CN111324943A - Traffic space-time process modeling management method and device - Google Patents
Traffic space-time process modeling management method and device Download PDFInfo
- Publication number
- CN111324943A CN111324943A CN202010031478.6A CN202010031478A CN111324943A CN 111324943 A CN111324943 A CN 111324943A CN 202010031478 A CN202010031478 A CN 202010031478A CN 111324943 A CN111324943 A CN 111324943A
- Authority
- CN
- China
- Prior art keywords
- traffic
- objects
- space
- dynamic
- time process
- 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.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Theoretical Computer Science (AREA)
- Remote Sensing (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses a traffic space-time process modeling management method and a traffic space-time process modeling management device based on a dynamic graph and a graph database, which belong to the field of intelligent city geographic information service.A traffic space-time process is effectively modeled and expressed by using the advantages of the dynamic graph, so that the space-time dynamics of traffic objects and the complex traffic relation among the objects are reflected; the method is used for storing the traffic spatiotemporal process into a Neo4j database, and historical backtracking is carried out by utilizing a Neo4j database, so that the rapid inversion of the traffic spatiotemporal process is supported; meanwhile, the storage mode has good expandability, and when the storage mode changes, the attributes in the nodes and the relations can be directly changed, so that the complex traffic relations among the traffic entities in the process of deeply analyzing the traffic space-time in the real world are facilitated, and a basis is provided for traffic planning and facility construction.
Description
Technical Field
The invention belongs to the field of intelligent city geographic information service, and particularly relates to a traffic spatiotemporal process modeling management method and device based on a dynamic graph and a graph database.
Background
The high-speed development of the society promotes the continuous upgrading of urban traffic, and traffic modeling is helpful for analyzing the travel rule of vehicles and provides decision support for intelligent traffic. The existing traffic modeling objects are mainly oriented to traffic infrastructure such as road networks, gas stations and the like, and extensive and intensive research is carried out based on the existing traffic modeling objects. However, these traffic modeling methods for service traffic infrastructure management are static and cannot effectively express the dynamics of the traffic spatiotemporal process and the correlation between traffic objects.
The traffic space-time process is used for describing the evolution process of a traffic entity in a specific time interval, is an organism coupled with time and space, and has obvious space-time attributes. Traffic entities involved in the traffic space-time process generate various interactions under the guidance of traffic rules, and the results of the traffic space-time process are generated. The modeling and expression of the traffic space-time process are beneficial to deeply analyzing the complex traffic relation among traffic entities in the traffic space-time process, and a basis is provided for traffic planning and facility construction.
Disclosure of Invention
Aiming at the defects or improvement requirements of the prior art, the invention provides a traffic space-time process modeling management method and a traffic space-time process modeling management device, so that the technical problem that the traffic modeling of the conventional service traffic infrastructure management cannot effectively express the dynamic property of the traffic space-time process and the relevance between traffic objects is solved.
To achieve the above object, according to one aspect of the present invention, there is provided a traffic spatiotemporal process modeling management method, including:
describing the space-time dynamic of the traffic objects covered by the traffic space-time process and the traffic relation among the traffic objects;
respectively modeling the traffic objects and the traffic relations among the traffic objects by utilizing a dynamic graph to obtain a traffic space-time process model, wherein nodes of the dynamic graph represent the traffic objects, and edges among the nodes represent the traffic relations among the traffic objects;
storing the traffic space-time process model constructed based on the dynamic graph into a target graph database;
and designing an access interface, and supporting dynamic query of a traffic space-time process and quick inversion of the traffic space-time process through the target graph database.
Preferably, the traffic object comprises a static object and a dynamic object; the static object is an infrastructure which comprises roads, traffic lights and traffic signs; the dynamic objects include vehicles and pedestrians.
Preferably, the traffic object is expressed in an object snapshot manner, and the time-space dynamic changes of the traffic object form a string of state sequences.
