A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems

Bike sharing provides an environment-friendly way for traveling and is booming all over the world. Yet, due to the high similarity of user travel patterns, the bike imbalance problem constantly occurs, especially for dockless bike sharing systems, causing significant impact on service quality and company revenue. Thus, it has become a critical task for bike sharing systems to resolve such imbalance efficiently. In this paper, we propose a novel deep reinforcement learning framework for incentivizing users to rebalance such systems. We model the problem as a Markov decision process and take both spatial and temporal features into consideration. We develop a novel deep reinforcement learning algorithm called Hierarchical Reinforcement Pricing (HRP), which builds upon the Deep Deterministic Policy Gradient algorithm. Different from existing methods that often ignore spatial information and rely heavily on accurate prediction, HRP captures both spatial and temporal dependencies using a divide-and-conquer structure with an embedded localized module. We conduct extensive experiments to evaluate HRP, based on a dataset from Mobike, a major Chinese dockless bike sharing company. Results show that HRP performs close to the 24-timeslot look-ahead optimization, and outperforms state-of-the-art methods in both service level and bike distribution. It also transfers well when applied to unseen areas

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

A Systematic Literature Review on Machine Learning in Shared Mobility

Shared mobility has emerged as a sustainable alternative to both private transportation and traditional public transport, promising to reduce the number of private vehicles on roads while offering users greater flexibility. Today, urban areas are home to a myriad of innovative services, including car-sharing, ride-sharing, and micromobility solutions like moped-sharing, bike-sharing, and e-scooter-sharing. Given the intense competition and the inherent operational complexities of shared mobility systems, providers are increasingly seeking specialized decision-support methodologies to boost operational efficiency. While recent research indicates that advanced machine learning methods can tackle the intricate challenges in shared mobility management decisions, a thorough evaluation of existing research is essential to fully grasp its potential and pinpoint areas needing further exploration. This paper presents a systematic literature review that specifically targets the application of Machine Learning for decision-making in Shared Mobility Systems. Our review underscores that Machine Learning offers methodological solutions to specific management challenges crucial for the effective operation of Shared Mobility Systems. We delve into the methods and datasets employed, spotlight research trends, and pinpoint research gaps. Our findings culminate in a comprehensive framework of Machine Learning techniques designed to bolster managerial decision-making in addressing challenges specific to Shared Mobility across various levels

Are shared electric scooters energy efficient?

Shared electric scooters (e-scooter) are booming across the world and widely regarded as a sustainable mobility service. An increasing number of studies have investigated the e-scooter trip patterns, safety risks, and environmental impacts, but few considered the energy efficiency of e-scooters. In this research, we collected the operational data of e-scooters from a major provider in Gothenburg to shed light on the energy efficiency performance of e-scooters in real cases. We first develop a multiple logarithmic regression model to examine the energy consumption of single trips and influencing factors. With the regression model, a Monte Carlo simulation framework is proposed to estimate the fleet energy consumption in various scenarios, taking into account both trip-related energy usage and energy loss in idle status. The results indicate that 40% of e-scooter battery energy was wasted in idle status in the current practice, mainly due to the relatively low usage rate (0.83) of e-scooters. If the average usage rate drops below 0.5, the wasted energy could reach up to 53%. In the end, we present a field example to showcase how to optimally integrate public transport with e-scooters from the perspective of energy efficiency. We hope the findings of this study could help understand and resolve the current and future challenges regarding the ever-growing e-scooter services

Optimization of Electric Scooter Rebalancing Tour through Mathematical programming

