Modelling the travel time of transit vehicles in real‐time through a GTFS‐based road network using GPS vehicle locations. (4th July 2020)
- Record Type:
- Journal Article
- Title:
- Modelling the travel time of transit vehicles in real‐time through a GTFS‐based road network using GPS vehicle locations. (4th July 2020)
- Main Title:
- Modelling the travel time of transit vehicles in real‐time through a GTFS‐based road network using GPS vehicle locations
- Authors:
- Elliott, Tom
Lumley, Thomas - Abstract:
- Summary: Predicting the arrival time of a transit vehicle involves not only knowledge of its current position and schedule adherence, but also traffic conditions along the remainder of the route. Road networks are dynamic and can quickly change from free‐flowing to highly congested, which impacts the arrival time of transit vehicles, particularly buses which often share the road with other vehicles, so reliable predictions need to account for real‐time and future traffic conditions. The first step in this process is to construct a framework with which road state (traffic conditions) can be estimated using real‐time transit vehicle position data. Our proposed framework implements a vehicle model using a particle filter to estimate road travel times, which are used in a second model to estimate real‐time traffic conditions. Although development and testing took place in Auckland, New Zealand, we generalised each component to make the framework compatible with other public transport systems around the world. We demonstrate the real‐time feasibility and performance of our approach in real‐time, where a combination of R and C++ was used to obtain the necessary performance results. Future work will use these estimated traffic conditions in combination with historical data to obtain reliable arrival time predictions of transit vehicles. Abstract : We present a framework for modelling transit vehicles and estimating traffic conditions in real‐time based solely on GTFS data, with anSummary: Predicting the arrival time of a transit vehicle involves not only knowledge of its current position and schedule adherence, but also traffic conditions along the remainder of the route. Road networks are dynamic and can quickly change from free‐flowing to highly congested, which impacts the arrival time of transit vehicles, particularly buses which often share the road with other vehicles, so reliable predictions need to account for real‐time and future traffic conditions. The first step in this process is to construct a framework with which road state (traffic conditions) can be estimated using real‐time transit vehicle position data. Our proposed framework implements a vehicle model using a particle filter to estimate road travel times, which are used in a second model to estimate real‐time traffic conditions. Although development and testing took place in Auckland, New Zealand, we generalised each component to make the framework compatible with other public transport systems around the world. We demonstrate the real‐time feasibility and performance of our approach in real‐time, where a combination of R and C++ was used to obtain the necessary performance results. Future work will use these estimated traffic conditions in combination with historical data to obtain reliable arrival time predictions of transit vehicles. Abstract : We present a framework for modelling transit vehicles and estimating traffic conditions in real‐time based solely on GTFS data, with an emphasis on the real‐time feasibility of the application. … (more)
- Is Part Of:
- Australian & New Zealand journal of statistics. Volume 62:Number 2(2020)
- Journal:
- Australian & New Zealand journal of statistics
- Issue:
- Volume 62:Number 2(2020)
- Issue Display:
- Volume 62, Issue 2 (2020)
- Year:
- 2020
- Volume:
- 62
- Issue:
- 2
- Issue Sort Value:
- 2020-0062-0002-0000
- Page Start:
- 153
- Page End:
- 167
- Publication Date:
- 2020-07-04
- Subjects:
- applications -- GTFS -- particle filter -- transit modelling -- transit networks
Statistics -- Periodicals
519.5 - Journal URLs:
- http://www.blackwellpublishers.co.uk/asp/journal.asp?ref=1369-1473 ↗
http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1467-842X ↗
http://onlinelibrary.wiley.com/ ↗ - DOI:
- 10.1111/anzs.12294 ↗
- Languages:
- English
- ISSNs:
- 1369-1473
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 1796.898000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 13673.xml