Preferably, the traffic relations include temporal topological relations between objects, spatial topological relations between objects, regular relations between objects and roads, and event relations.
Preferably, the time topological relation represents the relation expressed in the time sequence between the objects; the spatial topological relation represents adjacent and/or separated relation between objects; the regular relationship between the object and the road comprises the vehicle speed and driving direction limit and the traffic limit; the event relationship is a traffic event.
Preferably, the modeling the traffic objects and the traffic relations among the traffic objects by using the dynamic graph respectively to obtain a traffic spatiotemporal process model includes:
mapping the traffic objects to nodes of the dynamic graph, and acquiring attribute information of each object;
and finding an object node corresponding to the traffic relation according to the described traffic relation, mapping the traffic relation to the relation between corresponding nodes in the dynamic graph, and determining the attribute of each traffic relation so as to obtain a traffic space-time process model.
Preferably, the storing the traffic spatiotemporal process model constructed based on the dynamic graph to the target graph database comprises:
each node in the traffic space-time process model is correspondingly stored in a node in a target graph database, the relationship between the nodes in the traffic space-time process model is correspondingly stored in the relationship in the target graph database, and a storage attribute table is designed for the object and the relationship.
Preferably, the storage attribute table includes: a traffic space-time process table, a traffic object table, a traffic relation table and a traffic state table.
Preferably, the target map database is a Neo4j map database.
According to another aspect of the present invention, there is provided a traffic spatiotemporal process modeling management apparatus, including:
the defining module is used for describing the space-time dynamic of the traffic objects covered by the traffic space-time process and the traffic relation among the traffic objects;
the dynamic graph model building module is used for respectively modeling the traffic objects and the traffic relations among the traffic objects by utilizing a dynamic graph to obtain a traffic space-time process model, wherein nodes of the dynamic graph represent the traffic objects, and edges among the nodes represent the traffic relations among the traffic objects;
the storage module is used for storing the traffic space-time process model constructed based on the dynamic graph into a target graph database;
and the query module is used for designing an access interface and supporting dynamic query of a traffic space-time process and quick inversion of the traffic space-time process through the target graph database.
In general, compared with the prior art, the above technical solution contemplated by the present invention can achieve the following beneficial effects:
(1) the invention supports dynamic expression of traffic spatiotemporal processes. The traffic relation between traffic objects changes due to the fact that the geometric characteristics and the attribute characteristics of the traffic objects change along with the change of time. The invention can realize dynamic modeling of the traffic space-time process aiming at the defects of the static modeling of the existing infrastructure traffic facilities, and the method supports the change of the position, the attribute and the semantic of the object, can reflect the dynamic change of the traffic object and reflects the dynamic property of the traffic space-time process.
(2) The invention can embody the relevance between the traffic objects. The existing infrastructure-based traffic static modeling is based on traffic objects, and is expressed by modeling with the traffic objects as vectors, but complex traffic relations exist among the traffic objects, including the relations among object categories, the relations among object individuals and the like. The invention aims at the limitation of insufficient traffic relation expression capability of the existing traffic model, models and expresses the traffic relation in the traffic space-time process, and further can explore the deep reason of the evolution of the traffic space-time process.
(3) The invention can support efficient time-space inquiry and rapid inversion analysis of the traffic time-space process. The invention designs a complete storage structure, a space-time index and an access interface based on a dynamic graph model and a graph database, supports efficient space-time query of a traffic space-time process, and can realize efficient expression and inversion analysis of the traffic space-time process.
