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학술지 Predicting Short-Term Traffic Speed Using a Deep Neural Network to Accommodate Citywide Spatio-Temporal Correlations
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저자
이용진, 전현정, 손기민
발행일
202103
출처
IEEE Transactions on Intelligent Transportation Systems, v.22 no.3, pp.1435-1448
ISSN
1524-9050
출판사
IEEE
DOI
https://dx.doi.org/10.1109/TITS.2020.2970754
초록
The traffic speed on a given road segment is affected by the current and past speeds on nearby segments, and the influence further cascades into the rest of a transport network. Thus, a successful forecasting model should consider not only the impact of neighboring road segments but also that of distant segments. Based on this principle, the approach proposed here projects the topology of a real traffic network into the structure of a deep neural network in order to accommodate citywide spatial correlations as well as temporal dependencies. This approach leads to interesting model interpretations in terms of traffic state transition and propagation, which form a basis for extending the proposed forecasting model. The present study was conducted with a large-scale data set collected over 10 months, and traffic speeds were successfully forecasted for 170 road segments in Gangnam, Seoul, Korea.
KSP 제안 키워드
Deep neural network(DNN), Large-scale datasets, Temporal Correlation, Temporal dependencies, Traffic state transition, forecasting model, road segment, short-term, spatial correlation, spatio-Temporal, traffic network