METHOD AND SYSTEM FOR TRAFFIC PREDICTION BASED ON SPACE-TIME RELATION
First Claim
1. A method for determining spatial influences among sections, comprising:
- a spatial scope determining step of determining, for each of sections in a road network, a spatial scope having influence on the section, wherein the spatial scope is of a spatial order of N, which is an integer equal to or greater than 1;
an influential section extraction step of extracting, from the road network, neighboring sections of the section within the determined spatial scope, as N-th order influential sections for the section;
a spatial relation determining step of classifying the spatial relation between each of the sections and each of its N-th order influential sections into one of predefined types of spatial relation;
a correlation learning step of performing, for the classified type of spatial relation, correlation analysis based on historical traffic data of the section and its N-th order influential sections of this type of spatial relation, to learn a correlation between the section and its N-th order influential sections for this type of spatial relation; and
a spatial influence determining step of determining spatial influences of spatial order N for the section based on the learned correlation, wherein each of the spatial influences reflects an extent to which the section is influenced by one of its N-th order influential sections.
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Abstract
A system and method for traffic prediction based on space-time relation are disclosed. The system comprises a section spatial influence determining section for determining, for each of a plurality of sections to be predicted, spatial influences on the section by its neighboring sections; a traffic prediction model establishment section for establishing, for each of the plurality of sections to be predicted, a traffic prediction model by using the determined spatial influences and historical traffic data of the plurality of sections; and a traffic prediction section for predicting traffic of each of the plurality of sections to be predicted for a future time period by using real-time traffic data and the traffic prediction model. An apparatus and method for determining spatial influences among sections, as well as an apparatus and method for traffic prediction, are also disclosed. With the present invention, a spatial influence of a section can be used as a spatial operator and a time sequence model can be incorporated, such that the influences on a current section by its neighboring section for a plurality of spatial orders can be taken into account. In this way, the traffic condition in a spatial scope can be measured more practically, so as to improve accuracy of prediction.
116 Citations
23 Claims
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1. A method for determining spatial influences among sections, comprising:
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a spatial scope determining step of determining, for each of sections in a road network, a spatial scope having influence on the section, wherein the spatial scope is of a spatial order of N, which is an integer equal to or greater than 1; an influential section extraction step of extracting, from the road network, neighboring sections of the section within the determined spatial scope, as N-th order influential sections for the section; a spatial relation determining step of classifying the spatial relation between each of the sections and each of its N-th order influential sections into one of predefined types of spatial relation; a correlation learning step of performing, for the classified type of spatial relation, correlation analysis based on historical traffic data of the section and its N-th order influential sections of this type of spatial relation, to learn a correlation between the section and its N-th order influential sections for this type of spatial relation; and a spatial influence determining step of determining spatial influences of spatial order N for the section based on the learned correlation, wherein each of the spatial influences reflects an extent to which the section is influenced by one of its N-th order influential sections. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 22)
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16. An apparatus for determining spatial influences among sections, comprising:
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a spatial scope determining unit for determining, for each of sections in a road network, a spatial scope having influence on the section, wherein the spatial scope is of a spatial order of N, which is an integer equal to or greater than 1; an influential section extraction unit for extracting, from the road network, neighboring sections of the section within the determined spatial scope, as N-th order influential sections for the section; a spatial relation determining unit for classifying the spatial relation between each of the sections and its N-th order influential sections into one of predefined types of spatial relation; a correlation learning unit for performing, for the classified type of spatial relation, correlation analysis on historical traffic data of the section and its N-th order influential sections of this type of spatial relation, to learn a correlation between the section and its N-th order influential sections for this typo of spatial relation; and a spatial influence determining unit for determining spatial influences for the N-th order influential sections of the section based on the learned correlation, wherein each of the spatial influences reflects an extent to which the section is influenced by one of its N-th order influential sections. - View Dependent Claims (17, 18, 19, 20, 21, 23)
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Specification