Drawings
FIG. 1 is a schematic flow chart of a method provided by an embodiment of the present invention;
FIG. 2 is a schematic diagram of traffic objects and relationships involved in a traffic spatiotemporal process model according to an embodiment of the present invention;
FIG. 3 is a schematic view of a traffic situation according to an embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating the storage result of traffic condition at time T1 in a Neo4j database according to an embodiment of the present invention;
FIG. 5 is a schematic diagram illustrating an inversion of a traffic space-time process, for example, inquiring about speeding vehicles, according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The traffic space-time process modeling management method and device based on the dynamic graph and the graph database can effectively model and express the traffic space-time process by using the advantages of the dynamic graph, thereby reflecting the space-time dynamics of traffic objects and the complex traffic relation among the objects; the traffic spatiotemporal process is stored in a Neo4j map database by using the invention, and history backtracking is carried out by using the Neo4j map database, thereby supporting the quick inversion of the traffic spatiotemporal process; meanwhile, the storage mode has good expandability, and when the storage mode changes, the attributes in the nodes and the relations can be directly changed, so that the storage mode has wide application prospects.
Fig. 1 is a schematic flow chart of a method provided in an embodiment of the present invention, including the following steps:
s1: analyzing the traffic space-time process, abstracting, defining and expressing the traffic object space-time dynamics and traffic relations covered by the traffic space-time process;
in step S1, the traffic space-time process involves road sections, traffic light intersections (e.g., R1/R2 is horizontal and R3/R4 is vertical in fig. 3), pedestrians, traffic events, etc., and the participating objects of the traffic space-time process are abstracted and defined, as shown in fig. 2. In the embodiment of the invention, the traffic objects are divided into static objects and dynamic objects. Static objects mainly comprise infrastructures such as roads, traffic lights and traffic signs, dynamic objects comprise vehicles and pedestrians, and are expressed in a snapshot mode, and the temporal and spatial dynamic changes of the dynamic objects form a string of state sequences. Various classes of objects are described below:
SI＝(OID,STOID,T,Road,Des) (1)
in formula (1), SI represents a static infrastructure object, OID represents a traffic space-time process identifier to which the infrastructure belongs, STOID represents an object identifier of the infrastructure in a traffic space-time process, T represents recording time, Road represents Road information to which the infrastructure belongs, and Des is description of an effect on the infrastructure;
SR＝(OID,STOID,T,L,C) (2)
in formula (2), SR represents a static Road object, OID represents a traffic space-time process identifier to which the Road belongs, STOID represents an object identifier of the Road in the traffic space-time process, T represents recording time, L represents speed constraint of the Road on running vehicles, C represents a congestion coefficient, the calculation method is as shown in formula (3), Car _ Num represents the number of vehicles running on the Road, Road _ Length represents the Length of the Road, W represents the weight size set for the Road according to the Road grade and travel conditions, and the calculation result of C reflects the congestion condition of traffic;
DC＝(OID,STOID,T,Road,Direc,S) (4)
in formula (4), DC represents a dynamic vehicle object, OID represents a traffic space-time process identifier to which the vehicle belongs, STOID represents an object identifier of the vehicle in the traffic space-time process, T represents recording time, Road represents Road information on which the vehicle travels, direct represents a traveling direction of the vehicle, and S represents a traveling speed of the vehicle;
DP＝(OID,STOID,T,Road,Direc) (5)
in formula (5), DP represents a dynamic pedestrian object, OID represents a traffic space-time process identifier to which the pedestrian belongs, STOID represents an object identifier of the pedestrian in the traffic space-time process, T represents recording time, Road represents Road information on which the pedestrian is located, and direct represents a driving direction of the pedestrian.
