Simulating Annual Long-Distance Travel Demand
OPEN ACCESS
Author / Producer
Date
2019
Publication Type
Doctoral Thesis
ETH Bibliography
yes
Citations
Altmetric
OPEN ACCESS
Data
Rights / License
Abstract
Research in the field of transport planning focuses today on urban mobility and, thus, on daily activities. Hence, most of the surveys, models and simulations aim to add valuable contributions to this topic. However, long-distance travel is growing, because travelling has become more accessible and cheaper recently. Nowadays, almost half of all vehicle miles travelled are generated by long-distance travel. Therefore, it is crucial to transport planners and policy makers. Nevertheless, so far only few surveys or models cover this field. One of the reasons is the challenging task to capture data describing long-distance travel. This thesis shows that big data can potentially help to overcome this burden using GSM data. Due to different challenges there has been also no effort to develop an agent-based simulation for long-distance travel. The major obstacle is the time horizon that such a simulation has to cover. Long-distance travel can not be analyzed focusing on a single day, because long-distance journeys usually consume more time and are also planned well in advance. This thesis introduces an agent-based simulation covering long-distance and long-term travel demand and, thereby, closes the research gap described above. Furthermore, the thesis demonstrates that the simulation presented can adequately simulate large scale scenarios in reasonable time. The main value of such a simulation is support of policy makers for big infrastructural investments such as new bridges, tunnels or airports.
Permanent link
Publication status
published
External links
Editor
Contributors
Examiner: Axhausen, Kay W.
Examiner : Arentze, Theo A.
Book title
Journal / series
Volume
Pages / Article No.
Publisher
ETH Zurich
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Long-distance travel demand; agent-based modelling; Annual transport performance; C-TAP; Big Data; Continuous activity generation and scheduling
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
Notes
Funding
165900 - Long Distance Travel Demand Simulation (SNF)