the upstream intersection and the downstream intersection in the target area, the method further comprises:
acquiring a position point set formed by longitude, latitude and current time of a position of a target vehicle, and recording as: p= { longitue, latitude, time };
The driving event of the target vehicle from the start point p u to the destination p v is denoted as trip (u, v), and trip (u, v) = { p utime,pvtime,pulocation,pv location, state }; wherein u represents a starting point of a corresponding driving event; v represents the end point of the corresponding driving event; p u time represents the start time p v time of the corresponding driving event, and p u location represents the start position of the corresponding driving event; representing an end position of a corresponding driving event; state represents the state of the target vehicle during the occurrence of the corresponding driving event; if state=0, the target vehicle is in a passenger carrying state; if the state is not equal to 0, the target vehicle is in a passenger searching state; when p u is a passenger getting-on point and p v is a passenger getting-off point, representing that the target vehicle is a passenger carrying driving event; when p u is a passenger getting-off point and p v is a passenger getting-on point, representing that the target vehicle is a passenger searching driving event;
Mapping the driving event trip (u, v) of the target vehicle to a first region h u and a second region h v in the target region, obtaining the shortest driving time of the target vehicle from the first region h u to the second region h v in a time period t k, and recording as
The process of determining the target vehicle includes:
Receiving a riding request initiated by a target client on a pre-established intelligent traffic management platform;
Responding to the riding request and presenting a plurality of estimated riding fees and a plurality of vehicle types with the riding request to the target client;
Generating a vehicle calling request based on the estimated riding cost and the vehicle type selected by the target client, and pushing the vehicle calling request to a plurality of vehicle terminals;
And after triggering the vehicle calling request by a certain vehicle terminal, taking the vehicle corresponding to the certain vehicle terminal as the target vehicle.
2. The intelligent traffic management method based on big data according to claim 1, wherein before acquiring traffic flow data of an upstream intersection and a downstream intersection in a target area, the method further comprises:
Initializing a network structure of the self-organizing map neural network, the number of neurons and weight vectors, and setting a learning rate and a domain function; wherein one neuron corresponds to one environmental impact factor;
Normalizing the weight vector and the input vector, calculating the similarity degree between each neuron and the weight vector, and selecting a winning neuron;
updating the weight vector of the winning neuron, updating the learning rate and the domain function, and judging whether the learning rate is smaller than a preset numerical value or not or judging whether the updating frequency reaches a preset iteration frequency after the updating is completed;
If the learning rate is smaller than a preset value or the update times reach the preset iteration times, marking each neuron, and searching for the environmental influence shadow of the traffic flow data based on the marked neurons;
If the learning rate is greater than or equal to a preset numerical value and the update times do not reach the preset iteration times, carrying out normalization processing on the weight vector and the input vector again, and continuing to judge the learning rate or the update times until the environment influence shadow of the traffic flow data is found.
3. The big data based intelligent traffic management method according to claim 2, wherein the environmental impact shadow of traffic flow data comprises at least one of: road environment, signal lamp control strategy, weather, accident, traffic flow, vehicle speed.
