Systems and methods for detecting road congestion and incidents in real time
First Claim
1. A method comprising:
- dividing a candidate road into road segments by perpendicular bisectors;
receiving probe data from mobile devices in probe vehicles or on travelers on the candidate road, wherein the probe data includes geographic location probe data;
performing, using a processor, a first map-matching process, wherein the geographic location probe data is aligned to the candidate road or a specific lane on the candidate road, forming aligned probe data; and
performing, using the processor, a second map-matching process, wherein the aligned probe data is shifted to and grouped at a closest perpendicular bisector in a direction of travel for each mobile device, forming double map-matched probe data configured to be analyzed and compared with real time probe data aligned and shifted to a same geographic location.
1 Assignment
0 Petitions
Accused Products
Abstract
Apparatuses and methods are provided for determining real time traffic conditions. A candidate road is divided into road segments by perpendicular bisectors. A spatial sliding window is positioned over at least a portion of a road segment, wherein the spatial sliding window corresponds to a front end of the road segment in a direction of travel of the road segment. Real time probe data is received from mobile devices in probe vehicles or on travelers of the at least portion of the road segment within the spatial sliding window. The real time probe data is analyzed, and a computer program assists in determining the real time traffic conditions of the at least portion of the road segment within the spatial sliding window. Based on the analysis, the real time traffic conditions are reported.
30 Citations
16 Claims
-
1. A method comprising:
-
dividing a candidate road into road segments by perpendicular bisectors; receiving probe data from mobile devices in probe vehicles or on travelers on the candidate road, wherein the probe data includes geographic location probe data; performing, using a processor, a first map-matching process, wherein the geographic location probe data is aligned to the candidate road or a specific lane on the candidate road, forming aligned probe data; and performing, using the processor, a second map-matching process, wherein the aligned probe data is shifted to and grouped at a closest perpendicular bisector in a direction of travel for each mobile device, forming double map-matched probe data configured to be analyzed and compared with real time probe data aligned and shifted to a same geographic location. - View Dependent Claims (2, 3, 4, 5, 6)
-
-
7. A method comprising:
-
dividing a candidate road into road segments by perpendicular bisectors; positioning a spatial sliding window over at least a portion of a road segment, wherein the spatial sliding window aligns with a front end of the road segment in a direction of travel of the road segment; receiving real time probe data from mobile devices in probe vehicles or on travelers of the at least a portion of the road segment within the spatial sliding window, wherein the real time probe data has undergone first and second map-matching processes, wherein geographic location probe data from the real time probe data is aligned to the candidate road or a specific lane on the candidate road, forming aligned probe data, and wherein the aligned probe data is shifted to and grouped at a closest perpendicular bisector in a direction of travel for each mobile device, forming double map-matched probe data; analyzing, by a processor, the real time probe data; determining real time traffic conditions of the at least a portion of the road segment within the spatial sliding window, wherein the real time traffic conditions are determined based on a machine learning algorithm or a threshold based system, wherein the real time probe data is compared with a historical database of probe data aligned and shifted to the same road or lane segment for a same time period; and reporting the real time traffic conditions. - View Dependent Claims (8, 9)
-
-
10. An apparatus comprising:
-
at least one processor; and at least one memory including computer program code for one or more programs;
the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to at least perform;divide a candidate road into road segments by perpendicular bisectors; receive probe data from mobile devices in probe vehicles or on travelers on the candidate road, wherein the probe data includes the geographic location probe data; perform a first map-matching algorithm, wherein the geographic location probe data is aligned to the candidate road or a specific lane on the candidate road, forming aligned probe data; and perform a second map-matching algorithm, wherein the aligned probe data is shifted to and grouped at a closest perpendicular bisector in a direction of travel for each mobile device, forming double map-matched probe data configured to be analyzed and compared with real time probe data aligned and shifted to a same geographic location. - View Dependent Claims (11, 12, 13)
-
-
14. An apparatus comprising:
-
at least one processor; and at least one memory including computer program code for one or more programs;
the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to at least perform;divide a candidate road into road segments by perpendicular bisectors; position a spatial sliding window over at least a portion of a road segment, wherein the spatial sliding window corresponds to a front end of the road segment in a direction of travel of the road segment; receive real time probe data from mobile devices in probe vehicles or on travelers of the at least portion of the road segment within the spatial sliding window, wherein the real time probe data has undergone first and second map-matching processes, wherein geographic location probe data from the real time probe data is aligned to the candidate road or a specific lane on the candidate road, forming aligned probe data, and wherein the aligned probe data is shifted to and grouped at a closest perpendicular bisector in a direction of travel for each mobile device, forming double map-matched probe data; analyze the real time probe data; determine real time traffic conditions of the at least a portion of the road segment within the spatial sliding window, wherein the real time traffic conditions are determined based on a machine learning algorithm or a threshold based system, wherein the real time probe data is compared with a historical database of probe data aligned and shifted to the same road or lane segment for a same time period; and report the real time traffic conditions. - View Dependent Claims (15, 16)
-
Specification