2018
DOI: 10.1049/iet-its.2016.0244
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Compression algorithm of road traffic data in time series based on temporal correlation

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Cited by 10 publications
(4 citation statements)
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“…Recently, due to the extremely rapid increase in the amount of information transmitted, computer networksare overloaded due to the low bandwidth of existing communication channels. This problem can be solved in two ways: by replacing existing communication lines with new ones with greater bandwidth, or by introducing new methods of data compression [1][2][3][4][5][6][7]. The first method requires significant financial costs, in addition, it is not always possible to replace communication lines; more often it is more advisable to use existing lines for data transmission than to lay new ones.…”
Section: Introductionmentioning
confidence: 99%
“…Recently, due to the extremely rapid increase in the amount of information transmitted, computer networksare overloaded due to the low bandwidth of existing communication channels. This problem can be solved in two ways: by replacing existing communication lines with new ones with greater bandwidth, or by introducing new methods of data compression [1][2][3][4][5][6][7]. The first method requires significant financial costs, in addition, it is not always possible to replace communication lines; more often it is more advisable to use existing lines for data transmission than to lay new ones.…”
Section: Introductionmentioning
confidence: 99%
“…Chen et al [7] used the local linear embedding method to temporal and spatial feature extraction to achieve the visualisation of high dimensional data in low dimensions. Xu et al [8] and Wang et al [9] realise the feature extraction of road traffic spatial and temporal data using LZW encoding, respectively. Recently, with the flourish of machine‐learning and data‐mining methods [10–12], many traffic feature extraction methods are proposed.…”
Section: Introductionmentioning
confidence: 99%
“…Additionally, compression sensing can achieve data collection and compression simultaneously. With the redundancy trait of traffic states data, compression sensing technology achieves the estimation [8, 9] and feature extraction [14–16] of traffic states data. Based on the spatial‐temporal correlation and similar characteristics of road traffic states data, Xiao et al presented a spatial–temporal feature extraction model [17].…”
Section: Introductionmentioning
confidence: 99%
“…At present, more and more countries and research organisations focus on cycle research. Most countries have developed their own cycle for emission regulations, such as federal test procedure (FTP) transient test cycle, European stationary cycle (ESC) cycle, European transient cycle (ETC) cycle, world harmonised transient cycle (WHTC) cycle and so on [3,4]. Other research institutions have developed various functional RDCs of engine and vehicle.…”
Section: Introductionmentioning
confidence: 99%