272 research outputs found

    A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems

    Full text link
    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

    A dynamic approach to rebalancing bike-sharing systems

    Get PDF
    Bike-sharing services are flourishing in Smart Cities worldwide. They provide a low-cost and environment-friendly transportation alternative and help reduce traffic congestion. However, these new services are still under development, and several challenges need to be solved. A major problem is the management of rebalancing trucks in order to ensure that bikes and stalls in the docking stations are always available when needed, despite the fluctuations in the service demand. In this work, we propose a dynamic rebalancing strategy that exploits historical data to predict the network conditions and promptly act in case of necessity. We use Birth-Death Processes to model the stations' occupancy and decide when to redistribute bikes, and graph theory to select the rebalancing path and the stations involved. We validate the proposed framework on the data provided by New York City's bike-sharing system. The numerical simulations show that a dynamic strategy able to adapt to the fluctuating nature of the network outperforms rebalancing schemes based on a static schedule

    Hourly Demand Prediction of Shared Mobility Ridership

    Get PDF
    This research focuses on predicting the hourly number of bikes needed using Citi bike data. Micro mobility is the new trend that serves the transportation sector in any city. With the development of technology and introduction of new modes, comes new challenges. Bike sharing is the most developed and standard micro mobility device with extensive data sources. In this research we introduce the rebalancing bike sharing problem, which is very recent and interesting problem. Bikes are being ridden from a station and returned to another, not necessarily the same one of departure, this procedure can cause some stations to be empty while others to be full, as a result, there is a need for a method by which distribution of bikes among stations are done. Using year-round historical trip data obtained from one of the famous bike operators in New York that is Citi bike. The study aims to find the factors affecting bike ridership and then by utilizing some predictive algorithms such as, regression models, k-means, decision trees and random forest a model will be created to estimate the number of bikes needed in an hourly basis regardless of any specific stations initially. Where accuracy will be eventually calculated. The testing will be initially evaluating the data of Citi bike in New York, however, the same can be utilized to evaluate data from other cities worldwide and operators, as well as other micro mobility modes such as e-scooters, mopeds, and others. Initially the Prediction problem will be evaluated against the current data available in the open-source Citi-Bike data, however, weather factors, bike infrastructure, and some other open-source data can be integrated for better results

    ์‹ค์‹œ๊ฐ„ ๋™์  ๊ณ„ํš๋ฒ• ๋ฐ ๊ฐ•ํ™”ํ•™์Šต ๊ธฐ๋ฐ˜์˜ ๊ณต๊ณต์ž์ „๊ฑฐ ์‹œ์Šคํ…œ์˜ ๋™์  ์žฌ๋ฐฐ์น˜ ์ „๋žต

