Route Search Encoding via RNN for On-Demand Matching
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Solution Overview
Problem
Existing systems for on-demand services, such as taxi hailing, face challenges in quickly and efficiently identifying routes similar to a target route, which is crucial for matching service requests and improving service efficiency.
Innovation Solution
A system utilizing a Recurrent Neural Network (RNN) model to encode route information into a code of predetermined length, allowing similar routes to be encoded into the same code, enabling rapid identification and retrieval of matching routes from a database.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional route matching methods are used, then route similarity can be identified, but the searching speed and efficiency are insufficient
Solution Approach 1:
The patent applies preliminary action by pre-processing route information and encoding it into fixed-length codes before actual route matching occurs. The RNN model is trained in advance to learn route patterns, and the encoding process transforms variable-length route data into standardized representations that can be quickly compared during real-time matching, significantly reducing the time required for route similarity identification.
Solution Approach 2:
The patent transforms route information from its original variable-length format into fixed-length codes through the RNN encoding process. This parameter change in data representation allows for efficient storage and rapid comparison operations, as the fixed-length codes can be processed uniformly without requiring complex alignment or variable-length handling, thereby improving searching efficiency.
2Measurement precision
If detailed route information is stored and compared, then route similarity accuracy is improved, but the complexity of the system increases
Solution Approach 1:
The patent replaces complex mechanical comparison operations with a neural network-based encoding system. Instead of directly comparing detailed route information using traditional algorithms, the RNN model automatically extracts features and generates compact codes that capture essential route characteristics. This substitution simplifies the matching process while maintaining accuracy, as the neural network handles the complexity of detailed comparison internally.
Solution Approach 2:
The patent creates simplified copies of route information in the form of fixed-length codes generated by the RNN model. These codes serve as compressed representations that retain the essential characteristics needed for similarity determination while discarding redundant details. The copying process transforms complex route data into manageable formats that can be efficiently stored and compared without losing critical similarity information.
3Speed
If variable-length route codes are used, then route information flexibility is maintained, but the speed of code comparison and retrieval decreases
Solution Approach 1:
The patent applies preliminary action by transforming variable-length route data into fixed-length codes before the comparison operation. The RNN model processes the entire route information sequence and compresses it into a standardized fixed-length representation in advance, enabling rapid comparison without requiring runtime adaptation to different length formats. This pre-processing step maintains the flexibility of accepting variable-length input while ensuring fast comparison speed.
Solution Approach 2:
The patent segments the variable-length route information into fixed-size code units through the RNN encoding process. By dividing and compressing the route data into standardized fixed-length segments, the system achieves both speed and flexibility: the segmentation allows for efficient batch processing and uniform comparison operations, while the RNN model ensures that essential route characteristics are preserved in each fixed-length segment.
Data Source
AI summary
The present disclosure relates to systems and methods for searching for a route. The systems may perform the methods to obtain route information of a first route; encode the route information of the first route into a first code based on a target model; access a target database in at least one storage medium, wherein the target database includes a plurality of candidate codes encoded through the target model from a plurality of candidate routes; identify, from the plurality of candidate codes, a second code based on the first code, the second code being associated with at least one second route; and send information associated with the at least one second route to a receiving device.