01595137

Component

Short-term forecasting of traffic characteristics, such as traffic flow, speed, travel time, and queue length, has gained considerable attention from transportation researchers and practitioners over past three decades. While past studies primarily focused on traffic characteristics on freeways or urban arterials this study places particular emphasis on modeling the crossing time over one of the busiest US-Canada bridges, the Ambassador Bridge. Using a month-long volume data from Remote Traffic Microwave Sensors and a yearlong Global Positioning System data for crossing time two sets of ANN models are designed, trained, and validated to perform short-term predictions of (1) the volume of trucks crossing the Ambassador Bridge, and (2) the time it takes for the trucks to cross the bridge from one side to the other. The prediction of crossing time is contingent on truck volume on the bridge and therefore separate ANN models were trained to predict the volume. A multilayer feedforward neural network with backpropagation approach was used to train the ANN models. Predicted crossing times from the ANNs have a high correlation with the observed values. Evaluation statistics further confirmed the high forecasting capability of the trained ANN models. The ANN models from this study could be used for short-term forecasting of crossing time that would support operations of ITS technologies.

This paper was sponsored by TRB committee AHB15 Standing Committee on Intelligent Transportation Systems. Alternate title: Short-Term Prediction of Border Crossing Time and Traffic Volume: Case Study for U.S.-Canada Ambassador Bridge

01584066

16-4057

English

18p

2016

Transportation Research Board 95th Annual Meeting

Digital/other

Figures; Maps; References

Bridges; Case studies; Forecasting; International borders; Neural networks; Traffic volume; Travel time

Ambassador Bridge (Detroit, Michigan and Windsor, Ontario)

United States-Canada Border

Highways; Operations and Traffic Management; Planning and Forecasting

Transportation Research Board Annual Meeting 2016 Paper #16-4057

TRIS, TRB, ATRI

Jan 12 2016 5:47PM