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Titlebook: Enabling Smart Urban Services with GPS Trajectory Data; Chao Chen,Daqing Zhang,Hongyu Huang Book 2021 The Editor(s) (if applicable) and Th

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發(fā)表于 2025-3-21 19:32:13 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Enabling Smart Urban Services with GPS Trajectory Data
編輯Chao Chen,Daqing Zhang,Hongyu Huang
視頻videohttp://file.papertrans.cn/310/309164/309164.mp4
概述Addresses fundamental issues in trajectory data, including data map-matching, data compression, and data protection.Promotes smart mobility services to benefit a wide range of people including drivers
圖書封面Titlebook: Enabling Smart Urban Services with GPS Trajectory Data; Chao Chen,Daqing Zhang,Hongyu Huang Book 2021 The Editor(s) (if applicable) and Th
描述.With the proliferation of GPS devices in daily life, trajectory data that records where and when people move is now readily available on a large scale. As one of the most typical representatives, it has now become widely recognized that taxi trajectory data provides rich opportunities to enable promising smart urban services. Yet, a considerable gap still exists between the raw data available, and the extraction of actionable intelligence. This gap poses fundamental challenges on how we can achieve such intelligence. These challenges include inaccuracy issues, large data volumes to process, and sparse GPS data, to name but a few. Moreover, the movements of taxis and the leaving trajectory data are the result of a complex interplay between several parties, including drivers, passengers, travellers, urban planners, etc...In this book, we present our latest findings on mining taxi GPS trajectory data to enable a number of smart urban services, and to bring us one step closer tothe vision of smart mobility. Firstly, we focus on some fundamental issues in trajectory data mining and analytics, including data map-matching, data compression, and data protection. Secondly, driven by the re
出版日期Book 2021
關(guān)鍵詞GPS trajectory; urban services; smart mobility; spatial-temproal data ananlysis; data compression; map ma
版次1
doihttps://doi.org/10.1007/978-981-16-0178-1
isbn_softcover978-981-16-0180-4
isbn_ebook978-981-16-0178-1
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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發(fā)表于 2025-3-21 21:49:56 | 只看該作者
Trajectory Data Compressione issues, such as storage, communication, and computation. Online trajectory compression becomes a promising way to alleviate these issues. In this chapter, we first propose an online trajectory data compression algorithm which works on the basis of the SD-Matching algorithm. Similar to the SD-Match
板凳
發(fā)表于 2025-3-22 02:41:22 | 只看該作者
地板
發(fā)表于 2025-3-22 07:34:24 | 只看該作者
Hunting or Waiting: Earning More by Understanding Taxi Service Strategiese skills to . for low-performance taxi drivers. Thanks to the widely available large-scale taxi GPS trajectory data, service strategies of taxi drivers that are hidden in their trajectory data can be uncovered by data mining techniques. In this chapter, we investigate taxi service strategies from th
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發(fā)表于 2025-3-22 12:21:35 | 只看該作者
GreenPlanner: Planning Fuel-Efficient Driving Routeslized fuel consumption model (PFCM) for each driver, based on the individual driving behaviors embedded in the GPS trajectory data and the physical features (e.g., traffic lights, stop signs, road network topology) along the routes provided by road network data. Furthermore, we build a general PFCM
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發(fā)表于 2025-3-22 14:58:02 | 只看該作者
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Real-Time Imputing Trip Purpose Leveraging Heterogeneous Trajectory Data, previous studies have paid relatively less attention to the trip purpose imputation at an individual level and required no real-time response. To narrow the gaps, a two-phase probabilistic framework TripImputor is proposed in this chapter, to perform real-time taxi trip purpose imputation and pers
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發(fā)表于 2025-3-22 22:33:25 | 只看該作者
GPS Environment Friendliness Estimation with Trajectory Datarent road segments can be further predicted by estimating the negative impact of the urban environment on GPS accuracy, and we refer to this impact as GPS environmental friendliness (GEF). In this chapter, we propose a method for processing and analysing a large amount of historical bus GPS trajecto
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發(fā)表于 2025-3-23 02:52:23 | 只看該作者
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