Fleet Route Risk Profiling for Safer Vehicle Event Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current fleet management systems lack an effective method to create and utilize risk profiles that characterize the likelihood of vehicle events, such as speeding and collisions, to optimize route selection based on specific locations, vehicle types, and event types, leading to inefficient route planning and increased risk exposure.
Innovation Solution
A system and method that aggregate vehicle event information to create risk profiles specific to locations, vehicle types, and event types, determining route likelihoods and selecting the safest route by processing this data to provide optimized route recommendations to vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional fleet management systems are used for route planning, then route selection is simple and fast, but the likelihood of vehicle events increases due to lack of risk assessment
Solution Approach 1:
The risk profile is segmented into multiple dimensions including location-specific risk, vehicle-type-specific risk, and event-type-specific risk. Each dimension is analyzed separately and then integrated to provide comprehensive route safety assessment, allowing the system to manage complexity through structured decomposition of risk factors
Solution Approach 2:
The system performs preliminary aggregation of vehicle event data to create risk profiles before route selection is needed. By pre-processing historical event data and establishing risk characteristics in advance, the system enables faster real-time route decision-making without compromising safety assessment quality
2Reliability
If risk profiles are created and used for route optimization, then the likelihood of vehicle events decreases, but processing time and computational resources increase
Solution Approach 1:
The system aggregates vehicle event data and creates risk profiles in advance, before real-time route selection is required. This pre-processing approach stores processed risk information that can be quickly retrieved and applied during route determination, reducing real-time computational burden while maintaining comprehensive safety assessment
Solution Approach 2:
The system transforms raw vehicle event data into standardized risk profile parameters that characterize likelihood of events across different locations, vehicle types, and event types. This parameter transformation enables efficient comparison and evaluation of multiple routes without reprocessing raw data, balancing computational effort with safety assessment quality
Data Source
AI summary
Systems and methods for creating and using risk profiles for management of a fleet of vehicles are disclosed. The risk profiles characterize values representing likelihoods of occurrences of vehicle events, based on previously detected vehicle events. Exemplary implementations may: obtain vehicle event information for vehicle events that have been detected by the fleet of vehicles; aggregate the vehicle event information for multiple ones of the events to create one or more of a first risk profile, a second risk profile and/or a third risk profile; obtain a point of origin for and a target destination of a particular vehicle; determine a set of routes from the point of origin to the target destination; determine individual values representing likelihoods of occurrences of vehicle events along individual routes in the set of routes; select the first route from the set of routes; and provide the selected first route to the particular vehicle.