A Service-Based Approach to Traffic Sensor Data Integration and Analysis to Support Community-Wide Green Commute in China

Han, Yanbo; Wang, Guiling; Yu, Jian; Liu, Chen; Zhang, Zhongmei; Zhu, Meiling

doi:10.1109/tits.2015.2498178

Cited by 33 publications

(17 citation statements)

References 16 publications

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“…Refs. [28,29] proposed to model a bunch of related vehicles as a vehicle group to capture the companion pattern among them. They define vehicle group based on the co-occurrence of detection as follows.…”

Section: Vehicle Groupmentioning

confidence: 99%

Vehicle Identity Recovery for Automatic Number Plate Recognition Data via Heterogeneous Network Embedding

Chen

2020

Sustainability

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Automatic number plate recognition (ANPR) systems, which have been widely equipped in many cities, produce numerous travel data for intelligent and sustainable transportation. ANPR data operate at an individual level and carry the unique identities of vehicles, which can support personalized traffic planning. However, these systems also suffer from the common problem of missing data. Different from the traditional missing cases, we focus on the problem of the loss of vehicle identities in ANPR records due to recognition failure or other environmental factors. To address the issue, we propose a heterogeneous graph embedding framework that constructs a travel heterogeneous information network (THIN) and learns the embeddings of the entities to find the best matched vehicles for the unknown records. As a result, the recovery of vehicle identities is cast as an entity alignment task on a THIN. The proposed method integrates the vehicle group entities and context relations into the THIN for capturing the spatiotemporal relationships in vehicle travel and adopts a holographic embeddings model for better fitting the network structure. Empirically, we test it with a real ANPR dataset collected from Xuancheng, China, which has a densely-distributed camera network. The experiments demonstrate the effectiveness of the proposed graph structure under different missing rates. Further, we compare it with other embedding methods and the results support the superiority of holographic embeddings.

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Section: Vehicle Groupmentioning

confidence: 99%

Vehicle Identity Recovery for Automatic Number Plate Recognition Data via Heterogeneous Network Embedding

Chen

2020

Sustainability

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“…Based on our event capturing algorithm, we can capture the abnormal status of each device. In this section, we will introduce the dynamic service collaboration for correlating sensor streams ← the difference of their records' numbers; (5) ← getLongerSeries( , ); (6) ← getShorterSeries( , ); (7) ← interpolation( , ); (8) else (9) ← ; (10) ← ; (11) end if (12) ⟨ , ⟩ ← aligned data series from ⟨ , ⟩ based DTW (13) calculate correlation coefficient cor of ⟨ , ⟩; (14) if…”

Section: Dynamic Collaboration Among Servicesmentioning

confidence: 99%

“…(1) while receiving collaborate result (2) extract source service ; (3) if the result is a composed event (4) extract the valid routing path ⟨ , ⟩; (5) .weight = .weight+ ; (6) else (7) extract the invalid routing path ⟨ , ⟩; (8) .weight = .weight-; (9) if .weight< (10) remove from ; (11) end if (12) end if (13) for each in V (14) .weight = .weight+ ; (15) if .weight ≥ (16) add in ; (17) end if (18) end for (19) end while Algorithm 2: Updating the routing targets.…”

Section: Case Studymentioning

confidence: 99%

“…But ad hoc association and streaming-oriented queries can hardly be reflected. Following the thought of software-defined sensors, we proposed a service abstraction called proactive data service in our previous works [11,12]. With proactive data service, the sensor events generated directly by sensors can be transformed into multiple service events, which reveal more meaningful information.…”

Section: Introductionmentioning

confidence: 99%

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A Service‐Based Method for Multiple Sensor Streams Aggregation in Fog Computing

Zhang

Liu

Zhang

et al. 2018

Wireless Communications and Mobile Computing

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A surge in sensor data volume has exposed the shortcomings of cloud computing, particularly the limitation of network transmission capability and centralized computing resources. The dynamic intervention among sensor streams also brings challenges for IoT applications to derive meaningful information from multiple sensor streams. To handle these issues, this paper proposes a service-based method with fog computing paradigm based on our previous service abstraction, which can capture meaningful events from multiple sensor streams. In our service abstraction, we utilize correlation analysis method to capture events as variations of correlation among sensor streams. Facing inconsistent frequency and shift of correlation, we propose a Dynamic Time Warping- (DTW-) based algorithm to obtain sensor streams’ lag-correlation. For adaptively aggregating related events from different services, we also propose an event routing algorithm to assist the composition of cascaded events through service collaboration. This paper reports the tryout use of our method in Chinese power grid for detecting abnormal situations of power quality. Through a series of experiments based on real sensor data in power grid, we verified that our method can reduce the network transmission and computing resource with high accuracy.

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“…Xu et al (Xu, 2014) and Perera et al (Perera, 2015) provided IoT data resources as services to support accessing crossplatform data by URI through Web for IoT applications. In our previous work (Han, 2015). we proposed a stream data service model to access stream data continuously in realtime, and implemented a carpooling service based on the stream data services.…”

Section: Positioning Of Services In the Context Of Industry Iotmentioning

confidence: 99%

A decentralized and service-based approach to proactively correlating stream data

Han¹,

Liu

et al. 2016

Services Proceedings of the 2016 S2

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Stream data from devices and sensors are considered a typical kind of big data. Though being promising, they are useful only when we can reasonably correlate and effectively use them. Herein, services come back to the spotlight. The position paper reports some of our efforts in promoting service-based integration and correlation of such stream data in a real setting-monitoringand optimized coordination of individual devices in a power plant.

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A Service-Based Approach to Traffic Sensor Data Integration and Analysis to Support Community-Wide Green Commute in China

Cited by 33 publications

References 16 publications

Vehicle Identity Recovery for Automatic Number Plate Recognition Data via Heterogeneous Network Embedding

Vehicle Identity Recovery for Automatic Number Plate Recognition Data via Heterogeneous Network Embedding

A Service‐Based Method for Multiple Sensor Streams Aggregation in Fog Computing

A decentralized and service-based approach to proactively correlating stream data

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