Bikesharing systems are becoming popular all over the world. One of the remaining problems is that the rides are not uniformly distributed between stations and that certain stations fill up or empty over time. These empty and full stations lead to demand for bikes and return boxes (docks) that cannot be fulfilled; the situation leads to unsatisfied and possibly even lost customers. To avoid this situation, the provider redistributes bikes in the system. Although redistribution of bikes in such systems is well studied, the underlying demand has not yet been modeled to serve as an input to improve the redistribution. For this gap to be closed, demand for bikes and return boxes was modeled with data from the bikesharing system Citybike Wien in Vienna, Austria. In particular, the influence of weather and full or empty neighboring stations on demand was studied by using different count models. Furthermore, historic demand was used to show that forecasts from the model had improved. Last, the influence of new stations on the model parameters of a station and resultant structural breaks in the model were discussed.

A first step is presented toward a simulation tool for shared space zones, including all three prevailing individual modes of transport: cars, bicycles, and walking. Unlike on conventional roads, the behavior in shared spaces cannot be modeled by following a predefined path and strictly obeying traffic rules because the architectural design allows for many more degrees of freedom. Therefore, the research focused on two main aspects: finding a path for each individual and handling potential conflicts with other individuals. A simulation tool is needed because many urban planners see shared space as a modern design concept for busy urban roads. A growing number of cities are interested in experimenting with shared space zones but are uncertain about safety issues and the effectiveness of the design. Although mature simulation tools exist for conventional road designs, no such tool is available for shared space designs because of the added degrees of freedom in movement and more-complex social interactions. To tackle these problems, an infrastructure model was created to help all agents find a path to their destinations. A separate system for handling conflict detects when two agents, following their individual paths, might collide. Game theory is used to resolve these conflicts by maximizing a utility function for different strategies. First results give a preliminary assessment of the functionality of the proposed simulation model for shared space zones and its calibration that uses real trajectories from an existing shared space.

Over the last years multiple variations of the Social Force model have been proposed. While most of the available force-based models are calibrated on observed human movement data, validation for investigating the model characteristics, e.g. variance in parameter values, is still sparse. We present a novel methodology for validating Social Force based models which investigates the reproducibility of human movement behavior on the individual trajectory level with real-world movement data. Our approach estimates model parameter values and their distribution with non-linear regression on observed trajectory data, where the resulting variances of the parameter values represent the model's validity. We demonstrate our approach on a comprehensive (235 pedestrians) and highly accurate (within a few centimeters) set of human movement trajectories obtained from real-world pedestrian traffic with bidirectional flow using an automatic people tracking approach based on Kinect sensors. We validate the Social Force model of Helbing and Molnár (1995), Helbing and Johansson (2009) and Rudloff et al. (2011)

Several models for simulation of pedestrian movement have been proposed in recent decades. These models are primarily used in the planning and evaluation of large pedestrian infrastructures, such as transportation hubs, with a focus to increase comfort and safety for pedestrians. Although the number of proposed simulation models is increasing at a fast pace, not much is known about the properties of calibration procedures or the transferability of the models estimated in one setting to other settings. This paper compares three calibration methods for a slightly adapted social force model. The main emphasis lies in the characteristics of the data-generation process and the information contained in the data sets. The sensitivity of the model parameters of the calibrated model were investigated, and the transferability of the model to different scenarios was tested. Results revealed that the quality of the data had a strong effect on the suitability of different calibration strategies and that the information content in the scene under investigation limited the transferability of the results to other scenarios. These results suggest that several data sets with different characteristics do not need to be included in the calibration process to achieve a model that performs well in a wider variety of settings.

MATSim is an open-source simulation framework for mesoscopic traffic simulations that has gained popularity in recent years. In this paper, we present a MATSim model for the city of Vienna, with a particular emphasis on the intermodal routing framework used to create agent trips, and the development of a utility function to specify different agents’ mode preferences. To create agent activity chains, we use mobility diaries from the national transportation survey in Austria and disaggregate the available geospatial information to best fit the reported travel times. The novelty of the intermodal framework is the ability to create trips that do not consist of only one mode of transportation, but to also include bicycle, car, and demand-responsive transport (e.g., cab, car sharing) trips in combination with public transportation. To represent the different mobility behaviors of agents, we divide the population into groups and assign them different utility functions for transportation modes according to their socio-demographic characteristics. After presenting the validation of the model, we discuss ways to improve the model.