Hashing vehicle position data in real-time to detect behavior patterns
Systems and methods for detecting vehicle behavior patterns in real-time. One embodiment is a method of continuous vehicle behavior detection. The method includes receiving a vehicle behavior profile including one or more travel patterns that define a vehicle behavior, receiving track data of one or more vehicles, hashing the track data as it is received to generate hash values that uniquely identify cells that approximate locations of the one or more vehicles, and storing the hash values in a hash library. The method also includes analyzing the hash library as the hash values are stored to compare the cells with the one or more travel patterns in the vehicle behavior profile, and in response to determining a group of the cells match the one or more travel patterns, generating a message indicating the vehicle behavior is detected.
1. A method of continuous vehicle behavior detection, the method comprising:
receiving a vehicle behavior profile including one or more travel patterns that define a vehicle behavior;
receiving track data of one or more vehicles;
(a) hashing, by a computing device, the track data as it is received to generate geohash values that uniquely identify spatial cells that approximate locations of the one or more vehicles;
(b) storing the geohash values in a hash library;
(c) analyzing, by the computing device, the hash library as the geohash values are stored, for multiple of the one or more vehicles, to determine whether a group of different ones of the spatial cells over time matches any of the one or more travel patterns in the vehicle behavior profile; and
in response to determining the group of the different ones of a group of the spatial cells over time matches any of the one or more travel patterns;
generating, by the computing device, a message indicating the vehicle behavior is detected;
adjusting a resolution of the spatial cells for additional pattern analysis; and
repeating steps (a)-(c) for the spatial cells with the adjusted resolution for one or more additional travel patterns that define a vehicle behavior.
2. The method of claim 1 , further comprising:
based on the group of the different ones of the spatial cells over time matching any of the one or more travel patterns, detecting multiple of the one or more vehicles co-traveling based on a temporal analysis of the geohash values stored in the hash library; and
wherein the message indicates the multiple of the one or more vehicles are co-traveling.
3. The method of claim 1 , wherein analyzing the hash library is performed in real-time.
4. The method of claim 1 , further comprising:
assigning processing nodes of a distributed computing system to the spatial cells.
5. A non-transitory computer readable medium including programmed instructions executable by a processor, wherein the instructions, when executed, direct the processor to perform a method of continuous vehicle behavior detection, the method comprising:
receiving a vehicle behavior profile including one or more travel patterns that define a vehicle behavior;
receiving track data of one or more vehicles;
hashing the track data as it is received to generate geohash values that uniquely identify spatial cells that approximate locations of the one or more vehicles;
storing the geohash values in a hash library;
continually analyzing the hash library as the geohash values are stored, for multiple of the one or more vehicles, to determine whether a group of different ones of the spatial cells over time matches any of the one or more travel patterns in the vehicle behavior profile;
in response to determining the group of the different ones of the spatial cells over time matches any of the one or more travel patterns, generating a message indicating the vehicle behavior is detected; and
in response to determining the group of the different ones of the spatial cells over time do not match any of the one or more travel patterns, deallocating memory in the hash library for geohash values corresponding to the group of the different ones of the spatial cells that do not match any of the one or more travel patterns.
6. The non-transitory computer readable medium of claim 5 , wherein the method further comprises: based on the group of the different ones of the spatial cells over time matching any of the one or more travel patterns, detecting multiple vehicles co-traveling based on a temporal analysis of the geohash values stored in the hash library; and
wherein the message indicates the multiple of the one or more vehicles are co-traveling.
7. The non-transitory computer readable medium of claim 6 , wherein the temporal analysis is performed in real-time.
8. The non-transitory computer readable medium of claim 5 , wherein the method further comprises, based on the group of the different ones of the spatial cells over time matching any of the one or more travel patterns over a threshold length of time:
initiating an additional pattern analysis in response to detecting the vehicle behavior; and
adjusting a resolution of the cells for the additional pattern analysis.
9. A vehicle behavior detection system comprising:
a communication interface configured to receive a vehicle behavior profile including one or more travel patterns that define a vehicle behavior;
a vehicle behavior analytics platform comprising:
a continuous stream interface configured to receive track data of one or more vehicles; and
hash processor nodes configured to geohash the track data as it is received to generate hash values that uniquely identify spatial cells that approximate locations of the one or more vehicles;
a hash library configured to store the geohash values; and
a behavior detection processor configured to:
analyze the hash library as the geohash values are stored, for multiple of the one or more vehicles, to determine whether a group of different ones of the spatial cells over time matches any of the one or more travel patterns in the vehicle behavior profile;
in response to determining the group of the different ones of the spatial cells over time matches any of the one or more travel patterns, generate a message indicating the vehicle behavior is detected; and
in response to determining the group of the different ones of the spatial cells over time do not match any of the one or more travel patterns, deallocate memory in the hash library for geohash values corresponding to the group of the different ones of the spatial cells that do not match any of the one or more travel patterns.
10. The vehicle behavior detection system of claim 9 , wherein:
the behavior detection processor is configured to, based on the group of the different ones of the spatial cells over time matching any of the one or more travel patterns, detect multiple vehicles co-traveling based on a temporal analysis of the hash values stored in the hash library; and
wherein the message indicates the multiple of the one or more vehicles are co-traveling.
11. The vehicle behavior detection system of claim 10 , wherein the temporal analysis is performed in real-time.
12. The vehicle behavior detection system of claim 9 , wherein the hash processor nodes are assigned to the cells to distribute the hash processing.
13. The vehicle behavior detection system of claim 9 , wherein:
the behavior detection processor is configured to, based on the group of the different ones of the spatial cells over time matching any of the one or more travel patterns over a threshold length of time, initiate an additional pattern analysis in response to detecting the vehicle behavior, and to adjust a resolution of the cells for the additional pattern analysis.