Integrated collision avoidance and road safety management system

1. A method of determining a collision risk forecast and root cause of a collision risk for road segments of a road network comprising the steps of:

a computer receiving data representative of real time conditions, real time social events, and historic conditions associated with road segments from a plurality of devices;

the computer determining a probability of collision risk for each road segment including the root cause comprising the steps of the computer:

determining all road segments of the road network from the data representative of real time conditions and social events;

applying data representative of historical conditions to each road segment to down sample data to account for imbalances and determine a number of collisions in each road segment;

determining major factors of relevance for causing collisions for each road segment from data received representative of real time conditions, real time social events and historic conditions;

applying models to the major factors of relevance to determine conditional probabilities and dependencies causing collisions in each road segment;

spatially smoothing the conditional probability of each road segment to determine a collision risk index with continuous metrics to create a spatial low pass filter;

applying the spatial low pass filter to each road segment to remove discontinuities; and

simulating continuous probability to determine a road network risk estimation with a collision risk forecast and root cause of the collision risk for each road segment;

the computer sending a notification to at least some of the plurality of devices regarding the collision risk and root causes of collision for at least one road segment.

2. The method of claim 1 , wherein the plurality of devices receiving the notification are located in the at least one road segment with the collision risk.

3. The method of claim 1 , wherein the step of determining major factors of relevance for causing collisions in each road segment from data received representative of real time conditions, real time social events and historic conditions is determined by exploratory factor analysis.

4. The method of claim 1 , wherein the model applied to the major factors of relevance to determine conditional probabilities and dependencies causing collisions in each road segment is Bayesian Network Inference.

5. The method of claim 1 , wherein the plurality of devices are selected from a group consisting of: traffic signals, traffic cameras, security cameras, vehicle sensors personal devices of users in the at least one road segment, devices associated with emergency services, devices associated with traffic officials and devices associated with law enforcement.

6. The method of claim 1 , wherein the collision risk forecast is for a future time period from when the real time data was captured by the plurality of devices.

7. The method of claim 1 , wherein the collision risk forecast is specific to a type of driver behavior.

8. The method of claim 1 , wherein the collision risk forecast is specific to a type of vehicle.

9. The method of claim 1 , wherein the collision risk forecast is specific to a road type present in the each road segment.

10. A computer program product for determining a collision risk forecast and root cause of a collision risk for road segments of a road network using a decision making engine having a computer comprising at least one processor, one or more memories, one or more computer readable storage media, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by the computer to perform a method comprising:

receiving, by the computer, data representative of real time conditions, real time social events, and historic conditions associated with each road segment from a plurality of devices;

determining, by the computer, a probability of collision risk for each road segment including the root cause comprising the program instructions of:

identifying all road segments of the road network from the data representative of real time conditions and social events;

applying data representative of historical conditions to each road segment to down sample data to account for imbalances and determine a number of collisions in each road segment;

determining major factors of relevance for causing collisions in each road segment from data received representative of real time conditions, real time social events and historic conditions;

applying models to the major factors of relevance to determine conditional probabilities and dependencies causing collisions in each road segment;

spatially smoothing the conditional probability of each road segment to determine a collision risk index with continuous metrics to create a spatial low pass filter;

applying the spatial low pass filter to each road segment to remove discontinuities; and

simulating continuous probability to determine a road network risk estimation with a collision risk forecast and root cause of the collision risk for each road segment;

sending, by the computer, a notification to at least some of the plurality of devices regarding the collision risk and root causes of collision for the at least one road segment.

11. The computer program product of claim 10 , wherein the plurality of devices receiving the notification are located in the at least one road segment with the collision risk.

12. The computer program product of claim 10 , wherein the program instructions of determining, by the computer, major factors of relevance for causing collisions in each road segment from data received representative of real time conditions, real time social events and historic conditions is determined by exploratory factor analysis.

13. The computer program product of claim 10 , wherein the model applied to the major factors of relevance to determine conditional probabilities and dependencies causing collisions in each road segment is Bayesian Network Inference.

14. The computer program product of claim 10 , wherein the collision risk forecast is for a future time period from when the real time data was captured by the plurality of devices.

15. The computer program product of claim 10 , wherein the collision risk forecast is specific to a type of driver behavior.

16. The computer program product of claim 10 , wherein the collision risk forecast is specific to a type of vehicle.

17. The computer program product of claim 10 , wherein the collision risk forecast is specific to a road type present in each road segment.

18. A computer system for determining a collision risk forecast and root cause of a collision risk for road segments of a road network comprising a decision making engine having a computer comprising at least one processor, one or more memories, one or more computer readable storage media having program instructions executable by the computer to perform the program instructions comprising:

receiving, by the computer, data representative of real time conditions, real time social events, and historic conditions associated with each road segment from a plurality of devices;

determining, by the computer, a probability of collision risk for each road segment including the root cause comprising the program instructions of:

identifying all road segments of the road network from the data representative of real time conditions and social events;

applying data representative of historical conditions to each road segment to down sample data to account for imbalances and determine a number of collisions in each road segment;

determining major factors of relevance for causing collisions in each road segment from data received representative of real time conditions, real time social events and historic conditions;

applying models to the major factors of relevance to determine conditional probabilities and dependencies causing collisions in each road segment;

spatially smoothing the conditional probability of each road segment to determine a collision risk index with continuous metrics to create a spatial low pass filter;

applying the spatial low pass filter to each road segment to remove discontinuities; and

simulating continuous probability to determine a road network risk estimation with a collision risk forecast and root cause of the collision risk for each road segment;

sending, by the computer, a notification to at least some of the plurality of devices regarding the collision risk and root causes of collision for the at least one road segment.

19. The computer system of claim 18 , wherein the program instructions of determining, by the computer, major factors of relevance for causing collisions in each road segment from data received representative of real time conditions, real time social events and historic conditions is determined by exploratory factor analysis.

20. The computer system of claim 18 , wherein the model applied to the major factors of relevance to determine conditional probabilities and dependencies causing collisions in each road segment is Bayesian Network Inference.