E-scooter sharing schemes operational zones in Poland : dataset on voivodeship capital cities

In this paper, we present the vector dataset of the operational zones of e-scooter shared mobility services in the voivodeship capital cities in Poland. The data were acquired manually from the applications of a single provider of e-scooters for each city. The dataset contains not only the size and the position of the geographic service areas, or geofences of e-scooter sharing schemes, but also the size and position of no-parking zones, parking zones and low-speed zones, if applicable. The data can be used for various researches which cover the topic of micro-mobility, accessibility and broader issues connected with urban development and spatial management. The dataset captures the state of the e-scooter sharing scheme in the voivodeship capital cities in Poland at the beginning of August 2020. Additionally, the data are accompanied by the table of cities with identified providers of e-scooter sharing systems

Evaluating the Effectiveness of Bike Sharing Programs in Encouraging Sustainable Transportation in Urban Areas

Bike sharing programs have emerged as a popular solution to promote sustainable transportation in urban areas. This research abstract presents five key points that highlight the effectiveness of bike sharing programs in encouraging sustainable transportation. Firstly, these programs facilitate a modal shift by providing convenient access to bicycles, encouraging individuals to choose cycling as a sustainable transportation option and reducing reliance on private motorized vehicles, thereby decreasing carbon emissions. Secondly, bike sharing programs effectively address the last-mile problem by offering bicycles at strategic locations near transit hubs, providing a convenient and efficient mode of transportation for short-distance trips and complementing existing public transit systems. Thirdly, these programs enhance transportation accessibility by offering affordable rental options, enabling a broader range of people, including those without access to private vehicles or unable to afford their upkeep, to access transportation, thus promoting inclusivity and reducing transportation inequality. Moreover, bike sharing programs promote public health by encouraging regular cycling as a mode of transportation. This promotes physical activity and helps individuals meet recommended activity guidelines, resulting in a reduced risk of non-communicable diseases such as obesity, cardiovascular diseases, and diabetes, contributing to the overall sustainability and well-being of urban populations. Lastly, bike sharing programs generate valuable data that can inform urban transportation planning and infrastructure development. By analyzing usage patterns, trip durations, and popular routes, city planners can identify areas with high demand for cycling infrastructure, leading to the efficient allocation of resources and the optimization of urban transportation systems

Blind classification of e-scooter trips according to their relationship with public transport

E-scooter services have multiplied worldwide as a form of urban transport. Their use has grown so quickly that policymakers and researchers still need to understand their interrelation with other transport modes. At present, e-scooter services are primarily seen as a first-and-last-mile solution for public transport. However, we demonstrate that 50% of e-scooter trips are either substituting it or covering areas with little public transportation infrastructure. To this end, we have developed a novel data-driven methodology that autonomously classifies e-scooter trips according to their relation to public transit. Instead of predefined design criteria, the blind nature of our approach extracts the city’s intrinsic parameters from real data. We applied this methodology to Rome (Italy), and our findings reveal that e-scooters provide specific mobility solutions in areas with particular needs. Thus, we believe that the proposed methodology will contribute to the understanding of e-scooter services as part of shared urban mobility

Travel preferences for electric sharing mobility services:Results from stated preference experiments in four European countries

Electric sharing mobility services (ESMS) are gaining popularity as a promising solution for green transport. For sustainable mobility planning, it is important to understand the factors affecting the use behavior of ESMS and the substitution patterns of conventional transport modes. To that end, we carried out a stated preference experiment to elicit travel preference toward ESMS considering various alternatives, contexts, and traveler characteristics. Results from a scaled error component model applied to a large sample of respondents from four European countries (France, Italy, Netherlands, and Spain) show that ESMS have the potential to reduce dependency on private cars. While heterogeneity is found across countries, people at young ages, highly educated, with high income, and living in city centers are commonly associated with a higher probability of adopting ESMS for urban mobility. The substitution patterns reveal a relatively lower preference for ESMS from private car users compared to users of public transport and active modes. Operational implications are discussed for sharing mobility planners and operators to avoid unintended substitution effects

E-scooter and bike-share route choice and detours : modelling the influence of built environment and sociodemographic factors

Unidad de excelencia María de Maeztu CEX2019-000940-MMicromobility is often presented as a sustainable, affordable, and active urban transport option, in comparison to motorised modes. Understanding users routing preferences could help policymakers adapt and design facilities that attract a myriad of micromobility users. Whereas previous research largely focused solely on the built infrastructure, the ways in which sociodemographic factors affect micromobility route choice and infrastructure preferences are unclear. This study examines how elements of the built environment and sociodemographic attributes influence the route selection of 115 e-scooter and bike-share users in Barcelona, Spain. We also compare participants' GPS-tracked trips to the shortest path that they could have followed and develop a multilevel model to estimate how urban and sociodemographic factors affect the decision to deviate from the shortest path. The findings show that micromobility users rarely choose the shortest path since urban elements related to safety, accessibility and aesthetics seem to shape their wayfinding decisions. Results help us comprehend cyclists' and e-scooter riders' distinct route preferences and further illustrate how the gender identity of micromobility users influences route choice and detour. The models indicate that, on average, women take shorter detours than men. We observe gender differences in the way cyclists and e-scooter riders favour certain elements in their trips, such as parked cars and cycling infrastructure. Our findings offer valuable insights into how sociodemographic factors interact with infrastructure and built environment conditions to influence micromobility users' route choice and open up the potential to use these results to manage micromobility flows within cities

