다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Urbanization's rapid progress has modernized many people's lives but also engendered big issues, such as traffic congestion, energy consumption, and pollution. Urban computing aims to tackle these issues by using the data that has been generated in cities (e.g., traffic flow, human mobility, and geographical data). Urban computing connects urban sensing, data management, data analytics, and service providing into a recurrent process for an unobtrusive and continuous improvement of people's lives, city operation systems, and the environment. Urban computing is an interdisciplinary field where computer sciences meet conventional city-related fields, like transportation, civil engineering, environment, economy, ecology, and sociology in the context of urban spaces. This article first introduces the concept of urban computing, discussing its general framework and key challenges from the perspective of computer sciences. Second, we classify the applications of urban computing into seven categories, consisting of urban planning, transportation, the environment, energy, social, economy, and public safety and security, presenting representative scenarios in each category. Third, we summarize the typical technologies that are needed in urban computing into four folds, which are about urban sensing, urban data management, knowledge fusion across heterogeneous data, and urban data visualization. Finally, we give an outlook on the future of urban computing, suggesting a few research topics that are somehow missing in the community.

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Urban Computing: Concepts, Methodologies, and Applications

Optimization for dynamic ride-sharing: A review

Transportation Networks. Optimality. Cost Functions. Deterministic User Equilibrium Assignment. Stochastic User Equilibrium Assignment. Trip Table Estimation. Network Reliability. Network Design. Conclusions. References. Index.

Transportation network analysis

Bike Share: A Synthesis of the Literature

Astochastic, mixed-integer program (MIP) involving joint chance constraints is developed that generates least-cost vehicle redistribution plans for shared-vehicle systems such that a proportion of all near-term demand scenarios are met. The model aims to correct short-term demand asymmetry in shared-vehicle systems, where flow from one station to another is seldom equal to the flow in the opposing direction. The model accounts for demand stochasticity and generates partial redistribution plans in circumstances when demand outstrips supply. This stochastic MIP has a nonconvex feasible region. A novel divide-and-conquer algorithm for generating p-efficient points, used to transform the problem into a set of disjunctive, convex MIPs and handle dual-bounded chance constraints, is proposed. Assuming independence of random demand across stations, a faster cone-generation method is also presented. In a real-world application for a system in Singapore, the potential of redistribution as a fleet management strategy and the value of accounting for inherent stochasticities are demonstrated.

Fleet Management for Vehicle Sharing Operations

Large-Scale Vehicle Sharing Systems: Analysis of Vélib'

This paper describes a system to leverage data on cell phones to understand mobility patterns and presents a large-scale network design model for public transit while considering existing service offerings. The work is motivated by the rapid urbanization in growth market cities across the world, where adequate resources to develop detailed travel demand models may be absent. Urban growth is coupled with high cell phone penetration which transit operators can leverage to better match observed demand for travel with service offerings. Based on call detail records from a telecommunications operator in Abidjan, Cote d’Ivoire, this paper describes analytics and optimization techniques that result in system-wide journey time improvements of 10%. The main contribution of this paper is to demonstrate how big data analytics and optimization tools can transform data from opportunistic sensing in the real-world to improve urban mobility outcomes.

AllAboard: A System for Exploring Urban Mobility and Optimizing Public Transport Using Cellphone Data

The deep penetration of mobile phones offers cities the ability to opportunistically monitor citizens’ interactions and use data-driven insights to better plan and manage services. In this context, transit operators can leverage pervasive mobile sensing to better match observed demand for travel with their service offerings. With large scale data on mobility patterns, operators can move away from the costly and resource intensive transportation planning processes prevalent in the West, to a more data-centric view, that places the instrumented user at the center of development. In this framework, using mobile phone data to perform transit analysis and optimization represents a new frontier with significant societal impact, especially in developing countries.

/pdf/allaboard-a-system-for-exploring-urban-mobility-and-3h2oh61tbr.pdf

Rahul Nair

Papers

Fleet Management for Vehicle Sharing Operations

Large-Scale Vehicle Sharing Systems: Analysis of Vélib'

AllAboard: A System for Exploring Urban Mobility and Optimizing Public Transport Using Cellphone Data

AllAboard: a system for exploring urban mobility and optimizing public transport using cellphone data

Equilibrium network design of shared-vehicle systems