Geospatial Descriptor Generation for Rideshare Rendezvous
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Solution Overview
Problem
Conventional transportation management systems face challenges in facilitating timely and efficient rendezvous between ride requestors and providers due to insufficient basic location information, leading to difficulties in finding each other at pick-up locations, especially when traditional location markers are unclear or hard to read.
Innovation Solution
A transportation management system employs machine learning to generate human-understandable geospatial descriptors for specified pick-up locations, using reference expressions extracted from text communications and encoded map data to provide more expressive and specific location information, assisting ride requestors and providers in finding each other.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If basic location information (place name, street address, map pin) is provided to ride providers, then the system maintains simplicity in information provision, but ride requestors and providers have difficulty finding each other at pick-up locations
Solution Approach 1:
The system transforms location information from basic parameters (place name, address, coordinates) to enriched descriptors that include human-visible features, spatial relationships, and contextual details. This parameter enrichment makes location information more discriminative and easier to locate, directly resolving the contradiction between simplicity and effectiveness.
Solution Approach 2:
The system introduces an intermediary processing layer (machine learning model) that generates human-understandable geospatial descriptors. This intermediary translates basic location data into rich, context-aware descriptions that bridge the gap between simple information provision and effective rendezvous, without requiring manual input from users.
2Loss of information
If ride requestors and providers communicate via text messages or phone calls to exchange additional location information, then more detailed location descriptions can be provided, but the system complexity and communication overhead increase
Solution Approach 1:
The system implements self-service by automatically generating comprehensive geospatial descriptors without requiring user communication. The machine learning model autonomously enriches location information with relevant contextual details, eliminating the need for requestors and providers to exchange additional information through texts or calls.
Solution Approach 2:
The system performs preliminary enrichment of location information before it is presented to users. By pre-generating detailed geospatial descriptors with human-visible features and spatial relationships, the system ensures complete location information is available upfront, eliminating the need for subsequent communication to clarify location details.
3Ease of manufacture
If traditional location markers are used at pick-up locations, then the system maintains simplicity in implementation, but the markers are unclear or hard to read in many cases
Solution Approach 1:
The system changes the parameters of location markers from simple, static indicators to rich, context-aware descriptors that include human-visible features, spatial relationships, and environmental context. This transformation makes markers more detectable and interpretable without complicating the underlying system architecture.
Solution Approach 2:
The system adds another dimension to location information by incorporating human-visible features and spatial relationships beyond basic coordinates and names. This dimensional enrichment provides multiple cues for identification, making location markers clearer and easier to detect from different perspectives and contexts.
Data Source
AI summary
A disclosed method may include receiving geographic coordinates of a location at which two parties are to rendezvous, generating a human-understandable geospatial descriptor for the request location, and sending the descriptor to respective devices of the two parties for presentation to the two parties. Generating the human-understandable geospatial descriptor may include identifying a human-visible feature in the vicinity of the request location that is labeled within available map data, selecting, based on a descriptor generation model, a reference expression relative to the identified feature, and applying a grammar-based constructor to the label and the selected reference expression to form the human-understandable geospatial descriptor. The model may be tuned using machine learning. The two parties may include a ride requestor and a ride provider in a ridesharing service. The identified feature may be a point of interest, landmark, street name, intersection, marker, or structure.