Traffic relationships include temporal and spatial topological relationships between vehicle objects, as well as regular and incident relationships between vehicles and roads. The time topological relation refers to the relation expressed in the time sequence among the objects; the spatial topological relation represents that objects are adjacent and separated; regular relationships exist among roads, vehicles and pedestrians, and the regular relationships specifically include vehicle speed and driving direction restrictions, traffic restrictions and the like; traffic events are classified into general events and emergency events. The conventional events comprise changes of traffic intersection signal lamps, driving requirements on holidays and the like, and the emergency events comprise vehicle faults, sudden severe weather and the like. The various relationships are described as follows:
R＝(OID,STOIDi,STOIDj,T,Type,Des|i≠j) (6)
in equation (6), R represents a static infrastructure object, and OID represents a traffic spatiotemporal process identifier, STOID, to which the infrastructure belongsiAnd STOIDjRepresents the basisThe method comprises the following steps that an object identifier of a traffic relation is generated in the process of traffic space-time by a facility, T represents recording time, type represents type information of the traffic relation, and Des is description of the traffic relation;
s2: respectively modeling the traffic objects and the traffic relations by using the dynamic graph to realize the mapping of the traffic objects and the traffic relations to the graph nodes and edges;
in step S2, since the traffic object and the traffic relationship in the real world are both dynamically changed in space-time and constantly changing in object state and traffic relationship attribute, the traffic object and the traffic relationship are mapped to a dynamic graph for modeling expression:
firstly, modeling a traffic object, identifying a dynamic object by adopting a semi-local centrality algorithm, and correcting data in real time, so that the calculated amount is greatly reduced, and the influence of the scale of the dynamic object is basically avoided; then mapping the processed dynamic object and static object to the nodes of the dynamic graph, and respectively obtaining the attribute information of different dynamic/static objects according to the category; then according to the traffic relation defined in an abstract way, constructing a topology network through an adjacency matrix, finding corresponding object nodes, determining a topological relation among the nodes, and then determining a rule relation and an event relation related in a traffic time-space process by combining rules and events, thereby mapping the traffic relation to the relation among the corresponding nodes in a dynamic graph and determining the attributes of different types of relations; finally, the traffic space-time process is mapped to a dynamic graph for modeling, and the traffic space-time process model dynamic graph is expressed as a formula (7).
DoG＝{Gi|i＝1,2,3...} (7)
G＝(D,S,R) (8)
In equation (7), DoG represents a traffic model dynamic graph, GiA set of dynamic graphs is represented, each dynamic graph corresponding to a traffic spatiotemporal process. In equation (8), D ═ DCi,DP i1,2,3, representing the set of spatio-temporal dynamic object nodes involved in the traffic spatio-temporal process dynamic graph G, including pedestrians and vehicles, S ═ SI ·i,SR i1,2,3, representing the space-time static object involved in the traffic space-time process dynamic graph GSet of nodes, including road and traffic infrastructure, R ═ OID (STOID)i,STOIDjT, Type, Des | i ≠ j) represents the set of traffic relations involved in the traffic spatio-temporal process dynamics G. When the traffic object and the traffic relation change, the node attributes and the traffic relation among the nodes are changed along with the change of the corresponding graph, the formula (6) and the formula (7), so that the static-to-dynamic conversion of the graph is realized, and the dynamic expression of the traffic space-time process is further realized.
S3: storing a traffic space-time process model constructed based on the dynamic graph into a Neo4j database;
in step S3, each node in the traffic space-time process model is stored in a node in the Neo4j database, the relationship between nodes in the model is stored in a relationship in the Neo4j database, and a storage attribute table is designed for the object and the relationship;
1) traffic time-space process table
The traffic space-time process table is used to describe all the traffic space-time processes, and each field and type in the table are shown in table 1.
TABLE 1
| Field(s) | Name (R) | Type of field | Length of field | Whether or not it is empty | Remarks for note |
| ProgressID | Process identification | String | Process unique identification | ||
| CSTP_ID | Process ID | String | Main key | ||
| StartTime | Starting time | Date | |||
| EndTime | End time | Date |
2) Traffic object table
The traffic object table is used to describe all traffic objects including infrastructure, roads, vehicles and pedestrians. The infrastructure object table is used for describing infrastructures such as traffic lights, speed-limiting boards and the like in all traffic space-time processes, and each field and type in the table are shown in the table 2.
TABLE 2
| Field(s) | Name (R) | Type of field | Length of field | Whether or not it is empty | Remarks for note |
| ObjectID | Object identification | String | Object unique identification | ||
| CSTOI_ID | Facility ID | String | Main key | ||
| Time | Recording time | Date | |||
| Road | Belonging road | String | |||
| Description | Description of a facility | String |
The road object table is used to describe all roads, and each field and type in the table are shown in table 3.
