Automating synthetic trip data generation for an agent-based simulation of urban mobility
(2019) In Master’s Thesis in Geographical Information Science GISM01 20191Dept of Physical Geography and Ecosystem Science
- Abstract
- This paper explores the use of an automated pipeline to construct synthetic (artificially derived) trip data from aggregate socio-demographic sources to build a simulation of individual vehicles interacting with one another.
The study shows that quality data sources are required in order to do this effectively and accurately. It is shown that aspects of typical patterns and behaviours may still not be represented within the final simulation. It is often a complex, expensive and impractical exercise to obtain in situ measurements across an entire city to build simulation scenarios to help with effective planning and understanding of emissions at a fine resolution.
Since road traffic is a major source of harmful pollutant emissions, we... (More) - This paper explores the use of an automated pipeline to construct synthetic (artificially derived) trip data from aggregate socio-demographic sources to build a simulation of individual vehicles interacting with one another.
The study shows that quality data sources are required in order to do this effectively and accurately. It is shown that aspects of typical patterns and behaviours may still not be represented within the final simulation. It is often a complex, expensive and impractical exercise to obtain in situ measurements across an entire city to build simulation scenarios to help with effective planning and understanding of emissions at a fine resolution.
Since road traffic is a major source of harmful pollutant emissions, we explore the use of the simulation to generate emission outputs at a per vehicle level through simulation time.
The paper concludes that although a valid simulation scenario can be constructed from the derived synthetic dataset, new techniques need to be developed in order to obtain an equilibrium in the simulation to allow it to not only behaves as a valid urban mobility scenario but can also be calibrated to align to represent reality. (Less) - Popular Abstract
- We wanted to find out if a simulation could be built without field measurements that closely simulates real conditions using trips derived from aggregate population-level statistics. If this is possible, there are many benefits to being able to construct simulations of urban mobility that are not dependent on the installation of complicated equipment or the coordination of complex field surveys.
The focus of this paper is on creating a model that is able to generate this artificial trip data along with exploring techniques that can be used to evaluate the simulation’s outputs and behaviours. The study shows that quality data sources are required in order to do this effectively and accurately. It is shown that aspects of typical... (More) - We wanted to find out if a simulation could be built without field measurements that closely simulates real conditions using trips derived from aggregate population-level statistics. If this is possible, there are many benefits to being able to construct simulations of urban mobility that are not dependent on the installation of complicated equipment or the coordination of complex field surveys.
The focus of this paper is on creating a model that is able to generate this artificial trip data along with exploring techniques that can be used to evaluate the simulation’s outputs and behaviours. The study shows that quality data sources are required in order to do this effectively and accurately. It is shown that aspects of typical patterns and behaviours may still not be represented within the final simulation.
Since road traffic is a major source of harmful pollutant emissions, we explore the use of the simulation to evaluate emissions at an individual vehicle level.
The paper concludes that although a valid urban mobility scenario can be constructed, new techniques need to be developed to allow the simulation to align as close as possible to real expected traffic conditions for a given study area. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8991075
- author
- Dos Santos, Paul LU
- supervisor
- organization
- alternative title
- Automating the creation of artificial trip data for a simulation of urban mobility
- course
- GISM01 20191
- year
- 2019
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Geography, GIS, Agent-based Modelling, Simulation Science, Network Modelling, Simulation of Urban Mobility, Emissions, Environmental Modelling
- publication/series
- Master’s Thesis in Geographical Information Science
- report number
- 108
- language
- English
- id
- 8991075
- date added to LUP
- 2019-07-29 14:31:09
- date last changed
- 2019-07-29 14:31:09
@misc{8991075,
abstract = {{This paper explores the use of an automated pipeline to construct synthetic (artificially derived) trip data from aggregate socio-demographic sources to build a simulation of individual vehicles interacting with one another.
The study shows that quality data sources are required in order to do this effectively and accurately. It is shown that aspects of typical patterns and behaviours may still not be represented within the final simulation. It is often a complex, expensive and impractical exercise to obtain in situ measurements across an entire city to build simulation scenarios to help with effective planning and understanding of emissions at a fine resolution.
Since road traffic is a major source of harmful pollutant emissions, we explore the use of the simulation to generate emission outputs at a per vehicle level through simulation time.
The paper concludes that although a valid simulation scenario can be constructed from the derived synthetic dataset, new techniques need to be developed in order to obtain an equilibrium in the simulation to allow it to not only behaves as a valid urban mobility scenario but can also be calibrated to align to represent reality.}},
author = {{Dos Santos, Paul}},
language = {{eng}},
note = {{Student Paper}},
series = {{Master’s Thesis in Geographical Information Science}},
title = {{Automating synthetic trip data generation for an agent-based simulation of urban mobility}},
year = {{2019}},
}