Reports from various international organisations show that the importance of travel and transportation is growing. This is partly due to the urbanization and globalization, resulting in growing world trade and passenger traffic. Long-term investments are needed to map this increase, but also because governments cannot afford the limitations of transport to have a negative impact on the future competitiveness of their products. Traffic and transportation models can be used to make better long-term decisions.
On an international level, activity-based models have become the norm to model travel behaviour. The most important characteristic of these models is that the travel behaviour of persons or families is a product of the activities that they wish or have to perform. This means that travel is no longer seen as an isolated fact in these models, which is a great advantage in comparison to the classic models.
One such activity-based transport model is FEATHERS which makes it easy to create transport models for any study area. Being a state-of-the-art activity-based transport model, FEATHERS can easily tackle several Transport Demand Management scenario’s. In line with this, FEATHERS is also open wide for introducing extra sub-models, which can lead to even more detailed transport modeling. Being a modern transport model, FEATHERS can also be used as a starting point for integration with other models such as: emissions models, traffic safety models, electrification models…
Despite several evolutions, activity-based models are not common practice yet. However, during the last decade, activity-based transportation models have proven to lead to more realistic and policy-sound predictions. One of the advantages of these models is a more realistic description of people’s travel behaviour. Another advantage is a better understanding of people’s travel behaviour. Because of these advantages, researchers and policymakers are switching from conventional models to activity-based models.
How FEATHERS works?
2) Decision maker:
Based on information from the study area’s Data base, the Decision maker learns and represents travel behaviour that is necessary for the Scheduling engine.
3) Scheduling engine:
The core of FEATHERS. In this place activity-trips are predicted for each individual in the synthetic population.
4) Inference system:
What is FEATHERS?
FEATHERS is a highly advanced transport demand model that is able to predict trips for groups of individuals living in a certain area. However, in parallel to regular trips, FEATHERS is also able to predict individuals activities during the day in-between their regular trips. This means that for each trip and location, FEATHERS knows what activity individuals are involved in, at a particular place, at a particular moment in time and for how long. These activities, which enrich the trips during the day, open the door for the application of advanced transport demand scenarios where users of FEATHERS can achieve a multitude of answers to ever increasing transport demand related questions.
Advantages of FEATHERS
- Predicts trips and also activities of individuals
- Calculations are made with real-world data
- Easy to understand the output
- The output can be combined with other software or models
- emissions modelling to support lowering C02
- micro simulation models
- traffic safety models
- human health impact models
A model-based approach for evaluating the safety and environmental effects of traffic policy measures
The principal objective of this project concerned the development of a framework for the evaluation of traffic policy measures on:
In this framework different submodels were integrated, e.g. the activity-based transportation model FEATHERS, an agent-based traffic assignment model, and an emission, dispersion and exposure model, all into one overall model framework.
FEATHERS for the Seoul Metropolitan Area (SMA): Application and transferability
The project involved investigating the applicability of the current version of FEATHERS to the Seoul Metropolitan Area (South-Korea) and identifying relevant changes to the model that are required in order to enhance its accuracy when applied to this study area.