Traffic flow forecasting for intelligent transportation systems. Final report, January 1993-June 1995
The capability to forecast traffic volume in an operational setting has been identified as a critical need for intelligent transportation systems (ITS). In particular, traffic volume forecasts will directly support proactive traffic control and accurate travel time estimation. However, previous attempts to develop traffic volume forecasting models have met with limited success. The research focused on developing such models for two sites on the Capital Beltway in Northern Virginia. Four models were developed and tested for the single-interval forecasting problem, which is defined as estimating traffic flow 15 minutes into the future. The four models were the historical average, time series, neural network, and nonparametric regression models. The nonparametric regression model significantly outperformed the others. Based on its success on the single-interval forecasting problem, the nonparametric regression approach was used to develop and test a model for the multiple-interval forecasting problem. This problem is defined as estimating traffic flow for a series of time periods into the future in 15-minute intervals. The model performed well in this application. In general, the model was portable, accurate, and easy to deploy in a field environment. Finally, an ITS system architecture was developed to take full advantage of the forecasting capability. The architecture illustrates the potential for significantly improved ITS services with enhanced analysis components, such as traffic volume forecasting.
- Research Organization:
- Virginia Transportation Research Council, Charlottesville, VA (United States)
- OSTI ID:
- 104265
- Report Number(s):
- PB-95-239968/XAB; VTRC-95-R24; TRN: 52423235
- Resource Relation:
- Other Information: PBD: Jun 1995
- Country of Publication:
- United States
- Language:
- English
Similar Records
A data-driven operational model for traffic at the Dallas Fort Worth International Airport
A Public-Private-Academic Partnership to Advance Solar Power Forecasting