| Popis: |
Agent-based transport simulation models are a particularly useful tool to analyze demand-oriented transport policies and new mobility services, which have both gained significant attention lately. Since travel diaries, a traditional source to create the transport demand in agent-based transport models, are often hard to procure and not policy-sensitive, alternative approaches to creating travel demand representations for simulation scenarios are sought. In this study, a particularly efficient approach based on Big Data and a new, aspatial activity-based demand model with comparatively low input data requirements is established. Home, work, and education locations are informed based on mobile-phone-based origin-destination matrices. Other activity locations are modeled within the scope of the coevolutionary algorithm of the agent-based transport model, which is also responsible for finding suitable travel options of the modeled individuals. As a result, a comparatively lightweight process chain to create an agent-based transport simulation scenario is established, which is transferable to other regions. A basic quality evaluation of the created tool chain is carried out against a well-validated transport simulation model of the same region. c 2020 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 23rd EURO Working Group on Transportation Meeting. The authors thank Tim Hilgert for discussions on the actiTopp model. DZ thanks Luk Knapen, Tom Bellemans, and Bruno Kochan for discussion on the interaction of activity scheduling and transport simulations models during repeated research stays at IMOB. The authors acknowledge funding by the National Research Programme Big Data (NRP 75) of the Swiss National Science Foundation and thank Swisscom for providing mobile-phone-based OD matrices as well as the Swiss Bundesamt f¨ur Statsitik for granting access to the STATPOP census dataset. |
