A dynamic approach to rebalancing bike-sharing systems

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

A multiple type bike repositioning problem

This paper investigates a new static bicycle repositioning problem in which multiple types of bikes are considered. Some types of bikes that are in short supply at a station can be substituted by other types, whereas some types of bikes can occupy the spaces of other types in the vehicle during repositioning. These activities provide two new strategies, substitution and occupancy, which are examined in this paper. The problem is formulated as a mixed-integer linear programming problem to minimize the total cost, which consists of the route travel cost, penalties due to unmet demand, and penalties associated with the substitution and occupancy strategies. A combined hybrid genetic algorithm is proposed to solve this problem. This solution algorithm consists of (i) a modified version of a hybrid genetic search with adaptive diversity control to determine routing decisions and (ii) a proposed greedy heuristic to determine the loading and unloading instructions at each visited station and the substitution and occupancy strategies. The results show that the proposed method can provide high-quality solutions with short computing times. Using small examples, this paper also reveals problem properties and repositioning strategies in bike sharing systems with multiple types of bikes.published_or_final_versio

실시간 동적 계획법 및 강화학습 기반의 공공자전거 시스템의 동적 재배치 전략

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 건설환경공학부, 2020. 8. 고승영.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.공공자전거 시스템은 교통혼잡과 대기오염 등 여러 도시문제를 완화할 수 있는 교통수단이다. 대여소가 위치한 곳이면 언제 어디서든 이용자가 자전거를 이용할 수 있는 시스템의 특성상 수요의 시공간적 불균형으로 인해 대여 실패 또는 반납 실패가 발생한다. 시스템 실패를 예방하기 위해 운영자는 적절한 재배치 전략을 수립해야 한다. 운영자는 예측 수요 정보를 전제로 의사결정을 하므로 수요예측의 정확성이 중요한 요소이나, 수요의 불확실성으로 인해 예측 오차의 발생이 불가피하다. 본 연구의 목적은 공공자전거 수요의 불확실성과 시스템의 동적 특성을 고려하여 불만족 수요를 최소화하는 재배치 모형을 개발하는 것이다. 공공자전거 재배치 메커니즘은 순차적 의사결정 문제에 해당하므로, 본 연구에서는 순차적 의사결정 문제를 모형화할 수 있는 마르코프 결정 과정을 적용한다. 마르코프 결정 과정을 풀기 위해 복잡한 문제를 간단한 부문제로 분해하여 정확해를 도출하는 동적 계획법을 이용한다. 하지만 마르코프 결정 과정의 상태 집합과 결정 집합의 크기가 커지면 계산 복잡도가 증가하므로, 동적 계획법을 이용한 정확해를 도출할 수 없다. 이를 해결하기 위해 근사적 동적 계획법을 도입하여 근사해를 도출하며, 대규모 공공자전거 네트워크에서 가능해를 얻기 위해 강화학습 모형을 적용한다. 장래 공공자전거 이용수요의 불확실성을 모사하기 위해, 기계학습 기법의 일종인 random forest로 예측 수요를 도출하고, 예측 수요를 평균으로 하는 포아송 분포를 따라 수요를 확률적으로 발생시켰다. 본 연구에서는 관측 수요와 예측 수요 간의 차이인 예측오차에 빠르게 대응하는 재배치 전략을 개발하고 효과를 평가한다. 개발된 전략의 우수성을 검증하기 위해, 기존 연구의 재배치 전략 및 현실에서 적용되는 전략을 모형화하고 결과를 비교한다. 또한, 재고량의 안전 구간 및 안전재고량에 관한 민감도 분석을 수행하여 함의점을 제시한다. 개발된 전략의 효과를 분석한 결과, 기존 연구의 전략 및 현실에서 적용되는 전략보다 개선된 성능을 보이며, 특히 예측오차가 큰 대여소를 탐색하는 전략이 전체 대여소를 탐색하는 전략과 재배치 효과가 유사하면서도 계산시간을 절감할 수 있는 것으로 나타났다. 공공자전거 인프라를 확대하지 않고도 운영의 효율화를 통해 공공자전거 시스템의 이용률 및 신뢰성을 제고할 수 있고, 공공자전거 재배치에 관한 정책적 함의점을 제시한다는 점에서 본 연구의 의의가 있다.Chapter 1. Introduction １ 1.1 Research Background and Purposes １ 1.2 Research Scope and Procedure ７ Chapter 2. Literature Review １０ 2.1 Vehicle Routing Problems １０ 2.2 Bicycle Repositioning Problem １２ 2.3 Markov Decision Processes ２３ 2.4 Implications and Contributions ２６ Chapter 3. Model Formulation ２８ 3.1 Problem Definition ２８ 3.2 Markov Decision Processes ３４ 3.3 Demand Forecasting ４０ 3.4 Key Performance Indicator (KPI) ４５ Chapter 4. Solution Algorithms ４７ 4.1 Exact Solution Algorithm ４７ 4.2 Approximate Dynamic Programming ５０ 4.3 Reinforcement Learning Method ５２ Chapter 5. Numerical Example ５５ 5.1 Data Overview ５５ 5.2 Experimental Design ６１ 5.3 Algorithm Performance ６６ 5.4 Sensitivity Analysis ７４ 5.5 Large-scale Cases ７６ Chapter 6. Conclusions ８２ 6.1 Conclusions ８２ 6.2 Future Research ８３ References ８６ 초 록 ９２Docto

