Abstract
Traffic Prediction on a urban road network become more complex face to exponential growth in the volume of traffic, it is necessary to study the relationship between road segments before the prediction calculation. The spatial correlation theory has been well developed to interpret the dependency for understanding how time series are related in multivariate model. In large scale road network modeled by Multivariate Time Series, the Spatial-temporal dependencies detection can limit the use of only data collected from points related to a target point to be predicted. This paper present a Cross-Correlation as method to dependency analysis between traffic road segments, Scatterplot of Cross-Correlation is proposed to reveal the dependency, we provide a comparative analysis between a three correlation coefficients sush as Spearman, Kendal and Person to conclude the best one. To illustrate our study, the methodology is applied to the 6th road ring as the most crowded area of Bei**g.
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References
Baidu Research Open-Access Dataset. www.ai.baidu.com
Liao, B., et al.: Deep Sequence Learning with Auxiliary Information for Traffic Prediction (2018)
Yuan, N., Xoplaki, E., Zhu, C., Luterbacher, J.: A novel way to detect correlations on multi-time scales, with temporal evolution and for multi-variables (2016)
Chong, K., Sung, H.: Prediction of Road Safety Using Road/Traffic Big Data (2015)
Chen, Y.: A New Methodology of Spatial Cross-Correlation Analysis (2015)
Daxue, Q., Bao, X., Zhao, T., Zhang, Y., Zhou, Y., Feng, S.: Spatial cross correlations of traffic flows on urban road networks (2011)
Guo, S., et al.: Identifying the most influential roads based on traffic correlation networks (2019)
Su, F., Dong, H., Jia, L., Tian, Z., Sun, X.: Space-time correlation analysis of traffic flow on road network (2017)
Shi, K., Di, B., Zhang, K., Feng, C., Svirchev, L.: Detrended cross-correlation analysis of urban traffic congestion and NO2 concentrations in Chengdu (2018)
Hauke, J., Kossowski, T.: Comparison of values of person’s and spearman’s correlation coefficients on the same sets of data (2011)
William, B.M., Durvasula, P.K., Brown, D.E.: Urban freeway travel prediction: application of seasonal ARIMA and exponential smoothing models (1998)
Okutani, I., Stephanedes, Y.J.: Dynamic prediction of traffic volume through Kalman filtering theory (1984)
**e, Y., Zhang, Y., Ye, Z.: Short-Term Traffic Volume Forecasting Using Kalman Filter with Discrete Wavelet Decomposition (2007)
Zhang, Y., **e, Y.: Forecasting of Short-Term Freeway Volume with v-Support Vector Machines (2007)
Yu, G., Hu, J., Zhang, C., Song, G.: Short-term traffic flow forecasting based on Markov chain model (2003)
Wei, W., Wu, H., Ma, H.: An AutoEncoder and LSTM-Based Traffic Flow Prediction Method (2019)
Dai, X., Fu, R., Lin, Y., Li, L., Wang, F.Y.: DeepTrend: A Deep Hierarchical Neural Network for Traffic Flow Prediction (2017)
Chan, K.Y., Dillon, T.S.: On-road sensor configuration design for traffic flow prediction using fuzzy neural networks and Taguchi method (2013)
Pan, T.L., Sumalee, A., Zhong, R.X., Payoong, N.I.: Short-Term Traffic State Prediction Based on Temporal-Spatial Correlation (2013)
Baofeng, D.I., Kai, S., Kaishan, Z., Laurance, S., **aoxi, H.: Long-Term Correlations and Multifractality of Traffic Flow Measured By GIS for Congested and free-Flow Roads (2016)
Cheng, T., James, H.: Spatio-Temporal Autocorrelation of Road Network Data (2011)
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Redouane, B.B., Beghdad Bey, K. (2022). Road Segments Traffic Dependencies Study Using Cross-Correlation. In: Lejdel, B., Clementini, E., Alarabi, L. (eds) Artificial Intelligence and Its Applications. AIAP 2021. Lecture Notes in Networks and Systems, vol 413. Springer, Cham. https://doi.org/10.1007/978-3-030-96311-8_27
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DOI: https://doi.org/10.1007/978-3-030-96311-8_27
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