A Hybrid Short-Term Traffic Flow Prediction Model Based on Singular Spectrum Analysis and Kernel Extreme Learning Machine

Shang, Qiang; Lin, Ciyun; Yang, Zhaosheng; Bing, Qichun; Zhou, Xiaofeng

doi:10.1371/journal.pone.0161259

Cited by 36 publications

(27 citation statements)

References 61 publications

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“…Traffic analysts have utilized the spatial dependency of road segments to solve three typical problems in a traffic network: (1) short-term traffic forecasting [1, 2, 5, 6], (2) reliable path problem [7], and (3) missing data estimation [8]. …”

Section: Introductionmentioning

confidence: 99%

Using temporal detrending to observe the spatial correlation of traffic

2017

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This empirical study sheds light on the spatial correlation of traffic links under different traffic regimes. We mimic the behavior of real traffic by pinpointing the spatial correlation between 140 freeway traffic links in a major sub-network of the Minneapolis—St. Paul freeway system with a grid-like network topology. This topology enables us to juxtapose the positive and negative correlation between links, which has been overlooked in short-term traffic forecasting models. To accurately and reliably measure the correlation between traffic links, we develop an algorithm that eliminates temporal trends in three dimensions: (1) hourly dimension, (2) weekly dimension, and (3) system dimension for each link. The spatial correlation of traffic links exhibits a stronger negative correlation in rush hours, when congestion affects route choice. Although this correlation occurs mostly in parallel links, it is also observed upstream, where travelers receive information and are able to switch to substitute paths. Irrespective of the time-of-day and day-of-week, a strong positive correlation is witnessed between upstream and downstream links. This correlation is stronger in uncongested regimes, as traffic flow passes through consecutive links more quickly and there is no congestion effect to shift or stall traffic. The extracted spatial correlation structure can augment the accuracy of short-term traffic forecasting models.

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Section: Introductionmentioning

confidence: 99%

Using temporal detrending to observe the spatial correlation of traffic

2017

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“…Amongst other dimension reduction methods, we note applications of PCA based on singular-value decomposition [74,[79][80][81], non-negative matrix factorisation [82,83], local shrunk discriminant analysis [84], and singular spectrum analysis [79,85,86].…”

Section: Class 5: Dimension Reduction Methodsmentioning

confidence: 99%

Feature selection and extraction in spatiotemporal traffic forecasting: a systematic literature review

Pavlyuk

2019

Eur. Transp. Res. Rev.

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A spatiotemporal approach that simultaneously utilises both spatial and temporal relationships is gaining scientific interest in the field of traffic flow forecasting. Accurate identification of the spatiotemporal structure (dependencies amongst traffic flows in space and time) plays a critical role in modern traffic forecasting methodologies, and recent developments of data-driven feature selection and extraction methods allow the identification of complex relationships. This paper systematically reviews studies that apply feature selection and extraction methods for spatiotemporal traffic forecasting. The reviewed bibliographic database includes 211 publications and covers the period from early 1984 to March 2018. A synthesis of bibliographic sources clarifies the advantages and disadvantages of different feature selection and extraction methods for learning the spatiotemporal structure and discovers trends in their applications. We conclude that there is a clear need for development of comprehensive guidelines for selecting appropriate spatiotemporal feature selection and extraction methods for urban traffic forecasting.

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“…Zhang et al [26] found that ELMs with heterogeneous data exhibit improvements over linear models in terms of both the level and directional accuracy when handling traffic flow time-series data. Shang et al [27] proposed a shortterm traffic flow prediction model called SSA-KELM (singular spectrum analysis kernel ELM) to reduce the influences of uncertainty and nonlinearity on the expressway system.…”

Section: Introductionmentioning

confidence: 99%

Research on Air Traffic Flow Forecast Based on ELM Non-Iterative Algorithm

Zhang

Sun

et al. 2020

Mobile Netw Appl

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In this paper, the chaotic characteristics of air traffic flow are studied, ADS-B data easily available to ground aviation users are selected as the basic data of traffic flow, and a high-dimensional prediction model of air traffic flow time series based on the noniterative PSR-ELM algorithm is established. The prediction results of the proposed algorithm are then compared with those of the SVR algorithm, which requires iteration. Moreover, airspace operation data before and after the outbreak of the COVID-19 epidemic are selected as the experimental scene, and the prediction effects of time series with different degrees of chaos are comparatively analyzed. The experimental results reveal that the PSR-ELM algorithm achieves fast and accurate results, and, when the traffic flow state is sparse, the degree of chaos is reduced and the prediction effect is improved. The findings of this research provide a reference for air traffic flow theory.

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A Hybrid Short-Term Traffic Flow Prediction Model Based on Singular Spectrum Analysis and Kernel Extreme Learning Machine

Cited by 36 publications

References 61 publications

Using temporal detrending to observe the spatial correlation of traffic

Using temporal detrending to observe the spatial correlation of traffic

Feature selection and extraction in spatiotemporal traffic forecasting: a systematic literature review

Research on Air Traffic Flow Forecast Based on ELM Non-Iterative Algorithm

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