Comparing Implementation Strategies of Station-Based Bike Sharing in Agent-Based Travel Demand Models

Abstract

Shared mobility solutions such as bike sharing services play a key role to reduce greenhouse gas emissions in urban areas. In this paper, we present an approach to model station-based bike sharing in the multi-modal agent-based travel demand model mobiTopp. We compare different implementations of how agents choose their bike pick-up and drop-off stations. In addition to two variations of distance minimization, we also present a gravity approach to represent the reliability of a system. By also comparing different behavioral attitudes of the agents towards walking, a total of six scenarios were implemented and tested. The presented approach allows to easily test scenarios with a varying number of bikes and stations. We apply our algorithm to a model for the city of Hamburg, Germany, where the mobility behavior of a total of 1.9 million agents is modeled. Our simulations show plausible results. The average distances, utilization shares of each station, and other parameters match with values from the actual service. While the different strategies result in significantly different access times, and provide further new valuable insights and options for parameterization, differences in resulting demand are small. Overall, this model provides new methods to simulate bike sharing in travel demand models, thus helps to simulate an important mode of transport of the future.