Abstract
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.
Highlights
Automatic collection of Global Positioning System (GPS) data is the first step to look into people’s mobility habits [1]
Our methodology includes the following stages: a processing stage validates the data integrity and filters out invalid location points (Section 2.1); a segmentation method aggregates the location points into trips (Section 2.2); a map-matching method binds the e-bike trips with features in the road network (Section 2.3); road segments with similar characteristics are grouped (Section 2.4); we investigate the influence of the weather by combining weather conditions and trip data (Section 2.5)
This paper contributes to this line of research investigating whether the e-bike may represent a valid alternative for commuting trips
Summary
Automatic collection of Global Positioning System (GPS) data is the first step to look into people’s mobility habits [1]. GPS data facilitates several tasks: visualisation of trips’ origin, destination, trajectory as well as the estimation of travel times, distances and speeds, etc. To extract useful information from the massive amount of raw data generated by GPS devices [3], several processing steps must be carried out. The advantages of using GPS technologies in travel studies and the effectiveness of GPS data in capturing trip parameters have already been demonstrated [4,5,6]. Recent studies have evaluated the use of GPS data for both trip tracking and transportation-mode detection without the support of questionnaires [7,8]. In [9], the authors proposed a probabilistic map-matching approach to overcome uncertainty caused by poor quality of the GPS data captured by smartphones. The authors proposed a path finder approach to deal with this issue
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