Common goals of sustainable mobility approaches are to reduce the need for travel, to facilitate modal shifts, to decrease trip distances and to improve energy efficiency in the transportation systems. Among these issues, modal shift plays an important role for the adoption of vehicles with fewer or zero emissions. Nowadays, the electric bike (e-bike) is becoming a valid alternative to cars in urban areas. However, to promote modal shift, a better understanding of the mobility behaviour of e-bike users is required. In this paper, we investigate the mobility habits of e-bikers using GPS data collected in Belgium from 2014 to 2015. By analysing more than 10,000 trips, we provide insights about e-bike trip features such as: distance, duration and speed. In addition, we offer a deep look into which routes are preferred by bike owners in terms of their physical characteristics and how weather influences e-bike usage. Results show that trips with higher travel distances are performed during working days and are correlated with higher average speeds. Usage patterns extracted from our data set also indicate that e-bikes are preferred for commuting (home-work) and business (work related) trips rather than for recreational trips.

This paper presents a mobility-resource ownership model. The model captures inter-related personal mobility decisions: which transport mode (out of those available to a decision-maker) to use for a particular trip and which mobility resources (e.g., car, bicycle, transit season ticket or a combination) should the decision-maker own to enable the most “appropriate” set of transport modes. Importantly, the mobility decisions are not evaluated only for a single trip or a single day. In fact, for each decision-maker, an entire set of trips, observed over multiple days, is evaluated. We call this personal accessibility to travel. We present a two-step discrete choice model that includes both mode choice and ownership decisions. The model is estimated based on household travel survey data from Germany. This paper also investigates the simulation of travel times for non-chosen modes that are required as an input. The estimation results show significant effects of the personal accessibility and travel frequency on mobility-resource ownership decisions. To further validate the estimation, the forecasting and sensitivity analysis of the model for different scenarios is evaluated. The proposed model offers an efficient solution to situations when the impact of transport sustainability measures on mobility behaviour needs to be plausibly predicted.