Negative effects of traffic, like congestion, air and noise pollution are among the reasons why environmentally friendly solutions are promoted. Bike sharing (bs) is intended to strengthen cycling and public transport. Nevertheless, current transport models rarely consider cycling or even bs, in either detail or holistically. In this paper we present an agent based approach to model cycling and in particular bs within the multimodal simulation environment MATSim. Multimodal trips combining public transport and bs are included as well as within day rescheduling of bs trips as agents may not find a bike or empty return space (parking spot). To minimize such cases, choice probabilities were implemented, so that agents only start their bs trip, if sufficient bikes or parking spots are available. The modules presented in this paper were applied using a MATSim model of the city of Vienna. Agent based bs modelling is an inexpensive option to test the impact of a bike sharing system before implementation.
This paper presents a step-by-step method to generate a synthetic population for agent-based transport modelling as input to MATSim software, which requires an activity chain for each agent. We make use of high spatial resolution statistical raster (250 m) census data, applying all calculations at this scale. Due to the small sample, size of travel survey data an Iterative Proportional Fitting method is not suitable. Therefore, we devise a method utilizing Bayesian networks, maximum likelihood and Markov Chain Monte Carlo simulation to reproduce attribute distribution and fit to raster margins. Stratified sampling along households is employed to generate activity chains for the synthetic population.
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