01558260

Component

This paper focuses on an application of Autoregressive Integrated Moving Average (ARIMA) model to incorporate traffic information from neighboring links in forecasting short-term travel time along a corridor due to an incident. The model was developed using 181 “Vehicle Accident” type incidents that occurred along ~19-mile freeway segment in the city of Charlotte, North Carolina. The spatio-temporal context based data was combined and analyzed with the ARIMA model using pre-whitening of the cross-correlation function alongside lagged regression. Results indicate that the travel times for consecutive segments are highly correlated and, both, upstream and downstream segments of a target segment on a corridor will influence the current state of target segment. However, upstream segments are observed to have a higher effect than downstream segments. The mean absolute percent error (MAPE) and mean absolute deviation (MAD) of the developed lagged regression model for every segment was mostly less than 10%. Moreover, the results obtained from the validation of models at both segment- and corridor-level indicate that the estimated effect of incidents on travel time is almost equal to and close to the real-time observations. MAPE and MAD computed from validation data (26 incidents) are observed to be less than 10% for 95% of samples indicating an accurate estimation of the effect of incidents on travel time using the proposed method.

This paper was sponsored by TRB committee AHB20 Freeway Operations.

01550057

15-5553

English

19p

2015

Transportation Research Board 94th Annual Meeting

Digital/other

Figures; References; Tables

Forecasting; Freeways; Regression analysis; Spatial analysis; Traffic incidents; Traffic models; Travel time; Validation

Charlotte (North Carolina)

Highways; Operations and Traffic Management; Planning and Forecasting; I72: Traffic and Transport Planning

Transportation Research Board Annual Meeting 2015 Paper #15-5553

TRIS, TRB, ATRI

Dec 30 2014 1:52PM