A study of travel time modeling via time series analysis

Travel time information is a good operational measure of the effectiveness of transportation systems and can be used to detect incidents and quantify congestion. The ability to accurately predict freeway and arterial travel times in transportation networks is a critical component for many intelligent transportation systems (ITS) applications (e.g., advanced traffic management systems, in-vehicle route guidance systems). This paper focuses on the arterial travel time modeling by studying the travel time data, modeling and diagnostic checking so that short-term travel time can be predicted with reasonable accuracy. A 3.7-mile corridor on Minnesota State Highway 194 is chosen as our test site. The Global Positioning System (GPS) probe vehicle method is used in our data collection. The time series analysis techniques are then used in our model building, in particular, we focus on the autoregressive integrated moving average (ARIMA) model. Finally, the model established for each road section is verified via both the residual analysis and portmanteau lack-of-fit test. The near term goal of this study is to use the developed models to predict section travel times with reasonable accuracy