Transport planning strategies regard cycling promotion as a suitable means for tackling problems connected with motorized traffic such as limited space, congestion, and pollution. However, the evidence base for optimizing cycling promotion is weak in most cases, and information on bicycle patterns at a sufficient resolution is largely lacking. In this paper, we propose agent-based modeling to simulate bicycle traffic flows at a regional scale level for an entire day. The feasibility of the model is demonstrated in a use case in the Salzburg region, Austria. The simulation results in distinct spatio-temporal bicycle traffic patterns at high spatial (road segments) and temporal (minute) resolution. Scenario analysis positively assesses the model’s level of complexity, where the demographically parametrized behavior of cyclists outperforms stochastic null models. Validation with reference data from three sources shows a high correlation between simulated and observed bicycle traffic, where the predictive power is primarily related to the quality of the input and validation data. In conclusion, the implemented agent-based model successfully simulates bicycle patterns of 186,000 inhabitants within a reasonable time. This spatially explicit approach of modeling individual mobility behavior opens new opportunities for evidence-based planning and decision making in the wide field of cycling promotion

Walking is recognised as a healthy and sustainable mode of transport. Providing adequate infrastructure is pivotal for the promotion of walking and, subsequently, for achieving the benefits derived from its numerous positive effects. However, efficiently measuring the walkability at the street level remains challenging. In this paper, we present an indicator-based assessment model that can be used with open spatial data to evaluate segment-based walkability. The model incorporates eleven indicators describing the street segments and their close surroundings that are relevant for pedestrians, such as the presence and type of pedestrian infrastructure, road category, noise levels, and exposure to green and blue space. A weighted average calculation results in walkability index values for each street segment within a road network graph. The model’s generic approach and the ability to be used with open data ensure its reproducibility, adaptability, and scalability. The feasibility of the walkability model was shown using a case study for Salzburg, Austria. The model’s validity was evaluated through a large-scale study involving 660 full responses to an online survey. Participants provided ratings on the walkability of randomly selected street segments in Salzburg, which were compared with the calculated index, revealing a strong correlation (Spearman’s rank correlation = 0.82).

Systematic and reproducible analyses of walkability and bikeability are crucial for efficient and effective interventions to promote active mobility. Previous methods for segment-based walkability and bikeability are limited in applicability due to high data requirements, a constrained set of aspects considered, or are not openly available and adjustable. The lack of transparency, limited reproducibility, and the high effort required to re-implement customized and extended variants of such methods inhibit wider application and retard knowledge gain. The open source software NetAScore aims to fill this gap. It is based on open and widely available data, provides default mode profiles for modeling utilitarian bikeability and walkability, and is designed for customization. General applicability and suitability of the software and its underlying models were shown in two evaluation studies. Source code and documentation are available on GitHub and via doi.org/10.5281/zenodo.7695369.