openThe pursuit of Sustainable Development Goal 11 (SDG11), striving to create inclusive, resilient, and sustainable urban environments, has become a global priority. One of SDG11’s key objectives is to ensure safe, affordable, accessible, and sustainable transport systems for all. In response to this imperative, the concept of electric scooter (e-scooter) sharing has gained remarkable popularity. This innovative model allows users to access e-scooters on demand, providing a personalized last-mile solution that complements public transportation and has the potential to reduce private car ownership and greenhouse gas emissions. As e-scooters take on an increasingly pivotal role in urban mobility, the efficient management of their distri- bution and charging presents a critical challenge. One of the key problems in this sharing system is to ensure that e-scooter is not over-saturated and under-utilized. This thesis embarks on a comprehensive exploration of the com- plexities surrounding this challenge and proposes solutions to enhance the integration of e-scooter sharing into everyday urban life. The core idea revolves around conducting a night tour operation that allow operator to drop full-charge e-scooter, but also swap the battery of the low charged unit to re-balance the e-scooter distribution. Within this thesis, two mathematical programming formulations are presented in order to plan ahead the route and suggested actions at each station during the night tour. The first model, adapted from previous literature work on bike sharing systems rebalancing, provides a benchmark and introduces readers to the night rebalancing tour problem. The second model represents an original improved version, allowing night tour operators to swap only the battery rather than the whole e-scooter unit during operations. Both models confront the challenge of managing exponential growth in constraints and size of the solution space as the number of stations increases. In response, tailor-made branch-and-cut algorithms are developed to efficiently solve this problem. This scalable framework offers the potential to manage extensive e-scooter fleets and station networks within the city, enabling companies to enhance their operations, foster citizen trust, and establish e-scooter sharing systems as a dependable choice for daily last-mile transportation. This thesis aims to make the topic accessible and easily understandable, inviting broader participation and contributions to research in this vital field.The pursuit of Sustainable Development Goal 11 (SDG11), striving to create inclusive, resilient, and sustainable urban environments, has become a global priority. One of SDG11’s key objectives is to ensure safe, affordable, accessible, and sustainable transport systems for all. In response to this imperative, the concept of electric scooter (e-scooter) sharing has gained remarkable popularity. This innovative model allows users to access e-scooters on demand, providing a personalized last-mile solution that complements public transportation and has the potential to reduce private car ownership and greenhouse gas emissions. As e-scooters take on an increasingly pivotal role in urban mobility, the efficient management of their distri- bution and charging presents a critical challenge. One of the key problems in this sharing system is to ensure that e-scooter is not over-saturated and under-utilized. This thesis embarks on a comprehensive exploration of the com- plexities surrounding this challenge and proposes solutions to enhance the integration of e-scooter sharing into everyday urban life. The core idea revolves around conducting a night tour operation that allow operator to drop full-charge e-scooter, but also swap the battery of the low charged unit to re-balance the e-scooter distribution. Within this thesis, two mathematical programming formulations are presented in order to plan ahead the route and suggested actions at each station during the night tour. The first model, adapted from previous literature work on bike sharing systems rebalancing, provides a benchmark and introduces readers to the night rebalancing tour problem. The second model represents an original improved version, allowing night tour operators to swap only the battery rather than the whole e-scooter unit during operations. Both models confront the challenge of managing exponential growth in constraints and size of the solution space as the number of stations increases. In response, tailor-made branch-and-cut algorithms are developed to efficiently solve this problem. This scalable framework offers the potential to manage extensive e-scooter fleets and station networks within the city, enabling companies to enhance their operations, foster citizen trust, and establish e-scooter sharing systems as a dependable choice for daily last-mile transportation. This thesis aims to make the topic accessible and easily understandable, inviting broader participation and contributions to research in this vital field

Sustaining dockless bike-sharing based on business principles

Currently in urban areas, the value of money and fuel is increasing because of urban traffic congestion. As an environmentally sustainable and short-distance travel mode, dockless bike-sharing not only assists in resolving the issue of urban traffic congestion, but additionally assists in minimizing pollution, satisfying the demand of the last mile problem, and improving societal health. Despite the positives that this new transportation mode provides, currently there are few effective measures in place to make the development of dockless bike-sharing providers more sustainable. This study endeavors in establishing a foundation for resolving this problem through developing business models of dockless bike-sharing based on business theory and principles, and utilizing the largest dockless bike-sharing company in China as of November 2018 named Mobike as an example within these business models. The long-term sustainability issues of dockless bike-sharing are identified through various methods including an operational analysis of one of Mobike’s largest divisions located in Beijing, China, and potential solutions to those issues as well as policy implications are presented based on the research and analysis conducted