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ฑด์„คํ™˜๊ฒฝ๊ณตํ•™๋ถ€, 2020. 8. ๊ณ ์Šน์˜.The public bicycle sharing system is one of the modes of transportation that can help to relieve several urban problems, such as traffic congestion and air pollution. Because users can pick up and return bicycles anytime and anywhere a station is located, pickup or return failure can occur due to the spatiotemporal imbalances in demand. To prevent system failures, the operator should establish an appropriate repositioning strategy. As the operator makes a decision based on the predicted demand information, the accuracy of forecasting demand is an essential factor. Due to the stochastic nature of demand, however, the occurrence of prediction errors is inevitable. This study develops a stochastic dynamic model that minimizes unmet demand for rebalancing public bicycle sharing systems, taking into account the stochastic demand and the dynamic characteristics of the system. Since the repositioning mechanism corresponds to the sequential decision-making problem, this study applies the Markov decision process to the problem. To solve the Markov decision process, a dynamic programming method, which decomposes complex problems into simple subproblems to derive an exact solution. However, as a set of states and actions of the Markov decision process become more extensive, the computational complexity increases and it is intractable to derive solutions. An approximate dynamic programming method is introduced to derive an approximate solution. Further, a reinforcement learning model is applied to obtain a feasible solution in a large-scale public bicycle network. It is assumed that the predicted demand is derived from the random forest, which is a kind of machine learning technique, and that the observed demand occurred along the Poisson distribution whose mean is the predicted demand to simulate the uncertainty of the future demand. Total unmet demand is used as a key performance indicator in this study. In this study, a repositioning strategy that quickly responds to the prediction error, which means the difference between the observed demand and the predicted demand, is developed and the effectiveness is assessed. Strategies developed in previous studies or applied in the field are also modeled and