12,932 research outputs found

    A dynamic approach to rebalancing bike-sharing systems

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    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

    Modeling bike counts in a bike-sharing system considering the effect of weather conditions

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    The paper develops a method that quantifies the effect of weather conditions on the prediction of bike station counts in the San Francisco Bay Area Bike Share System. The Random Forest technique was used to rank the predictors that were then used to develop a regression model using a guided forward step-wise regression approach. The Bayesian Information Criterion was used in the development and comparison of the various prediction models. We demonstrated that the proposed approach is promising to quantify the effect of various features on a large BSS and on each station in cases of large networks with big data. The results show that the time-of-the-day, temperature, and humidity level (which has not been studied before) are significant count predictors. It also shows that as weather variables are geographic location dependent and thus should be quantified before using them in modeling. Further, findings show that the number of available bikes at station i at time t-1 and time-of-the-day were the most significant variables in estimating the bike counts at station i.Comment: Published in Case Studies on Transport Policy (Volume 7, Issue 2, June 2019, Pages 261-268

    A Framework for Integrating Transportation Into Smart Cities

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    In recent years, economic, environmental, and political forces have quickly given rise to โ€œSmart Citiesโ€ -- an array of strategies that can transform transportation in cities. Using a multi-method approach to research and develop a framework for smart cities, this study provides a framework that can be employed to: Understand what a smart city is and how to replicate smart city successes; The role of pilot projects, metrics, and evaluations to test, implement, and replicate strategies; and Understand the role of shared micromobility, big data, and other key issues impacting communities. This research provides recommendations for policy and professional practice as it relates to integrating transportation into smart cities

    Predicting bicycle arrivals in a Bicycle Sharing System network: A data science driven approach grounded in Zero-Inflated Regression

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    The adoption of bicycle sharing systems (BSS) is growing in order to improve the way people move around cities, but also to stimulate the development of a more sustainable urban mobility. For the proper functioning of a BSS, it is important to have bicycles permanently available at the stations for users to start their trips, so the literature has undertaken efforts, from the perspective of the service operator, to improve the process of redistribution of bicycles and thus ensure their availability at the different stations. Since the guarantee of available bicycles cannot be assured, this work proposes to develop, from the cyclist's perspective, a proof of concept on the feasibility of informing the user about the possibility of starting a trip in a pre-defined time interval. The main contributions of this work are: (i) the ability to predict how many bicycles will arrive at a given station is a feasible improvement for BSS, (ii) the models developed through the Zero-Inflated Regression approach are a path that can be explored to improve prediction and (iii) unprecedented methodological contribution to the literature on BSS focusing on the end-user's decision power about whether or not it will soon be possible to start a trip.A adoรงรฃo de sistemas de bicicletas partilhadas (BSS) vem crescendo com o objetivo de melhorar a forma como as pessoas se deslocam pelas cidades, mas tambรฉm para estimular o desenvolvimento de uma mobilidade urbana mais sustentรกvel. Para o bom funcionamento de um BSS รฉ importante que haja bicicletas permanentemente disponรญveis nas estaรงรตes para os utilizadores iniciarem as suas viagens, pelo que a literatura tem empreendido esforรงos, sob a รณtica do operador do serviรงo, para melhorar o processo de redistribuiรงรฃo das bicicletas e assim garantir a sua disponibilidade nas diferentes estaรงรตes. Como a garantia de bicicletas disponรญveis nรฃo pode ser assegurada, este trabalho propรตe-se desenvolver, sob a รณtica do ciclista, uma prova de conceito sobre a viabilidade de informar o utilizador acerca da possibilidade de iniciar uma viagem num intervalo de tempo prรฉ-definido. As principais contribuiรงรตes deste trabalho sรฃo: (i) a capacidade de previsรฃo de quantas bicicletas chegarรฃo a uma determinada estaรงรฃo รฉ uma melhoria viรกvel para os BSS, (ii) os modelos desenvolvidos atravรฉs da aproximaรงรฃo Zero-Inflated Regression sรฃo um caminho que pode ser explorado para melhorar a previsรฃo e (iii) contributo metodolรณgico inรฉdito ร  literatura sobre os BSS com foco no poder decisรณrio do utilizador final sobre se serรก, ou nรฃo, possรญvel iniciar uma viagem em breve

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

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    ํ•™์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
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