Dynamic Trip Ranking via Latent Preference Modeling
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Solution Overview
Problem
Current trip planning systems for public transportation fail to accurately reflect real-world passenger preferences and time-dependent variations, often suggesting routes that do not align with actual traveler choices due to static assumptions about optimal routes and preferences.
Innovation Solution
A computer-implemented method and system that utilizes dynamic latent variables to model passenger preferences, converting actual trips into pair-wise preferences and applying a ranking function to re-rank trip suggestions based on time-dependent modeling, incorporating sequences of services and change points matrices through non-negative matrix factorization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static trip planning algorithms are used to determine routes, then computational simplicity is maintained, but accuracy in reflecting real passenger preferences deteriorates
Solution Approach 1:
The patent transforms static trip planning into a dynamic system by introducing time-dependent latent variables that capture passenger preferences at different times. The planning algorithm now adapts to temporal variations in passenger behavior, making the system dynamic rather than static, which resolves the contradiction between simplicity and accuracy.
Solution Approach 2:
The patent changes the parameters of the trip planning system by introducing time-dependent parameters and latent variables that represent passenger preferences. These parameters vary over time and are learned from historical data, allowing the system to accurately reflect real passenger preferences while maintaining computational tractability through parameterized models.
2Measurement precision
If time-dependent passenger preferences are incorporated into trip planning, then recommendation accuracy improves, but computational complexity increases
Solution Approach 1:
The patent manages computational complexity by parameterizing time-dependent preferences into latent variables with manageable dimensions. Instead of modeling all possible preferences explicitly, the system uses a compact parameter representation that can be efficiently computed and updated, balancing accuracy with computational feasibility.
Solution Approach 2:
The patent applies local quality by focusing computational resources on learning and modeling only the most relevant time-dependent preference patterns rather than all possible variations. The system identifies and models key temporal patterns in passenger behavior, applying complexity only where it provides the most value for recommendation accuracy.
3Reliability
If static assumptions about optimal routes are made, then algorithm speed is maintained, but relevance to actual traveler choices deteriorates
Solution Approach 1:
The patent replaces static route optimization with dynamic route planning that adapts to time-dependent passenger preferences. The system computes routes based on preferences that vary over time, maintaining computational efficiency through dynamic programming and cached computations while significantly improving relevance to actual traveler choices.
Solution Approach 2:
The patent performs preliminary computation of time-dependent preference patterns and caches results for efficient retrieval during trip planning. By pre-computing and storing learned preference models, the system maintains fast response times during actual use while incorporating complex time-dependent preferences, thus preserving productivity while improving reliability.
Data Source
AI summary
A method and system are disclosed for generating a list of trips on an associated transportation network, the list ranked in accordance with time-dependent modeling of passenger preferences. User preferences of choosing a specific public transportation service or change point are modeled by a set of latent variables. Any actual trip on the network is converted into a set of pairwise preferences implicitly made by the passenger during the trip. Sequences of services matrices and change points matrices from the retrieved set of trips and non-negative factorization of the services and change points matrices is performed to smooth the matrices. The set of pairwise preferences are used to learn a ranking function and the output of a journey planner is re-ranked using the ranking function.