Effects of population sampling on agent-based transport simulation of on-demand services
OPEN ACCESS
Loading...
Author / Producer
Date
2022
Publication Type
Conference Paper
ETH Bibliography
yes
Citations
Altmetric
OPEN ACCESS
Data
Abstract
Large-scale agent-based simulations require higher computing resources than are usually available. Consequently, many applications rely on downscaling, that is, simulating with smaller population samples in which the results are then scaled. Existing studies have shown a need to investigate the impact of downscaling on the output statistics of such simulations. Downscaling is a common practice in transport modeling. In this study, we investigate the impacts of population downscaling on a ride-sharing service with a focus on vehicle occupancy and wait time, travel time and detour time. Our findings reveal that if transport modelers want to model on-demand services with ride sharing, it is strongly recommended to use a 100% population, or when using a smaller population sample, to estimate the relative biases of their desired metrics compared to the results of a 100% population in order for their results to be applicable for real-world situations.
Permanent link
Publication status
published
External links
Editor
Book title
Journal / series
Volume
201
Pages / Article No.
305 - 312
Publisher
Elsevier
Event
13th International Conference on Ambient Systems, Networks and Technologies (ANT 2022)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Agent-based simulations; Population sampling; MATSim; Downscaling; Shared mobility
Organisational unit
03521 - Axhausen, Kay W. (emeritus) / Axhausen, Kay W. (emeritus)
02655 - Netzwerk Stadt u. Landschaft ARCH u BAUG / Network City and Landscape ARCH and BAUG