A Historical‐Trajectories‐Based Map Matching Algorithm for Container Positioning and Tracking

Positioning and tracking of containers is becoming an urgent demand of container transportation. Map matching algorithms have been widely applied to correct positioning errors. Because container trajectories have the characteristics of low sampling rate and missing GPS points, existing map matching...

Full description

Bibliographic Details
Main Authors: Gao, C. (Author), Li, W. (Author), Zhang, W. (Author)
Format: Article
Language:English
Published: MDPI 2022
Subjects:
Online Access:View Fulltext in Publisher
LEADER 02494nam a2200361Ia 4500
001 10-3390-s22083057
008 220425s2022 CNT 000 0 und d
020 |a 14248220 (ISSN) 
245 1 0 |a A Historical‐Trajectories‐Based Map Matching Algorithm for Container Positioning and Tracking 
260 0 |b MDPI  |c 2022 
856 |z View Fulltext in Publisher  |u https://doi.org/10.3390/s22083057 
520 3 |a Positioning and tracking of containers is becoming an urgent demand of container transportation. Map matching algorithms have been widely applied to correct positioning errors. Because container trajectories have the characteristics of low sampling rate and missing GPS points, existing map matching algorithms based on the shortest path principle are not applicable for container positioning and tracking. To solve this problem, a historical‐trajectories‐based map matching algorithm (HTMM) is proposed. HTMM mines the travel time and the frequency in historical trajectories to help find the local path between two adjacent candidate points. HTMM first presents a path re-construction method to calculate the travel time of historical trajectories on each road segment. A historical path index library based on a path tree is then developed to efficiently index historical paths. In addition, a location query and tracking method is introduced to determine the location of containers at given time. The performance of HTMM is validated on a real freight trajectory dataset. The experimental results show that HTMM has more than 3% and 5% improvement over the ST‐ Matching algorithm and HMM‐based algorithm, respectively, at 60–300 s sampling intervals. The positioning error is reduced by half at a 60 s sampling interval. © 2022 by the authors. Licensee MDPI, Basel, Switzerland. 
650 0 4 |a container positioning and tracking 
650 0 4 |a Container positioning and tracking 
650 0 4 |a Containers 
650 0 4 |a historical trajectories 
650 0 4 |a Historical trajectory 
650 0 4 |a map matching 
650 0 4 |a Map matching 
650 0 4 |a Map-matching algorithm 
650 0 4 |a path reconstruction 
650 0 4 |a Path reconstruction 
650 0 4 |a Positioning and tracking 
650 0 4 |a Positioning error 
650 0 4 |a Sampling interval 
650 0 4 |a Trajectories 
650 0 4 |a Trajectory-based 
650 0 4 |a Travel time 
650 0 4 |a Travel-time 
700 1 |a Gao, C.  |e author 
700 1 |a Li, W.  |e author 
700 1 |a Zhang, W.  |e author 
773 |t Sensors