4. An intelligent traffic management system based on big data, characterized in that the system comprises:
The data acquisition module is used for acquiring traffic flow data of an upstream intersection and a downstream intersection in a target area, wherein the target area comprises a traffic road area which is determined in advance or in real time;
the correlation calculation module is used for calculating the spatial correlation and the time correlation of the traffic flow data;
The intelligent traffic management module is used for carrying out intelligent traffic management on the upstream intersection and the downstream intersection according to the spatial correlation and the time correlation;
wherein the process of calculating the spatial correlation of the traffic flow data by the correlation calculation module comprises:
Processing the traffic flow data into traffic flow time sequences with the same time interval, and marking the traffic flow time sequence of an upstream intersection as q i and the traffic flow time sequence of a downstream intersection as q j;
The cross correlation coefficient C ij of the traffic flow time sequence q i of the upstream intersection and the traffic flow time sequence q j of the downstream intersection is calculated, and as the spatial correlation of the traffic flow data, there are:
Wherein D (q i) is the variance of the traffic flow time sequence q i of the upstream intersection;
D (q j) is the variance of the traffic flow time series q j at the downstream intersection;
cov (i, j) is the covariance of the traffic flow time series q i at the upstream intersection and the traffic flow time series q j at the downstream intersection;
the process of calculating the time correlation of the traffic flow data by the correlation calculation module comprises the following steps:
Acquiring a vehicle flow data set of an intersection formed by the upstream intersection and the downstream intersection, and marking the vehicle flow data set as N is a positive integer;
Calculating the vehicle flow data set The covariance of (2) is:
Where d is the time step, d=0, 1,2, 3..n-1; i=1, 2, 3..n;
acquiring the vehicle flow data set The autocorrelation function r (i) (d) of (a) has:
When the autocorrelation coefficient r (i) (d) reaches 0 for the first time, the autocorrelation coefficient r (i) (d) is noted as d (i);
When d is less than or equal to d (i), the time correlation exists between the previous d time steps and the traffic flow data at the current moment, and when d is more than d (i), the time correlation does not exist between the previous d time steps and the traffic flow data at the current moment;
The system also comprises a data restoration module, which is used for restoring the missing data in the traffic flow data after the traffic flow data of the upstream intersection and the downstream intersection in the target area are acquired, and comprises the following steps:
Wherein a s represents the weight of traffic flow data;
y (h) represents traffic flow data at the time of data loss;
y (h-m) represents traffic flow data at the h-m th time;
y (h-1) represents traffic flow data at the h-1 th time;
y (h+1) represents traffic flow data at the h+1th time;
y (h+m) represents traffic flow data at the h+m-th time;
The system also comprises a driving matching module, which is used for acquiring a position point set formed by longitude, latitude and current time of the position of the target vehicle after acquiring traffic flow data of an upstream intersection and a downstream intersection in the target area, and marking as follows:
p＝{longitue，latitude，time}；
the driving event of the target vehicle from the start point p u to the destination p v is denoted as trip (u, v), and trip (u, v) = { p utime,pvtime,pulocation,pv location, state }; wherein u represents a starting point of a corresponding driving event; v represents the end point of the corresponding driving event; p u time represents the start time p v time of the corresponding driving event, and p u location represents the start position of the corresponding driving event; p v location represents the end position of the corresponding driving event; state represents the state of the target vehicle during the occurrence of the corresponding driving event; if state=0, the target vehicle is in a passenger carrying state; if state=0, the target vehicle is in a passenger searching state; when p u is a passenger getting-on point and p v is a passenger getting-off point, representing that the target vehicle is a passenger carrying driving event; when p u is a passenger getting-off point and p v is a passenger getting-on point, representing that the target vehicle is a passenger searching driving event;
Mapping the driving event trip (u, v) of the target vehicle to a first region h u and a second region h v in the target region, obtaining the shortest driving time of the target vehicle from the first region h u to the second region h v in a time period t k, and recording as
The process of determining the target vehicle by the driving matching module comprises the following steps:
Receiving a riding request initiated by a target client on a pre-established intelligent traffic management platform;
Responding to the riding request and presenting a plurality of estimated riding fees and a plurality of vehicle types with the riding request to the target client;
Generating a vehicle calling request based on the estimated riding cost and the vehicle type selected by the target client, and pushing the vehicle calling request to a plurality of vehicle terminals;
