Abstract
Mobility is a must for human life on this planet, because important activities like working or shopping cannot be done from home for everyone. Present modes of transports contributes significantly to green house gas emissions while the efforts to reduce these emissions can be improved in many countries. Pathways to a more sustainable form of mobility can be modelled using travel demand models to aid decision makers. However, to project human behavior into the future one should analyze the changes in the past to understand the drivers in mobility change. Mobility surveys provide sets of activity diaries, which show changes in travel behavior over time. Those activity diaries are one of the inputs in activity-based demand generation models like travel activity pattern simulation (TAPAS). This paper shows a method of using probability distributions between person and diary groups. It offers an opportunity for an increased heterogeneity in travel behavior without sacrificing too much accuracy. Additionally it will present the use case of temporal back- and forecasting of changes in activity choices of existing mobility survey data. The results show the possibilities within this approach together with its limits and pitfalls.
Highlights
Mobility is needed in any form of human society
One way to support the decision makers to forecast travel demand by travel demand generation models, which simulate the demand of mobility for a given set of parameters in a specific region
Understanding why people leave their homes, how this behavior has changed over the years and how to forecast it is crucial for the quality of travel demand modeling to show pathways to a sustainable transport system with respect to the mobility needs
Summary
Mobility is needed in any form of human society. the sectors of energy generation and food production are transforming towards sustainability for decades, the mobility sector in the industrialized countries is still heavily dependent on individual modes of transports, namely cars and trucks. One way to support the decision makers to forecast travel demand by travel demand generation models, which simulate the demand of mobility for a given set of parameters in a specific region The outcome of such models represents the reaction of the population to hypothetical introduced measures resulting in changes of trips and their length, performed activity, and used mode of transport. In a method of fore- and backcasting, we will display an opportunity to allow and reflect individual travel behavior changes over time only by changing the probabilities without a change in the used diaries or doing a complete resurvey of mobility patterns Resulting, it will be shown how well they can be projected into the future by forecasting later MiDs from the past data and the previous ones from the recent one.
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