Abstract
Nowadays, there is an extensive body of literature that demonstrates the methods of forecasting traffic flows, which includes artificial neural networks, Kalman filtering, support vector regression, (seasonal) ARIMA models. However, seldom articles use two or more than two methods to predict the traffic flows and compare their difference within the forecasting process , which might be gradually recognized as a potentially important research area in the future. Two of the most commonly adopted methods, Space-Time Autoregressive Integrated Moving Average (STARIMA) and the Elman Recurrent Neural Network (ERNN ), an Artificial Neural Network, have been firstly harnessed to establish the space-time predicting models. Secondly, according to the successfully trained models, the dissertation conducts the multi-dimensional comparison based on four aspects: interpretability; ease of implementation; running time and instability. Finally, some possible improvements are put forward according to their forecasting performance which also indirectly reflects their unique features and application environments.
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References
Bennett RJ (1975) The representation and identification of spatio-temporal systems: an example of population diffusion in North-West England. Trans Inst Br Geograph, 73–94
Button KS, Ioannidis JP, Mokrysz C, Nosek BA, Flint J, Robinson ES, Munafò MR (2013) Power failure: why small sample size undermines the reliability of neuroscience. Nat Rev Neurosci 14(5):365–376
Chatfield C, Weigend A (1994) Time series prediction: forecasting the future and understanding the past. Int J Forecast 10(1): 161–163
Cheng T, Wang J (2008) Integrated spatio-temporal data mining for forest fire prediction. Trans GIS 12(5):591–611
Cheng T, Wang J, Li X (2011) A hybrid framework for space-time modeling of environmental data. 环境数据时空建模的混合框架. Geograph Anal 43(2):188–210
Cheng T, Haworth J, Wang J (2012) Spatio-temporal autocorrelation of road network data. J Geogr Syst 14(4):389–413
Deutsch SJ, Pfeifer PE (1981) Space-time ARMA Modeling with contemporaneously correlated innovations. Technometrics 23(4):401–409
Dong C, Shao C, Xiong Z, Li J (2009) Short-term traffic flow forecasting of road network based on Elman neural network. J Transp Syst Eng Inf Technol, 146–149
Dougherty M, Cobbett M (1997) Short-term inter-urban traffic forecasts using neural networks. Int J Forecast 13(1):21–31
Feng H, Chen D, Lin Q, Chen C (2006) Multi-scale network traffic prediction using a two-stage neural network combined model. In: 2006 international conference on wireless communications, networking and mobile computing, pp 1–5. IEEE
Giacomini R, Granger CW (2004) Aggregation of space-time processes. J Econometr 118(1):7–26
Giles C, Lawrence S, Tsoi A (2001) Noisy time series prediction using recurrent neural networks and grammatical inference. Mach Learn 44(1):161–183
Kamarianakis Y, Prastacos P (2005) Space–time modeling of traffic flow. Comput Geosci 31(2):119–133
Lin SL, Huang HQ, Zhu DQ, Wang TZ (2009) The application of space-time ARIMA model on traffic flow forecasting. In: 2009 international conference on machine learning and cybernetics, vol 6, pp 3408–3412. IEEE
Liu H, van Zuylen HJ, van Lint H, Salomons M (2006) Predicting urban arterial travel time with state-space neural networks and kalman filters. Transp Res Rec J Transp Res Board 1968(1):99–108
Lutkerpohl H (1987) Forecasting aggregated vector ARMA processes. Springer, Berlin, p 323
Martin RL, Oeppen JE (1975) The identification of regional forecasting models using space: time correlation functions. Trans Inst Br Geograph, 95–118
Mcdonnell J, Waagen D (1994) Evolving recurrent perceptrons for time-series modeling. IEEE Trans on Neur Net 5(1): 24–38
Perry R, Aroian LA (1979) ‘Of time and the river: time series in M dimensions, the one-dimensional autoregressive model. In: Proceedings of the American Statistical Association, Statistical Computing Section
Pfeifer PE, Bodily SE (1990) A test of space-time arma modelling and forecasting of hotel data. J Forecast 9(3):255–272
Pfeifer PE, Deutsch SJ (1980) A STARIMA model-building procedure with application to description and regional forecasting. Trans Inst Br Geograph, 330–349
Tinline R (1971) Linear operators in diffusion research. In: Chisolm MDI et al (eds) Regional forecasting. Butterworths, London
van Lint JWC, Hoogendoorn SP, van Zuylen HJ (2005) Accurate freeway travel time prediction with state-space neural networks under missing data. Transp Res Part C Emerg Technol 13(5–6):347–369
Wang J, Cheng T, Heydecker BG, Howarth J (2010) STARIMA for journey time prediction in London. In: Proceedings of the 5th IMA conference on mathematics in transport. IMA
Williams B, Hoel L (2003) Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: theoretical basis and empirical results. J Transp Eng ASCE 129(6):664–672
Wu C, Ho J, Lee D (2004) Travel-time prediction with support vector regression. IEEE Trans Intell Transp Sys 5(4):276–281
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Shi, H., Yue, Y., Zhou, Y. (2019). The Comparison Between Two Different Algorithms of Spatio-Temporal Forecasting for Traffic Flow Prediction. In: Geertman, S., Zhan, Q., Allan, A., Pettit, C. (eds) Computational Urban Planning and Management for Smart Cities. CUPUM 2019. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-030-19424-6_18
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