A data stream-based evaluation framework for traffic information systems

Geisler, Sandra; Quix, Christoph; Schiffer, Stefan

doi:10.1145/1878500.1878505

Cited by 9 publications

(5 citation statements)

References 13 publications

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“…The Cooperative Cars (CoCar) project at Aachen University is developing a data stream mining platform for automotive systems [22]. Examples of its application include queue-end detection and traffic state estimation.…”

Section: Related Workmentioning

confidence: 99%

Vehicle Embedded Data Stream Processing Platform for Android Devices

Akiyama¹,

Nakamoto²,

Yamaguchi³

et al. 2015

ijacsa

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Abstract-Automotive information services utilizing vehicle data are rapidly expanding. However, there is currently no data centric software architecture that takes into account the scale and complexity of data involving numerous sensors. To address this issue, the authors have developed an in-vehicle datastream management system for automotive embedded systems (eDSMS) as data centric software architecture. Providing the data stream functionalities to drivers and passengers are highly beneficial. This paper describes a vehicle embedded data stream processing platform for Android devices. The platform enables flexible query processing with a dataflow query language and extensible operator functions in the query language on the platform. The platform employs architecture independent of data stream schema in in-vehicle eDSMS to facilitate smoother Android application program development. This paper presents specifications and design of the query language and APIs of the platform, evaluate it, and discuss the results.

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Section: Related Workmentioning

confidence: 99%

Vehicle Embedded Data Stream Processing Platform for Android Devices

Akiyama¹,

Nakamoto²,

Yamaguchi³

et al. 2015

ijacsa

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“…The user-defined Map function transforms given GPS coordinates and precision level to a geodetic coordinate system. The user-defined aggregate detects whether a given road segment has a traffic queue-end [9].…”

Section: Query Typesmentioning

confidence: 99%

Adaptive input admission and management for parallel stream processing

Balkesen

Tatbul

Özsu

2013

Proceedings of the 7th ACM International Conference on Distributed Event-Based Systems

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In this paper, we propose a framework for adaptive admission control and management of a large number of dynamic input streams in parallel stream processing engines. The framework takes as input any available information about input stream behaviors and the requirements of the query processing layer, and adaptively decides how to adjust the entry points of streams to the system. As the optimization decisions propagate early from input management layer to the query processing layer, the size of the cluster is minimized, the load balance is maintained, and latency bounds of queries are met in a more effective and timely manner. Declarative integration of external meta-data about data sources makes the system more robust and resource-efficient. Additionally, exploiting knowledge about queries moves data partitioning to the input management layer, where better load balance for query processing can be achieved. We implemented these techniques as a part of the Borealis stream processing system and conducted experiments showing the performance benefits of our framework.

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“…Traffic is often used as the application area in geo-streaming papers [3,4,5,9]. However, the geo-streams are often from stationary sensors such as induction loops.…”

Section: Related Workmentioning

confidence: 99%

Trajectories for novel and detailed traffic information

Krogh

Andersen

Torp

2012

Proceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoStreaming

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Trajectories based on GPS tracks have been studied for a number of years but only to a limited degree been used for analyzing and monitoring traffic. This paper shows how novel and important information about traffic can be computed from trajectories. Concretely the paper proposes to compute the central metric free-flow speed from trajectories, instead of using point-based measurements such as inductionloops. This free-flow speed is widely used to compute and monitor the congestion level. The paper argues that the actual travel-time is a more accurate metric. The paper suggests a novel approach to analyzing individual intersections that enables traffic analysts to compute queue lengths and estimated time to pass an intersection. Finally, the paper uses associative rule mining for evaluating green waves on road stretches. Such information can be used to verify that signalized intersections are correctly coordinated, and navigational device manufacturers to advice drivers in real-time on expected behavior of signalized intersections. The main conclusion is that trajectories can provide novel insight into the actual traffic situation that is not possible using existing approaches. Further, extracting this information requires no expensive changes to the road-network infrastructure, which is a problem with the technologies currently used.

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A data stream-based evaluation framework for traffic information systems

Cited by 9 publications

References 13 publications

Vehicle Embedded Data Stream Processing Platform for Android Devices

Vehicle Embedded Data Stream Processing Platform for Android Devices

Adaptive input admission and management for parallel stream processing

Trajectories for novel and detailed traffic information

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