TABLE 3
| Field(s) | Name (R) | Type of field | Length of field | Whether or not it is empty | Remarks for note |
| ObjectID | Object identification | String | Object unique identification | ||
| CSTOR_ID | Road ID | String | Main key | ||
| Time | Recording time | Date | |||
| Speed_limit | Speed limitation | Double | |||
| Coefficient | Congestion coefficient | Double | Value of 0 to 1 |
The vehicle object table is used to describe all vehicles, and the fields and types in the table are shown in table 4.
TABLE 4
| Field(s) | Name (R) | Type of field | Length of field | Whether or not it is empty | Remarks for note |
| ObjectID | Object markSign board | String | Object unique identification | ||
| CSTOC_ID | Vehicle ID | String | Main key | ||
| Time | Recording time | Date | |||
| Road | Driving road | String | |||
| Direction | Direction of rotation | String | |||
| Speed | Speed of rotation | Double |
The pedestrian object table is used to describe all pedestrians, and each field and type in the table are shown in table 5.
TABLE 5
| Field(s) | Name (R) | Type of field | Length of field | Whether or not it is empty | Remarks for note |
| ObjectID | Object identification | String | Object unique identification | ||
| CStop_ID | Pedestrian ID | String | Main key | ||
| Time | Recording time | Date | |||
| Road | Driving road | String | |||
| Direction | Direction of rotation | String |
3) Traffic relation table
The traffic relation table is used for describing all traffic relations, and each field and type in the table are shown in table 6.
TABLE 6
4) Traffic state table
In the process of the traffic space-time, a series of changes occur to the traffic object, the state table is used for recording the change situation of the state of the traffic object in the process of the traffic space-time, and each field and type in the table are shown in the table 7.
TABLE 7
| Field(s) | Name (R) | Type of field | Length of field | Whether or not it is empty | Remarks for note |
| ObjectID | Object identification | String | Relationship unique identifier | ||
| CSTO_ID | Object ID | String | Main key | ||
| RegisterTime | Time stamp | Date | Time of registration | ||
| RegisterStat | Registration status | Document | |||
| RegisterAttr | Registering attributes | Document | |||
| Enclosure | Accessories | Document | Description of related Art document |
After completing the attribute design, establishing respective attribute information aiming at different objects and traffic relations.
S4: and designing an access interface, and realizing dynamic query management and inversion of a traffic space-time process through a Neo4j database.
The invention is characterized in that aiming at the defect of static modeling of the traffic infrastructure by the existing method, the dynamic graph is used for explicitly expressing the dynamic property of the traffic space-time process, the relevance among traffic objects can be reflected, and dynamic management is carried out by means of a Neo4j graph database, so that the traffic space-time process in the real world is accurately simulated.
In another embodiment of the present invention, there is also provided a traffic spatiotemporal process modeling management apparatus, including:
the defining module is used for describing the space-time dynamic of the traffic objects covered by the traffic space-time process and the traffic relation among the traffic objects;
the dynamic graph model building module is used for respectively modeling the traffic objects and the traffic relations among the traffic objects by utilizing a dynamic graph to obtain a traffic space-time process model, wherein nodes of the dynamic graph represent the traffic objects, and edges among the nodes represent the traffic relations among the traffic objects;
the storage module is used for storing the traffic space-time process model constructed based on the dynamic graph into the target graph database;
and the query module is used for designing an access interface and supporting dynamic query of a traffic space-time process and quick inversion of the traffic space-time process through the target graph database.
The specific implementation of each module may refer to the description of the method embodiment, and the embodiment of the present invention will not be repeated.
