CN112015837A - Urban road high-frequency path analysis method, system and storage medium - Google Patents
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Abstract
The invention relates to a method, a system and a storage medium for analyzing urban road high-frequency paths, which comprises the steps of obtaining historical vehicle passing data of each section of an urban road in a certain time period; counting the historical data of the paths of the vehicles running on the road network according to road sections or intersections; setting a minimum support degree and a minimum confidence threshold in a high-frequency path algorithm; and calculating the high-frequency road section and the high-frequency path by using the historical data of the path of the vehicle running on the road network and using a correlation rule correlation algorithm. The method and the device have the advantages that the algorithm in the association rule is applied to the calculation scene of the traffic high-frequency path, the high-frequency road sections are screened by matching the vehicle passing data based on the basic road network and setting the threshold value, and the high-frequency path is calculated by utilizing the association rule correlation algorithm.
Description
Technical Field
The invention relates to the technical field of urban traffic management, in particular to an urban road high-frequency path analysis method, an urban road high-frequency path analysis system and a storage medium.
Background
In daily traffic trip, according to road network structure and route selection, combine the use of some navigation instrument, can prefer to select main road or OD short circuit must pass through the place, lead to partial road to use the frequency high, actual traffic demand surpasss the traffic service ability of road far away, and then causes the traffic jam, consequently, to the discernment on high frequency route, be favorable to controlling key node, alleviate the traffic pressure on high frequency route, and then reduce the emergence frequency that the traffic jam, be favorable to the promotion of urban traffic management ability.
Disclosure of Invention
The urban road high-frequency path analysis method, system and storage medium provided by the invention can be used for identifying high-frequency paths.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for analyzing high-frequency paths of urban roads comprises the following steps,
s01, acquiring historical vehicle passing data of each road section of the urban road in a certain time period;
s02, counting the historical data of the vehicle driving on the road network according to the road sections or intersections;
s03, setting a minimum support degree and a minimum confidence threshold in a high-frequency path algorithm;
and S04, calculating the high-frequency road section and the high-frequency path by using the historical data of the paths of the vehicles running on the road network and using a correlation rule correlation algorithm.
Further, the historical vehicle passing data in S01 includes unique vehicle identification information, road network information, and vehicle passing information.
Further, the step S04, the obtaining of the high-frequency road segment and the high-frequency route by using the historical data of the routes traveled by the vehicle on the road network and the calculation by the association rule correlation algorithm specifically includes:
defining:
set of vehicle path items: the vehicle with the identity marks, the collection of various combinations of each road section or intersection in the driving path, and the collection with k elements is called a k-item set;
parameter 1: the minimum support degree is used for screening the vehicle path item sets with too low frequency to be reached, can be configured and is adjusted according to the actual city vehicle passing data condition;
parameter 2: the minimum confidence coefficient is used for screening a high-frequency vehicle path k-item set, generating a related high-frequency vehicle path rule and then generating a vehicle high-frequency path, and the minimum confidence coefficient can be configured and adjusted according to the actual city vehicle passing data condition;
step 1: collecting data;
step 2: constructing a vehicle path data set to generate a candidate 1-item set;
(1) constructing a path item set by using the acquired vehicle passing data and data of one day, and sequencing the paths of each license plate driven in one day according to time to form a vehicle path set;
(2) calculating the time difference of adjacent paths in each path set, and dividing the set with the time difference larger than a set value into a plurality of path sets to form a vehicle path data set D on the same day;
(3) taking a path data set DD of historical data at a certain time, splitting the data set into single road sections and intersections, and performing aggregation and de-duplication to generate an item set consisting of one element to obtain a plurality of candidate 1-item sets di (i ═ 1.. N);
step 3: screening a frequent vehicle path 1 item set by using the minimum support degree to generate a candidate 2 item set;
calculating the frequency of all candidate 1-item sets in the DD data set, wherein the frequency is (the number of the item sets)/the total number of the data set, and screening out frequent 1-item sets D1 with the frequency greater than the minimum support degree;
the frequent item set D1 is arranged and combined pairwise to generate a candidate 2-item set;
step 4: circularly and iteratively screening a frequent k item set to generate a candidate k + 1-item set;
calculating the occurrence frequency of the candidate k-item set in the data set, screening out a frequent k-item set Dk with the frequency greater than the minimum support degree, and generating a candidate k +1 item set through permutation and combination;
iterating according to the steps, and stopping iterating until the frequency of all candidate item sets is lower than the minimum support degree or the candidate item sets cannot be regenerated, so as to obtain a final vehicle path frequent item set;
step 5: generating a high-frequency path from the vehicle path frequent item set;
arranging and combining elements of the frequent item set to form possible paths which arrive back and forth in sequence, and calculating the reliability of each combination, namely the conditional probability of the back road section in the presence of the front road section, wherein the reliability is the frequency of the simultaneous presence of the front road section and the back road section/the frequency of the presence of the set of the front road section;
and screening out frequent item sets with the reliability higher than the minimum reliability, namely the frequent item sets are high-frequency paths.
Furthermore, the collected data of step1 is used for collecting the identification license plate or unique id of the vehicle through the detection monitoring equipment on the urban road, and collecting the related vehicle passing data.
Further, the vehicle passing data comprises vehicle passing time, road section/intersection id.
Further, the detection monitoring equipment comprises an electronic police, a bayonet and a detector.
On the other hand, the invention also discloses an urban road high-frequency path analysis system, which comprises the following units,
the historical vehicle passing data acquisition unit is used for acquiring historical vehicle passing data of each road section of the urban road in a certain time period;
the route history data determining unit is used for counting the route history data of the vehicles running on the road network according to road sections or intersections;
the minimum support degree and minimum confidence degree threshold setting unit is used for setting a minimum support degree and a minimum confidence degree threshold in a high-frequency path algorithm;
and the high-frequency path determining unit is used for calculating a high-frequency road section and a high-frequency path by using the historical data of the paths of the vehicles running on the road network and using an association rule correlation algorithm.
In a third aspect, the present invention also discloses a computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of the above method.
According to the technical scheme, the method for analyzing the urban road high-frequency path comprises the steps of enabling an algorithm in an association rule to be in a calculation scene of the traffic high-frequency path, matching vehicle passing data based on a basic road network, screening high-frequency road sections by setting a threshold value, and calculating the high-frequency path by using the association rule (the existing algorithm in unsupervised learning) related algorithm.
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FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention.
As shown in fig. 1, the method for analyzing a high-frequency path of an urban road according to this embodiment includes
S01, acquiring historical vehicle passing data of each road section of the urban road in a certain time period;
s02, counting the historical data of the vehicle driving on the road network according to the road sections or intersections;
s03, setting a minimum support degree and a minimum confidence threshold in a high-frequency path algorithm;
and S04, calculating the high-frequency road section and the high-frequency path by using the historical data of the paths of the vehicles running on the road network and using a correlation rule correlation algorithm.
The following is a detailed description:
the historical vehicle passing data in the step S01 includes vehicle unique identification information, road network information, and vehicle passing information.
And S04, based on the historical data of S02 and the minimum support degree and the minimum confidence threshold value of 03, extracting all the passing data of the marked high-frequency road sections, and finding out the road sections with strong sequential relevance among the high-frequency road sections according to the data by an association rule algorithm in a recommendation algorithm, wherein the high-frequency road sections form a final high-frequency path according to the driving direction.
Wherein the motivation originally proposed for association rule mining was proposed for a Market Basket Analysis (Market Basket Analysis) problem; analyzing the shopping habits of the customers by finding the association between different commodities put into a shopping basket by the customers; the discovery of this association can help retailers learn which items are frequently purchased simultaneously by customers, thereby helping them develop better marketing strategies.
Two processes of association rule mining:
in the first stage, all high frequency items (frequency items) are first found from the data set;
in the second stage, Association Rules (Association Rules) are generated from these high frequency item groups.
The association rule of the embodiment of the invention is as follows:
the method comprises the steps of analyzing a high-frequency traveling path of an urban road as a shopping basket of a supermarket in a shopping mall, wherein each person is considered as a vehicle, each object bought in the shopping basket is considered as a road section or an intersection where the vehicle runs in the urban road, analyzing the occurrence frequency and probability of the shopping quantity of each commodity or the running frequency of each road section or intersection, obtaining the simultaneous occurrence frequency and sequence of two or more elements, and providing the market with far and near reference or a path with high traveling frequency of the urban road.
The invention constructs an urban road trip model (based on a shopping basket model) by applying an association rule algorithm (shopping basket analysis model) to the traffic field, performs corresponding data format design adjustment in the algorithm, performs organization and transformation aiming at the existing urban road traffic data, applies the input of the algorithm, and adjusts corresponding parameters according to actual conditions to obtain the effect of being enough to use.
The probability used by the association rule is a priori probability, and there are two important axioms:
if a non-empty set of items is frequent, all subsets thereof must also be frequent; if the set of items is infrequent, all of its supersets must also be infrequent.
Based on the two common theories, calculating a high-frequency path corresponding to the urban road trip model:
if a non-empty high frequency path is high frequency, all its sub-paths must also be high frequency;
if the path is infrequent, all of its extended extension paths must also be non-high frequency.
The specific process (in combination with traffic) of the mining of the traffic urban road high-frequency path association rule of the invention is as follows:
the algorithm in the association rule is multiple, such as Apriori algorithm, modified FP-growth algorithm, even Prefix span algorithm which can directly calculate the sequence, and the like, the general ideas are consistent, and the algorithm can be used for calculating the high-frequency path, wherein the modified traffic data structure and the Apriori algorithm are used;
specifically enumerating the following algorithm, the steps are as follows;
basic concept:
set of vehicle path items: vehicle with ID, collection of various combinations of each road section (or intersection) in driving path, collection with k elements called k-item set
Parameter 1: the Minimum Support (Minimum Support) is used for screening the vehicle path item sets with too low frequency and not reached, and can be configured and adjusted according to the actual city vehicle passing data condition
Parameter 2: the Minimum Confidence (Minimum Confidence) is used for screening a high-frequency vehicle path k-item set, generating a related high-frequency vehicle path rule and then generating a vehicle high-frequency path, and can be configured and adjusted according to the actual city vehicle passing data condition;
step 1: collecting data
Data collection can provide the identification license plate or unique id of vehicle through the detection supervisory equipment (electronic police, bayonet, detector, etc.) on the urban road to can provide relevant data of passing: vehicle passing time, road segment/intersection id, etc.;
step 2: constructing a vehicle path data set to generate a candidate 1-item set;
(1) constructing a path item set by using the acquired vehicle passing data and data of one day, and sequencing the paths of each license plate driven in one day according to time to form a vehicle path set;
(2) calculating the time difference of adjacent paths in each path set, and dividing the set with overlarge time difference (such as more than one hour) into a plurality of path sets to form a vehicle path data set D on the same day;
(3) taking a path data set DD of historical data (such as 30 days) at a certain time, splitting the data set into single road sections and intersections, and aggregating and removing duplication to generate an item set consisting of one element to obtain a plurality of candidate 1-item sets di (i is 1.. N);
step 3: screening a frequent vehicle path 1 item set (with minimum support) to generate a candidate 2 item set;
calculating the frequency of all candidate 1-item sets in the DD data set, wherein the frequency is (the number of the item sets)/the total number of the data set, and screening out frequent 1-item sets D1 with the frequency greater than the minimum support degree;
the frequent item set D1 is arranged and combined pairwise to generate a candidate 2-item set;
the frequent 1-item set obtained by calculation in the step can also be stored and recorded as a high-frequency road section to be used as a traffic data reference;
step 4: circularly and iteratively screening a frequent k item set to generate a candidate k + 1-item set;
calculating the occurrence frequency of the candidate k-item set in the data set, screening out a frequent k-item set Dk with the frequency greater than the minimum support degree, and generating a candidate k +1 item set through permutation and combination;
iterating according to the steps, stopping iterating until the frequency of all candidate item sets is lower than the minimum support degree or the candidate item sets cannot be regenerated, and obtaining the final vehicle path frequent item set
step 5: generating a high-frequency path from the vehicle path frequent item set;
arranging and combining elements of the frequent item set to form possible paths which arrive back and forth in sequence, and calculating the reliability of each combination (namely the conditional probability of the back road section in the front road section), wherein the reliability is the frequency of the simultaneous existence of the front road section and the back road section/the frequency of the existence of the set of the front road section;
and screening out frequent item sets with the confidence degree higher than the minimum confidence degree, namely the frequent item sets are high-frequency paths.
