Effects of population sampling on agent-based transport simulation of on-demand services
Open access
Date
2022Type
- Conference Paper
ETH Bibliography
yes
Altmetrics
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. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000662210Publication status
publishedExternal links
Editor
Book title
The 13th International Conference on Ambient Systems, Networks and Technologies (ANT) / The 5th International Conference on Emerging Data and Industry 4.0 (EDI40)Journal / series
Procedia Computer ScienceVolume
Pages / Article No.
Publisher
ElsevierEvent
Subject
Agent-based simulations; Population sampling; MATSim; Downscaling; Shared mobilityOrganisational 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
More
Show all metadata
ETH Bibliography
yes
Altmetrics