Automated scenario recognition and reporting using neural networks

1. An incident avoidance system, comprising:

a plurality of imaging sensors; and

a neural computing system configured to:

detect several instances of a particular pattern of behavior over a history of detection of the neural computing system;

identify a particular set of circumstances that has been commonly associated with the several instances of the particular pattern of behavior over the history of detection of the neural computing system;

identify a cause of the particular pattern of behavior based at least on the particular set of circumstances;

determine a preventative action based on the particular set of circumstances and the identified cause;

associate the preventative action with the particular pattern of behavior, wherein the particular pattern of behavior is classified as a known pattern of behavior;

receive, after determining the preventative action, an image feed from at least one of the plurality of imaging sensors;

analyze the image feed to identify a pattern of behavior exhibited by subjects within the image feed;

determine that the identified pattern of behavior matches the known pattern of behavior; and

perform the preventative action when the particular set of circumstances is detected a subsequent time, wherein performing the preventative action comprises sending a command to one or more remote devices, wherein one or both of a type of the command or the one or more remote devices are selected based on a result of the determination that the identified pattern of behavior matches the known pattern of behavior, and wherein at least one of the one or more remote devices comprises a transit system device.

2. The incident avoidance system of claim 1 , wherein:

identifying the cause comprises receiving data from one or more external sources;

the received data is associated with a time that is within a threshold range of the identified pattern of behavior; and

the cause is further identified based at least in part on at least a portion of the received data.

3. The incident avoidance system of claim 2 , wherein:

the received data comprises one or more elements selected from the following: a time of day, weather data, timetable data, non-transit event data, or transit system device data.

4. The incident avoidance system of claim 1 , wherein:

one or both of a type of the command or the one or more remote devices are selected further based on the identified cause.

5. The incident avoidance system of claim 1 , wherein the neural computing system is further configured to:

at a later time, identify a set of circumstances matching the identified cause; and

send a command that implements the preventative action.

6. A method of detecting and avoiding incidents, comprising:

detecting several instances of a particular pattern of behavior over a history of detection of a neural computing system;

identifying a particular set of circumstances that has been commonly associated with the several instances of the particular pattern of behavior over the history of detection of the neural computing system;

identifying a cause of the particular pattern of behavior based at least on the particular set of circumstances;

determining a preventative action based on the particular set of circumstances and the identified cause;

associating the preventative action with the particular pattern of behavior, wherein the particular pattern of behavior is classified as a known pattern of behavior;

receiving after determining the preventative action, at a neural computing system, an image feed from at least one imaging sensor;

analyzing the image feed to identify a pattern of behavior exhibited by subjects within the image feed;

determining that the identified pattern of behavior matches the known pattern of behavior; and

performing the preventative action when the particular set of circumstances is detected a subsequent time, wherein performing the preventative action comprises sending a command to one or more remote devices, wherein one or both of a type of the command or the one or more remote devices are selected based on a result of the determination that the identified pattern of behavior matches the known pattern of behavior, and wherein at least one of the one or more remote devices comprises a transit system device.

7. The method of detecting and avoiding incidents of claim 6 , further comprising:

identifying an additional plurality of patterns of behavior from additional image feeds;

receiving sensor data from one or more sensors of a transit system;

analyzing the additional plurality of patterns of behavior and the sensor data to identify a common set of circumstances between at least one of the plurality of patterns of behavior and at least a portion of the sensor data; and

predicting a subsequent occurrence of the at least one of the plurality of patterns of behavior.

8. The method of detecting and avoiding incidents of claim 6 , further comprising:

determining that the identified pattern of behavior involves an emergency scenario, wherein the command comprises an alert detailing a type of the emergency scenario, and wherein the one or more remote devices comprises an emergency agency system.

9. The method of detecting and avoiding incidents of claim 6 , further comprising:

prior to receiving the image feed, analyzing a plurality of image feeds;

receiving information related to a plurality of patterns of behavior, wherein each of the plurality of patterns of behavior is associated with at least one of the plurality of image feeds, wherein the information comprises a characterization of at least one known pattern of behavior present in each of the plurality of image feeds; and

storing each of the plurality of known patterns of behavior.

10. The method of detecting and avoiding incidents of claim 6 , wherein:

the at least one imaging sensor comprises a LIDAR device.