Project Details
Projekt
Factors influencing the quality of macroscopic travel demand models in person transport
Subject Area
Traffic and Transport Systems, Intelligent and Automated Traffic
Term
from 2015 to 2019
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 262231088
Travel demand models are an important tool of transport planning serving the preparation of transport policy decisions. They should appropriately replicate the reality in quantitative terms as well as with regard to the transport processes and the interdependencies. For assessing the quality of a travel demand model the modeling practice most commonly compares observed and modeled traffic volumes at selected cross sections. One the one hand this results from the fact that traffic volumes can be easily surveyed. On the other hand traffic volumes represent the decisive value for many planning tasks and are employed by decision makers to determine the impacts of a specific state. Problems resulting from this are rarely discussed. Increasingly also travel times and travel distance distributions as well as distance dependent modal split shares are considered for the validation of travel demand models. However, principal statements how input data and modeling assumptions influence the model quality are not yet available. Objective of this project is to identify the factors influencing the quality of macroscopic travel demand models for person transport and to quantify the importance of selected factors. The model quality is influenced by the precision of the input data (land use data, network data, behavioral data), by the segmentation of the transport supply (level of detail of the network) and the travel demand (traffic analysis zones, person groups, trip purposes, transport modes), by the assumptions for the modeling of the decision processes of travelers as well as by the numerical exactness of the model. In order to assess the quality of travel demand models quality measures referred to in literature are classified and evaluated. The quality measures are then applied to macroscopic travel demand models commonly used in practice. Selected factors are varied systematically in order to derive statements on the importance of these factors. To measure the quality of a model reference data are required revealing the actual behavior of travelers in a representative manner. For this geocoded data from household surveys are used and data from a virtual reality are generated. As a result statements on the influencing factors and recommendations for measuring and ensuring the quality of travel demand models are issued offering impulses for further research in the field of travel demand modeling and advice for the practice of transport modeling.
DFG Programme
Research Grants