And after triggering the vehicle calling request by a certain vehicle terminal, taking the vehicle corresponding to the certain vehicle terminal as the target vehicle.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211188349.3A CN115691164B (en) | 2022-09-27 | 2022-09-27 | Intelligent traffic management method and system based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211188349.3A CN115691164B (en) | 2022-09-27 | 2022-09-27 | Intelligent traffic management method and system based on big data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115691164A CN115691164A (en) | 2023-02-03 |
| CN115691164B true CN115691164B (en) | 2024-05-03 |
Family
ID=85064602
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211188349.3A Active CN115691164B (en) | 2022-09-27 | 2022-09-27 | Intelligent traffic management method and system based on big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115691164B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103971520A (en) * | 2014-04-17 | 2014-08-06 | 浙江大学 | Traffic flow data recovery method based on space-time correlation |
| CN104408913A (en) * | 2014-11-03 | 2015-03-11 | 东南大学 | Traffic flow three parameter real time prediction method taking regard of space-time correlation |
| CN111583649A (en) * | 2020-05-15 | 2020-08-25 | 重庆大学 | Method for predicting characteristic parameters of traffic flow by using RFID (radio frequency identification) space-time data |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| BR102017019865A2 (en) * | 2017-09-15 | 2019-04-16 | Velsis Sistemas E Tecnologia Viaria S/A | PREDICTIVE, INTEGRATED AND INTELLIGENT SYSTEM FOR TRAFFIC TRAFFIC TIME CONTROL |
-
2022
- 2022-09-27 CN CN202211188349.3A patent/CN115691164B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103971520A (en) * | 2014-04-17 | 2014-08-06 | 浙江大学 | Traffic flow data recovery method based on space-time correlation |
| CN104408913A (en) * | 2014-11-03 | 2015-03-11 | 东南大学 | Traffic flow three parameter real time prediction method taking regard of space-time correlation |
| CN111583649A (en) * | 2020-05-15 | 2020-08-25 | 重庆大学 | Method for predicting characteristic parameters of traffic flow by using RFID (radio frequency identification) space-time data |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115691164A (en) | 2023-02-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109191845B (en) | Bus arrival time prediction method | |
| US9132742B2 (en) | Electric vehicle (EV) charging infrastructure with charging stations optimumally sited | |
| CN114435138B (en) | Vehicle energy consumption prediction method and device, vehicle and storage medium | |
| CN103440768B (en) | Dynamic-correction-based real-time bus arrival time predicting method | |
| WO2021082464A1 (en) | Method and device for predicting destination of vehicle | |
| CN110472999B (en) | Passenger flow mode analysis method and device based on subway and shared bicycle data | |
| CN111190942B (en) | Urban road parking spot overall analysis method based on data mining technology | |
| CN108364464B (en) | Probability model-based public transport vehicle travel time modeling method | |
| CN110570678A (en) | Method and device for predicting total travel time of bus from starting point to end point | |
| CN112509317B (en) | Bus real-time arrival prediction method, device and equipment based on machine learning algorithm | |
| CN104599002A (en) | Order value predicting method and equipment | |
| CN114418606A (en) | Network taxi appointment order demand prediction method based on space-time convolutional network | |
| CN118172941B (en) | Traffic light control method and device based on wireless communication | |
| CN110189537B (en) | Parking guidance method, device and equipment based on space-time characteristics and storage medium | |
| CN112258850A (en) | Edge side multi-sensor data fusion system of vehicle-road cooperative system | |
| CN114443303A (en) | Resource allocation method, device, equipment and medium | |
| CN106297296A (en) | A kind of fine granularity distribution method hourage based on sparse tracing point data | |
| CN113159371B (en) | Unknown target feature modeling and demand prediction method based on cross-modal data fusion | |
| CN114036411A (en) | Route planning method, device, equipment and medium | |
| CN115691164B (en) | Intelligent traffic management method and system based on big data | |
| CN116704750B (en) | Traffic state identification method based on clustering algorithm, electronic equipment and medium | |
| Xu et al. | Research on estimation method of mileage power consumption for electric vehicles | |
| CN110046535B (en) | Intelligent travel time prediction system, method and storage medium based on machine learning | |
| CN106781470B (en) | Method and device for processing running speed of urban road | |
| CN115660156A (en) | Method and system for predicting passenger flow congestion delay at traffic station |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20240408 Address after: Room 3, Office, 25th Floor, Green Space Business Center, No. 2588 Hongguangshan Road, Shuimogou District, Urumqi, Xinjiang Uygur Autonomous Region, 830000, 202-203 Applicant after: Xinjiang Beiying beichuang Information Technology Co.,Ltd. Country or region after: China Address before: Room 7f, 2nd Floor, No. 2, Ezhangtan Street, Baiyun District, Guangzhou, Guangdong Province (self declaration) Applicant before: Guangzhou Yuxin Information Technology Co.,Ltd. Country or region before: China |
|
| TA01 | Transfer of patent application right | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |