CN112905856A - Method for constructing high-speed traffic data set with space-time dependence - Google Patents
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Abstract
The invention relates to the technical field of data mining, in particular to a method for constructing a high-speed traffic data set with space-time dependence, which comprises the steps of acquiring data of monitoring points of different high-speed road sections; carrying out data preprocessing after aligning the acquired data according to a time sequence; constructing a space-time dependency relationship of the processed data; and storing the constructed data as graph structure data as a high-speed traffic data set. The method constructs the high-speed traffic data set from the time dimension and the space dimension simultaneously so as to reflect the characteristics of the high-speed traffic data relatively truly. The method has important significance for improving model prediction capability and data visualization capability.
Description
Technical Field
The invention relates to the technical field of data mining, in particular to the technical field of traffic data mining, and more particularly relates to a construction method of a high-speed traffic data set with space-time dependence.
Background
The traffic data is the basis of application and research in the traffic field, and the traffic data acquired by the detector on the expressway has the characteristic of multi-source heterogeneity. These data may include: at the current moment, geographic information of the monitoring point, state information of the monitoring point, traffic data flow data, other data influencing traffic weather and hot spot events and the like, the data of a period of time before the measuring point, and the data of a period of time N days before the measuring point.
In the current construction process of the high-speed traffic data set, the high-speed traffic data set is constructed singly from a time dimension or a space dimension, so that a part of information of the high-speed traffic data is lost. When the time dimension is singly considered, the constructed data set only contains single monitoring point data, so that the spatial dependence of adjacent monitoring points is lost; when the spatial dimension is considered singly, the constructed data set comprises the spatial dependency relationship among the monitoring points at the same moment, and the dependency relationship among the monitoring points in the time sequence is lost.
The multi-sensor data node time sequence graph model refers to that a space-time network can be formed by sensing data acquired by multiple sensor nodes, namely, a plurality of sensor nodes are mutually influenced at the same time to form a static graph structure. The graph data structures at different moments form a time sequence chart, and the time sequence chart displays the process of mutual influence and dynamic evolution among all perception data.
Therefore, it is necessary to construct a high-speed traffic sequence diagram data set from a time dimension and a space dimension at the same time, construct a high-speed traffic data set close to reality, reflect the characteristics of high-speed traffic data more truly, and improve the traffic flow prediction effect and the data visualization effect of the training model.
Disclosure of Invention
The invention aims to solve the problem that part of high-speed traffic data information is lost due to the fact that only single construction can be carried out from a time dimension or a space dimension when a high-speed traffic data set is constructed at present, and provides a construction method of a high-speed traffic data set with space-time dependence.
The invention adopts the following technical scheme: a method for constructing a high-speed traffic data set with space-time dependence comprises the following steps:
the method comprises the following steps: acquiring multi-source heterogeneous data of monitoring points of different high-speed road sections at different moments;
step two: carrying out data preprocessing after aligning the acquired data according to a time sequence;
step three: constructing a space-time dependency relationship of the processed data; at the same time, the spatial dependence relationship between the monitoring points is determined by means of the highway network relationship, namely at the same time, the directional relationship is formed between different monitoring points, the information of the edges in the graph is stored by using a two-dimensional array, the directional relationship between the monitoring points is determined according to the driving direction, and the adjacent matrix in the spatial dimension is obtained and expressed as
；
At different times, the connection between the monitoring points in two adjacent time segments is realized by means of an adjacency matrix in the spatial dimension
Determining, i.e. in time-sequential direction, by means of an adjacency matrix
Determining the relationship between a monitoring point at a certain moment and a monitoring point at the next moment according to the directional relationship, and obtaining an adjacency matrix in the time dimension as
(ii) a When two time slices are involved, a adjacency matrix is obtained:
step four: and storing the constructed data as graph structure data as a high-speed traffic data set.
Further, the specific process of the first step includes:
according to the target area, selecting the number of monitoring points
The multi-source heterogeneous data obtained by the monitoring points have M dimensions to obtain the first dimension
Time sequence data of each monitoring point
(ii) a Wherein
Represents the first
The multi-source heterogeneous data of each field in time sequence.
Furthermore, the multi-source heterogeneous data comprises geographic information of monitoring points and state information of the monitoring points; the upstream and downstream measuring points acquire upstream and downstream traffic flow data and charging information of upstream and downstream toll stations; whether the measuring point is on holidays or not at the moment; road condition information of the current measuring point; measuring hot events of cities upstream and downstream of the point; weather condition information of a high-speed area is detected.
Further, the upstream and downstream traffic flow data and the upstream and downstream toll station charging information include instantaneous speed, vehicle type, lane occupancy, and vehicle capacity of a single vehicle.
Further, the hot events are hot events of cities at two ends of the expressway section of the target monitoring point, including a concert, a sports meeting and a large conference, and are obtained by automatically capturing and analyzing the hot events in the social network.
Further, the road condition information of the current measuring point obtains the statistical result of the congestion condition of the target road section from a third-party service high-grade map and a Baidu map.
The weather condition information comprises rainfall, visibility, wind direction and wind level.
Further, the specific process of the second step comprises:
adopting uniform time scale for the multi-source heterogeneous data of the obtained monitoring points to obtain the time sequence data again
(ii) a And processing the missing part in the data by adopting linear interpolation and adjacent interpolation.
Further, for the missing part, linear interpolation processing is adopted, and the time is the same
To go to
When the field has missing condition, it is determined from two known time points which are the nearest
And
is obtained by linear interpolation, i.e.
。
Furthermore, when data cross-border occurs after linear interpolation processing, adjacent interpolation processing is adopted, namely, time is subjected to time instant interpolation processing
To go to
When a field is out of bounds, two non-out-of-bounds moments which are closest to each other are used
And
is updated to
。
Further, the specific process of step four includes:
graph structure data is represented by nodes and edges; respectively taking multi-source heterogeneous data obtained by the processed monitoring points as graph node data; storing adjacency matrix A as edge set information, adjacency matrix A comprising chronologically adjacent matrices
And an adjacency matrix in the spatial dimension.
The invention has the beneficial effects that:
firstly, the spatial information of the monitoring points is associated with multi-source heterogeneous data influencing traffic flow based on the geographic information of a highway network, and the multi-source heterogeneous traffic flow time related data is constructed into graph structural data by utilizing the connectivity data of the highway network, so that the method can more truly reflect the characteristics of high-speed traffic data compared with the traditional construction method;
secondly, the method can enable the model to better extract relevant features, and further improve the prediction precision of the relevant model; on one hand, the method can be used as the basis for making decisions by management departments; on the other hand, road condition information can be provided for drivers, the drivers are guided to carry out optimization on driving paths, the environmental pollution can be reduced, and meanwhile, the related theoretical technology of traffic flow data is favorably developed.
Thirdly, the method can enable the high-speed traffic visualization to be more vivid.
Drawings
FIG. 1 shows a flow chart of the present invention;
FIG. 2 is a flow chart of data preprocessing of step two of the present invention;
fig. 3 shows a flow chart of step three of the present invention.
Detailed Description
Further refinements will now be made based on the figure embodiments. The following description is not intended to limit the embodiments to one preferred embodiment. But is intended to cover alternatives, modifications and equivalents, which may be included within the spirit and scope of the embodiments as defined by the appended claims.
A method for constructing a high-speed traffic data set with space-time dependence is disclosed, wherein the whole process is demonstrated by taking high-speed traffic flow data as an example. The high-speed traffic flow data is collected by monitoring points distributed on an expressway and is space-time data. The data collected in real time is lost due to equipment, data transmission and the like, and the data needs to be preprocessed; and then constructing a high-speed traffic data set with space-time dependence.
The steps are as follows in detail in connection with fig. 1.
The method comprises the following steps: acquiring multi-source heterogeneous data of monitoring points of different high-speed road sections;
determining the number of monitoring points in the highway network according to the target area
M represents that the multi-source heterogeneous data obtained by the monitoring points has M dimensions to obtain the first dimension
Time sequence data of each monitoring point
(ii) a Wherein
Representing the first of the multi-source heterogeneous data acquired at the watch point
A field.
Collecting multi-source heterogeneous data, wherein the data comprises geographic information and state information of monitoring points; the upstream and downstream measuring points acquire upstream and downstream traffic flow data and charging information of upstream and downstream toll stations; whether the measuring point is on holidays or not at the moment; road condition information of the current measuring point; measuring hot events of cities upstream and downstream of the point; weather condition information of a high-speed area is detected. According to the required problems: and giving the flow of the previous 30 minutes, and counting the flow data in one day according to 5 minutes to obtain flow values on each monitoring point corresponding to 288 time segments.
The upstream and downstream traffic flow data and the upstream and downstream toll station charging information comprise the instantaneous speed of a single vehicle, the vehicle type, the lane occupancy rate and the vehicle capacity. The data may select a corresponding field from a database of monitoring point traffic. The size of the vehicle capacity directly affects the vehicle density, the inter-vehicle distance and the like of the road section and the traffic flow directly related factors. For example, the rollover of a large vehicle has a great influence on surrounding trolleys, so that a driver of a small vehicle can select to enlarge the distance or change lanes when encountering the large vehicle. Therefore, the composition proportion of the vehicle type has certain influence on the current traffic flow, and the statistics of the specific gravity of various vehicle types at the same time also has practical significance.
The holiday factor means that the national legal holidays can cause additional population migration, such as home return, travel, etc. Traffic flow may also be affected.
The hot events are hot events of cities at two ends of the expressway section of the target monitoring point, including a concert, a sports meeting and a large conference, and are obtained by automatically capturing and analyzing the hot events of the social network.
And obtaining the statistical result of the congestion condition of the target road section from the third-party service height map and the Baidu map according to the road condition information of the measuring point at the moment. Besides obtaining real-time data from the traffic management system, the third-party service also feeds GPS positioning information of the service user back to the server for movement track analysis, so that the real-time statistics of the congestion degree of the road section is realized. And the congestion condition has direct influence on the traffic flow, so that an open interface of a third-party navigation service can be called to obtain real-time road condition information of a target road section to assist the prediction of the traffic flow.
The weather condition information comprises rainfall, visibility, wind direction and wind level. Natural phenomena on roads, such as rain, snow, fog, glare, etc., can cause drivers to subjectively change driving speed and distance.
Step two: performing data preprocessing after aligning the acquired data according to a time sequence, as shown in fig. 2;
adopting uniform time scale for the data of the obtained monitoring points to obtain the time sequence data again
；
And processing the missing part in the data by linear interpolation and adjacent interpolation. For missing parts, linear interpolation processing is adopted, and the time is the same
To go to
When the field has missing condition, it is determined from two known time points which are the nearest
And
is obtained by linear interpolation, i.e.
。
When data cross-border occurs after linear interpolation processing, adopting adjacent interpolation processing, namely for time
To go to
When a field is out of bounds, two non-out-of-bounds moments which are closest to each other are used
And
is updated to
。
After the time on each monitoring point is unified, in order to supplement missing values in time sequence data more quickly and accurately, the missing values on each monitoring point have the characteristics of simplicity and convenience due to linear interpolation; the adjacent interpolation has the characteristics of rapidness and stability. And filling by adopting linear interpolation and adjacent interpolation in sequence.
Step three: constructing a spatiotemporal dependency relationship of the processed data, as shown in fig. 3;
at the same time, the spatial dependence relationship between the monitoring points is determined by means of the highway network relationship, namely at the same time, the directional relationship is formed between different monitoring points, the information of the edges in the graph is stored by using a two-dimensional array, the directional relationship between the monitoring points is determined according to the driving direction, and the adjacent matrix in the spatial dimension is obtained and expressed as
；
At different times, the connection between the monitoring points in two adjacent time segments is realized by means of an adjacency matrix in the spatial dimension
Determining, i.e. in time-sequential direction, by means of an adjacency matrix
Determining the relationship between a monitoring point at a certain moment and a monitoring point at the next moment according to the directional relationship, and obtaining an adjacency matrix in the time dimension as
；
When two time slices are involved, a adjacency matrix is obtained:
the spatial distance between monitoring points is adopted for constructing the spatial dependency relationship and obtaining the adjacency matrix representation
Then the adjacency matrix at time t can be represented as
。
The adjacency matrix represents spatial information, At represents temporal information, but is a single representation,
the adjacency matrix at time t is represented.
And so on, involving multiple time slices. That is, considering the time sequence dependency and the directional relationship formed between different monitoring points, the dependency relationship only exists on two adjacent time slices and is expressed by two-dimensional array, then
Sequential adjacency matrix construction
. A spatio-temporal dependent adjacency matrix may be constructed for the data obtained in step two:
step four: and storing the constructed data as graph structure data as a high-speed traffic data set.
Graph structure data is represented by nodes and edges; respectively taking multi-source heterogeneous data obtained by the processed monitoring points as graph node data; storing adjacency matrix A as edge set information, adjacency matrix A comprising chronologically adjacent matrices
And adjacency matrices in the spatial dimension
Thereby treating the adjacency matrix as part of the high-speed traffic data set.
In this embodiment, t is divided into 288 time segments, the monitoring point data on each time segment is stored as graph node information, and an adjacency matrix a is stored as edge set information, where the adjacency matrix a includes an adjacency matrix in time sequence
And adjacency matrices in the spatial dimension
. The stored graph structure data is used as a high-speed traffic flow data set.
The high-speed traffic data set can be used for research work in related fields such as high-speed traffic and the like, for example, traffic accidents such as traffic accidents occur on expressways, and related models are built by using the high-speed traffic data set, and the actual scenes can be pre-judged and analyzed from the time and space angles, so that vehicles coming and going can be reminded through the front, normal traffic can be realized, and secondary accidents, traffic jam and the like are avoided. Therefore, the traffic data set is beneficial to developing relevant theoretical techniques in the traffic field and has positive significance for subsequent research.