The high-frequency path with sequence can also be directly obtained through a sequence pattern algorithm, and the steps are similar to those of an association rule algorithm.
In summary, the present invention can be used in the following applications:
a. and the high-frequency road section provides relevant road sections optimized by departments such as city management planning, traffic management and the like (road sections are widened, and the adjustment and optimization are matched with the intersection signals), and provides the reference of the police-out destination of the traffic police.
b. And the high-frequency path is controlled and planned (work day peak shifting trip on and off duty and limiting number) aiming at vehicles in the head and tail area of the high-frequency path, and a ready-made police instructs to reasonably guide the flow to the non-high-frequency path, so that the pressure of the high-frequency path is reduced.
On the other hand, the invention also discloses an urban road high-frequency path analysis system, which comprises the following units,
the historical vehicle passing data acquisition unit is used for acquiring historical vehicle passing data of each road section of the urban road in a certain time period;
the route history data determining unit is used for counting the route history data of the vehicles running on the road network according to road sections or intersections;
the minimum support degree and minimum confidence degree threshold setting unit is used for setting a minimum support degree and a minimum confidence degree threshold in a high-frequency path algorithm;
and the high-frequency path determining unit is used for calculating a high-frequency road section and a high-frequency path by using the historical data of the paths of the vehicles running on the road network and using an association rule correlation algorithm.
In a third aspect, the present invention also discloses a computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of the above method.
It is understood that the system provided by the embodiment of the present invention corresponds to the method provided by the embodiment of the present invention, and the explanation, the example and the beneficial effects of the related contents can refer to the corresponding parts in the method.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (8)
1. A method for analyzing urban road high-frequency paths is characterized by comprising the following steps: comprises the following steps of (a) carrying out,
s01, acquiring historical vehicle passing data of each road section of the urban road in a certain time period;
s02, counting the historical data of the vehicle driving on the road network according to the road sections or intersections;
s03, setting a minimum support degree and a minimum confidence threshold in a high-frequency path algorithm;
and S04, calculating the high-frequency road section and the high-frequency path by using the historical data of the paths of the vehicles running on the road network and using a correlation rule correlation algorithm.
2. The urban road high-frequency path analysis method according to claim 1, characterized in that: the historical vehicle passing data in the step S01 includes unique vehicle identification information, road network information, and vehicle passing information.
