Travel time is the time required to traverse a route between any two points of interest and it is an important parameter that can be used to measure the effectiveness of transportation systems. The ability to accurately predict freeway and arterial travel times in transportation networks is a critical component for many Intelligent Transportation Systems (ITS) applications. In this paper, we focus on the application of using time series models to study the arterial travel time prediction problem for urban roadways and a section of Minnesota State Highway 194 is chosen as our case study. We use the Global Positioning System (GPS) probe vehicle method to collect data. The time series modeling is then developed, in particular, we focus on the autoregressive integrated moving average (ARIMA) model due to the non-stationary property of the data collected. The section models established for the corridor are verified via both the residual analysis and portmanteau lack-of-fit test. Finally, based on the models developed we present our prediction results. Our study indicates the potential and effectiveness of using the ARIMA modeling in the prediction of travel time. The method presented in this paper can be easily modified and applied to short-term arterial travel time prediction for other urban areas.