Various public transport (PT) agencies publish their route and timetable information with the General Transit Feed Specification (GTFS) as the standard open format. Timetable data are commonly used for PT passenger routing. They can also be used for studying the structure and organization of PT networks, as well as the accessibility and the level of service these networks provide. However, using raw GTFS data is challenging as researchers need to understand the details of the GTFS data format, make sure that the data contain all relevant modes of public transport, and have no errors. To lower the barrier for using GTFS data in research, we publish a curated collection of 25 cities' public transport networks in multiple easy-to-use formats including network edge lists, temporal network event lists, SQLite databases, GeoJSON files, and the GTFS data format. This collection promotes the study of how PT is organized across the globe, and also provides a testbed for developing tools for PT network analysis and PT routing algorithms.

Efficient public transport (PT) networks are vital for well-functioning and sustainable cities. Compared to other modes of transport, PT networks feature inherent systemic complexity due to their schedule-dependence and network organization. Because of this, efficient PT network planning and management calls for advanced modeling and analysis tools. These tools have to take into account how people use PT networks, including factors such as demand, accessibility, trip planning and navigability. From the PT user perspective, the common criteria for planning trips include waiting times to departure, journey durations, and the number of required transfers. However, waiting times and transfers have typically been neglected in PT accessibility studies and related decision-support tools. Here, we tackle this issue by introducing a decision-support framework for PT planners and managers, based on temporal networks methodology. This framework allows for computing pre-journey waiting times, journey durations, and number of required transfers for all Pareto-optimal journeys between any origin–destination pair, at all points in time. We visualize this information as a temporal distance profile, covering any given time interval. Based on such profiles, we define the best-case, mean, and worst-case measures for PT travel time and number of required PT vehicle boardings, and demonstrate their practical utility to PT planning through a series of accessibility case studies. By visualizing the computed measures on a map and studying their relationships by performing an all-to-all analysis between 7463 PT stops in the Helsinki metropolitan region, we show that each of the measures provides a different perspective on accessibility. To pave the way towards more comprehensive understanding of PT accessibility, we provide our methods and full analysis pipeline as free and open source software.Peer reviewe

The emergence of micromobility services in the form of dockless shared e-scooters has resulted in a wide range of behavioral changes in urban environments. In order to effectively steer these changes towards sustainability targets, the characteristics of e-scooter trips and users’ behaviors should be understood further. However, there is a lack of systematic literature reviews in this domain. To address this gap, we provide a two-fold systematic literature review. The first aspect focuses on the categorization of temporal and spatial patterns of shared e-scooter usage. The second aspect focuses on a deeper understanding of e-scooter users’ behaviors, utilizing the principles of persona design. The analysis of temporal patterns highlights the commonality of midday, evening, and weekend peak usage across cities, while spatial patterns suggest e-scooters are used for traveling to recreational and educational land use, as well as city center areas. The synthesis of findings on users’ behaviors has resulted in six categories, with four user types based on usage frequency (one time, casual, power, and non-adopters), and two motivation-based personas (users who are not satisfied with current mobility options and users who have had positive travel experience from e-scooter usage). The overall findings provide important lessons for evaluating this emerging mobility service, which should be considered for steering its development in public-private stakeholder networks.