compared with the results to verify the effectiveness of the strategy. Besides, the effects of various safety buffers and safety stock are examined and appropriate strategies are suggested for each situation. As a result of the analysis, the repositioning effect by the developed strategy was improved compared to the benchmark strategies. In particular, the effect of a strategy focusing on stations with high prediction errors is similar to the effect of a strategy considering all stations, but the computation time can be further reduced. Through this study, the utilization and reliability of the public bicycle system can be improved through the efficient operation without expanding the infrastructure.๊ณต๊ณต์ž์ „๊ฑฐ ์‹œ์Šคํ…œ์€ ๊ตํ†ตํ˜ผ์žก๊ณผ ๋Œ€๊ธฐ์˜ค์—ผ ๋“ฑ ์—ฌ๋Ÿฌ ๋„์‹œ๋ฌธ์ œ๋ฅผ ์™„ํ™”ํ•  ์ˆ˜ ์žˆ๋Š” ๊ตํ†ต์ˆ˜๋‹จ์ด๋‹ค. ๋Œ€์—ฌ์†Œ๊ฐ€ ์œ„์น˜ํ•œ ๊ณณ์ด๋ฉด ์–ธ์ œ ์–ด๋””์„œ๋“  ์ด์šฉ์ž๊ฐ€ ์ž์ „๊ฑฐ๋ฅผ ์ด์šฉํ•  ์ˆ˜ ์žˆ๋Š” ์‹œ์Šคํ…œ์˜ ํŠน์„ฑ์ƒ ์ˆ˜์š”์˜ ์‹œ๊ณต๊ฐ„์  ๋ถˆ๊ท ํ˜•์œผ๋กœ ์ธํ•ด ๋Œ€์—ฌ ์‹คํŒจ ๋˜๋Š” ๋ฐ˜๋‚ฉ ์‹คํŒจ๊ฐ€ ๋ฐœ์ƒํ•œ๋‹ค. ์‹œ์Šคํ…œ ์‹คํŒจ๋ฅผ ์˜ˆ๋ฐฉํ•˜๊ธฐ ์œ„ํ•ด ์šด์˜์ž๋Š” ์ ์ ˆํ•œ ์žฌ๋ฐฐ์น˜ ์ „๋žต์„ ์ˆ˜๋ฆฝํ•ด์•ผ ํ•œ๋‹ค. ์šด์˜์ž๋Š” ์˜ˆ์ธก ์ˆ˜์š” ์ •๋ณด๋ฅผ ์ „์ œ๋กœ ์˜์‚ฌ๊ฒฐ์ •์„ ํ•˜๋ฏ€๋กœ ์ˆ˜์š”์˜ˆ์ธก์˜ ์ •ํ™•์„ฑ์ด ์ค‘์š”ํ•œ ์š”์†Œ์ด๋‚˜, ์ˆ˜์š”์˜ ๋ถˆํ™•์‹ค์„ฑ์œผ๋กœ ์ธํ•ด ์˜ˆ์ธก ์˜ค์ฐจ์˜ ๋ฐœ์ƒ์ด ๋ถˆ๊ฐ€ํ”ผํ•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ๊ณต๊ณต์ž์ „๊ฑฐ ์ˆ˜์š”์˜ ๋ถˆํ™•์‹ค์„ฑ๊ณผ ์‹œ์Šคํ…œ์˜ ๋™์  ํŠน์„ฑ์„ ๊ณ ๋ คํ•˜์—ฌ ๋ถˆ๋งŒ์กฑ ์ˆ˜์š”๋ฅผ ์ตœ์†Œํ™”ํ•˜๋Š” ์žฌ๋ฐฐ์น˜ ๋ชจํ˜•์„ ๊ฐœ๋ฐœํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ๊ณต๊ณต์ž์ „๊ฑฐ ์žฌ๋ฐฐ์น˜ ๋ฉ”์ปค๋‹ˆ์ฆ˜์€ ์ˆœ์ฐจ์  ์˜์‚ฌ๊ฒฐ์ • ๋ฌธ์ œ์— ํ•ด๋‹นํ•˜๋ฏ€๋กœ, ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ˆœ์ฐจ์  ์˜์‚ฌ๊ฒฐ์ • ๋ฌธ์ œ๋ฅผ ๋ชจํ˜•ํ™”ํ•  ์ˆ˜ ์žˆ๋Š” ๋งˆ๋ฅด์ฝ”ํ”„ ๊ฒฐ์ • ๊ณผ์ •์„ ์ ์šฉํ•œ๋‹ค. ๋งˆ๋ฅด์ฝ”ํ”„ ๊ฒฐ์ • ๊ณผ์ •์„ ํ’€๊ธฐ ์œ„ํ•ด ๋ณต์žกํ•œ ๋ฌธ์ œ๋ฅผ ๊ฐ„๋‹จํ•œ ๋ถ€๋ฌธ์ œ๋กœ ๋ถ„ํ•ดํ•˜์—ฌ ์ •ํ™•ํ•ด๋ฅผ ๋„์ถœํ•˜๋Š” ๋™์  ๊ณ„ํš๋ฒ•์„ ์ด์šฉํ•œ๋‹ค. ํ•˜์ง€๋งŒ ๋งˆ๋ฅด์ฝ”ํ”„ ๊ฒฐ์ • ๊ณผ์ •์˜ ์ƒํƒœ ์ง‘ํ•ฉ๊ณผ ๊ฒฐ์ • ์ง‘ํ•ฉ์˜ ํฌ๊ธฐ๊ฐ€ ์ปค์ง€๋ฉด ๊ณ„์‚ฐ ๋ณต์žก๋„๊ฐ€ ์ฆ๊ฐ€ํ•˜๋ฏ€๋กœ, ๋™์  ๊ณ„ํš๋ฒ•์„ ์ด์šฉํ•œ ์ •ํ™•ํ•ด๋ฅผ ๋„์ถœํ•  ์ˆ˜ ์—†๋‹ค. ์ด๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ๊ทผ์‚ฌ์  ๋™์  ๊ณ„ํš๋ฒ•์„ ๋„์ž…ํ•˜์—ฌ ๊ทผ์‚ฌํ•ด๋ฅผ ๋„์ถœํ•˜๋ฉฐ, ๋Œ€๊ทœ๋ชจ ๊ณต๊ณต์ž์ „๊ฑฐ ๋„คํŠธ์›Œํฌ์—์„œ ๊ฐ€๋Šฅํ•ด๋ฅผ ์–ป๊ธฐ ์œ„ํ•ด ๊ฐ•ํ™”ํ•™์Šต ๋ชจํ˜•์„ ์ ์šฉํ•œ๋‹ค. ์žฅ๋ž˜ ๊ณต๊ณต์ž์ „๊ฑฐ ์ด์šฉ์ˆ˜์š”์˜ ๋ถˆํ™•์‹ค์„ฑ์„ ๋ชจ์‚ฌํ•˜๊ธฐ ์œ„ํ•ด, ๊ธฐ๊ณ„ํ•™์Šต ๊ธฐ๋ฒ•์˜ ์ผ์ข…์ธ random forest๋กœ ์˜ˆ์ธก ์ˆ˜์š”๋ฅผ ๋„์ถœํ•˜๊ณ , ์˜ˆ์ธก ์ˆ˜์š”๋ฅผ ํ‰๊ท ์œผ๋กœ ํ•˜๋Š” ํฌ์•„์†ก ๋ถ„ํฌ๋ฅผ ๋”ฐ๋ผ ์ˆ˜์š”๋ฅผ ํ™•๋ฅ ์ ์œผ๋กœ ๋ฐœ์ƒ์‹œ์ผฐ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ด€์ธก ์ˆ˜์š”์™€ ์˜ˆ์ธก ์ˆ˜์š” ๊ฐ„์˜ ์ฐจ์ด์ธ ์˜ˆ์ธก์˜ค์ฐจ์— ๋น ๋ฅด๊ฒŒ ๋Œ€์‘ํ•˜๋Š” ์žฌ๋ฐฐ์น˜ ์ „๋žต์„ ๊ฐœ๋ฐœํ•˜๊ณ  ํšจ๊ณผ๋ฅผ ํ‰๊ฐ€ํ•œ๋‹ค. ๊ฐœ๋ฐœ๋œ ์ „๋žต์˜ ์šฐ์ˆ˜์„ฑ์„ ๊ฒ€์ฆํ•˜๊ธฐ ์œ„ํ•ด, ๊ธฐ์กด ์—ฐ๊ตฌ์˜ ์žฌ๋ฐฐ์น˜ ์ „๋žต ๋ฐ ํ˜„์‹ค์—์„œ ์ ์šฉ๋˜๋Š” ์ „๋žต์„ ๋ชจํ˜•ํ™”ํ•˜๊ณ  ๊ฒฐ๊ณผ๋ฅผ ๋น„๊ตํ•œ๋‹ค. ๋˜ํ•œ, ์žฌ๊ณ ๋Ÿ‰์˜ ์•ˆ์ „ ๊ตฌ๊ฐ„ ๋ฐ ์•ˆ์ „์žฌ๊ณ ๋Ÿ‰์— ๊ด€ํ•œ ๋ฏผ๊ฐ๋„ ๋ถ„์„์„ ์ˆ˜ํ–‰ํ•˜์—ฌ ํ•จ์˜์ ์„ ์ œ์‹œํ•œ๋‹ค. ๊ฐœ๋ฐœ๋œ ์ „๋žต์˜ ํšจ๊ณผ๋ฅผ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ, ๊ธฐ์กด ์—ฐ๊ตฌ์˜ ์ „๋žต ๋ฐ ํ˜„์‹ค์—์„œ ์ ์šฉ๋˜๋Š” ์ „๋žต๋ณด๋‹ค ๊ฐœ์„ ๋œ ์„ฑ๋Šฅ์„ ๋ณด์ด๋ฉฐ, ํŠนํžˆ ์˜ˆ์ธก์˜ค์ฐจ๊ฐ€ ํฐ ๋Œ€์—ฌ์†Œ๋ฅผ ํƒ์ƒ‰ํ•˜๋Š” ์ „๋žต์ด ์ „์ฒด ๋Œ€์—ฌ์†Œ๋ฅผ ํƒ์ƒ‰ํ•˜๋Š” ์ „๋žต๊ณผ ์žฌ๋ฐฐ์น˜ ํšจ๊ณผ๊ฐ€ ์œ ์‚ฌํ•˜๋ฉด์„œ๋„ ๊ณ„์‚ฐ์‹œ๊ฐ„์„ ์ ˆ๊ฐํ•  ์ˆ˜ ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ณต๊ณต์ž์ „๊ฑฐ ์ธํ”„๋ผ๋ฅผ ํ™•๋Œ€ํ•˜์ง€ ์•Š๊ณ ๋„ ์šด์˜์˜ ํšจ์œจํ™”๋ฅผ ํ†ตํ•ด ๊ณต๊ณต์ž์ „๊ฑฐ ์‹œ์Šคํ…œ์˜ ์ด์šฉ๋ฅ  ๋ฐ ์‹ ๋ขฐ์„ฑ์„ ์ œ๊ณ ํ•  ์ˆ˜ ์žˆ๊ณ , ๊ณต๊ณต์ž์ „๊ฑฐ ์žฌ๋ฐฐ์น˜์— ๊ด€ํ•œ ์ •์ฑ…์  ํ•จ์˜์ ์„ ์ œ์‹œํ•œ๋‹ค๋Š” ์ ์—์„œ ๋ณธ ์—ฐ๊ตฌ์˜ ์˜์˜๊ฐ€ ์žˆ๋‹ค.Chapter 1. Introduction ๏ผ‘ 1.1 Research Background and Purposes ๏ผ‘ 1.2 Research Scope and Procedure ๏ผ— Chapter 2. Literature Review ๏ผ‘๏ผ 2.1 Vehicle Routing Problems ๏ผ‘๏ผ 2.2 Bicycle Repositioning Problem ๏ผ‘๏ผ’ 2.3 Markov Decision Processes ๏ผ’๏ผ“ 2.4 Implications and Contributions ๏ผ’๏ผ– Chapter 3. Model Formulation ๏ผ’๏ผ˜ 3.1 Problem Definition ๏ผ’๏ผ˜ 3.2 Markov Decision Processes ๏ผ“๏ผ” 3.3 Demand Forecasting ๏ผ”๏ผ 3.4 Key Performance Indicator (KPI) ๏ผ”๏ผ• Chapter 4. Solution Algorithms ๏ผ”๏ผ— 4.1 Exact Solution Algorithm ๏ผ”๏ผ— 4.2 Approximate Dynamic Programming ๏ผ•๏ผ 4.3 Reinforcement Learning Method ๏ผ•๏ผ’ Chapter 5. Numerical Example ๏ผ•๏ผ• 5.1 Data Overview ๏ผ•๏ผ• 5.2 Experimental Design ๏ผ–๏ผ‘ 5.3 Algorithm Performance ๏ผ–๏ผ– 5.4 Sensitivity Analysis ๏ผ—๏ผ” 5.5 Large-scale Cases ๏ผ—๏ผ– Chapter 6. Conclusions ๏ผ˜๏ผ’ 6.1 Conclusions ๏ผ˜๏ผ’ 6.2 Future Research ๏ผ˜๏ผ“ References ๏ผ˜๏ผ– ์ดˆ ๋ก ๏ผ™๏ผ’Docto

