Spatio-temporal compression of trajectories in road networks

Popa, Iulian Sandu; Zeitouni, Karine; Oria, Vincent; Kharrat, Ahmed

doi:10.1007/s10707-014-0208-4

Cited by 58 publications

(12 citation statements)

References 23 publications

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“…We conclude the scientific fundamentals with an observation regarding the impact of incorporating a more global context in the process if the mobile data compression. Namely, by incorporating the knowledge about an existing road network -i.e., its graph-based representation with speed values associated with the edges -there is a possibility of a MOD-based data compression [13]. Clearly, this could further increase the overall benefits, in terms of the overall MODwide savings.…”

Section: Compression Of Mobile Location Data Fig 3 E U Distance Funmentioning

confidence: 99%

Compression of Mobile Location Data

Trajcevski¹,

Wolfson²,

Scheuermann³

2017

Encyclopedia of Database Systems

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Section: Compression Of Mobile Location Data Fig 3 E U Distance Funmentioning

confidence: 99%

Compression of Mobile Location Data

Trajcevski¹,

Wolfson²,

Scheuermann³

2017

Encyclopedia of Database Systems

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“…Richter et al (2012) introduced a semantic trajectory compression method, which utilizes reference points in a transportation network to replace raw and redundant GPS trajectory data points. Popa et al (2015) proposed an extended data model and a transportation network partitioning algorithm to increase trajectory compression rates without increasing the compression error. The limitation of the first approach is that no optimal solution is provided to adjust the online data sampling rate.…”

Section: Introductionmentioning

confidence: 99%

Efficient Data Collection and Accurate Travel Time Estimation in a Connected Vehicle Environment Via Real-Time Compressive Sensing

Lin

Peeta

2019

J. Big Data Anal. Transp.

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Connected vehicles (CVs) can capture and transmit detailed data such as vehicle position and speed through vehicle-tovehicle and vehicle-to-infrastructure communications. The wealth of CV data provides new opportunities to improve safety and mobility of transportation systems, which can overburden storage and communication systems. To mitigate this issue, we propose a compressive sensing (CS) approach that allows CVs to capture and compress data in real-time and later recover the original data accurately and efficiently. We evaluate our approach using two case studies. In the first study, we use our approach to recapture 10 million CV basic safety message (BSM) speed samples as well as other BSM variables. The results show that we can recover the original speed data with root-mean-squared error as low as 0.05 MPH. In the second study, a freeway traffic simulation model is built to evaluate the impact of our approach on travel time estimation. Multiple scenarios with various CV market penetration rates, On-board unit (OBU) capacities, compression ratios, arrival rate patterns, and data capture rates are simulated for our experiments. As a result, our approach provides more accurate estimation than conventional data collection methods by achieving up to 65% relative reduction in travel time estimation error. With a low compression ratio, our approach can still provide accurate estimation, therefore reducing OBU hardware costs. Lastly, our approach can improve travel time estimation accuracy when CVs are in traffic congestion as it provides a broader spatial-temporal coverage of traffic conditions and can accurately and efficiently recover the original CV data.

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“…Similarly, Wang et al [20] applied the network KDE to characterize commercial facilities by depicting the spatial distribution patterns of customer numbers and satisfaction. Moreover, Popa et al [21] compressed trajectory data in road networks with deterministic error bounds to systematically transmit and store such data. Brinkhoff [22] proposed a framework that supports generating network-based moving objects for the further exploration of spatiotemporal database analyses.…”

Section: Introductionmentioning

confidence: 99%

Investigating Road-Constrained Spatial Distributions and Semantic Attractiveness for Area of Interest

Meng

Xing

et al. 2019

Sustainability

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An area of interest (AOI) refers to an urban area that attracts people’s attention within different urban functions through cities. The wide availability of big geo-data that are able to capture human activities and environmental socioeconomics enable a more nuanced identification of AOIs. Current research has proposed various approaches to delineate continuous AOI patterns using big geo-data. However, these approaches ignore the effects of urban structures such as road networks on reshaping AOIs, and fail to investigate the attractiveness and certain functions within AOIs. To fill this gap, this paper proposes a systematic framework to investigate the spatial distribution of road-constrained AOIs and analyze the semantic attractiveness. First, we propose an Epanechnikov-based kernel density estimation (KDE) with a bandwidth selection strategy to extract road-constrained AOIs. Then, we establish semantic attractiveness indices regarding AOIs based on the textual information and the number of review data. Finally, we investigate in detail the spatial distribution and semantic attractiveness of AOIs in Yuexiu, Guangzhou. The results show that road-constrained AOIs can not only effectively capture the human activity patterns influenced by urban structures, but also depict certain urban functions including entertainment, public, service, hotel, education, and food functions. This method provides a quantitative reference to monitor urban structures and human activities to support city planning.

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Spatio-temporal compression of trajectories in road networks

Cited by 58 publications

References 23 publications

Compression of Mobile Location Data

Compression of Mobile Location Data

Efficient Data Collection and Accurate Travel Time Estimation in a Connected Vehicle Environment Via Real-Time Compressive Sensing

Investigating Road-Constrained Spatial Distributions and Semantic Attractiveness for Area of Interest

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