Crowdsensing-driven route optimisation algorithms for smart urban mobility

Urban rörlighet anses ofta vara en av de främsta möjliggörarna för en hållbar statsutveckling. Idag skulle det dock kräva ett betydande skifte mot renare och effektivare stadstransporter vilket skulle stödja ökad social och ekonomisk koncentration av resurser i städerna. En viktig prioritet för städer runt om i världen är att stödja medborgarnas rörlighet inom stadsmiljöer medan samtidigt minska trafikstockningar, olyckor och föroreningar. Att utveckla en effektivare och grönare (eller med ett ord; smartare) stadsrörlighet är en av de svåraste problemen att bemöta för stora metropoler. I denna avhandling närmar vi oss problemet från det snabba utvecklingsperspektivet av ITlandskapet i städer vilket möjliggör byggandet av rörlighetslösningar utan stora stora investeringar eller sofistikerad sensortenkik. I synnerhet föreslår vi utnyttjandet av den mobila rörlighetsavkännings, eng. Mobile Crowdsensing (MCS), paradigmen i vilken befolkningen exploaterar sin mobilkommunikation och/eller mobilasensorer med syftet att frivilligt samla, distribuera, lokalt processera och analysera geospecifik information. Rörlighetavkänningssdata (t.ex. händelser, trafikintensitet, buller och luftföroreningar etc.) inhämtad från frivilliga i befolkningen kan ge värdefull information om aktuella rörelsesförhållanden i stad vilka, med adekvata databehandlingsalgoriter, kan användas för att planera människors rörelseflöden inom stadsmiljön. Såtillvida kombineras i denna avhandling två mycket lovande smarta rörlighetsmöjliggörare, eng. Smart Mobility Enablers, nämligen MCS och rese/ruttplanering. Vi kan därmed till viss utsträckning sammanföra forskningsutmaningar från dessa två delar. Vi väljer att separera våra forskningsmål i två delar, dvs forskningssteg: (1) arkitektoniska utmaningar vid design av MCS-system och (2) algoritmiska utmaningar för tillämpningar av MCS-driven ruttplanering. Vi ämnar att visa en logisk forskningsprogression över tiden, med avstamp i mänskligt dirigerade rörelseavkänningssystem som MCS och ett avslut i automatiserade ruttoptimeringsalgoritmer skräddarsydda för specifika MCS-applikationer. Även om vi förlitar oss på heuristiska lösningar och algoritmer för NP-svåra ruttproblem förlitar vi oss på äkta applikationer med syftet att visa på fördelarna med algoritm- och infrastrukturförslagen.La movilidad urbana es considerada una de las principales desencadenantes de un desarrollo urbano sostenible. Sin embargo, hoy en día se requiere una transición hacia un transporte urbano más limpio y más eficiente que soporte una concentración de recursos sociales y económicos cada vez mayor en las ciudades. Una de las principales prioridades para las ciudades de todo el mundo es facilitar la movilidad de los ciudadanos dentro de los entornos urbanos, al mismo tiempo que se reduce la congestión, los accidentes y la contaminación. Sin embargo, desarrollar una movilidad urbana más eficiente y más verde (o en una palabra, más inteligente) es uno de los temas más difíciles de afrontar para las grandes áreas metropolitanas. En esta tesis, abordamos este problema desde la perspectiva de un panorama TIC en rápida evolución que nos permite construir movilidad sin la necesidad de grandes inversiones ni sofisticadas tecnologías de sensores. En particular, proponemos aprovechar el paradigma Mobile Crowdsensing (MCS) en el que los ciudadanos utilizan sus teléfonos móviles y dispositivos, para nosotros recopilar, procesar y analizar localmente información georreferenciada, distribuida voluntariamente. Los datos de movilidad recopilados de ciudadanos que voluntariamente quieren compartirlos (por ejemplo, eventos, intensidad del tráfico, ruido y contaminación del aire, etc.) pueden proporcionar información valiosa sobre las condiciones de movilidad actuales en la ciudad, que con el algoritmo de procesamiento de datos adecuado, pueden utilizarse para enrutar y gestionar el flujo de gente en entornos urbanos. Por lo tanto, en esta tesis combinamos dos prometedoras fuentes de movilidad inteligente: MCS y la planificación de viajes/rutas, uniendo en cierta medida los distintos desafíos de investigación. Hemos dividido nuestros objetivos de investigación en dos etapas: (1) Desafíos arquitectónicos en el diseño de sistemas MCS y (2) Desafíos algorítmicos en la planificación de rutas aprovechando la información del MCS. Nuestro objetivo es demostrar una progresión lógica de la investigación a lo largo del tiempo, comenzando desde los fundamentos de los sistemas de detección centrados en personas, como el MCS, hasta los algoritmos de optimización de rutas diseñados específicamente para la aplicación de estos. Si bien nos centramos en algoritmos y heurísticas para resolver problemas de enrutamiento de clase NP-hard, utilizamos ejemplos de aplicaciones en el mundo real para mostrar las ventajas de los algoritmos e infraestructuras propuestas

