Short-term traffic forecasting is playing an increasing role in modern transport management. Although many short-term traffic forecasting methods have been explored, the spatiotemporal dependency of traffic flow, an important characteristic of traffic dynamics that can benefit the forecasting of traffic changes, is often neglected in short-term traffic forecasting. This paper first investigates the spatiotemporal dependency of traffic flow using cross-correlation analysis and then discusses its implications in terms of traffic forecastability and real-time data effectiveness. This can help us to understand traffic flow, and hence improve the performance of forecasting models.
References
1.
AhmedS ACookA R, 1982, “Application of time-series analysis techniques to freeway incident detection”Transportation Research Record number 841, pp 19–21
2.
BennettR J, 1979Spatial Time Series: Analysis, Forecasting, Control (Pion, London)
3.
ChristensenR, 2001Advanced Linear Modeling: Multivariate, Time Series, and Spatial Data; Nonparametric Regression and Response Surface Maximization (Springer, Berlin)
4.
ClarkS DDoughertyM SKirbyH R, 1993, “The use of neural networks and time series models for short term traffic forecasting: A comparative study”, in Transportation Planning Method Proceedings, PTRC 21st Summer Annual Meeting, 13–17 September, Manchester PTRC, 1 Vernon Mews, London, pp 151–162
5.
DaileyD J, 1993, “Travel time estimation using cross-correlation techniques”Transportation Research B2797–107
6.
DavisG ANihanN L, 1991, “Nonparametric regression and short-term freeway traffic forecasting”Journal of Transportation Engineering117178–188
7.
DiaH, 2001, “An object-oriented neural network approach to short-term traffic forecasting”European Journal of Operational Research131253–261
8.
HeadK L, 1995, “Event-based short-term traffic flow prediction model”Transportation Research Record number 1510, pp 45–52
9.
IshakSKothaPAlecsandruC, 2003, “Optimization of dynamic neural network performance for short-term traffic prediction”Transportation Research Record number 1836, pp 45–56
10.
KernerB S, 2004The Physics of Traffic: Empirical Freeway Pattern Features, Engineering Applications, and Theory (Springer, Berlin)
11.
PettyK FBickerPOstlandMRiceJSchoenbergFJiangJ, 1998, “Accurate estimation of travel times from single-loop detectors”Transportation Research A321–17
12.
SeddonP A, 1972a, “Another look at platoon dispersion: 2. The diffusion theory”Traffic Engineering and Control13388–390
13.
SeddonP A, 1972b, “Another look at platoon dispersion: 3. The recurrence relationship”Traffic Engineering and Control13442–444
14.
SmithB LDemetskyM J, 1994, “Short-term traffic flow prediction: Neural network approach”Transportation Research Records number 1453, pp 98–104
15.
SmithB LWilliamsB MOswaldR K, 2002, “Comparison of parametric and nonparametric models for traffic flow forecasting”Transportation Research C10303–321
StathopoulosAKarlaftisM G, 2003, “A multivariate state space approach for urban traffic flow modeling and prediction”Transportation Research C11121–135
18.
StephenedesY JMichalopoulosP GPlunR A, 1981, “Improved estimation of traffic flow for real-time control”Transportation Research Record number 795, pp 28–39
19.
VlahogianniE IGoliaJ CKarlaftisM G, 2004, “Short-term traffic forecasting: Overview of objective and methods”Transport Reviews24533–557
20.
VlahogianniE IGoliaJ CKarlaftisM G, 2005, “Incorporating spatial data in short-term urban traffic flow forecasting using genetically-optimized temporal neural networks”TRB 2005 Annual Meeting CD-ROMTransportation Research Board, Washington, DC
21.
VythoulkasP C, 1993, “Alternative approaches to short term forecasting for use in driver information systems”, in Transportation and Traffic Theory: Proceedings of the 12th International Symposium on Transportation and Traffic Theory Berkeley, CA, 21–23 July (Elsevier, Amsterdam) pp 485–506
22.
Wherry, 1984Contributions to Correlational Analysis (Academic Press, New York)