To more clearly illustrate the idea of the present invention, the traffic spatiotemporal process modeling and management method based on dynamic graph is further described below with reference to fig. 3 to 5, using a mobile vehicle of a main traffic road as example data. The method comprises the following specific steps:
step 1: analyzing the urban traffic space-time process, and abstracting, defining and expressing the traffic object space-time dynamics and traffic relations covered by the traffic space-time process;
in the first embodiment, the selected position where the traffic space-time process occurs is an intersection in a certain area, as shown in fig. 3, static objects included in the traffic space-time process are four road segments from R1 to R4, I1 represents a traffic light, and I2 represents a traffic restriction indicator; the related dynamic objects are 6 vehicle objects of C1-C6, and 5 pedestrian objects of P1-P5. Wherein, roads R1-R4 are connected with each other, vehicles C1 and C2 drive to R4 on the road R3, vehicles C3 and C4 drive to R3 on the road R4, vehicles C5 drive to R2 on the road R1, and vehicles C6 drive to R1 on the road R2; pedestrians P1 and P2 await traffic ready to cross road R3, pedestrians P3 and P4 are crossing road R2, and pedestrian P5 is crossing road R1. In the process of the traffic space-time, roads R1-R4 have a connection relationship, vehicles C3 and C4 have an adjacent topological relationship, and pedestrians P1 and P2, and pedestrians P3 and P4 have an adjacent topological relationship. The regular relationship comprises the restriction of traffic lights I1 to all vehicles at the intersection, and when the vehicles pass through the transverse road, the longitudinal road stops for waiting; the restriction board I2 is a restriction for roads R1 and R2, and restricts large trucks above 2 tons to 6: the vehicle cannot run on roads R1 and R2 between 00 and 22: 00; an event is involved, and the event E1 indicates that the vehicles C3 and C4 have rear-end collisions, so that road congestion is caused when accidents are handled.
Step 2: respectively modeling the traffic objects and the traffic relations by using the dynamic graph to realize the mapping of the traffic objects and the traffic relations to the graph nodes and edges;
in the traffic space-time process shown in fig. 3, each traffic object is taken as a node of the graph, including 4 road objects of R1-R4, 2 infrastructure of I1 and I2, 6 vehicle objects of C1-C6, and 5 pedestrian objects of P1-P5, which are mapped to 17 nodes of the graph in total, including 11 dynamic nodes and 6 static nodes, each node has corresponding attribute information, and then the relationships between the objects are mapped to the relationships between the nodes of the graph, including topological connection relationships between roads, regular relationships between roads and vehicles, and event relationships between vehicles, infrastructure and pedestrians, thereby completing the modeling process. When the state of the object and the relationship changes, the nodes in the dynamic graph and the relationship among the nodes also change.
And step 3: storing the urban traffic spatio-temporal process model constructed based on the dynamic graph into a Neo4j graph database;
each node in the model is correspondingly stored in a node in the Neo4j graph database, and the relationship between the nodes in the model is correspondingly stored in the relationship in the Neo4j graph database, so that the attribute table information of the nodes and the relationship is perfected.
The results of the traffic spatiotemporal process at time T1 stored in the Neo4j map database are shown in FIG. 4.
And 4, step 4: and designing an access interface, and realizing dynamic query management and inversion of a traffic space-time process through a Neo4j database.
In step 4, for example, a speeding vehicle on the route R1 is queried, query conditions such as time and speed are set, and the query results are displayed in a list form as shown in fig. 5.
The traffic space-time process modeling and management method based on the dynamic graph has the advantages that: a traffic space-time process model is designed based on the graph, so that the model can express the dynamics of a traffic space-time process and can reflect the traffic relation among traffic objects. In addition, the method facilitates the storage of the model through the Neo4j map database, and meanwhile, the relation between the vehicle and the road can be visually reflected through a Web interface. The method has good expandability, and when the relationship between roads and vehicles changes, the attributes of the nodes and the relationship in Neo4j can be directly changed, so that the method can be suitable for different traffic space-time processes.