Bicycling to Level the Field: A Study of Divvy in Chicago

With the onset of COVID-19, norms across the world shifted, including the way people moved in major cities. In order to conduct a comparative analysis, understanding transportation habits before COVID-19 hit cities is important. In this paper, I have focused on Divvy bikes in the city of Chicago, which are touted as a means to achieving first- and last-mile transit especially in underserved communities. I am interested in initiating the line of inquiring into who Divvy bikes served during a time when there was major fear around high transmission of COVID-19 on trains and buses due to the close proximity. Behaviorally, bicycling also become very popular after the pandemic started and it would be helpful to understand how much of those habits become apparent through Divvy usage. In order to get a snapshot before COVID-19 hit Chicago, I limited this project to quantifying 2019’s Divvy usage. I conducted research by utilizing the City of Chicago Data Portal and its closed-sourced data analysis tool for creating maps, pie charts and histograms. I narrowed down my use of Divvy data to the peak usage months of 2019. I found that the common users of Divvy bikes were young, male users belonging to affluent neighborhoods. In total, those who bought annual subscriptions initiated more trips than those who didn’t. However, non-subscribers made longer trips than subscribers. Understanding such trends grants insight into whose needs micromobility technology serves and also facilitates the process of conducting future comparative research

Smart balancing of E-scooter sharing systems via deep reinforcement learning: a preliminary study

Nowadays, micro-mobility sharing systems have become extremely popular. Such systems consist in fleets of dockless electric vehicles which are deployed in cities, and used by citizens to move in a more ecological and flexible way. Unfortunately, one of the issues related to such technologies is its intrinsic load imbalance, since users can pick up and drop off the electric vehicles where they prefer. In this paper we present ESB-DQN, a multi-agent system for E-Scooter Balancing (ESB) based on Deep Reinforcement Learning where agents are implemented as Deep Q-Networks (DQN). ESB-DQN offers suggestions to pick or return e-scooters in order to make the fleet usage and sharing as balanced as possible, still ensuring that the original plans of the user undergo only minor changes. The main contributions of this paper include a careful analysis of the state of the art, an innovative customer-oriented rebalancing strategy, the integration of state-of-the-art libraries for deep Reinforcement Learning into the existing ODySSEUS simulator of mobility sharing systems, and preliminary but promising experiments that suggest that our approach is worth further exploration

Analysis and Optimization of Servicing Logistics for Self-Driving E-Scooters

In recent years, the shared scooter market has seen tremendous growth along with other micromobility industries as the future means of urban transport. One particularly interesting innovation that companies have begun experimenting with in this field is that of self-driving e-scooters. This thesis presents a study on the benefits of an autonomous or teleoperated scooter fleet with self-assembly capabilities: the ability to cluster nearby scooters and reduce the number of locations for servicing. To this end, the application is tackled as two separate optimization problems in clustering and routing. The full algorithm pipeline is described and several metrics evaluated against independent variables and algorithm parameters using real-world GBFS scooter data collected over several months. This thesis shows that self-assembly reduces total service times by as much as 50%, and can serve as a stepping stone for early adoption of the technology while more complex capabilities are being developed

The Impact of Cybersecurity and Innovation on Mobility Technology Acceptance

Not only in the mobility industry, innovative digital technologies are associated with opportunities but also pose risks in terms of cybersecurity. Consumers’ perception of a technology might thereby be impacted by their personal affinity to innovation and cybersecurity risk, affecting the attitude towards using. We developed an empirical model based on Davis’ Technology Acceptance Model (TAM) to analyze this impact in an online survey (n= 260). While both innovation and cybersecurity have an effect on attitude towards using, consumers do not perceive a direct tension between the two. Our study does thereby make both theoretical and practical contributions. On the one hand, we propose an extended TAM model that can easily be applied to further industries and digital technologies. On the other, we derive recommendations for the mobility industry, e.g., how to ease negative effects of cybersecurity risk perception, and increase customers’ attitude toward using

A Spatiotemporal Study and Location-Specific Trip Pattern Categorization of Shared E-Scooter Usage

This study analyzes the temporally resolved location and trip data of shared e-scooters over nine months in Berlin from one of Europe’s most widespread operators. We apply time, distance, and energy consumption filters on approximately 1.25 million trips for outlier detection and trip categorization. Using temporally and spatially resolved trip pattern analyses, we investigate how the built environment and land use affect e-scooter trips. Further, we apply a density-based clustering algorithm to examine point of interest-specific patterns in trip generation. Our results suggest that e-scooter usage has point of interest related characteristics. Temporal peaks in e-scooter usage differ by point of interest category and indicate work-related trips at public transport stations. We prove these characteristic patterns with the statistical metric of cosine similarity. Considering average cluster velocities, we observe limited time-saving potential of e-scooter trips in congested areas near the city center