Metropolitan bicycle-sharing system in the Polish context of various needs of cities, towns, and villages

Bicycle-sharing systems (BSSs) have started to play an important role in the transport systems of cities worldwide as a sustainable alternative to the dominant motorised mobility culture. BSSs have also expanded over time to include regions and metropolitan areas as well as small towns and rural areas. The purpose of this paper is to identify and compare the goals of connecting individual communes in a metropolitan area to a metropolitan bicycle system. The authors applied a case study of the MEVO metropolitan bicycle system consisting of electrically assisted bicycles, introduced in 2019 in 14 communes of the Gdańsk-Gdynia-Sopot Metropolitan Area (GGSMA) in Poland. The study used GGSMA-designated metropolitan zoning to group the goals pursued by the participants when joining the project. This paper is the first to identify the goals that inclined small towns and rural areas to accede to the BSS. The results show that the largest cities in the metropolis that make up its core count on bike sharing to solve the problems of congested city centres, while small towns and rural areas see the BSS as an opportunity to improve the quality of life of the inhabitants, as the first mode of public transport, as an opportunity to be closer and more identified with the metropolitan core, and as a chance to develop tourism and recreation.

A Systematic Review of Station Location Techniques for Bicycle-Sharing Systems Planning and Operation

Designing or expanding a bicycle-sharing system (BSS) involves addressing the infrastructure’s location of the bicycle stations. Station location is an essential factor for designing and implementing a new system or for its operation. In a complex spatial optimization context, geographic information systems (GIS) can support this decision problem. There are also numerous ways of subdividing the broad spectrum of location-allocation models used in previous studies. However, a station location comprehensive review and systematization with the specific aim of characterizing the state of the art of BSS is missing. The present research aimed to provide a comprehensive systematization for station location problems, criteria, and techniques, seeking to identify the current state of practice. We searched scientific publication databases to collect relevant publications—the final list comprised 24 papers for the literature review. The systematization addresses the two major problems concerning bicycle station location: initial network design and operation improvement (where changes in operating a BSS are implemented). Based on the literature, we propose a set of four main criteria for choosing appropriate places for bike stations (or parking) in a city: “bike network”, “operator”, “user”, and “city infrastructure”. The sub-criteria mentioned in the literature are categorized based on the proposed classification and new sub-criteria are suggested. We also group location modeling techniques into three categories: “mathematical algorithms”, “multi-criteria decision making”, and “GIS”. Combining GIS and multi-criteria decision making (MCDM) has received more attention in recent years to locate bike stations, evaluate their operating performance, and have more accurate and practical results.

Categorizing Bicycling Environment Quality Based on Mobile Sensor Data and Bicycle Flow Data

The bicycle is a healthy and sustainable transport mode due to its emission-free characteristics. To increase bicycle use, it is fundamental to provide bicycle-friendly environments. To better monitor bicycle environments, this study proposed the concept of bicycling environment quality (BEQ), which was defined by perceived satisfaction and conflict level. Data collection was conducted at 19 road segments in five sites located in Beijing, China. Then, speed-related and acceleration-related bicycling behavior indicators (BBIs) were extracted from data collected using sensors on mobile phones, while bicycling environment indicators (BEIs), such as bicycle flow, were extracted from recorded data. Taking the BBIs and BEIs as input attributes, a two-level BEQ classification assessment model based on a random forest (RF) algorithm was constructed. The proposed RF-based classification assessment model was able to produce approximately 77.35% overall correct classification. The results demonstrate the feasibility of using GPS data in evaluating BEQ. In addition, a novel dockless bicycle-sharing system (DBS)-based framework for bicycle traffic monitoring is discussed, which is of great significance in the sustainable development of bicycles. This study provides a theoretical method for objective BEQ assessment. It can further be used by planners and road administrators to monitor and improve BEQ and by individual cyclists for optimal route choice.