In the description of the present invention, it is to be understood that the terms "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, are merely for convenience of description of the present invention, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the present invention.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A traffic space-time process modeling management method is characterized by comprising the following steps:
describing the space-time dynamic of the traffic objects covered by the traffic space-time process and the traffic relation among the traffic objects;
respectively modeling the traffic objects and the traffic relations among the traffic objects by utilizing a dynamic graph to obtain a traffic space-time process model, wherein nodes of the dynamic graph represent the traffic objects, and edges among the nodes represent the traffic relations among the traffic objects;
storing the traffic space-time process model constructed based on the dynamic graph into a target graph database;
and designing an access interface, and supporting dynamic query of a traffic space-time process and quick inversion of the traffic space-time process through the target graph database.
2. The method of claim 1, wherein the traffic objects comprise static objects and dynamic objects; the static object is an infrastructure which comprises roads, traffic lights and traffic signs; the dynamic objects include vehicles and pedestrians.
3. The method of claim 2, wherein the traffic objects are expressed in object snapshots, and the spatiotemporal dynamics of the traffic objects form a string of state sequences.
4. The method according to any one of claims 1 to 3, wherein the traffic relations comprise temporal topological relations between objects, spatial topological relations between objects, regular relations between objects and roads and event relations.
5. The method of claim 4, wherein the temporal topological relation represents an association between objects expressed in chronological order; the spatial topological relation represents adjacent and/or separated relation between objects; the regular relationship between the object and the road comprises the vehicle speed and driving direction limit and the traffic limit; the event relationship is a traffic event.
6. The method of claim 1, wherein the modeling the traffic objects and the traffic relationships between the traffic objects using the dynamic graph to obtain a traffic spatiotemporal process model comprises:
mapping the traffic objects to nodes of the dynamic graph, and acquiring attribute information of each object;
and finding an object node corresponding to the traffic relation according to the described traffic relation, mapping the traffic relation to the relation between corresponding nodes in the dynamic graph, and determining the attribute of each traffic relation so as to obtain a traffic space-time process model.
7. The method according to claim 1 or 6, wherein storing the traffic spatiotemporal process model constructed based on dynamic graph to a target graph database comprises:
each node in the traffic space-time process model is correspondingly stored in a node in a target graph database, the relationship between the nodes in the traffic space-time process model is correspondingly stored in the relationship in the target graph database, and a storage attribute table is designed for the object and the relationship.
8. The method of claim 7, wherein storing the attribute table comprises: a traffic space-time process table, a traffic object table, a traffic relation table and a traffic state table.
9. The method of claim 8, wherein the target graph database is a Neo4j graph database.
10. A traffic spatiotemporal process modeling management device, comprising:
the defining module is used for describing the space-time dynamic of the traffic objects covered by the traffic space-time process and the traffic relation among the traffic objects;
the dynamic graph model building module is used for respectively modeling the traffic objects and the traffic relations among the traffic objects by utilizing a dynamic graph to obtain a traffic space-time process model, wherein nodes of the dynamic graph represent the traffic objects, and edges among the nodes represent the traffic relations among the traffic objects;
the storage module is used for storing the traffic space-time process model constructed based on the dynamic graph into a target graph database;
and the query module is used for designing an access interface and supporting dynamic query of a traffic space-time process and quick inversion of the traffic space-time process through the target graph database.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010031478.6A CN111324943B (en) | 2020-01-13 | 2020-01-13 | Traffic space-time process modeling management method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010031478.6A CN111324943B (en) | 2020-01-13 | 2020-01-13 | Traffic space-time process modeling management method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111324943A true CN111324943A (en) | 2020-06-23 |
