Dockless Shared Bicycle Flow Control by Using Kernel Density Estimation Based Clustering

Since dockless sharing bicycles have become an indispensable means of everyday life for urban residents, how to effectively control the supply and demand balance of bikes has become an important issue. This study aims to apply Kernel Density Estimation based (KDE-based) clustering analysis and a threshold-based reverse flow incentive mechanism to encourage the users of bicycles to adjust the supply and demand actively. And it takes Shanghai Jing’an Temple and its surroundings as the research area. Its practical steps include: (1) compilation and processing of the needed data, (2) application of KDE-based clustering, partitioning, and grading, and (3) incentives calculation based on dockless shared bicycle flow control system. The study finds that the generalization function of KDE-based clustering can be used to estimate the density value at any point in the study area to support the calculation of the incentive mechanism for bicycle reverse flow

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

Towards smart open dynamic fleets

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-33509-4_32Nowadays, vehicles of modern fleets are endowed with advanced devices that allow the operators of a control center to have global knowledge about fleet status, including existing incidents. Fleet management systems support real-time decision making at the control center so as to maximize fleet perform‐ ance. In this paper, setting out from our experience in dynamic coordination of fleet management systems, we focus on fleets that are open, dynamic and highly autonomous. Furthermore, we propose how to cope with the scalability problem as the number of vehicles grows. We present our proposed architecture for open fleet management systems and use the case of taxi services as example of our proposal.Work partially supported by Spanish Government through the projects iHAS (grant TIN2012-36586-C03) and SURF (grant TIN2015-65515-C4-X-R), the Autonomous Region of Madrid through grant S2013/ICE-3019 (“MOSI-AGIL-CM”, cofunded by EU Structural Funds FSE and FEDER) and URJC-Santander (30VCPIGI15).Billhardt, H.; Fernández, A.; Lujak, M.; Ossowski, S.; Julian Inglada, VJ.; Paz, JFD.; Hernández, JZ. (2016). Towards smart open dynamic fleets. En Multi-Agent Systems and Agreement Technologies. Springer. 410-424. https://doi.org/10.1007/978-3-319-33509-4_32S41042

Combining Reinforcement Learning With Genetic Algorithm for Many-To-Many Route Optimization of Autonomous Vehicles

This study introduces an approach for route optimization of many-to-many Demand-Responsive Transport (DRT) services. In contrast to conventional fixed-route transit systems, DRT provides dynamic, flexible, and cost-effective alternatives. We present an algorithm that integrates DRT with the autonomous shuttles at Korea National University of Transportation (KNUT), allowing dynamic route modifications in real-time to accommodate incoming service calls. The algorithm is designed to take into account the shuttle's current position, the destinations of passengers already on board, the current locations and destinations of individuals who have requested shuttle services, and the remaining capacity of the shuttle. The algorithm has been developed to combine genetic algorithms and reinforcement learning. The performance evaluation was conducted using a simulation model that emulates KNUT's campus and the adjoining local community area. The simulation results show significant improvements in two key metrics, specifically the 'Request to Pick-up Time' and 'Request to Drop-off Time' during high-demand periods over the single-shuttle operation. Additional simulation test with random service requests and stochastic passenger walking distances showed the potential adaptability across different settings

Integrating operations research into green logistics:A review

Logistical activities have a significant global environmental impact, necessitating the adoption of green logistics practices to mitigate environmental effects. The COVID-19 pandemic has further emphasized the urgency to address the environmental crisis. Operations research provides a means to balance environmental concerns and costs, thereby enhancing the management of logistical activities. This paper presents a comprehensive review of studies integrating operations research into green logistics. A systematic search was conducted in the Web of Science Core Collection database, covering papers published until June 3, 2023. Six keywords (green logistics OR sustainable logistics OR cleaner logistics OR green transportation OR sustainable transportation OR cleaner transportation) were used to identify relevant papers. The reviewed studies were categorized into five main research directions: Green waste logistics, the impact of costs on green logistics, the green routing problem, green transport network design, and emerging challenges in green logistics. The review concludes by outlining suggestions for further research that combines green logistics and operations research, with particular emphasis on investigating the long-term effects of the pandemic on this field.</p

Semi-Automated Location Planning for Urban Bike-Sharing Systems

Bike-sharing has developed into an established part of many urban transportation systems. However, new bikesharing systems (BSS) are still built and existing ones are extended. Particularly for large BSS, location planning is complex since factors determining potential usage are manifold. We propose a semi-automatic approach for creating or extending real-world sized BSS during general planning. Our approach optimizes locations such that the number of trips is maximized for a given budget respecting construction as well as operation costs. The approach consists of four steps: (1) collecting and preprocessing required data, (2) estimating a demand model, (3) calculating optimized locations considering estimated redistribution costs, and (4) presenting the solution to the planner in a visualization and planning front end. The full approach was implemented and evaluated positively with BSS and planning experts

Routing Applications in Newspaper Delivery

-The goal of this report is to give an up-to-date account of routing applications in the newspaper business. We describe the newspaper supply chain, and focus on the “last mile” distribution that has been advocated as an application of arc routing in the literature. A literature survey is provided, followed by a discussion of the arc routing model and its adequacy to newspaper applications. A more general and normally more adequate model: The Node, Edge, and Arc Routing Problem, is discussed. Characteristics of routing problems in carrier delivery are presented, together with a case study from the development of a web-based route design and revision system. Finally, summary, conclusions, and prospects for the future are given