.png){kind=icon}
Return
Transit performance assessment based on graph analytics
.png){kind=icon}
PRE
AI
DOI：10.1080/23249935.2019.1596991.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
GPS-equipped public transit vehicles generate a massive amount of location information, yet analytical methods based on Geographic Information System and Relational Database Management Systems are limited in their ability to handle these data for transit performance assessment. Graph analytics approach appears well suited for addressing these limitations; however, existing graph data models that have been used to represent the transit network do not provide the flexibility to incorporate mobility context from Automatic Vehicle Location (AVL) feeds with the geographic context of the network. This research work presents a new graph model that accounts for the mobility and geographical contexts of transit networks yet capable of processing a large volume of AVL data feeds for transit performance assessment. The efficacy of the proposed graph model and analytics method has been demonstrated in using simple graph queries to retrieve operational-level performance indicators such as schedule adherence, bus stops and routes activity levels.
Keywords:
Transit performance
graph data model
graph metrics
graph analytics
AVL data feeds
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.1
Papers:
939
Citations:
2.2K
Organization
Cited Papers
ShareSaveShareSaveApplications of Graph Theory and Network Science to Transit Network Design.png)
TRANSPORT REVIEWS
TRANSPORT REVIEWSIF9.9
PREAI
ShareSave
ShareSaveShareSaveShareSaveShareSave
View more