| CN111324943B CN111324943B (en) | 2022-05-24 |
Family
ID=71168744
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010031478.6A Active CN111324943B (en) | 2020-01-13 | 2020-01-13 | Traffic space-time process modeling management method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111324943B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104866670A (en) * | 2015-05-25 | 2015-08-26 | 武汉大学 | GPS spatial-temporal trajectory-based road network topological change automatic detection method and system |
| CN105574259A (en) * | 2015-12-14 | 2016-05-11 | 华南理工大学 | Internet word frequency-based city cognitive map generation method |
| CN108765948A (en) * | 2018-06-05 | 2018-11-06 | 厦门大学 | A kind of the bus dispatching method and system of elasticity |
| US20180330242A1 (en) * | 2017-05-10 | 2018-11-15 | Verizon Patent And Licensing Inc. | Sensor-based predictive outage system |
-
2020
- 2020-01-13 CN CN202010031478.6A patent/CN111324943B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104866670A (en) * | 2015-05-25 | 2015-08-26 | 武汉大学 | GPS spatial-temporal trajectory-based road network topological change automatic detection method and system |
| CN105574259A (en) * | 2015-12-14 | 2016-05-11 | 华南理工大学 | Internet word frequency-based city cognitive map generation method |
| US20180330242A1 (en) * | 2017-05-10 | 2018-11-15 | Verizon Patent And Licensing Inc. | Sensor-based predictive outage system |
| CN108765948A (en) * | 2018-06-05 | 2018-11-06 | 厦门大学 | A kind of the bus dispatching method and system of elasticity |
Non-Patent Citations (3)
| Title |
|---|
| XUEFENG GUAN: "Wavelet-HST: A Wavelet-Based Higher-Order Spatio-Temporal Framework for Urban Traffic Speed Prediction", 《IEEE ACCESS》 * |
| 刘博: "面向交通车辆图像的图拓扑表示方法", 《中国优秀博硕士学位论文全文数据库(硕士) 信息科技辑》 * |
| 成波: "一种面向时空对象及其关联关系动态变化表达的概念数据模型", 《地球信息科学》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111324943B (en) | 2022-05-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110298500B (en) | Urban traffic track data set generation method based on taxi data and urban road network | |
| Fischer | GIS and network analysis | |
| CN111275965B (en) | Real-time traffic simulation analysis system and method based on internet big data | |
| CN107490384B (en) | Optimal static path selection method based on urban road network | |
| US10373494B1 (en) | Method and apparatus for estimating a parking event based on device signal observations | |
| CN113763712B (en) | Regional traffic jam tracing method based on travel event knowledge graph | |
| WO2023040539A1 (en) | Vehicle stream relocating condition display method and apparatus, device, medium, and product | |
| CN112347596B (en) | Urban public transport network optimization method | |
| CN115100867B (en) | Urban intelligent traffic simulation method based on digital twins | |
| US20230137263A1 (en) | Method and apparatus for generating structured trajectories from geospatial observations | |
| CN111324943B (en) | Traffic space-time process modeling management method and device | |
| CN114676203A (en) | Traffic knowledge map construction method and device, electronic equipment and storage medium | |
| CN112883195B (en) | Traffic knowledge graph construction method and system for individual travel | |
| CN116822627A (en) | Construction method of urban traffic knowledge graph | |
| CN115293585B (en) | Method for building dynamic emission list of road network motor vehicle based on GIS-T | |
| Pincay et al. | Partitioning space to identify en-route movement patterns | |
| CN110942622B (en) | Parking lot planning method based on real-time operation big data of parking lot | |
| Botian et al. | Multi School Bus Routing Problem with Road Safety Factor via A Heuristic Algorithm | |
| Lombard et al. | Modelling the time-dependent vrp through open data | |
| Cao et al. | Fusion and integration framework study on road network and dynamic traffic data in vehicle navigation | |
| CN109993974B (en) | Intelligent traffic-induced chain type progressive network construction method based on sliding window | |
| US20230132499A1 (en) | Method and apparatus for generating structured trajectories from geospatial observations | |
| US20230135578A1 (en) | Method and apparatus for generating structured trajectories from geospatial observations | |
| Wang et al. | Highway network topology construction and traffic distribution based on multi-source data | |
| CN116932929A (en) | Target road section screening method, device, computer equipment and storage medium |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |