Venue Trip Detection via Influence Graphs
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Solution Overview
Problem
Current mapping and navigation services face challenges in accurately determining the impact of venues and points of interest on regional road traffic, as they rely on popularity and event size rather than actual traffic influence, leading to inefficiencies in traffic management and analysis.
Innovation Solution
A system that processes probe data to identify venue-related trips and computes a traffic impact score by generating an influence graph and applying weighting factors to determine the specific traffic impact of venues on road networks, enabling real-time detection and analysis of traffic congestion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If probe data is processed to identify venue-related trips and compute traffic impact scores, then traffic management precision is improved, but computational complexity increases
Solution Approach 1:
The system segments the complex task of traffic impact analysis into distinct modules: probe data processing module, trip identification module, influence graph generation module, and traffic impact score computation module. Each module handles a specific aspect of the analysis, making the overall system more manageable and efficient despite the increased computational requirements.
Solution Approach 2:
The patent introduces an influence graph as an intermediary data structure that mediates between raw probe data and final traffic impact scores. This influence graph captures the relationships between venues, trips, and road segments, allowing the system to process complex spatial and temporal dependencies in a structured manner without overwhelming computational complexity.
2Measurement precision
If weighting factors are applied to roads based on trip counts, then traffic analysis accuracy is improved, but data processing time increases
Solution Approach 1:
The system performs preliminary aggregation of trip counts and computation of weighting factors during data collection phases, before final traffic impact analysis is required. By pre-processing and storing these weighting factors in the influence graph, the system avoids redundant computations during real-time traffic monitoring, thus improving measurement accuracy without proportionally increasing processing time during critical analysis phases.
3Speed
If real-time probe data processing is implemented, then traffic monitoring responsiveness is improved, but computational resource consumption increases
Solution Approach 1:
The influence graph serves multiple functions simultaneously: it stores spatial relationships between venues and road segments, captures temporal patterns of trips, holds pre-computed weighting factors, and provides the framework for generating traffic impact scores. This multi-functionality reduces the need for separate data structures and computations, thereby improving real-time monitoring responsiveness while controlling computational resource consumption.
Data Source
AI summary
An approach is provided for detecting venue trips and related road traffic. The approach involves, for example, processing probe data to identify a trip related to the venue. The trip is traveled by a probe vehicle generating the probe data within a timeframe associated with an event occurring at the venue. The approach also involves generating an influence graph comprising one or more roads used by the probe vehicle during the trip. The approach further involves determining a traffic parameter for the one or more roads of the influence graph. The approach further involves computing a traffic impact score for the venue, the event, or a combination thereof based on the traffic parameter.