Mechanism Design with Predicted Task Revenue for Bike Sharing Systems

Bike sharing systems have been widely deployed around the world in recent years. A core problem in such systems is to reposition the bikes so that the distribution of bike supply is reshaped to better match the dynamic bike demand. When the bike-sharing company or platform is able to predict the revenue of each reposition task based on historic data, an additional constraint is to cap the payment for each task below its predicted revenue. In this paper, we propose an incentive mechanism called {\em TruPreTar} to incentivize users to park bicycles at locations desired by the platform toward rebalancing supply and demand. TruPreTar possesses four important economic and computational properties such as truthfulness and budget feasibility. Furthermore, we prove that even when the payment budget is tight, the total revenue still exceeds or equals the budget. Otherwise, TruPreTar achieves 2-approximation as compared to the optimal (revenue-maximizing) solution, which is close to the lower bound of at least $\sqrt{2}$ that we also prove. Using an industrial dataset obtained from a large bike-sharing company, our experiments show that TruPreTar is effective in rebalancing bike supply and demand and, as a result, generates high revenue that outperforms several benchmark mechanisms.Comment: Accepted by AAAI 2020; This is the full version that contains all the proof

MODELING AND ANALYSIS OF AN AUTONOMOUS MOBILITY ON DEMAND SYSTEM

Ph.DDOCTOR OF PHILOSOPH

A Data-Driven Based Dynamic Rebalancing Methodology for Bike Sharing Systems

Mobility in cities is a fundamental asset and opens several problems in decision making and the creation of new services for citizens. In the last years, transportation sharing systems have been continuously growing. Among these, bike sharing systems became commonly adopted. There exist two different categories of bike sharing systems: station-based systems and free-floating services. In this paper, we concentrate our analyses on station-based systems. Such systems require periodic rebalancing operations to guarantee good quality of service and system usability by moving bicycles from full stations to empty stations. In particular, in this paper, we propose a dynamic bicycle rebalancing methodology based on frequent pattern mining and its implementation. The extracted patterns represent frequent unbalanced situations among nearby stations. They are used to predict upcoming critical statuses and plan the most effective rebalancing operations using an entirely data-driven approach. Experiments performed on real data of the Barcelona bike sharing system show the effectiveness of the proposed approach