System and method for road traffic pattern calculation

1 . A traffic pattern data calculation system, comprising:

at least one non-transitory memory configured to store computer executable instructions; and

at least one processor configured to execute the computer executable instructions to:

receive, for a road segment, historical traffic data from a plurality of vehicle probes and complementary data from one or more external data sources, for a predefined most-recent time window;

extract, from the received historical traffic data and the complementary data, secondary traffic data based on a filtering criterion;

calculate, for the road segment, traffic pattern data, based on the extracted secondary traffic data; and

update a map database with the traffic pattern data of the road segment for the predefined most-recent time window, based on the calculation; and

cause a mapping platform routing engine to generate navigation instructions based on the updated map database.

2 . The traffic pattern data calculation system of claim 1 , wherein the at least one processor is further configured to:

determine an advancing time interval for the predefined most-recent time window, based on one or more attributes of the road segment;

advance the predefined most recent time window by the determined advancing time interval; and

calculate updated traffic pattern data for the road segment based on the advanced predefined most-recent time window.

3 . The traffic pattern data calculation system of claim 1 , wherein the filtering criterion comprises at least one of:

complementary data that is calendar event data;

complementary data that is a non-recurring incident data; and

the historical traffic data that is associated with a traffic confidence value that is less than or equal to a predefined traffic confidence threshold.

4 . The traffic pattern data calculation system of claim 3 , wherein to extract the secondary traffic data based on the filtering criterion, the at least one processor is configured to:

exclude the historical traffic data corresponding to the complementary data; and

combine the historical traffic data associated with the traffic confidence value more than the predefined traffic confidence threshold.

5 . The traffic pattern data calculation system of claim 4 , wherein the combining comprises an arithmetic averaging based on a recurrent formula.

6 . The traffic pattern data calculation system of claim 1 , wherein the traffic pattern data comprises a speed value.

7 . The traffic pattern data calculation system of claim 1 , wherein the traffic pattern data comprises a traffic congestion parameter value.

8 . The traffic pattern data calculation system of claim 1 , wherein the one or more attributes of the road segment comprise at least one of: a functional classification of the road segment or a controlled access limitation associated with the road segment.

9 . A method for calculation of a traffic pattern data, the method comprising:

receiving, for a road segment, historical traffic data from a plurality of vehicle probes and complementary data from one or more external data sources, for a predefined most-recent time window;

extracting, from the received historical traffic data and the complementary data, secondary traffic pattern data based on a filtering criterion;

calculating, for the road segment, the traffic pattern data, based on the extracted secondary traffic data; and

update a map database with the traffic pattern data of the road segment for the predefined most-recent time window, based on the calculation; and

cause a mapping platform routing engine to generate navigation instructions based on the updated map database.

10 . The method of claim 9 , further comprising:

determining an advancing time interval for the predefined most-recent time window, based on one or more attributes of the road segment;

advancing the predefined most-recent time window by the determined advancing time interval; and

calculating updated traffic pattern data for the road segment based on the advanced predefined most-recent time window.

11 . The method of claim 9 , wherein the filtering criterion comprises at least one of:

complementary data that is calendar event data;

complementary data that is a non-recurring incident data; and

the historic traffic data that is associated with a traffic confidence value that is less than or equal to a predefined traffic confidence threshold.

12 . The method of claim 11 , wherein extracting the secondary traffic data based on the filtering criterion comprises:

excluding the historical traffic data corresponding to the complementary data; and

combining the historical traffic data associated with the traffic confidence value more than the predefined traffic confidence threshold.

13 . The method of claim 12 , wherein the combining comprises an arithmetic averaging based on a recurrent formula.

14 . The method of claim 9 , wherein the traffic pattern data comprises a speed value and a traffic congestion parameter value.

15 . The method of claim 9 , wherein the one or more attributes of the road segment comprise at least one of: a functional classification of the road segment or a controlled access limitation associated with the road segment.

16 . A computer programmable product comprising a non-transitory computer readable medium having stored thereon computer executable instruction which when executed by one or more processors, cause the one or more processors to carry out operations for calculating traffic pattern data, the operations comprising:

receiving, for a road segment, historical traffic data from a plurality of vehicle probes and complementary data from one or more external data sources, for a predefined most-recent time window;

extracting, from the received historical traffic data and the complementary data, secondary traffic data based on a filtering criterion;

calculating, for the road segment, the traffic pattern data, based on the extracted secondary traffic data; and

updating a map database with the traffic pattern data of the road segment for the predefined most-recent time window, based on the calculation; and

causing a mapping platform routing engine to generate navigation instructions based on the updated map database.

17 . The computer programmable product of claim 16 , wherein the operations further comprise:

advancing the predefined most-recent time window by the determined advancing time interval; and

calculating updated traffic pattern data for the road segment based on the advanced predefined most-recent time window.

18 . The computer programmable product of claim 16 , wherein the filtering criterion comprises at least one of:

complementary data that is calendar event data;

complementary data that is a non-recurring incident data; and

the historical traffic data that is associated with a traffic confidence value that is less than or equal to a predefined traffic confidence threshold.

19 . The computer programmable product of claim 18 , wherein the combining comprises an arithmetic averaging based on a recurrent formula.

20 . The computer programmable product of claim 16 , wherein the traffic pattern data comprises a speed value.