Travel speed estimation from cellular networks using modified Data Swarm Clustering algorithm

Basyoni, Yarah

doi:10.1109/icengtechnol.2014.7016816

Cited by 2 publications

(5 citation statements)

References 12 publications

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“…Due to the temporal and spatial information contained in mobile phone signaling data, they are commonly employed to estimate and predict traffic and pedestrian flow. Basyoni [2] transformed cellular-network-based mobile phone data into vehicle-based data using data clustering algorithms without auxiliary systems, achieving good results for traffic flow estimation. Chen [3] investigated the feasibility of using mobile phone data for dynamic pedestrian flow prediction and reached an accuracy of over 75%.…”

Section: Literature Reviewmentioning

confidence: 99%

Mobile Phone Data Feature Denoising for Expressway Traffic State Estimation

Shou

Xie

et al. 2023

Sustainability

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Due to their wide coverage, low acquisition cost and large data quantity, the mobile phone signaling data are suitable for fine-grained and large-scale estimation of traffic conditions. However, the relatively high level of data noise makes it difficult for the estimation to achieve sufficient accuracy. According to the characteristics of mobile phone data noise, this paper proposed an improved density peak clustering algorithm (DPCA) to filter data noise. In addition, on the basis of the long short-term memory model (LSTM), a traffic state estimation model based on mobile phone feature data was established with the use of denoising data to realize the estimation of the expressway traffic state with high precision, fine granules, and wide coverage. The Shanghai–Nanjing Expressway was used as a case study area for method and model verification, the results of which showed that the denoising method proposed in this paper can effectively filter data noise, reduce the impact of extreme noise data, significantly improve the estimation accuracy of the traffic state, and reflect the actual traffic situation in a fairly satisfactory manner.

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Section: Literature Reviewmentioning

confidence: 99%

Mobile Phone Data Feature Denoising for Expressway Traffic State Estimation

Shou

Xie

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…Furthermore, it was assumed that each vehicle has at least a switched-on CP aboard (100% penetration rate). Vehicular-based CP data were extracted in a previous research endeavour by a model that did not use a secondary reference system [3] from which SMS was estimated for 8 h at aggregation intervals of: 1, 3, and 5 min [4].…”

Section: Case Study Descriptionmentioning

confidence: 99%

“…As the CP LA is changed (by the CP transition from one location to another), an LA update is exerted, by a process known as 'handover', to preserve the received signals strength by the CP. Spatiotemporal data of the CPs can be collected from cellular networks from which several types of traffic and mobility data can be extracted by using various data mining algorithms [3,4].…”

Section: Introductionmentioning

confidence: 99%

“…Alternatively, SMS data were estimated from vehicular-based traffic data with temporal dimensions extracted from CP data with <2 MAPE [4]. The vehicular-based traffic data were extracted from augmented CP data by using a clustering technique (modified Data Swarm Clustering).…”

Section: Introductionmentioning

confidence: 99%

“…The vehicular-based traffic data were extracted from augmented CP data by using a clustering technique (modified Data Swarm Clustering). The full details of the estimation procedure can be found in [3,4]. The clustering model was able to eliminate the noise in CP-based data presenting a more realistic dataset of the traffic stream.…”

Section: Introductionmentioning

confidence: 99%

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