3. The urban road high-frequency path analysis method according to claim 1, characterized in that: the step S04, which is to calculate the high-frequency road segment and the high-frequency route by using the historical data of the routes traveled by the vehicle on the road network and using the association rule correlation algorithm, specifically includes:
defining:
set of vehicle path items: the vehicle with the identity marks, the collection of various combinations of each road section or intersection in the driving path, and the collection with k elements is called a k-item set;
parameter 1: the minimum support degree is used for screening the vehicle path item sets with too low frequency to be reached, can be configured and is adjusted according to the actual city vehicle passing data condition;
parameter 2: the minimum confidence coefficient is used for screening a high-frequency vehicle path k-item set, generating a related high-frequency vehicle path rule and then generating a vehicle high-frequency path, and the minimum confidence coefficient can be configured and adjusted according to the actual city vehicle passing data condition;
step 1: collecting data;
step 2: constructing a vehicle path data set to generate a candidate 1-item set;
(1) constructing a path item set by using the acquired vehicle passing data and data of one day, and sequencing the paths of each license plate driven in one day according to time to form a vehicle path set;
(2) calculating the time difference of adjacent paths in each path set, and dividing the set with the time difference larger than a set value into a plurality of path sets to form a vehicle path data set D on the same day;
(3) taking a path data set DD of historical data at a certain time, splitting the data set into single road sections and intersections, and performing aggregation and de-duplication to generate an item set consisting of one element to obtain a plurality of candidate 1-item sets di (i ═ 1.. N);
step 3: screening a frequent vehicle path 1 item set by using the minimum support degree to generate a candidate 2 item set;
calculating the frequency of all candidate 1-item sets in the DD data set, wherein the frequency is (the number of the item sets)/the total number of the data set, and screening out frequent 1-item sets D1 with the frequency greater than the minimum support degree;
the frequent item set D1 is arranged and combined pairwise to generate a candidate 2-item set;
step 4: circularly and iteratively screening a frequent k item set to generate a candidate k + 1-item set;
calculating the occurrence frequency of the candidate k-item set in the data set, screening out a frequent k-item set Dk with the frequency greater than the minimum support degree, and generating a candidate k +1 item set through permutation and combination;
iterating according to the steps, and stopping iterating until the frequency of all candidate item sets is lower than the minimum support degree or the candidate item sets cannot be regenerated, so as to obtain a final vehicle path frequent item set;
step 5: generating a high-frequency path from the vehicle path frequent item set;
arranging and combining elements of the frequent item set to form possible paths which arrive back and forth in sequence, and calculating the reliability of each combination, namely the conditional probability of the back road section in the presence of the front road section, wherein the reliability is the frequency of the simultaneous presence of the front road section and the back road section/the frequency of the presence of the set of the front road section;
and screening out frequent item sets with the reliability higher than the minimum reliability, namely the frequent item sets are high-frequency paths.
4. The urban road high-frequency path analysis method according to claim 3, characterized in that:
the collected data of step1 is passed through the detection monitoring equipment on the urban road, and the identification license plate or the unique id of the vehicle is collected, and the relevant data of passing the vehicle is collected.
5. The urban road high-frequency path analysis method according to claim 4, wherein:
the vehicle passing data comprises vehicle passing time, road section/intersection id.
6. The urban road high-frequency path analysis method according to claim 4, wherein: the detection monitoring equipment comprises an electronic police, a bayonet and a detector.
7. The utility model provides an urban road high frequency path analytic system which characterized in that: comprises the following units of a first unit, a second unit,
the historical vehicle passing data acquisition unit is used for acquiring historical vehicle passing data of each road section of the urban road in a certain time period;
the route history data determining unit is used for counting the route history data of the vehicles running on the road network according to road sections or intersections;
the minimum support degree and minimum confidence degree threshold setting unit is used for setting a minimum support degree and a minimum confidence degree threshold in a high-frequency path algorithm;
and the high-frequency path determining unit is used for calculating a high-frequency road section and a high-frequency path by using the historical data of the paths of the vehicles running on the road network and using an association rule correlation algorithm.
8. A computer-readable storage medium, storing a computer program which, when executed by a processor, causes the processor to carry out the steps of the method according to any one of claims 1 to 6.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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| CN114170796A (en) * | 2021-11-20 | 2022-03-11 | 无锡数据湖信息技术有限公司 | Algorithm improved congestion propagation analysis method |
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| CN115662158B (en) * | 2022-10-31 | 2023-08-29 | 东南大学 | Traffic overflow-oriented critical path signal control optimization method |
| CN116453333A (en) * | 2023-03-24 | 2023-07-18 | 阿波罗智联(北京)科技有限公司 | Method for predicting main traffic flow path and model training method |
| CN116453333B (en) * | 2023-03-24 | 2024-04-16 | 阿波罗智联(北京)科技有限公司 | Method for predicting main traffic flow path and model training method |
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