Application of the VISEVA Demand Generation Software to Berlin Using Publicly Available Behavioral Data

Abstract

In this paper the EVA algorithm developed by Lohse is applied in order to generate Berlin’s average workday traffic based on a minimum of data input. Behavioral parameters are derived from the German travel survey “Mobilitat in Deutschland (MiD)”. The EVA approach allows generating trip purpose- and time-dependent OD matrices from general input data used in transport modeling. This model output can be used for standard OD-matrix-based static or dynamic assignment, but provides us with primary activity location choice and scheduling information necessary to generate initial conditions for agent-based transport simulation packages like MATSIM. The paper describes the basic concept of the EVA model and specifications of the Berlin scenario. Since the range of possible input data for demand generation is limited, our aim was to use the established demand generation model VISEVA with a minimum of input data, which has to be commonly available and easy to purchase (making transfer of transport models to other study areas easier). The model output is displayed and compared with output resulting from Berlin’s official demand generation model. Besides that, the simulation results are compared to real-world data from traffic counts. It can be shown that even though we reduce data requirements to a minimum, the results have a structure adequate for Berlin and could serve as input for initial condition generation for MATSIM.

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