In the foregoing detailed description, reference is made to the accompanying drawings which form a part hereof, and in which is shown by way of illustration specific embodiments in accordance with the embodiments. Although these examples are described in sufficient detail to enable those skilled in the art to practice the embodiments, it is to be understood that these examples are not limiting, such that other examples may be used and that corresponding modifications may be made without departing from the spirit and scope of the embodiments.
Claims (10)
1. A method for constructing a high-speed traffic data set with space-time dependence is characterized by comprising the following steps: the method comprises the following steps:
the method comprises the following steps: acquiring multi-source heterogeneous data of monitoring points of different high-speed road sections at different moments;
step two: carrying out data preprocessing after aligning the acquired data according to a time sequence;
step three: constructing a space-time dependency relationship of the processed data; at the same time, the spatial dependence relationship between the monitoring points is determined by means of the highway network relationship, namely at the same time, the directional relationship is formed between different monitoring points, the information of the edges in the graph is stored by using a two-dimensional array, the directional relationship between the monitoring points is determined according to the driving direction, and the adjacent matrix in the spatial dimension is obtained and expressed as
；
At different times, the connection between the monitoring points in two adjacent time segments is realized by means of an adjacency matrix in the spatial dimension
Determining, i.e. in time-sequential direction, by means of an adjacency matrix
Determining the relationship between a monitoring point at a certain moment and a monitoring point at the next moment according to the directional relationship, and obtaining an adjacency matrix in the time dimension as
(ii) a When two time slices are involved, a adjacency matrix is obtained:
step four: and storing the constructed data as graph structure data as a high-speed traffic data set.
2. The construction method according to claim 1, characterized in that: the specific process of the first step comprises the following steps:
determining the number of monitoring points in the highway network according to the target area
The multi-source heterogeneous data obtained by the monitoring points have M dimensions to obtain the first dimension
Time sequence data of each monitoring point
(ii) a Wherein
Representing the first of the multi-source heterogeneous data acquired at the watch point
A field.
3. The construction method according to claim 1 or 2, characterized in that: the multi-source heterogeneous data comprises geographic information of monitoring points and state information of the monitoring points; the upstream and downstream measuring points acquire upstream and downstream traffic flow data and charging information of upstream and downstream toll stations; whether the measuring point is on holidays or not at the moment; road condition information of the current measuring point; measuring hot events of cities upstream and downstream of the point; weather condition information of a high-speed area is detected.
4. The construction method according to claim 3, wherein: the upstream and downstream traffic flow data and the upstream and downstream toll station charging information comprise the instantaneous speed, vehicle type, lane occupancy rate and vehicle capacity of a single vehicle; and obtaining the statistical result of the congestion condition of the target road section from the third-party service height map and the Baidu map according to the road condition information of the measuring point at the moment.
5. The construction method according to claim 3, wherein: the hot events are hot events of cities at two ends of a target monitoring point highway section, comprise a singing meeting, a sports meeting and a large conference, and are obtained by automatically capturing and analyzing the hot events in a social network; the weather condition information comprises rainfall, visibility, wind direction and wind level.
6. The construction method according to claim 1 or 2, characterized in that: the specific process of the first step comprises the following steps:
adopting uniform time scale for the multi-source heterogeneous data of the obtained monitoring points to obtain the time sequence data again
；
And processing the missing part in the data by linear interpolation and adjacent interpolation.
7. The construction method according to claim 6, wherein: for missing parts, linear interpolation processing is adopted, and the time is the same
To go to
When the field has missing condition, it is determined from two known time points which are the nearest
And
is obtained by linear interpolation, i.e.
。
8. The construction method according to claim 7, wherein: when data cross-border occurs after linear interpolation processing, adopting adjacent interpolation processing, namely for time
To go to
When a field is out of bounds, two non-out-of-bounds moments which are closest to each other are used
And
is updated to
。
9. The construction method according to claim 1, characterized in that: the concrete process of the third step comprises:
involving a plurality of time segments, because of the dependency on time sequence and the directional relation formed between different monitoring points, the dependency exists only on two adjacent time segmentsTwo-dimensional array representation, then
Sequential adjacency matrix construction
For the data obtained in step two, a spatio-temporal dependent adjacency matrix may be constructed:
10. the construction method according to claim 1 or 9, characterized in that: the concrete process of the step four comprises:
graph structure data is represented by nodes and edges; respectively taking multi-source heterogeneous data obtained by the processed monitoring points as graph node data; storing adjacency matrix A as edge set information, adjacency matrix A comprising chronologically adjacent matrices
And adjacency matrices in the spatial dimension
。
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| CN111710154A (en) * | 2020-05-15 | 2020-09-25 | 湖州师范学院 | Expressway traffic flow prediction method |
| CN111897875A (en) * | 2020-07-31 | 2020-11-06 | 平安科技（深圳）有限公司 | Fusion processing method and device for urban multi-source heterogeneous data and computer equipment |
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