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Method and Apparatus to Recover the Untrusted Trajectory Dynamically

IP.com Disclosure Number: IPCOM000234133D
Publication Date: 2014-Jan-14
Document File: 4 page(s) / 282K

Publishing Venue

The IP.com Prior Art Database

Abstract

Global Positioning System (GPS) is now becoming one of major data sources for moving object information collection because of its high accuracy and easy availability. It could be used to collect moving object data all day and all weather.GPS data is widely used in Intelligent Transportation System (ITS) and Connected Vehicle (CV) fields and it is the foundation of trajectory analysis, moving pattern extraction, traffic state estimation, traffic flow guidance and so on.The accuracy, coverage and integrity of GPS data have a big impact on the analysis results. However, GPS signals often drift or even lose due to the obstructions from buildings, tunnels and trees etc in city. Therefore, it is quite important and necessary to recover the GPS trajectory data accurately.

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Method and Apparatus to Recover the Untrusted Trajectory Dynamically

Global Positioning System (GPS) is now becoming one of major data sources for moving object information collection because of its high accuracy and easy availability. It could be used to collect moving object data all day and all weather.

GPS data is widely used in Intelligent Transportation System (ITS) and Connected Vehicle (CV) fields and it is the foundation of trajectory analysis, moving pattern extraction, traffic state estimation, traffic flow guidance and so on.

The accuracy, coverage and integrity of GPS data have a big impact on the analysis results. However, GPS signals often lose due to the obstructions from buildings, tunnels and trees etc in city. Therefore, it is quite important and necessary to recover the GPS trajectory data accurately.

This disclosure proposes a Method and Apparatus leverage real-time traffic state and position excursion feature to recover the lost GPS trajectory dynamically. Leverage the real-time traffic state of the road network to estimate the most reasonable driving path dynamically.

Personalized historical driving pattern is involved to refine average travel speed.

Take time interval of lost trajectory and position excursion features of each road-segment as the major decision objectives.

Key Findings

Key Findings:

:


1) The traveling route of the vehicle usually tends to vary with changes of real-time road condition;


2) The vehicle on a road-segment usually tends to follow the general traffic flow. That means the travel time of a specific vehicle spending on a road segment basically equal to the average travel time of vehicles on the segments during this period.


3) The travel time of a specific driver could be corrected by considering personalized historical driving pattern


4) The position excursion of GPS signal...