Travel time estimation using NiagaraST and latte

Tufte, Kristin; Jin, Li; Maier, David; Papadimos, Vassilis; Bertini, Robert L.; Rucker, James

doi:10.1145/1247480.1247617

Cited by 19 publications

(19 citation statements)

References 2 publications

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“…Current literature on integrated processing of historical and streaming data has focused on developing efficient query processing models for data integration [22,8,9,25,4,1,5,27]. Frameworks [13,4,22,8] have been proposed to address complex event processing (CEP) over the integration of historical and streaming data from systems perspective.…”

Section: Related Workmentioning

confidence: 99%

“…Such an integrated analysis of historical and streaming data is required by many emerging applications including network monitoring for intrusion detection [23,4], financial trading, real-time bidding [28], and traffic monitoring [25]. To address the demands of such applications, data stream warehousing systems, such as TidalRace [16], have recently emerged.…”

Section: Introductionmentioning

confidence: 99%

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Estimating quantiles from the union of historical and streaming data

2016

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Modern enterprises generate huge amounts of streaming data, for example, micro-blog feeds, financial data, network monitoring and industrial application monitoring. While Data Stream Management Systems have proven successful in providing support for real-time alerting, many applications, such as network monitoring for intrusion detection and real-time bidding, require complex analytics over historical and real-time data over the data streams. We present a new method to process one of the most fundamental analytical primitives, quantile queries, on the union of historical and streaming data. Our method combines an index on historical data with a memory-efficient sketch on streaming data to answer quantile queries with accuracy-resource tradeoffs that are significantly better than current solutions that are based solely on disk-resident indexes or solely on streaming algorithms. Disciplines Electrical and Computer Engineering CommentsThis is a manuscript of a proceeding published as Singh, Sneha Aman, Divesh Srivastava, and Srikanta Tirthapura. "Estimating quantiles from the union of historical and streaming data. Systems have proven successful in providing support for real-time alerting, many applications, such as network monitoring for intrusion detection and real-time bidding, require complex analytics over historical and real-time data over the data streams. We present a new method to process one of the most fundamental analytical primitives, quantile queries, on the union of historical and streaming data. Our method combines an index on historical data with a memory-efficient sketch on streaming data to answer quantile queries with accuracy-resource tradeoffs that are significantly better than current solutions that are based solely on disk-resident indexes or solely on streaming algorithms.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating quantiles from the union of historical and streaming data

2016

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“…Characterizations of unique traffic patterns per road are addressed in [24], whereas [22] correlates GPS points with neighboring ones in space and time, in order to assess traffic status. For travel time estimation, [18] combines archived data with live traffic feeds in a streaming-oriented mode. In contrast, we opt for multi-resolution, succinct traffic statistics intended for easily perceptible portrayal and quick notifications.…”

Section: Related Workmentioning

confidence: 99%

“…Similar platforms have emerged lately [1,7,10,11,18], as vehicle positions and derived statistics are inherently fluctuating, potentially intermittent, and ever more voluminous to be hosted by a traditional DBMS. In a spirit close to ours, the objective is to turn quantitative samples (raw positions) into qualitative estimates (e.g., average speed, expected travel times).…”

Section: Introductionmentioning

confidence: 99%

Event Processing and Real-Time Monitoring over Streaming Traffic Data

Patroumpas

Sellis

2012

Lecture Notes in Computer Science

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Abstract. Tracking mobility of humans, animals or merchandise has recently given rise to a wide variety of location-based services and monitoring applications. In this paper, we particularly focus on real-time traffic surveillance over densely congested road networks in large metropolitan areas. In such a setting, streaming positional updates are being frequently relayed into a central server from numerous moving vehicles (buses, taxis, passenger cars etc.). Our analysis concerns two important aspects. First, we outline characteristics of a robust processing engine that is capable to efficiently manage such massive, transient, and perhaps noisy geospatial data. Our objective is to provide online aggregates and reliable estimates regarding the current traffic situation at multiple levels of resolution. At a second step, we design a framework for effective multi-modal dissemination of derived information to the end users, in the form of interactive maps for intuitive visualization as well as instant notifications via message feeds. As a proof of concept, we also report on our ongoing development of EPOPS; in its current version, this functional prototype of the proposed scheme is able to deliver cross-platform geographic, textual, and even multimedia content through web and smartphone interfaces.

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“…A fundamental requirement to smart transportation is to collect the dynamic vehicular location data to form the basis to build an effective traffic information system [4]. The collected large-scale vehicular dataset is subject to further analysis, such as traffic estimation [15], hot spot detection [9], driving pattern recognition [8], traffic mining [5], and similar routes discovery [3], before the goals of smart transportation can be achieved. Real-time vehicular data collection is the first step toward smart transportation.…”

Section: Introductionmentioning

confidence: 99%

Exploration of ground truth from raw GPS data

Mao

Luo

Tan

et al. 2012

Proceedings of the ACM SIGKDD International Workshop on Urban Computing

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To enable smart transportation, a large volume of vehicular GPS trajectory data has been collected in the metropolitanscale Shanghai Grid project. The collected raw GPS data, however, suffers from various errors. Thus, it is inappropriate to use the raw GPS dataset directly for many potential smart transportation applications. Map matching, a process to align the raw GPS data onto the corresponding road network, is a commonly used technique to calibrate the raw GPS data. In practice, however, there is no ground truth data to validate the calibrated GPS data. It is necessary and desirable to have ground truth data to evaluate the effectiveness of various map matching algorithms, especially in complex environments. In this paper, we propose truthFinder, an interactive map matching system for ground truth data exploration. It incorporates traditional map matching algorithms and human intelligence in a unified manner. The accuracy of truthFinder is guaranteed by the observation that a vehicular trajectory can be correctly identified by human-labeling with the help of a period of historical GPS dataset. To the best of our knowledge, truthFinder is the first interactive map matching system trying to explore the ground truth from historical GPS trajectory data. To measure the cost of human interactions, we design a cost model that classifies and quantifies user operations. Having the guaranteed accuracy, truthFinder is evaluated in terms of operation cost. The results show that truthFinder makes the cost of map matching process up to two orders of magnitude less than the pure human-labeling approach.

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Travel time estimation using NiagaraST and latte

Cited by 19 publications

References 2 publications

Estimating quantiles from the union of historical and streaming data

Estimating quantiles from the union of historical and streaming data

Event Processing and Real-Time Monitoring over Streaming Traffic Data

Exploration of ground truth from raw GPS data

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