    Toward Sustainability: Bike-Sharing Systems Design, Simulation and Management

    Get PDF
    The goal of this Special Issue is to discuss new challenges in the simulation and management problems of both traditional and innovative bike-sharing systems, to ultimately encourage the competitiveness and attractiveness of BSSs, and contribute to the further promotion of sustainable mobility. We have selected thirteen papers for publication in this Special Issue

    A systematic literature review

    Get PDF
    Albuquerque, V., Dias, M. S., & Bacao, F. (2021). Machine learning approaches to bike-sharing systems: A systematic literature review. ISPRS International Journal of Geo-Information, 10(2), 1-25. [62]. https://doi.org/10.3390/ijgi10020062Cities are moving towards new mobility strategies to tackle smart citiesโ€™ challenges such as carbon emission reduction, urban transport multimodality and mitigation of pandemic hazards, emphasising on the implementation of shared modes, such as bike-sharing systems. This paper poses a research question and introduces a corresponding systematic literature review, focusing on machine learning techniquesโ€™ contributions applied to bike-sharing systems to improve citiesโ€™ mobility. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) method was adopted to identify specific factors that influence bike-sharing systems, resulting in an analysis of 35 papers published between 2015 and 2019, creating an outline for future research. By means of systematic literature review and bibliometric analysis, machine learning algorithms were identified in two groups: classification and prediction.publishersversionpublishe

    A spatio-temporal deep learning model for short-term bike-sharing demand prediction

    Get PDF
    Bike-sharing systems are widely operated in many cities as green transportation means to solve the last mile problem and reduce traffic congestion. One of the critical challenges in operating high-quality bike-sharing systems is rebalancing bike stations from being full or empty. However, the complex characteristics of spatiotemporal dependency on usage demand may lead to difficulties for traditional statistical models in dealing with this complex relationship. To address this issue, we propose a graph-based neural network model to learn the representation of bike-sharing demand spatial-temporal graph. The model has the ability to use graph-structured data and takes both spatial -and temporal aspects into consideration. A case study about bike-sharing systems in Nanjing, a large city in China, is conducted based on the proposed method. The results show that the algorithm can predict short-term bike demand with relatively high accuracy and low computing time. The predicted errors for the hourly station level usage demand prediction are often within 20 bikes. The results provide helpful tools for short-term usage demand prediction of bike-sharing systems and other similar shared mobility systems
    • โ€ฆ
    corecore